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In this paper, we address the extended dissipativity (ED) performance of delayed T–S fuzzy model (TSFM)-based wind generation systems (WGSs). First, a concept of coupled leakage time-varying delay (CLTVD) is proposed to construct a more widespread TSFM. Second, the relaxed term with time-delay-product function (TDPF) is introduced. Then, a befitting Lyapunov-Krasovskii functional (LKF) is constructed, which can handle the delay and its derivation. Third, by using valid integral inequalities, new stabilization criteria are established. Meanwhile, the desired fuzzy quantized control with CLTVD is designed. Finally, simulation results are given to show the validity and superiority of our derived results.

This study aims to evaluate the supply aspect of chicken eggs. The Variables used in this research were egg stock, egg demand, and selling prices with alternatives are adding suppliers, adding customers, reducing supplier. The method of this research using Fuzzy AHp where The membership sets of fuzzy model are Triangular and Trapezodial membership function. The results of the research indicate that the main priority as a decision support for the supply of chicken egss is the selling price to customer (0,67), so that the alternative priority option is the addition of suppliers (0,75) to anticipate imbalances between the number of eggs sold and stock availability in the warehouse.

Multiple sclerosis (MS) is a chronic autoimmune and inflammatory disease affecting the central nervous system (CNS). Magnetic resonance imaging (MRI) provides sufficient imaging contrast to visualize and detect MS lesions, particularly those in the white matter (WM). A robust and precise segmentation of WM lesions from MRI provide essential information about the disease status and evolution. The proposed FPSOPCM segmentation algorithm included an initial segmentation step using fuzzy particle swarm optimization (FPSO). After extraction of WM, atypical data (outliers) is eliminated using possibilistic C-means (PCM) algorithm, and finally, a Mamdani-type fuzzy model was applied to identify MS. The objective of the work presented in this paper is to obtain an improved accuracy in segmentation of MR images for MS detection.

PurposeThis paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.Design/methodology/approachIn this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.FindingsThe finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.Originality/valueThe developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.

Activity recognition and classification are emerging fields of research that enable many human-centric applications in the sports domain. One of the most critical and challenged aspects of coaching is improving the performance of athletes. Hence, in this paper, the Adaptive Evolutionary Neuro-Fuzzy Inference System (AENFIS) has been proposed for sports person activity classification based on the biomedical signal, trial accelerator data and video surveillance. This paper obtains movement data and heart rate from the developed sensor module. This small sensor is patched onto the user’s chest to get physiological information. Based on the time and frequency domain features, this paper defines the fuzzy sets and assess the natural grouping of data via expectation-maximization of the probabilities. Sensor data feature selection and classification algorithms are applied, and a majority voting is utilized to choose the most representative features. The experimental results show that the proposed AENFIS model enhances accuracy ratio of 98.9%, prediction ratio of 98.5%, the precision ratio of 95.4, recall ratio of 96.7%, the performance ratio of 97.8%, an efficiency ratio of 98.1% and reduces the error rate of 10.2%, execution time 8.9% compared to other existing models.

Sport training is a sporting performance preparation phase that consists of four parts: Training in conditioning, technical Training, Training in attitude, Training in psychology. The challenging factor of the sports training session is the realization of approximate and uncertainty. The valued idea involves the definition of fuzzy sets and rules and membership functions to overcome the challenges. A fuzzy logical explanation enables successful ambiguous situations, which are complex, continuous, and more practical, closer to the real world and human thought. The concept, which is highly valued, includes defining fluids and rules and membership functions to test training exercises for strength. This paper proposed a Fuzzy, assisted artificial intelligence monitoring framework (FAAIMF) that evaluates all athletes’ behaviors in an outdoor training setting using wearable inertial sensors. Data obtained from sensor fitted machines, feedback, and proper implementation requirements are considered in the design. The Random Forest Classifier uses a Discreet Transform Wavelet (DWT) to effectively and accurately effectively and accurately id. Second, the relative orientation of the wearable inertial sensors on a shield and thigh of a material from which the knee angle of flexion-extension is determined. The proposed method in various non-constrained settings for the exact classification of sports activities and accurate movement techniques assessment.

This paper proposed an Effort Estimation Model for optimizing the deployment of Web Applications Based Fuzzy and Practical Models. Fuzzy logic approach is applied for estimating the development effort, which is compared with practical efforts model in the development process with interpreting the historical data available for the existing functionalities. This paper presented effort estimation model that involves two levels development and requirements for web applications built on three-tier architecture using Microsoft technologies. The first level estimates published by Project Managers and the second level estimates presented by Project Leaders or Developers for any new requirement or enhancements. The model considers the classification of each task as either Low or Medium or High complexity. These tasks pertain to the lowest level parts in bottom-up estimation. Efforts are estimated for designing, coding and unit testing of these tasks and the efforts are summed up to get the effort estimation for the higher level which is a feature to be implemented. The paper also discusses about the application of the effort estimation model by taking a new requirement as a case study. The first level estimates calculated using the effort estimation model has a variance of about 25% when compared with the actual effort. This variance is very much acceptable considering the fact that the first level estimates can be tolerable up to 35%. The proposed effort estimation model would help the project managers to efficiently control the project, manage the resources effectively, and improve the software development process and also trade off analyses among schedule, performance, quality and functionality. Fuzzy logic is used to verify the claims made in efforts estimation. It is proposed a new relation between the number of data and efforts value membership for actual data.

The subcontractor selection decision is inherently a multicriterion problem. It is a decision of strategic importance for companies. The nature of this decision is usually complex and unstructured. Management science techniques might be helpful tools for solving these kinds of decision-making problems. In this research, the fuzzy logic method and the analytic hierarchy process were applied for the selection of suitable subcontractors in an apparel supply chain. In general, many factors, such as quality level, price offer, and delivery delay, were considered to determine the most suitable and reliable subcontractors that fit the company's strategy. This survey is carried out using the database of an apparel company manufacturing denim products.

Fuzzy systems are widely used for solving complex and non-linear problems that cannot be addressed using precise mathematical models. Their performance, however, is critically affected by how they are constructed as well as their fuzzy rule base. Inspired by neural networks that apply a multi-layer structure to improve their performance, we propose a multi-layer fuzzy model with modified fuzzy rules to improve the approximation ability of fuzzy systems without losing efficiency. In practical applications, the fuzzy rule base extracted from numerical data is often incomplete, which makes a fuzzy system less robust. To address this problem, a non-linear function is used as the consequent of each fuzzy rule based on fuzzy-rule clustering to enhance the approximation ability of the fuzzy rule base. In addition, exact matching of fuzzy rules is employed based on the fuzzy rule’s antecedent for prediction. By doing so, only one rule will be triggered in each layer, which is very efficient. Experimental results from two simulated functions and three practical applications confirm that our proposed multi-layer fuzzy model can outperform other well-established fuzzy models in terms of accuracy and robustness without sacrificing efficiency.

At present, the teaching of architectural art in China is still relatively traditional, and there are still some problems in the actual teaching. Based on this, this study combines the Naive Bayesian classification algorithm with the fuzzy model to construct a new architectural art teaching model. In teaching, the Naive Bayesian classification algorithm generates only a small number of features for each item in the training set, and it only uses the probability calculated in the mathematical operation to train and classify the item. Moreover, by combining the fuzzy model, the materials needed for architectural art teaching can be quickly generated, and the teaching principles and implementation strategies of architectural art are summarized. In addition, this paper proposes an attribute weighted classification algorithm combining differential evolution algorithm with Naive Bayes. The algorithm assigns weights to each attribute based on the Naive Bayesian classification algorithm and uses differential evolution algorithm to optimize the weights. The research shows that the method proposed in this paper has certain effect on the optimization of architectural art teaching mode.

An experimental study combined with a mathematical modeling of vacuum drying of cocoa beans was carried out to enhance the drying kinetics without compromising the quality of dried products. Experiments were conducted to compare the effects of variation of temperature and pressure on the drying process. The temperature was varied between 40 and 60 °C, while the pressure between 735 and 245 mmHg. The experimental study investigated and analyzed the effects of various process parameters on drying kinetics and quality (in terms of color and pH value). Results demonstrated that the cocoa beans dried at 50 °C temperature and 735 mmHg pressure had the highest drying kinetics, lowest acidity, and best color among all the temperature and pressure values. Compared to convective drying conducted at 60 °C temperature and atmospheric pressure (760 mmHg), vacuum oven drying consistently showed better drying performance with a higher quality of dried products. Modeling for vacuum drying kinetics of cocoa beans was performed with semi-theoretical drying kinetic and fuzzy models to verify the experimental results. The graphical representations of the results demonstrated a good fit between the fuzzy model and the experimental data compared with semi-theoretical drying kinetic models. Finally, a suitable model was proposed for optimum drying conditions compared with the fuzzy and drying kinetic models that could play an important role in designing future advanced dryers.

Control of slab camber is an important topic in hot strip rough rolling process. In this study, a slab camber control system, composed by a camber prediction model and a gap levelling value calcula...

This paper considers the yaw-moment control issue for uncertain electric vehicle systems with sensor failures and actuator saturation. By employing the interval type-2 (IT2) fuzzy approach, the uncertain electric vehicle systems are constructed as an IT2 fuzzy model. In this model, the parameter uncertainties are described through the membership functions (MFs) with lower and upper bounds. Then, an IT2 fuzzy state-feedback controller, which can share different MFs with the IT2 fuzzy model, is designed. The sensor failures can be quantified by a variable varying in a given interval. Meanwhile, the saturation nonlinearities can be handled by employing a norm-bounded strategy. By means of the Lyapunov stability approach, sufficient conditions of the controller design are derived to achieve the desired performance. Finally, simulation results based on the electric vehicle systems are presented to demonstrate the effectiveness of the proposed control scheme.

The shipbuilding industry has evolved in recent years. Some quantitative and qualitative factors influence competitiveness of shipyards. The development of shipbuilding processes depends on evoluti...

Decision making has become a problem in environments full of uncertain, vague and imprecise information. They face many problems to train computer systems to simulate human thinking to make the right decision. Different methodologies and approaches have been used to train computers to understand and mimic human thinking. This paper proposes a fuzzy model for a bone disease to have the right diagnosis answer, as a human expertise doctor. and to prove that using fuzzy logic has a significant ability to mimic human thinking. The model accepts inputs in the different forms as physiological and clinical parameters and all data based on medical expertise, using a rule-based fuzzy system approach applied with fourteen rules to have final accurate output decisions. it has been tested in the orthopedic unit against the real existing diagnosis answer from expertise doctor and found that is capable of assisting medical experts in diagnosing diseases and provide good health services to their patients.

Cancer disease is assumed as a gathering of diseases which is initiated because of uncontrolled cell growth. An early analysis of Lung Nodules (LN) can possibly enhance the prognosis and in future can save numerous lives every year. In the proposed research work, the LN detection from the ELCAP lung image database is analyzed by image segmentation and classification techniques. Initially, the exact portion of the lung image is achieved and then it is subjected to pre-processing where the image contrast level is enhanced by the imadjust function of MATLAB. Next to that, the potential nodules are segmented by the Fuzzy C-Means (FCM) and then some features are extracted for effective classification. Based on the selected or extracted feature sets, the images are classified as two types (nodule detected and normal lung) by the proposed classifier i.e. Artificial Neural Network (ANN) with weight optimization. The performances of the proposed algorithm and classifier are tested on the chosen datasets in terms of sensitivity, specificity and accuracy. The results demonstrate that ANN with Oppositional based Ant Lion Optimization (OALO) algorithm achieves high accuracy and less execution time compared to existing algorithms.

In this paper, we consider various approaches to the problem of filtering in nonlinear dynamic systems and their algorithms. The Strong Tracking Unscented Kalman Filter, based on the combination of Unscented Kalman Filter and Strong Tracking Kalman Filter, provides stability to the uncertainty of the process model directly using a suboptimal scaling factor (SSF). The softening coefficient is part of the SSF and it improves the smoothness of the system state assessment. The coefficient is determined empirically and is included in the entire filtering process, which leads to a loss of accuracy in the time segments in which the process model is defined. The paper explores the use of Takagi --- Sugeno fuzzy model (T-S model) to adjust in real time the softening coefficient when the object's dynamics changes. As a result of a comparative analysis of the accuracy of the studied filters for the nonlinear model, it was found that the new filter using a fuzzy logical adaptive system possesses good smoothness of assessment and the greatest accuracy

The article is devoted to the study of the problem of labor potential management employee. Under management is a purposeful influence on the employee in order to improve the quality of his work in order to increase the efficiency of the enterprise and increase its profits. It has been proposed to use a scenario approach in the management of labor potential at the microeconomic level. The main purpose of the scenario approach is to build scientifically sound scenarios for the development of economic phenomena and use them in the process of supporting management decisions. A step-by-step procedure for managing labor potential at the microeconomic level, which includes four stages: analysis of factors influencing the labor potential of the employee and building a cognitive model, collecting statistical information, assessing the individual level of labor potential based on fuzzy model, forming employee ratings based on individual assessments, development of scenarios of labor potential management at the macro level using a cognitive model. The proposed approach has been tested on the example of labor potential management of pharmacy network employees. The main factors influencing the level of labor potential of a pharmacy network employee have been determined. The fuzzy inference rule is used to calculate individual estimates of labor potential of employees. The model of management of labor potential of the employee of a drugstore network allows to make reasoned decisions concerning motivation of the personnel, and also to substantiate scenarios of management of the enterprise for increasing of level of labor potential of employees of a drugstore network. When developing scenarios for assessing the impact of factors on the level of labor potential of the employee in the dynamics of a simulation experiment using a pulse model.

During the digital forensic process, different software and hardware tools are used. The devices from which the digital evidences are collected have been varied in parallel with the developments in technology. The issue of identifying the mobile phone owner’s friends and assessing his relationship with them with the help of digital evidences collected from the Android mobile phones has been studied in the literature and it is still under investigation. The software developed in this work enables accessing a variety of data that have evidential value in the court proceedings; these include physical and logical acquisition of images from mobile phones with Android operating system, extracting images for investigations, examining different file types in images, and databases. This software can collect and examine the evidences and then produce reports. At the same time, it can identify criminals or people with potentially have connections to those people whose accounts are under investigations by using developed analysis model which examines the relationships between social media applications` data, phone contacts and calling histories collected from the mobile devices. In this work, the evidences examined by using a novel software developed by the authors which performs multiple tasks using a single interface and the corresponding results are presented.

This special issue of the Journal of Intelligent & Fuzzy Systems contains selected articles of Fuzzy model for human autonomous computing in extreme surveillance and it’s applications

In this article, the authors conducted an evaluating study on the impact of content quality on subscriber growth inside the TikTok social network. To achieve this goal, the essence of the TikTok network was analyzed, and the main variables that affect the quality of content and promotion were identified. As a result, a fuzzy model for assessing the impact was built, and qualitative and quantitative analysis of its work was carried out.

Dynamic Movement Primitives (DMPs), which represent movement plans based on a set of nonlinear differential equations with well-defined attractor dynamics, have been adapted for movement planning. In this paper, we present a trajectory generation method for mobile agent from multiple demonstration using DMPs. The original DMPs can be only used to learn from single demonstration. In order to adapt multiple demonstrations of different tasks, we use DMPs with Takagi-Sugeno fuzzy model to realize autonomous trajectory planning. The proposed scheme is capable to regenerate trajectory in the presence of obstacles even when the goal position is altered. A simulation example is given to confirm the effectiveness of the proposed scheme.

A road freight vehicle fleet represents the basic means for the work of a transport company, for which reason it is the most important element of its business doing. Its work directly influences the volume of the income from and costs of business operations of a transport company. The correct sizing and management of the road freight vehicle fleet is of essential significance to the cost-effectiveness of the enterprise and the satisfaction of demands for transporting. The defining of a road freight vehicle fleet and the selection of the vehicles that it will consist of are a complex problem, which should be approached from several aspects. In the paper, a fuzzy model for determining the justifiability of investing in the renewal of a truck road freight vehicle fleet and the assessment of the time period needed for return on such investment is presented. The forecasts of the expected volume of transport, i.e. income from transport, have been made on the routes with constant flows of freight for the realistic pessimistic and optimistic variants for the recommended period of the exploitation of a vehicle.

Purpose Nowadays, there are various internet of things (IoT) applications covering many aspects of daily life. Many people own numerous smart objects that use these IoT applications. The purpose of this study is determining suitable IoT applications for each user which is a relevant challenge because it is amulti-criteria decision-making. Design/methodology/approach To solve this challenge, the authors propose fuzzy analytical hierarchy process model. Based on the opinions of IoT experts, the model and the hierarchy were designed to assess and compare three crucial IoT criteria, namely, object, application and providers. Findings The results indicated that the application criterion is far more relevant for users other than the two criteria. The findings of this study offer insights into more effective decision-making for IoT application developers and providers. Originality/value This study contributes to the IoT through proposing a fuzzy model to classify IoT applications. The findings provide meaningful implications for IoT application providers.

The increased popularity of smart home technologies, lower prices of technical infrastructure and the growing demand for these technologies accelerated the development of smart systems. Automatic irrigation of the soil with the help of automatic systems will prevent sufficient irrigation of the soil in dry periods. This can cause the cultivated area and garden plants wither and eventually die. In this study, a smart plant irrigation system is developed, aiming to address the need for these systems in such cases. The system here is created using various sensors, and, through application of a fuzzy model, it can determine the amount of water required for a specific period. The fuzzy model determines the exact amount of water needed by the plant, hence prevents undesirable situations like excessive or insufficient watering of the plant. This enables planning for soil irrigation. The system proposed in this paper is a low-cost and easy-to-install one that can be applied in any agriculture.

Due to the intermittent and random nature of wind energy, wind power interval prediction (WPIP) is important to weaken the uncertainty and support the planning and scheduling of the power system. To improve the quality of WPIP, a novel fuzzy interval prediction model (FIPM) based on the lower upper bound estimation method is proposed in this paper. In the frame of the FIPM, a novel inter type-2 (IT-2) fuzzy model is designed to construct the lower and upper bounds of the prediction interval (PI), in which an IT-2 fuzzy c-regression algorithm is used to partition the data space and identify the fuzzy model. The gravitational search algorithm is employed to optimize the FIPM by minimizing coverage width-based criterion to reach a tradeoff between the interval width and coverage probability. In order to verify the effectiveness of the proposed method, existing interval prediction approaches are adopted in comparative experiments with 17 datasets extracted from five wind fields. The experimental results show that the proposed IT-2 FIPM achieves significant performance with huge promotion in the quality of the PI compared to the traditional forecasting models.

Methods for decision making as semi-structured goals which based on creation of linguistic model are considered in this article. Pattern of fuzzy model system is presented. Creation fuzzy model for project quality with substantive interpretation for input (projector experience, universality, projector error bar, project complexity, quality of basic data, engineering time) and output (project error) variables is reported in main body. Rule blokes are indicated to estimate output variable. The first rule bloke – projector rating- consists of 36 fuzzy rules that help taking into account quality of projector’s work. The second rule bloke consists of 81 fuzzy rules. Fuzzy model and surfaces of fuzzy inference are created in fuzzyTECH 5.54d. In the capacity of defuzzification method is used method of the best compromise. Obtained results point at capital saving due to inventory decrease on basis of analysis of project quality.

The Panji Deep Area in Huainan Coalfield, Southern North China is a newly discovered deep coal-bearing block with abundant coalbed methane (CBM) resources. To date, systematic geological studies regarding CBM reservoir in this region are still deficient, thereby seriously hindering the further exploitation and utilization of CBM in Huainan Coalfield. Results indicate that No.13–1 coal seam in the Panji Deep Area has good material conditions for CBM accumulation and preservation. All the coal samples from the No.13–1 coal seam are medium ash and medium−high volatile bituminous coals (Ro,max, 0.72–0.82%), which have different proportions of macerals and minerals. Pore structure and morphology analyses indicate that No.13–1 coal samples have various pore types and complex pore structures. Among them, the ink-bottle shaped pores are mainly observed with a pore size diameter of 4 nm. The gas content of No.13–1 coal samples is 16.11–21.83 m3/t (avg. 18.16 m3/t), which is strongly affected by the effective thickness of coal reservoir, reservoir pressure and temperature. Gas content is significantly positively correlated with burial depth, reservoir pressure, and temperature below 700 m, 6 MPa, and 34 °C, respectively. However, the upward trend tends to be constant after these demarcation points. Numberous micropores hold the retention capacity of coal matrix to methane molecules, thus the residual gas content increases along with the volume and surface area of micropores. By contrast, the correlations between gas content and coal thickness and roof thickness are not obvious. The total CBM resource of No.13–1 coal seam is approximately reserve of 205.36 × 108 m3. Moreover, the total CBM resources in the northern and southern parts of the Panji Deep Area are 73.83 × 108 m3 and 131.53 × 108 m3, respectively. On the basis of multi-level fuzzy model and the evaluated factors, the most favorable areas for CBM development of the No.13–1 coal seam were predicted to be around Zhutuan and south Gugou town in the Panji Deep Area.

The robust stabilization problem of memristor-based coupled neural networks (MNNs) is addressed in this paper. Firstly, the fuzzy model of MNNs is obtained by considering the properties of memristor and corresponding circuit, some predictable assumptions on the boundedness and Lipschitz continuity of activation functions are formulated. Secondly, based on T-S fuzzy theory and Lyapunov-Krasovskii functional method, robust stabilization criteria are derived in form of linear matrix inequalities (LMIs). Finally a numerical example is presented to demonstrate the effectiveness of the proposed robust stabilization criteria, which well supports theoretical results.

The features of the proposed fuzzy model for assessing the crisis state of enterprises have been disclosed. The MATLAB software environment has been selected as the environment for building a fuzzy output system. In the model of a fuzzy assessment of the crisis state of enterprises, the following input linguistic variables have been highlighted: the relative level of financial status, the probability of bankruptcy, the level of information security, the level of innovation potential. The terms of the input variables and the result variable have been described. The rule base for fuzzy inference system has been formulated. The results of modeling the assessment of the crisis state of enterprises have been represented by a fuzzy inference procedure.

More than 40 years after fuzzy logic control appeared as an effective tool to deal with complex processes, the research on fuzzy control systems has constantly evolved. Mamdani fuzzy control was originally introduced as a modelfree control approach based on expert?s experience and knowledge. Due to the lack of a systematic framework to study Mamdani fuzzy systems, we have witnessed growing interest in fuzzy model-based approaches with Takagi-Sugeno fuzzy systems and singleton-type fuzzy systems (also called piecewise multiaffine systems) over the past decades. This paper reviews the key features of the three above types of fuzzy systems. Through these features, we point out the historical rationale for each type of fuzzy systems and its current research mainstreams. However, the focus is put on fuzzy model-based approaches developed via Lyapunov stability theorem and linear matrix inequality (LMI) formulations. Finally, our personal viewpoint on the perspectives and challenges of the future fuzzy control research is discussed.

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information.

Game Analytics is an important research topic in digitalentertainment. Data log is usually the key to understand players’behavior in a game. However, alpha and beta builds may need aspecial attention to player focus and immersion. In this paper, wepropose t he us e of player’s focus detection, through theclassification of pictures. Results show that pictures can be usedas a new source of data for Game Analytics, feeding developerswith a better understanding of players enjoyment while in testingphases .

Modern computer sciences and information technologies are anticipated to bring transformative influence in part that mobile communication technologies play in society. To completely take advantage of the services bestowed by modern computer sciences and information technologies, the evidence of the economic and business case is an essential prerequisite. Existing research engulfs several transformative computing methods based on sensors area obtainable as service contain optimize resource management, data processing/storage and security provisioning. With transformative computing being on edge, real-time data must be necessitated for healthcare data analytics. The conventional cloud server cannot address the latency requirements of healthcare IoT sensors. To survive with how to handle these services, we introduce a hybrid method integrating Sugeno Fuzzy Inference (SFI) and Model-free Reinforcement Learning to enhance healthcare IoT and cloud latency. The objective is to lessen high latency between healthcare IoT devices. The proposed Sugeno Fuzzy Model-free Reinforcement Learning Data Computing (SF-MRLDC) method uses a Sugeno Fuzzy Inference model integrated with a Model-free Reinforcement Learning model data computing in a healthcare IoT data analytics environment. The simulation results of the SF-MRLDC method show that it is computationally efficient in terms of latency by ensuring better response time.

In agriculture with 4.0 technologies, developing a decision model with a 0.0 cost is attractive to small farmers. In water management, if this approach could be used to promote sustainability and optimization, it could become a pathway to reach the sustainable development goal in 2030. The core of this work is the development of a 4.0 mathematical model (based on fuzzy concepts) to verify the benefits of the production of lettuce irrigated with magnetically treated water at different replacement rates. This approach is achieved using computational 4.0 software and manual methods. The aim of mathematical modeling is to understand or explain a natural phenomenon associated with a given area of knowledge, and fuzzy-rule-based systems have been widely used in different types of in-depth research. In agriculture with 4.0 technologies, developing a decision model with a 0.0 cost is attractive to small farmers. In water management, if this approach could be used to promote sustainability and optimization, it could become a pathway to reach the sustainable development goal in 2030. The core of this work is the development of a 4.0 mathematical model (based on fuzzy concepts) to verify the benefits of the production of lettuce irrigated with magnetically treated water at different replacement rates. This approach is achieved using computational 4.0 software and manual methods. The aim of mathematical modeling is to understand or explain a natural phenomenon associated with a given area of knowledge, and fuzzy-rule-based systems have been widely used in different types of in-depth research.

The special issue of IEEE Transactions On Fuzzy Systems s dedicated to the memory of Prof. Robert John, one of the pioneers of type-2 fuzzy sets and systems, who passed away during its preparation. This special issue focuses on type-2 model-based control and demonstrates the broadening of the type-2 fuzzy logic controller field. It is demonstrated that type-2 FMB control framework can be established by adopting type-2 fuzzy sets into type-1 fuzzy-model-based (FMB) designs. Besides the parameter uncertainty, the type-2 fuzzy sets have also demonstrated their potential to deal with the intrinsic uncertainty caused by specific control mechanisms, such as the sample-data control mechanism and event-triggered control mechanism.