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

This special issue of the Journal of Intelligent & Fuzzy Systems contains selected articles of WorldCist 2018, the 6th World Conference on Information Systems and Technologies held in Naples, Italy, 27–29 March 2018.

At present new sophisticated attacks make organizations’ IT infrastructure (ITI) break-in more professional and dangerously effective. All organizations must oppose this properly designed and centralized information security (IS) management systems. Learn from the past helps to avoid the consequences of serious IS incidents in the future. Therefore, IS management is necessary for rapidly detecting IS incidents, minimizing loss and destruction caused by then, mitigating the vulnerabilities exploited and restoring organizations’ ITIs. This process can be implemented based on Security Operations Centers (SOCs) and Security Intelligence Centers (SICs) as their next evolution step. SOCs’ main functions and serious limitations are defined. The SICs’ concept and functioning are analyzed. The main areas of further research conclude the paper.

In order to intuitively analyze the changing rules and characteristics of coal mine accidents in China, we should find the approximate algebraic fitting relation between the number of gas accidents and time variation and better provide theoretical support for the intelligent prediction and prevention of gas accidents. According to the changing trend of the figure of gas accident, the numerical analysis method of discrete data was used to establish different mathematical models of coal mine gas accidents based on exponential function, power function and polynomial respectively by MATLAB software, and compare the errors of different models. Comparing with the mathematical models, it can be concluded that the law of year and the number of gas accidents in coal mines accord with the results of the fourth-degree polynomial fitting, indicating that the number of gas accidents shows a quantitative law of fitting, the intelligent prediction for the number of gas accident is realized. The results of the model show that in recent years in our country, gas treatment technology and management have achieved remarkable results, and the correlation prediction and analysis have been carried out based on the model results.

In order to improve the quality of food safety information disclosure in traceability system, the study of the influence of related factors on their internal relations in the process of information disclosure was carried out. First, a model of food safety information disclosure between food enterprises and government regulators was built based on the theory of evolutionary game. And then, the route of model evolution was intelligent simulated by Matlab, and different key parameters was adjusted individually to lead the evolution to an ideal condition. The result shows that the problem of false food safety information cannot be solved effectively only relying on government supervision. The power of societal supervision is necessary to clear the market of false food safety information. The combination of the increase in governmental penalty, lowering of governmental supervision cost, improvement of societal supervisory strength, and increase the societal losses of the enterprises due to false food safety information, will effectively result in a quality increase of food safety information published by enterprises, and paves its way for the formation and perfection of an ideal food traceability system.

This paper constructs evaluation index system of input-output of Smart City on the basis of the present evaluation researches of Smart City, urban information and urbanization from the view of input-output theory; and via utilizing related methods of Data Envelopment Analysis (DEA) takes intelligent evaluation research of the change of effectiveness and construction efficiency of Smart City construction in Wuhan among 2013–2016. The result reveals Smart City construction of Wuhan has got a certain achievements and relative efficiency. So, Wuhan should stress overall layout, achieve key breakthrough, exert local features and tamp human resources foundation, perfect relevant system, guarantee innovation enthusiasm, improve financing system and promote industrial innovation so as to improve cluster scale of urban construction input, technological innovation capability and promote the construction of Smart City.

In order to analyze the change trend in risk financial assets of the retail business, this paper which is combined with the advantages of grey prediction system and Markov chain, establishes the “grey differential-Markov chain” intelligent prediction and evaluation model according to business characteristics. Meanwhile, the modeling steps are introduced, and the solution methods, model validation results, model prediction and evaluation methods are introduced in detail through specific cases. Through specific analysis, this paper aims to show that the model has high prediction accuracy and wide application value in the field of financial retail business.

Since the 1980s, the number of international strategic alliances has been on the rise and the alliance has become a novel mode of corporate growth. Therefore, the selection of strategic partners has always been relevant to the fulfillment of strategic objectives of a corporation. Since the conflicts among partners’ motivations can easily lead to the breakup of a strategic alliance, the pursuit of an appropriate strategic partner has become essential to the success of an enterprise. This paper mainly conducts a case study on the French auto-manufacturer Renault with the grey system theory and DEA analysis. Relevant data from Bloomberg BusinessWeek of 2015 have been studied, and the data concerns 19 auto-manufacturers including Group Renault in four consecutive fiscal years from 2012 to 2015. After the data analysis, an intelligent method of selecting appropriate strategic partners has been proposed. According to the intelligent method, among the 19 decision-making units on the list, Nissan Motor Company (DMU8) and Daimler AG (DMU3) are the most suitable strategic partners to form an alliance with Renault (DMU13).

The public sector is the manager and supervisor of the PPP project, and the financial compensation for the PPP project should be considered as many factors as possible. We set up a game model of private participation in PPP project. We analyzed their influence on financial compensation from 3 aspects: the marginal cost of financial funds, the proportion of private equity of PPP project and the scale of investment, and the financial compensation of the PPP project is optimized intelligently. On this basis, the countermeasures and suggestions about the financial compensation of the PPP project are put forward.

Using the panel data of China’s top five high-tech industries from 1995–2015, and adopting two-stage model, we intelligent analysis the influence that the innovation outlay of non-R&D has on the innovation efficiency of the high-tech industry. It has been concluded that the performance of our country’s high-tech industries vary from one to another, among which the highest one is computer and office equipment manufacturing and the lowest one is the manufacturing of the aerospace vehicles and its equipment. The average number of the former is 0.909, while the average number of the latter is 0.125. The mean number of the former is three times higher than the latter. In the innovation outlay, the expense on the technical reform inhibited the performance of the high-tech industry, the expense on the technology introduction and absorption has a positive effect on the high-tech industry, and there is no obvious connection between the domestic purchase expense and the performance of the high-tech industry.

The application of an intelligent logistics information platform has promoted the development of logistics informatization, and how user usage intention can be improved is an important link for accelerating the promotion of the intelligent logistics information platform. The influencing factor model of user usage intention on an intelligent logistics information platform was developed based on the technology acceptance model, and empirical verification was performed for research hypotheses based on a questionnaire survey to identify the key factors that influence user usage intention and the influencing paths. Results show that information resource, management service, and platform technology have significantly positive influences on user perceived usefulness (PU); management service, platform technology, and application effect have significantly positive influences on perceived ease of use (PEOU); PEOU has a prominently positive influence on PU; and PEOU and PU have significantly positive influences on user usage intention. On this basis, suggestions for optimizing intelligent logistics information platform were proposed.

Based on the climatic, surface and human factors of desertification, an index system and an intelligent model of desertification forewarning are established for the downstream of Tarim River. Based on the remote sensing, climate, land surface, and human data in 1990, 2000, and 2006, the ArcGIS software is used to quantify and rasterize the data of each factor. The forewarning model reveals the distribution of desertification degree in the three study periods on a scale of 30×30 m and the results are used to conduct the correction and verification of model parameters. The development trend of desertification in the study area in 2015 is then forecasted under the conditions of “intermittent water conveyance” and “no water conveyance” respectively. Through the parameter correction, the accuracy of the simulated distribution of desertification degree can be more than 90%, showing a good performance of the model. The intermittent water conveyance plays a certain role in the reversal of desertification in the downstream of Tarim River, which is a necessary measure to prevent a complete desertification. However, the reversal effect is only confined to a limited extent along the river channel and cannot thoroughly change the current situation of desertification in the study area.

As essential consumer goods for daily life, agricultural products tend to go bad from the farm to the table. Therefore, people put forward higher requirements for the quality of agricultural products. How to manage and control the quality of agricultural products and to control the quality of agricultural products by chain type are the cores of scientific research. The purpose of combining Agent technology with agricultural product quality safety traceability system was to establish the quality and safety traceability system of agricultural products based on Multi-Agent in the study. Artificial intelligence, intelligent control and intelligent detection technologies were used effectively in the system. The framework of quality and safety traceability system of agricultural products proposed in this study can be used to analyze modules in the quality of agricultural products. Through a number of Agent refining divisions of labor, the quality safety early warning and supervision control for the supply chain of agricultural products was carried out. The performance evaluation system of agricultural product quality safety traceability system based on Multi-Agent was established, so as to provide a systematic evaluation basis for the quality supply of agricultural products.

Wood modification is a processing method to change the physical and chemical properties of wood, and it can improve its corrosion resistance, flame retardancy and other properties. With the rapid development of the furniture industry, the application of high-end technology is the inevitable way to meet the needs of the vast majority of consumers. Therefore, this paper based on the PSO algorithm, the properties of the modified wood were grasped as a whole and then used the virtual reality technology, and applied the design of modified wood to the furniture field which could ensure the safety of users at the same time and also take into account the aesthetic needs of the user. In this paper, the 3D model based on virtual reality technology could be modified and the accuracy of nodes was counted, and its feasibility was determined through its use.

With the progress of modern society, human knowledge explodes, which calls for more convenient, faster and more effective methods of education. In this research, we focus on the construction of the computer aided intelligent tutoring system based on a new teaching model, and its influence on teachers’ teaching self-efficacy. Firstly, on the basis of the actual demand of ICAI system, the theoretical basis and development principle of intelligent tutoring system were expounded, and the system construction of student model and teacher model was studied. Secondly, the implementation of the quantitative assessment of cognitive ability and the teaching strategy base in DCB-ITS student model were studied and the implementation of the intelligent tutoring system was analyzed. Finally, in order to test the efficiency of the system, we carried through an empirical study. A survey to 209 college teachers was conducted, the results show that teachers who make use of the intelligent tutoring system during the teaching activities report higher scores of teaching self-efficacy, which indicates that compared with the traditional teaching model, the computer aided intelligent teaching model is of great help for teachers in making them feel more confident about teaching effectively.

The optimal path planning for tourist guides in tourist areas can effectively improve the utilization of tourism time. This paper studies and analyzes the application and improvement of ant colony algorithm in terminal tourism route planning under Android platform, and proposes an optimal tour guide path planning model based on ant colony algorithm. By giving the time constraint of tour guide path planning and the total length of tour guide path, we solve the objective function and complete the tour guide path planning for the best group in the tourist area. The experimental results show that the proposed model and the optimal path planning algorithm are more optimized.

This paper has analyzed the intelligent parking system algorithm based on camera calibration model. The camera model has been established, the initial calibration image collection is acquired by the initial image generation module, the calibration image is updated according to the output of the error analysis module, and the accuracy analysis module is used to determine whether the algorithm is over. Through the initialization, real-time updating and real-time arrangement of the time windows of the feasible paths, the collision-free path planning of multiple AGVs is realized. Experiments show that the proposed method has better robustness and flexibility and effectively improves the overall operation efficiency of intelligent parking system.

Music recognition is an interdisciplinary field, in the field of music retrieval and automatic music has very important application value in technology. In order to study the improvement method of music recognition for piano music, this paper compared the characteristics of music signals and speech signals around music related theories, discussed the selection of dimension of feature vectors, and used RBF neural network to identify 88 monosyllabic pianos. At the same time, the characteristics and calculation methods of the sound level contour with high frequency in western music and chord recognition were studied, and the specific formulas were given. The final study shows that: The improved method gives intermediate weights more inclined note nest, which has a higher accuracy than the traditional method and fault tolerance.

In view of the increasingly prominent congestion in urban traffic, it will be of great use to study the reasonable Lane configuration and optimization strategy. This paper analyzes the control and optimization algorithm for dynamic green wave used in city traffic intelligent control. The fuzzy programming model of single target is set up, and genetic algorithm is used to solve the optimal timing scheme of single intersection signal lamp. On the basis of this, the scheme of coordination control model of green wave is introduced to optimize the distribution of adjacent intersections. The optimization results show that the proposed scheme can effectively alleviate the traffic congestion and reduced the parking delay.

Under the environment of cloud, particle swarm algorithm is widely used in intelligent computer field. The combination model of the logistics service is solved. However, in solving workflow and call problems, the traditional algorithm consumes more time and does not meet the logistics application scenarios. In this paper, the particle swarm optimization algorithm was optimized and improved. The execution order of users was used to re arrange the algorithm. The experimental results showed the high efficiency of the algorithm and rapid computing speed. In order to solve the problem of particle swarm algorithm “premature”, a jamming algorithm was designed in this research. When the similarity of particle swarm was greater than a limit value, the particle position was updated optimally, and the local optimal solution and global optimal solution were retained. The particle swarm optimization algorithm could successfully avoid that the particle swarm optimization got into the local dead loop problem when searching for the optimal solution. It could be seen based on particle swarm optimization algorithm experimental results that the algorithm had high superiority in computational efficiency and speed.

In order to solve the contradiction between spectrum resource and system capacity in the mobile communication system and the radius of the housing estate has been decreasing, the microcell has appeared. In this article, the scheme which combined ray tracing and genetic algorithm were studied to solve the problem of urban microcellular network planning. The author used the ray tracing to calculate the propagating characteristics of the micro-cellular scene in a precise way and the genetic algorithm to find the specific location of the urban microcellular base station. And at the same time, basing on the characteristics of the site selection of the urban microcellular base station, the improved genetic algorithm which proposed in the literature was adopted. The final study shows that basing on the simulation of urban microcells, combining with the scheme of the ray tracing and improving genetic algorithm can solve the problem of urban microcell location.

The construction of long-span spatial structure is an important measure of the science and technology level of a country’s architecture and is widely used in various landmark buildings. Therefore, it is of great significance to adopt effective methods to monitor the health of long-span space structures and ensure the safety of structures during construction and use. Based on the improved particle swarm optimization algorithm, different fitness functions were selected for the health monitoring project of the gymnasium in Hai Lake New District of Xining according to the structural characteristics. The location of strain sensors and acceleration transducer was optimized to provide a reference for the establishment of a long-span spatial structure health monitoring system. The validity of the method was verified through experiments, so as to provide important practical significance and theoretical support for the study of spatial structure detection.

With the rapid development of modern science and technology, more and more high-tech has appeared in front of the common people, in particular, the recent rise of artificial intelligent robots and other supernova scientific developments. In order to move the robot according to the direction of the system design, it is necessary to plan the robot’s walking path to a certain extent; this requires the use of specific algorithms to achieve. This paper was based on the application of path planning at the present stage, the artificial immune and ant colony fusion algorithm were used to plan and analyze the AGV path, the ant colony algorithm based on the artificial immune algorithm to enhance the recognition function of the immune system and the ant searching for food secretion pheromone as the optimal path finding method was the basic algorithm developed in this paper, the path planning of AGV was analyzed and studied.

With the rapid development of Internet technology, social networks have been widely used in the world, and some of them are really active and some people are just browsing. Based on this, the collective efficacy was proposed and the three element interaction determinism was studied. The similarity between social network users was calculated and integrated, and the collective efficacy was studied. 60 members of the four network groups were interviewed, and 15 influencing factors of network group efficacy were coded. 230 college students were investigated by questionnaire and the multi factor analysis method was integrated. The influence of community members’ efficacy on their community involvement was studied through the initiative, focusing on sharing information and other dependent variables, attitudes, interests, values, personality and other independent variables. The research results showed that increased awareness of the degree of interpersonal similarity will increase the degree of involvement of social network group members, and psychological involvement played an intermediary role in perceived interpersonal similarity and the increases of perceived interpersonal similarity will enhance the formation of the sense of group efficacy.

The frequent trading activities of electronic commerce make the online transaction volume of Chinese enterprises increase year by year, but many enterprises still follow the traditional marketing strategy, which is not conducive to the long-term development of enterprises. Online precision marketing system model based on big data was built, Hadoop + MapReduce precision marketing model platform was implemented, all the data were stored in a distributed storage system, data mining technology was used to deal with it and provide the basis for enterprise decision making. China’s H group was studied. The “user portrait database” and the corresponding E-R map were constructed. The height subdivision factor with strong correlation was selected for cluster analysis, and the product was subdivided by cluster analysis. This study has certain reference significance for the collection and mining of online data of enterprises in our country and contributes to the long-term healthy development of the enterprise.

At present, the domestic securities registration is basically controlled by third parties, and it is not necessary to be identified after the transaction. Therefore, if a third party’s operating platform is maliciously attacked, it may change some of the information on the equity securities asset. Block chain application technology can transfer securities assets in the network system to check whether the security of securities assets through the P2P system. As the entire stock transaction process is stored in the block chain, the risk can be greatly reduced; the application of block chain technology can effectively prevent the securities assets from being tampered with in the course of the transaction. Aiming at the problem that the block chain technology is used to prevent the data from being tampered with in the course of the transaction, the storage system of securities trading based on block chain was proposed. Transactions of securities assets based on block chains were realized through the design of the system. The system was designed by using Java technology. Block chains were used as a basis for design ideas, and securities transactions were written into block chains and transferred to database.

Leisure sport is a rising industry in recent decades. Its development speed is far more than that of other industries. Despite the rapid development of sports industry, the number of leisure sports enterprises in China is still scarce. Therefore, this paper put forward the research on the competitiveness of leisure sports, combined the data of leisure sports industry with computer technology, and solved the problem of lagging development of sports in China through statistical operation. In order to optimize the model, we added the association rule algorithm to further improve the accuracy of data, so as to further expand the leisure sports market. Through a comprehensive test of the function of the algorithm, the results show that the system is feasible in the use of the system.

At present, with the development of music, the variety of music is gradually enriched, and the piano music of ASEAN is one of the most popular music types. However, traditional music recognition is difficult to identify such a variety of music, and it cannot meet the needs of people, the design of the piano music library and the display platform is urgent and necessary, based on this, the article first established the N-grams index database, and built a music feature index database based on the learning N-grams algorithm which contains only the soprano part, finally we tested the algorithm built in this paper, and the results show that the retrieval speed of the database designed in this paper has reached the requirement, the algorithm designed in this paper has a certain degree of recognition, it can meet the daily needs of the people for the piano music of ASEAN.

With the continuous development of modern network, because of the increasing prominent and correlation of the huge network system, the original source independent network system has been difficult to meet the needs of the development of modern network. Therefore, we need to add the correlation between each ON/OFF source in the classical ON/OFF model based on the time series. On the basis of theoretical analysis and simulation experiment, the new C-ON/OFF model was formed after the improvement of ON/OFF model. Through quantitative analysis, the two parameters of and with the physical meaning in the model were discussed, and the relationship between the normalized self-covariance of the generated traffic. In order to verify the validity and practicability of the proposed model, the experiments of different parameters of the two groups were comparatively analyzed. The experimental results show that the model has good advantages in the physical meaning and complexity under longer correlation, and the C-ON/OFF network traffic model can be applied to the design of the network traffic monitoring system.

The development of information technology brings new hope for our traditional education mode. However, the combination of information technology and teaching in our country is still in the initial stage. Especially, the research of school district function guidance system based on cognitive guidance algebra I is less. In this paper, the intelligent guidance system was the main research topic, and the development of the project of cognitive guidance algebra I in America was summarized, so as to help the related scholars to better understand the gap between the research of intelligent guidance system and the world level in China. Finally, through the study of the relevant theoretical basis, the development of educational intelligent guidance system was discussed, and finally a relatively complete intelligent guidance system was established.

This study aims to use neural network theory to analyze and evaluate computer network security. Firstly, the evaluation model of computer network security was given based on relevant literature, and the corresponding index system was constructed, including 19 indicators of management security, physical security, and logical security. Then, the index normalization standard was proposed and the index security level was set. Finally, computer network security was evaluated according to the neural network. Results show that security management strategy has the greatest impact on computer network security, followed by routing control and data encryption.

Driverless vehicles interacting with other traffic participants, such as cars, pedestrians and bicycles inevitably will inevitably interact in complex traffic environment. During the interaction, all potential collisions must be avoided to ensure driving safety. Based on the model predictive control, this paper analyzed the active obstacle avoidance algorithm for unmanned vehicles, and proposed a collaborative trajectory planning program for unmanned vehicles with multiple collision and collision management. The coordinated collision avoidance rules based on angle change were used to solve the consistency problem of distributed collision avoidance. The simulation results show that the scheme can effectively solve the multiple collision problems between UAVs.

Faced with the problems of low production efficiency and unreasonable industrial organization exist in the development of agriculture, this paper studied the multi-objective optimization of sustainable agricultural industrial structure based on genetic algorithm in order to optimize the allocation of agricultural industrial structure. First of all, this paper expounded the current agricultural development background, and then put forward the research status of multi-objective optimization algorithm, based on which points out the advantages of genetic algorithm. Then, establish the model system that is based on the optimal allocation of industrial structure. Use the improved genetic algorithm to solve the problem, and modify the operator design to obtain the optimal algorithm for the sustainable agricultural optimization sequence. The results of the algorithm evaluation show that the optimized industrial structure benefit value and sustainability both have been improved.

The research of face recognition began in the 70s of last century. With the rapid development of science and technology and computer technology, it has been used in the application of property management, which has become a new research direction. Based on this, the research status of intelligent property and face recognition at home and abroad was investigated. On this basis, the concept and principle of face recognition technology were analyzed, and PCA algorithm was proposed for face recognition. According to the basic theory of PCA algorithm, it was applied to face recognition technology. The principle of PCA algorithm was proposed, and the principal component analysis method was deduced and applied to face recognition. According to the performance of PCA algorithm in face recognition technology, a new wavelet transform method was used in face recognition algorithm. After extracting the facial feature vector, the wavelet transform method combined with PCA algorithm was obtained. Finally, the proposed algorithm was applied to intelligent residential property management system. The results show that the algorithm can effectively improve the recognition accuracy and can meet the design requirements, which provides a theoretical basis for the subsequent system research.

In previous studies, due to the sparsity and chaos of distributed data, such a result would lead to a local convergence phenomenon by using PSO algorithm, resulting in low accuracy of data mining. So this time we proposed a data mining algorithm based on neural network and particle swarm optimization. At the beginning, we calculated the global kernel function of differentiated distributed data mining and mixed to build the mining decision model. The training error was used as the constraint condition of mining optimization to realized data optimization mining. The results showed that the differential distributed data mining with this algorithm has higher accuracy and stronger convergence.

At this stage, the technical and tactical information acquisition technology has become the key factor to improve the performance of athletes. In this paper, “manual + automation” information collection method was selected. At the same time, in order to improve the speed of information collection, a kind of intelligent prompt automatic completion algorithm was designed and proposed. In the algorithm, the optimization algorithm was improved in view of the poor convergence in the original algorithm, and then the intelligent completion algorithm with more restrictive was further proposed. By collecting the standard document of dynamic standard information and embedding the basic video file, the algorithm was beneficial to the algorithm and automatic completion and intelligent prompt visual features in the video retrieval and analysis. In addition, in terms of technology, the video semantic description was carried out based on the AVI format, and the video retrieval and video analysis based on sports tactical competition were realized, thus providing complete technical support for coaches and athletes in scientific competitions, and improving the level of athletes’ skills and tactics.

With the network technology development and the current attention to network security, the intrusion detection technology of the network system research has being regarded more important. The previous system intrusion detection section has not been able to meet and adapt to the needs of the current rapid development of the network era. The construction of the framework of intrusion detection system is our primary job. This research adopts the genetic attribute reduction algorithm based on rough set and neural network intrusion detection system simulation analysis, through the simple computer algorithm and system simulation analysis of intrusion mode simulation model establishment. The results show that the study has made great success.

The precision of price appraisal is directly linked to the sound development of a market-oriented economy and public benefit. Market comparison approach is a widely used price-appraisal algorithm. However, when the sample size is small, the computing precision of the market comparison approach is severely impaired. To realize precise, intelligent computing under the conditions of a small sample size and a low degree of proximity between samples and the object to be evaluated, this study adopted grey correlation and fuzzy mathematics to optimize the market comparison approach for price appraisal. The proposed algorithm used grey correlation analysis to quantify the weight of various attributes influencing price appraisal. The price correlation between samples and the object to be evaluated was confirmed. The fuzzy mathematical method was then used to obtain the relative weight of samples. The proposed intelligent price-appraisal algorithm was constructed to improve the market comparison approach. Finally, several residences in Hangzhou, Zhejiang province, China were chosen for an empirical analysis and for the verification of the feasibility of the improved price-appraisal algorithm. Results suggest that (1) The mean error of the algorithm is only 0.99%, indicating high computing precision; (2) When the number of objects to be evaluated is large, a computer system can be used to aid the intelligent computing and to efficiently perform intelligent computing. In conclusion, the proposed algorithm can maintain high computing precision under the conditions of a small sample size and a low degree of proximity, and the algorithm can be used as a theoretical basis to realize intelligent mass appraisal. Overall, the proposed algorithm is worthy of further development because it is highly operational.

Studies on path dependence (PD) and lock-in have indicated several limitations, such as chaotic literature information, interdisciplinarity complexity, and evolution vagueness. In this study, literature on path dependence and lock-in are obtained from the database of Web of Science Core Collection from 2001 to 2017. The knowledge mining model of the evolution of path dependence and lock-in is constructed based on knowledge mining process and content. The evolution of this area is also explored. Results show that the evolution of path dependence and lock-in can be categorized as follows: (1) English, American, and German scholars have led the research on path dependence and lock-in. (2) The research topics have undergone four stages. (3) The research levels have been classified as policy system (macro-level), regional economy (meso-level), and organizational path dependence (micro-level). (4) The research design can be represented as theory basis, case discussion, lock-in or reformation, and path creation. (5) The research method can be transformed from case study into lock-in mathematical modeling. A panoramic knowledge mapping of path dependence and lock-in is displayed by the research system to enhance understanding on research trajectories and future research directions.

Intelligent exercise recommendation is a research focus in the field of online learning that can help learners quickly find exercises suitable for them from the exercise bank. However, exercise recommendation differs from product or film recommendation because of some special requirements. First, the recommended exercises must cover all knowledge points geared toward the learning objective of the learner. Second, the difficulty of exercises must match the knowledge level of the target learner. In response to the above requirements, this study proposes an exercise recommendation algorithm that integrates learning objective and assignment feedback. This algorithm considers not only the coverage of knowledge points but also the knowledge level of learners to help them find highly suitable exercises. According to this algorithm, the learning objective of the learner must be initially identified to obtain a course knowledge set that suits his/her learning objective. Second, the understanding of the learner about the knowledge set must be judged based on the assignment feedback. Third, suitable exercises are recommended based on the knowledge level of the learner and the course knowledge structure. The proposed algorithm is experimentally verified by using a real-world dataset and by comparing it with other algorithms. The experimental results show that the proposed algorithm significantly outperforms the other algorithms in both precision and recall. Based on these results, the proposed algorithm can achieve an excellent recommendation performance.

Sentiment analysis mainly studies the emotional tendencies of texts from grammar, semantic rules and other aspects. The texts from social network are characterized by less words, irregular grammar, data noise and so on, which have increased the difficulty of emotion analysis. In order to improve the performance of machine learning in sentiment analysis, this study proposed the Majority Decision Algorithm to classify the emotional tendentious of the text in WeChat, combined the characteristics of five classifiers and integrated the classification results of five classifiers, eventually the text can be classified in WeChat. Firstly, this study utilized the BlueStacks to crawl the cache of WeChat Moment developed by Tencent company. Secondly, the cache was processed by Python to get the WeChat dataset. After the Chinese word segmentation, data cleaning and segmentation, the sentiment classification experiment were carried out using different classifiers. Finally, a Majority Decision Algorithm composed of five classifiers was established. It included, Naive Bayes (sklearn), Naive Bayes (SnowNLP), SVM (linear), SVM (RBF) and SGD. Then, the comparison was carried out between the performance of the algorithm and the five classifiers. Results show that the precision rates of the five classifiers are 0.8598, 0.8154, 0.8511, 0.8739 and 0.8678; the recall rates are 0.8544, 0.8482, 0.9380, 0.9226 and 0.9349; F1 scores are 0.8571, 0.8315, 0.8924, 0.8975 and 0.9001, respectively. The algorithm of the Precision rate, Recall rate and F1 score were 0.8804, 0.9349 and 0.9069, respectively, indicating that algorithm in current study significantly improved the performance, which can be effectively applied into the new text form of WeChat Moment. The study can provide theoretical reference for sentiment classification of Chinese text based on machine learning.

To overcome disadvantages of conventional Doppler estimation and compensation for non-stationary underwater platforms, a novel algorithm based on joint time-frequency searching is proposed to track and rapidly compensate the time-variant Doppler factor in underwater acoustic OFDM (Orthogonal Frequency Division Multiplexing) communication. The algorithm utilizes the comb pilot’s subcarriers combined with frequency-domain resampling to calculate the channel impulse response under different Doppler factors. Then the correct Doppler factor is achieved and eliminated by seeking the sparest channel impulse response, which is accessed using compressed sensing method. Experiment results obtained by Monte Carlo simulation and sea-trial in shallow-water are provided, demonstrating that the new algorithm can effectively track and rapidly compensate the time-variant Doppler factor, thus ensuring the stable underwater acoustic OFDM mobile communication for non-stationary underwater platforms.

Android systems typically run on resource-constrained hand-hold devices. How to efficiently utilize Java heaps is one of the most important issues of concerns to the developers. Developers often use profilers to observe the utilization efficiency of Java objects, hoping to find out memory allocation bottlenecks, identify and solve problems such as memory leaks, etc. However, currently there lacks a low-overhead and efficient Java object profiler on Android and its Java virtual machines.

In this paper, we design and implement a novel and low-overhead Java object profiler based on the Address-Chain technique, on Android 6.0 and its ART virtual machine, which uses an AOT (ahead-of-time) compiler and has complex garbage collection algorithms. Our profiler records the allocation site, the class information of the object, the object size, the birth time and death time of the object, the physical memory trace of the object movements with time stamps, the last access time and the access regular pattern, etc., for every Java object. The data profiled can help the developers to detect memory leaks, implement optimizations like pretenuring and tune the performance of garbage collector, etc.

The Java object profiling mechanism proposed in this paper has low execution time overhead, imposes no overhead on the Java heap and does not modify any existing key data structure of the ART Virtual Machine, including the object layouts, class layouts and any others. By caching object access event in global register and removing redundant instrumentation, on Nexus7 and Android 6.0, the read/write barriers overheads of the profiler are about 19% on average for EEMBC, SciMark and other workloads. The I/O overheads are about 28% and the total execution overheads are about 51% on average.

Path planning for human beings is based on scene understanding and semantic map as well as geometry features. The point cloud map can help robots to perform path planning. However, it is quite different from the natural way of human beings. In this paper, we propose to construct a novel framework for topological semantic map. Specifically, we construct a 2D semantic map by projecting 3D scene semantic information recognized by convolutional neural network onto a 2D plane. 3D reconstruction of the environment is achieved by RGB-D SLAM 3D space mapping algorithm. The intersections in the 2D map are recognized offline, and the semantic annotation of the intersection in the topological map is utilized to build up a complete object-based semantic map. Experimental results demonstrate the feasibility of the proposed approach.

In the trading activities, the liquidity level of assets and its impact on future earnings has always been a concern for investors. This paper calculates the liquidity of the stock market by constructing a frictionless asset model. At the same time, we test the application of the liquidity level premium index in the LA-CAPM model, mainly draws the following conclusions: (1) Although the individual liquidity of the stock has different individual characteristics, but showing a common trend of change. Stock individual liquidity level premium level and the stock market performance of the opposite, when the market is good, the stock individual liquidity level premium is low. (2) The liquidity premium of the market portfolio is generally inversely related to the market index, and the liquidity premium between the different market combinations is significantly different. (3) In different portfolios, the portfolio of large, value-based company stocks is superior to the portfolio of small, growing corporate stocks. The results of this study have important reference significance for the construction of liquidity index and the research of liquidity and asset pricing.

In order to effectively improve the data transmission efficiency of wireless sensor networks (WSN), a new wireless sensor network chain routing algorithm is proposed based on adaptive backoff-adjusted medium access control. At first, the optimal transmission back-off time of undetermined frames is estimated by the number of active nodes in this algorithm, and the performance evaluation index of WSN is presented with area coverage, transmission distance and residual energy. At the same time, the number of collisions and transmission attempt rate are given with adaptive backoff-adjusted medium access control, and the chain routing algorithm is build. Finally, the key factors influencing the algorithm are deeply studied through experimental platform and numerical simulation with MATLAB. The results show that, compared with the traditional chain routing algorithm and progressive best backoff algorithm, the algorithm has great advantages in terms of chaining efficiency, node life cycle and delay.

Generative Adversarial Networks have demonstrated potential on a variety of generative tasks, although they are regarded as unstable and sometimes they miss modes. We propose Auto-encoder Generative Adversarial Networks - a convolutional neural network combining auto-encoders with Generative Adversarial Networks. The former brings more information to Generative Adversarial Networks to reduce problems of miss modes and the latter makes the picture generated more coherent because it can better handle multiple modes in the output. We also show that image composition is available for Auto-encoder Generative Adversarial Networks so that it can be used for many feature-based tasks. Besides, we can generate different samples by adding a random noise to a feature vector.

The spectrum cluster algorithm is opposite in the other cluster algorithm has the obvious superiority that can distinguish the non-raised distribution the cluster, suits extremely in many actual problems. With this prior model this paper combines the fuzzy set system to propose the new data mining algorithm. At the same time, we build a fuzzy comprehensive evaluation model and analyze the evaluation system of physical education. The result shows that the fuzzy comprehensive evaluation method is improved by using the grey relational grade, which avoids the disadvantages of adopting the principle of maximum membership degree. Therefore, this evaluation method can be used in the evaluation of public sports teaching in colleges and universities.

In this paper, the spatial dobbin model is used to analyze the spatial impact of local government competition and land price on foreign direct investment. The data came from 69 prefecture level cities in five provinces in Central China, time from 2005–2015. The empirical research shows that the higher the level of regional economic development, the better the road traffic conditions, the lower the industrial land price and the higher the degree of government competition, the more the FDI will flow into the region. The impact of environmental regulation on FDI is not obvious. In other areas, the level of economic development, the increase of industrial land price and the enhancement of environmental regulation intensity will promote the level of foreign investment in this area, while the traffic improvement in other areas and the intensification of government competition will inhibit FDI in the region. The results show that the means of attracting FDI among governments in order to achieve economic goals are available and effective.

Teaching quality evaluation is important to improve teaching quality in universities. Fuzzy comprehensive evaluation is a comprehensive method of qualitative analysis and quantitative analysis. It has been widely applied in social life, economic management and engineering technology. The scientific practice has proved that the rational distribution of the weight factors and the selection of the evaluation model directly affect the accuracy and reliability of the fuzzy comprehensive evaluation results. How to determine the weight of multiple indicators into a single index is the focus of the present study. The simulation results show that the parameters of the gaussian kernel function optimization method designed in this article has strong advantages. Also, the results show that the algorithm presented in this paper can accurately reflect the quality of teaching quality, and at the same time realize the accurate determination of the weight of teaching quality evaluation index.

The technology adoption of a firm is endowed with complexity if the production technology upgrades continually and the price competition become intensified. This paper establishes a supply chain involving one technology supplier and two manufacturing enterprises. The supplier provides a kind of production technology and offers its upgraded versions for enterprises to produce products. On this basis, this paper studies the problem that whether or not to buy this kind of production technology in the perspective of each enterprise, explores the problem that when to buy an upgraded version of production technology in the perspective of each enterprise, and analyzes the price competition between two kinds of products. The results show that the asymmetric Nash equilibrium can effective avoid the prisoner’s dilemma of technology investment, and help to maximize the interest of the social welfare system that includes the technology supplier, manufacturing enterprises and consumers.

The multiple attribute group decision making (MAGDM) problem, in which both the attribute weights and the expert weights are correlated and the attribute values take the form of interval grey linguistic (IGL) variables, is investigated in this paper. First, based on the relative concepts of IGL variables and the operation rules between IGL variables, two new aggregation operators are developed, which are the interval grey linguistic correlated ordered geometric (IGLCOG) operator and the induced interval grey linguistic correlated ordered geometric (I-IGLCOG) operator. Then, some desirable properties of the I-IGLCOG operator are studied, such as commutativity, idempotency, monotonicity and boundedness. Next, the IGLCOG and I-IGLCOG operator-based approaches are developed to solve the abovementioned MAGDM problem. Finally, an illustrative examples are given to verify the developed approach and demonstrate its practicality and effectiveness.

An efficient reverse logistics structure plays an important role in improving market competitiveness. The complexity of reverse logistics operations, customer service improvement, and costs elimination highlight the necessity of reverse operations outsourcing to the third-party reverse logistics providers (3PRLPs). Investigating and selecting an appropriate 3PRLP is recognized as a significant issue by manufacturers. This problem is affected by uncertainty, basically due to the vagueness intrinsic to the assessment of qualitative factors. This paper aims to propose a structured approach to prioritizing 3PRLPs based on sustainability criteria under fuzzy environment which accommodate the uncertainty associated with the vagueness of qualitative criteria. The proposed approach is composed of two main steps in which the first step employed the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to select the effective criteria and the second step used Mamdani Fuzzy Inference System (FIS) model to cope with the vagueness that exists in the 3PRLPs evaluation process. If-then scenarios are employed to design rules of a FIS model which are devised by experts. The Experts’ knowledge about the problem is incorporated into the FIS system. This is a significant benefit of the proposed approach, in comparison with approaches which incorporate fuzzy set theory with multi-criteria decision-making models. An industrial case study is conducted to highlight the real-life applicability of the proposed approach. In addition, a sensitivity analysis is performed to confirm the robustness.

Smarandache (1998) initiated neutrosophic sets as a new mathematical tool for dealing with problems involving incomplete, indeterminant and inconsistent knowledge. By simplifying neutrosophic sets, Smarandache (1998) and Wang et al. (2010) proposed the concept of single valued neutrosophic sets and studied some properties of single valued neutrosophic sets. Recently, Bao and Yang (2017) introduced n-dimension single valued neutrosophic refined rough sets by combining single valued neutrosophic refined sets with rough sets and further studied the hybrid model from two perspectives–constructive viewpoint and axiomatic viewpoint. A natural problem is: Can the supremum and infimum of n-dimension single valued neutrosophic refined rough approximation operators be given? Following the idea of Bao and Yang, in this paper, let X be a set, H _n (X) and L _n (X) denote the family of all n-dimension single valued neutrosophic refined upper and lower approximation operators in X, respectively. We can define appropriate order relation ≦ on H _n (X) (resp., L _n (X)) such that both (H _n (X), ≦) and (L _n (X), ≦) are complete lattices. In particular, both (H, ≦) and (L, ≦) are complete lattices, where H and L denote the family of single valued neutrosophic upper and lower approximation operators in X, respectively.

In this paper, the prediction of damage results for complex network is considered under grey information attack. Firstly, in order to construct more realistic networks, a new algorithm is proposed to generate 3 types of fully connected networks (normal scale-free network, scale-free network with cutoff, random network). Secondly, robustness of the 3 networks is analyzed under grey information attack. And then, a new method is proposed to predict the damage results by training the BP neural network. Thirdly, the effects of different topological parameters on the damage results are analyzed and a new method is proposed to find central nodes of the network. Finally, the damage results of a real bus network under grey information attack are predicted by the proposed method and several suggestions are given to help protect the real bus network.

The aim of this paper is the study of fuzzy quotient hypervector spaces. In this regards several kinds of fuzzy quotient hypervector spaces are introduced, according many approaches. Also, the relationship between these definitions is studied. In particular, it is investigated when these concepts are equivalent.

RNA-sequencing technology helps to consider the expression of thousands of genes, simultaneously. The large-scale gene expression data include a huge number of genes versus a few samples. Therefore, the algorithms that among huge number of unrelated genes can accurately detect genes associated with specific disease can be useful for experts in early detect and treat the disease.

A two-phase search algorithm is proposed in this paper to discover the biomarkers in the RNA-seq gene expression dataset for the prostate cancer diagnosis. After statistical noise removing from the original large-scale dataset, a multi-objective optimization process is proposed to select the best non-dominated subset of genes with the maximum classification accuracy and the minimum number of genes, simultaneously. Finally, the proposed cache-based modification of the sequential forward floating selection (CMSFFS) algorithm is applied to the selected subset of genes to discover the most discriminant genes.

The obtained results show that the proposed algorithm is able to achieve the classification accuracy, sensitivity and specificity of 100% in the large scale RNA-seq prostate cancer dataset by selecting only three biomarkers.

This paper defines the similarity degree of fuzzy sets with bi-implication. Based on this definition, a new fuzzy inference method, namely guaranteed similarity-degrees inference method (GSI method) is proposed. Considering the FMP problem, four kinds of calculation formulas of GSI method are exhibited. Fuzzy reasoning formulas based on the new method with the commonly used implications are discussed. As an illustration of its effectiveness, the proposed method is implemented to some numerical examples from aggregate production planning. Moreover, the authors show that under certain conditions the commonly used fuzzy reasoning methods such as the CRI method, the triple I method, and the FRI method (which based on ∧ -→ composition) all belong to GSI method, the Turksen’s AARS (the approximate analogical reasoning schema) is a partial approximation of the GSI method, and the Raha’s method is a special case of the GSI method. The proposed method is of good property to distinguish the input conditions, and its weighted mode provides the algorithm with a wide range of adaptability and flexibility and facilitates the model optimization.

In many real decision-making problems, since the assessment information is usually inaccurate and incomplete, decision makers may be hard to get enough information and precisely quantify their opinions. To solve this problem, we aim to introduce interval-valued dual hesitant fuzzy rough set over two universes model. By integrating interval-valued dual hesitant fuzzy set and rough set models, this paper provides a systematic framework for the study of interval-valued dual hesitant fuzzy rough set over two universes, which can better handle imprecise and uncertain information. Firstly, in constructive approach, we define the lower and upper approximation operators under an interval-valued dual hesitant fuzzy relation, and some relevant properties are discussed. Then, we establish a general decision-making approach based on the proposed model. Finally, two practical examples are provided to elaborate the created method and illustrate its effectiveness.

We define the Interval Neutrosophic Hesitant Fuzzy Choquet Integral (INHFCI) operator as a useful tool for multicriteria decision making (MCDM). The INHFCI operator generalizes both the interval neutrosophic hesitant fuzzy ordered weighted averaging operator and the interval neutrosophic hesitant fuzzy weighted averaging operator. A modified version of the score function to make comparison among Interval Neutrosophic Hesitant Fuzzy elements is proposed. We develop an approach for multicriteria decision making based on the interval neutrosophic hesitant fuzzy choquet integral operator that applies to our proposed score function. Finally the model is illustrated with the help of an example.

Image segmentation, which becomes more and more prevalent in computer vision, plays a requisite part in the fields of object detection, tracking and even virtual or augmented reality. Early segmentation methods that relied on hand-crafted features have fast been superseded by deep learning algorithms. Nonetheless, deep learning algorithms are hardly applied in real object segmentation because of a lack of ground truth labels. This work introduces the use of 3D models to generate segmentation training dataset. This system projects 3D models to the 2D plane and merges 2D images with different backgrounds to obtain training images. In this process, the ground truth labels would be allowed to obtain automatically without manual annotation, since the position of objects is known in the picture. Experimental results indicate that synthetic images can be used to train on existed networks such as FCNs and DeepLab and trained models achieve relatively accurate segmentation results on real images. Moreover, the modified model based on DeepLab-CRF-LargeFOV achieves more precise segmentation results by strengthening its localization and edge performance.

This paper compares tracking performances of the inverse hysteresis model-based feedforward compensator and the feedback-feedforward combined controller for the time-varying hysteresis nonlinearity of a piezoelectric-stack-actuator-driven (PSA-driven) system. Three different inverse hysteresis models, including Bouc-Wen, polynomial, and Prandtl-Ishlinskii (PI), are adopted to design feedforward compensator for the PSA-driven compliant system. Particle swarm optimization (PSO) scheme is employed to estimate model parameters of the Bouc-Wen and PI hysteresis models, respectively, whereas the least-mean-square-error based criterion is utilized in identifying the polynomial-based hysteresis model. Although solely feedforward compensation approach seems workable for systems with rate-independent hysteresis or slow tracking trajectories, large modeling errors are inevitable when fast trajectory or rate-dependent hysteresis is facing. To improve tracking performances, conventional PID control is augmented to the feedforward controllers. The resultant scheme is denoted as the feedback-feedforward combined control. To compare control performances of the solely feedforward and feedback-feedforward combined scheme, a number of experiments focusing on sinusoidal trajectories with different frequency have been implemented on the PSA-driven stage system. The results indicate that significant performance improvement can be achieved provided PID control is augmented to the solely feedforward approach. When different hysteresis models are of concerned, PI hysteresis model performs better in either feedforward or combined cases.

In this paper, we introduce the notion of Alexandrov L-(neighborhood) filters and Alexandrov L-convergence structures as a topological viewpoint of fuzzy rough sets. We investigate the categorical relations among Alexandrov L-neighborhood filters, L-fuzzy preorders and Alexandrov L-convergence structures. Moreover, we investigate their topological properties and give examples.

Optimization is a process that is followed to improve an objective function. The aim of the optimization is to improve the solution of a problem developed by a scientist or an engineer. In any optimization algorithm, the initial conditions have a big effect on quality of the final found solution. One of the new optimization algorithms is See-See Partridge Chicks Optimization (SSPCO) algorithm, which is modeled upon the behavior of the partridge chicks. Chaotic behaviors are frequent in the natural phenomena. Indeed, many real world behaviors which resemble random, are chaotic. As these behaviors are highly sensitive to the initial conditions, they show such really random-like behaviors that can’t be predicted (while they are not random at all). In this paper, we have implemented a chaotic SSPCO (CSSPSO) algorithm. We have used two different chaotic functions and we have lastly compared the results with the state of the art algorithms. The results indicate that chaos has a positive effect and the CSSPCO algorithm outperforms the state of the art algorithms.

In this study, a novel strategy for current control of distribution static compensator (D-STATCOM) based on fuzzy inference system (FIS) is proposed. The D-STATCOM is utilized for compensating a non-linear load drawbacks in power system by injecting compensating currents. The D-STATCOM is connected between the non-linear load and sinusoidal power source. The control system of the D-STATCOM is designed based on the instantaneous power theory. In addition, two other current control strategies consisting of hysteresis current control (HCC) and adaptive hysteresis current control (AHCC), are investigated and implemented. The results of the HCC and the AHCC are compared with the proposed strategy. Comparison between the obtained results showed that the proposed current control strategy is drastically superior to other strategies due to its less harmonic distortion, lower and constant switching frequency, lower transient current peak and more successful reactive power compensation. The effectiveness of the proposed strategy is verified by extensive simulations in MATLAB/Simulink environment.

The growing attention of the recycling of WEEE led to the making of critical studies on how to manage this process such as to determine one of the most appropriate outsourcing firms. In the recent years, the determination of the most suitable firm to be managed for the recycling of WEEE for private and public institutions is a critical decision that has high level importance in terms of environmental, economic, social and even technological. For this aim, we try to determine the best alternative outsourcing firm for management of WEEE process for Municipality of Eyüp which is a district located in European side of Istanbul by using suggested MCDM methodology based on HFS. In this paper, a multi criteria decision making (MCDM) methodology based on hesitant fuzzy enveloped TOPSIS that gives experts extra flexibility in using linguistic terms to give their assessments has been proposed to determine the best outsourcing firm for Waste of Electrical and Electronic Equipment (WEEE). We have developed a mechanism based on hesitant fuzzy sets (HFS) for enabling decision makers to be easier in evaluation process of WEEE management.

This paper proposes a new multi-objective interior search algorithm (MOISA) for solving multi-objective optimization problems. Multi-objective complex mathematical models need to be solved by meta-heuristic algorithms in such a way that Pareto-optimal solutions are obtained; therefore, a new algorithm is presented in this paper for solving such models. The process of the interior search algorithm (ISA) is based on principles of interior design and decoration. This algorithm divides all elements, except the most suitable one, into two groups. In the first group, which is called the artistic composition group, algorithm changes the composition of elements to achieve a more desirable view. In the second group, which is called the mirror group, the algorithm places a mirror between the group elements and the most suitable element to find a better view. This paper uses the principles of the ISA in conjunction with the concepts of the non-dominated sorting and crowding distance to present the proposed MOISA, which is capable of obtaining near-optimal non-dominated solutions from solution space and identifying accurate Pareto fronts. To evaluate the performance of the foregoing algorithm, the related results of solving six models and a maximal covering location-allocation model are compared with several standard multi-objective evolutionary algorithms in terms of different metrics. This comparison shows that the results of the proposed MOISA are better than those obtained from other tested algorithms. Based on the solved numerical examples, the algorithm presented in this paper has many advantages over existing algorithms.

This paper investigates a principal-agent problem between the operator of an information service online platform and the content provider in which the content provider’s effort to develop new content is unobservable to the operator and the market demand is unknown to both parties and characterized by an uncertain variable. The purpose of the operator is to maximize the total profit earned on the information service’s online platform by designing a contract, whereas the content provider determines the price and the effort level. Next, we apply the critical value criteria to establish two different principal-agent models from the perspective of symmetric and asymmetric effort information. Subsequently, the optimal contracts, the optimal price and the information value of the effort information are derived, respectively. Finally, the impacts of the model parameters on the operator’s profit and the effort’s information value are presented.

The notion of hesitant fuzzy set is introduced by V. Torra, which is a very useful tool to express peoples’ hesitancy in daily life. The notion of pseudo-BCI algebra is introduced by W. A. Dudek and Y. B. Jun, which is a kind of nonclassical logic algebra and close connection with various non-commutative fuzzy logic algebras. In this paper, hesitant fuzzy theory is applied to pseudo-BCI algebras. The new concepts of hesitant fuzzy filter and anti-grouped hesitant fuzzy filter in pseudo-BCI algebras are proposed, and their characterizations are presented. Also, the relationships between fuzzy filters and hesitant fuzzy filters are discussed. Moreover, by introducing the notion of tip-extended pair of hesitant fuzzy filters, a new union operation (generated by the union of two hesitant fuzzy filters) is defined and it is proved that the set of all hesitant fuzzy filters in pseudo-BCI algebras forms a bounded distributive lattice about intersection and the new union.

In this paper, we build a metric semi-linear space L G * for lattice intuitionistic fuzzy numbers (LIFNs). We give new concepts of some operations, for example, a geometric binary addition operation, a nonnegative multiplication operation, geometric differences, and propose a new formula to calculate the derivative and integral of geometric lattice intuitionistic fuzzy functions in L G * , we introduce and establish the positive solutions of the initial value problem for geometric lattice intuitionistic fuzzy differential equation (IVP for GLIFDE), and additionally show several practical applications in this semi-linear space with the medical intuitionistic fuzzy models.

With extensive applications of fuzzy numbers, many methods for fuzzy arithmetic especially the basic operations have been developed based on Zadeh’s extension principle. Among these methods, the interval arithmetic approach and the standard approximation method are the most important and commonly used exact and approximate methods, respectively. In this paper, regarding the continuous and strictly monotone functions of triangular fuzzy numbers, we propose an inverse distribution approach to deriving the exact or well approximate membership functions for arithmetic results by embedding the credibility measure of fuzzy sets into fuzzy arithmetic. Besides, some non-complicated and complicated examples are given to illustrate the performance of the new approach, together with a detailed comparison with the interval arithmetic approach and the standard approximate method. Furthermore, the inverse distribution approach is also applied to the fuzzy system reliability calculation based on fault tree compared with several current related researches.

A type of similarity measure between two vague soft sets, which contains subitem satisfying properties instead of single value parameters defined within a value range, is introduced. Some properties of the proposed similarity measure are studied. Eight detailed expression examples derived from the subitem are given. A real-world problem in handling landmark preference is solved based on this technique of similarity measure of vague soft sets. The results of an analysis of the landmark preference example are based on a comparison of the proposed similarity measure with the existing similarity measures, which is meant to demonstrate the advantages of the proposed approach.

In this research paper, the notions of rough m-polar fuzzy (mF, for short) sets and mF soft sets, as new hybrid models for soft computing, are introduced and some of their fundamental properties are investigated. Rough degrees of mF sets are computed. In addition, potential applications of rough mF sets and mF soft sets to decision-making problems are explored.

A fuzzy graph is a representation tool for many real networks. But, due to some restriction on edges, fuzzy graphs are limited to represent for some networks. In this study, generalized fuzzy graphs and generalized directed fuzzy graphs are discussed to avoid such restrictions. A pipeline network is expressed as a generalized directed fuzzy graph. In the pipeline network, the generalized membership values of edges and vertices are determined by the capacity of the pipelines. Also, fuzzy r-cuts and fuzzy (r₁, r₂, …, r _n )-cuts in the generalized fuzzy graphs are defined.

Graph theory includes two unavoidable graphs, namely Euler graphs and Hamiltonian graphs. In this study, generalized fuzzy Euler graphs (GFEGs) and generalized fuzzy Hamiltonian graphs (GFHGs) are defined to express uncertain system like routes, networks. Here, even degrees of all vertices of graphs do not assure that the graphs are GFEG. Similarly, other properties of crisp Euler graphs and crisp Hamiltonian graphs are tested. Two algorithms are prepared to find Eulerian circuit in GFEGs and Hamiltonian circuit in GFHGs. In modern life, planning of efficient routes is very important for business and industry purposes. An application on planning of routes is described.

Differential evolution (DE) is a versatile and fast evolutionary algorithm (EA) for real world global optimization problems. It has been widely applied to diverse areas as a remarkable search technique. However, because of large step sizes in mutation, DE is known to be incapable of exploiting the existing population than exploring the search region. DE inherent drawback is avoiding the true optima. To heal its poor exploitation, hybridization with local search techniques will be a healthy choice, because local search strategies are proven to be robust in exploitation. In this paper, we hybridize DE variant, JADE with two gradient based local search (LS) techniques, Steepest Decent Method (SDM) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. The new algorithm is known as hybidJADE. The SDM is applied iteratively to locate promising solutions in the evolution, afterwards BFGS is applied to fine tune the elite solutions. If BFGS found solutions are in the neighborhood of current best solution, a restart is incorporated, where JADE and BFGS explore the population. The performance of hybridJADE is tested on 15 benchmarks from literature. The obtained experimental results are compared with the results of two state-of-the-art algorithms, JADE, and jDE. The results show significance performance of hybridJADE in terms of success rate on various benchmarks. The sensitivity analysis of hybridJADE to its various parameters is also presented.

In many applications, we need to numerically express the difference of two objects (notions) by means of distance and similarity between the corresponding interval type-2 fuzzy sets (IT2 FSs). In this paper, we propose two new signed distances between interval type-2 trapezoidal fuzzy numbers (IT2 TrFNs) and some properties of their have been introduced. We also give an approach to construct similarity measures using the signed distance for IT2 TrFNs. Several illustrative examples are given to demonstrate the practicality and effectiveness of the proposed measures.

Currently, the concern with renewable resources has led the society to create several initiatives to approach the subject, proposing viable solutions. One of the biggest challenges is the conservation of the environment due to its unbridled degradation. Thus, companies must act in an ecologically responsible way so as not to threaten future survival, thus contributing to sustainable development. The present study applied the FAHP method to optimize the processes aiming to eliminate production bottlenecks in the manufacture of concrete blocks with the use of foundry sand from a metalworking industry located in the South of Brazil. Results demonstrated that the prioritized alternatives were transportation outsourcing (24.24%), while the analysis of toxicity and resistance within the norms obtained the same prioritization (22.38%), these alternatives contributed to suppress the bottleneck of production process. The industry hired a third party company with reference in the market, so there was no involvement with labor laws, in addition, the analysis of toxicity contributed with reduction costs and wasted time; consequently, reducing in 100% the chance of Contamination, due to the adoption of correct practices. Resistance within the norms helped in the reduction of the rework (98%) due to breaks and chipping. The improvements found through the FAHP method were achieved through the degree of importance of alternatives, in order to maximize the profit of the industry.

This paper presents some capabilities of automation system from distribution planning and operation points of view. In this regard, the optimized switch placement and Distribution Feeder Reconfiguration (DFR) strategies are applied to the network to investigate the reliability of the system based on failure rate reduction. The objective functions to be minimized are the total switch cost in switch placement problem and System Average Interruption Frequency Index (SAIFI); System Average Interruption Duration Index (SAIDI) and active power loss as well as voltage deviation from nominal in the feeder reconfiguration problem. Also, through this analysis and by selecting the location of capacitors as well as ON/OFF status of the placed switched capacitors and tap position of Under Load Tap Changer (ULTC) transformers as control variables, the concepts of capacitor placement and Volt/Var Control (VVC) are evaluated. The problems are solved by using the Modified Social Spider Optimization (MSSO) algorithm as a proper method to solve the mixed-integer nonlinear programming problem. The proposed distribution automation planning and operation strategies are applied to a 69 bus IEEE distribution test system and simulation results are demonstrated and discussed.

Due to the uncertainty of real world problems and the limitation of human’s knowledge to understand the complex problems, it is very difficult for one to apply only a single type of uncertainty method to cope with such problems. One can develop a more powerful new model to solve decision making problems by incorporating the advantages of many other different theories of uncertainty. Fuzzy sets, soft sets and rough sets are very useful mathematical models for dealing with uncertainty. Combinations of these models result into several useful hybrid models. In view of this, in this research paper, the concepts and methods of rough soft sets and fuzzy sets are used to construct a new soft rough fuzzy set model. We employ the concept of soft rough fuzzy sets to graphs and investigate some properties of this model. We apply this new model to describe and resolve some multi-criteria decision-making problems.

The study of attribute reductions is one of the main problems in information systems. In this paper, multi-granulation variable precision rough set (for short, MVPRS) model is introduced. Several kinds of attribute reduction in an inconsistent decision information system based on this model are proposed. Relationships among these attribute reductions given. Some examples are given to illustrate that the unmentioned relationships among these attribute reductions are often not maintained.

In this paper, based on a type-2 fuzzy set, structures of fuzzy truth values on a linearly ordered set are investigated. Some special type-2 fuzzy sets are first presented. Then, rough approximations of fuzzy truth values based on a type-2 fuzzy set are introduced. Next, topological and rough structures of fuzzy truth values based on a type-2 fuzzy set are obtained. Finally, rough equal relations of fuzzy truth values based on a type-2 fuzzy set are proposed. These results will be helpful for the study of type-2 fuzzy sets.

The scheduling problem of cyclic multi-type parts robotic cell with blocking is studied in this paper. For simultaneously optimizing robotic move sequence and part input sequence, an effective chemical reaction optimization (ECRO) is proposed. In ECRO, a new encoding method, robotic activity encoding, is shown for transferring double sequence into single sequence. To construct feasible solution, a novel rule, order insertion (OI) rule, is presented. To obtain initial population more effectively, insertion robotic activity method (IRAM) is firstly addressed. What’s more, for enhancing the efficiency of ECRO, elementary reaction operators are designed according to properties of feasible solutions. From the simulation results, compare to stochastic generated solution method (SGSM), IRAM is outstanding. The performance of ECRO is better than branch and bound (BB) method and beam search (BS) algorithm.

Multi-criteria decision making (MCDM) is a discipline in which several contradictory criteria are evaluated in a decision making process. There are many MCDM techniques described in the literature, and important considerations have been made in this area because the hesitant evaluations of decision makers, regarding both the criteria and the alternatives, have to be taken into consideration. This study focuses on the MCDM problem using axiomatic design (AD), which is one of the "state of the art" and efficient MCDM techniques related to hesitant evaluations, which are defined as hesitant fuzzy linguistic term sets (HFLTS). In this study, AD is used throughout a methodology in which hesitancy is taken into account in the assessments of the decision makers. System and design ranges are determined in light of the hesitant evaluations of the field experts, and alternatives are listed in the case of both the unweighted and weighted fuzzy environments.

In the case of crucial technical systems, the necessary condition for the proper management of the operation and maintenance is the minimisation of threat of the inability state occurrence. Expressing this objective in an analytical form is an essential element of the operation correctness assessment and optimal maintenance management. It involves modelling of the technical state and the operation and service activities in a coherent way. Unfortunately, in the case of huge and complex technical systems the analytical and stochastic models are inadequate. Therefore, it was decided to use a system approach to the issues under consideration. Firstly, as a fundamental, the operational potential was introduced. Subsequently, the potential was described in the fuzzy technical state space. Thanks to it, the amount of the operational potential which can be transformed into the effects of the system operation and the degree of threat of the inability state occurrence were expressed in an analytical form. The proposed theory was applied to analyse the results of the operational tests accomplished by using an OP-650k-040 power boiler. As a result, the usefulness of the approach in the case of complex, crucial technical systems of strategic significance has been proven.

In this paper, a novel method for optimal solution in long-run is proposed to solve the test sequencing problem under unreliable tests that exist widely in real applications. The fault diagnosis process is presented and reformulated as a typical Markov Decision Process model, with the uncertainty of the tests is descripted by false alarm and detection probabilities which are to be the transition probabilities of the model. Moreover, the repeated test is adopted to improve the reliability of fault diagnosis. The cost and information gain are both considered in test chosen to achieve fast diagnosis with minimum cost. An application on the launcher device of a missile is studied in detail, as well as the comparisons in diagnostic performance among the proposed solution and the ones of traditional methods. Both the simulation and results illustrate the validity and feasibility of the proposed method.

Project managers normally investigate how to reduce the total completion time of projects undertaken subject to the pre-determined objectives. The purpose of this paper is compression of total projects’ duration subject to influencing factors such as cost, time, quality and risk. In the present study, by considering factors affecting project success such as cost, time, quality and risk, project crashing and fast tracking are both employed then a fuzzy multi-objective non-linear model is proposed. Each project is associated with uncertainty and the lack of consideration of these uncertainties might lead to project failure. In addition, a fuzzy approach has been employed for incorporating the uncertainties associated with activities. In the proposed model, crashing and fast tracking of projects were considered simultaneously for the first time. According to a real case study considered in this paper, after using the proposed model, the cost of compression is also reduced. In addition, we determined which activities have to be crashed or fast tracked in order to attain the objective. The proposed methodology can be practically applied through mega projects such as construction and “Engineering, Procurement, and Construction” (EPC) projects where the deadline closes to being achieved. As another contribution, a unique feature of the proposed model is its capability for taking both crashing and fast-tracking simultaneously into consideration.

Machine learning is successful in many applications including securing a network from unseen attack. The application of learning algorithm for detecting anomaly in a Network has been fundamental since few years. With increasing use of machine learning techniques it has become important to study to what extent it is good to be dependent on them. Altogether a different discipline called ‘Adversarial Learning’ have come up as a separate dimension of study. The work in this paper is to test the robustness of online machine learning based IDS to carefully crafted packets by attacker called poison packets. The objective is to observe how a remote attacker can deviate the normal behavior of machine learning based classifier in the IDS by injecting the network with carefully crafted packets externally, that may seem normal by the classification algorithm and the instance made part of its future training set. This behavior eventually can lead to a poison learning by the classification algorithm in the long run, resulting in misclassification of true attack instances. This work explores one such approach with SOM and SVM as the online learning based classification algorithms.

The aim of this paper is to extend the concept of the solidarity value to interval-valued (IV) cooperative games and develop a fast and simplified method for computing the IV solidarity values for a special class of IV cooperative games under some weaker conditions. In the method, we find a size (or coalition) monotonicity-like condition so that we can compute the IV solidarity values of a special class of IV cooperative games via determining their lower and upper bounds by utilizing the lower and upper bounds of the IV coalitions’ values, respectively. In addition, some important and useful properties of IV solidarity values are discussed and the feasibility and applicability of the method proposed in this paper are verified with numerical examples as well as comparison analysis with existing methods is conducted.

Single-valued neutrosophic hesitant fuzzy sets (SVNHFSs) have recently become a subject of great interest for researchers, and have been applied widely to multi-criteria decision-making (MCDM) problems. In this paper, the single-valued neutrosophic hesitant fuzzy geometric weighted Choquet integral Heronian mean operator, which is based on the Heronian mean and Choquet integral, is proposed, and some special cases and the corresponding properties of the operator are discussed. Moreover, based on the proposed operator, an MCDM approach for handling single-valued neutrosophic hesitant fuzzy information where the weights are unknown is investigated. Furthermore, an illustrative example to demonstrate the applicability of the proposed decision-making approach is provided, together with a sensitivity analysis and comparison analysis, which proves that its results are feasible and credible.

Aggregating individual interval multiplicative comparison matrices (IMCMs) and then ranking all the alternatives are very important in group decision making. However, the traditional methods may make the final result become overall average and does not always satisfy the Pareto optimal axiom. What’s more, the ranking of the alternatives may rely on parameter or the possibility degree. In order to make the aggregated interval multiplicative comparison matrix (IMCM) and the ranking more reliable, this paper develops a novel method to aggregate interval multiplicative comparison matrices (IMCMs) and then applies logarithmic least squares method (LLSM) to rank alternatives. Firstly, DMs’ interval multiplicative comparison matrices (IMCMs) are converted into two-dimensional coordinates. Next, the minimum Euclidean distance model is constructed and plant growth simulation algorithm (PGSA) is applied to find the aggregated points. Then the logarithmic least squares method (LLSM) is employed to derive the weight vectors and the ranking of alternatives can be obtained. Finally, numerical examples are provided to show the advantage and efficiency of the proposed method.

Weapon target allocation (WTA) is a classic NP-complete problem in the field of military operations research. In this paper, we addressed the multi-constraint WTA problems in multilayer defense scenario. To solve large-scale WTA problems effectively, a distributed MAX-MIN Ant System (MMAS) algorithm based on distributed computing framework Spark was developed and improved. An experiment environment comprising virtual machines was built for implementing the distributed MMAS. First, a small-scale WTA example, whose theoretical optimal solution can be obtained by existing optimization software, was taken as a benchmark problem to assess the performance of distributed MMAS. The result shows that it can find high-quality and robust approximate solutions. Then a large-scale WTA problem was constructed and used to further evaluate the performance of distributed MMAS in the experiment environment. The result shows that the distributed MMAS can also achieve high-quality approximate solutions with high robustness and computational efficiency even for large scale WTA problems. Our study demonstrates it is a promising approach for solving large-scale iteration-dependent optimization problems like WTA by means of incorporating heuristic optimization algorithms such as Ant Colony Optimization into distributed computing framework.

In this paper, we introduce the notion of fuzzy filters on equality algebras and study a fuzzy filter generated by a fuzzy set. We also solve the open problem which was presented by Kadji, Lele and Tonga in [Soft Computing, 21 (2017) 1913-1922]. In addition, we denote the set of all cosets of a fuzzy filter f by E/f, and prove that the structure (E/f, ∧ , ∼ , f₁) is an equality algebra, and is isomorphic to the (E/f_f(1), ∧ , ∼ , f_f(1)), where f_f(1) = {x ∈ E|f (x) = f (1)}. At the meantime, we define fuzzy congruences on equality algebras and show that there is one-to-one correspondence between fuzzy filters and fuzzy congruences. Finally, we investigate some topological properties of uniform topology induced by the special family of extreme fuzzy filters.

In this paper, we introduce a fuzzy Mellin transform method for solving Hermite fuzzy differential equations. The fuzzy Mellin transform reduce the problem of solving a Hermite fuzzy differential equation to a problem of solving a difference equation, whose inverse transform gives the solution of the fuzzy differential equation at hands. Under some conditions, we also give some Hyers-Ulam stability result of Hermite fuzzy differential equations.

Search engines are as important as recommender systems for hotel selections. However, the recommended lists of search engines are usually non-personalized and low accuracy. In order to deal with these issues in search engines, a comprehensive mechanism for hotel recommendation is proposed. In this mechanism, we consider users’ personalized preferences by identifying users’ attributes about interest, trust and consumption capacity. Meanwhile, the quantification method for each attribute is presented by using fuzzy theory. Moreover, this paper improves the method to evaluate the hotel, which respects to the criteria price, rating, and online review by using fuzzy theory. In addition, this proposed approach uses TOPSIS, a classical multi-criteria decision making method, to improve the accuracy further. Finally, a case study is conducted based on Tripadvisor.com to illustrate the validity of the proposed method for hotel recommendation in search engines. The results of the case study indicate that it not only solves the problem of non-personalization, but also improves the accuracy in search engine.

The main purpose of this paper is to introduce and examine the concepts of deferred statistical convergence of order β and strong r-deferred Cesáro summability of order β for sequences of fuzzy numbers and give some relations between deferred statistical convergence sequences of order β and strongly r-deferred Cesáro summable sequences of order β, where β ∈ (0, 1] and p ∈ ℝ + .

Mathematics Subject Classification: 40A05, 40C05, 46A45, 03E72.

In this paper, we investigate channel coordination of project supply chain based on uncertainty theory. The project is conducted by one manufacturer, whose materials are provided by different suppliers. To complete the project before its deadline, the manufacturer needs to consider two elements – the on-site task duration and the material delivery lead time. Due to the lack of historical data, these two elements are assumed as uncertain variables. Through modeling and analyzing, the time-based contract is proved to achieve channel coordination if the manufacturer can decide the following two terms: the targeted material delivery date, and the fraction and the timing of the delayed payment. The manufacturer and his suppliers can have a win-win outcome by contract design. The impacts of variables’ uncertainty degrees are discussed as well. Generally speaking, uncertainty degrees’ increases bring more risks to the project’s completion. However, it is proved that the manufacturer can still keep his profit by changing the bonus and penalty even if the on-site duration and material delivery lead time’s uncertainty degrees increase.

The main goal of this paper is to discuss the generalized Bosbach states on EQ-algebras. The notions of generalized Bosbach states of type I and generalized Bosbach states of type II (resp. III, IV, V, VI and VII) are proposed. The equivalent characterizations of these generalized Bosbach states on special EQ-algebras are given. The properties of these generalized Bosbach states and the relationship between them are investigated.

We give the notion of fuzzy congruences in a non-associative semigroup briefly known as Abel-Grassmann’s groupoid. First, we study fuzzy full, fuzzy self-conjugate, fuzzy normal subgroupoids and show that fuzzy kernel κ and fuzzy trace τ of a congruence make up a congruence pair (κ, τ) . Then, we investigate fuzzy idempotent-separating congruence and show that μ_(κ,τ) is a unique fuzzy congruence. Next, we study the lattice of fuzzy congruences, fuzzy congruence relations φ_min and φ_max and show a relation between fuzzy kernel and fuzzy trace of ρ_max in a completely inverse AG^**-groupoid.

In this paper, we study fractional partial differential equations (FPDEs) under Caputo gH-differentiability with uncertainty in type of fuzziness. Using Banach fixed point theorem, we show that the equilibrium point of the problem is stable. The stability is understood in the sense of Lyapunov stability. Moreover, by constructing a basic space of integral solutions, we prove global existence of fuzzy decay solutions of the problem. Some examples are also given to illustrate our main results.

In M-fuzzifying interval spaces, some properties, including M-fuzzifying dense property, M-fuzzifying decomposable property, M-fuzzifying ramification property and M-fuzzifying straight property, are introduced and their relations are studied. Based on these properties, notions of M-fuzzifying Bryant-Webster spaces and M-fuzzifying join spaces are introduced. It is proved that the category of M-fuzzifying Bryant-Webster spaces and M-fuzzifying convexity preserving mappings can be characterized by the category of M-fuzzifying join spaces and M-fuzzifying join preserving mappings.

Hesitant fuzzy soft set (HFSS) is considered as a powerful tool to capture uncertain information in group decision-making process. In this communication, we propose generalized correlation coefficient for hesitant fuzzy sets (HFS) and further extend for HFSS. Using generalized correlation coefficient of HFSS, we define generalized correlation efficiency which shows the significance of the HFSS. We also propose an algorithm to apply generalized correlation coefficient in group decision-making (GDM) problem, where information is presented in the hesitant fuzzy soft environment. Finally, with the help of an example, we show the effectiveness of the proposed algorithm which uses the concept of generalized correlation efficiency.

Retail store selection is an important decision for both customers and retailers because it is directly linked to customer satisfaction and profitability of retailers. Because of the competitive market conditions, retailers severely try to find out what they should do to be preferred by potential customers, and consequently to grow their sales. In this context, the criteria influencing customers’ store selection decision have to be analyzed. Although the relationship between customer preferences and retail store attributes has been widely studied through exploratory studies, a comprehensive framework using a multi-criteria decision making method under uncertainty to provide an overall assessment for retail stores has not yet been proposed. Pythagorean fuzzy sets are quite capable of representing uncertainty and vagueness in a decision making process by providing a larger domain to experts in expressing their opinions. Therefore, in this study, a novel interval-valued Pythagorean fuzzy WASPAS method is developed to evaluate the performance of retail stores. The obtained results are compared with crisp WASPAS and interval-valued intuitionistic WASPAS methods and it is revealed that the proposed method provides reliable and informative outputs.

The aim of this paper is to develop the spectral theory of prime L-ideals in ordered semigroups. Firstly the concepts of an L-fuzzy ideal and that of an L-fuzzy prime ideal are given and discussed. And then a topological space, called the L-fuzzy prime spectrum of an ordered semigroup, has been gained. This topological space is T₁ and sober. Furthermore some homeomorphic spaces of this topological space are investigated. Finally, a contravariant functor from the category of commutative ordered semigroups into the category of sober topological spaces is obtained.

In the present paper, we initiate a multi attribute group decision making problems in the presence of multi attribute and multi decision in decision making with preferences. Then resolving the problem, using two different approximation strategies, that is, seeking common reserving difference and seeking common rejecting difference, four kinds of dominance based multi-granulation rough sets are presented, namely, dominance based optimistic multi-granulation rough sets and dominance based pessimistic multi-granulation rough sets and their applications in solving a multi agent conflict analysis decision problem. The proposed method addresses the limitations of the Pawlak model and Sun’s conflict analysis model and thus improve these models. Finally, the results on labor management negotiation problems show that the proposed algorithms are more effective and efficient for feasible consensus strategy when compared with Sun’s technique.

Interval valued linguistics preference relation is one of the model of simple uncertain linguistic preference structures (additive or multiplicative). It can be easily and conveniently used to express the experts evaluations over the considered alternatives in group decision making under uncertainty. The primary purpose of this paper is to define uncertain multiplicative linguistic soft set (UMLSS) and study some of its properties. Some basic algebraic operations such as the ’AND’ and ’OR’ operations are also introduced in UMLSS. Using multiplicative linguistic sets deviation operation [21] into ULMSS, we derive uncertain multiplicative virtual linguistic soft set (UMVLSS). Based on the UMVLSS, we introduce the generalized linguistic deviation operator. An approach based on virtual linguistic soft set deviation to rank the alternatives in the group decision making problem is also provided. Finally a numerical example is provided to justify the new approach and illustrate the validity of our approach to the selection of feasible candidate(s) in a software company problem.

Soft set, as a parametrized family of subsets of a crisp universal set, has more ability to handle uncertain information. Pawlak introduced the concept of rough set to deal with uncertainty. He used equivalence relation to approximate a set. Many authors generalized the concept and used binary relations to approximate a set. In this paper, we used soft binary relations to approximate a set. We approximate a set by using the aftersets and foresets. In this way we get two sets of soft sets, called the lower approximation and upper approximation with respect to the aftersets and foresets. We applied these concepts on semigroups and approximations of subsemigroups, left (right) ideals, interior ideals and bi-ideals of semigroups are studied. Moreover, some examples are considered to illustrate the paper. In the last we studied the homomorphic images of the lower and upper approximations.

Here, necessary corrections on the proof the Theorem 1 of Xu (J Intell Fuzzy Syst 33(3): 1563-1575, 2017) are stated in brief. Throughout, we use the same notations and equation numbers as in Xu.

Concerning the discrete stochastic multi-attribute decision making (SMADM) problems in which attribute values are interval neutrosophic numbers and the attribute weight is incompletely known, a novel SMADM method based on the cumulative prospect theory (CPT) and generalized Shapley function is proposed. Firstly, the value prospect of each attribute under interval neutrosophic environment is calculated as well as subjective probability weights respectively, and the prospect function values are obtained according to the formulation of prospect function. Then, following the maximum deviation principle, the optimization model with respect to the incompletely known attribute weight information is constructed to obtain the optimal fuzzy measure, and the optimal weights of each attribute based on the formulation of generalized Shapley function are generated. Further, by aggregating the above prospect function values and the optimal weights, the values of the comprehensive prospect function are obtained and then alternatives are ranked. Finally, an illustrate example is presented to demonstrate the validity and feasibility of the proposed method.