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Wireless sensor networks (WSNs) enable seamless data gathering and communication, facilitating efficient and real-time decision-making in IoT monitoring applications. However, the energy required to maintain communication in WSN-based IoT networks poses significant challenges, such as packet loss, packet drop, and rapid energy depletion. These issues reduce network life and performance, increasing the risk of delayed packet delivery. To address these challenges, this work presents a novel energy-efficient distributed neuro-fuzzy routing model executed in two stages to enhance communication efficiency and energy management in WSN-based IoT applications. In the first stage, nodes with high energy levels are predicted using a fusion of distributed learning with neural networks and fuzzy logic. In the second stage, clustering and routing are performed based on the predicted eligible nodes, incorporating thresholds for energy and distance with two combined metrics. The cluster head (CH) combined metric optimizes cluster head selection, while the next-hop combined metric facilitates efficient multi-hop communication. Extensive simulation results demonstrate that the proposed model significantly enhances network lifetime compared to EANFR, RBFNN T2F, and TTDFP by 9.48%, 25%, and 31.5%, respectively. [ABSTRACT FROM AUTHOR]

Predicting the number of COVID-19 cases offers a reflection of the future, and it is important for the implementation of preventive measures. The numbers of COVID-19 cases are constantly changing on a daily. Adaptive methods are needed for an effective estimation instead of traditional methods. In this study, a novel method based on neuro-fuzzy and FPA is proposed to estimate the number of COVID-19 cases. The antecedent and conclusion parameters of the neuro-fuzzy model are determined by using FPA. In other words, neuro-fuzzy training is carried out with FPA. The number of COVID-19 cases belonging to twenty countries including USA, India, Brazil, Russian, France, UK, Italy, Spain, Argentina, Germany, Colombia, Mexico, Poland, Turkey, Iran, Peru, Ukraine, South Africa, the Netherlands and Indonesia is estimated. Time series is created using the number of COVID-19 cases. Daily, weekly and monthly estimates are realized by utilizing these time series. MSE is used as the error metric. Although it varies according to the example and problem type, the best training error values between 0.000398027 and 0.0286562 are obtained. These best test error values are between 0.0005607 and 0.409867. The best training and test error values are 0.000398027 and 0.0005607, respectively. In addition to FPA, the number of cases is also predicted with the algorithms such as particle swarm optimization, harmony search, bee algorithm, differential evolution and their performances are compared. Success score and ranking are created for all algorithms. The scores of FPA for the daily, weekly and monthly forecast are 71, 77 and 62, respectively. These scores have shown that neuro-fuzzy training based on FPA is successful than other meta-heuristic algorithms for all three prediction types in the short- and medium-term estimation of COVID-19 case numbers. [ABSTRACT FROM AUTHOR]

Introduction Due to water resources limitations in the country, future decisions will be based on future status. In the present study, to investigate the effect of climate change on hydrological components (minimum temperature, maximum temperature, precipitation, and sunshine hours), the information of Kamalvand water gauging station from the headwaters of Kashkan River located in Khorramabad City and Khorramabad meteorological stations were used. For this purpose, taking into account the climate information by LARS-WG software, their values were predicted in the future under three scenarios including A1B, A2, and B1 in three time periods of 34 years from 2011 to 2113. Considering the limited water resources, making management decisions will require knowing the future state of water resources. This phenomenon can cause considerable damage in vulnerable areas. Therefore, as water and its related issues are among the main concerns of mankind in the coming periods, it is necessary to evaluate the occurrence of climate change and the extent of its impact on water resources. According to the importance of knowing the amount of river flow in hydrological studies and water resources management, and the lack of information about the changes in the amount of river flow in the coming years, this study was designed and implemented to predict the daily flow of the Kashkan River in Khorramabad City in the coming years. To achieve this purpose, with the application of the atmospheric general circulation model and various intelligent neural models, the prediction of river flow with high accuracy under different climate change scenarios was examined. Material and Methods This watershed forms an important part of the water-rich tributaries of the Karkhe River and covers about one- third of the land of Lorestan Province. In this study, the data of the Kamalvand River gauge station from the headwaters of the Keshkan River located in Khorram Abad have been used. In this study, relying on the ability of the artificial neural network, the application of this method was evaluated along with two hybrid models including neuro-fuzzy (CANFIS) and neuro-genetics (ANN-GA). The water crisis can be considered one of the challenges facing different regions of the country in the coming years. Managing water resources and dealing with the water shortage crisis requires knowing the state of hydrological components in the coming years. For this purpose, in this study, the status of meteorological parameters and the amount of river flow in the coming years were investigated. To achieve this goal, the capability and application of the LARS-WG model in forecasting meteorological parameters and intelligent neural models were used in river flow forecasting. Results and Discussion Based on the obtained results, the trend of increasing minimum and maximum temperature and evaporation and transpiration was predicted until 2113. Regarding the parameters of precipitation and solar radiation, a decrease in precipitation and an increase in radiation were predicted from 2080 to 2113. Comparing the performance of intelligent neural models in predicting river flow showed the superiority of the neural-fuzzy model over artificial neural and neuro-genetic models. river flow prediction with the neuro-fuzzy model until 2113 under scenarios A1B, A2, and B1 indicated that the lowest amount of river flow will be observed in scenario A1B and the highest amount will be observed in scenario B1. The temporal changes of the river flow during different seasons showed that the river flow will increase in spring, autumn, and winter. In general, according to the changes in meteorological parameters and the observed values of the river flow, the description of the changes in the river flow in scenario A1B was closer to reality. This makes it necessary to properly manage the river flow, especially in the summer season of 2080-2113. Results The results indicated that minimum and maximum temperatures and evapotranspiration during the next years will increase. In scenario A2, the precipitation changes trend was decreasing and solar radiation was increasing, however in other scenarios trend of increasing and decreasing. Then the discharge amount under different scenarios was calculated. The forecasting discharge values by intelligent models showed that the CANFIS model had more accuracy than the ANN and ANN-GA. The results of the optimized structure of CANFIS illustrated that the minimum discharge value in the future will occur in scenario A1B and the maximum discharge amount will be recorded in scenario B1. The evaluation of the seasonal trend showed that the flow rate increased in spring, autumn, and winter compared to the base period by 20.60, 17.31, and 9.27%, respectively. The lowest river flow in summer will occur under the AIB scenario during 2080-2113. rivers are one of the most important effective factors in the geomorphological processes of the earth and the hydrological cycle. Effective factors in very diverse hydrological processes and their applications in designed models are very difficult and the existence of high uncertainties and strong nonlinearity of the data complicates the issue. Long-term records of hydrological data show the temporal changes in discharge caused by climate change and vegetation changes. [ABSTRACT FROM AUTHOR]

The Adaptative neuro-fuzzy inference system (ANFIS) has shown great potential in processing practical data from control, prediction, and inference applications, reflecting advantages in both high performance and system interpretability as a result of the hybridization of neural networks and fuzzy systems. Matlab has been a prevalent platform that allows to utilize and deploy ANFIS conveniently. On the other hand, due to the recent popularity of machine learning and deep learning, which are predominantly Python-based, implementations of ANFIS in Python have attracted recent attention. Although there are a few Python-based ANFIS implementations, none of them are directly compatible with scikit-learn, one of the most frequently used libraries in machine learning. As such, this paper proposes Scikit-ANFIS, a novel scikit-learn compatible Python implementation for ANFIS by adopting a uniform format such as fit() and predict() functions to provide the same interface as scikit-learn. Our Scikit-ANFIS is designed in a user-friendly way to not only manually generate a general fuzzy system and train it with the ANFIS method but also to automatically create an ANFIS fuzzy system. We also provide four kinds of representative cases to show that Scikit-ANFIS represents a valuable addition to the scikit-learn compatible Python software that supports ANFIS fuzzy reasoning. Experimental results on four datasets show that our Scikit-ANFIS outperforms recent Python-based implementations while achieving parallel performance to ANFIS in Matlab, a standard implementation officially realized by Matlab, which indicates the performance advantages and application convenience of our software. [ABSTRACT FROM AUTHOR]

High levels of poverty in rural areas constitute one of the main challenges for developing countries. Since agricultural employment is the main source of income in these areas, the design of tools that simulate and help public policymakers will be remarkably useful. This work proposes the development of a model for agricultural employment in Colombia, considering input variables such as education, contract, and income, and the output is the amount of agricultural employment. Real data measured in Colombia are used for the design and adjustment of the model. To design the fuzzy system for an agricultural employment model, the methods employed are fuzzy C-means clustering and neurofuzzy systems. The systems were tested with different cluster configurations, and a fuzzy system was obtained with an adequate distribution of the fuzzy sets and the respective rules that relate the sets. It was observed that as the clusters increase, the adjustment function decreases. The implementation of neuro-fuzzy systems to model agricultural employment will allow public policymakers to generate guidelines that adjust to their political agendas with a lower degree of uncertainty. [ABSTRACT FROM AUTHOR]

High levels of poverty in rural areas constitute one of the main challenges for developing countries. Since agricultural employment is the main source of income in these areas, the design of tools that simulate and help public policymakers will be remarkably useful. This work proposes the development of a model for agricultural employment in Colombia, considering input variables such as education, contract, and income, and the output is the amount of agricultural employment. Real data measured in Colombia are used for the design and adjustment of the model. To design the fuzzy system for an agricultural employment model, the methods employed are fuzzy C-means clustering and neuro-fuzzy systems. The systems were tested with different cluster configurations, and a fuzzy system was obtained with an adequate distribution of the fuzzy sets and the respective rules that relate the sets. It was observed that as the clusters increase, the adjustment function decreases. The implementation of neuro-fuzzy systems to model agricultural employment will allow public policymakers to generate guidelines that adjust to their political agendas with a lower degree of uncertainty.

Abstract It has become increasingly difficult for medical practitioners to recognize illness in recent years due to the emergence of new diseases from their myriad causes on a daily basis. Due in large part to inadequate diagnostic and monitoring infrastructure, a substantial amount of illness and death are associated with lung cancer (LC). The aim of the paper is to find lung cancer early and help patients receive curative treatment. Quitting smoking or never starting is the best way to mitigate the potential for disease-related death. As a result, cutting-edge detection and monitoring technologies must be developed to enable rapid, accurate, and timely diagnosis. Fuzzy logic (FL) is one of the best approaches to modeling complex and uncertain systems; therefore, it helps us deal with these challenges. Fuzzy expert system for lung cancer [FES-LC] detection and prediction on Internet of medical things (IoMT) is employed to overcome the challenges. Hence, an enhanced adaptive neuro-fuzzy inference framework [ANF-IF] is proposed in the current research. The cloud-based application of an adaptive neuro-fuzzy inference system yields four risk categories: not at risk, slightly at risk, moderately at risk, and severely at risk. New methods and theoretical frameworks have made it possible to diagnose LC in its earliest stages with the help of magnetic nanoparticles (MNPs), which allow researchers to overcome the limitations of conventionally slow diagnostic efficiency. The proposed system exhibits a precision of 93.4%, accuracy of 95.1%, specificity of 90.6%, sensitivity of 92.8%, false positive rate of 0.22%, false negative ratio of 0.18%, and classification accuracy of 98.2%. The proposed method outperforms all methods and provides better lung cancer detection accuracy than others.

Live video broadcasting is a popular application properly considered in lately developed standard, Versatile Video Coding (H.266/VVC). In live video broadcasting, both the bandwidth and buffer volume are limited, while a high quality level is demanded. In order to solve these problems, a Quantization Parameter Estimation Algorithm (QEA) is proposed. The core of the proposed algorithm is a neuro-fuzzy system that changes the Quantization Parameter (QP) gradually to produce a bandwidth-compliant bit rate and prohibit buffer saturation and starvation while providing high quality. The estimation is conducted according to the proportional, integral, and derivative components of the bit error. In other words, the proposed QEA is a Proportional-Integral-Derivative (PID) controller. The optimal parameters of the neuro-fuzzy system are obtained through the training process. The required data set for the training process is established by taking advantage of dynamic programming. The experiments affirm that the proposed approach achieves the target rate with an average error equal to 1.41% and fully respects the buffering boundaries. This method has at least a 2.48% bit rate reduction rather than other QEAs. Meanwhile, the proposed QEA is faster than other algorithms. [ABSTRACT FROM AUTHOR]

The revolutionary future of proton-exchanging membrane fuel cells (PEMFC) has recently garnered a great deal of excitement, as has their green energy source. Maximizing the production of electricity from PEMFC is crucial to maintaining effectiveness. This research article thoroughly analyzes a research study using a strategy known as MPPT, or maximum power point tracking that uses the neuro-fuzzy method for PEMFC operating under diverse temperatures, pressures, and joining constraints. The neuro-fuzzy controller cleverly regulates the point of maximal operation of a hydrogen fuel cell system, allowing exact adherence to the highest possible power scale. According to simulation results, the neuro-fuzzy MPPT technique improves PEMFC validity across a wide range of operating scenarios.

Airplanes generate severe turbulent airflow, known as trailing vortices, which can have an adverse control impact on following airplanes. Aviation authorities worldwide impose separation standards between leading and following airplanes to reduce the capacity of airports to handle large volumes of traffic. Optimizing these separations is highly sought after. These separations are optimized based on several factors such as airplane size, weight, and design in addition to weather conditions. The recognition of trailing vortices is usually done manually through examination of pilot reports and analysis of flight data recorders (FDRs). This process can be subjective and depends greatly on the analyst's experience. Automated processes based on Artificial Intelligence (AI), such as Machine Learning (ML) and Computational Intelligence (CI), have shown a potential to better deal with such problems. Results obtained from these various techniques are presented to show their degree of appropriateness. Flight event identification, such as encounters with airplane vortices, wind shear, turbulence, and uncommanded control, can help establish aircraft warning and monitoring systems to enhance automatic flying, especially during landing and take-off. The developed model can attain great assurance recognition of the aircraft wake turbulence by supporting the air traffic controllers with the decision information in actual work to ensure aircraft safety. The success rates obtained from the investigated CI models compare favorably well to results obtained by either manual and/or other CI-based techniques when applied to vortex identification.

Abstract Parkinson's Disease (PD) is a complex, degenerative disease that affects nerve cells that are responsible for body movement. Artificial Intelligence (AI) algorithms are widely used to diagnose and track the progression of this disease, which causes symptoms of Parkinson's disease in its early stages, by predicting the results of the Unified Parkinson's Disease Rating Scale (UPDRS). In this study, we aim to develop a method based on the integration of two methods, one complementary to the other, Ensembles of Self-Organizing Map and Neuro-Fuzzy, and an unsupervised learning algorithm. The proposed method relied on the higher effect of the variables resulting from the analysis of the initial readings to obtain a correct and accurate preliminary prediction. We evaluate the developed approach on a PD dataset including speech cues. The process was evaluated with root mean square error (RMSE) and modified R square (modified R2). Our findings reveal that the proposed method is effective in predicting UPDRS outcomes by a combination of speech signals (measures of hoarseness). As the preliminary results during the evaluation showed numbers that proved the worth of the proposed method, such as UPDRS = 0.955 and RMSE approximately 0.2769 during the prediction process.

Neuro-fuzzy techniques have been widely used in many applications due to their ability to generate interpretable fuzzy rules. Ensemble learning, on the other hand, is an emerging paradigm in artificial intelligence used to improve performance results by combining multiple single learners. This paper aims to develop and evaluate a set of homogeneous ensembles over four medical datasets using hyperparameter tuning of four neuro-fuzzy systems: adaptive neuro-fuzzy inference system (ANFIS), Dynamic evolving neuro-fuzzy system (DENFIS), Hybrid fuzzy inference system (HyFIS), and neuro-fuzzy classifier (NEFCLASS). To address the interpretability challenges and to reduce the complexity of high-dimensional data, the information gain filter was used to identify the most relevant features. After that, the performance of the neuro-fuzzy single learners and ensembles was evaluated using four performance metrics: accuracy, precision, recall, and f1 score. To decide which single learners/ensembles perform better, the Scott-Knott and Borda count techniques were used. The Scott-Knott first groups the models based on the accuracy to find the classifiers appearing in the best cluster, while the Borda count ranks the models based on all the four performance metrics without favoring any of the metrics. Results showed that: (1) The number of the combined single learners positively impacts the performance of the ensembles, (2) Single neuro-fuzzy classifiers demonstrate better or similar performance to the ensembles, but the ensembles still provide better stability of predictions, and (3) Among the ensembles of different models, ANFIS provided the best ensemble results. [ABSTRACT FROM AUTHOR]

Parkinson's Disease (PD) is a complex, degenerative disease that affects nerve cells that are responsible for body movement. Artificial Intelligence (AI) algorithms are widely used to diagnose and track the progression of this disease, which causes symptoms of Parkinson's disease in its early stages, by predicting the results of the Unified Parkinson's Disease Rating Scale (UPDRS). In this study, we aim to develop a method based on the integration of two methods, one complementary to the other, Ensembles of Self-Organizing Map and Neuro-Fuzzy, and an unsupervised learning algorithm. The proposed method relied on the higher effect of the variables resulting from the analysis of the initial readings to obtain a correct and accurate preliminary prediction. We evaluate the developed approach on a PD dataset including speech cues. The process was evaluated with root mean square error (RMSE) and modified R square (modified R2). Our findings reveal that the proposed method is effective in predicting UPDRS outcomes by a combination of speech signals (measures of hoarseness). As the preliminary results during the evaluation showed numbers that proved the worth of the proposed method, such as UPDRS = 0.955 and RMSE approximately 0.2769 during the prediction process. [ABSTRACT FROM AUTHOR]

Conventional methods for estimating porosity from core data are often limited by their spatial coverage, time-consuming nature, high cost, and inability to capture the entire underground reservoir. To address these challenges, this paper proposes a soft computing method using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate porosity in a conventional gas reservoir. The approach involves integrating well-logging data and the ANFIS model with a Particle Swarm Optimisation (PSO) training algorithm to predict the underground porosity model in the Hassi R’mel region of the Algerian Sahara. The choice of this hybrid method was based on its superior performance compared to other models. Although the Hassi R’mel reservoirs are of Triassic clay sandstones, originated by the fluviatile depositional environment that lay on top of the Hercynian surface, the characterisation of their properties still requires refinement to improve the reservoir performance and address the problems faced using appropriate technologies. With an average porosity of 15% and permeability ranging from 250 to 650 mD, the ANFIS method shows excellent accuracy compared to core data, and a reliability of 85%. Overall, the ANFIS-PSO hybrid model proves to be a dependable and efficient technique for porosity prediction, surpassing traditional methods. [ABSTRACT FROM AUTHOR]

A neuro-fuzzy computing system combines neural network learning with fuzzy model technique and interpretability into a single unit. Over the past ten years, several neuro-fuzzy systems have been developed. Among these, the adaptive neuro-fuzzy inference system (ANFIS) gives the best design parameters in the least period of time and offers a logical and focused technique for modelling. They are well known for accurately imitating a range of real-world obstacles. Noise pollution's impact on hearing is one such topic that is regularly raised. Specific occupational hazards for laborers include noise, vibration, and low temperatures. Investigating the combined effects of these three physical dangers on employees' physiological markers was the goal of this study, so these three main factors impacting hearing impact are temperature, vibration, and noise intensity, according to the research review. These characteristics interact in complicated, unreliable, and nonlinear ways. Because of this, it is difficult to completely explore using conventional methods. This paper develops a neuro-fuzzy model to predict how noise pollution would affect hearing as a function of noise intensity, vibration, and temperature. On the Fuzzy Logic Toolbox in MATLAB, the model is implemented using ANFIS. The specific fuzzy model that the authors created was employed to assist gather the data for the current investigation. The input/output data sets were split into two groups: 30% were used to assess the model's validity, and the remaining 70% were used to prepare the model for usage. [ABSTRACT FROM AUTHOR]

Streamflow, a pivotal variable in water resources management, holds profound significance in shaping the decision-making processes of hydrologic projects. This paper tries to delve into the exploration of the stage-discharge relationship using three machine learning methods (MLMs) namely multi-layer neural networks (MLNN), radial basis neural networks (RBNN), and neuro-fuzzy systems (ANFIS) to predict and simulate mean daily stage-discharge data derived from two monitoring stations, Bulakbasi and Karaozü, Kizilirmak River, Turkey. Root mean square error (RMSE), Mean absolute percentage error (MAPE), coefficient of determination (R2), and the Developed Discrepancy Ratio (DDR) metrics were utilized to MLMs' performance assessment. The performance evaluation indices (RMSE, MAEP, R2, DDR) for the preeminent MLNN model applied to Bulakhbashi and Karasu stations were determined as (0.29, 1.57, 0.9998, 17.62) and (1.71, 6.56, 0.9980, 6.65), respectively. The MLNN model contributed to a notable enhancement in the RMSE performance index for the aforementioned stations, exhibiting improvements of 87% and 56%, respectively. These results affirm the MLNN's proficiency in accurately capturing the stage-discharge at both monitoring stations.

The capability of a neuro-fuzzy control approach for frequency fluctuation damping in an isolated hybrid microgrid (IHMG) system (DEG/WTG /PV/FC/ESSs) is investigated in this paper. Due to the intermittent behavior of renewable energy sources (RESs) like wind turbines and photovoltaic arrays and the time-varying nature of demands, frequency fluctuation is more likely, specifically in the grid-connected mode. Model parametric uncertainties as well as load changes, wind power, and solar irradiation variations are the main uncertainty sources of the IHMG system. In the suggested approach, a neuro-fuzzy output feedback controller with three inputs that are inspired by PID control is designed considering the power balance between demands and generations, by optimizing fuzzy membership functions’ locations. The proposed controller is compared with two popular other methods on the investigated IHMG system in terms of time-domain characteristics. The outcome illustrates remarkable merit compared to the state-of-the-art methods in the presence of simultaneous disturbances and the model parametric uncertainties.

The primary goal of this research paper is to inculcate AI approaches in thermal power plants to raise automation at power plants by expanding the integrated control idea to thermal power plants and implementing it using AI approaches. The recommended control method will be analysed and contrasted with established strategies. The crucial condition will be evaluated even though the suggested type of control system doesn't come up with an effective result. The development of the conceptual model is predicated on the state of power generation technologies. There will be no suggestion to use newer or different technology, such as fluidised bed combustion, sliding pressure units or ultra-supercritical plants, in the process of producing electricity. This study specifically focuses on the operational features of the control system instead of the hardware technologies. [ABSTRACT FROM AUTHOR]

Nowadays, spoken digit recognition is widely used for various applications. It is a technique used in automatic speech recognition to identify and classify the language spoken by a particular speaker. Several techniques and methods for spoken digit recognition have been proposed. Most of the method includes artificial neural networks, Gaussian mixture models and neuro-fuzzy approach. However, there needs to be a more efficient algorithm that gives more satisfactory and better results. Therefore, to enhance the results in terms of accuracy and running time, a hybrid adaptive neuro-fuzzy approach called KANFIS-SCG is proposed in this paper. This approach combines the Adaptive Network-Based Fuzzy Inference System (ANFIS) and a fast supervised learning algorithm called the Scaled Conjugate Gradient (SCG). In addition, the K-means clustering algorithm is used to reduce the number of fuzzy inference rules. The features are extracted from speech samples using the Mel Frequency Cepstral Coefficient (MFCC) algorithm. Furthermore, the covariance matrix provides more precise information about the features. The experiment was carried out in two languages, namely Hindi and English, and the database was prepared for both languages where utterances were digits from zero to nine. The proposed method KANFIS-SCG gives average recognition rates of 99.12% and 98.54% for Hindi and English, respectively, for the speaker-dependent system, which is better than the existing ANFIS method. [ABSTRACT FROM AUTHOR]

Fall armyworm (FAW) Spodoptera frugiperda (J. E. Smith) is a major pest affecting cereal production in Africa. Biological control (BC) technologies are being promoted as a sustainable alternative to chemical control, which can lead to health risks and environmental hazards. However, effective deployment of these technologies requires site-specific recommendations. In this study, we use a step-by-step modelling approach to map suitable sites for BC technologies, focusing on the parasitoid Cotesia icipe using the FAW level of infestation, the ecological niche of the parasitoid, and the FAW host crop. The level of pest infestation was estimated using an evolutionary adaptive Neuro-Fuzzy inference system (R2 > 0.89) while the pest ecological niche was obtained using the maximum entropy algorithm (area under curve, AUC > 0.9). A fuzzy operator was used to combine all fuzzified variables into a single layer that represents the landscape's overall suitability for C. icipe in maize farms. Our computational findings indicate that C. icipe holds substantial promise as a BC agent in maize farms, with suitability levels consistently surpassing 90% throughout maize cropping seasons. The findings demonstrate that the utilization of artificial intelligence, combined with data science and knowledge representation, serves as an effective advisory tool for guiding the deployment of BC agents, such as parasitoids, for the sustainable management of FAW. This approach enables informed decision-making and enhances the efficacy of FAW management strategies by providing valuable insights and recommendations based on data-driven and computer intelligence analyses.

Cloud computing provides tool-based IT services to customers worldwide. It is built on a fee-for-service model and enables the hosting of large programs from consumer, scientific, and commercial domains. However, cloud application hosting data centers have enormous energy consumption, which leads to high operating expenses and a carbon footprint. Cloud centers get a lot of requests from different users every day. The centers need strong servers that consume a lot of energy and peripherals in order to run in order to meet these needs. It should go without saying that reducing cloud center energy usage requires efficient resource utilization. We have taken a novel hybrid method in this study. Neural fuzzy systems have been employed for the dynamic resource allocation in the cloud with the goal of minimizing energy consumption and load prediction, and the ant colony optimization method has been utilized for the virtual machine's movement. The experimental results show that the proposed strategy works optimally, as compared to other methods in the literature that have the same objective. When compared to other comparable approaches, the suggested solution has an average resource loss of 23.1 % in 720 time periods and an average request denial rate that is 6.5 % lower. Moreover, extensive testing with the SPECpower benchmark has confirmed the proposed methodology, yielding a forecast accuracy of over 97 % for the proposed energy consumption model.

Pornography is a severe problem in Indonesia, apart from drugs. This can be seen based on data from the Ministry of Communication and Informatics in 2021 which found 1.1 million pornographic content online. The increasing number of access to pornographic content sites on the internet can prove this. Several studies have been conducted to produce preventive formulas. However, this research flow has not been effective in solving the problem. This is because the results of the identification value in the output image obtained are not quite right. This study proposes a procedure for identifying pornographic content in digital images as an alternative approach for the early stages of a destructive content access prevention system. The formulation uses the YCbCr color space to analyze human skin on image objects that represent exposed body parts and the classification process with the Neuro Fuzzy approach. The performance of this formula was tested on 100 digital images of random categories of human objects (usually covered, skimpy, and naked) taken from the internet. The test results are at a relatively good level of accuracy, with a weight of 70% for the entire test data.

In this study, the problems of accurate exemplification and a trustworthy test for judging DNA base-calling were discussed. The study of this problem was done by utilizing a model to gather data and develop a suitable design according to the problem's criteria. The height, peakness, and spacing of the first most likely candidate (the base), as well as the peakness and height of the second most likely candidate, are the three input variables in our model. By gathering data from base calling, these three attributes may be computed in the three subsystems and utilized in the main system to calculate the analysis value for each base in DNA sequencing. The MATLAB program was used to create this model. It's looking into applying the neuro fuzzy algorithm to solve the problem. These methods are delivering successful outcomes with high workplace performance and easy access to very precise data.

In recent times, the neural image captioning (NIC) is considered as a primitive problem artificial intelligence (AI) in which creates a connection between computer vision (CV) and natural language processing (NLP). However, recent attribute-based and textual semantic attention-based models in NIC still encounter challenges related to irrelevant concentration of the designed attention mechanism on the relationship between extracted visual features and textual representations of corresponding image's caption. Moreover, recent NIC-based models also suffer from the uncertainties and noises of extracted visual latent features from images which sometime leads to the disruption of the given image captioning model to sufficiently attend on the correct visual concepts. To solve these challenges, in this paper, we proposed an end-to-end integrated deep fuzzy-neural network with the unified attention-based semantic-enhanced vision-language approach, called as FuzzSemNIC. To alleviate noises and ambiguities from the extracted visual features, we apply a fused deep fuzzy-based neural network architecture to effectively learn and generate the visual representations of images. Then, the learnt fuzzy-based visual embedding vectors are combined with selective attributes/concepts of images via a recurrent neural network (RNN) architecture to incorporate the fused latent visual features into captioning task. Finally, the fused visual representations are integrated with a unified vision-language encoder–decoder for handling caption generation task. Extensive experiments in benchmark NIC-based datasets demonstrate the effectiveness of our proposed FuzzSemNIC model. [ABSTRACT FROM AUTHOR]

This work deals with the problem of choosing a controller for the production of biodiesel from the transesterification process through temperature control of the chemical reactor, from the point of view of automatic control, by considering such aspects as the performance metrics based on the error and the energy used by the controller, as well as the evaluation of the control system before disturbances. In addition, an improvement method is proposed via a neuro-fuzzy controller tuned with a metaheuristic algorithm to increase the efficiency of the chemical reaction in the reactor. A clear improvement is shown in the minimization of the integral of time multiplied squared error criterion (ITAE) performance index with respect to the proposed method (8.1657 × 10 4 ) in relation to the PID controller (7.8770 × 10 7 ). Moreover, the integral of the total control variation (TVU) performance index is also shown to evaluate the power used by the neuro-fuzzy controller (25.7697), while the PID controller obtains an index of (32.0287); this metric is especially relevant because it is related to the functional requirements of the system since it quantifies the variations of the control signal. [ABSTRACT FROM AUTHOR]

In this study, a hybrid method based on the marine predators algorithm (MPA) and adaptive neuro-fuzzy inference system (ANFIS) is presented to identify nonlinear systems exhibiting symmetrical or asymmetrical behavior. In other words, the antecedent and conclusion parameters of the ANFIS are adjusted by the MPA. The performance of the MPA is evaluated on eight nonlinear systems. The mean squared error is used as the error metric. Successful results were obtained on the eight systems. The best mean training error values belonging to the eight systems are 1.6 × 10−6, 3.2 × 10−3, 1.5 × 10−5, 9.2 × 10−6, 3.2 × 10−5, 2.3 × 10−3, 1.7 × 10−5, and 8.7 × 10−6. In the ANFIS training carried out to solve the related problems, the performance of the MPA was compared with the butterfly optimization algorithm, the flower pollination algorithm, moth–flame optimization, the multi-verse optimizer, the crystal structure algorithm, the dandelion optimizer, the RIME algorithm, and the salp swarm algorithm. The results have shown that the performance of the MPA mostly outperforms other algorithms in both training and testing processes. [ABSTRACT FROM AUTHOR]

This paper deals with the detection of single- and double-switching faults (open-circuit type "O-C") which appear in the inverter of a photovoltaic solar pumping system. Our system as a whole contains a photovoltaic module, a DC/DC step-up converter controlled by perturbation and observation maximum power point tracking technique, a three-phase DC/AC inverter controlled by the sinusoidal pulse width modulation technique, a three-phase induction motor, and a water pump. The used techniques to detect this type of faults are based on artificial intelligence (AI) (neural networks and neuro-fuzzy networks); we use AI as an observer to the inverter in order to detect the faults using extracted features from the inverter output currents. Both of the proposed fault diagnosis techniques show a good performance and high accuracy with less than ±5% of error for neuron-fuzzy and ±7% for artificial neural network and a response time of less than 0.1 s, which is a satisfying speed to detect the faults before a total degradation or any undesirable effects. This paper fulfills an identified need for faults diagnosis of a three-phase inverter in photovoltaic solar pumping systems using AI. The effectiveness of the AI techniques was evaluated for O-C fault detection by simulation tests using the MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

Nowadays, an enormous amount of data is available on the WWW and exponentially growing as well. A lot of users use social networking websites such as Twitter, Facebook, Instagram, and Google+ as common platforms for sharing and exchanging views and opinions on any topics/events. The researchers have considered the reviews and views of the users on these platforms for sentiment analysis, opinion mining, question answering, text summarization, etc. The paper proposes a novel approach for identifying reviews or opinion of users having sarcasm in the text patterns at the clause level. The sentences are classified into four categories such as Simple Sentences, Compound Sentences, Complex Sentences, and Compound-Complex Sentences based on the rules derived from a decision tree. The Simple Sentences and Complex Sentences alone are considered for analysing the sentence patterns where a positive sentiment contrasts with a negative polarity and vice-versa. The decision tree and neuro-fuzzy rules are used on sentence structures to classify the sentences into sarcastic and non-sarcastic sentence patterns. Membership functions are used to map the fuzzy rules and linguistic grading is used for grading the sarcastic patterns. The proposed approach is evaluated on Twitter Dataset and found that the results are promising with the recent and relevant work. [ABSTRACT FROM AUTHOR]

Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-consuming and it is inaccurate due to inconstancies in microbial multiplicity. In this study, the applicability of four hybrid neuro-fuzzy (ANFIS) methods, ANFIS with genetic algorithm (GA), ANFIS with particle swarm optimization (PSO), ANFIS with sine cosine algorithm (SCA), and ANFIS with marine predators algorithm (MPA), was investigated in predicting BOD using distinct input combinations such as potential of hydrogen (pH), dissolved oxygen (DO), electrical conductivity (EC), water temperature (WT), suspended solids (SS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (T-P) acquired from two river stations, Gongreung and Gyeongan, South Korea. The applicability of multi-variate adaptive regression spline (MARS) in determination of the best input combination was examined. The ANFIS-MPA was found to be the best model with the lowest root mean square error and mean absolute error and the highest determination coefficient. It improved the root mean square error of ANFIS-PSO, ANFIS-GA, and ANFIS-SCA models by 13.8%, 12.1%, and 6.3% for Gongreung Station and by 33%, 25%, and 6.3% for Gyeongan Station in the test stage, respectively. [ABSTRACT FROM AUTHOR]

The gabion weirs serve the same functions that their counterpart impervious weirs do. However, they have the advantage of being eco-friendly, more stable, and economical in low to medium-head cases. Dissolved oxygen is one of the major determinants for the assessment of the purity of water. The purpose of the present work is to illustrate the comparison of multiple linear regression (MLR), neural network (NN), neuro-fuzzy system (NFS), deep neural network (DNN), and reported empirical models for the prediction of gabion weir aeration performance efficiency (APE20) with experimental results which are collected from the laboratory test. The NFS with four shaped membership functions, NN, DNN, MLR, and existing empirical models, are generated with the same input parameters, and their potentials are assessed to statistical appraisal indices. The results show that the DNN with the highest value of R2 (0.935) and NSE (0.934) and having the least errors in validating phase is the outperforming proposed model in the prediction of the APE20, which the NN model follows with R2 (0.917) and NSE (0.917). However, except trapezoidal shaped NFS model with R2 (0.873) and NSE (0.852) and MLR with R2 (0.905) and NSE (0.897), the remaining models of NFS-based and empirical relations could not perform better in validating phase. The sensitivity performance test is too conducted to find the relative relevance of the input parameter on the results of the APE20, where discharge per unit width (q) is found to be the most significant parameter, followed by the drop height (H0).

Neuro-fuzzy systems combine the abilities of both artificial neural networks and fuzzy systems. They are easily trainable and provide a certain level of interpretability. Their performance has been assessed in different application domains and many attempts have been made to improve it using ensemble learning. However, to the best of our knowledge, no study has investigated the performance of heterogeneous neuro-fuzzy ensembles in a medical context. In this study, we constructed, evaluated, and compared the performance of 26 heterogeneous neuro-fuzzy ensembles on four medical datasets. The five single classifiers used were based on the Takagi-Sugeno-Kang (TSK) and Mamdani fuzzy inference systems. The metrics employed to measure the performance of the ensemble classifiers were the accuracy, precision, and recall. Additionally, the Borda count method and Scott-Knott statistical test were used to rank and cluster the classifiers, respectively. The results show that ensemble classifiers rarely outperform their base classifiers. Moreover, ensembles composed of TSK base classifiers performed best. In addition, we noticed that ensembles comprising four base learners achieved the best performance. Finally, no ensemble classifier managed to score high-performance values across the four datasets.

The immune system goes through a profound transformation during pregnancy, and certain unexpected maternal complications have been correlated to this transition. The ability to correctly examine, diagnoses, and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is scalable. Many approaches for disease diagnosis/classification have been established with the use of data mining concepts. However, such methods do not provide an appropriate classification/diagnosis model. Furthermore, single learning approaches are used to create the bulk of these systems. Classification issues may be made more accurate by combining predictions from many different techniques. As a result, we used the Ensembling of Neuro-Fuzzy (E-NF) method to perform a high-level classification of medical diseases. E-NF is a layered computational model with self-learning and self-adaptive capabilities to deal with specific problems, such as the handling of imprecise and ambiguous data that may lead to uncertainty concerns that specifically emerge during the classification stage. Preprocessing data, Training phase, Ensemble phase, and Testing phase make up the complete procedure for the suggested task. Data preprocessing includes feature extraction and dimensionality reduction. Besides such processes, the training phase includes the fuzzification process of medical data. Moreover, training of input data was done using four types of NF techniques: Fuzzy Adaptive Learning Control Network (FALCON), Adaptive Network-based Fuzzy Inference System (ANFIS), Self Constructing Neural Fuzzy Inference Network (SONFIN) and/Evolving Fuzzy Neural Network (EFuNN). Later, in the ensemble phase, all the NF methods' predicted outcomes are integrated, and finally, the test results are evaluated in the testing phase. The outcomes indicate that the method could predict impaired glucose tolerance, preeclampsia, gestational hypertensive abnormalities, bacteriuria, and iron deficiency anaemia better than the others. In addition, the model exposed the capability to be utilized as an autonomous learning strategy, specifically in the early stages of pregnancy, examinations, and clinical guidelines for disease interventions. [ABSTRACT FROM AUTHOR]

The ongoing effort to create methods for detecting and quantifying fatigue damage is motivated by the high levels of uncertainty in present fatigue-life prediction approaches and the frequently catastrophic nature of fatigue failure. The fatigue life of high strength aluminum alloy 2090-T83 is predicted in this study using a variety of artificial intelligence and machine learning techniques for constant amplitude and negative stress ratios (R = -1). Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM), a random forest model (RF), and an extreme-gradient tree-boosting model (XGB) are trained using numerical and experimental input data obtained from fatigue tests based on a relatively low number of stress measurements. In particular, the coefficients of the traditional force law formula are found using relevant numerical methods. It is shown that, in comparison to traditional approaches, the neural network and neuro-fuzzy models produce better results, with the neural network models trained using the boosting iterations technique providing the best performances. Building strong models from weak models, XGB helps to predict fatigue life by reducing model partiality and variation in supervised learning. Fuzzy neural models can be used to predict the fatigue life of alloys more accurately than neural networks and traditional methods. [ABSTRACT FROM AUTHOR]

The aim of this research is to manage the voltage of an active distribution grid with a low X/R ratio and multiple Photovoltaic Distributed Generators (PVDGs) operating under varying conditions. This is achieved by providing a methodology for coordinating three voltage-based controllers implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The first controller is for the On-Line Tap Changer (OLTC), which computes its adequate voltage reference. Whereas the second determines the required Active Power Curtailment (APC) setpoint for PVDG units with the aim of regulating the voltage magnitude and preventing continuous tap operation (the hunting problem) of OLTC. Finally, the last component is an auxiliary controller designed for reactive power adjustment. Its function is to manage voltage at the Common Coupling Point (CCP) within the network. This regulation not only aids in preventing undue stress on the OLTC but also contributes to a modest reduction in active power generated by PVDGs. The algorithm coordinating between these three controllers is simulated in MATLAB/SIMULINK and tested on a modified IEEE 33-bus power distribution grid (PDG). The results revealed the efficacy of the adopted algorithm in regulating voltage magnitudes in all buses compared to the traditional control method. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has led to the deployment of many battery-powered sensors in various applications to gather, process, and analyze meaningful data. Clusters of sensors provide for more efficient data collection and increased scalability in such contexts. A low-latency, long-lived routing strategy is described for WSNs that can connect to the Internet of Things. In this research, we present a neuro-fuzzy approach to energy-efficient routing (NFEER) for IoT-enabled WSNs. The novelty of the proposed algorithms is the multiple parameters for the routing in IoT-enabled WSN as consideration of CH distance to sink, cluster size, and residual energy of CH. These variables are used to find the most efficient path across the network, which will help mitigate the hotspot issue. During the operation on the condition "consider only those nodes which have energy greater than the pre-defined threshold energy," the NFEER relies on energy thresholds to restrict the set of candidate nodes. Extensive simulations are performed to specify the effectiveness of the NFEER, and it elongates stability period by 27.98%, 13.97%, and 10.91% as compared to existing protocols. The stability duration, residual energy, network lifetime, and throughput are enhanced by the proposed method as compared to PSO-Kmean, BMHGA, and FSO-PSO. [ABSTRACT FROM AUTHOR]

Aeration is a physical process through which air is brought into contact with water, and thereby both times of contact and contact area between water and air are enhanced. Gabion weirs are eco-friendly alternative structures having the least negative environmental impact. The paper examines the performance of the neuro-fuzzy, neural network, and Gaussian process regression (GPR) models in estimating the gabion weir oxygen aeration efficiency using the test data sets with regards to the evaluation indicators. The model utilizes non-dimensional inputs: Froude number, Reynolds number, the ratio of mean size to weir length, and porosity of gabion weir particles. The GPR with normalized poly kernel model having the highest value of correlation coefficient (CC), 0.9132, and value of root mean square error (RMSE), 0.392, outperforms all applied models and is followed by the neural network model with CC 0.8952 and RMSE 0.398. The neuro-fuzzy triangular shape with CC 0.8865 and RMSE 0.07576 is an equally performing model; nevertheless, all applied AI-based models are mostly performing well, but all mathematical models are exceptionally performing poorly except the nonlinear model with CC 0.8612 and RMSE 0.0029. To assess the relative relevance of input parameters on the output results, sensitivity and parametric analysis are also performed. [ABSTRACT FROM AUTHOR]