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

Mobile Ad hoc networks (MANET) are multi hop networks, which are self organized, self configured and their topologies are changed dynamically. The destination node may be out of range of source node and routes are changing with time due to mobility of nodes. So a routing procedure is required to establish a path between them to send packets. Based on mobility pattern of MANET nodes, we have proposed four reactive routing models of MANET, which utilize position information of GPS, enabled nodes to restrict the search of a new stable path in a smaller cone-shape request zone. Among the proposed models, first three are being recommended for open field networks whereas last one is for urban area networks. We have compared our models with LAR, LARDAR and TN-CMAD and have shown the proposed models performed better in terms of overhead packets and energy consumption but a little increase of path-finding error.

Social networks helps to build relationships where two or more concepts, objects, or people are connected, or in state of being connected which is multidimensional and dynamic in nature. The interactive aspect of information extraction in online social networks instigates from considerations of different parameters which levied to the invention of new metrics. These metrics normally based on their ability to adapt to existing positioning or ranking indicator approaches with intent on activities and relationships among users in modern online social network which evolves with time. Existing work on network topology analysis is mainly focused on acquiring global properties such as interactions on either synthetic network or real world data provided by some authors without involving actual scenario of social network data. This research mainly focus on supervised learning using localize properties of known influential user in terms of links evolving from online social networks data. In addition, capture top K real time influential users from the evolving social network graph of known influential user. To achieve this, we propose two approaches, first an optimal Weight based Evolving Friends Follower Ranking (WEFFR) influence ranking algorithm to assign weights by capturing adaptive degree of relationship and secondly we combine WEFFR algorithm with Page Rank algorithm (WEEFRPR) to measures influence of nodes using reciprocal influence. The experiment results on Twitter network of known influential users shows that proposed approach performs better as compare to well-known existing approaches.

Cloud storage service permits data owners to store their encrypted content in the cloud storage servers and provide multiple users the authority to get access to the content. In this paper, we have proposed and evaluated a Secure lightweight Multi-User Searchable Encryption Scheme (SLMSES) for cloud storage. SLMSES achieves the requirement of small search time, concise index, and efficient user authorization. The scheme introduces a third party Manager which allows the users to subscribe tp data owners and get access to their files. The other job is to reduce the workload in the cloud by delegating their task of keyword searching. SLMSES employs the concept of bucketization for storing the files in the cloud. This allows the fast search for Manager as well as the cloud by narrowing the search space. Implementation and performance evaluation is conducted, which indicates that SLMSES is efficient and ready to be deployed, where subscriber base is large an stable.

With the advancements in cognitive radio network procedures, such as blind sensing, sensing transmission trade-off, energy aware protocols etc, effectively overcomes the power constraint issues of CBAN. In present work, such primary issues have been addressed with the help of look-up table incorporated with energy harvesting (EH). It delivers maximum achievable throughput with minimum energy consumption to obtain self-sustainable pervasive wireless networks. The experimental work has been carried out in three different scenarios (Echelon 1, 2 and 3). The key components such as look-up table and energy harvesting have also been investigated on different system parameters (like energy consumption, normalized throughput, saved and residual energy). The simulation is done with NS-2 and results are shown in Matlab for clarity. Results depict that proposed model consumes less energy and also provides a better normalised achievable throughput as compared to the conventional model.

Owing to its integration with cyber, Industrial Internet of Things (IIoT) is susceptible to integrity attacks, thereby inflicting fatal consequences both in industrial and economic domains. Compared to traditional networks, IIoT based on Software Defined Network (SDN) provides various network security enhancements thereby decreasing the effects of the integrity attacks. In an industrial process, anomaly detection with negligible false positives is the ideal intrusion detection mode, where the prerequisite of storing the attack patterns or acquiring the exhaustive knowledge of the devices in IIoT is not required. This research is an extension of our previous work, which employed a hybrid of specification and anomaly detection methods to recognize anomalies of critical components from a water treatment test bed at the Singapore University of Technology and Design (SUTD). The proposed work defines invariants for all the processes of the test bed. Any conflict from the invariants is notified as an intrusion and the compromised device is identified. The validation is done through Mininet tool with the testbed dataset. Out of the 30 successful attacks, this effort discovers 29 attacks with the detection rate of 96.5% and false positive rate of 6.5%.

The network lifetime often faces great challenges for wireless multi-hop networks since the nodes are typically self-powered in the networks. Intelligent multipoint relays (MPR) selection and energy efficiency have recently been considered to be the major solutions which address to suppress unnecessary messages and reduce energy consumption in the networks, respectively. After extensive studies, a strategy based on Signal to Interference plus Noise Ratio (SINR), together with residual energy aware multipoint relays selection (SRE-MPR) is proposed in this work. In SRE-MPR, a generic model of energy consumption is introduced where SINR is also taken into account. Simulation results demonstrate that outstanding performance can be achieved in comparison with classical Optimized Link State Routing Protocol (OLSR) and Residual Energy-based OLSR (REOLSR2) on the number of alive nodes, throughput and overhead, respectively.

In mobile applications, user behavior clustering analysis divides users into different groups so that operators can provide personalized services, optimize page organization and functional design. On the basis of clickstream and custom event, this paper first proposed two collection schemes and formats of user behavior data, then a two-layer clustering algorithm and a fuzzy clustering algorithm are proposed respectively. The former first-layer clustering adopts an improved DBSCAN algorithm, which replacesthe neighborhood radius with a newly-designed user session similarity and has optimized the merge condition of clusters. While, the latter directly extracts feature vectors of user sessions and use an improved FCM algorithm, where an effective method is used to initialize membership matrix to accelerate the convergence speed and giving a weighted value to membership matrix solves the problem of local optimum. Experimental results show that both algorithms are effective and matches the advance assigned user test distributions, which proves that the two clustering methods are applied to different analysis scenarios, depending on the business requirement of developers.

The Internet of Things (IoT) has been emerged as one of the most optimistic solution that reducing the gap between physical and cyber world. In IoT, sensors and actuators gather the sensory information; communicate with each other to fulfill application requirements. As sensor nodes are running on batteries, which cannot be replaced, so the development of the green model in IoT for shifting the real world data into the cyber world is challenging task. As IoT is deployed in large area and become more complex, so wireless sensor network current techniques cannot be applied in it directly. To achieve energy efficient network in the field of Internet of Thing, this paper addresses these challenging issues by introducing an efficient deployment scheme. This paper contributes: (1) A hierarchical cluster model for IoT network deployment; (2) An optimal Relay node ( ℝ ℕ ) election technique with respect to node density in the network and their deployment strategy by using their communication area as threshold distance for next ℝ ℕ selection within the cluster; (3) An efficient energy routing algorithm to meet the requirement of proposed model. The analysis of experimental results show that proposed method is more efficient in terms of energy consumption and suits IoT requirements.

With the construction and application of large-scale datacenters, the issue of resource allocation in cloud computing becomes a serious concern. Although the current static allocation method can make applications get corresponding resources, there still exist some shortcomings such as resource surpluses or shortages. This kind of problem is more crucial in real-time requirements of mobile cloud computing service. Therefore, it is necessary to establish a forecasting model to predict the future resource demands, and then perform on-demand distribution, which can effectively reduce the unnecessary daily network management fees and address the issues mentioned above. This paper focuses on CPU resource forecasting, establishing three forecasting models including Markov chain, weighted Markov chain and stacking weighted Markov chain. By comparing and analyzing the experiment results, the most reasonable forecasting model is found and explained.

With the accelerated process of agricultural modernization, the accurate acquisition of agricultural environmental information has become a major trend. A field microclimate monitoring system based on wireless sensor network is constructed based on the latest concept model of the Internet of things and fuzzy control theory; it mainly composed of data acquisition system, data storage system and visualization platform for big data analysis. The data acquisition system is highly integrated with data acquisition and data transmission to obtain real-time data of farmland environment in different terrain areas, including meteorology, hydrology, soil, growth etc. Wireless transmission will transmit real-time data through the GPRS network to the big data analysis platform. The big data analysis platform presents the site data information and analyzes the rule of historical data through visualization technology, realizes the meteorological disaster early warning and forecast, and provides effective decision-making service information based on the agricultural fuzzy theory. Finally, we analyze all kinds of hardware interference problems and software defects encountered in the debugging process, and propose new solutions through the experimental data obtained from actual production applications. It has been proved that the field microclimate monitoring system runs steadily and meets the demand of agricultural monitoring.

Wireless Sensor Networks (WSNs) are vulnerable to various localization attacks where attackers intended to provide improper beacons or manipulate the location determination. Attack classification for localization in WSNs is not only the condition, prerequisite and premise of threat analysis, but, more significantly, a vital part of the security anomaly detection. In this paper, a localization attack recognition method using a deep learning architecture was proposed. To enhance the classification performance, a good feature representation was established through combining location features with topological indexes based on the complex network theory. The ability of Stacked Denoising Autoencoder (SDA) to learn the underlying features from input data was exploited. Back-propagation algorithm was performed to update weights through a stochastic gradient descent method. The proposed approach could efficiently distinguish the Sybil attacks, Replay attacks, Interference attacks, Collusion attacks and normal beacons. Extensive experiments demonstrated that the proposed algorithm can achieve an average classification accuracy of 94.39% and was more robust and efficient even in the existent of huge baneful beacons.

In order to solve the contradiction between wireless network application requirements and increasingly scarce spectrum resources, cognitive wireless network technology emerged. Based on the characteristics of wireless network nodes and combining multiple hybrid control strategies, this paper proposes a multi-priority dual-clock probability detection CSMA(MPDCPD-CSMA) protocol with a monitoring mechanism. The field-programmable gate array (FPGA) hardware circuit is used as an experimental research platform for the first time. Cognitive wireless network MAC protocol design and implementation. The design took full advantage of the flexibility of the FPGA, using a hardware description language Verilog HDL and schematic input combined with the QuartusII9.0 circuit design. By comparing the statistical values of the circuit system with the theoretical values, it is verified that the design has the characteristics of good real-time performance, high reliability, and strong portability. It can effectively reduce system node energy consumption, improve system throughput, and can be applied to wireless networks.

Uncovering the potential treatment associations of the drug-disease pairs is a research focus of drug repositioning. However, it is time-consuming and costly to verify the potential treatment relation between a drug and a disease by “wet” experiment methods. Fortunately, along with the accumulation of large amount of data and the development of machine learning methods, lots of computational methods to predict the drug-disease treatment associations have been proposed. In order to build the prediction model based on machine learning techniques, both plenty of positive and negative training samples are required. In the case of biological experiments, however, we can only verify whether a drug cures a disease, yet we are unable to answer whether a drug definitely cannot treat a disease. Correspondently, there are only positive and unlabeled samples in the data. Being lack of validated negative samples, most computational methods assume the unlabeled samples to be negative ones and randomly select some unlabeled samples and positive samples to train the prediction models. Obviously, the unlabeled samples are not necessarily negative, and some of them may be positive just remaining uncovered via experiments. In this paper, we propose a method called PUDrDi which directly make use of the positive and unlabeled samples to train a Biased-SVM classifier. Moreover, we combine the drug and disease features together to represent a drug-disease pair, in which we use chemical substructures and symptoms as the features to represent drugs and diseases respectively. The experiment results demonstrate that PUDrDi outperforms some other methods. The case study further shows the practicality of PUDrDi.

As the performance of modern multi-core processors is significantly increases, the total energy consumption in the systems also increases drastically. Dynamic Voltage and Frequency Scaling (DVFS) is considered as one of the efficient schemes for achieving the aim of energy saving. In this paper, we consider scheduling a task set, whose release times, deadlines and execution requirements are given, on DVFS-enabled multi-core processor system. Our main aim is to meet the execution requirements of all the tasks, and to minimizethe overall energy consumption on the processor with effective utilization of resources. Instead of seeking optimal solutions with high complexity, we aim to design algorithms suitable for real-time systems, with good performances. We come up with a simple algorithm for task scheduling and energy awareness by considering deadline constraint. We further consider the distribution of deadline and task scheduling, which guarantee that all tasks meet their execution requirements, and tries to minimize the overall energy consumption. Case based simulations for various applications and task characteristics and evaluations using a practical processor’s power configuration indicate that our proposed algorithm has a less energy consumption performance and good resource utilization in terms of saving processor energy, though it has low complexity. Besides, the proposed algorithm is easy to be implemented in practical systems.

Generation Expansion Planning (GEP) aims to define the least cost capacity expansion plan to meet forecasted demand inward a pre-defined reliability criterion and emission constraint over a planning horizon. This paper presents the application of Differential Evolution (DE), Opposition-based Differential Evolution (ODE) and Self-adaptive Differential Evolution (SaDE) algorithms to GEP problem, where the power generating system of an Indian state Tamil Nadu is taken as study region. GEP problem has been solved for short-term (6-years) and long-term (12-years) planning horizon by considering least-cost, reliable supply and lowest emission to the environment using DE, ODE and SaDE also validated by Dynamic Programming (DP). GEP problem is solved for seven diverse cases such as, Case 1: Base case, Case 2: GEP with Energy Conservation (EC), Case 3: GEP with high penetration of Renewable Energy Sources (RES), Case 4: GEP with penalty costs on emissions from high emission plants (HEP), Case 5: GEP with energy storage technologies (EST), Case 6: Combination of Cases 2, 3&4 and Case 7: Combination of Cases 2, 3, 4&5. The results simultaneously provide the type and capacity of each power plant need to be expanded in each year of the planning horizon at least cost.

Microbial activities are the indicators of soil strength. The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics, phosphate solubilization (PS), bacterial population (BP), and 1-aminocyclopropane-1-carboxylate ACC-deaminase activity. More specifically, fuzzy c-means clustering (FCM)-FIS, Wang and Mendel’s (WM) fuzzy inference systems (FIS), adaptive neuro-fuzzy inference system (ANFIS), and subtractive clustering (SC) and have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics. Experimental measurements were performed using controlled pot experiment using minimal salt media. Three experimental parameters, including temperature, pH, and incubation period have been used as inputs of FCM-FIS, SC-FIS, ANFIS, and WM-FIS methods. The SC-FIS method has the best estimation accuracy for the PS (R² of 0.99) and BP (R² of 0.94) than the rest three FIS methods.

Pythagorean fuzzy sets, which is based on intuitionistic fuzzy sets (IFSs), is an important tool to solve problems and has attracted a large number of researchers in different fields. As we know, studies have focused on interval-valued Pythagorean fuzzy set and aggregated operators. However, few studies focus on point operators. This paper introduces and discusses what is the pythagorean fuzzy point operators, study their properties and relationships, which is seen as the extensions of intuitionistic fuzzy sets. The uncertainty regarding to Pythagorean fuzzy set could be decreased if we use the pythagorean fuzzy point operators. In the end, pythagorean fuzzy multi-attributes decision making based on analytic hierarchy procedure is put forward to cope with the complicated MADM (multi-attributes decision making) issues which can be very useful when we face the multi-level analysis.

Failure Modes and Effects Analysis (FMEA) is a common technique used in several manufacturing and service industries for eliminating failures and potential problems using the evaluation of failure modes of a new or an existing product, process, or system. The risk analysis is carried out by calculating the Risk Priority Number (RPN), which is a product of three factors, Occurrence (O), Severity (S), and Detection (D). In the literature, modeling uncertainties are used to improve the FMEA process and overcome the inefficiencies of traditional RPN. One of the common uncertainties in FMEA is the epistemic uncertainty that is essentially modeled using the Dempster-Shafer theory (DST). In this study, a novel risk-based fuzzy evidential approach is proposed by using interval-valued DST and fuzzy axiomatic design (FAD) to assess the risk of failure modes with fuzzy belief structures. The efficiency of the proposed model was investigated with the help of an example and the results are compared with riskless evaluations. Reviewing the results shows the information content of failure modes decrease relatively when risk is taken into account, in fact failure modes become relatively more critical than those in the case where no risk is considered.

Diversity and accuracy of classifiers are widely recognized to be two key factors for a successful ensemble. The increase of diversity among classifiers must lead to the decrease of the average accuracy of that, and vice verse. Therefore, finding a tradeoff between the diversity and the accuracy of classifiers can make the ensemble perform the best. Existing ensemble pruning approaches always find the tradeoff using diversity measures and heuristic algorithms separately. Those ensemble pruning approaches based on diversity measures, using different strategies, cannot exactly find the tradeoff; Those approaches based on heuristic algorithms cannot also exhaustively search for that. To address the issue, Combining Weak-link Co-evolution Binary Artificial Fish swarm algorithm and Complementarity measure for Ensemble Pruning (CWCBAFCEP) is proposed using a combination of the proposed Weak-link Co-evolution Binary Artificial Fish Swarm Algorithm (WCBAFSA) and COMplementarity measure (COM). First, the classifiers in a constructed initial pool of classifiers are pre-pruned using COM, which significantly reduce the computational complexity of ensemble pruning. Second, the final ensemble extracted from the remaining classifiers after pre-pruning can be efficiently achieved using the proposed WCBAFSA. Experimental results on 25 datasets from the UCI Machine Learning Repository demonstrate that CWCBAFCEP performs much better than the original ensemble and other state-of-the-art ensemble pruning approaches, and that its effectiveness and efficiency. It provides a new research idea for ensemble pruning.

This paper presents a technique of motion planning of robot using Fuzzy Method and Genetic Algorithm along with Three Path concept in a dynamic environment which contains both static and dynamic obstacles. The algorithm is divided into two phases. In the first phase, a path or two is generated by using Three Path Method or Fuzzy-GA while considering static obstacles only. The information about the distances and angles of obstacles from the robot is acquired by using the concept of Three Path. A collision free path is selected by using Three Path concept. When all the three paths are blocked by static obstacles Fuzzy Method is used for obstacles avoidance. Genetic Algorithm is used to find optimal range of the linguistic values of the variables for the membership functions. In the second phase, the moving obstacles are avoided along the path/paths generated in the first phase by considering the velocities of the moving obstacles and the velocity of the robot. Results show that the technique of motion planning of mobile robots using Fuzzy-GA along with Three Path concept is computationally efficient as compared to other hybrid methods for motion planning in dynamic environment.

The general characteristics observed in Autism is decrease in communication skill, interaction and shows behavioral changes. The reasons for these can be studied by understanding their visual sensory processing. The research work presented here uses image stimuli to study the behavior in children by understanding when and where they look. A Fuzzy based Eye Gaze Point estimation (FEGP) has been proposed which observes the gaze coordinates of the child, analyze the eye gaze parameters to assess the difference in visual perception of an autistic child in comparison to a normal child. The approach helps to identify the visual behavior difference in autistic children with a performance level indicator, visualization and inferences that can be used to tune their learning programs with an attempt to meet their counterparts.

A Kriging-based global optimization method is proposed to solve black-box unconstrained design problems in this work. Firstly, the non-convex Kriging optimization problem is converted into the two convex programing problems by the canonical dual transform to quickly get global optimal solution. Then, PSO (Particle Swarm Optimization) algorithm is adopted to find next promising design point by exploring and optimizing the transformed problems. The proposed method not only reduces the computational burden, but also effectively balances local and global search behavior. Some well-known numerical test functions and a real engineering example are investigated to illustrate that the presented method can further enhance the feasibility, validity and robustness of the optimization process in contrast with other global optimization algorithms.

Nowadays, Semi-Supervised Learning lies at the core of the Machine Learning field trying to effectively exploit unlabeled data as much as possible, together with a small amount of labeled data aiming to improve the predictive performance. Depending on the nature of the output class, Semi-Supervised Classification and Semi-Supervised Regression constitute the basic components of Semi-Supervised Learning. Various studies deal with the implementation of Semi-Supervised Classification techniques in many real world problems over the last two decades in contrast with Semi-Supervised Regression, which is deemed to be a more general and slightly touched case. This survey aims to provide a detailed review of Semi-Supervised Regression methods and implemented algorithms in recent years. Our in-depth study reveals the relatively few studies that deal with this specific problem. Moreover, we seek to classify these methods by proposing a schema and categorizing all the related methods that have been developed in recent years according to specific criteria.

Brain Computer Interface (BCI) enables us to record and process the information generated by the brain and process them. Due to high variability of the Electroencephalogram (EEG) data, multiple trails are recorded for a particular task. The present work aims to improve the accuracy for motor imagery task classification by selecting the most prominent trail from the multiple trails recorded during motor imagery. In this paper, we propose a novel weight optimization algorithm for common spatial filtering (CSP) using evolutionary algorithms (i.e. cuckoo search algorithm (CSA), firefly algorithm (FA) and gravitational search algorithm (GSA)) to select the most prominent trial from the multiple trails recorded for feature extraction. The features extracted from the selected trials were thus used for motor imagery task classification. The performance was evaluated on the extracted features from the selected trials using two classifiers namely linear discriminant analysis (LDA) and support vector machines (SVM). It is observed that FA with band power as a feature gives the best performance in comparison to the earlier reported methods i.e. average, error based and alternating direction method of multipliers (ADMM).

Sea squirts are cultivated mainly in Korea, Japan, and China. Sea squirt sorting during the harvesting process is labor-intensive and time-consuming as there is no automatic sorting technology for sea squirts. In this study, we developed and evaluated an automatic sea squirt sorting algorithm based on sea squirt color information analyzed using the hue-saturation-value (HSV) color model and the regression equation of the projected area and weight of the sea squirt. The developed algorithm recognizes sea squirts during the sorting process based on the threshold range of sea squirt color values and their weight based on measurements of the projected area. In 100 repeated experiments conducted with mixed products containing sea squirts, mussels, and Styela clava, the average sea squirt recognition rate of the developed algorithm was 98.5%, and the sorting performance based on animal weight and grade was ≥95.5% at an average speed of 1,050 kg/h.

Voice activity detection (VAD) identifies the presence/absence of human speech in a frame of a given speech signal. Presence/Absence of human speech can easily be identified in clean speech signal but its accuracy decreases with decreasing Signal-to-Noise ratio (SNR) value. Robust VAD helps to enhance the efficiency of speech signal based automated applications like speech enhancement, speaker identification, hearing aid devices etc. In this paper, a new feature of speech signal- “Peak of Log Magnitude Spectrum (PLMS)” is introduced and used for VAD. This newly defined feature PLMS along with three existing acoustic features(MFCC;RASTA-PLP and Formant Frequency) are used to train SVM classifier for VAD. Experimentally, it is found that coefficients of PLMS play most prominent role. Experimentally, it is also observed that the accuracy of the trained SVM classifier for VAD is the highest when compared with other state of the art methods (Sohn VAD and VAD G.729).

Decision-making is very important activities in the various applications of science, engineering, and technology. A decision can be derived in three manners by these applications: (1) by developing a mathematical model, (2) taking domain experts advice, (3) developing an expert system. However, accurate mathematical model may not be developed for the domain that might not be completely interpreted. Moreover, the problem with the second method is that the human intervention is not possible all the time and the expenditure of hiring a domain expert may be high. Decision-making, using expert system or controller induces great interest among the researchers and professionals. Expert systems or controllers are capable enough to counter unpredictability, noise, and vagueness. Fuzzy set theory is commonly used in building the expert systems and controllers due to its ease and similarity to human reasoning. Therefore, the proposed approach is based on fuzzy logic for decision making. The proposed model is explained through a case study. The result of the proposed work is compared and judged by the results of earlier studies. The result depicts that the proposed method has a better performance and effectiveness than existing studies.

Emotion is a property by which human beings and machines can be differentiated as machines are emotionless while human beings are not. If the emotion of a speaker is recognized then others can interact accordingly. This paper presents a new approach for recognizing all the six basic emotions (Happy, anger, fear, sadness, boredom and neutral) from the speech signals more effectively. To recognize the emotion of a speaker, pitch value and two wavelet packet feature vectors derived from speech signals are used. Principal Component Analysis (PCA) has been applied to reduce the dimension of feature vectors. Random Forest (RF) and Support Vector Machine (SVM) classifiers are trained separately based on these reduced feature vectors. The experimental results show that the accuracy of emotion recognition with Random Forest classifier is 86.11% while with SVM classifier it is 84.41%. Experimentally, it is also found that clean speech of 1 sec duration is sufficient enough to recognize emotion of the speaker.

Consensus is a significant part that supports the identification of unknown information about animals, plants and insects around the globe. It represents a small part of Deoxyribonucleic acid (DNA) known as the DNA segment that carries all the information for investigation and verification. However, excessive datasets are the major challenges to mine the accurate meaning of the experiments. The datasets are increasing exponentially in ever seconds. In the present article, a memory saving consensus finding approach is organized. The principal component analysis (PCA) and independent component (ICA) are used to pre-process the training datasets. A comparison is carried out between these approaches with the Apriori algorithm. Furthermore, the push down automat (PDA) is applied for superior memory utilization. It iteratively frees the memory for storing targeted consensus by removing all the datasets that are not matched with the consensus. Afterward, the Apriori algorithm selects the desired consensus from limited values that are stored by the PDA. Finally, the Gauss-Seidel method is used to verify the consensus mathematically.

The augmented growing of visual cryptography in multimedia image transmission or data transmission over unsecured networks leads in safekeeping for confidential information. Generally two techniques are employed to afford secure transmission namely data hiding and cryptography. Cryptography is the main objective of recent research work in which the way of achieving secure transmission over the network be contingent on the interest of data encryption. This encryption process encrypts the constituent of data such as manuscript, image, audial, and audiovisual to make the data unconceivable or incomprehensible during transmission. A novel secret key generation based on Improved Bat Optimized Piecewise Linear Chaotic Map is proposed for image encryption. Our proposed secret key is intended for image encryption owing to the progression of mixing, permutation, double diffusion and confusion with the size of 128 bit to perform secure transmission. The success of our proposed method is revealed by the tentative results and comparison with the existing techniques in terms of sensitivity analysis, Information Entropy, correlation coefficient and, Encryption speed.

A hybrid renewable energy scheme comprising of the wind and solar PV electric power systems with appropriate maximum power point tracking is presented in this paper. The maximum power point tracking for the wind generator is carried out using Adaptive Neuro Fuzzy Inference System. The MPPT technique adopted for the photovoltaic power generation system is the Incremental Conductance (IC) algorithm. A power flow control scheme based on fuzzy logic is developed to regulate the power transaction from the wind and solar power sources as well as for the battery charging and discharging. Based on the available velocity of wind and solar insolation and based on the electrical demand different modes of operation are selected automatically using the ANFIS based control strategy. Considering the non linearity’s of the converters and the unpredictable nature of the renewable sources an advanced adaptive controller is necessary. The proposed ANFIS controller performs well and the proposed idea has been validated using MATLAB/Simulink and the simulation results are reported.

Benefited on the open source software movement, many code search tools are proposed to retrieve source code over the internet. However, the retrieved source code rarely meets user needs perfectly so that it has to be changed manually. This is because the retrieved source code is concretely over-specific to some particular context. To solve this problem, we propose an Abstract Change Pattern Model (ACPM) to ensure the context-specific source code general for various contexts. This model consists of the ACP abstracting and the ACP concretizing algorithms. The former exploits the abstractly context-aware change pattern from the code changes. Based on the change pattern, the latter transforms the context-specific source code into the correct one meeting different user needs. To evaluate ACPM, we extract 7 topics and collect 5-6 code snippets per topic from the Github, while performing 5 different experiments where we explore 2 sensitivity-related rules and use them to raise the accuracy gradually. Our experimental results show that ACPM is feasible and practical with 73.84% accuracy.

The online smartphone-based human activity recognition (HAR) has a variety of applications such as fitness tracking, healthcare…etc. Currently, the signals generated from smartphone-embedded sensors are used for HAR systems. The smartphone-embedded sensors are utilized in order to provide an unobtrusive platform for HAR. In this paper, we propose a deep convolution neural network (CNN) model that provides an effective and efficient smartphone-based HAR system. For automatic local features extraction from the raw time-series data, we use the CNN while simple time-domain statistical features are used to extract more distinguishable features. Furthermore, we explore the impact of a novel data augmentation on the recognition accuracy of the proposed model. The performance of the proposed method is evaluated using two public data sets (UCI and WISDM) which are collected using smartphones. Experimentally, we show how the proposed model establishes the state-of-the-art performance using these datasets. Finally, to demonstrate the applicability of the proposed model for online smartphone-based HAR, the computational cost of the model is evaluated.

Energy storage using batteries is emerging as a fundamental element of standalone power system based on non conventional energy sources like wind and solar to increase the penetration level of these sources. The planning of standalone power system incorporating renewable sources and storage necessitates a vigilant study on modeling of a storage system. In most of the planning study reported in literature pertaining to battery storage, charging efficiency (CE) of a battery is assumed to be fixed at constant value. However, CE and State of charge (SOC) of the battery both are correlated. In this paper, Interval Type(IT)-2 fuzzy logic has been applied for determining CE of battery relative to a specific SOC. For evaluating reliability indices i.e. Expected Energy not served (EENS), probabilistic analysis using analytical method has been applied to the standalone power system, situated near Kandla Port in Gujarat, India. The effect of considering CE of battery as a function of SOC has been compared with the constant value of CE of battery for the different groups consisting of solar-battery storage, wind turbine(WT)-battery storage, and wind-solar battery storage systems.

Even though finding out distance is the central core of k-Nearest Neighbor classification techniques, similarity measures are often favored against distance in various realistic scenarios and situation. Most of the similarity measures, which are used to classify an instance, are based on geometric model. Their effectiveness decreases with the increases in the number of dimensions. This paper establishes an efficient technique called ARSkNN for finding out class of any given instance using a measure based on an unique similarity, that does no longer compute distance, for k-NN classification. Our empirical results show that ARSkNN classification technique is better than the previous established k-NN classifiers. The performance of algorithm was verified and validated on various datasets from different domains.

With the higher compression ratio, the decoded image produces annoying compression artifacts near block boundaries, ringing artifacts near original edges and corner outliers at block corner. These coding artifacts are caused by quantization and transformation process of discrete cosine transform (DCT). This paper proposes a novel deblocking algorithm that removes block discontinuities by taking into account the ringing, blurring and corner outlier artifacts. The proposed deblocking technique consists of two frequency related modes (smooth and detailed region mode) and corner outlier mode have been proposed and then applied median filter. The proposed technique has been applied to a number of reconstructed images and their performance is compared with conventional methods on the basis of standard metrics such a PSNR-B, BBM and MOS. Experimental simulation results illustrate that proposed technique improves the perceptual quality of reconstructed images and thus outperforms the all existing methods.

Recently, many software companies have shifted to shorter release cycles from the traditional multi-month release cycle. Evolution and transition of release cycles may affect the test effort in the system. This paper analyses 25 traditional releases containing 1210 classes and 69 rapid releases containing 2616 classes of four Open Source Java systems. Correlations between 48 Object Oriented metrics and 2 test metrics were evaluated to identify the best indicators of test effort. The results show that (i) correlation between OO and test metrics remain irrespective of release models, (ii) test effort required in Rapid Release (RR) models (shorter release cycles) is slightly more as compared to Traditional Release (TR) models, (iii) Out of 18 machine learning algorithms instance based machine learning algorithms IBK and K star followed by Multi-Layer Perceptron (MLP) and additive regression are able to predict the test effort accurately in classes.

This paper models the single and bi-objective brain emotional intelligent controllers for the dual stator winding induction motor (DSWIM) drive. The main purpose of this paper is performance improvement of the DSWIM drive control system and power losses reduction of the inverter in the DSWIM drive at low speeds. In the vector control method, it is difficult to estimate flux at low speeds. To solve the mentioned problem, researchers have used from the free capacity of the two windings of the stator. This paper presents three proposed methods: 1. Using the idea of rotor flux compensation based on classical PI controller at low speeds, the motor works in its standard operating mode; 2. Proposed Method 1 is reformed and improved based on the bi-objective brain emotional controller; and 3. Proposed Method 2 is improved using single-objective brain emotional controller in the speed control loops of the DSWIM drive. The proposed methods are simulated in MATLAB/Simulink software.

Due to the limited focus range of optical imaging system, and the locations or focus of different objects in the same scene are different, multiple objects cannot be focused at the same time. In order to solve this problem and make the underwater image clearer, we propose a fusion method based on the sparse matrix in this paper. Firstly, we transform the source image into sparse image by sparse transform and get the clearity of the image based on the sparsity. Then, the clearity image will be segmented into focus regions. After that, the focus regions and non-focus regions are fused respectively based on different fusion algorithms. Finally, the focus regions and non-focus regions are combined to get the enhanced image. The experiments in the end show that the fusion method we proposed in this paper has higher information entropy, correlation entropy, standard deviation, and average gradient, so it can enhance the underwater multi-focus image and can be applied to the underwater object detection.

Contrasted with common obstacle avoidance mode based on single sensor or solo algorithm, this article put forward an intelligent pattern based on Combination from CNN-based Deep Learning Method and liDAR-based Image Processing approach. As for Deep Learning method, a 10-layer Convolutional Neural Network (CNN) is designed which comes to a high recognition accuracy of 97 percent in Tensorflow and success rate of obstacle avoidance is over 90 percent. With regard to liDAR-based Image Processing approach, decision is made by a special method of counting the number of Point Cloud Data (PCD) which is generated by 2D liDAR and a success rate over 90 percent is achieved as well. When two kinds of methods work together, a robust success rate of 100 percent is realized. Meanwhile, Inertial Measurement Unit (IMU) and Xbox360 are taken into consideration for Pose Estimation and Data Collection. Finally, all functions are integrated in Robot Operation System (ROS) on platform of nVidia Jetson TX1.

Software cost estimation is the process of predicting the most realistic and valid amount of effort necessary for the development of any software. The cost estimation of any software is a difficult assignment due to the involvement of many factors that anyhow affect the estimation process. In literature, many cost estimation models have been developed for more than a decade to maintain accuracy in estimation of the cost of software projects. But, it is found that these models are inefficient to estimate the exact cost of software development because of uncertainties and lack of accuracy associated with them. In this paper, Alla F. Sheta models have been taken for optimization, which are the modified versions of the very famous Boehm’s COCOMO model. Parameters of the Sheta models have been tuned enough by the proposed method to estimate and minimize the consequences of different factors that affect the overall software development cost. Experimental work has been carried out in MATLAB environment and analysis of results is performed on the basis of Magnitude of Relative Error (MRE), Prediction (PRED) at 0.25, Value Accounted For (VAF) and Mean Magnitude of Relative Error (MMRE). Estimation accuracy of the proposed work is tested on NASA software project dataset. It is found that the proposed method shows good estimation capabilities over other state-of-the-art cost estimation models.

3D Reconstruction has been an enduring problem in Image understanding and computer vision. There is an increasing interest on 3D information in the general public due to rapid development of 3D imaging techniques, marketing of 3D movies and games, and low cost of depth cameras. Numerous algorithms that have been proposed for performing 3D reconstruction using different variants of Iterative Closest Point(ICP) algorithm focusses mainly on reducing the computation time. The accuracy of 3D reconstruction is not taken in to consideration. An efficient, accurate, real time and active 3D reconstruction method using Kinect sensor is developed in this paper focusing on improving the accuracy of 3D reconstruction in less computation time. An Artificial Bee colony based ICP algorithm is proposed by incorporating several efficient variants of ICP algorithm. The proposed algorithm is intended to improve the accuracy and stability of the standard ICP algorithm. The performance of the proposed algorithm is satisfactory when compared with structured light technique and several ICP variants with respect to accuracy, complexity and computation speed.

Smartphone has been used for recognizing the different motion activities. However, current studies focus on either improving algorithm factor or adjusting neural network structure factor rather than on time cost factor and actual application factor. A novel method to consider these four factors comprehensively enhancing recognition of motion state accuracy is proposed. An architecture of the Bi-LSTM neural network and the TensorFlow machine learning system are used to classify the motion state and evaluate its experimental results. In addition, the Bi-LSTM neural network is compared with other neural network structures. Meanwhile, using the data captured by the accelerometer sensor and gyroscope sensor of the smartphone tests the Bi-LSTM neural network model. Experimental results show that using Bi-LSTM neural network and TensorFlow machine learning system to extract motion state characteristics, this method makes the motion state identification achieve 86.7% accuracy and the Bi-LSTM neural network model is better than other neural network models considering above four factors. The model of Bi-LSTM neural network can be used for other time-series fields such as signal recognition, action analysis, etc. This study provides a new method, which considers the four factors, to enhance the accuracy of the motion state classification.

Conversion of the conventional vehicle (CV) into the plug-in hybrid electric vehicle (PHEV) is one of the promising solutions to improve transport sustainability and reduce outdoor air pollution. Energy management is crucial for the performance of PHEV. The paper presents combine rule based-artificial bee colony optimization algorithm for energy management of converted plug-in hybrid electric vehicle (CPHEV). The diesel operated parallel hybrid topology is considered for study with the designed electric powertrain. NOx and PM are considered as optimization parameters along with specific fuel consumption. The performance based on fuel consumption and emissions (NOx and PM) is analyzed by considering sample Indian urban and highway driving cycle. The complete vehicle is simulated using MATLAB Simulink linked with coding. The results of converted PHEV obtained is compared with conventional one for both driving cycles for analysis of the fuel consumption and emissions considering real-time benchmarking norms. The results indicate that the combine rule based-artificial bee colony strategy keeps pollution under control required as per BSIII norms.

Robot path planning is integral to many robotic applications. In this work, three optimization objectives are presented: path length, degree of path smoothness, and degree of security. Due to the lack of local search ability, the optimal solution set is difficult to be obtained with the traditional method especially when the search space is very irregular. And the simple local search algorithm is often trapped into local optimization. A new method with local search is introduced to improve the SPEA2 in this work. The proposed method sets up an external population dedicated to local search, which can increase the local search ability of the method while retaining good global searching ability. In addition, the new crossover operator and the individual update strategy are used for proposed method. The simulation results shows that the proposed method is better than that of SPEA2, NSGA-2 and PESA. It was found that the model proposed in this work is practical for robot path planning.

Handwritten word recognition is considered as an active research area since long because of its various real life applications. The key obstacle of this research problem is the huge variation of the writing styles of different individuals. In addition to that the complex shapes of alphabet make the recognition process more difficult. A holistic word recognition approach is proposed here in order to classify 80-class handwritten city name images written in Bangla script. Based on the negative refraction property of the light, a novel shape-based feature vector of size 186 is generated from each of the word images. Effectiveness of the feature vector is tested on a database containing total 12000 handwritten word images having equal number of samples from each class. The proposed method achieves a reasonably good recognition accuracy of 87.50% which proves better while comparing with some of the recently published feature vectors used for similar job. The reported result is achieved by combining the classifiers namely Sequential Minimal Optimization (SMO), Simple Logistic and CV Parameter Selection embedded with SMO. To verify the robustness of the present method it is also applied on handwritten word images written in Roman and Devanagari scripts separately and it is found that our method obtains satisfactory result on the both the cases.

Human Cancer Cell lines have gained a lot of attention since it helps in studying cancer biology and various treatment options. Recently various large-scale drug screening experiments were performed providing access to genomic and pharmacological data. This data helps in predicting drug responses which eventually contributes to the development of personalized cancer treatment. Heterogeneous nature of cancer raises the serious need for therapeutic agents with an essence of personalized treatment. Thus considering the assumption that similar drugs exhibit similar drug responses, we have developed kernelized similarity based regularization matrix factorization framework for predicting anti-cancer drug responses. Drug-Drug chemical structure similarity and Tissue-Tissue similarity (gene expression) are taken as key descriptors to formulate the objective function. The kernel function is used to map non-linear relationships between drugs and tissues. Our aim is to provide an efficient anti-cancer drug response prediction approach to establish the protocol for personalized treatment and new drugs designing. The proposed framework is validated using publicly available tumor datasets: GDSC and CCLE. Proposed KSRMF is further compared with three states of art algorithms using GDSC and CCLE drug screens. We have also predicted missing drug response values in the dataset using KSRMF. KSRMF outperforms other counterparts even though gene mutation data is not incorporated while designing the approach. An average mean square error of 3.24 and 0.504 is achieved using GDSC and CCLE drug screens respectively. The obtained results show that the proposed framework has quite potential to improve anti-cancer drug response prediction. Our analysis showed how data integration can help in achieving the goal of personalized cancer treatment.

The paper introduces a methodological approach based on genetic algorithms to calibrate microscopic traffic simulation models. The specific objective is to test an automated procedure utilizing genetic algorithms for assigning the most appropriate values to driver and vehicle parameters in AIMSUN. The genetic algorithm tool in MATLAB^® and AIMSUN micro-simulation software were used. A subroutine in Python implemented the automatic interaction of AIMSUN with MATLAB^®. Focus was made on two roundabouts selected as case studies. Empirical capacity functions based on summary random-effects estimates of critical headway and follow up headway derived from meta-analysis were used as reference for calibration purposes. Objective functions were defined and the difference between the empirical capacity functions and simulated data were minimized. Some model parameters in AIMSUN, which can significantly affect the simulation outputs, were selected. A better match to the empirical capacity functions was reached with the genetic algorithm-based approach compared with that obtained using the default parameters of AIMSUN. Overall, GA performs well and can be recommended for calibrating microscopic simulation models and solving further traffic management applications that practioners usually face using traffic microsimulation in their professional activities.

A linear method based on local statistical parameters of the image to remove the speckles of ultrasound carotid artery medical image is presented in this article. Speckle is the main drawback of medical images and it should be removed before any further processing of images like edge detection and registration. The focus of this article is to filter the speckle efficiently and effectively even at higher density of noise. The filter is designed by keeping in mind that the local statistical parameters are important rather than global statistical parameters. The weighting factor is designed such that it is high for similar areas and thus results into more smoothing without destroying the useful information, whereas it is low at the edges and thus less smoothing will be done. The filter is applied with the help of 5×5 sliding window. The noise ranging from 0.01–0.09 of variance is unnaturally inserted in the medical images through Matlab. The efficiency calculating parameters like Signal to Noise Ratio (SNR), Quality Index (Q), Mean Square Error (MSE), Similarity Index Measure (SSIM) and Edge Preserved Index (EPI) were used to evaluate the proposed technique. The suggested method is also compared with the existing local statistical mean variance filter for the said parameters in order to analyse the performance of the filter.

A methodology to systems identification based on Evolving Fuzzy Kalman Filter, is proposed in this paper. The mathematical formulation using an evolving Takagi-Sugeno (TS) structure, is presented: the offline Gustafson Kessel (GK) algorithm is used for initial parametrization of antecedent of the fuzzy Kalman filter inference system, considering an initial data set; and an evolving version of the GK algorithm is developed for online parametrization of antecedent of the fuzzy Kalman filter inference system. A fuzzy recursive version of OKID (Observer/Kalman Filter Identification) algorithm is proposed for parametrizing the matrices A, B, C, D and K (state matrix, input influence matrix, output influence matrix, direct transmission matrix, and Kalman gain matrix, respectively), in the consequent of the fuzzy Kalman filter inference system. Computational and experimental results from the estimation of the states and outputs of a dynamic system and a two-degree-of-freedom (2DoF) Helicopter, respectively, show the efficiency and applicability of the proposed methodology.

Distributivity equation has been widely studied involving different classes of logical connectives or aggregation operators, such as implications, uninorms, t-operators and their generalizations. In this paper, we follow on these works by investigating the distributivity for uninorms and Mayor’s aggregation operators.

We propose project evaluation criteria by considering not only the Six Sigma approach, but also European Foundation for Quality Management (EFQM) model features, and introduce an evaluation model for completed Six Sigma projects. The proposed model uses seven main criteria: leadership, policies/strategies, main performance results, social results, employees, cooperation, and resources. These main criteria are enhanced by 18 sub-criteria. Due to the evaluation scale being based on human judgments, fuzzy set theory is an obvious methodology choice to account for uncertainty in the evaluation data. Type-2 fuzzy sets can provide flexibility for uncertainty by considering membership functions and their footprints. We also propose an evaluation methodology for weighting the main and sub-criteria and for ranking completed Six Sigma projects through a fuzzy analytic network process (ANP) method with interval type-2 fuzzy sets. From our analysis, performance results were found to have the highest weight among the seven main criteria, while achieving project objectives was found to be the most effective criteria.

Recent studies have shown sparse representation learning is a potentially promising method in pattern classification, but very few focused on class imbalanced problems involved in its applications and practice. This problem is particularly important, since it causes suboptimal classification performances, especially when the cost of misclassifying a minority-class example is substantial. Unlike the prior test sample sparse representation on balanced data sets, which cannot reflect the data distribution in real applications, we proposed a novel sparse representation learning algorithm called Balanced Sparse Representation Classifier (BSRC), considering the contribution from heavily under-represented of minority classes. Our solution first estimates the contribution of training sample in each class, and then identifies the nearest neighbors with the largest contributions. After that, the test data is expressed based on linear combination of all the nearest samples. Finally, the decision has been made according to sum of contribution for each class. Moreover, we also present the kernel extension of the proposed classifier to deal with complex data. Experimental results also show that with the proposed learning approach, it is possible to design better method to tackle the class imbalance problem in sparse representation learning.

Investment project assessment is one of the most critical activities in the investment process, which requires a trade-off between multiple attributes exhibiting vagueness and imprecision with the involvement of a group of experts. The multiple attribute group decision-making (MAGDM) method based on trapezoidal interval type-2 fuzzy sets (IT2FSs) is suitable for the decision makers to deal with this problem. However, some shortcomings in the arithmetic operations of trapezoidal IT2FSs, and some of ranking methods in some cases are invalid. In this paper, the arithmetic operations of trapezoidal IT2FSs are redefined, which can overcome some shortcomings of the ones developed in existing literature. And then, a new ranking method of trapezoidal IT2FSs based on the incentre point of fuzzy sets is developed. In order to verify the proposed method, thirteen fuzzy sets are used in comparison with some of the existing methods, and the comparison results demonstrate the superiority of the proposed method. Finally, the proposed method is integrated into the technique for order preference by similarity to the ideal solution (TOPSIS) method and an illustrative example in investment project assessment is presented to evaluate the effectiveness of the proposed methods.

Traffic congestion has become a serious phenomenon in the cities. In order to achieve the effective control of intersections, multi-lane four-phase intersection is studied. The corresponding queue length model and vehicular delay model are established. Aiming at the dynamic uncertainty problem in the intersection, a type-2 fuzzy logic controller is designed. The green time of each phase is dynamically decided according to the real-time traffic information for purpose of achieving the smallest vehicular average delay, so as to enhance the traffic efficiency in the intersection. The excellent performance of the designed controller is confirmed through simulation experiments under different conditions. Finally, in view of the difficulty of parameter settings in type-2 fuzzy controller, DNA evolutionary algorithm is applied to online optimize and adjust the parameters of membership function. One group of parameters is difficult to fit all traffic situations, so on-line optimization and adjustment is necessary for reflecting the real-time change of traffic flow in time, which is of great significance for the practical application. The experimental results indicate that the online optimized type-2 fuzzy traffic control method has better effect.

A hypergraph is one of the most developing area for modeling various practical problems in different fields, including computer science, biological sciences, social networks and psychology. Our main discussion in this research paper is to apply the notion of intuitionistic fuzzy sets to extend the theory of hypergraphs. We introduce the concept of isomorphism, dual intuitionistic fuzzy hypergraph, intuitionistic fuzzy line graph and 2-section of an intuitionistic fuzzy hypergraph. We present some applications of intuitionistic fuzzy hypergraphs in planet surface networks, selection of authors of of intersecting communities in a social network and grouping of incompatible chemical substances. We design certain algorithms to construct dual intuitionistic fuzzy hypergraph, intuitionistic fuzzy line graph and the selection of objects in decision-making problems.

A linear programming with triangular intuitionistic fuzzy parameters is focused in this paper. As a shortcoming, all the solution approaches of the literature for this problem are constructed based on ranking functions, where, use of different ranking functions may result in different solutions. In this study for the first time an approach with no ranking function is developed for the problem. For this aim, the triangular intuitionistic fuzzy objective function is decomposed to a multi-objective function, and the problem is converted to a multi-objective crisp problem. As another contribution, in order to solve the obtained multi-objective problem for its efficient solutions, a new multi-objective optimization approach was developed and suited to the obtained crisp multi-objective problem. The computational experiments of the study, show the superiority of the proposed multi-objective optimization approach over the existing approaches of the literature.

The aim of this paper is to obtain some common α-fuzzy fixed point theorems under generalized Θ-contraction in the setting of complete metric space. In this way, we generalize various results of literature including the main result of Hancer et al. (Fixed Point Theory, 18 (2017), 229-236). We also provide an example to show the significance of the results investigated in this paper.

Blind source separation (BSS) is an advanced method of signal processing. Essentially, the problem in BSS is to separate and estimate the original signal from the observed mixed signal source without knowing the characteristics of the original signal. Independent component analysis (ICA) is a popular approach for blind source separation, and because its traditional search scheme is based on a gradient algorithm, a convergence problem will arise. In order to overcome the defect, this paper proposed to apply Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) to conduct accelerated computing of the rate of convergence of a demixing matrix in ICA. However, the PSO converges prematurely, and the population diversity is reduced rapidly, so that the optimal solution falls into the local optimum. In order to increase the diversity of PSO, GPSO-based ICA algorithm (GPSO-ICA) is proposed that has the exploring ability of GSA, so that the ICA algorithm has a higher convergence rate and better ability to escape local optimization. A series of comparisons is implemented for the ICA algorithms based on PSO, GSA, and GPSO. The results show that GPSO-ICA has better performance than the other methods.

In this paper, we propose a new correlation coefficient between intuitionistic fuzzy sets. We then use this new result to compute some examples through which we find that it benefits from such an outcome with some well-known results in the literature. As in statistics with real variables, we refer to variance and covariance between two intuitionistic fuzzy sets. Then, we determined the formula for calculating the correlation coefficient based on the variance and covariance of the intuitionistic fuzzy set, the value of this correlation coefficient is in [−1,1]. Then, we develop this direction to build correlation coefficients between the interval-valued intuitionistic fuzzy sets and apply it in the pattern recognition problem.

This paper proposes global template dynamic time warping (GTDTW) algorithm for gesture recognition with the wearable gloves. The method is applied to both isolated and continuous gesture recognition. A gesture segmentation system based on GTDTW is also proposed for continuous gesture recognition. The global template is obtained based on statistical methods. For a defined gesture, states which have a large proportion are selected as important states. They form the global template for the defined gesture. Global template more fully expresses the characteristics of the defined gesture which can improve gesture recognition rate. Global template also has a smaller length than normal template of Dynamic Time Warping (DTW) so that time consumption of GTDTW is low and gesture recognition system has a better real-time performance. Experimental evaluations on both isolated and continuous gesture recognition show the effectiveness of the proposed method. The time consumption is obviously reduced and recognition rate is improved that up to 98.8% for isolated gesture recognition. For continuous gesture recognition, the proposed method has high segmentation rate and recognition rate is up to 95.6%.

A generalized fuzzy entropy based on double adaptive ant colony algorithm for image thresholding segmentation is proposed. The new algorithm first attempts to propose the adaptive pheromone concentration at the initial time and the adaptive global updating rules, which uses the double adaptive mechanism to automatically select the generalized fuzzy entropy parameters. The threshold of the image is obtained by introducing the parameters into the complement of the generalized fuzzy entropy, and then the optimal segmentation of the image is obtained. Compared with the existing image thresholding segmentation algorithms, in most cases, simulating results indicate that the new algorithm has less background information and clearer target information. In addition, it is superior to the existing algorithms in performance and greatly improves the stability and convergence speed.

Generally, in transportation problem, full vehicles (e.g., light commercial vehicles, medium duty and heavy duty trucks, etc.) are to be booked, and transportation cost of a vehicle has to be paid irrespective of the fulfilment of the capacity of the vehicle. Besides the transportation cost, total time that includes travel time of a vehicle, loading and unloading times of products is also an important issue. Also, instead of a single item, different types of items may need to be transported from some sources to destinations through different types of conveyances. The optimal transportation policy may be affected by many other issues like volume and weight of per unit of product, unavailability of sufficient number of certain types of vehicles, etc. In this paper, we formulate a multi-objective multi-item solid transportation problem by addressing all these issues. The problem is formulated with the transportation cost and time parameters as fuzzy variables. Using credibility theory of fuzzy variables, a chance-constraint programming model is formulated, and is then transformed into the corresponding deterministic form. Finally numerical example is provided to illustrate the problem.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

Blood transfusion services are a vital section component of the healthcare system all over the world. Literature on studying and modeling of these systems is surprisingly sparse. In this paper, we expand a generalized network optimization model for the complex supply chain of blood, which is a regionalized blood bank system. In this paper; the purpose of blood is red blood cells (RBC). This system consists of collection sites, testing and processing facilities, storage facilities, distribution centers, as well as points of demand, which are classically, include hospitals. Our major contribution is to develop a novel Hybrid stochastic programming, multi-choice goal programming and robust optimization (SMCGR) approaches to simultaneously model two different types of uncertainties by including stochastic scenarios for total blood donations and polyhedral uncertainty sets for demands. Real numerical studies are implemented to verify our mathematical formulation and also show the benefits of the SMCGR approach. The performance improvements achieved by the valid inequalities and Pareto-optimal cuts are demonstrated in real world application.

The sum of membership and non-membership degrees of the Pythagorean fuzzy set is greater than one with their square sum less than or equal to one. Thus, as an extension of intuitionistic fuzzy set, the Pythagorean fuzzy set is a powerful tool to describe fuzziness and uncertainty. The aim of this study is to introduce some new operators for aggregating Pythagorean fuzzy information and apply them to multi-attribute decision making. Considering the advantages of the power average operator and Muirhead mean, we introduce the power Muirhead mean operator and investigate it under Pythagorean fuzzy environment. Thus, some new Pythagorean fuzzy aggregation operators, such as the Pythagorean fuzzy power Muirhead mean and the weighted Pythagorean fuzzy power Muirhead mean are developed. The prominent advantage of these proposed operators is that they consider the relationships between fused data and the interrelationships between all aggregated values, thereby obtaining more information in the process of multi-attribute decision making. Furthermore, we introduce a novel approach to multi-attribute decision-making problems based on the proposed operators. Finally, we provide a numerical example to illustrate the validity of the proposed approach.

In order to avoid the environmental emission of fossil fuels, renewable energy resources expansion trend is undeniable. This paper analyzes the possibility of cooperation between neighboring independent microgids in a smart grid with renewable resources considering the security assets and vulnerabilities. The ultimate goal is to decrease the overall security risks on the smart grid. Modeling of microgrids cooperation is formulated via a coalition game definition considering the permissible amount of their vulnerabilities. Load shedding minimization can be obtained during cyber-attack occurrence for each member of coalition microgrids. This paper presents a comprehensive algorithm for optimal coalition of neighboring microgrids based on a utility function calculation which has three major components: Budget for Attack (BA), Budget for Defense (BD) and microgrid relationship effectiveness, to contribute more understanding, microgrid relationship effectiveness formulation is developed by the introduction of friction and influence matrices. Proportional Fairness (PF) index is employed to illustrate the improvement of utility function in case of optimal coalition in comparison with non-cooperative state. Microgrids coalition optimal choice based on suggested algorithm is validated by probability attack and loss of load probability (LOLP) simulation for each microgrid before and after the coalition form.

In this paper, a new method, namely, the dynamic delay partitioning method, is firstly developed to solve the problems of stability analysis and stabilization for a class of unknown nonlinear systems. To study the system stability and facilitate the design of fuzzy controller, Takagi-Sugeno (T-S) fuzzy models are employed to represent the system dynamics of the unknown nonlinear systems. Different from previous results, the delay interval [0, d (t)] is partitioned into some variable subintervals by employing dynamic delay partitioning method. Thus, new delay-dependent stability criteria for fuzzy time-delay systems is derived, which is less conservative than previous results. By the criteria, the approach to design a fuzzy controller is developed. Two examples are provided to demonstrate the effectiveness for conservatism reduction.

In industrial engineering, the components of a critical system are capable of being in partial failure modes, except for “perfect state” and “complete failure”, and the failure behavior of those usually manifests as dynamicity and dependence. However, traditional dynamic fault trees (DFTs), which represent an event as a dichotomous variable, and the extended ones in probability risk assessment cannot actually grasp the dynamic properties of some multi-state systems (MSSs). For these issues, this article further extends the classical DFT language for sequential, prior and trigger-dependent MSSs and presents a unified framework of probability risk analysis based on the dynamic Bayesian net (DBN). First, three types of multi-state dynamic gates (MSDGs) for representation of the above-mentioned failure behavior were defined, and the algorithm for mapping MSDGs to DBNs was proposed. Next, this paper employs the classic Markov chain based on the improved approach of Kronecker algebra to verify these models. Finally, combining a specific example of a shield excavation system, we discuss how the MSDGs can be adopted as a compact modeling language and analyze the dynamic probability risk of the system by compiling the model into a DBN.

Fuzzy rough set is a hybrid tool for handling indeterminate, inconsistent and uncertain information that exist in real life. Graph theory has numerous applications in various disciplines, including modern sciences and technology, database theory, computer networks, expert systems and image capturing. In this paper, we apply fuzzy rough set theory to graph theory and investigate a new kind of graph structure which is called fuzzy rough digraphs. Then we describe some of their theoretical properties. Finally, we consider applications of fuzzy rough digraphs in decision-making to demonstrate the applicability, feasibility, effectiveness of the proposed algorithm.

In this paper, we introduce a generalized fuzzy soft rough model. A pair of new fuzzy soft rough approximations namely, fuzzy soft I ℙ -lower approximation and fuzzy soft I ℙ -upper approximation is proposed and their related properties are presented. Furthermore, we have succeeded to reduce the fuzzy soft boundary region comparing with already known in the literature. Finally, we obtain the structure of fuzzy soft ideal rough topologies induced by fuzzy soft sets and fuzzy ideals.

The aim of this paper is to introduce and study the concepts of fuzzifying semipre-θ-neighborhood system of a point, fuzzifying semipre-θ-closure of a set, fuzzifying semipre-θ-interior of a set, fuzzifying semipre-θ-open sets and fuzzifying semipre-θ-closed sets in fuzzifying topological spaces, and investigate some of their basic properties. Also, we present two types of functions in a fuzzifying topological spaces called fuzzifying strong β -irresolute and fuzzifying weak β -irresolute functions. The interrelations of these functions with the parallel existing allied concepts are established. Further, several characterizations of these functions along with different conditions for their existence are obtained.

This paper investigates the optimal structure switching in the microgrid for improving the electrical services for cost minimization and power quality improvement. The proposed framework makes use of some tie and sectionalizing switches for changing the microgrid feeding path to the electrical consumers and thus minimizing the resistive power losses. This concept is assimilated with the mobile storage capability of electric vehicles to improve the operation of the microgrid. The wöhler curve is used to model the degradation costs of battery charging and discharging process in the electric vehicles. The proposed problem is formulated as a mixed-integer nonlinear optimization problem which is solved using the social spider optimization algorithm. Also, an effective two-phase modification method is developed to improve the algorithm diversity and avoid the premature convergence. An IEEE test system is used to investigate the performance of the proposed model.

The present work is focused on hesitant fuzzy multi-attribute decision making (HF-MADM) problem with the hesitant fuzzy information based on a new aggregation operator. To begin with, we present the new hesitant fuzzy dual Muirhead mean (HFDMM) to deal with MADM problems, including the HFDMM and the hesitant fuzzy dual weighted Muirhead mean (HFDWMM) operator, the main advantages of the two aggregation operators are that they can capture interrelationships of multiple attributes among any number of attributes by a parameter vector P and make information aggregation process more flexible by the parameter vector P. In addition, some properties of HFDMM and HFDWMM are obtained and some special cases are discussed where the parameter vector takes some different values. Moreover, we present a new method to solve the MADM problems with hesitant fuzzy information based on HFDWMM. Finally, an illustrative example is provided to show the feasibility and validity of the new method, the influences of parameter vector P on the decision making results are investigated and the advantages of proposed methods by comparing with the other existing methods are also analyzed by the example.

Branciari defined the integral contractions to generalize the Banach contraction principle. Moreover recently Phiangsungnoen proved a fixed point theorem to generalize the ordered structure and contractive conditions with admissible mappings. In this article, we prove some coincidence and common fixed point theorems for a pair of L-fuzzy mappings satisfying integral β-admissible and generalize integral contractions. These results generalize the above famous results and many others in the literature. We define two types of fuzzy functional inclusions and prove existence of their solutions as a consequence of our results. Some nontrivial examples are given to provide significance of ourresults.

In this paper, we further study the theory of A-subsets in lattice implication algebras. To begin with, we introduce the propositions of homomorphism image and original image of A-subsets in lattice implication algebras. In addition, the notion of A*-subsets is introduced and some properties of A*-subsets are also investigated in lattice implication algebras. Finally, we introduce the notion of involutory LI-ideals with respect to an LI-ideal A and denote the set of all of them by S _A (L). Then S _A (L) can be made into a complete Boolean lattice and a lattice implication algebra with respect to the suit operations, respectively.

This paper deals with fuzzy permutation graph which is introduced as a fuzzified model of a crisp permutation graph. We have defined two types of complements of a fuzzy permutation graph viz. p-complement and f-complement. We have considered various examples to encounter the existence and non-existence of the f-complement graph as a fuzzy permutation graph and established a sufficient condition for the existence of the same. Also, we have calculated the possible number of fuzzy permutation graphs that can represent the f-complement of a fuzzy permutation graph for a given permutation depending on the edge-membership values of the fuzzy permutation graph. We have also introduced and characterized fuzzy comparability graph. Lastly, we have discussed a real-life application of fuzzy permutation graph.

Deep learning algorithms have recently been applied to solving challenging problems in medicine such as medical image classification and analysis. In some areas, those algorithms have outperformed the human medical experts experience in diagnosis. Thus, in this paper we apply three different deep networks to solve the problem of brain hemorrhage identification in CT images. The motivation behind this work is the difficulty that radiologists encounter when diagnosing a hemorrhagic brain CT image, in particularly in the early stages of the brain bleeding. Autoencoder (AE), stacked autoencoder (SAE), and convolutional neural network (CNN) are employed and trained to classify the CT images into hemorrhagic or non-hemorrhagic. Experimentally, it was found that all employed networks performed differently in terms of accuracy, error reached, and training time. However, stacked autoencoder has achieved a higher accuracy and lesser error compared to other used networks.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

A complex fuzzy set is a set whose membership values are vectors in the unit circle in the complex plane. To enhance and extend the applicability of complex fuzzy sets, this paper investigates and develops different types of distance measures for complex fuzzy sets. Several distance measures of complex fuzzy sets are introduced. After that, the application of these distances to continuity problems of complex fuzzy operations is given.

As a foundation to study the similarity measure with applications in pattern recognition, (normalized) Hamming distances and (normalized) Euclidean distances between hyper structures based on two and three parameters are established. An example to show that these distances are not metrics in general is provided, and conditions for these distances to be metrics are considered. The notions of length fuzzy ideals with four types are introduced, and related properties and their relations are investigated. Relations between length fuzzy ideals and length fuzzy subalgebras are investigated. Conditions for a length fuzzy subalgebra to be a length fuzzy ideal are provided. Characterizations of a length fuzzy ideal are considered.

In the last years peer-to-peer (P2P) networks have become an important and prevalent architecture in the Internet and have been commonly applied into many scenarios such as big data storage and management, cloud computing, vehicular networks and social networking. The overall performance of P2P networks mainly depends on cooperation between peers, so as to encourage each peer to contribute its resource to others and reduce free-riding behaviors. Then each peer has to face a dilemma decision that how much of its available bandwidth it will allocate to uploads for others and how much it will reserve for its own downloads. In this paper we consider bandwidth allocation in P2P networks where both upstream and downstream flows traverse one common access link with finite capacity, and formulate the utility maximization model for bandwidth allocation. The model is difficult to resolve since it is an interplay problem between upload and download decisions of each peer. In order to achieve the optimal bandwidth allocation, we present a heuristic scheme using Particle Swarm Optimization (PSO), and discuss the performance with numerical examples in different network scenarios. Simulation results show that the scheme can achieve the global optimum within reasonable iterations.

The purpose of this paper is to provide an efficient numerical technique for solving two-dimensional nonlinear fuzzy Fredholm integral equations. The fuzzy Gauss quadrature rule is applied to the above mentioned equation, which reduces the fuzzy integral equations to the nonlinear approximate equations that can be easily solved by a numerical iterative algorithm. The convergence of the presented numerical method is investigated under several mild conditions. Finally, some illustrative numerical experiments are provided to illustrate the efficiency and accuracy of the proposed method.

In this paper, a mixed-integer non-linear programming model has been proposed to design an integrated cellular manufacturing system with type-2 fuzzy parameters. The integrated cellular manufacturing system consists of the following elements: cell formation, cellular layout and operator and tool assignment. One of the important features of the proposed model is considering total expected cost criteria to operator assignment. Tool costs, inter and intra cell movement costs and machine constant costs are type-2 fuzzy variables. In order to defuzzification of parameters the critical value (CV)-based reduction method is used to reduce type-2 fuzzy variables into type-1 fuzzy variables and finally the centroid defuzzification method defuzzifies fuzzy costs to crisp numbers of the costs. Furthermore, to solve a small-sized numerical example, the Branch and Bound algorithm with the Lingo software has been used. Because of NP-hardness of the model for large-sized problems and no exist benchmark to validate the performance of the proposed model, three tuned meta-heuristic algorithms (i.e., particle swarm optimization, differential evolution and fire fly) are presented too. The results show that the particle swarm optimization algorithm has better statistically performances in most problems.

Stock evaluation is a significant decision-making activity for investors. Due to the complexity of stock exchange market, evaluation information may be fuzzy and stochastic in the meantime. Therefore, it is essential to conduct the research on fuzzy stochastic multi-criteria decision-making (FSMCDM) methods. In this paper, at the beginning, we propose interval neutrosophic probability from the definition of neutrosophic probability. Then, a novel MCDM method is proposed with interval neutrosophic probability based on regret theory, in which criteria values are interval neutrosophic numbers (INNs). The method proposed with interval neutrosophic probability may be much better than methods using classic probability or fuzzy probability. Next, we apply the proposed method to stock selection problems and discuss the influence of parameters on ranking results. Finally, we compare regret theory with prospect theory, which is also an important theory of bounded rationality just like regret theory, to emphasize the characteristics the regret theory. Moreover, a comparative analysis is also conducted between the proposed method and existing methods under interval neutrosophic environment to demonstrate efficiency and applicability of the proposed method.

To date, type-2 fuzzy sets have attracted much more interest of the researchers, which are more capable to handle uncertain and imprecision information than type-1 fuzzy sets. Heronian mean is a classical mean aggregation operator which can effectively consider the interrelationships between various arguments. In this paper, we extend Heronian mean to trapezoidal interval type-2 fuzzy environment and present some aggregation operators, such as trapezoidal interval type-2 fuzzy generalized Heronian mean (TIT2FGHM) operator and trapezoidal interval type-2 fuzzy weighted generalized Heronian mean (TIT2FWGHM) operator. Then, we further discuss several desirable properties and particular cases when the parameters take diverse values in detail. Moreover, concerning multiple attribute group decision making problems in which the decision attributes are interdependent and the attribute values take the forms of interval type-2 fuzzy numbers, we propose a new approach based on TIT2FWGHM operator. Finally, we provide a numerical example of providers selection to illustrate the practicality and effectiveness of the proposed method and give a comparison analysis between the proposed method and existing methods.

This paper presents a multi-objective method to optimally place the Distribution Static Synchronous Compensator (D-STATCOM) in a distribution system using the multi-objective genetic algorithm (MOGA). In the proposed method, the average current total harmonic distortion (THD), installation and operation cost functions are considered as the objective functions, where input harmonic limitations for individual buses are chosen as the optimization constraints. The application of this optimization is based on a novel forward-backward harmonic load flow method by comparing input current THD, device generated harmonics and impacts of output THD on the network. The performance of this algorithm is evaluated using the Matlab software on the radial IEEE 33-bus test system. The simulation results verify the capability of the MOGA in accurately determining the optimal location of the D-STATCOM in radial distribution systems.

Recent years, fuzzy inference systems have been commonly used for time series forecasting. It is well known that fuzzy inference systems can produce good forecasting. Although fuzzy inference systems like adaptive network fuzzy inference system have been preferred by many of researchers, these systems have many of problems. If data set contains many explanatory variables, the number of rules will increase dramatically. Classical fuzzy inference systems need to estimate too many parameters for a reasonable forecasting performance. In this study, a new fuzzy inference system is proposed for time series forecasting. The proposed inference system uses fuzzy c-means method for clustering and pi-sigma neural network for fuzzy modelling. Moreover, the proposed system can generate probabilistic outputs (forecasts) under favour of subsampling block bootstrap method. The performance of the proposed method was investigated by using some data sets. It is understood that the proposed inference system can produce better forecast results.

In this paper, we consider a left quantale module represented by Q-module as a universal set and we introduce the notion of rough Q-submodule with respect to a Q-submodule of Q-module. Some results about homomorphic images of rough Q-submodules are also generalized and improved in the field of Q-modules. Next, the concepts of set-valued homomorphism and strong set-valued homomorphism of Q-modules are introduced, and related properties are investigated. The notions of generalized lower and upper approximation operators, by means of a set-valued mapping are defined.

In this paper, we propose a method for finding an optimum solution of the nonlinear optimization problem with equality constraints. The original problem is replaced by a sequence of unconstrained problems of the augmented Lagrangian function. The subproblems are minimized by using quasi-Newton methods. The Hessian matrix of the augmented Lagrangian is updated by using a new secant approximations which is positive definite at every iteration. A set of computational results on test problems from CUTEr collection are presented.

In the present paper, we introduce the concept of lacunary statistical boundedness of order β for sequences of fuzzy numbers and give some relations between lacunary statistical boundedness of order β and statistical boundedness.

In a complex financial market, people pay more attention to the portfolio selection model in some fuzzy environment. Some properties and definitions of semivariance and entropy are given and proved in this paper. Then, the fuzzy tri-objective mean-semivariance-entropy portfolio model is proposed based on credibility theory when the return rates are fuzzy numbers. We present a novel layer-by-layer tolerance evaluation method to solve the proposed model in this paper. Finally, a numerical example is given to illustrate the effectiveness of the model and method proposed in this paper. There are two improvements in our proposed method: one is that the evaluation function idea is considered in the process of laying objective functions; the other is that the preference degree and subjective wills are presented by tolerant amounts of objective function values.

Due to different knowledge background and language habit, decision makers generally provide multi-granularity unbalanced linguistic terms to assess possible alternatives in linguistic decision situation. In this paper, a new linguistic term transformation method is proposed to transform multi-granularity unbalanced linguistic terms into 2-tuple linguistic values of any fixed linguistic term set. The new method consists of transformation of linguistic term and 2-tuple linguistic representation of transformation, the aim of transformation of linguistic term is to obtain a unified information of multi-granularity unbalanced linguistic term based on its triangle membership function, 2-tuple linguistic representation of transformation is to transform the unified information as 2-tuple linguistic values of any fixed linguistic hierarchy. A practical decision making problem is utilized to illustrate the practicality of the new method, more important, it is compared with existing important transformation methods in the practical decision making problem, the result shows that the new method is an alternative method to transform multi-granularity unbalanced linguistic term sets.

Uncertain differential equation is an essential tool in dealing with uncertain dynamic system, which is driven by canonical Liu process. This paper mainly studies two classes of nonlinear uncertain differential equations with exponential and power forms by two analytic methods. The corresponding solutions are obtained, and some examples are given to illustrate the effectiveness of these methods.

Multifactor uncertain differential equation is a type of differential equation driven by the multiple Liu processes. Stability of a multifactor uncertain differential equation plays a very important role in differential equation which means insensitivity of the state of a system to small changes in the initial state. This paper presents a concept of the p-th moment stability of multifactor uncertain differential equation. Some stability theorems for the solution of multifactor uncertain differential equation are given, in which some sufficient conditions for a multifactor uncertain differential equation being stable in p-th moment and a sufficient and necessary condition for a linear multifactor uncertain differential equation being stable in p-th moment are provided. In addition, this paper discusses the relationships among stability in p-th moment, stability in measure and stability in mean.

Linguistic intuitionistic fuzzy number (LIFN) is a special intuitionistic fuzzy number where the membership and non-membership are expressed by linguistic terms and can more easily describe the vagueness and uncertainty in the real world. The Heronian mean (HM) provides an aggregation operator which can consider the interrelationship among the aggregated arguments. Nevertheless, the traditional HM can only aggregate crisp numbers rather than any other types of arguments. In this paper, we firstly define some new operational rules of the LIFNs based on Einstein operations, then the HM operator is extended to the LIFNs and some linguistic intuitionistic fuzzy Heronian mean operators based on Einstein operations are proposed, such as linguistic intuitionistic fuzzy Einstein Heronian mean (LIFEHM) operator, weighted linguistic intuitionistic fuzzy Einstein Heronian mean (WLIFEHM) operator. Further, some desired properties of these operators and some special cases with respect to the different parameter values in these operators are discussed. Finally, a decision-making approach is developed for multiple-attribute group decision-making (MAGDM) problems with linguistic intuitionistic fuzzy information and an example is given to demonstrate the effectiveness and superiority of the proposed method.

We extend the definitions of the Gaussian Malliavin calculus operators to fuzzy stochastic processes. We consider Skorohod fuzzy stochastic differential equations, which the integrands of the stochastic integrals are not adapted to the filtration generated by a Wiener process. Such equations with randomness, fuzziness and non-adapted processes can be applied in financial models. We apply the fuzzy Malliavin derivative and related topics to discuss the existence and uniqueness of solutions.

This paper aims at putting forward several types of convergence concepts of complex uncertain random sequences. The relations among convergence concepts are derived by some limit theorems. In addition, for illustrating of convergence theorems, lots of examples are derived. Finally, a lot of counterexamples about relationships between convergence concepts are stated.

The semigroups have many applications in finite state machines, transformations etc. So, the abstract concept of neutrosophic cubic sets were required to be established in semigroups. This motivate the authors to present the idea of neutrosophic cubic semigroups and neutrosophic cubic points. In this work, we study the truth, indeterminacy and falsehood in algebraic structures and deduce some results. We generalize the concept of fuzzy points, intuitionistic fuzzy points and cubic points by introducing the concept of neutrosophic cubic points. Based on neutrosophic cubic points, we generalize the idea of (α, β)-fuzzy ideals, (α, β) -intuitionistic fuzzy ideals and (α, β)-cubic ideals in semigroups. Particularly, we give the idea of neutrosophic cubic (∈, ∈ ∨ q)-ideals (resp., subsemigroups, generalized bi-ideals, bi-ideals, quasi-ideals, interior ideals, prime and semiprime ideals) of semigroups.

In this paper, we define aggregation operators for triangular cubic linguistic hesitant fuzzy sets which include triangular cubic linguistic hesitant fuzzy (geometric) operator, triangular cubic linguistic hesitant fuzzy weighted geometric (TCLHFWG) operator, triangular cubic linguistic hesitant fuzzy ordered weighted geometric (TCHFOWG) operator and triangular cubic linguistic hesitant fuzzy hybrid geometric (TCLHFHG) operator. Furthermore, we relate these aggregation operators to develop an approach to multi-attribute decision-making with triangular cubic linguistic hesitant fuzzy information. Finally, a numerical example is providing to demonstrate the submission of the established approach.

In this paper we introduce and study some difference double sequence spaces of fuzzy numbers by using Hausdorff metric associated with sequence of Orlicz functions. We make an effort to study some algebraic, topological properties and inclusion relations between these sequence spaces. We show that the new formed sequence spaces are complete with respect to the metric D M , Δ m , p , u ( x , y ) . Finally, we establish some relation between Δ ^r -statistically convergent and Δ ^r -statistically (C, 1, 1) summable double sequences of fuzzy numbers under the slowly oscillating and statistically slowly oscillating conditions in certain sense, respectively.

Aim at achieving the energy conservation and fully taking advantage of the multi-radio resource for multi-radio wireless sensor networks (MRWSNs), the interval type-2 fuzzy logic (IT2FL) based energy-optimal radio resource management mechanism is proposed, by taking the complex uncertainties existed in MRWSNs into consideration. The contribution of this paper is as follows. Firstly, the IT2FL inference mechanism is proposed to handle the complex uncertainties better. In the proposed IT2FL inference mechanism, three important factors, i.e., the transceiver energy consumption, the residual energy, and the channel quality, are considered as the input variables and the selection probability of each transceiver is regard as output variable. Secondly, the proposed IT2FL is utilized to the decision-making of the energy-efficient radio resource allocation in MRWSNs, when there are multiple new/delivery tasks. Following that, full simulations are deployed, in order to validate the proposed IT2FL based radio resource management mechanism can effectively improve the network performance, in terms of the energy efficient, throughput, data transmission success rate, and prolong the network lifetime etc.

In this paper, a notion of modified ⊤-convergence spaces (initially defined by Fang and Yue in FSS, 2017) is given. Then the relationships between the modified ⊤-convergence spaces and types of lattice-valued convergence spaces are established.

It is well known while dealing with uncertainty, fuzzy sets are assumed to be more efficient than ordinary. In this article, the existence results for a certain types of the system of fuzzy differential inclusions with integral types of local conditions have been obtained. Two systems of fuzzy differential inclusions are considered, the existence result for first system is obtained by using a well-known topological fixed point result, while for the other, a multivalued fixed point result is used. This study has generalized many results present in the literature. To validate the study, non-trivial examples are provided.

Intrusion Detection System (IDS) detects the intrusions and produces alerts. Automated Intrusion Response System (AIRS) selects and triggers the appropriate response based on some criteria to mitigate the intrusion without delay. The big challenges in the automated response selection process are a precise measurement of importance weight for each criterion and response prioritization for the specific category of attacks. Analytic hierarchy process (AHP) uses the pair-wise comparison of each criterion and does not require the accurate quantification but is unable to handle the vagueness or uncertainty in the importance judgment. This paper presents the framework called Fuzzy Rule-Based Automatic Intrusion Response Selection System (FRAIRSS) for automated response selection. Fuzzy AHP model has been created in order to deal with precise measurement and uncertainty in the importance judgment of each criterion. Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multi-criteria decision making (MCDM) approach has been applied in order to resolve the response prioritization. Fuzzy Rule-based inference system is modeled to select the appropriate response from the prioritized response sets for each category of attacks. The framework has been simulated in MATLAB with various attack scenarios and it is found that FRAIRSS is selecting most appropriate response under the given attack scenarios.

Regression model is a powerful analytical tool for estimating the relationships between explanatory variables and the response variable. Traditionally, it is often assumed that the data are observed precisely and characterized by crisp values. However, in many cases, those data are collected in an imprecise way and characterized in terms of uncertain variables. In this paper, the residual analysis of uncertain regression models is provided. Furthermore, an approach to obtain the forecast value and the confidence interval of the response variable for the new explanatory variables is given. Finally, a numerical example of the uncertain regression model is documented.

In this article, an argument Kalman filter is exposed for the fast updating of a neural network. The argument Kalman filter is developed based on the extended Kalman filter, but the recommended scheme has the next two advantages: first, it has less computational complexity because it only employs the Jacobian argument instead of the full Jacobian, second, its gain is ensured to be uniformly stable based on the Lyapunov approach. The commented scheme is applied for the modeling of two Takagi-Sugeno fuzzy models.

This paper presents a learning algorithm for fuzzy neural networks based on unineurons able to generate interpretation provided by the model through fuzzy rules. The learning algorithm is based on ideas from Extreme Learning Machine, to achieve a low time complexity, and pruning method based on F-scores resulting in accurate models using low complexity resources, using only training data in a single step. Experiments considering binary pattern classification are detailed. Results and statistical evaluation suggest the suggested approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability through a process of pruning performed in simple steps.

Uncertain spring vibration equation is a type of uncertain differential equations, whose external force is affected by an uncertain interference. The solution and inverse uncertainty distribution of solution of uncertain spring vibration equation in different cases have been derived, respectively. This paper proves an existence and uniqueness theorem of solution for general uncertain spring vibration equation in different cases under linear growth condition and Lipschitz condition.

Recently, assembly line balancing problem with uncertain task time gains more and more attention in the literature. Task time uncertainty may overload workstations. Uncertain task time attributes were studied in the frameworks of the learning theory, fuzzy theory, and probability theory. In this paper, we use a new method, which is the uncertainty theory, to model the uncertain task time as the historical task time information is unavailable. We incorporate the uncertainty into the constraints of the line balancing type-1 problem and propose two new optimization models. We also derive some useful theorems related to the optimal solutions. Further, we develop an algorithm based on the branch and bound remember algorithm to solve the models. Finally, numerical studies are conducted to illustrate our models and to show the efficiency of the proposed algorithm.