Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

 With the rapid development of China’s economy, early-warning of operational risk has attracted significant attention of financial risk researchers in recent years. To analysis dynamic early-warning of operational risks, Chinese manufacturing industry publicly listed companies were selected as research object. A measure model of early-warning index of operational risks in Chinese manufacturing industry listed companies based on grey Kalman Filter was constructed. With the early-warning results of sample listed companies in manufacturing industry through calculating process noise covariance, measurement noise covariance and Kalman gain matrix; thirdly, a model of investors’ reactions was designed to test correctness of early-warning index of operational risks. Results demonstrate that, for the case study, it can get precise result of early-warning results by using Kalman filter, and investors react negatively towards the operational risks. The conclusions contribute to prior literatures on the dynamic evaluation of early-warning of operational risks by providing operational processes.

There are many problems in the supply chain management of Chinese enterprises, which restricts the development of enterprises and the improvement of benefits. In order to improve the decision-making ability of Internet of Things (IoT) applications in enterprise supply chain management, the collaborative filtering algorithm is optimized. The algorithm determines the collaboration type according to the nature of the optimization target, and calculates the user similarity based on the user browsing behavior, so as to seek the recommendation function of the user to best evaluate the individual. According to the two objectives of the shortest time and the highest overall satisfaction in emergency dispatching in enterprise supply chain management, a two-level planning model of commodity scheduling is constructed, the time function of the supply chain delivery process calculated. The simulation results of the model show that the demand forecasting algorithm based on neighborhood rough set and GA-SVM is used to predict the demand of chain retail supply chain, which achieves high prediction accuracy. The maximum error is 5.71%, the minimum error is 0.60. The average error is %, which is 2.84%. The research in this paper has implications for enterprise application of IoT in supply chain management. The use of decision-making model can greatly improve the operational efficiency of the supply chain.

With the advent of the Internet + era, traditional piano teaching is facing unprecedented opportunities and challenges. In order to realize the intellectualization of piano network teaching, this paper constructs a piano note recognition algorithm. According to MIDI files and corresponding piano audio files, the algorithm builds training and test waveform data to calculate the corresponding annotation files, and recompiles the multi-note model to realize the multi-note recognition system. The multi tone HMM acoustic model and multi note model are established by using Internet technology. The matching degree between the test audio and the multi note models is calculated respectively, and then an Internet + piano intelligent network teaching system model is constructed. The test results show that when the number of states of the multi-note recognition system is 7, the correct recognition rate is the highest. Through this study, the piano intelligent network teaching system model has produced new inspiration, which can be more accurate when the multi-note recognition state is 7.

With the development of Internet and information technology, enterprises are facing more challenges. It is urgent and necessary to innovate the mode of enterprise management. In this paper, from the perspective of contingency, a multi-objective genetic algorithm is constructed to introduce Internet of Things technology into enterprise management innovation. Through the function to solve the relationship between the various operating entities of the enterprise, the algorithm obtains the optimal solution of vehicle distribution. Firstly, contingency theory is introduced into the innovative design of enterprise information system to optimize the logistics distribution path under the environment of the Internet of Things (IoT). The multi-objective genetic algorithm steps are designed, and the non-dominant set is constructed. The crossover and mutation operations of the objectives are combined to get the genetic sub-classes, and then the relationships among the parties in the enterprise logistics innovation management activities are solved. The experimental results show that the shortest running time of the algorithm is 0.56 seconds and the longest running time is 2.48 seconds. The average running time in the whole process is not more than 1 second, which meets the actual needs. The genetic algorithm can help enterprises to arrange the distribution path of logistics fleet reasonably. The research in this paper has enlightening effect on the management innovation of enterprises under the information environment, and further expands the application field of the IoT, which has practical significance.

Intelligent interference communication technology is an important direction for the development of a new generation of anti-jamming communication system through the recognition of complex electromagnetic interference environment and the use of learning and intelligent decision-making methods to achieve efficient and reliable information transmission. A channel prediction algorithm based on intelligent interference communication technology is proposed, and only a small-scale fading channel model is considered. Under the background of rapid development of information technology, the level of anti-interference of electronic communication is further improved, and the reliability of information transmission is effectively guaranteed. Under the background of fully expounding the physical layer security technology, the anti-interference scheme in large-scale multi-antenna system in electronic communication is designed. Based on the physical layer security communication model in the full-duplex network, a channel prediction scheme is innovatively proposed to reduce the impact of imperfect CSI. Through this measure to improve network security performance, the proposed anti-interference scheme was tested. The test results show that because Massive MIMO can direct the transmit beam and energy to the user direction, in the physical layer security scenario, for malicious eavesdroppers in the network, it cannot steal information and is difficult to interfere with electronic communication.

Edge computing applications have the characteristics of huge scale and sensitive quality of service. However, due to the “long tail delay” problem of user access requests across the heterogeneous environment of edge networks, wide area networks and data centers, the quality of experience of edge users has seriously decreased. Therefore, a time reduction rule calculation algorithm based on Internet of Things (IoT) delay application-driven measurement mechanism is proposed, which can be applied to multi-source heterogeneous information fusion, big data fusion and information fusion security. The system architecture features of edge computing applications are reviewed, and the causes and classifications of long tail delays are analyzed. The main theories and methods of network delay measurement are introduced, and the optimization techniques for long tail delay are summarized. Finally, the online optimization operation environment is proposed thoughts and challenges. The research results show that the GXDGC algorithm proposed is effective for the application of driving measurement technology in IoT delay. Users’ access to online real-time big data needs to span complex heterogeneous network environments such as edge networks, wide area networks, data center networks, etc. Due to the superposition of delays, any increase in delays in online real-time big data processing will inevitably lead to end-to-end long-tail delays. Therefore, it is necessary to design an integrated optimization mechanism to control end-to-end online real-time big data network delays.

Atmospheric scintillation brings additional noise to the detection system and reduces the receiving sensitivity of the system. It is urgent and necessary to introduce computer technology to improve the sensitivity of the system. Based on this, a computer simulation algorithm is proposed to simulate the atmospheric scintillation model in laser communication to analyze the sensitivity of the detection system. Taking Kalman filter estimation algorithm as the core of system state estimation, the problem of detection noise caused by atmospheric scintillation in laser communication is analyzed by means of computer simulation simulation algorithm. A Kalman filter estimation algorithm based on adaptive process-measurement model is proposed, and the computer simulation experiment of intensity scintillation caused by turbulence is carried out. The experimental results show that the error mean, covariance and root mean square error of the adaptive process model are the smallest. The second-order adaptive process model plays an important role in removing additional noise and improving system sensitivity, which meets our expectation of model order selection. The proposed method has good robustness, real-time and accuracy. Computer simulation algorithm is applied to the optimization of laser communication, which brings enlightenment to the optimization of laser communication technology and the application of computer intelligent algorithm.

Due to the large amount of pollutant discharge, the environmental pollution capacity of the coastal waters of the Yellow Sea and Bohai Sea in China is seriously overloaded, which has caused unacceptable impact on the marine ecological environment. Based on this, this paper is based on the cloud computing model of marine environmental management scientific decision-making evaluation algorithm, constructing a model of complex network dynamic correlation characteristics, and externally describing water bloom. Through the weight parameters in the cloud model, it is effectively applied to the evaluation of marine environmental management science decision support system. According to the complexity characteristics of cyanobacterial bloom management decision, the cyanobacteria bloom control decision model is inferred, and the intuitionistic fuzzy rough set algorithm is improved. The similarity is calculated, the best matching case is found, and the experts are adjusting to form a governance plan. The research results show that the scientific decision-making evaluation algorithm of marine environment management based on cloud computing model is effective for the verification of marine environmental management science decision support system evaluation system, embedding eutrophication evaluation method and cyanobacteria water bloom governance decision-making method into water quality remote monitoring and cyanobacteria water China’s governance decision-making system, to achieve the decision-making of cyanobacteria bloom control in the lake water body.

With the development of the Internet, the application of multimedia in College English teaching is becoming more and more demanding, especially in the area of accurate information recommendation. In order to improve the accuracy of push information in College English multimedia teaching broadcasting, a particle swarm optimization (PSO) algorithm is proposed in this paper. By designing a massless particle to simulate the foraging behavior of birds, the algorithm searches for precise targets to obtain the optimal solution. Combining with Internet technology, the weight of data transmitted by multimedia broadcasting signal is calculated by setting an independent address, and then the interactive data flow between teachers and students is analyzed to optimize the design of multimedia broadcasting terminal. The results show that the indicators with higher scores are mainly distributed in the construction of network teaching platform. The three indicators with the highest scores are convenience, module integrity and management and maintenance, which are 2.825, 2.715 and 2.696, respectively. Through this study, it brings new inspiration to the intellectualization of College English teaching. Multimedia broadcasting design can solve the problem of interference in terminal broadcasting transmission by setting independent addresses.

In this paper, a novel signal candidate generation method and a new joint coding and probability peak to average power ratio (PAPR) reduction scheme are proposed for a Luby transform (LT) coded orthogonal frequency division multiplexing (OFDM) system. When a few LT packets are mapped into an OFDM symbol, all subcarriers are automatically divided into several blocks. We permutate these packets and assign them different subcarrier blocks to generate different signal candidates instead of multiplying by many phase rotation vectors and using active constellation extension, and the transmitted symbol will be the one whose PAPR is the smallest. Moreover, through introducing one phase rotation vector, the proposed algorithm is modified, and the PAPR reduction performance is enhanced. Simulation results prove that our proposed scheme can not only fully explore the spatial freedom brought by the mapping relationship between LT packets and OFDM symbols but also can obtain effective PAPR reduction performance. Since the permutation operation does not change the degree value of each packet, the new scheme can still maintain good decoding performance.

Music education plays a particularly important role in China’s existing education systems at all levels. With the development of Internet of Things (IoT) technology, interactive teaching methods are more and more widely used. Therefore, a piano teaching system model design algorithm is proposed based on the IoT technology to design the function of the piano teaching system, which is of great help to improve the quality of piano teaching. The current mature technical framework and development language are compared, the key technologies of the IoT using the system architecture is determined, and the structural design methods of the piano teaching system analyzed, mainly using the SSH framework (ie Stmts, Spring and Hibernate’s model, based on this paper, a model design algorithm is proposed for building a piano teaching system based on the IoT technology. Finally, the algorithm and system are tested and implemented through experiments. The research results show that the algorithm uses the hardware underlying direct control development method, with an average scan of 12 times in 0.3 s, the recognition probability can be increased to 0.999719, the algorithm is effective, and the designed piano teaching system is fully functional. The research in this paper provides a theoretical reference for the wide application of IoT technology and the optimal design of piano teaching system.

The emergence of resource conflicts and overload control problems during the playback of smart TV terminals has brought many obstacles to the operation of smart TV terminals, which has seriously affected the user experience of smart TV terminal users. In this regard, the adaptive media playback algorithm is optimized for smart TV terminals. This method performs dynamic priority preemptive scheduling on exclusive resources according to resource characteristics and application priorities to optimize resource allocation and improve media playback. The feedback control algorithm is used to perform QoS scheduling on shared resources until QoS proportional fairness is achieved, and QoS proportional compression is used to eliminate resource overload. Finally, a DASH server on Apache and implements an analog DASH client using Python are built. In order to verify the performance of the algorithm, the research results show that the adaptive media playback algorithm has the overload control capability, which only solves the resource conflict and improves the response performance under heavy load of the system, and the algorithm consumes only 4.5% of the overall system QoS. Compared with the existing methods, it is about 30% lower, which is more suitable for resource scheduling of smart TV terminals. The research in this paper shows that the adoption of QoS scheduling mechanism contributes to the optimization of media playback resources and allocation ratio, thus making the playback process of smart TV terminals, which provides a reference for the optimization of smart TV terminal playback.

The research technique of human motor nerve is more complicated. In order to improve the understanding level of human motor nerve structure, a fusion architecture of human motion nerve and neural network computer driven by education are proposed. The human motor nerve simulation model is established by using the computer data simulation model. The model proposes an optimization scheme from the algorithm flow, the data transformation technology used to improve the real-time performance of the neural network and enhance the dynamic capture of the motor nerve changes. In order to further improve the architecture of the neural network and computer architecture driven by sports, a neural network algorithm is added to realize the sequence optimization of data to test the authenticity and efficiency of the human neural network simulation model neural network algorithm. In this paper, the feasibility and functionality of the algorithm are tested with comparative experiments. The results show that the neural network algorithm neural network algorithm not only has a calculation time of only 12 seconds. Moreover, the calculation accuracy is high, achieving a high level of accuracy of 98%. On the other hand, other algorithms have lower accuracy and longer calculation time. The research shows that the neural network algorithm can improve the human motor nerve capture and optimize the architecture, which can provide reference for the fusion of human motion nerve and computer technology in the future.

In a certain network environment, the use of teaching evaluation assistant decision-making system can further promote the rationality and fairness of teaching evaluation. Two screening algorithms are proposed, which combine with the influence factors in the automatic evaluation model of physical education teaching, delete the relevant factors and leave them behind. After two deep screening, the accuracy of the results is improved. By introducing the artificial neural network technology into the evaluation of physical education teachers’ teaching quality, the evaluation factors of neurons are calculated to establish the evaluation model of BP neural network. Secondly, the factors affecting the evaluation results in the evaluation model of BP neural network are decomposed and screened by using the second screening method, and a certain amount of training and learning is carried out for the teaching quality data. The experimental results show that the second screening algorithm is effective and can improve the accuracy of the results of automatic evaluation of physical education teaching. By establishing the automatic evaluation model of physical education teaching, it can provide reference for the evaluation and assistant decision-making of physical education teaching quality in Vocational colleges.

With the improvement of people’s living standards, people’s pursuit of material and high quality of living environment must combine the concept of human and nature to integrate urban landscape design with the surrounding ecological environment. The most interesting thing is the landscape design of most coastal cities. Based on this, a virtual environment is proposed based on virtual reality technology and intelligent algorithm, using 3-bit binary to represent the digital factor and creating the virtual environment by simulating the display environment. After constructing the basic model of coastal landscape, the landscape design of coastal areas using virtual reality technology is analyzed, and the parametric design method of new images in landscape design analyzed, the landscape design of virtual reality technology in coastal areas analyzed. The research results show that the display degree of DEM is 50% and the image display detail degree is 90% by using virtual reality technology. Combined with 3DMAX, it can be transferred to the 3DMAX operation surface to perform real editing and simulation of the coastal garden environment. Thus, the algorithm proposed in this paper is effective. The research in this paper shows that it is feasible to apply virtual reality technology and intelligent algorithm to landscape design in coastal areas, and has achieved certain effects.

At present, in the context of the Internet of Things (IoT), more and more teaching institutions have integrated Internet technology into the English network teaching system. Therefore, based on the IoT technology, an English speech recognition algorithm is proposed based on the network teaching system, and develops the function of the network teaching system oriented to English speech intonation. In this paper, the function of the platform and the design of the key modules of the system are proposed in detail. During the operation of the recognition model, the collected speech signals are preprocessed, effective speech feature parameters are extracted, and each frame feature parameter is composed into a vector sequence. The different sequences are classified, and the speech intonation system is developed based on the classification results. Then the improved particle filter algorithm is used to further improve the accuracy and speed of English speech system recognition, and the function of English speech network teaching system is optimized based on this. Experiments show that the excerpts from the China Daily website in the system of this article, most of them can be spliced with word primitives, reaching more than 90% of the total number of words in the measured text, reflecting the system’s higher word and segment coverage and high accuracy. The research in this paper has certain theoretical reference value for the construction and optimization of English phonetic system and the further development and use of IoT technology.

The recommendation system is an important means to solve the “information overload” of e-commerce today. Consumer psychology believes that consumer psychology dominates consumer behavior, and consumer behavior is the external manifestation of consumer psychology. Therefore, the personalized recommendation algorithm of user consumption psychology is studied based on the specific perspective of local group-buying e-commerce. By constructing a user social relationship network, the personalized recommendation algorithm is evaluated and the final recommendation result is obtained. A personalized recommendation model is proposed based on multi-dimensional space, which is compared with the existing personalized recommendation model. The simulation results show that the improved collaborative filtering recommendation method has a large recall rate and accuracy during the daytime. And F value; when the number of recommended results is small at night, the traditional recommendation method has a slightly larger recall rate, accuracy rate and F value, but as the number of recommended results increases, the recommended effects decrease. In general, the proposed method of the recommended algorithm has a good effect. The method proposed in this paper can improve the accuracy of recommendation and partially eliminate the cold start problem of users, which has certain enlightenment for the expansion of personalized recommendation algorithm and the improvement of e-commerce user management.

The development of English teaching mode and students’ speculative ability training mode is slow. In order to improve the quality of English teaching and improve students’ speculative ability, the English teaching mode and the cultivation of students’ speculative ability based on Internet of Things (IoT) are proposed and typical cases are analyzed. The computer data simulation model is used to construct an English teaching innovation model. The model proposes an optimization scheme from the algorithm flow, and uses the data transformation technology to improve the real-time teaching and strengthen the efficiency of teaching management. In order to further improve the students’ speculative ability in English teaching, the particle swarm optimization algorithm is added to the model to realize the sequence optimization of the data, and the authenticity and efficiency of the particle swarm optimization algorithm in the English teaching model are verified. The test results show that the particle swarm algorithm can self-improve and repair functions, continuously improve the accuracy of the English teaching model, and optimize the teaching method. The research shows that the particle swarm optimization algorithm can improve the quality of English teaching and optimize the architecture, which can provide reference for the future integration of English teaching and computer technology.

With the increasing number of electric vehicles, the location problem of charging stations has been paid more and more attention. It is more efficient and scientific to select electric vehicle charging stations through intelligent algorithms. Aiming at the location selection of electric vehicle charging station based on time satisfaction, a bi-level planning model is constructed for electric vehicle charging station location, and introduces genetic algorithm into the model to scientifically calculate the location of charging station. The candidate data string is extracted by genetic algorithm, and the text candidate string and the image candidate string are obtained. The candidate string is used as the document attribute to construct the electric vehicle charging station location plan, and then the ideal charging station address is solved. Finally, the method is applied. It is used in the planning analysis of the area near Chaowai Street in Chaoyang District, Beijing. The research results show that the six charging points calculated by the method can meet the demand of the charging vehicles of the residents in the planned area, which is in line with the actual situation of the planned area. This also shows that the double-layer planning model is used for site selection. The research in this paper shows that the genetic algorithm can be effectively used in the location problem, which can improve the efficiency of work and the accuracy of site selection. The relevant conclusions can provide a theoretical reference for the development of site selection.

The transmission of ECG signal is a key technology of wireless body area network technology center. An ECG emotion classification algorithm based on body area network is proposed. In this method, SV vector function is used to fit ECG signals, and the fitting parameters are obtained. After estimating the channel characteristics, the fixed-point parameters are transmitted. Firstly, the wireless body area network technology is analyzed, because the body area network can deal with long-distance dependence and capture the semantic information of input text. Wireless body area network is used to extract the grammatical features of input text. Then, based on the wireless field of network technology, the principle of support vector machine (SVM) is proposed. On the basis of the emotion classification model, an algorithm based on speech recognition is constructed, and the input text vector obtained by CNN is used to represent the emotion category of the output layer. Finally, the experimental results show that the algorithm is effective, and the emotional classification model can obtain the highest accuracy in multiple data sets. The results show that the algorithm can not only fit the waveform of ECG emotional signals well, reduce the compression ratio and achieve a certain fitting effect, but also improve the detection and transmission ability of ECG (emotional) ECG signals.

With the rapid development of the IoT, the traditional traffic scheduling optimization model is difficult to adapt to the development needs of emerging services, bringing new challenges and problems to data center management. In order to solve the problem of data traffic management in data center network, a clustering algorithm is constructed to analyze its key technologies. The algorithm divides the data to be observed into a certain number of “class clusters” by some predetermined features, so that the similarity of the data in the cluster is measured by a certain “distance function” within each “class cluster". By analyzing the RFID automatic radio frequency identification technology, a data classification model based on RFID automatic radio frequency identification technology is constructed. The original data of the unbalanced state is processed based on the hierarchical partitioning method, and the sampling data analysis result is obtained. The results of data training experiments on the model show that for the prediction of a few samples, the prediction of the unbalanced data set has been further improved, and the AUC value has reached 98.72%. Research has provided new ideas for the operation and management of data centers.

 Along with the rapid increasement of flights and projects of extending and building airports, the probability of flight delays is also increasing. People begin to pay more attention to the prediction of flight delays in a large civil aviation air traffic network. In this paper, we employ a deep learning (DL) model— the convolutional long short-term memory network (conv-LSTM), to address the airport delay prediction in network structure. The spatiotemporal variables including flight delays of airport, air route congestion, airport throughput and flow control are input into an end-to-end learning architecture as a spatiotemporal sequence. The future flight delays in airport will be output by the model. Experiments show that conv-LSTM possess stronger ability to capture temporal and spatial characteristic than traditional LSTM.

With the development of China’s Internet, wireless network technology has been upgraded, and the rapid development of wireless network technology requires more advanced intelligent information processing technology to match. Therefore, based on the development of wireless network, this paper proposes intelligent tourism Management scheme. Firstly, the Wide & Deep Learning exploratory tourism route recommendation model was constructed. Then the Wide & DSSM exploratory recommendation algorithm combining the traditional recommendation algorithm with the depth model was proposed. Finally, the model and algorithm were tested through experiments. In this paper, the algorithm was used to study the semantic space vectors of both the user dimension and the Travel line dimension, and the data of the user dimension was fully utilized, which brought a significant improvement to the performance of the recommended system.

In the performance of vocal music, the human body noise science is a problem that cannot be ignored. The wrong pronunciation often causes noise diseases. It is necessary to analyze the localization of vocal noise. Based on this article, the research of vocal art performance and human noise science based on wireless sensor system is studied. At first, this article briefly describes the relationship between vocal art performance and human noise, and then puts forward the importance of noise localization. Aiming at the problem of noise localization, several target localization algorithms are proposed. At the same time, these algorithms are weighted to reduce the complexity and improve the calculation accuracy. The simulation results test confirmed the effectiveness of the target location weighting algorithm without increasing the computational complexity, and the calculation accuracy was significantly improved.

The construction of the artificial translation scoring model based on BP neural network has a positive effect on the improvement of college students’ translation performance. In order to promote the application of this kind of system in English teaching in our country, in this study, the author summarized the subjective topic scores in English teaching in our country, and then the author constructed the artificial translation scoring model of BP neural network. Compared with the example application, the comparison results show that the artificial translation scoring system based on BP neural network is more effective than the traditional scoring method. This study aims to provide reference for the continuous improvement and development of our English language teaching model.

There is a certain correlation between human health and actual exercise. This has always been one of the focuses of many scholars. When analyzing the movement of human body, a lot of modern technology and equipment need to be used. In order to better realize the physical health of young people in our country, the health service system based on sensor technology has been put forward. By introducing accelerometer technology, real-time data capture and analysis of human motion posture and movement trajectory were carried out. At the same time, the reliability of the algorithm was improved. In order to verify the feasibility of the method, a practical case was verified. Experimental results showed that the method was more scientific and reasonable in motion capture analysis.

At this stage, the rapid development of computer technology and information technology in China has provided favorable conditions for the development of the game. In order to pursue the game experience, how to use artificial intelligence in the game has become a new research hotspot. Therefore, the current situation of artificial intelligence used in games was investigated, and the principles of Unity3D game engine were studied; then the intelligent behavior model for NPC was established by using the behavior tree as the basic algorithm, and the AI architecture of the agent in the game was designed; moreover, combined with the above analysis, the behavior tree model based on Q learning algorithm was calculated, and the application of Unity3D in the game was completed; finally, a game model was developed in combination with Unity3D game engine. The results show that the behavior tree based on Unity3D game engine can realize NPC’s intelligent behavior simply and efficiently, and the system can run at a good speed, which has theoretical guidance for the follow-up research of game artificial intelligence and simulation training.

Traditional information recommendation system using only the user’s score is calculated and recommended, although to a certain extent, can obtain the implied characteristics of users or resources, but the lack of enough semantic interpretation, affecting the effects of recommendation. This article studied and analyzed the recommendation based on attribute coupled matrix decomposition algorithm in the application of Internet of things, on the foundation of the matrix decomposition model successively introduced global offset and time offset, in order to improve the prediction accuracy and the quality is recommended. In this paper, the algorithm is proved by experiment and the prediction accuracy of the algorithm is improved.

With the continuous improvement of the social and economic level, the investment in fixed assets in the whole society is increasing steadily, while the phenomenon of uncontrollable investment is becoming more and more serious. Therefore, it is very important to increase the investment estimate in the early stage of the project construction. Based on this, in this paper, by studying the BP neural network, a mathematical model of the prediction of engineering cost based on the improved BP neural network model was proposed; then, taking a 15-storey tall building in a residential district as a prediction object, by collecting and sorting out engineering cost data similar to the predicted object, the improved BP neural network model was estimated and trained; finally, the prediction of the engineering cost data for the project was carried out, and the actual results were compared with the estimation results of the traditional prediction model; thus, the speediness and accuracy of the proposed improved BP neural network model in the field of the prediction of engineering cost were verified.

With the rapid development of wireless networks, the issue of information security in wireless communications has gradually emerged, and it has become one of the biggest obstacles to the popularization of this technology. To meet the increasing security and reliability requirements of new network environments, wireless network security standards and protocols are constantly being updated and enhanced. Based on the current development of wireless networks in Beijing, this paper discusses the optimization of encryption protocols in network security protocols, introduces the PrefixSpan algorithm and improves its algorithms, and then performs data mining based on Prefix, and analyzes the intrusion lines and their correlations. Finally, the feasibility of using the improved PrefixSpan algorithm to optimize the encryption protocol is verified through experiments.

With the popularity of computer technology, big data, Internet of things and other new IT technologies appear. With the rapid transformation of the times, the traditional logistics industry can no longer meet the needs. Therefore, the new logistics method has been studied by various technicians. Petri network logistics is one of the new methods. The knowledge flow modeling of supply chain under Petri network is expounded. The basic theory and method of logistics are analyzed. Using the particle swarm optimization (PSO) algorithm of data Petri network to combine the Internet of things with Petri network, the innovation research is carried out and the supply chain knowledge flow model is built. In the test of information operation efficiency of the algorithm, it is proved that our algorithm is feasible.

Industrial cluster is a common phenomenon in the process of industrialization. It is an external scale formed by agglomeration effect. Cluster enterprises based on their comparative advantages, innovation will be in the system innovation, management innovation, technological innovation, corporate culture innovation and other all-round development. The empirical results show that the development of industrial clusters attracts and promotes a large number of intermediary service-oriented organizations, as well as institutions providing research and development and technical support, providing innovative incubation platform. The diffusion behavior of technological innovation of enterprises in clusters plays an important role in upgrading industrial clusters, but uncontrolled imitation behavior will reduce the expected profits of enterprises taking the lead in innovation and increase innovation risks. At the same time, the supporting innovation service system integrates and improves the factors gathering and optimization needed for innovation, and provides a technological support platform for enterprises in clusters to innovate.Therefore, the government’s policy should be biased towards establishing an effective mechanism for the interaction between scientific and technological innovation and industrial clusters, so as to achieve a good situation of the interaction between economy and science and technology.

The security of massive data has always been the focus of computer security research. With the increase of data storage, the computing platform of single node can not deal with the increasing security of massive data. It is urgent to use distributed computing platform to improve computing efficiency and detection accuracy. The physical deployment of intrusion detection system on cloud computing platform consists of monitoring server, Hadoop master server, IDS server, node and IDS terminal management. The experimental results show that the proposed intrusion detection system based on Hadoop cloud node has better detection effect. This paper searches for the optimal weights, and then begins the training of the neural network. The whole process uses the Hadoop framework of distributed computing platform to implement the genetic algorithm and the neural network algorithm in the cloud computing platform. At the same time, the algorithm is improved to improve the efficiency and accuracy of intrusion detection. The results show that the intrusion detection technology is very effective to protect the application system and help it against various types of intrusion attacks.

The effective supply guarantee of fresh agricultural products is a systematic problem. Farmers should not only pursue economic effectiveness, but also ensure the quality of products. This paper analyzes the supply decision of agricultural products based on negative exponential utility function and game analysis. The negative exponential utility function has been widely used in the study of the risk preferences of farmers. This paper analyses the impact of price fluctuation on the supply of agricultural products. At the same time, it analyses the problem of quality and safety from the perspective of the main body of agricultural products supply under the framework of economics. The income of agricultural products is an important factor affecting their behavioral decision-making. The result shows that: (1) price affects utility values by affecting the profit values and the mean risk-aversion coefficients; (2) market competition gives producers and operators inherent motivation to improve the quality of agricultural products. In the process of production decision-making, it is necessary to ensure that the profit of supplying safe agricultural products is not less than that of supplying conventional agricultural products. Therefore, the government can adopt economic incentive and policy incentive mechanism to protect the economic interests of the main body of safe agricultural products supply.

At present, there is less software related to sport technical behavior recognition, and there are few studies on the classification and identification of detailed actions. By introducing computer technology to analyze the efficiency and regularity of sports, not only the characteristics of athletes can be excavated, but also the visibility and dynamic tracking of sport training can be provided. The process of sports education is a fast and complex systematic process. Through the interactive system of physical education, we can use different methods to collect sports data and make a comparative analysis of athletes’ movements. Through the data mining of the relationship between athletes’ physiological indexes and sports load, the unreasonable link in sports training can be avoided. Also, in sports training, we can use computer vision and modern biomechanics to construct a virtual sports education situation. With the classification accuracy as the fitness function, this paper collects the data through the network database, and returns the corresponding sport training parameters on this basis. The results showed that the accuracy of the model was nearly 98%, which met the actual demand. Therefore, the development of sports education assistant system can provide strong support for the process control of sports training and education.

There is little research on the relationship between financial innovation and economic growth, and the research on the synergy between the two is basically blank. Based on this, from a general perspective, through constructing the corresponding subsystems in combination with financial innovation and economic growth, establishing the corresponding synergy model, and discovering the synergy development relationship by studying the degree of synergy in the past period, this study builds a BP neural network simulation model to predict the degree of synergy between financial innovation and economic growth in 2018 on the basis of practice. At the same time, this study compares it with the actual situation to verify its effectiveness. Through analysis, the research model has certain effectiveness, which is basically consistent with the actual development trend. The research proposes that the main trend of financial innovation from the perspective of generalized virtual economy is Internet finance. This is the first time to study this issue from a new perspective, theory and method, which expands the existing research results.

Competitive sports require athletes to operate in real time, and there are many uncertainties. At present, there are few applications of artificial intelligence in the prediction of competitive sports, and the relevant literature about fitness motivation is rare. Based on this, this study is based on the machine learning algorithm and uses the support vector machine to build the competitive sports model and fitness motivation evaluation. At the same time, this study combines the actual situation to construct a corresponding factor analysis model for racing sports, and this factor analysis is a combination of data mining and machine learning. Only by adopting appropriate measures can students’ motivation of physical fitness be effectively fostered and stimulated, their active participation in physical exercise and lifelong fitness habits be fostered. On the basis of traditional SVM method, PCA-SVM model is constructed to further improve the prediction accuracy and validity of fitness motivation. In this paper, the principal components of eight kinds of operation behavior are extracted; fitness motivation is not only the direct reason for college students to participate in fitness exercise, but also the motive force of fitness behavior. Grid Search algorithm is selected to optimize the parameters of SVM. The recognition rate of Grid Search-SVM is 94.79%, and satisfactory results are obtained.

At present, artificial intelligence for sports static image recognition is mostly in the action judgment stage, but less analysis on the action detail stage. Based on this, based on machine learning, this study uses static images and video sequences as carriers to improve traditional algorithm research and to perform motion gesture recognition. Through performance analysis, this paper explores the traditional algorithm and uses parameter analysis to improve the feature extraction and classification of traditional algorithms. Moreover, this paper uses the multi-scale feature approximation calculation method to improve the speed of the algorithm to extract features, and the algorithm is tested using the UCF motion data set and the self-created motion data set. In addition, this paper obtains representative motion video through data collection to test the effectiveness of the proposed algorithm. The research shows that the proposed algorithm has good performance and can provide theoretical reference for subsequent related research.

Estimating the compensation risk of agricultural insurance is a hotspot of current research. The related research mainly focuses on the calculation and simulation of catastrophe risk that agricultural insurance may face. On the whole, the compensation risk of agricultural insurance mainly comes from the agricultural disasters, especially the agro meteorological disasters. Compared with property insurance, the overall compensation rate of agricultural insurance is much higher than that of property insurance, so agricultural insurance belongs to high-risk business operation. In the research, the support vector machine is used as the research technology, and the forecast model corresponding to the insurance market is constructed. At the same time, this paper constructs SVM prediction model and VAR-based SVM prediction model. Finally, the prediction accuracy of the SVM prediction model and the VAR-based SVM prediction model are compared and analyzed. The research shows that the prediction accuracy of VAR-based SVM prediction model is higher, that is, it is easier to draw near-realistic prediction results based on parameter optimization. This paper summarizes the research, puts forward its inadequacies and merits, and provides theoretical reference for subsequent related research.

The accelerated development of urbanization in China started with economic globalization and industrialization, and also in the process of economic system transition. Generally speaking, urbanization is an important indicator of a region’s economic development and social development. Urbanization equity is an important direction of sustainable economic and social development. From the economic dimension, urbanization can promote division of labor, specialization and accumulation of human capital through agglomeration effect. This paper analyzes the social equity and urbanization by using fuzzy logic and factor analysis model. Under the condition of market economy, migrant workers have no competitive advantage in the labor market because of their low educational level. At the same time, the treatment of migrant workers in social security, cultural education and economic welfare is lower than that of urban residents. Under the unbalanced economic development reality, the economic absorption effect leads to the migrant workers rushing to the developed big cities. The results shows that by taking the dimensions of social justice of the rural migrant worker as the independent variable, psychological urbanization for the dependent variable, the regression Equation of F value is 90.424, P = 0.000, less than 0.05 level of significance. Also, we make correlation analysis on all factors of social justice and urbanization. The experimental results show the effectiveness of proposed method.

Green building is the development of sustainable development concept in architectural field. While the construction industry has brought great benefits to the development of national economy, its high investment, high pollution and inefficient development mode has also produced a huge energy load. Therefore, from the perspective of environmental and economic sustainability, the development of green buildings is particularly important. In this paper, the author makes economic benefit analysis of green building based on fuzzy logic and bilateral game model. By introducing such factors as economic benefits, cognition and government policies, this paper construct an evolutionary game model, which provides a basis for improving the economic benefits of green buildings. The results show that the first factor affecting enterprise decision-making is the incremental profit of green building developers, followed by the government’s incentive policy. After the evolution of the market, the final strategic choice will be stabilized to higher economic benefits. Generally speaking, green buildings need to effectively control incremental costs and consider scale benefits. Through management efficiency innovation and policy stimulation, the problems of huge investment cost and long payback period can be solved, so as to improve the economic benefits of green building development.

At present, the application of artificial intelligence in the identification and classification of sports technology is still relatively small, and it is difficult to effectively improve the training and competition quality of athletes. Based on this, this study takes badminton as an example for analysis. Moreover, based on the complexity and multi-deformation of this motion, this study uses machine learning as the basic algorithm to design a real-time classification algorithm for badminton action. At the same time, this paper improves the traditional algorithm, designs an improved training model, and verifies the effectiveness of the design algorithm by experimental method. In addition, this paper constructs a feature statistics and pace training system with the support of machine learning algorithms through statistical analysis and statistical badminton technical features and realizes the intelligentization of badminton batting action classification and recognition. Finally, this paper designs a comparative test for system functional testing. The system test shows that the system can effectively improve the action classification and recognition effect and can provide theoretical reference for subsequent related research.

The lack of effective evaluation of online education is a worldwide malpractice, and it is impossible to help students improve the correctness of online learning choices through existing reviews. Based on the current mainstream sentiment lexicon and text sentiment analysis, the authors use machine learning method to analyze the sentiment orientation of the legal course review text, through method that combines PMI and SVM. At the same time, this paper uses LibSVM tool to train and predict data, collect and pre-process data through network data collection, and, based on traditional algorithms, propose improved experimental scheme based on their respective advantages and disadvantages. In addition, the model proposed in this study is used to classify and process the emotional text, and the two methods are combined to obtain the final result. Finally, this paper combines experiments to analyze the performance of the comprehensive model proposed in this study. The research shows that the classification effect of the text sentiment analysis of model is good, it can be applied to practice, and it can provide theoretical reference for subsequent related research.

Athletes have a large amount of video information, so how to capture effective information is the key to improving athletes’ training efficiency and improving the quality of the game. From the perspective of deep learning, this study analyzes and improves traditional algorithm models based actual needs, and jointly learns multi-scale features. At the same time, in view of the problem of over-fitting in the model training process, this study uses the sparse pyramid pool strategy to adjust the pool parameterization process and reduce the complexity of feature description. In addition, the research designs experiment to analyze the performance of the improved algorithm model and select the appropriate database to analyze the recognition effect of the algorithm model. The research shows that the algorithm of this research has a certain improvement in the recognition effect of athletes, and the recognition effect matching the artificial design features can be obtained, and it can provide theoretical reference for subsequent related research.

Ball sports have great variability in the game and the intelligent control of the rules of ball movement can effectively improve the training effect of athletes. However, the current research on artificial intelligence of spherical motion trajectory prediction points is basically blank. Based on this, this study is based on deep learning technology, and obtains the main experimental data through network data collection in the research and builds the table tennis spatial position image data set under various environments with accurate annotation based on the traditional deep learning. At the same time, the convolutional neural network is used as the location recognition algorithm, and a prediction algorithm for predicting the trajectory of table tennis is proposed based on the recurrent neural network. In addition, this paper designs comparative experiments to analyze the effectiveness of the algorithm model, and evaluates the real-time recognition, location and trajectory prediction capabilities, and conducts quantitative analysis. The research shows that the algorithm has certain practical effects and can provide theoretical reference for subsequent related research.

With the rapid expansion of chain network, enterprises meet the consumption demand scattered around in a large range. In this paper, SOM neural network algorithm is introduced for empirical test. Design includes the structure of the fuzzy neural network identification and parameter identification, structural identification include input space division and the number of fuzzy rules to determine. Through summarizing and analyzing the characteristics of chain retail enterprises, this paper proposes to build a hierarchical and differentiated incentive mechanism by cultivating retail culture. The result shows that the knowledge staff is been higher the education level, the work creativity is stronger, cooperates the demand to the team members. In the era of the knowledge economy, knowledge has replaced capital as the core source of the enterprise core competence. The performance evaluation of knowledge workers is complex and the performance of the general staff is often easier to get a more objective evaluation. In conclusion, performance characteristics of knowledge workers should include general knowledge staff quality, knowledge staff performance behavior and performance results three aspects of characteristics.

Due to the lack of uniform standards for pathological cell detection, it is difficult to identify. In order to improve the accuracy of pathological cell identification, this study combines the actual situation of cell detection based on traditional particle algorithm to construct a C-V model based on level set algorithm and curve evolution theory, which realizes the effective separation of different substances inside the cell. At the same time, in order to effectively extract the characteristics of cell images, this paper uses the global research method to extract the features of the research object and adopts the improved gray level co-occurrence matrix to extract the texture features, thus effectively improving the feature extraction quality. In addition, in order to study the accuracy of the algorithm model identification in this study, this paper designs a comparative experiment for performance analysis. The research shows that the proposed algorithm model has good performance, can achieve accurate recognition and feature extraction of pathological cells, has certain practical effects, and can provide theoretical reference for subsequent related research.

Traditional nuclear magnetic resonance technology has grayscale inhomogeneity in brain tumor detection, which directly affects the formulation of follow-up treatment plans. In order to improve the detection effect of nuclear magnetic resonance on brain tumors, this study uses a convolutional neural network as the basis algorithm to construct an algorithm model suitable for multimodal MRI image recognition. At the same time, combined with the actual case, this paper uses the model to segment and identify brain tumors, and this paper combines the principle of machine learning and collects data for data training to construct a multi-channel deep deconvolution network model. In addition, in order to explore the effectiveness of the algorithm in this study, the performance analysis was carried out by comparative experiment method, and the multi-faceted performance of the model was studied, and the corresponding test result images were obtained. Through experimental comparison, it can be seen that the algorithm model constructed in this study has certain validity, can be applied to practice, and can provide theoretical reference for subsequent related research.

The Internet of Things (IOT) is the main technical support of smart agriculture. The sensor equipment of the Internet of Things (IOT) in agriculture is developing in the direction of low cost, self-adaptation, high reliability and low power consumption. In the future, the sensor network will gradually have the characteristics of distributed, multi-protocol compatibility, self-organization and high throughput. In this paper, the authors analyze the intelligent agricultural system and control mode based on fuzzy control and sensor network. Intelligent agriculture is based on the most efficient use of various agricultural resources to minimize agricultural energy consumption and costs. It is supported by Internet of Things technologies such as comprehensive perception, reliable transmission and intelligent processing. Using ROF technology, the WiFi signal is pulled far, and the wireless coverage is expanded greatly. At the same time, through the combination of wireless sensor technology such as ZigBee, the transmission and centralized control of sensing signals are realized, and the monitoring system of intelligent agricultural greenhouse is established. The simulation results show that the system can effectively improve the level of intelligence and information of agricultural greenhouse management, and greatly improve crop production efficiency.

Reasonable fire risk assessment system can demonstrate the occurrence of fire and ensure the safe evacuation of fire. The selected indicators of the evaluation system play a fundamental role in the establishment of the system. In the evaluation model, the general problem is transformed into a specific mathematical model by using the method of fuzzy information processing, which makes the evaluation result more direct and measurable. This paper uses a measure of feature attributes to measure the contribution of clusters, that is, the method of calculating the weight of features. When the value of the equilibrium discriminant function reaches the minimum value, the clustering result under the optimal condition can be obtained. Then, the author analyzes the fire risk assessment and factor analysis of buildings based on multi-target decision and fuzzy mathematical model. The simulation results show that the improved fuzzy model proposed in this paper makes the calculation results more accurate. The fire risk analysis and control system based on the theory of fuzzy information processing can be widely used in various high-rise buildings to ensure safety.

 In order to realize the intelligent evaluation of effective teaching quality and make up for the lack of research in this aspect, in the research, BP neural network is used as the basis for model construction analysis. Political education in colleges and universities is an important course, and its teaching quality evaluation is particularly important. Through comparative analysis, LMBP is selected as the learning algorithm, and the neural network evaluation model mechanism of college classroom teaching quality evaluation system is determined through theory and practical methods, and the simulation model is simulated by MATLAB as a simulation tool. At the same time, this paper uses the experimental method to carry out simulation training experiments in the MATLAB neural network toolbox, select the training algorithm for comparative analysis, and display the results in the form of statistical graphs. In addition, this paper sets the convergence speed and error curve as evaluation indicators, determines the appropriate training algorithm, and verifies the validity of the model. The research indicates that the BP teaching quality evaluation model based on BP neural network is a reasonable and feasible evaluation model and can provide theoretical reference for subsequent related research.

 With the full spread of various IT application systems, a large number of business data are stored in the business systems of enterprises. In this paper, the author analyzes the aviation industry management mode based on big data analysis. In this paper, the author analyses aviation industry management model and exchange rate index analysis based on error correction model and fuzzy mathematics. The BP algorithm uses the error of the output layer to estimate the error of the direct predecessor layer of the output layer, and then gradually estimates the error forward, and thus, the error of all layers is obtained. The weights and thresholds of the layers are adjusted according to the error so that the modified network output can approach the expected value. The aviation industry data include both the financial and internal data of airlines, and the external data such as flight information and user data. From the ETL process, building an enterprise data warehouse is an important strategy for the development of the aviation industry. It has a positive effect on the application of automatic data mining and business intelligence in the aviation industry. On this basis, we put forward relevant suggestions for aviation industry management.

Technological innovation in manufacturing industry is a kind of R&D activity that produces new technologies, including input and output of technological innovation. In this paper, the authors analyze the lean production and technological innovation in manufacturing industry based on SVM algorithms and data mining technology. Data mining can discover novel, effective, potential and ultimately understandable data patterns from a deeper level, and encode the data to predict the development trend of enterprises. The machine learning support vector machine method is used to analyze and model the collected data. At the same time, we constructed a decision tree using random forest, and explained the significance of the training algorithm through the visualization results. The simulation results show that learning growth dimension and market dimension have the greatest impact on business model innovation. In the context of TEC, business model innovation must pay attention to market grasp and customer demand oriented, so as to improve the competitiveness of manufacturing enterprises.

Using the panel data for China’s 30 provinces from 2008 to 2016, this paper analyzes the impact of producer services agglomeration on green economic efficiency at its spillover effects, through spatial autocorrelation test and the establishment of spatial econometric models. It comes to the results as follows: First, China’s regional green economic efficiency is significant positive spatial dependence. Second, the producer services specialized agglomeration not only inhibits the green economic efficiency of one region but also has significantly negative spatial spillover effects on adjacent areas, while the producer services diversified agglomeration only enhance the green economic efficiency in the region. Third, the impact of the agglomeration mode selection of producer services industry on green economic efficiency in the eastern region is basically consistent with the empirical analysis at the national level, while the green economic efficiency improvement in the central region only benefits from producer services specialized agglomeration, and the green economic efficiency in the western region is not significantly affected by the producer services agglomeration mode selection.

Currently, there is a certain fluctuation in the real estate industry, so it is particularly important to analyze the solvency of real estate enterprises. In order to find a reliable model suitable for studying the difference in house prices, this study collects the research data through data collection, and uses the K-means clustering method to construct the corresponding model as a basic research in combination with the machine learning research method. At the same time, this paper compares the analysis effects of several common machine learning models and finds the advantages and disadvantages of these methods through mathematical statistics. In addition, combined with practice, this paper constructs a nonlinear generalized additive model, and based on machine learning technology, validates the validity of the model based on data analysis, the collected predictors. In view of the improvement of the solvency of real estate enterprises, diversified operation of real estate enterprises can maintain reasonable cash flow and make up for the defect of poor liquidity of real estate. Furthermore, this paper uses the stability method to find the optimal model. In addition, the generalized additive model effectively reveals the complex nonlinear relationship between continuous predictors and house prices. Through research, it can be seen that the nonlinear generalized additive model based on machine learning can play an important role in real estate industry forecasting and has certain theoretical reference significance for subsequent related research.

With the advent of green economy, it is of great significance to objectively calculate the green innovation efficiency of provincial industrial enterprises in China for achieving sustainable economic development. In this paper, the author analyzes the regional technological innovation and green economic efficiency based on DEA model and fuzzy evaluation. Based on the latest development of traditional efficiency and productivity analysis theory, this study calculates the green innovation efficiency of 30 provincial industrial enterprises by using SBM model, while considering the relaxation of economic input-output problem. The results show that the SBM model improves the accuracy and authenticity of the economic efficiency evaluation of green innovation. The efficiency of green economy in most provinces is on the rise. At the same time, the intensity of R&D and industrial structure play a positive role in improving the efficiency of green economy. Through cluster analysis, the differences and causes of green economic efficiency of regional industrial enterprises are analyzed. In addition, the provinces should also consider the factors affecting the green economic efficiency of industrial enterprises, implement the innovation-driven development strategy in an all-round way, and promote the development of green economy.

In the present paper we present a new approach to the fuzzification of groups, which is defined by the hazy associative law (a new fuzzy associative law) on hazy binary operations. It is also called an M-hazy group. The basic idea is to generalize the binary operation of classical algebras to fuzzy binary operation. Some properties of M-hazy groups and M-hazy subgroups are discussed. Besides, we also study the homomorphisms between two M-hazy groups.

Cloud computing is a new framework, which is facing a numerous type of challenges including resource management and energy consumption of data centers. One of the most important duties of cloud service providers is to manage resources and schedule tasks for reducing energy consumption in data centers. In this paper, fuzzy logic is used for finding most adequate DC, improved DVFS algorithm is used for selecting ideal host and developed version of EDF-VD algorithm is utilized for Task scheduling and load balance in cloud computing. Our approach improvement to the current methods including EEVS, DVFS, Homogeneous, MBFD and EEVS-N.

In this paper, a mixed-integer nonlinear programming model is developed for a general edible oil closed loop supply chain network design problem under hybrid uncertainty which is then transformed to its linear counterpart. In order to cope with the hybrid uncertainty in input parameters, scenario-based and fuzzy- based parameters, a new approach is proposed including a novel robust fuzzy programming and an efficient method based on the Me measure. Furthermore, the performance of the proposed model is compared with that of other models. Finally, numerical studies and simulation are performed to verify our mathematical formulation and demonstrate the benefits of the proposed model.

This paper proposes a self-recovery method of fragile watermarking. Generally, self-recovery methods embed two types of data into the original image: check-bits for tamper detection and reference data for image recovery. Generating reference data is the primary challenge of every self-recovery method for more tamper resiliency and higher reconstruction quality. The proposed Multi-Rate Reference Embedding (MRRE) method makes unique reference data with several redundancy rates, instead of generating multiple reference data. According to the proposed methodology, the image is compressed by a source coding algorithm and the compressed data is separated into ten parts. Each part is protected by a channel coding algorithm based on pre-assigned redundancy rates. A fuzzy-based rate allocation system is used to assign the redundancy rates based on the importance of data. The generated data is packetized and randomly embedded into an image block. For tamper detection purpose, check-bits are generated by an MD5 hash function for every block. Both reference data and check-bits are embedded into three least significant bits (LSB) of the image pixels. To increase restoration efficiency, the proposed MRRE method provides ten scales of image recovery named highly-scalable self-recovery. The simulation results show an improvement in both tamper tolerability and reconstruction quality in comparison with the most recent methods.

Obviously, financial aspect is the most important pillar of technology development. Furthermore, the role of venture capital in developing small and medium size knowledge-based institutions is vital. However, startup portfolio selection and venture capital firms’ syndication have always been critical challenges in VC industry and the need for integrated methods based on sophisticated quantitative techniques are always being felt. In this research, startup portfolio optimization is simulated which is more similar to real world problems rather than other research. In order to attain this goal, preferences of startups as decision-makers and the interaction between investees and investors are considered. Concerning the complexity of the problem, the best-known model to simulate this problem is an agent-based modeling and also by using harmony search algorithm, the optimization procedure is successfully implemented. Through implementing this procedure, not only the portfolio’s return on investment is optimized but also venture capital firms’ syndication and their share is then determined. Finally, several numerical illustrations are solved using the proposed combinatorial model.

In solving real life fractional programming problem, we often face the state of uncertainty as well as hesitation due to various uncontrollable factors. To overcome these limitations, the fuzzy rough approach is applied to this problem. In this paper, an efficient method is proposed for solving fuzzy rough multiobjective integer linear fractional programming problem where all the variables and parameters are fuzzy rough numbers. Here, the fuzzy rough multiobjective problem transformed into an equivalent multiobjective integer linear fractional programming problem. Furthermore, from the obtained problem, five crisp multiobjective integer linear fractional programming problems are constructed and the resultant problems are solved as a crisp integer linear programming problem by using Dinkelbach concept. Finally, the effectiveness of the proposed procedure is illustrated through numerical examples.

General type-2 fuzzy logic systems (GT2 FLSs) have drawn great attentions since the alpha-planes representation of general type-2 fuzzy sets (GT2 FSs) was proposed. The iterative of type-reduction (TR) algorithms are difficult to apply in practical applications. In the enhanced types of algorithms, the Nagar-Bardini (NB) algorithms decrease the computation complexity greatly. In terms of the Newton-Cotes quadrature formulas of numerical integration techniques, the paper extends the NB algorithms to three different forms of weighted NB (WNB) algorithms according to the comparisons between the sum operation in NB algorithms and the integral operation in continuous version of NB (CNB) algorithms. The NB algorithms just become a special case of the WNB algorithms. Four simulation examples are used to illustrate and analyze the performances of the WNB algorithms while performing the centroid TR of GT2 FLSs. It also shows that, in general, the WNB algorithms have smaller absolute error and faster convergence speed compared with the NB algorithms, which provides the potential value for T2 FLSs designers and users.

Hypothesis tests are a statistical decision-making tool for testing if a hypothesized parameter value is supported by the sample data or not. Vagueness and impreciseness in the sample data require fuzzy techniques to be employed in the analysis. These techniques can be based on intuitionistic fuzzy sets, hesitant fuzzy sets, type-2 fuzzy sets, neutrosophic sets, or spherical fuzzy sets. In this paper, Z-fuzzy numbers are used to capture the vagueness in the sample data and develop Z-fuzzy hypothesis testing. A Z-fuzzy number is represented by a restriction function that is usually a triangular or trapezoidal fuzzy number and a reliability function representing the confidence level to the restriction function. Illustrative examples for left and right sided hypothesis testing and sensitivity analyses are presented.

Natural monotonic linguistic language is widely used to express experts’ uncertain subjective appraisal opinion, such as “More than”, “At least”, “Less than” and “At most”, which reveals explicit information about performance range and implicit information about his hiding preference on a linguistic scale. A novel computational method for monotonic hesitant fuzzy linguistic terms is developed to transfer experts’ uncertain appraisal information to decision-making data, which can systematically consider and mine expert’s obvious explicit and hidden implicit appraisal information. Specially, the comprehensive meanings of monotone decreasing and increasing hesitant fuzzy linguistic terms are investigated, in which both explicit and implicit appraisal information are explored to reveal its actual meaning. Additionally, Weibull distribution functions with three parameters are fitted considering the comprehensive meaning of monotone increasing appraisals, which is determined by a multi-objective programming model following ABC classification method. Symmetry principle is employed to confirm the expression of monotone decreasing appraisals, which are transferring from monotone increasing appraisals with same length of domain field. Moreover, feasibility analysis is explored to show the influence of parameters on decision-making precision. Finally, a numerical study is conducted to show the feasibility and advantage of the new method, which can effectively improve the precision of computational transfer by comparing to previous method.

Medical and health text documents pose a challenge for data handling and retrieving the relevant and meaningful documents. Automatically retrieval of significant knowledge with a better understanding of medical and health documents is a challenging task. One popular approach for thematically understand the medical and health text documents and finding the topics from these documents is topic modeling. In this research, we propose a novel topic modeling approach Fuzzy k-means latent semantic analysis (FKLSA) by using the fuzzy clustering. Our method generates local and global term frequencies through the bag of words (BOW) model. Principal component analysis is used for removing high dimensionality negative impact on global term weighting. Previous work shows that in medical and health documents redundancy issue has a negative impact on the quality of text mining. Therefore, the main achievement of FKLSA is the handling of the redundancy issue in medical and text documents and discover semantically more precise topics. FKLSA is socially utilized for finding the themes from medical and health text corpus. These topics are further used for text classification and clustering tasks in text mining. Experimental results show that FKLSA performs better than LDA and RedLDA for redundant corpora. FKLSA’s time performance is also stable with an increase in number of topics and thus better than LDA and LSA on a big twitter heath dataset. Quantitative evaluations of the real-world dataset for health and medical documents show that FKLSA gives a higher performance as compared to state-of-the-art topic models like Latent Dirichlet allocation and Latent semantic analysis.

  Evaluating the performances of a set of entities called decision making units (DMUs) which convert multiple inputs into multiple outputs has long been considered as a difficult task because one is dealing with complex economics. This work proposes an inequality approach to evaluate the performances of DMUs. Inequalities consist of expressions of the production possibility set and the line segments joining the evaluated DMU to the positive output-axes. However, in real-world application involving performance measurement, inputs and outputs are often imprecise and fluctuated. In this case, a fuzzy inequality approach is proposed to evaluate the performances. What is more, fuzzy relative efficiency is dependent upon the number of solutions. Furthermore, the minimal element is used to distinguish the fuzzy relative efficient DMUs. Finally, two numerical examples are used to illustrate the fuzzy approach and compare the results with those obtained with alternative fuzzy approaches.

 This paper presents a novel adaptive fuzzy sliding mode (AFSM) control scheme for a vehicle steer-by-wire (SbW) system. Initially, the dynamics of the SbW system are described by a second-order differential equation where the Coulomb friction and the self-aligning torque are treated as external disturbances. Furthermore, an AFSM controller is designed for the SbW system, which utilizes an adaptive law to estimate both the Coulomb friction and the self-aligning torque, a sliding mode control component to deal with the parametric uncertainties and unmodeled dynamics, and a fuzzy strategy to strike a good balance between the chattering-alleviation and the tracking precision. The stability of the control system is verified in the sense of Lyapunov, and the selection of control parameters is provided in detail. Lastly, experiments are carried out under various road conditions. The experimental results demonstrate that the developed AFSM controller possesses superiority in terms of higher tracking accuracy, stronger robustness and a better balance between the control precision and smoothness in comparison with a conventional sliding mode (CSM) controller and a boundary layer-based adaptive sliding mode (BLASM) controller.

This paper proposes a novel multi-agent unit commitment model under Smart Grid (SG) environment to minimize the demand satisfaction error and production cost. This is a distributed solution applicable in non-deterministic environments with stochastic parameters intending to solve Distributed Stochastic Unit Commitment (DSUC) problem. We use multi-agent reinforcement learning (RL) in which agents learn as independent learners to cooperatively satisfy the demand profile. The learning mechanism proceeds using a reward signal, which is given based on the performance of the entire system as well as the impact of the joint action of the agents. The learning agent utilizes a novel multi-agent version of Fuzzy Least Square Policy Iteration (FLSPI) as a model-free RL algorithm to approximate Q-function. Based on this approximation, the agent makes the best decision to achieve the goals while considering the constraints governing the system. Uncertainty sources in our definition of the problem are fluctuations in the predicted demand function, random productions of clean energy generators and the possibility of accidental failure in power generators. Training for one time interval (i.e. one season or one year) consisting of several time intervals (i.e. days) can be simultaneously conducted by one trial in our method. We have conducted our experiment in two different frameworks. These frameworks are defined based on the problem complexity in terms of the number of generators, the uncertainties in the environment and the system constraints. The results show that the learning agent learns to satisfy the demand profile as well as other constrains.

In this paper, the notions of (semi) topological basic algebra and (semi) topological implication basic algebra are introduced, along with evaluating their properties. Then, different operations are defined based on basic algebras and the relationship between semicontinuity and continuity of operations is considered. In addition, the separation axioms on (semi) topological basic algebras are investigated by considering some conditions implying that a (semi) topological basic algebra becomes a T _i - space, for i ∈ {0, 1, 2}. In the sequel, some relations between (weak) ideals and (weak) filters of basic algebras are obtained and (left) topological (implication) basic algebra is constructed by using the concepts of (weak) filters, which is a zero dimensional, normal, disconnected, locally compact and completely regular (left) topological space. Further, the notion of quotient basic algebras are presented along with evaluating the interaction of topological basic algebras and topological quotient basic algebras. Finally, it is proved that there is an implication basic algebra IB * with cardinality n + 1 and filter F^* = F ∪ {z^*}, which z * ∉ IB for any implication basic algebra IB of cardinality n and filter F. Accordingly, it is proved that there is at least one nontrivial regular and normal topological implication basic algebra of cardinality n.

In the field of engineering economy, engineering investment selection is a common problem, where the preference information is usually intuitionistic and fuzzy. To deal with the consistency and integrity of the information in the selection process, the aim of this article is to extend the superiority and inferiority ranking method and use the interval-valued intuitionistic fuzzy theory, where the individual evaluation values and the weights information of criteria and decision-makers are all described by interval-valued intuitionistic fuzzy numbers. First, some concepts of interval-valued intuitionistic fuzzy set are introduced. Then, the interval-valued intuitionistic fuzzy superiority and inferiority ranking (IVIF-SIR) method is developed. Moreover, an engineering investment selection model based on IVIF-SIR method is investigated. Finally, an illustration of choosing investment alternatives is used to prove the developed approach and a comparative study is also use to demonstrate the effectiveness.

Reliability in the electric power system is fundamental to the development of society, for which rapid and accurate methods of fault identification are required. Faults in distribution insulators are hardly visible and the fault behavior is often intermittent, which makes its diagnosis a difficult task. Fault diagnosis with the ultrasound equipment has been used efficiently since this equipment is directional and not influenced by sunlight. However, the interpretation of the signal generated by this equipment requires an experienced operator and they are also susceptible to provide false diagnostics. The use of advanced algorithms to classify electrical system conditions has been proven as a great alternative to automate operator decisions. This article proposes the use of artificial intelligence algorithms such as single-layer and multilayer Perceptron for classification of distribution insulators conditions. The use of artificial neural networks for insulator classification is an innovative subject. Some researchers have already worked on partial discharges however not specifically for fault classification in insulators of distribution networks. The application of this technique can make the inspection of the electrical system automated and, in this way, more accurate and efficient. The results of the analysis showed that the application of signal linearization technique joint with artificial intelligence is a good alternative to locate faults in insulators.

Hyperparameter optimization is a crucial step in the implementation of any machine learning model. This optimization process includes regularly modifying the hyperparameter values of the model in order to minimize the testing error. A deep neural learning model hyperparameter optimization process includes optimizing both the model parameters and architecture. Optimizing a model’s parameters involves deciding the values of parameters, such as learning rate and batch size. Optimizing architectural hyperparameters includes deciding the shape of the deep neural learning model, i.e., the number of layers of individual types and the number of neurons in a certain layer. The state-of-the-art hyperparameter optimization methods don’t optimize the position of the hyperparameter within the model architecture. In this work, we study the effect of changing a hyperparameter within the deep learning model architecture. Thus, we propose an architectural position optimization (ArchPosOpt) method for model architectural hyperparameter optimization. ArchPosOpt extends three different hyperparameter optimization techniques, namely grid search, random search, and Tree-structured Parzen Estimator (TPE), to include a new dimension of hyperparameter optimization problem – the hyperparameter position. We show through a set of experiments that the position of the hyperparameters does matter for model performance as well as the hyperparameter values.

Intelligent optimized energy management and prediction model in electric vehicles received attraction of the researchers in the last couple of years. Several techniques and models have been proposed in the literature for optimized energy management and control, but the trade-off between occupant comfort index and the energy consumption is still a significant challenge to the research community. In this paper, we have proposed a model based on learning to optimization and learning to control for user comfort maximization and efficient energy consumption. The proposed model is comprised of three layers; prediction module, learning to optimization module and learning to control module. In the prediction module, we have used the Kalman filter for noise removal and prediction of environmental parameters. In learning to optimization module, the bat algorithm has been used for user comfort maximization and energy consumption minimization. Furthermore, we have used the learning module with optimization module in order to tune the user preferences parameters in the comfort index formula used in the bat optimization algorithm. Likewise, the learning module has been used with the conventional fuzzy logic controller in order to improve its performance. In the conventional fuzzy logic controller, the membership functions boundaries are usually determined through hit and trial method, and once the membership functions are determined, they remain fixed for the entire process. In the learning to control module, the membership functions tuning is carried out. The membership functions are continuously tuned to get effective results. Experimental results indicate that the proposed method performs better as compared to the conventional methods and achieves improved user comfort with reduced energy consumption.

The spatial colocation problem is totally different from the traditional association rule problem, as it operates on spatial data and not on conventional transaction data. In this work, a spatial colocation mining framework is proposed that mines spatial colocation of image-objects present in images using a tensor factorization approach. The framework takes in image data directly, tensorize it and perform the mining task, thus eliminating the need of converting into a transaction based approach. An interestingness measure called, spatial dominance is also proposed in this work. This measure is an indicator of the prevalence of the mined colocation pattern. Algorithms are designed in this framework, first to map the classified pixels as members of image-objects, which is a pre-stage before mining and second to find spatial colocation patterns. Experiment results are provided to show the strength of the spatial colocation mining algorithm.

In this paper, an iterative numerical method has been developed to solve nonlinear fuzzy Volterra integral equations based on three-point quadrature formula. The error estimation of the method is obtained based on Lipschitz condition and in order to confirm the yielded theoretical results, we perform the iterative method on some numerical examples.

 An unscented Kalman filter can be applied for the experimental learning of the solar dryer for oranges drying and the greenhouse for crop growth to know better the processes and to improve their performances. The contributions of this document are: a) an unscented Kalman filter is designed for the learning of nonlinear functions, b) the unscented Kalman filter is applied for the experimental learning of the two mentioned processes.

The prompt enhancement of Telecom turned to be a vibrant and economical industry, which comprises an intrinsically great perspective for customer churn, requiring exact churn prediction models. In recent times, there has been phenomenal responsiveness in the development of feature selection methods for a large number of datasets. Through this research work, a High Relevancy and Low Redundancy (HRLR) approach by consuming Vague Set (VS) has proposed for selecting the subset of features from the features set. This proposed method is based on the Minimum Redundancy and Maximum Relevancy (MRMR) approach by using Vague Set. The proposed HRLR-VS method is based on the filtered approach feature selection, where the features are selected only when the measure of feature-class relevancy is maximized and a measure of feature-feature redundancy is minimized. The collaboration of similarity measures and ranking algorithms are prepared by utilizing the vital notions of Vague Sets information energies by Information Gain, Gain Ratio, and Chi-Square methods. The projected approach has been employed with the Particle Swarm Optimization for probing the best feature subset. Further, it measures the efficacy of the projected approach HRHL-VS for telecommunication dataset. The performance metrics like Accuracy, Kappa Statistics, True Positive Rate, Precision, F-Measure, Recall, MAE, RRSE, RMSE and RAE are considered in this paper for evaluating the proposed HRLR-VS method. The proposed HRRL-VS method has compared with existing literature approaches like mRMR and FCBF. From the result obtained in this paper, the proposed HRLR-VS method better results in all aspects for selecting the feature subset in telecommunication dataset.

Attribute and class noises are the two important sources of Corruptions (noise) contained in real-world datasets which may deteriorate data interpretation and accuracy. Class noise has potentially serious negative impacts compared to attribute noise, however, the existing major class noise detection methods are not able to address this problem efficiently. To overcome issues related to detection and the elimination of class noise, we suggest a new noise filtering approach able to identify and remove class noise, called Multi-Iterative Partitioning Class Noise Filter (MIPCNF). Since there is no single filter that consistently outperforms its counterparts in all database types and in different levels of noise, our approach relies on an algorithm in which several rounds of class noise detection are performed on different partitions of the data using several classifiers. Therefore, we use different filtering strategies: iterative noise filter, partitioning filter and ensemble-based filter. The experimental results, on 14 real-world datasets, and statistical analysis, show that our method is not only overcoming the higher noise but also over-performing latest class noise detection and elimination strategies in different levels of noise.

In today’s world, there have been lots of unique optical character recognition systems. One drawback of these systems is that they cannot work effectively on natural scene images where the text is not only subject to different orientations, lightning, and background but can be of multiple scripts as well. The paper, proposes a state of the art algorithm to detect texts of different dialects and orientations in an image. The whole text detection pipeline is divided into two parts. First, extraction of probable text regions in an image is performed based on a combination of statistical filters, which results in a high recall. These regions are then fed to an Artificial Neural Networks (ANN) based classifier which classifies whether the proposed regions are text or non-text, which increases the overall precision. The validity of the algorithm is verified on the most challenging bilingual text detection dataset MSRA-TD500 and a promising F1 score of 0.67 is reported.

An e-learning system offering a personalised learning path will be vastly appealing to the learners. Adaptive techniques when employed in e-learning can sustain the interest and motivation of the learners and help them to complete the enrolled courses successfully. In addition, it would improve their performance and thus, enhance the overall learning experience. Personalisation takes into consideration the characteristics of the individual learner and the diversity in his/her needs. The main challenge is finding a match between these individual characteristics and the sequence of the learning content. It is a complex task to implement as it involves selection of the appropriate material from a vast amount of the available learning materials. It is a challenge to perform this process manually as it requires both technical savvy and pedagogical skills. In this paper, a stigmergy model is proposed, which was applied to build a customised learning path. The aim was to provide personalisation that satisfied the needs of an individual in a widely heterogeneous e-learning environment. Compared with the traditional teaching method, this tailored learning path, generated using the proposed approach, shows promise and was found to enhance the performance of the learners.

Scientific workflow applications include a set of tasks, which have complex inter dependencies with each other, along with a large number of parallel tasks. The problem of scheduling such application tasks involves careful decisions on determining the sequence in which it can be processed, causing high impact on the cost of execution and makespan (execution time), when executed on a cloud computing system. Achieving optimal schedule, which can optimize both of these objectives while keeping the dependencies between tasks intact is a real challenge. In this work, a non-dominated sorting based particle swarm optimization approach to find an optimal schedule for workflow applications in cloud computing systems is proposed. A graph is used to represent tasks in the workflow and the dependencies among tasks. The optimization problem is modelled using integer programming formulation, subject to capacity and dependency constraints among tasks and Virtual Machines (VM). Simulation studies and result comparison with other representative algorithms in the literature shows that the proposed algorithm is promising.

The figurative language involving sarcasm on social networks is evolving the way how the humans use computers to communicate. Consequently, artificial intelligence techniques are applied in various scenarios to make the social networking more intelligent - for instance, identification of figurative language. Identifying both literal and non-literal meaning is not easy for a machine and it is hard even for people. Therefore, novel and exact frameworks ready to identify figurative languages are important. In sarcasm detection, this is even more challenging because sarcasm changes the polarity of an evidently positive or negative expression into its inverse. To maintain a distance for a sarcastic message being comprehended in its unintended actual meaning, in micro-blogging sites, for example messages on Twitter, sarcasm is frequently set apart with a hashtag for example, ’#sarcastic’, '#sarcasm', ’#not’ etc. Moreover, the customer reviews may also contain some element of sarcasm. To contribute to this area, we gathered the data of tweets and reviews from Twitter, thesarcasmdetector.com, and Kaggle and proposed a mechanism for detecting sarcasm automatically using a classifier. A detailed experimental study was also conducted to evaluate the proposed mechanism. The results of this study were quite promising and proved the effectiveness of our approach.

Wireless sensor networks (WSNs) found application in many diverse fields, starting from environment monitoring to machine health monitoring. The sensor in WSNs senses information. Sensing and transmitting this information consume most of the energy. Also, this information requires proper processing before final usages. This paper deals with minimising the redundant information sensed by the sensors in WSNs to reduce the unnecessary energy consumption and prolong the network lifetime. The redundant information is expressed in terms of the overlapping sensing area of the working sensors set. A mathematical model is proposed to find the redundant information in terms of the overlapping area. A combined meta-heuristic approach is used to achieve the optimal coverage, and the effect of the overlapping area is considered in the objective function to reduce the amount of redundant information sensed by the working sensors set. Improved genetic algorithm (IGA) and Binary ant colony algorithm (BACA) are used as meta-heuristic tools to optimise the multi-objective function. The objective was to find the minimum number of sensors that cover a complete scenario with minimum overlapping sensing region. The results show that optimal coverage with the minimum working sensor set is achieved and then by incorporating the concept of overlapping area in the objective function, sensing of redundant information is further reduced.

 Since the non-singleton fuzzy logic controllers (NFLCs) can effectively reflect the uncertainty brought by the inputs, they are used for balance control and position control of the mobile two-wheeled self-balancing robot (MTWSBR) in this paper. The similarity between the inputs and the antecedent fuzzy sets as the firing strength, that is, the similarity-based non-singleton fuzzy logic controller (Sim-NFLC), is proposed to deal with the problem of information loss caused by the standard non-singleton fuzzy logic controller (Sta-NFLC) in terms of the interaction of the inputs and antecedents. A comparative study among singleton fuzzy logic controllers (SFLCs), Sta-NFLCs and Sim-NFLCs, and interval type-2 fuzzy logic controllers (IT2FLCs) and general type-2 fuzzy logic controllers (GT2FLCs) are also shown. The simulation results show that the performance of Sim-NFLCs is better than that of SFLCs and Sta-NFLCs. The similarity-based general type-2 fuzzy logic controller (Sim-NGT2FLC) gets the best performance in handling the input uncertainty.

 Vision-based Sign Language Recognition has been an open research problem since decades. Many existing methods for sign recognition works well under restricted laboratory conditions but failed to support real-time scenarios because extraction of manual and non-manual movements with constantly changing shapes of signs are considered as tedious problem in machine vision and machine learning. To overcome these shortcomings, an interactive real time class level gesture similarity based sign recognition using Artificial Neural Network is presented in this paper. The method uses the sign images and starts with enhancing the image quality. The quality enhancement is performed by equalizing the histograms of luminance and contrast. The features of hand as subunits from quality improved image have been extracted by template matching techniques. Extracted features are used to generate neural network and trained with different class of signs. The classification is performed by measuring the class level gesture similarity measure towards each class of signs and images. Based on the measure estimated, the method classifies the image and sign. The result produced to the user has been iterated based on the actions provided by the user. The method is capable of iterating the result and recognition till the user gets satisfied. The method produces higher accuracy in sign recognition and reduces the false ratio.

Researchers have studied several different types of directed shortest path (SP) problems under fuzzy environment. However, few researchers have focused on solving this problem in an interval-valued fuzzy network. Thus, in order to light these, we investigate a generalized kind of the SP problem under interval-valued fuzzy environment namely all pairs shortest path (APSP) problem. The main contributions of the present study are fivefold: (1) In the interval-valued fuzzy network under consideration, each arc weight is represented in terms of interval-valued fuzzy number. (2) We seek the shortest weights between every pair of nodes in a given interval-valued fuzzy network based on a dynamic approach. (3) In contrast to most existing approaches, which provide the shortest path between two predetermined nodes, the proposed approach provides the interval-valued fuzzy shortest path between every pair of nodes. (4) Similarly to the competing methods in the literature, the proposed approach not only gives the interval-valued fuzzy weights of all pairs shortest path but also provides the corresponding interval-valued fuzzy APSP. (5) The efficiency of the proposed approach is illustrated through two applications of APSP problems in wireless sensor networks and robot path planning problems.

This study investigated fault information estimation and diagnosis using a novel approach based on an integrated fault estimator and state estimator for an electric motor in coal mine. The proposed scheme uses a self-constructing fuzzy unscented Kalman filter (UKF) system to simultaneously estimate the system state and approximate the fault information. To achieve this, a generalized linear discrete-time system of the electric motor in coal mine without faults was first transformed into an equivalent standard state-space system with faults. Then, the self-constructing fuzzy UKF system was designed in order to obtain the fault information. According to fault information obtained fault detection experiments based on fuzzy clustering were performed with the proposed scheme and the fault feature parameters required for fault isolation were determined. Finally, the scheme was applied to an electric motor in coal mine to demonstrate the effectiveness of the proposed fault estimation and diagnosis approach. Results of the simulation illustrate the effectiveness of the proposed method.

Uncertain differential equation with jumps is a type of uncertain differential equation driven by both Liu process and uncertain renewal process. Many concepts of stability for uncertain differential equation with jumps have been investigated. This paper presents a concept of exponential stability for uncertain differential equations with jumps, and gives a sufficient and necessary condition for the linear uncertain differential equation with jumps being exponentially stable. The relationships among stability in measure, stability in in mean, stability in p-th moment, and almost sure stability for uncertain differential equation are also discussed.

Grasshopper optimization algorithm (GOA) is proposed for imitating grasshopper’s behavior in nature, which has the disadvantages of slow convergence speed and unbalanced exploration and exploitation, etc. Therefore, an algorithm called GOA_jDE, which combines GOA and jDE is proposed to improve the optimization performance. Firstly, the adaptive strategy is introduced into DE to improve the global search ability in the proposed algorithm. Secondly, the combination of jDE and GOA greatly improves the convergence efficiency while maintaining the population diversity. Finally, it can be observed in the work that the proposed algorithm improves the convergence speed and calculation precision. In the subsequent experiments, 14 well-known test benchmark functions are used to compare the advantages of GOA_jDE. The experimental results illustrate that the performance of proposed algorithm has significant improvement, which also proves the feasibility and effectiveness. Considering the complexity of engineering problems, three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) are used to evaluate the performance of the proposed algorithm. In addition, the classical engineering design results proves the merits of this algorithm in solving real problems with unknown search spaces.

Maintenance Scheduling and Routing (MS&R) is critical for the offshore wind farm to reduce maintenance cost. Although different models are proposed, the turbine operating conditions and the forecasted wind resources in the maintenance horizon are still less accounted in these current models. To address this issue, this research proposes a novel mathematical model to optimize the MS&R problem by highlighting the significance of turbine production loss (PL) before and during maintenance activities. In the proposed methodology, the PL term takes the most up-to-date wind turbine power curve and the forecasted wind resources as model inputs. Subsequently, a novel Genetic Algorithm (GA) solver is designed to minimize the PL of wind turbines together with the technician salaries and the transportation costs. The outcome of the proposed model gives a detailed maintenance plan with maintenance schedules, vessel routes, technician assignments, and cost breakdowns. Validation of the proposed model is implemented on real-world data collected from an offshore wind farm with several 4 MW wind turbines. The result demonstrates the effectiveness and superiority of the proposed method, and some practical findings are also summarized in the conclusions.

We study the problem of Aleksandrov in fuzzy n-normed spaces and prove that every surjective fuzzy function preserving unit n-distance is affine, and thus is a fuzzy n-isometry. Finally, we show that every fuzzy function preserving two fuzzy unit n-distances confirmers the result of Benz theorem when target space is fuzzy n-strictly convex.

 We study, in this paper, some notions related to Pythagorean fuzzy soft sets (PFSSs) along with their algebraic structures. We present operations on PFSSs and their peculiar characteristics and elaborate them with real life examples and tabular representation to develop the affluence of linguistic variables based on Pythagorean fuzzy soft (PFS) information. We present an application of PFSSs to the multi-criteria group decision-making (MCGDM) related to site selection, accompanied by Algorithm and flow chart. We develop PFS TOPSIS method and PFS VIKOR method as extensions of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) respectively. Finally, we rendered an application in stock exchange investment problem and tackled it by both PFS TOPSIS and PFS VIKOR methods.

In this paper we define the notions of a norm and a prenorm on BL-algebras and we give some methods for constructing them. We introduce (pre)metrics on BL-algebras and find some connections between them and (pre)norms. Using prenorms, we find some conditions that a BL-algebra become a metric space. Finally, we show that if there exists a prenorm on a BL-algebra A, then for any filter F in A, there are topologies U and V on A and on A/F respectively such that ( A , U ) and ( A / F , V ) are (semi)topological BL-algebras.

 Traditional clinical diagnostic aid systems for medical images are facing challenges of reliability and interpretability. Artificial intelligence has the potential to bring driving changes to disease diagnosis methods through rapid traversal of medical images and efficient classification. However, the application of artificial intelligence in the field of medical image still faces challenges. Our method combines the multiple modalities of attention which consider the most discriminative part in the images. The proposed classification method is tested on the microscopic image dataset with 40 leukocyte categories, which achieves top-1 accuracy of 84.21% and top-5 accuracy of 99.44% during the testing procedure. And experiments on the dermoscopic image dataset show that our method has good generalization ability across multiple imaging modalities.

Investigating clusters of experts is an interesting topic in the large-group decision-making (LGDM) problem, since being familiar with patterns (groups) of experts is beneficial to some other actions needed for decision-making (e.g., reconciliation of opinions derived from different expert groups). However, not too much attention has been paid to expert clustering in the LGDM problem under a linguistic environment. Besides, it seems that only the decision information is utilized to group experts while the auxiliary (outside) knowledge (e.g., expertise and occupation) about these experts has not been fully considered during the clustering process. To address this issue, this study proposes a hybrid method integrating outside knowledge about experts with practical preference information under the interval-valued linguistic environment to cluster experts. The method consists of four elements: pre-clustering of experts according to the given knowledge, the optimization model to transform the interval-valued 2-tuple linguistic (IV2TL) decision information, the data envelopment analysis-discriminant analysis (DEA-DA) model to deal with a two-cluster issue, and iterative clustering based on the DEA-DA model to cluster experts into multiple clusters. The feasibility and validity of the proposed method are illustrated with a real-world example. A comparison with the maximal tree clustering method in the linguistic environment is provided.

 A normal wiggly hesitant fuzzy set (NWHFS) is viewed as a powerful and useful tool to dig the potential uncertainty of decision makers (DMs) in the process of expressing their preferences, which can be regarded as an extended form of the traditional hesitant fuzzy set (HFS). The NWHFSs have the ability of both reserving the original hesitant fuzzy information completely and exploring potential fuzziness of DMs, which assist the DMs in advancing the decision-making efficiency and derive the reasonable ranking orders finally. To fully exert the strengths of the combined power average and Muirhead mean operators, based on the proposed distance measure of normal wiggly hesitant fuzzy elements (NWHFEs), we extend the power Muirhead mean (PMM) to the normal wiggly hesitant fuzzy environment and develop the normal wiggly hesitant fuzzy PMM (NWHFPMM) and its weighted form included. After that, several representative cases and attractive properties of the proposed normal wiggly hesitant fuzzy operators are investigated in depth. Finally, a novel MADM method for solving normal wiggly hesitant fuzzy decision-making problems is developed, then a numerical example is performed to analyze the strengths of our proposed method, which in the way of comparing with other existing studies.

Recently, the TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) method, which can characterize the decision makers’ psychological behaviors under risk, has been introduced to handle multiple attribute group decision making (MAGDM) problems. Moreover, the probabilistic linguistic term sets (PLTSs) are effective tool for depicting uncertainty of the MAGDM problems. In this paper, we extend the TODIM method to the MAGDM with PLTSs. Firstly, the definition, comparative method and distance of PLTSs are simply introduced, and the steps of the classical TODIM method for MAGDM problems are presented. Then, on the basis of the conventional TODIM method, the extended TODIM method is proposed to deal with MAGDM problems in which the attribute values are depicted in the PLTSs, and its significant characteristic is that it can fully consider the decision makers’ bounded rationality which is a real action in decision making. Finally, a numerical example for green supplier selection is proposed to verify the developed approach and its practicality and effectiveness.

Nowadays there are lots of fatal diseases are growing at a rapid rate. We consider about four primary diseases like jaundice, diabetes mellitus, yellow fever, and cholera. In this paper, we design a novel method with the help of fuzzy and FPGA system for prediction multiple diseases in a rural area. Association rule mining technique helps to define fuzzy rules, which implemented on both Spartan3-E and Artix-7 FPGA kit. Due to this implementation, it is easier to design a cost-effective and portable system for multi-disease prediction. The innovation lies in design a low power FPGA and meticulousness method for identification, prediction of four fatal diseases. The whole plan has tested on Xilinx and Cadence tool for generating RTL model and Layout design.

This paper organizes a two-stage DEA models by taking into account undesirable output with fuzzy stochastic data. A normal distribution with fuzzy component adopted for inputs, intermediate outputs, desirable and undesirable outputs. We propose, finally, a linear and feasible model in deterministic form. To achieve this aim, a possibility-probability approach is applied on a reform of two-stage DEA models occupied with undesirable outputs. A case study in the banking industry is presented to exhibit the efficacy of the procedures and demonstrate the applicability of the proposed model.

AMS Mathematics Subject Classification (2000): 62A86, 90C70

Pythagorean fuzzy sets (PFSs), as an extension of intuitionistic fuzzy sets (IFSs) for dealing with uncertainty information, have attracted considerable attention in the decision-making area. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is one of the most popular decision-making approaches. In the TOPSIS method, the desired alternative should have not only the shortest distance from the positive ideal solution, but also the farthest distance from the negative ideal solution. Similarity measures play an important role in assessing the degree between ideal and proposal alternatives in decision-making. Thus, this paper aims to provide an extended TOPSIS by developing new similarity measures with PFSs and applying it to multi-criteria decision-making (MCDM) problems. The main contributions of this paper are as follows: (1) development of three new similarity measures with PFSs, and investigation of their properties; (2) extension of the TOPSIS method based on the proposed similarity measures; and (3) establishment of a Pythagorean fuzzy decision-making method using the improved TOPSIS method. A case study on the selection of a project delivery system is conducted to show the applicability of the presented approach.

 The measure of the similarity between intuitionistic fuzzy sets (IFSs) is an important topic in IFSs theory. In this paper, we propose two computational formulae for similarity measures on IFSs based on a quaternary function called intuitionistic fuzzy equivalence. We first propose the concept of intuitionistic fuzzy equivalence. Then we give a computational formula for intuitionistic fuzzy equivalencies (i.e., Eq. (1)), which is obtained from combining dissimilarity functions and fuzzy equivalencies. Based on Eq. (1), we obtain two computational formulae for similarity measures on IFSs. The first one is obtained by aggregating Eq. (1). The second one is obtained by respectively aggregating the numerator and the denominator of Eq. (1). We also examine some properties of the proposed similarity measures on IFSs. Finally, we make a comparison between the proposed similarity measures on IFSs and those existing ones in the literature through several counter-intuitive cases.

Backward uncertain differential equation is a specical type of differential equation driven by a Liu process. There are some concepts of stability such as stability in measure, stability in mean, stability in p-th moment, stability moment exponential and almost sure stability of backward uncertain differential equations have been proposed. As a supplement, this paper gives a concept of stability in uncertain distribution of backward uncertain differential equation. Some sufficient conditions for a backward uncertain differential equation being stable in uncertain distribution are provided. In addition, this paper further discusses their relationships among stability in uncertain distribution, stability in uncertain measure, stability in mean and stability in p-th moment. Last, this paper discusses some examples to illustrate the theoretical considerations.