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The internet content increases exponentially day-by-day leading to the pop-up of irrelevant data while searching. Thus, the vast availability of web data requires curation to enhance the results of the search in relevance to searched topics. The proposed F-CapsNet deals with the content curation of web blog data through the novel integration of fuzzy logic with a machine learning algorithm. The input content to be curated is initially pre-processed and seven major features such as sentence position, bigrams, TF-IDF, cosine similarity, sentence length, proper noun score and numeric token are extracted. Then the fuzzy rules are applied to generate the extractive summary. After the extractive curation, the output is passed to the novel capsule network based deep auto-encoder where the abstractive summary is produced. The performance measures such as precision, recall, F1-score, accuracy and specificity are computed and the results are compared with the existing state-of-the-art methods. From the simulations performed, it has been proven that the proposed method for content curation is more efficient than any other method. [ABSTRACT FROM AUTHOR]

Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services. [ABSTRACT FROM AUTHOR]

In an agricultural country like India farmer's contribution is very vital. Though a lot of work is going on to increase the yield quality, security of the crops is also the question on the front because if the crops are destroyed or damaged due to animals or thefts the farmers are helpless. Hence we propose a system to help a farmer to identify where an animal or thief has entered the field using Fuzzy Classification. In machine learning many classifiers are there like Neural network, Convolutional neural network, Probabilistic approach, K-nearest neighbor, SVM, etc. But little literature is found on Fuzzy classifiers. Hence we introduce here an approach to classify the images of farm at fencing with and without animals using Fuzzy systems. [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]

Traffic patterns in urban areas present a complex and dynamic system that is characterized by inherent uncertainties. The presented study is a traffic light control system with feedback. The controller of the system is designed in a fuzzy and conventional way and is applied to a network of two junctions. The verification is performed using the MATLAB fuzzy toolbox platform (for designing the fuzzy controller) and Aimsun platform for microsimulation of the two junctions using the two types of controllers. To accomplish the control of the system a fuzzy controller on heuristic rules proposed to allow adaptive traffic control on signalized junctions in urban environments. The Fuzzy Toolbox in MATLAB is used to simulate the fuzzy controller. The Aimsun traffic simulator is used to model and simulate a traffic network of two intersections. The green light duration in the Aimsun model is based on the results for the two controllers from two separated experiments. Simulations of Aimsun models with the two types of controllers, the fuzzy and the conventional one, are compared. The experiment is performed under the premise that the traffic flow is oversaturated. Findings show that in a network of two junctions both controllers perform in a similar manner for the first junction. However, for the second junction, the fuzzy controller tends to have some advantages over the conventional controller with regard to higher traffic flow. In conclusion, the overall performance of the fuzzy controller is better than the one of the conventional controller, but further research is needed for more complex traffic networks.

Bearings are essential components of rotating machinery used in mechanical systems, and fault diagnosis of bearings is of great significance to the operation and maintenance of mechanical equipment. Deep learning is a popular method for bearing fault diagnosis, which can effectively extract the in-depth information of fault signals, thus achieving high fault diagnosis accuracy. However, due to the complex deep structure of deep learning, most deep learning methods require more time and resources for bearing fault diagnosis. This paper proposes a bearing fault diagnosis method combining feature engineering and fuzzy broad learning. First, time domain, frequency domain, and time-frequency domain features are extracted from the bearing signals. Then the stability and robustness indexes of these features are evaluated to complete the feature engineering. The features obtained by feature engineering are used as the input of the fault diagnosis model, and three sets of experimental data validate the model. The experimental results show that the proposed method can achieve the bearing fault diagnosis accuracy of 96.43% on the experimental bench data, 100% on the Case Western Reserve University dataset, and 100% on the centrifugal pump bearing fault dataset, with a time of approximately 0.28 s. The results show that this method has the advantages of accuracy, rapidity, and stability of bearing fault diagnosis.

The problem of strictly asynchronous dissipative control for a class of continuous time nonlinear generalized Markov jump systems under Takagi-Sugeno fuzzy rules is studied. Firstly, by constructing a less conservative mode independent Lyapunov function, the sufficient conditions for stochastic stability and strict dissipation of generalized system are discussed. Then, the hidden Markov model that is widely used in practice is introduced. By combining the state transition probability with the conditional probability of the hidden Markov process, and through Schur transformation, a fuzzy state feedback controller is designed, which can operate asynchronously with the original system, so as to ensure the stochastic stability and strict dissipation of the closed-loop system. Finally, numerical simulation using Matlab linear matrix inequality(LMI) toolbox is applied to verify the effectiveness of the conclusion. [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]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wireless Sensor Networks (WSN) is an emerging networking technology that allows for the low-cost, unattended monitoring of a variety of environments, thus WSN technologies have become increasingly important. Simultaneously, lack of security and poor energy efficiency is considered the major concerns in many routing protocols in various existing WSN protocols. Security and energy efficiency concerns are mainly due to the emergence of various kinds of malicious attacks because of advancements in networking technology. To mitigate these issues secured transmission is essential in any routing protocol. In order to provide safe data transfer in WSN-based smart environment applications, the TABEESR protocol (Trust Analysis Based Energy Efficient and Safe Routing) was developed. The proposed routing protocol encompasses four phases such as network creation and node deployment, grading for trust evaluation, route identification process and data transmission. The trust evaluation is performed with the aid of sensor packet features to categorize the positive nodes and negative nodes. The path identification process includes two processes such as cluster head selection through red fox optimization and relay node selection using a fuzzy interference system for energy efficient and secure path selection. A public static agreement key based cryptography technique is introduced for secure data transmission. The proposed TABEESR is tested with MATLAB software to validate its performance. SMEER, SHER, SRPA and AFSA are the prior routing protocols in WSN that are considered to compare the performance of the proposed routing protocol. The proposed TABEESR algorithm attained 95(%) of Average Residual Energy, 91 (%) of packet delivery ratio, 1350 (sec) network lifetime, 0.131 (Mbps) of throughput and 7(%) Packet Loss. Moreover, the proposed PSAKC algorithm attained 5.8 (%) information loss, 3.8 (sec) encryption time, 2.3 (sec) decryption time, 6 (sec) execution time, and 6.5 (J) energy consumption which is better than the prior methods. The proposed routing algorithm is applicable for providing security in both server to device and device to device in various smart applications. [ABSTRACT FROM AUTHOR]

To accurately rank various web services can be a very challenging task depending on the evaluation criteria used, however, it can play an important role in performing a better selection of web services afterward. This paper proposes an approach to evaluate trust prediction and confusion matrix to rank web services from throughput and response time. AdaBoostM1 and J48 classifiers are used as binary classifiers on a benchmark web services dataset. The trust score (TS) measuring method is proposed by using the confusion matrix to determine trust scores of all web services. Trust prediction is calculated using 5-Fold, 10-Fold, and 15-Fold cross-validation methods. The reported results showed that the web service 1 (WS1) was most trusted with (48.5294%) TS value, and web service 2 (WS2) was least trusted with (24.0196%) TS value by users. Correct prediction of trusted and untrusted users in web services invocation has improved the overall selection process in a pool of similar web services. Kappa statistics values are used for the evaluation of the proposed approach and for performance comparison of the two above-mentioned classifiers.

Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the uncertainty inherent in the modelling process, from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference systems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. In the current study, fuzzy set theory is applied to groundwater-quality related decision-making in an agricultural production context; the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) are used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices and hydrological data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the present research employs widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended as it outperforms both MFL and LFL in terms of accuracy when assessing groundwater quality using irrigation indices.

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]

This research work confronts a sender-based responsive and novel protocol named "Intelligent Energy-Aware Multiple restraints Secured Optimal Routing (IEAMSOR)" protocol for WSNs. In order to deal with the various emerges like packet routing, node mobility, and energy optimization as well as energy balancing in WSNs. The proposed protocol accounts for the basic QoS restraints such as Delay, HopCount and Energy Level for each link of 'n' number of routes and predicts the best optimal path among these in-between sender and receiver nodes throughout the route discovery process. It also assures the energy level of each node existing on the route during the route reply process. It incorporates the modified mobility prediction approach in order to estimate the stableness of link failure time for every link of each path during the route reply process. The main objective of this work to achieve the energy balancing among the nodes is achieved through fuzzy rules based node's trust classification is introduced and based on this energy weight of each node is adjusted according to their trustworthiness. It accomplishes the path sustainment process when the link among the two nodes goes down. Moreover, the proposed model has been given careful attention for selecting additional substitute routes throughout link failure. The experimental results have seemed that the IEAMSOR protocol performs better than the existing traditional protocols. [ABSTRACT FROM AUTHOR]

Reconfigurable manufacturing system is the new type of manufacturing system which is designed for a part family and it can change its function and capacity by rearranging of software and hardware components whenever required. In a manufacturing system, products and/or product families are needed to be scheduled to get more productivity and profit. In the present work, fuzzy logic based model has been prepared for scheduling of the part families for reconfigurable manufacturing system considering the industrial case. For scheduling of products, three criteria have been considered; reconfiguration effort, profit over cost and due date. Fuzzy rules have been developed according to behavior of these criteria. Fuzzy logic model has been made in MATLAB. This model can be used to predict the schedule for maximum for considering reconfiguration effort and due date. Results of the model have been compared with weighted aggregate sum method. [ABSTRACT FROM AUTHOR]

Rule-based models are applicable to model the behavior of complex and nonlinear systems. Due to limited experience and randomness involving constructing information granules, an insufficient credible rules division could reduce the model’s accuracy. This paper proposes a new rule-based modeling approach, which utilizes density-based spatial clustering of applications with noise (DBSCAN)-based information granules to construct the rules. First, bear in mind the advantages of density-based clustering, DBSCAN is proposed to generate data structures. Based on these data structures, two rule-based models are constructed: 1) models using DBSCAN clusters to construct granules and rules directly and 2) models generating subgranules in each DBSCAN cluster for rule formation. Experiments involving these two models are completed, and obtained results are compared with those generated with a traditional model involving fuzzy C-means-based granules. Numerical results show that the rule-based model, which builds rules from subgranules of DBSCAN structures, performs the best in analyzing system behaviors. [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]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Managing the container yard operations can be challenging as a result of various uncertainties associated with storing and retrieving containers from the yard. These associated uncertainties occur because the arrival of a truck to pick up the container is random, so the departure time of the container is unknown. The problem investigated in this paper emerges when newly arrived containers of different sizes, types and weights require storage operation in the same yard where other containers have already been stored. This situation becomes more challenging when the time of departure of existing container is not known. This study develops a new Fuzzy Knowledge-Based optimisation system named 'FKB_GA' for optimal storage and retrieval of containers in a yard that contains long stay pre-existing containers. The containers' duration of stay factor is considered along with two other factors such as the similarity (containers with same customer) and the quantity of containers per stack. A new Multi-Layered Genetic Algorithm module is proposed which identifies the optimal fuzzy rules required for each set of fired rules to achieve a minimum number of container re-handlings when selecting a stack. An industrial case study is used to demonstrate the applicability and practicability of the developed system. [ABSTRACT FROM AUTHOR]

Summary: DNA signals mainly differ from standard digital signals due to their biological data contents. Owing to unique properties of DNA signals the conventional signal processing techniques, such as digital filters, suffers with spectral leakage and results in insignificant noise suppression in DNA sequence analysis. This article presents an intelligent noise suppression window filter (INSWF) for DNA signal analysis. The filter demises the signal by separating high‐level frequency contents and by identifying nucleotides with high fuzzy membership contribution at particular locations. The nucleotide contents of signals are later filtered by application of median filtering employing a combination of s‐shaped and z‐shaped filters. The fundamental characteristic of codons usage that causes uneven nucleotides segmentation has been tackled by finding the best fit of the curve in biological contents of filter. One of the fuzzy correlations existing between codons and median that nucleotides incorporated to reduce the signal noise to a larger magnitude. The INSWF filter outperformed the existing fixed‐length digital filters tested over 250 benchmarked and random datasets of various species. A notable enhancement of 45% to 130% was achieved by significantly suppressing signal noise as compared with conventional digital filters in DNA sequence analysis. [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]

Recently, a kind of heuristic optimization algorithm named gravitational search algorithm (GSA) has been rapidly developed. In GSA, there are two main parameters that control the search process, namely, the number of applied agents (Kbest) and the gravity constant (G). To balance exploration and exploitation, a fuzzy system containing twelve fuzzy rules is proposed to intelligently control the parameter setting of the GSA. The proposed method can enhance the convergence ability and yield better optimization results. The performance of fuzzy GSA (FGSA) is examined by fifteen benchmark functions. Extensive experimental results are tested and compared with those of the original GSA, CGSA, CLPSO, NFGSA, PSGSA and EKRGSA.

The competitiveness of a travel chain largely depends on the travel conditions along the sequence of journeys within the chain. This paper shows a method to analyse and to optimize the service quality along a travel chain. Travel comfort is a very important qualitative feature of the public transportation service, where travel comfort is used in a broader sense to describe ride quality and transfer quality including mobility, information, safety, security, and naturally comfort aspects. The analysis of travel comfort in the literature regards public transportation services. Several synthetic indices, which consider user judgment about service aspects, were developed to describe travel comfort, and comprehensive analyses have been published. However, to describe the competitiveness of the public transport the focus from the individual services should be moved toward the integrated service of the travel chain from the beginning to the end. The characteristics of travel comfort along the travel chain should be described and the location and rate of necessary interventions should be identified. In this paper we analyse the travel comfort features of travel chains. This paper proposes a method, which describes the travel comfort characteristics with synthetic indices based on the individual comfort indices of travel components, and uses a fuzzy approach to give an overall analysis of comfort conditions along the travel chain. The proposed method helps to identify the quality fluctuation and the weak points of a travel chain and makes the attractiveness of alternative travel chains comparable. An illustrative case study was carried out for one of the major transportation corridor of Budapest (Hungary), to exemplify the approach, where the validity of the method was tested as well. The results confirmed the usefulness and applicability of the methodology; by its application very valuable insights can be gained regarding the location and type of the necessary interventions. The results of our research are helpful to evaluate the actual service level of sustainable alternatives of individual car usage and to promote modal shift towards sustainable transportation modes.

This study was conducted in the KVRS to; evaluate the seasonal eco-toxicity of water, soil and sediment samples; establish the suitability of using temperate biotest batteries in tropical systems; classify the toxicity of samples using Fuzzy Rules to estimate potential ecological risks. 143 water, sediment, and soil samples were collected during dry and rainy seasons in plantation areas. Pseudokirchneriella subcapitata, Aliivibrio fischeri and Arthrobacter globiformis bioassays were used to assess the toxicity of the samples. Results were categorized and classified into toxicity classes. Dry season presented a significantly higher mean inhibition of 31% than 5% shown by rainy season samples (p < 0.001) in the bacterial bioassays, indicating a lower concentration of contaminants due to flooding and increased surface runoff. A few sediment samples resulted into 100% inhibition of A. globiformis, implying organisms were physiologically inactive upon exposure to contaminants. Seventy-three percent of samples posed little or no toxic potential risk, 25% posed critical risk and 1% posed elevated critical risk, implying the KVRS ecosystem might be at risk if the extensive usage of pesticides in the area is not well managed and monitored. The temperate micro-biotests can be used in tropical systems, but with further research on suitable organisms and standardized methods.

Understanding the reasons behind the decisions of complex intelligent systems is crucial in many domains, especially in healthcare. Local explanation models analyse a decision on a single instance, by using the responses of the system to the points in its neighbourhood to build a surrogate model. This work makes a comparative analysis of the local explanations provided by two rule-based explanation methods on RETIPROGRAM, a system based on a fuzzy random forest that analyses the health record of a diabetic person to assess his/her degree of risk of developing diabetic retinopathy. The analysed explanation methods are C-LORE-F (a variant of LORE that builds a decision tree) and DRSA (a method based on rough sets that builds a set of rules). The explored methods gave good results in several metrics, although there is room for improvement in the generation of counterfactual examples.

Partial discharge (PD) source location using acoustic emission (AE) is widely utilized by many transformer manufacturers and power utility engineers in routine and critical situation for optimal operation of the electrical power system as well as further risk management and repair planning. The PD detection is not enough to take solution, so identification of PD source is essential to restore apparatus condition. This work aim is to localize the defect geometrically by means of TDOA (time difference of arrival) signals from the sensors fixed on the power transformer. The solution for PD source location is acquired by making these nonlinear equations as optimization problem. In this technique, the inertia weight is effec-tively regulated by using 49 and 9 simple IF-THEN fuzzy rules to improve the global optimal solution and impairs the local convergence problem and improves the accuracy in estimating the PD source location. The simulation results reveal that PD location accuracy with minimum of maximum deviation error, absolute error and relative error is better when compared to other constant parameter intelligent methods which were reported in the literature.

Millions of people encounter coronary artery disease annually, and this disease constitutes a large number of patients. Although considerable progress has been made in medical science, but the early detection of this disease is still a challenging issue. The aim of this paper is to describe developing a fuzzy expert system that will help to detect CAD at an early stage. Rules were extracted with the aid of doctors and fuzzy approach was taken to cope with the present uncertainty in the medical domain. The results indicate a high level of correct detection of normal and abnormal groups of people. The suggested methodology can help the doctors in medical diagnosis