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In the field of biomedicine, enormous data are generated in a structured and unstructured form every day. Soft computing techniques play a major role in the interpretation and classification of the data to make appropriate decisions for making policies. The field of medical science and biomedicine needs efficient soft computing-based methods which can process all kind of data such as structured data, categorical data, and unstructured data to generate meaningful outcome for decision-making. The soft-computing methods allow clustering of similar data, classification of data, predictions from big-data analysis, and decision-making on the basis of analysis of data. A novel method is proposed in the paper using soft-computing methods where clustering mechanisms and classification mechanisms are used to process the biomedicine data for productive outcomes. Fuzzy logic and C-means clustering are devised as a collaborative approach to analyze the biomedicine data by reducing the time and space complexity of the clustering solutions. This research work is considering categorical data, numeric data, and structured data for the interpretation of data to make further decisions. Timely decisions are very important especially in the field of biomedicine because human health and human lives are involved in this field and delays in decision-making may cause threats to human lives. The COVID-19 situation was a recent example where timely diagnosis and interpretations played significant roles in saving the lives of people. Therefore, this research work has attempted to use soft computing techniques for the successful clustering of similar medical data and for quicker interpretation of data to support the decision-making processes related to medical fields., Competing Interests: The authors have none to declare for conflicts of interest with respect to this article., (Copyright © 2022 Yanping Wu and Md. Habibur Rahman.)

This paper sketches a new approach using Fuzzy Cognitive Maps (FCMs) to operably map and simulate digital transformation in architecture and urban planning. Today these processes are poorly understood. Many current studies on digital transformation are only treating questions of economic efficiency. Sustainability and social impact only play a minor role. Decisive definitions, concepts and terms stay unclear. Therefore this paper develops an open experimental testbed for sustainable and innovative environments (ETSIE) for three different digital transformation scenarios using FCMs. A traditional growth-oriented scenario, a COVID-19 scenario and an innovative and sustainable COVID-19 scenario are modeled and tested. All three scenarios have the same number of components, connections and the same driver components. Only the initial state vectors are different and the internal correlations are weighted differently. This allows for comparing all three scenarios on an equal basis. The Mental Modeler software is used. This paper presents one of the first applications of FCMs in the context of digital transformation. It is shown that the traditional growth-oriented scenario is structurally very similar to the current COVID-19 scenario. The current pandemic is able to accelerate digital transformation to a certain extent. But the pandemic does not guarantee for a distinct sustainable and innovative future development. Only by changing the initial state vectors and the weights of the connections an innovative and sustainable turnaround in a third scenario becomes possible., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Höhl W.)

Objective: In biological data analysis, protein sequence and structural motifs are an amino-acid sequence patterns that are widespread and used as tools for detecting the cancer at an earlier stage. To improve the cancer detection with minimum space and time complexity, Distribution based Fuzzy Estimate Spectral Clustering (DFESC) technique is developed. Methods: Initially, the protein sequence motifs are taken from dataset to form the cluster. The Distribution based spectral clustering is applied to group the protein sequence by measuring the generalized jaccard similarity between each protein sequences. To develop the clustering accuracy, soft computing technique namely fuzzy logic is applied to calculate membership value of each sequence motifs. Results: The outcome showed that the presented DFESC technique effectively identifies the cancer in terms of clustering accuracy, false positive rate, and cancer detection time and space complexity. Conclusion: Based on the observations, evaluation of DFESC technique provides improved result for premature detection of cancer using protein sequence and structural motifs., (Creative Commons Attribution License)

MicroRNAs (miRNAs) act as a major biomarker of cancer. All miRNAs in human body are not equally important for cancer identification. We propose a methodology, called FMIMS, which automatically selects the most relevant miRNAs for a particular type of cancer. In FMIMS, miRNAs are initially grouped by using a SVM-based algorithm; then the group with highest relevance is determined and the miRNAs in that group are finally ranked for selection according to their redundancy. Fuzzy mutual information is used in computing the relevance of a group and the redundancy of miRNAs within it. Superiority of the most relevant group to all others, in deciding normal or cancer, is demonstrated on breast, renal, colorectal, lung, melanoma and prostate data. The merit of FMIMS as compared to several existing methods is established. While 12 out of 15 selected miRNAs by FMIMS corroborate with those of biological investigations, three of them viz., "hsa-miR-519," "hsa-miR-431" and "hsa-miR-320c" are possible novel predictions for renal cancer, lung cancer and melanoma, respectively. The selected miRNAs are found to be involved in disease-specific pathways by targeting various genes. The method is also able to detect the responsible miRNAs even at the primary stage of cancer. The related code is available at http://www.jayanta.droppages.com/FMIMS.html .

Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless, their operational efficiency and longevity might be impeded by energy limitations. The low energy adaptive clustering hierarchy (LEACH) protocol has been specifically developed with the objective of achieving energy consumption equilibrium and regularly rotating cluster heads (CHs). This study presents a novel technique, namely the hierarchical fuzzy logic controller (HFLC), which is integrated with the LEACH protocol to enhance the process of CH selection and effectively prolong the network's operational lifespan. The HFLC system employs fuzzy logic as a means to address the challenges posed by uncertainty and imprecision. It assesses many aspects, including residual energy, node proximity, and network density, in order to make informed decisions. The combination of HFLC with LEACH demonstrates superior performance compared to the conventional LEACH protocol in terms of energy efficiency, stability, and network durability. This study emphasizes the potential of intelligent and adaptive mechanisms in improving the performance of WSNs by improving the survivability of nodes by reducing the energy consumption of the nodes during the communication of network process. It also paves the way for future research that integrates soft computing approaches into network protocols. [ABSTRACT FROM AUTHOR]

The present study investigates the compressive strength performance of polylactic acid (PLA) polymer material parts printed using the Fused Deposition Modelling (FDM) three-dimensional (3D) printing process, with a particular emphasis on various machine input parameters. The face centred central composite design matrix approach was employed for experimental modelling, which was subsequently utilised as a knowledge base for the fuzzy algorithm. A hybrid evolutionary algorithm, i.e., Genetic-Algorithm (GA) assisted with Fuzzy Logic Methodology (FLM), was used to optimize input process parameters and compressive strength of FDM technique fabricated polymer material parts. The study concluded that the maximum compressive strength observed with GA integrated FLM was 49.7303 MPa at input factors (layer thickness-0.16 mm, temperature 208°C, infill-pattern-Honeycomb, infill-density-60% and speed/extrusion velocity-41 mm/s) which is higher than the experimental (47.08 MPa) and fuzzy predicted (47.101 MPa) value. This evolutionary hybrid soft computing methodology has optimized the compressive strength of PLA polymer material parts at optimum parameters combination set. [ABSTRACT FROM AUTHOR]

Optimizing computational offloading in Mobile Edge Computing (MEC) environments presents a multifaceted challenge requiring innovative solutions. Soft computing, recognized for its ability to manage uncertainty and complexity, emerges as a promising approach for addressing the dynamic multi-objective evaluation inherent in computational offloading scenarios. This paper conducts a comprehensive review and analysis of soft computing approaches for Dynamic Multi-Objective Evaluation of Computational Offloading (DMOECO), aiming to identify trends, analyze existing literature, and offer insights for future research directions. Employing a systematic literature review (SLR) methodology, we meticulously scrutinize 50 research articles and scholarly publications spanning from 2016 to November 2023. Our review synthesizes advancements in soft computing techniques, including fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning, as applied to computational offloading optimization within MEC environments. Within this comprehensive review, existing approaches are categorized and analyzed into distinct research lines based on methodologies, objectives, evaluation metrics, and application domains. The evolution of soft computing-based DMOECO strategies is emphasized, showcasing their effectiveness in dynamically balancing various computational objectives, including energy consumption, latency, throughput, user experience, and other pertinent factors in computational offloading scenarios. Key challenges, including scalability issues, lack of real-world deployment validation, and the need for standardized evaluation benchmarks, are identified. Insights and recommendations are provided to enhance computational offloading optimization. Furthermore, collaborative efforts between academia and industry are advocated to bridge the theoretical developments with practical implementations. This study pioneers the use of SLR methodology, offering valuable perspectives on soft computing in DMOECO and synthesizing state-of-the-art approaches. It serves as a crucial resource for researchers, practitioners, and stakeholders in the MEC domain, illuminating trends and fostering continued innovation in computational offloading strategies. [ABSTRACT FROM AUTHOR]

Non-linear optical (NLO) parameter of a series of donor–acceptor substituted N-salicylidene-4-benzenesulfonylaniline (SBSA) derivatives with a contrast of first hyperpolarisability has been investigated using density functional-based theory. The derivative of SBSA with both the donor and acceptor substitution (NMe2-SBSA-NO2) exhibits the highest first hyperpolarisability ($\sim 3 \times$ ∼ 3 × 103 a.u in gas phase) value. Frequency and solvent polarity responsive behaviours of first hyperpolarisability are utilised to demonstrate the variation of NLO as switchable electrical parameter. Each of the studied compounds displays very high NLO response at frequency corresponding to the absorption maxima of their electronic state. Likewise, the increase of solvent polarity induces a substantial increase of first hyperpolarisability. The computation of NLO responsive first hyperpolarisability by varying the frequency and solvent dielectric constant within a wide domain is very tedious and time-consuming. To overcome the lacuna, we implemented machine learning tools such as artificial neural networks (ANNs), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS) to predict the computation data of the NMe2-SBSA-NO2 and moderately good results were obtained using ANFIS as soft computing tool. [ABSTRACT FROM AUTHOR]

The novelty of this work is that it is the first to introduce complex-valued suprametric spaces and apply it to Fractal Generation and mixed Volterra–Fredholm Integral Equations. In the realm of fuzzy logic, complex-valued suprametric spaces provide a robust framework for quantifying the similarity between fuzzy sets; for instance, utilizing a complex-valued suprametric approach, we compared the similarity between fuzzy sets represented by complex-valued feature vectors, yielding quantitative measures of their relationships. Thereafter, we establish related fixed point results and their applications in algorithmic and numerical contexts. The study then delves into the generation of fractals, exemplified by the Barnsley Fern fractal, utilizing sequences of affine transformations within complex-valued suprametric spaces. Moreover, this article presents two algorithms for soft computing and fractal generation. The first algorithm uses complex-valued suprametric similarity for fuzzy clustering, iteratively assigning fuzzy sets to clusters based on similarity and updating cluster centers until convergence. The distinctive pattern of the Barnsley Fern fractal is produced by the second algorithm's repetitive affine transformations, which are chosen at random. These techniques demonstrate how well complex numbers cluster and how simple procedures can create complicated fractals. Moving beyond fractal generation, the paper addresses the solution of mixed Volterra–Fredholm integral equations in the complex plane using our results, demonstrating numerical illustrations of complex-valued integral equations. [ABSTRACT FROM AUTHOR]

Fuzzy Time Series (Fts) models are experiencing an increase in popularity due to their effectiveness in forecasting and modelling diverse and intricate time series data sets. Essentially these models use membership functions and fuzzy logic relation functions to produce predicted outputs through a defuzzification process. In this study, we suggested using a Second Order Type-1 fts (S-O T-1 F-T-S) forecasting model for the analysis of time series data sets. The suggested method was compared to the state-of-theart First Order Type 1 Fts method. The suggested approach demonstrated superior performance compared to the First Order Type 1 Fts method when applied to household consumption data from the Magene Regency in Indonesia, as measured by absolute percentage error rate (APER). [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy logic based heart disease risk prediction system. The system collects patient medical data using a user-friendly GUI and processes the same with fuzzy logic soft computing technique. The prediction model also introduces three supreme attributes, viz., Serum creatinine, Echocardiogram (LVEF) and Breathlessness that inform about the diminished blood flow, deteriorated heart muscles and severity while breathing respectively. The proposed system works on 47 logical operations processed over 10 clinical inputs. The study reveals 5 distinct levels of heart risks as output and final results matches with probable cardiac risk. The study yielded an accuracy of 96% along with specificity and sensitivity of 94.73 and 96.67% respectively. Comparison with previous research papers and presentation of ROC and PR-curves highlights the effectiveness of fuzzy logic in the medical science. [ABSTRACT FROM AUTHOR]

Background: The main goal of the paper is to define the level of energy efficiency in the economies of selected countries in the Balkan region that have opted for the EU Green Deal, a circular economy, and a transition to carbon neutrality. Energy efficiency, as a determinant of carbon neutrality, was selected as an indicator for analysis because it records particularly unfavorable indicators in the region under observation. The research was carried out on a sample of seven Balkan countries and their surrounding areas. An initial qualitative analysis was followed by a quantitative analysis based on a combination of statistical methods and soft computing. Six indicators were selected for the analysis covering a period of 30 years (1990–2020). Results: A significant obstacle to the green transition and the region's transition to a circular economy and carbon neutrality is energy efficiency and energy related pollution—the reliance of most countries on coal-fired thermal power plants for electricity generation. The research results showed the following: (a) the degree of economic development and membership in the European Union are not significantly related to the level of energy efficiency; (b) most of the sampled countries are in the initial stages of introducing activities to achieve carbon neutrality; and (c) only Slovenia has documented consistent indicators and evident advancements in its efforts to achieve carbon neutrality. Based on the research findings, proposals for improvements were made in the direction of policymaking and in a methodological sense. Conclusions: The implementation of circularity and carbon neutrality as a long-term goal of the European Union is not necessarily related to the level of economic development, nor can its trajectory be exclusively ascertained by means of data processing and monitoring. A more precise understanding of a carbon-neutral future can be achieved through the incorporation of qualitative data to a greater extent, a realistic evaluation of historical facts and their repercussions, as well as projections of the effects that reality and global developments after 2022 will have on each country. [ABSTRACT FROM AUTHOR]

Soft rough fuzzy sets (SRFS s) represent a powerful paradigm that integrates soft computing, rough set theory, and fuzzy logic. This research aimed to comprehensively investigate the various dimensions of SRFS s within the domain of approximation structures. The study encompassed a wide spectrum of concepts, ranging from covering approximation structures and soft rough coverings to soft neighborhoods, fuzzy covering approximation operators, and soft fuzzy covering approximation operators. We introduced three models of SRFS s based on covering via the core of soft neighborhood. We discussed and analyzed our models' characteristics and properties. The relations between our models for soft fuzzy covering sets and Zhan's model for soft rough fuzzy covering were presented. [ABSTRACT FROM AUTHOR]

Defect density is an essential software testing and maintenance aspect that determines the quality of software products. It is used as a management factor to distribute limited human resources successfully. The availability of public defect datasets facilitates building defect density prediction models using established static code metrics. Since the data gathered for software modules are often subject to uncertainty, it becomes difficult to deliver accurate and reliable predictions. To alleviate this issue, we propose a new prediction model that integrates gray system theory and fuzzy logic to handle the imprecision in software measurement. We propose a new similarity measure that combines the benefits of fuzzy logic and gray relational analysis. The proposed model was validated against defect density prediction models using public defect datasets. The defect density variable is frequently sparse because of the vast number of none‐defected modules in the datasets. Therefore, we also check our proposed model's performance against the sparsity level. The findings reveal that the developed model surpasses other defect density prediction models over the datasets with high and very high sparsity ratios. The ensemble learning techniques are competitive choices to the proposed model when the sparsity ratio is relatively small. On the other hand, the statistical regression models were the most inadequate methods for such problems and datasets. Finally, the proposed model was evaluated against different degrees of uncertainty using a sensitivity analysis procedure. The results showed that our model behaves stably under different degrees of uncertainty. [ABSTRACT FROM AUTHOR]

The adaptive neuro-fuzzy inference system (ANFIS), an efficient soft computing approach, has both high interpretability and self-learning ability. ANFIS can effectively handle the both classification and regression of low-dimensional data, but it deals poorly with high-dimensional big data due to the curse of dimensionality. This paper proposes a highly interpretable deep neural-fuzzy system (DNFSA)-based algorithm to efficiently and effectively train fuzzy classifiers. It integrates several novel techniques: (1) independent membership functions (MFs) and fuzzy c-means clustering, which initialize rule by fuzzy c-means clustering to train Takagi–Sugeno–Kang fuzzy system efficiently and (2) maximal information coefficient, which identifies interesting relationships between pairs of variables, so as to enhance the interpretability and generalization performance. Specifically, by viewing improved ANFIS as the base learner, we construct DNFSA in the fashion of parallel layer by layer. A visualized structure can be obtained automatically and presented to the users for better understanding of DNFSA. Furthermore, the proposed DNFSA can effectively determine which base learners should be abandoned from a set of available sub-fuzzy systems, which reveals that it may be better to ensemble many sub-fuzzy systems instead of all at hand. Experiments are conducted to confirm the DNFSA can significantly reduce the rules along with the parameters to have better interpretability. [ABSTRACT FROM AUTHOR]

The majority of technological processes include dynamic systems, which are characterized by structural and parameter uncertainty. Conventional control procedures based on such models are unlikely to produce the desired performance since deterministic models cannot appropriately characterize these uncertainties. When dealing with complex nonlinear systems that are characterized by ill-defined and unknown elements, fuzzy-logic based system is an effective method. Fuzzy controllers—whose rule basis is built on the expertise of human experts—are frequently utilized in this context to achieve desired performance. This knowledge might not be adequate for some complex processes, thus different methods for creating IF-THEN rules have been suggested. In this paper, the design of higher-order fuzzy sets is discussed and a generalized novel algorithm is developed for implementing type-n fuzzy sets and it is applied for controlling the angle of steering for a vehicle in such a way that the vehicle tracks the desired path. In the proposed methodology, fuzzification and defuzzification are taking place n number of times, and defuzzification of higher order fuzzy values up to type-1 fuzzy set is performed using the center of gravity method. The obtained type-1 fuzzy values are used in the rule base and inference mechanism for calculating the final crisp output. The significance of the present research is that the computation cost is not as high as expected because both fuzzification and defuzzification processes are recursive and the reduced new type-1 fuzzy values have the additional ability to model the uncertainty and vagueness as compared to normal type-1 fuzzy values. A comparison of the controllers based on the type of fuzzy sets is done based on the square of error (SE). Both the disturbance signal and uncertainty scenarios were also considered. The use of higher-order fuzzy sets facilitates the controller to give the more accurate results for controlling the vehicle and the corresponding simulation results suggest the same. [ABSTRACT FROM AUTHOR]

There are several methods are used to calculate the power quality indicators. These objectives are now served by the most recent soft computing theories, such as neural networks, fuzzy time series, fuzzy logic theory, and other techniques. The fuzzy logic theory is one of the methods used to calculate the power quality indicators for the distribution electric network in this paper. It resolves the issue of forecasting the voltage values on distribution electrical networks' buses and enhances the quality of electricity in the face of fluctuating consumer loads and uncertainty. This paper proposed a device that has the ability for monitoring the quality of electricity domestically which leads to getting rid of many problems such as voltage deviations and asymmetrical faults. The fuzzy logic theory is implemented to develop the algorithm for this purpose. The actual data is recorded at the distribution transformer and implement the fuzzy logic theory by using a mathematical model as well as the simulation on MATLAB for optimizing the power quality indicators with the help of fuzzy logic theory and results are discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

A new era of technological developments has been ushered in by the growth of the Internet of Things (IoT), with telerobotic systems significantly contributing to the automation of crucial medical, diagnostic, and pharmaceutical processes. This study presents a thorough analysis of access control mechanisms in the context of the IoT to improve the security and usability of these cutting-edge technologies. The Fuzzy Logic Trust-Based Access Control Algorithm (FL-TACA) offers a novel strategy that optimizes energy efficiency and allocates access rights. It offers low-latency operations and data security as a secure communication solution for the telerobotics community. The technique decreases vulnerabilities to denial of service, eavesdropping, and replay attacks by allocating access permissions based on the least privileges. Finally, integrating FL-TACA promises to improve the caliber and effectiveness of patient care by enabling intelligent and independent activities that lessen the workload on medical staff.

In the evolving landscape of manufacturing, the integration of intelligent control theory stands as a pivotal advancement, driving both process optimization and the paradigm of smart manufacturing. This review delves into the multifaceted applications of intelligent control theory, emphasizing its role in equipment, operations, and controls optimization. With a focus on three primary methodologies—fuzzy logic, neural networks, and genetic algorithms—the paper elucidates their biological parallels and their significance in simulation, modeling, and optimization. The transformative potential of smart manufacturing, synonymous with Industry 4.0, is also explored, highlighting its foundation in data, automation, and artificial intelligence. Drawing from a comprehensive analysis of recent literature, the review underscores the growing interest in this domain, as evidenced by the surge in publications and citations over the past decade. The overarching aim is to provide contemporary discourse on the applications and implications of intelligent control theory in the realms of process optimization and smart manufacturing. [ABSTRACT FROM AUTHOR]

Models of artificial intelligence (AI) and numerical weather prediction (NWP) have been widely used to successfully forecast solar radiation. Nevertheless, hybridized versions of these models can improve prediction accuracy and reliability and lessen the biased outcomes that can arise from one model in order to take advantage of the power elements of each model. In this research, a hybrid soft computing method based on particle swarm optimization (PSO) of fuzzy logic systems (FLSs) is applied to hybridize the outputs of an optimized FLS with an NWP model to forecast daily solar radiation. By building a spatially and temporally independent system in two stages, the applied hybrid technique focuses on the design of a practical and general model. In the first stage, the FLS is optimized using the simulated annealing algorithm (SA). In the second stage, a new FLS is designed using the PSO algorithm to estimate the final prediction. The PSO-FLS utilizes the outputs of both the SA-FLS and the NWP model in a straightforward and practical manner. The NWP model that is utilized is the fifth generation of atmospheric reanalysis of the global climate, or ERA5, developed by the European Centre for Medium-Range Weather Forecasts. The findings regarding the prediction procedures of FLSs, NWP, and the hybrid model are reported and compared using a data set gathered from nine stations in Saudi Arabia. The results demonstrate that the hybridization process can increase the accuracy and reliability of AI-based predictions while reducing their bias. [ABSTRACT FROM AUTHOR]

One of the soft computing technologies that has been widely developed is fuzzy logic. One of the research themes that uses fuzzy logic is the assessment system in research. To do this, an application that can be used to calculate and record the results of lecturers in learning. The purpose of this study is to apply fuzzy logic with the Mamdani method for lecturer research activities at the University of Graha Nusantara Padangsidimpuan. The steps of the research using the Mamdani method: generating input variables taken from Sinta accredited articles, Simlitabmas grant articles, and articles in international and national journals. generate input variables taken from Sinta accredited articles, Simlitamas grant articles, and articles in international and national journals. In higher education, teachers play an important role in producing skilled and qualified graduates. Therefore, it is necessary to measure student satisfaction with the performance of lecturers at the UGN Padangsidimpuan campus. To determine student satisfaction, observations and questionnaires were distributed to respondents. The questionnaire as a tool and data collection method to measure student satisfaction with lecturer performance is the Mamdani fuzzy logic method. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic impacted the educational process since the teaching process has been forced to go online in many countries. This enforced change revealed the weaknesses and strengths of the national educational systems and particular institutions. This article aims to analyse the impact of COVID-19 at selected European universities and assess the satisfaction of students, teachers, IT staff and management. This study is unique for its systematicity and complexity - it aggregates the opinions of all interested groups of stakeholders, distinguishes several time periods (before, during and after the pandemic), and allows the respondents to express hesitance in their evaluation. The evaluation model uses fuzzy sets to capture the uncertainty and to aggregate the opinions of different stakeholder groups. The empirical results show that most of the satisfaction development is the same or similar for all institutions examined. Then, the pandemic strongly influenced the satisfaction of all stakeholder groups at the universities examined. This impact was mostly negative, however, several lessons learnt have been revealed. Therefore, it was shown that it is highly beneficial to include these aspects to obtain a reliable picture of overall satisfaction. [ABSTRACT FROM AUTHOR]

Due to growing environmental issues and strict carbon emission (CEM) guidelines imposed throughout the globe, low-carbon emission of machine tools, which aims to reduce carbon intensity and improve process efficiency, has evolved as an emerging issue that has encouraged a lot of research into accurate prediction of energy-related performance characteristics such as energy efficiency (EE), power factor (PF) and associated CEM of machine tools. In practice, EE and PF are the two significant indicators of a machine tool's effective electrical energy utilization. In the present study, three soft computing techniques, multi-gene genetic programming (MGGP), least square-support vector machine (LS-SVM) and fuzzy logic, are employed to model a machine tool's EE, PF and associated CEM. The experiments were performed on a CNC lathe machine tool to capture the data required for development of models using soft computing techniques. The performance of the models was evaluated on six statistical indicators i.e. coefficient of determination (R2), mean absolute error, root mean square error, mean square error, sum of squared error and relative percentage error. In all cases, R2 values were found in the range of 94–99% in training and 84–94% in testing, signifying a strong relationship between the experimental and predicted values. The models' comparative performance evaluation reveals that the LS-SVM consistently outperforms the MGGP and fuzzy logic. Further, hypothesis testing i.e. mean paired t-test and variance of F-test, were conducted to validate the goodness of fit of the developed models. The developed models can be used to eliminate the need for advanced costly laboratory set-up and time-consuming measurement procedures required for performing experiments. [ABSTRACT FROM AUTHOR]

This study proposes and evaluates a combined soft computing method to mitigate agricultural flood damage downstream of reservoirs, in which a fuzzy inference system and evolutionary optimization are linked. A flood damage function was developed by linking a Mamdani fuzzy inference system and Hydrological Engineering Centre—River Analysis System two-dimensional (HEC RAS-2D) model. First, an expert panel proposed the fuzzy rules of flood damage to develop the damage function. Then, this function was utilized in the reservoir operation model, in which evolutionary optimization methods were applied to optimize release from the reservoir. Finally, a decision-making system consisting of the two stages of evaluation were used for selecting the best algorithm. In the first stage, the performance of the penalty functions was considered to exclude some inappropriate algorithms. Then, the fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) finalized the best solution of the reservoir management. Based on the results in the case study, either the firefly algorithm or the differential evolution algorithm is the best method to mitigate flood damage. The outputs corroborated the robustness of the developed method to mitigate potential flood damage. The maximum flood damage was reduced 40% compared with the natural flow. Moreover, average flood damage and inundation duration in the simulated period were mitigated considerably. It is recommended to apply the proposed method in flood mitigation studies in which overcoming flood damage data scarcity is a challenge. [ABSTRACT FROM AUTHOR]

This paper presents a model for determining fuzzy evaluations of partial indicators of the availability of continuous systems at coal open pits using a neuro-fuzzy inference system. The system itself is a combination of fuzzy logic and artificial neural networks. The system availability is divided into partial indicators. By combining the fuzzy logic and artificial neural networks, a model is obtained that has the ability to learn and uses expert judgment for that learning. This paper deals with the ECC system (bucket wheel excavator-conveyor-crushing plant) of the open pit Drmno-Kostolac, which operates within the Electric Power Company of Serbia. The advantage of a model of this type is that it does not rely on the historical experiences of experts and usual predicted values for the fuzzy evaluation of partial indicators, which are based on the assumption that similar systems affect availability in a similar way. The fuzzy evaluation of partial indicators is based on historical data for the specific system for which the model was created. As such, it can more accurately predict continuous systems availability on the basis of expert evaluations in the appropriate time period. Another advantage of this model is that the availability is estimated on a quarterly basis, which gives a more accurate view because it uses a smaller time period with more similar characteristics and, thus, includes certain external influences which are related to the quarterly meteorological conditions. [ABSTRACT FROM AUTHOR]

It is one of the topics that have been studied extensively on maximum power point tracking (MPPT) recently. Traditional or soft computing methods are used for MPPT. Since soft computing approaches are more effective than traditional approaches, studies on MPPT have shifted in this direction. This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT. The meta-heuristic training algorithms used are particle swarm optimization (PSO), harmony search (HS), cuckoo search (CS), artificial bee colony (ABC) algorithm, bee algorithm (BA), differential evolution (DE) and flower pollination algorithm (FPA). The antecedent and conclusion parameters of neuro-fuzzy are determined by these algorithms. The data of a 250 W photovoltaic (PV) is used in the applications. For effective MPPT, different neuro-fuzzy structures, different membership functions and different control parameter values are evaluated in detail. Related training algorithms are compared in terms of solution quality and convergence speed. The strengths and weaknesses of these algorithms are revealed. It is seen that the type and number of membership function, colony size, number of generations affect the solution quality and convergence speed of the training algorithms. As a result, it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem. [ABSTRACT FROM AUTHOR]

In recent years, a large number of manipulator robots have been deployed to replace or assist humans in many repetitive and dangerous tasks. Yet, these robots have complex mechanisms, resulting in their non-linearity of kinematics and dynamics as well as intensive computations. Therefore, relying on soft computing techniques are a common and alternative key to model and control these systems. In particular, fuzzy logic approaches have proven to be simple, efficient, and superior to relevant well-known methods and have sparked greater interest in robotic applications. To help researchers meet their needs easily and quickly in finding relevant research works on fuzzy-based solutions, this article adapted to provide an in-depth review of the currently updated fuzzy logic approaches for collision-free path planning of serial manipulator robots operating in complex and cluttered workspaces. In addition to a comprehensive description of fuzzy hybridization with other artificial intelligence techniques description. Further, this article attempts to present the main solutions with a summary and visualization of all basic approaches that path-planning problems may subtend in the decision-making process. Finally, the paper suggests some potential challenges and explores research issues for future work. [ABSTRACT FROM AUTHOR]

Ransomware attacks continue to pose a significant threat to organizations and individuals worldwide. The attackers' ability to constantly evolve and adapt their tactics challenges traditional cybersecurity approaches to keep pace. Ransomware attacks targeting the healthcare industry accounted for 45% of all reported cyberattacks. The nature and scale of attacks and the increasing healthcare technology adoption will continue to pose ransomware attack risks. However, by collecting and analyzing large volumes of data and applying soft computing techniques, cybersecurity experts can improve their ability to detect and prevent ransomware attacks. As a result, soft computing offers options for detecting and preventing malware attacks. Using methods from the field of soft computing, such as fuzzy logic, neural networks, and genetic algorithms, makes it possible to conduct a thorough analysis of large data sets. These can yield insightful information that can help recognize and react to ransomware attacks. These techniques can also help to decrypt files that have been encrypted using ransomware. [ABSTRACT FROM AUTHOR]

(1) Objectives: Reliability is one of the major aspects for enhancing the operability, reusability, maintainability, and quality of a system. A software component is an independent entity that deploys to form a functional system (CBSS). The component becomes unreliable mainly because of errors introduced during its design and development; it is essential to estimate the reliability of a software component in advance. This research work proposes a novel Mamdani Fuzzy-Inference (M-FIS) model to estimate the components' reliability and provides an intuitive solution for industry personnel; (2) Scope: The technology moves forward from traditional monolithic software development to scalable, integrated, business-driving software applications. Henceforth, the proposed paradigm can give a preliminary estimate of the reliability of software components, and it helps developers and vendors to produce it at high-quality; (3) Methods: In the component development and realization phase, failure data is unavailable; hence, designing metrics, inspections, statistical methods, soft-computing techniques are used to predict early reliability. The present work applies soft computing techniques to validate metrics. Moreover, estimating premature reliability reduces follow-up effort and component-development cost and time; (4) Finding: The proposed model aids the project manager in better estimating and predicting a components' reliability. Adopting both an expert-based fuzzy inference system and an unsupervised, or self-learning, algorithm provides the basis for cross checking, and concludes with a better decision in an ambivalence state. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption. [ABSTRACT FROM AUTHOR]

Public transport ridership has fallen drastically over the past few years, resulting in an increased number of private vehicles on the road, negatively impacting the road infrastructure with escalated levels of traffic congestion, pollution, road accidents, etc. Policy interventions provide a viable solution to the problem of ridership as they influence the decision-making of the public. In this article, an attempt was made to model the choice behaviour of employees working in the city of Thiruvananthapuram, India based on soft computing techniques. The choice data were collected through a revealed preference survey and mode choice models were developed using Artificial Neural Networks (ANN), Neuro-Fuzzy, and Fuzzy logic taking socio-economic characteristics, trip characteristics, and the identified latent factors as inputs. The results from the developed models are encouraging and they show good statistical validity. To formulate passenger attraction policies, the major latent factors that affect public bus ridership rates were examined. Consequently, suitable transport improvement policies are recommended based on the most influencing latent factors. Finally, based on the results of the sensitivity analysis, several policy interventions to enhance public transport ridership are recommended. [ABSTRACT FROM AUTHOR]

Harmonic contamination, non-linear loads, voltage sag-swell and other issues can all have an impact on the quality of electricity. The fluctuation of load side voltage has made it extremely difficult to maintain power quality in recent years. The load side voltage variation solved by optimization technique is suitable for attain good result. The work consist of additional solar system provided with Maximum Power Point Tracking controlled by a Particle Swarm Optimization Technique and fuzzy, Artificial Neural Network based Proportional Integral control technique, which is used in the Unified Power Quality Conditioner, in order to balance and increase to maintain the load side power. Total harmonic distortion has decreased from an average of 2.77 percent for voltage to 1.16 percent, and from an average of 4.48 percent to 2.02 percent for current. Results of the simulation run using MATLAB software. [ABSTRACT FROM AUTHOR]

The present study explores the potential of magnetic field assisted finishing (MFAF) in precise finishing of femoral knee implant made of bio-titanium alloy at the nanometric level. The process parameters, viz. rotational speed of tool, feed rate and carbonyl iron particle (CIP) concentration of MFAF have significant influence on the final surface topography of implants and thus selection of optimal set of their values is a difficult but an important task. In the present work, central composite rotatable design of response surface methodology is considered for nanofinishing of titanium alloy based femoral knee implants. Based on the experimental results, a polynomial regression prediction model for surface roughness is established in terms of MFAF process parameters. The fuzzy logic model with the help of ANOVA analysis is employed to analyze the effect of individual and interaction of parameters on surface roughness of implants. Finally, for further optimizing the final surface roughness of knee implant and determining the best settings of MFAF parameters, an improved salp swarm algorithm (ISSA) is proposed by introducing two important enhancements in basic SSA. The results reveal that SSA and ISSA metaheuristics achieved an improvement in final surface finish of knee implant by 4.04% and 14.24%, respectively, in comparison to default experimental trials during finishing. [ABSTRACT FROM AUTHOR]

This paper presents the development and validation of five different soft computing methods for flotation performance prediction: (1) two models based on fuzzy logic (Mamdani and Takagi-Sugeno fuzzy inference system) and (2) three models based on artificial neural networks. Copper content in the ore feed, collector dosage in the rougher and the scavenger flotation circuits, slurry pH in the rougher flotation circuit and frother consumption were selected as input parameters to estimate the copper grade and recovery of final concentrate, as well as the copper content in the final tailings of the flotation plant. The training and evaluation of the proposed models were performed on the basis of real process data collected by the multiannual monitoring of industrial flotation plant operation in "Veliki Krivelj Mine". The results showed that the proposed soft computing-based models well describe the behavior of the industrial flotation plant in a wide range of circumstances. Among the proposed algorithms, artificial neural networks gave the most accurate predictions for the final copper concentrate grade and recovery (R2 = 0.98 and R2 = 0.99, respectively) and copper content in final tailings (R2 = 0.87). At some points, fuzzy logic models are almost equally efficient, but artificial neural networks had lower values for all error functions. [ABSTRACT FROM AUTHOR]

Fuzzy system is proven to be one of the well-effective approximation tool in soft computing techniques. The image de-noising in modern multimedia system is strongly demanded issue which has been focussed in this work and an optimal solution with the help of fuzzy inference technique has been provided. An improved and approximated anisotropic diffusion scheme has been proposed by using fuzzy based diffusion coefficient functions. The anisotropic diffusion has been redefined by formulating the diffusion coefficients in terms of degrees of noisiness of each pixel which tends to sufficiently smooth the impulse noisy pixels along with preservation of edge pixels. The proposed fuzzy rule based diffusion coefficient is applied in basic Perona–Malik diffusion as well as selective advanced diffusion scheme and tested on various standard images at different noise densities. The proposed diffusion scheme based on fuzzy rule shows the effective results on images having impulsive noise densities upto 50 % . [ABSTRACT FROM AUTHOR]

The article "Novel fuzzy logic expert system-based edge detection for X-ray images" by Kalaipriyan Thirugnanasambandam has been retracted by the publisher due to concerns about compromised editorial handling and peer review processes, inappropriate references, and being out of scope for the journal. The retraction was part of an investigation that found several articles with similar issues. The authors of the retracted article disagree with the decision to retract it. [Extracted from the article]