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Cardiovascular disease (CVDs) is a rapidly rising global concern due to unhealthy diets, lack of physical activity, and other factors. According to the World Health Organization (WHO), primary risk factors include elevated blood pressure, glucose, blood lipids, and obesity. Recent research has focused on accurate and timely disease prediction to reduce risk and fatalities, often relying on predictive models trained on large datasets, which require intensive training. An intelligent system for CVDs patients could greatly assist in making informed decisions by effectively analyzing health parameters. CEP has emerged as a valuable method for solving real-time challenges by aggregating patterns of interest and their causes and effects on end users. In this work, a fuzzy rule-based system is proposed for monitoring clinical data to provide real-time decision support. A fuzzy rule based on clinical and WHO standards ensures accurate predictions. The integrated approach uses Apache Kafka and Spark for data streaming, and the Siddhi CEP Engine for event processing. Additionally, numerous cardiovascular disease-related parameters are passed through CEP Engine to ensure fast and reliable prediction decisions. To validate the effectiveness of the approach, simulation is done with real-time, unseen data to predict cardiovascular disease. Using synthetic data (1000 samples), and categorized it into "Very Low Risk, Low Risk, Medium Risk, High Risk, and Very High Risk." Validation results showed that 20% of samples were categorized as very low risk, 15–45% as low risk, 35–65% as medium risk, 55–85% as high risk, and 75% as very high risk. [ABSTRACT FROM AUTHOR]

Power dispatch is a real-time process that requires accurate decisions to be made in a short period of time. It is necessary to improve the calculation speed as much as possible while ensuring the accuracy of the calculation, in order to meet the real-time requirements. Therefore, a design method for error-proof adaptive optimization model of power dispatch under time redundancy control is proposed. Build an intelligent monitoring platform for the operation status of power dispatch equipment, and use this platform to achieve real-time analysis and processing of power grid operation data. Use state estimation method to obtain accurate state estimation results of the power system, use fuzzy logic method for error analysis, reduce the impact of errors, and improve the operational efficiency and stability of the power system. Finally, by controlling time redundancy, the stability of basic parameters such as voltage and frequency in the power system is ensured, achieving rapid start–stop of generator units and flexible load adjustment and improving the schedulability and economy of the power system. Experimental results show that the proposed method can effectively reduce the root mean square error and average absolute error of error prevention results at the power dispatching terminal and ensure the stability of power system operation, and the model calculation speed is fast, which verifies the effectiveness of the method. [ABSTRACT FROM AUTHOR]

This article presents an investment recommender system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and pre-trained weights from a Multimodal Neural Network (MNN). The model is designed to support the investment process for the customers and takes into consideration seven factors to implement the proposed investment system model through the customer or potential investor data set. The system takes input from a web-based questionnaire that collects data on investors' preferences and investment goals. The data is then preprocessed and clustered using ETL tools, JMP, MATLAB, and Python. The ANFIS-based recommender system is designed with three inputs and one output and trained using a hybrid approach over three epochs with 188 data pairs and 18 fuzzy rules. The system's performance is evaluated using metrics such as RMSE, accuracy, precision, recall, and F1-score. The system is also designed to incorporate expert feedback and opinions from investors to customize and improve investment recommendations. The article concludes that the proposed ANFIS-based investment recommender system is effective and accurate in generating investment recommendations that meet investors' preferences and goals. [ABSTRACT FROM AUTHOR]

A radial basis function neural network PID controller under fuzzy rules (FUZZY-RBF-PID) was designed for the electro-hydraulic position servo system under the influence of uncertain factors such as load mutation, and load stiffness change. Firstly, the mathematical model of the system is established, and the frequency domain and time domain analysis of the system are carried out. Secondly, based on the analysis results, a radial basis function (RBF) neural network PID controller is designed, and fuzzy rules are innovatively used to adjust the learning rate of PID parameters in the RBF neural network learning algorithm in real time. Thirdly, the simulation results show that under the action of the FUZZY-RBF-PID controller, the unit step response of the system has high steady-state accuracy, fast response speed, and under the condition of large load stiffness, the system can recover to the steady-state value faster after being disturbed. At the same time, when the input signal is the sinusoidal signal of 10 HZ, the system under the action of the FUZZY-RBF-PID controller has no obvious phase lag phenomenon, and the tracking error is minimal. The proposed method can effectively improve the comprehensive performance of the electro-hydraulic position servo system under the influence of uncertain factors. [ABSTRACT FROM AUTHOR]

In recent years, toughened glass has become widely used in architectural applications due to its high structural strength, shatter resistance, soundproofing, heat resistance, and durability. However, despite its strong capabilities, toughened glass can explode completely when subjected to heavy impact, causing security-related constraints in areas prone to smash-and-grab attempts. This issue can only be prevented by enhancing the quality of toughened glass, particularly its tensile strength, compressive strength, and durability. To achieve this goal, a novel technique called the "fuzzy hybrid arithmetic war strategy" (FHAWS) approach is proposed in this paper. The approach utilizes the "hybrid arithmetic war strategy" (HAWS) algorithm to determine the optimal solution with a faster convergence rate. The HAWS algorithm integrates the standard Arithmetic Optimization (AO) algorithm and War Strategy Optimization (WSO) algorithm. The fuzzy rule concept is introduced for regulating the improper searching algorithm. The proposed FHAWS approach optimizes toughened glass characteristics such as splitting strength, flexural strength, tensile strength, compressive strength, stress, temperature, fragmentation, heating time, cooling time, visual light, durability, and production cost. The experimental analysis illustrates that the proposed FHAWS approach is more efficient and robust in enhancing the quality of toughened glass than other compared state-of-the-art approaches. Overall, the paper proposes a novel approach to optimize the characteristics of toughened glass, addressing a critical issue in the architectural industry. However, the specific details of the approach and experimental results should be examined in the paper to assess the method's effectiveness comprehensively. [ABSTRACT FROM AUTHOR]

Digitalization occupies entire world irrespective of the fields including the construction field study and it plays a major role to identify the better structure using the various design software that are available in the market. The identification of suitable connection is very important and tedious task for the structural engineers. For fulfilling the structural engineers' requirements, this study proposes a new Intelligent Fuzzy Rule-based Connection Prediction Method (IFR-CPM) to predict the types of joints for the extended endplate long bolted joint by applying the newly generated fuzzy rules. Here, the fuzzy rules have been generated by considering the different values of moments and rotations to find the type of joints. The proposed prediction system is evaluated by conducting different experiments and proved as better than the existing system with respect to the prediction accuracy. [ABSTRACT FROM AUTHOR]

In recent years, agriculture has incorporated various sensors to obtain crop data. Nutrients, conductivity, and pH in soil are the key factors affecting the growth of crops. Before using fertilizers, farmers need to master the soil information of the orchard to decide the ratio of nitrogen, phosphorus, and potassium to be fertilized and the amount of water to be irrigated. Although existing outdoor technology sensing solutions can detect soil nutrient information, there are high maintenance costs and systems that do not provide analysis recommendations, which are very unfriendly to farmers. Aiming at the above problems, we propose the fuzzy neural network of fertilization rate and fuzzy neural network of fertilization rate to provide farmers with the most direct fertilization and irrigation suggestions. This paper uses an automated self-propelled soil identification system that enables path planning in an orchard. After arriving at the measurement point designated by the farmer, we use the soil probe measurement system to measure soil nutrients within a certain range. The system provides farmers with suggestions on fertilization and medication and grasps the growth status of the orchard area. The advantage of this is that it can reduce the maintenance cost caused by the long-term installation of the system. The cost of our vehicle-mounted system is 150,000 NT dollars, which saves 350,000 NT dollars a year and 50,000 NT dollars in maintenance costs compared to installing ten outdoor weather station sensors. Compared with fixed weather station solutions, mobile unmanned vehicles can analyze more land areas and bring more precise benefits to farmers. [ABSTRACT FROM AUTHOR]

This paper proposes a novel positioning and anti-swing controller based on fuzzy sliding mode for an overhead crane. For the underactuated characteristics of the overhead crane, the trolley displacement and load swing angle are integrated into the same sliding mode surface. The boundness and convergence of each state on the sliding mode surface are analyzed. Appropriate fuzzy rules are introduced to adjust the control quantity to ensure that the system state is always on this surface. The sliding mode function is used to replace the tracking error signal to reduce the dependence of the control method on the system model, so that the control system has better robustness to parameter changes and external disturbances. The experiment and results analysis show that this proposed method can effectively deal with the dynamic deviation of the system model and external random disturbances. The load swing has a good suppression effect and the positioning of the trolley can be accomplished quickly and accurately. The proposed positioning and anti-swing controller based on fuzzy sliding mode is able to provide the high-quality and fast transportation of overhead cranes. [ABSTRACT FROM AUTHOR]

A solar cooker is essential for cooking nutrient-dense food as opposed to LPG, which is avoidable. In the current situation, the solar cooker's incorporation of a nanoparticle covering resulted in improved thermal performance and shorter cooking times. It has been attempted to forecast thermal performance using the Fourier series and to validate the study of cooking liquid temperatures using the Fourier series. Additionally, the logical modeling of the complete cooking process with the climatic conditions has been fuzzified in order to create fuzzy rules. Also, a thermal measurement of the cooking process was conducted during the experiment to support the fuzzy inference. From the study, it can be inferred that fuzzy logic modeling and Fourier series analysis can both be used to simulate the proposed system under various climatic settings that are comparable to the local climatic conditions in Chennai. In comparison to Pt values that have a larger dependence, a solar box cooker study of the environment and economy is applied to determine total carbon dioxide emissions (%) that reduce a period when this design was used. There is a clear correlation between minimization of CO2 emission and Pt values. The overall efficiency was determined to be 52.6% when boiling water and 38.99% when the oil was at its steady-state temperature. [ABSTRACT FROM AUTHOR]

Our country has a vast territory, and rail transit is very important to the development of our country's national economy. In this paper, key technologies for a digital twin-based shop floor management and control system are investigated, and the concept is designed and implemented. By adding a digital twin between the business management layer and the production execution layer of the traditional workshop management and control system through the fuzzy rule neural network, a new workshop management and control system architecture on the basis of the virtual is formed, enabling intellectual management and control of the workshop. The results of the study found that the integration of the digital twin into the conventional shop floor management and control system led to changes in the composition, processes and information integration of the management and support system. For the purpose of comparing the system scheduling of the high-speed railway on the basis of the vague rule neural network with the traditional method, we made statistics on the system scheduling before and after the transformation. In terms of manufacturing volume, after the output exceeds 200, the speed of the traditional manufacturing method lags behind the fuzzy rule neural network by nearly 50%. [ABSTRACT FROM AUTHOR]

This work proposes a new intelligent disease prediction system for predicting the disease and also knowing the current status of the dead diseases such as diabetic, heart and cancer diseases. More number of people are affecting and losing their life early due to these diseases so that these are also called as dead diseases. The proposed disease prediction and monitoring system consists of two phases such as feature selection and classification phases. In the feature selection phase, a newly proposed feature selection algorithm called conditional random field and mutual information-based feature selection algorithm is used for identifying the most contributed features that are used to enhance the prediction accuracy. In the classification phase, a newly proposed fuzzy-aware multilayer backpropagation neural network is applied for predicting and monitoring the diabetic disease and heart disease effectively. Here, newly generated fuzzy rules are also incorporated for making effective decision on patient records. The proposed prediction and monitoring system is used to predict and monitor the heart, diabetic and cancer diseases. The experiments have been conducted for evaluating the performance of the proposed disease prediction and monitoring system by using UCI Machine Learning Repository datasets and also proved that as better than the existing disease prediction systems in terms of precision, recall, F-measure and prediction accuracy. [ABSTRACT FROM AUTHOR]

Nature-inspired optimization techniques have been applied in various fields of study to solve optimization problems. Since designing a Fuzzy System (FS) can be considered one of the most complex optimization problems, many meta-heuristic optimizations have been developed to design FS structures. This paper aims to design a Takagi–Sugeno–Kang fuzzy Systems (TSK-FS) structure by generating the required fuzzy rules and selecting the most influential parameters for these rules. In this context, a new hybrid nature-inspired algorithm is proposed, namely Genetic–Grey Wolf Optimization (GGWO) algorithm, to optimize TSK-FSs. In GGWO, a hybridization of the genetic algorithm (GA) and the grey wolf optimizer (GWO) is applied to overcome the premature convergence and poor solution exploitation of the standard GWO. Using genetic crossover and mutation operators accelerates the exploration process and efficiently reaches the best solution (rule generation) within a reasonable time. The proposed GGWO is tested on several benchmark functions compared with other nature-inspired optimization algorithms. The result of simulations applied to the fuzzy control of nonlinear plants shows the superiority of GGWO in designing TSK-FSs with high accuracy compared with different optimization algorithms in terms of Root Mean Squared Error (RMSE) and computational time. [ABSTRACT FROM AUTHOR]

A lot of scientific problems in various domains from modelling sky as mosaics to understanding Genome sequencing in biological applications are modelled as workflows with a large number of interconnected tasks. Even though many works are cited in the literature on workflow scheduling, most of the existing works are focused on reducing the makespan alone. Moreover, energy efficiency is considered only in a few works included in the literature. Constraints about the dynamic workload allocation are not introduced in the existing systems. Moreover, the optimization techniques used in the existing systems have improved the QoS with little scalability in the cloud environment since they consider only the infrastructure as the service model. In this work, a new algorithm has been proposed based on the proposal of a new Multi-Objective Optimization model called F-NSPSO using NSPSO Meta-heuristics. This method allows the user to choose a suitable configuration dynamically. When compared to NSPSO an energy reduction of at least 10% has been observed for F-NSPSO for Montage, Cybershake, and Epigenomics workflow applications. Compared to the NSPSO algorithm F-NSPSO algorithm shows at least 13%, 12%, and 21% improvement in average makespan for Montage, Cybershake, and Epigenomics workflow applications respectively. [ABSTRACT FROM AUTHOR]

Software evolution, an integral part of the software development process, encompasses frequent and numerous changes and updates that may lead to complex and poor-quality systems. Software engineers and quality practitioners continually refactor the software components to mitigate the negative effects of code and design smells. Although these smells are not directly connected to design and coding bugs, they are high indicators of technical debt that may arise as bugs in future software releases. Therefore, refactoring plays a significant role in the overall software evolution process. To fix the underlying code and design smells, they must be first detected and classified. As code smells have been the focus of research in the literature, this paper focuses on detecting design smells. A novel design smell detection scheme is proposed using adaptive neuro-fuzzy approaches. This scheme can be seamlessly integrated with any refactoring scheduling and prioritization models as it is efficiently designed using a fast-training scheme based on a neuro-fuzzy architecture. In addition, the design smell detection solution relies on a reduced set of software metrics. A large custom dataset with more than 30,000 class records is created to assess the performance of the design smell detection solution. The reported performance scores confirm the superiority of the proposed technique over the existing ones. The accuracy, precision, recall, and area-under-the-curve (AUC) scores attained 0.97, 0.98, 0.98, and 0.99, respectively. Thanks to the high detection scores, design smell refactoring sequencing and prioritization routines can be further enhanced. [ABSTRACT FROM AUTHOR]

Online stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers' choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM. [ABSTRACT FROM AUTHOR]

Hierarchical reinforcement learning (HRL) is a promising method, which decomposes complex tasks into a series of sub-tasks. However, at present, most HRL methods have slow convergence speed and are difficult to be widely applied to such scenarios in real life. In this paper, we propose an efficient hierarchical reinforcement learning algorithm with fuzzy rules (HFR), a novel framework for integrating human prior knowledge with hierarchical policy network, which can effectively accelerate the optimization of policy. The model presented in this paper uses fuzzy rules to represent the human prior knowledge, making the rules trainable because of the derivability of the fuzzy rules. In addition, a switch module that adaptively adjusts the decision-making frequency of the upper-level policy is proposed to solve the limitation of manual tuning. Experiment results demonstrate that HFR has a faster convergence rate than the current state-of-the-art HRL algorithms, especially in complex scenarios, such as robot control tasks. [ABSTRACT FROM AUTHOR]

Crying is how babies express their needs eliciting care and providing information to parents and physicians about their health and physical status. Automatic crying-pattern analysis is performed to assist in the diagnosis of infant's emotional and physical conditions. Despite its usefulness, in this field, the use of such techniques requires machine learning approaches, besides specialists to tune the algorithms to get the best performance possible. To address the lack of knowledge or specialists, we propose a multi-class, auto-tuned classification algorithm based on fuzzy rules and the full model selection paradigm to discriminate among three different crying patterns through features extracted with Mel frequency cepstral coefficients. The obtained results showed an accuracy superior to 98% in all the experiments performed. Although the classification improvement is small its value lies in its automatic fine-tuning capacity, obtaining the best classifier for a given dataset releasing inexpert users from the complex trial–error configuration task. [ABSTRACT FROM AUTHOR]

The problem of adaptive learning from evolving and possibly non-stationary data streams has attracted a lot of interest in machine learning in the recent past, and also stimulated research in related fields, such as computational intelligence and fuzzy systems. In particular, several rule-based methods for the incremental induction of regression models have been proposed. In this paper, we develop a method that combines the strengths of two existing approaches rooted in different learning paradigms. More concretely, our method adopts basic principles of the state-of-the-art learning algorithm AMRules and enriches them by the representational advantages of fuzzy rules. In a comprehensive experimental study, TSK-Streams is shown to be highly competitive in terms of performance. [ABSTRACT FROM AUTHOR]

Rule-based models constructed by "IF-THEN" fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management. [ABSTRACT FROM AUTHOR]

The human society has been identified as more prone for diseases. Various drugs with different compounds are available to treat the diseases. However, the medical practitioner cannot be sure about the application of the drug and what would be exact drug should be feed to the patient. To hook this, a multi feature drug compound analysis model is presented in this paper. The method keeps track of medical records related to various patients and the details of drugs being provided to them. Using these treatment data set, the method applies machine learning techniques to generate and predict the success rate of different drugs. To perform this, the method first split the records based on the disease and for each of them the list of medicines and compounds given has been identified. Based on these data, a set of patterns are generated according to various compounds of drug provided. Further, the method estimates the success influence measure (SIM) for different drug components. Estimated success influence measure is used to generate the fuzzy rules. Based on the rule generated, the method performs success rate prediction for various drug compounds. The method produces noticeable growth in the success rate prediction. [ABSTRACT FROM AUTHOR]

Early detection of cervical tumour is very important to minimise deaths due to cervical cancer. Further it provides a deep insight into the anatomical information of the normal and abnormal cervix and helps in planning for a good treatment well in advance. Numerous techniques are used to detect malignancy through image segmentation. One such segmentation technique is discussed here. The proposed technique uses Artificial Neural Network Fuzzy Inference system (ANFIS) and watershed segmentation techniques for image classification and processing and compares the results with known techniques. A comprehensive set of fuzzy rules was used in the experiment to classify abnormal images to the corresponding malignancy. The experiment shows that the proposed technique is feasible and provides greater accuracy in detection of tumour types. [ABSTRACT FROM AUTHOR]

Crowdsourcing is a procedure for demonstrating data outsourcing to a wide range of individual workers rather than considering a unique entity or a company. Crowdsourcing has made different kinds of chances for some trying issues by utilizing human knowledge. In order to attain an optimal global assignment technique, it is necessary to gather information regarding the location of the entire workers. There occur few security issues during information gathering that causes severe threat to all workers. To overcome the concerns based on privacy-preserving, this paper proposes a privacy-preserving model based on Fuzzy with the Black widow and Spider Monkey Optimization (BW–SMO). The fuzzy can be used to cluster the query solution. To optimize the query selection, we exploited the Black widow optimization algorithm incorporated with the Spider Monkey optimization algorithm. The parameters of both algorithms are controlled by the Fuzzy logic controller. Thus, our proposed frameworks of Fuzzy with BW–SMO effectively solve optimizing selection and join queries with low cost, latency, and securing data. The proposed model will be compared with the existing models to show the system's effectiveness. [ABSTRACT FROM AUTHOR]

The YARA rules technique is used in cybersecurity to scan for malware, often in its default form, where rules are created either manually or automatically. Creating YARA rules that enable analysts to label files as suspected malware is a highly technical skill, requiring expertise in cybersecurity. Therefore, in cases where rules are either created manually or automatically, it is desirable to improve both the performance and detection outcomes of the process. In this paper, two methods are proposed utilising the techniques of fuzzy hashing and fuzzy rules, to increase the effectiveness of YARA rules without escalating the complexity and overheads associated with YARA rules. The first proposed method utilises fuzzy hashing referred to as enhanced YARA rules in this paper, where if existing YARA rules fails to detect the inspected file as malware, then it is subjected to fuzzy hashing to assess whether this technique would identify it as malware. The second proposed technique called embedded YARA rules utilises fuzzy hashing and fuzzy rules to improve the outcomes further. Fuzzy rules countenance circumstances where data are imprecise or uncertain, generating a probabilistic outcome indicating the likelihood of whether a file is malware or not. The paper discusses the success of the proposed enhanced YARA rules and embedded YARA rules through several experiments on the collected malware and goodware corpus and their comparative evaluation against YARA rules. [ABSTRACT FROM AUTHOR]

Information technology has spread widely, and extraordinarily large amounts of data have been made accessible to users, which has made it challenging to select data that are in accordance with user needs. For the resolution of the above issue, recommender systems have emerged, which much help users go through the process of decision-making and selecting relevant data. A recommender system predicts users' behavior to be capable of detecting their interests and needs, and it often uses the classification technique for this purpose. It may not be sufficiently accurate to employ single classification, where not all cases can be examined, which makes the method inappropriate to specific problems. In this research, group classification and the ensemble learning technique were used for increasing prediction accuracy in recommender systems. Another issue that is raised here concerns user analysis. Given the large size of the data and a large number of users, the process of user needs analysis and prediction (using a graph in most cases, representing the relations between users and their selected items) is complicated and cumbersome in recommender systems. Graph embedding was also proposed for resolution of this issue, where all or part of user behavior can be simulated through the generation of several vectors, resolving the problem of user behavior analysis to a large extent while maintaining high efficiency. In this research, individuals most similar to the target user were classified using ensemble learning, fuzzy rules, and the decision tree, and relevant recommendations were then made to each user with a heterogeneous knowledge graph and embedding vectors. This study was performed on the MovieLens datasets, and the obtained results indicated the high efficiency of the presented method. [ABSTRACT FROM AUTHOR]

In this paper, we propose to use fuzzy fusion rules to improve the performances of the CA-CFAR and OS-CFAR detectors for non-coherent MIMO radars in homogenous and non-homogenous Pareto background. First, the membership function for each individual detector is computed. At the fusion center, the membership functions are combined using four fuzzy fusion rules, namely; the "MIN", "MAX", "algebraic product" and the "algebraic sum" to yield a binary decision after defuzzification. The obtained results show that for a number of nodes equal to four, the performance is the best for a high number of receivers. In homogeneous background, the "algebraic product" fusion rule gives the best result when SCR > 4 dB whereas the "algebraic sum" is the best when SCR < 4 dB for the CA-CFAR. For the OS-CFAR, it is clearly shown that the "algebraic product" is the best. In non-homogenous case, the "algebraic sum" fusion rule gives the best results for the CA-CFAR, and the "algebraic product" for the OS-CFAR. [ABSTRACT FROM AUTHOR]

Abstractive text summarization using attentional recurrent neural network (sequence-to-sequence) models have proven to be very effective. In this paper, a novel hybrid approach is presented for generating abstractive text summaries by combining fuzzy logic rules (which selects extractive sentences) with bidirectional long short-term memory (Bi-LSTM) which further produces abstractive summary. Bi-LSTM uses attention mechanism and Adam optimizer for updating network weights. The proposed approach utilizes fuzzy measures and inference to extract textual information from the document to find the most relevant sentences. These relevant sentences are given as input to Bi-LSTM to produce an abstractive summary of the significant sentences. The proposed FLSTM model is evaluated using ROUGE toolkit. The experiment is performed on standard datasets (i.e., DUC and CNN/daily mail). Another salient feature of this work is merging of DUC 2003–2004, DUC 2006–2007 datasets to generate a larger dataset to achieve better results. The FLSTM model is compared with other state-of-the-art models, and the empirical results suggested that the proposed FLSTM model outperforms all other models. [ABSTRACT FROM AUTHOR]

As recent development of technology, it enables patients to get treatment remotely from doctors through audio conversation. The fourth highest number of death every year is caused by diabetics. Almost 50% to 80% of patients can avoid diabetics if the cause is found at the early stage. In this paper, we propose a new methodology to detect Diabetes at an early stage and recommend few attributes in which the patient needs to be careful in order to avoid diabetics. The proposed methodology makes use of fuzzy logic and kNN classifier to find out the caution attributes and recommends them as soon as possible. The proposed algorithm detects the audio signals from patients or clinical labs to process the data. We implemented our proposed methodology on Pima Indian dataset and compared with existing algorithms and the result shows that our algorithm outperforms existing algorithms. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), energy optimization and the provision of security are the major design challenges. Since the wireless sensor devices are energy constrained, the issue of high energy consumption by the malicious nodes must be addressed well in order to enhance the network performance by making increased network lifetime, reduced energy consumption and delay. In the past, many researchers worked in the provision of new techniques for providing improved security to WSN in order to enhance the reliability in the routing process. However, most of the existing routing techniques are not able to achieve the required security through the use of intelligent techniques for safeguarding the sensor nodes from malicious attacks. In order to address these problems, a new fuzzy temporal clustering-based secured communication model with trust analysis and outlier detection has been developed in this research work. For this purpose, a new fuzzy temporal rule-based cluster-based routing algorithm with trust modelling and outlier detection for monitoring the nodes participating in the communication has been proposed. In addition, a fuzzy temporal rule- and distance-based outlier detection algorithm is also proposed in this paper for distinguishing the malicious nodes from other nodes within each cluster of the network and has been used in the secured routing algorithm. The proposed secure routing algorithm uses the temporal reasoning tasks of explanation-based learning and prediction as well as spatial constraints for making efficient routing decisions through the application of trust and key management techniques for performing effective authentication of nodes and thereby isolating the malicious nodes from communication through outlier detection. By applying these two proposed algorithms for communication in the proposed work, it is proved through experiments that the proposed secure routing algorithm and the outlier detection algorithm are able to perform secured and reliable routing through genuine cluster head nodes more effectively. Moreover, these two algorithms provide improved quality of service with respect to the reliability of communication, packet delivery ratio, reduction in end-to-end delay and reduced energy consumption. [ABSTRACT FROM AUTHOR]

Gliomas are dreadful and common type of brain tumor. Therefore, treatment planning is significant to increase the survival rate of gliomas patients. The large structural and spatial variation between tumors makes an automated detection more challenging. Brain magnetic resonance imaging is utilized for tumor evaluation on the basis of automated segmentation and classification methods. In this work, triangular fuzzy median filtering is applied for image enhancement that helps in accurate segmentation based on unsupervised fuzzy set method. Gabor features are extracted across each candidate's lesions, and similar texture (ST) features are calculated. These ST features are supplied to extreme learning machine (ELM), and regression ELM leaves one out for tumor classification. The technique is evaluated on BRATS 2012, 2013, 2014 and 2015 challenging datasets as well as on 2013 Leader board. The proposed approach shows better results and less computational time. [ABSTRACT FROM AUTHOR]

Three-phase transformers (TPT) play a significant and crucial function in the power networks in order to connect the sub-systems and deliver the electrical energy to final customers. The TPT are one of the most high-priced equipment in modern power networks, and therefore their working condition should be constantly monitored to prevent their breakdown, power outages and huge financial damage. Accordingly, this paper presents a hybrid method for detection and classification of incipient faults in TPT using dissolved gas analysis techniques (DGAT) information and rule-based machine learning method. In the developed method, the most informative and important items of DGAT data out of 14 items selected by association rules mining technique (ARMT) are employed as the input of adaptive neuro-fuzzy inference system (ANFIS). The ARMT is implemented to select the items, which have maximum information and can train the ANFIS more accurately. Furthermore, in order to enhance the accuracy of ANFIS and improve its robustness in different implementations, black widow optimization algorithm is applied for ANFIS training. In order to evaluate the performance of developed method on real issues, two industrial data collections obtained from Iran-Transfo Company chemical laboratory and Damavand power substations are used. The obtained results through MATLAB simulations proved that the developed method has high fault detection and classification accuracy, robust function in different implementations, short run time and simple structure. [ABSTRACT FROM AUTHOR]

In this technological world demanding latest updations in the domain knowledge, it is no surprise that e-learning has become a more viable option to a range of people from beginners to get knowledged and the experts to get updated in a particular domain. Nevertheless, the evolution of e-learning systems is yet to provide full adaptability to the e-learners due to several weaknesses in the systems. Normally, e-learners have varying degrees of progress in their respective learning methodology. Over a period of time, this affects the e-learners performance while providing the same course to all e-learner. Hence, there is a need to create the adaptive e-learning environment to offer the appropriate e-learning contents to all the e-learners. This proposed work puts forth a fuzzy-based novel, intelligent and adaptive e-learning context for a programming language and offers appropriate domain contents to the e-learners which shall update the e-learners better than the previous works. The dependency relation among the concepts in the programming language is provided using fuzzy cognitive map which in turn paves the way for the development of the existing e-learning system. The fuzzy sets and the fuzzy rules represent the e-learners knowledge level and help in providing appropriate recommendations for the previous and subsequent related concepts in a fuzzy cognitive map. Evaluations of the proposed intelligent e-learning system provide promising results in the precise categorization of e-learners and to find their true knowledge. [ABSTRACT FROM AUTHOR]

Image fusion plays a significant role in various computer vision applications. However, designing an efficient image fusion technique is still a challenging task. In this paper, a novel deep belief networks based image fusion framework is proposed to improve the performance of the image fusion process further. We have initially, evaluated the fusion dataset by applying various feature extraction techniques. Thereafter, features selection techniques are applied to select potential features. Finally, the image fusion machine learning model is built by using a deep belief network model. Extensive experiments reveal that the proposed technique outperforms existing image fusion techniques. [ABSTRACT FROM AUTHOR]

The handwritten digit recognition issue turns into one of the well-known issues in machine learning and computer vision applications. Numerous machine learning methods have been utilized to resolve the handwritten digit recognition problem. However, sometimes the digit is not completely present in the image due to issues related to scanning or environmental conditions (light, illumination, dirt, etc.). Although different efficient methodologies of handwritten digit recognition are proposed, there is not much work done on fragmented handwritten digit recognition. The objective of the proposed research work is to handle this circumstance to assemble a consistent digit recognition system that can precisely handle three types (English, Bangla, and Devanagari) of fragmented handwritten digit images. To solve the confusion, a technique is created to classify handwritten digits based on geometrical functions that are utilized to calculate handwritten digit features to assess if a digit belongs to a specific class. A grading scheme and a set of specified fuzzy rules determine the performance of classification. Experiments have been directed on the three familiar datasets, i.e., MNIST database (English), NumtaDB (Bangla) and Deva numeral database (Devanagari). Since fragmented digit delivers a lesser amount of information, the work also attempts to create a tentative size threshold above which outcomes become erratic and whether such thresholds are standardized or vary depending on other factors. Since the fragmented handwritten digital image does not have a public database, a method is formed to produce repeatable fragmented handwritten digital images from the entire image. Experimental outcomes validate that the proposed approach is effective in recognizing fragmented handwritten digits to an acceptable degree of fragmentation. [ABSTRACT FROM AUTHOR]

The number and diversity of machine learning applications causes an increasing need for understanding computational models and used data. This paper deals with a framework design of easily interpretable rules of the Takagi-Sugeno-Kang (TSK) fuzzy model. The proposed framework aggregates TSK fuzzy rules and association rules by calculating overlapping value intervals of variables appearing in both antecedent and consequent parts of fuzzy and association rules. Besides a simple insight into rule interconnections of the rule-based models, the framework provides an assessment of fuzzy rule importance, and in accordance with other rules and the complete TSK fuzzy model. The proposed framework is developed and illustrated by analysing traffic accidents with pedestrian involvement. It provides a deeper understanding of the built rule-based model, as well as more readable identification of significant accident causes. The framework can be used in many domains of analysis modelling and decision making processes where computational model understanding is crucial. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tri-directional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities. [ABSTRACT FROM AUTHOR]

A relevant and suitable content recommendation is an important and challenging task in e-learning. Relevant terms are retrieved in a recommender system that should also cope with varying user preferences over time. This paper proposes a novel recommendation system which provides suitable contents by refining the final frequent item patterns evolving from frequent pattern mining technique and then classifying the final contents using fuzzy logic into three levels. This is achieved by generating frequent item patterns after consolidating the user interest changes with an extended error margin quotient. Moreover, fuzzy rules are used in this work to enable the rule mining constraints for accommodating all types of learners while applying rules on the pattern tables. This method aims at mining the data stream preferences into equal-sized windows and caters to the varying user interest ratings over time. Experiments prove its efficiency and accuracy over existing conventional methods. [ABSTRACT FROM AUTHOR]

In this paper, a new epileptic seizure detection method using fuzzy-rules-based sub-band specific features and layered directed acyclic graph support vector machine (LDAG-SVM) is proposed for classification of electroencephalogram (EEG) signals. Wavelet transformation is used to decompose the input EEG signals into various sub-bands. The nonlinear features, namely approximate entropy, largest Lyapunov exponent and correlation dimension, are extracted from each sub-band. In this proposed work, sub-band specific feature subset that is reduced in size and capable of discriminating samples is selected by employing fuzzy rules. For classification purpose, a new LDAG-SVM is used for detecting epileptic seizure. Every sub-band has its own characteristics. If appropriate features which characterize the specific sub-band are selected, then the classification accuracy is improved and computational complexity is reduced. The important advantage of the fuzzy logic is its close relation to human thinking. Due to the lengthy record and intra-professional variability, automation of epileptic detection is inevitable. Fuzzy rules are the natural choice of employing human expertise to build machine learning system. Performances of the proposed methods are evaluated using two different benchmark EEG datasets, namely Bonn and CHB-MIT. The performance measures such as classification accuracy, sensitivity, specificity, execution time and receiver operating characteristics are used to measure and analyze the performances of the proposed classifier. The proposed LDAG-SVM with fuzzy-rules-based selected sub-band specific features provides better performance in terms of improved classification accuracy with reduced execution time compared to existing methods. [ABSTRACT FROM AUTHOR]

LTE-Advanced, the 4.5 G technology is built upon 3GPP specifications, is going to rule the wireless network world. This work proposes an energy efficient, QoS-awarescheduler for transmission of real-time data in downlink LTE-advanced networks. A basic LTE-Advanced scheduler has been developed by combining Carrier Aggregation with multi input and multi output (MIMO) features to provide the required bandwidth. We have formulated the scheduling problem as a gain of weighted transmission rates of all possible combinations of various resources required by the channel for transmitting data. The overall process of resource allocation for the downlink scheduling is then optimized using fuzzy rules. The main focus of our study is the cell edge users, and so Voronoi algorithm was used for the first time in LTE-Advanced networks to detect those cell edge users.The results proves that the Carrier Aggregation with MIMO scheduling scheme is energy efficient, QoS-aware and benefits the cell edge users with increased coverage. This scheduling scheme attains excellent throughput with improved fairness for real-time data transmission among the mobile users. [ABSTRACT FROM AUTHOR]

The security level of the medical images in hospitals and scan centers are increased by steganography technique, which imposes the secret image into cover image in order to improve the security level. In this paper, an efficient edge based steganography algorithm is proposed using fuzzy logic. Fuzzy rules are constructed which are used to detect the edges in both cover and secret image. The construction of fuzzy rules optimizes the edge detection process by finding the thin and thick edges in both cover and secret image. The Discrete Wavelet Transform is applied on the edge detected cover and secret image, which produces sub bands as Approximate, Horizontal, Vertical and Diagonal. The weak edge coefficients in cover image are overlaid by the coefficients of the secret image, which produces highly secured steganography image. The performance of the proposed steganography technique is analyzed in terms of Peak Signal to Noise Ratio, Mean Square Error and Entropy. [ABSTRACT FROM AUTHOR]

This paper focuses on ensemble methods for Fuzzy Rule-Based Classification Systems (FRBCS) where the decisions of different classifiers are combined in order to form the final classification model. The proposed methods reduce the FRBCS complexity and the generated rules number. We are interested in particular in ensemble methods which cluster the attributes into subgroups of attributes and treat each subgroup separately. Our work is an extension of a previous ensemble method called SIFRA. This method uses frequent itemsets mining concept in order to deduce the groups of related attributes by analyzing their simultaneous appearances in the databases. The drawback of this method is that it forms the groups of attributes by searching for dependencies between the attributes independently from the class information. Besides, since we deal with supervised learning problems, it would be very interesting to consider the class attribute when forming the attributes subgroups. In this paper, we proposed two new supervised attributes regrouping methods which take into account not only the dependencies between the attributes but also the information about the class labels. The results obtained with various benchmark datasets show a good accuracy of the built classification model. [ABSTRACT FROM AUTHOR]

Advanced traveller information system is an important intelligent transportation systems application area, which provides information to transport users and managers in order to improve the efficiency and effectiveness of the transportation system, in the face of increasing congestion in urban cities around the world. So far very limited research attention has been focused on long-term travel time prediction (i.e. predicting greater than 60 min ahead). Long-term travel time forecasts can play a critical role in journey planning decisions for both private road users and logistics operators. In this paper, we have considered a fuzzy neural network incorporated with both imprecise and numerical information and developed a hybrid long-term travel time prediction model, which shows the better prediction capability than naive methods and highlights the importance of different data variables. The model combines the learning ability of neural networks and the knowledge extraction ability of fuzzy inference systems. The model was validated by using travel time data compiled from electronic toll tags on a 14 km length section of the CityLink tollway in Melbourne, Australia. The validation results highlight the ability of the fuzzy neural network model to accommodate imprecise and linguistic input information, while providing reliable predictions of travel times up to a few days ahead. [ABSTRACT FROM AUTHOR]

With the increasing popularity of location-based social networks (LBSNs), community detection has emerged as an important and practical issue. One of the main shortcomings of the previous methods is that cluster's centers have been selected randomly in clustering the communities; therefore, different results are obtained in each execution. This paper proposes an intelligent method to detect overlapping communities in LBSNs with respect to user check-ins and the attributes of venues and users. In the proposed approach, clustering is performed on the two-segment user-venue edges. The advantage of this approach is the use of subtractive clustering method, which determines cluster centers with a probability, based on the potential of each data point. Furthermore, the specified fuzzy rules have a significant role in determining the appropriate number of cluster centers. The paper concentrates on the data obtained from the Foursquare LBSN. The empirical results of the proposed approach are compared with the previous approach to clustering edges in multi-attribute and multi-mode ( M2-clustering) algorithm, and a significant improvement is observed in the cost function of the community detection. [ABSTRACT FROM AUTHOR]

The robotics casts a vital part in day-to-day life. A large majority of the robots are employed in industries, factories and in our neighbourhood ecosystem. In this regard, diverse kinds of robots are employed for specific purposes. Standing out amongst them are the hexapod robots, endowed with six legs or wheels to move about in the surroundings in a consistent manner. As they have to move quickly in the atmosphere overcoming the roadblocks so as to arrive at the target with the least possible time-frame, we have come up with an innovative technique through this document. The epoch-making technique lends a helping hand to boost the momentum of the hexapod robot thus enabling the realization of the target with a short span of time. For the purpose, we have elegantly employed the Fuzzy rule base technique, to fine-tune the efficiency in performance of the so as to exploit him to the maximum in this sophisticated world of innovation. The milestone methods find it elegantly executed in the working platform of the mighty MATLAB and the outcomes are subjected to deep assessment. [ABSTRACT FROM AUTHOR]

Classifying the sentences that describe Events is an important task for many applications. In this paper, Event patterns are identified and extracted at sentence level using term features. The terms that trigger Events along with the sentences are extracted from web documents. The sentence structures are analyzed using POS tags. A hierarchal sentence classification model is proposed by considering specific term features of the sentence and the rules are derived. The rules fail to define a clear boundary between the patterns and create ambiguity and impreciseness. To overcome this, suitable fuzzy rules are derived which gives importance to all term features of the sentence. The fuzzy rules are constructed with more variables and generate sixteen patterns. Artificial Neuro-Fuzzy Inference System (ANFIS) model is proposed for training and classifying the sentence patterns for capturing the knowledge present in sentences. The obtained patterns are assigned linguistic grades based on previous classification knowledge. These grades represent the type and quality of information in the patterns. The membership function is used to evaluate the fuzzy rules. The patterns share the membership values between [0-1] which determines the weights for each pattern. Later, higher weighted patterns are considered to build Event Corpus, which helps in retrieving useful and interested information of Event Instances. The performance of the proposed approach classification is evaluated for 'Crime' Event by crawling documents from WWW and also evaluated for benchmark dataset for 'Die' Event. It is found that the performance of the proposed approach is encouraging when compared with recently proposed similar approaches. [ABSTRACT FROM AUTHOR]

In a fuzzy classifier, the maping 'inputs-output' is described by the linguistic 〈If-then〉 rules, the antecedents in which contain the fuzzy terms 'low,' 'average,' 'high,' and so on. To increase the correctness, the fuzzy classifier is learned by using experimental data. The problems with equal and different costs of various classification errors are discussed. A new criterion is offered for problems with undistinguishable types of errors, in addition to the two known criteria. A new one implies that the distance between the desired and real fuzzy results of classification for the cases of a wrong decision is weighted by the penalty factor. The learning criteria are generalized for problems of classification with the cost matrix. The conducted computer experiments on the wine recognition and heart disease diagnostics problems show that the best quality parameters of tuning fuzzy classifiers are achieved by a new learning criterion. [ABSTRACT FROM AUTHOR]

The e-learners' excellence can be improved by recommending suitable e-contents available in e-learning servers that are based on investigating their learning styles. The learning styles had to be predicted carefully, because the psychological balance is variable in nature and the e-learners are diversified based on the learning patterns, environment, time and their mood. Moreover, the knowledge about the learners used for learning style prediction is uncertain in nature. This paper identifies Felder-Silverman learning style model as a suitable model for learning style prediction, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in the learning style predictions. The evaluations have used the Gaussian membership function based fuzzy logic for 120 students and tested for learning of C programming language and it has been observed that the proposed model improved the accuracy in prediction significantly. [ABSTRACT FROM AUTHOR]