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Food safety is critical in protecting consumers from foodborne diseases. The public currently classifies and determines food ingredients and their quality based on appearance, aroma, and other characteristics. Existing food inspection machines often focus on single characteristics, resulting in incomplete and inaccurate information. Hence, developing methods that analyse multiple characteristics is necessary for high-accuracy classification. This research proposed an effective stacked ensemble deep transfer learning algorithm using eight popular transfer learning algorithms as a base classifier and combining them with the Adaptive Neuro-Fuzzy Inference System as a meta-classifier to analyse imaging, odour, and capacitive sensing approaches. Twenty-four food samples classified according to freshness, maturity, ripeness, and disease levels were analysed using the proposed stacked ensemble EfficientNet algorithm, achieving the highest accuracy rate of 0.916 and 0.933 in food ingredient classification and quality determination, respectively. This research demonstrated the system's reliability for deployment in classifying food ingredients in dishes. [ABSTRACT FROM AUTHOR]

Seismic designs and numerical analyses required fundamental parameters such as damping ratio and shear modulus. In this study, large-scale triaxial cyclic tests were used to investigate the dynamic properties of limestone ballast and electric arc furnace (EAF) slag ballast. The term ‘shear stiffness’ is typically reported in a normalized form using shear modulus. As the laboratory test results showed, an increase in confining pressure, loading frequency and anisotropy raises the shear modulus of materials. Shear modulus and damping ratio values do not appear to be significantly affected by an increase in loading cycles. Loading frequency plays the most significant role in changing damping ratio values. An adaptive neuro-fuzzy inference system (ANFIS) was also used to predict the normalized shear modulus and the damping ratio in this study. The results of the developed model were consistent with those of the laboratory tests. Moreover, the relations among the dynamic properties were estimatedly determined using the nonlinear regression method. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) methods, including Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs), Support Vector Machines (SVMs), Deep Learning (DL), Genetic Programming (GP) and Hybrid Algorithms, have proven to be important tools for accurate groundwater level (GWL) modelling. Through an analysis of the results obtained in numerous articles published in high-impact journals during 2001–2023, this comprehensive review examines each method's capabilities, their combinations, and critical considerations about selecting appropriate input parameters, using optimisation algorithms, and considering the natural physical conditions of the territories under investigation to improve the models' accuracy. For example, ANN takes advantage of its ability to recognise complex patterns and non-linear relationships between input and output variables. In addition, ANFIS shows potential in processing diverse environmental data and offers higher accuracy than alternative methods such as ANN, SVM, and GP. SVM excels at efficiently modelling complex relationships and heterogeneous data. Meanwhile, DL methods, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), are crucial in improving prediction accuracy at different temporal and spatial scales. GP methods have also shown promise in modelling complex and nonlinear relationships in groundwater data, providing more accurate and reliable predictions when combined with optimisation techniques and uncertainty analysis. Therefore, integrating these methods and optimisation techniques (Hybrid Algorithms), tailored to specific hydrological and hydrogeological conditions, can significantly increase the predictive capability of GWL models and improve the planning and management of water resources. These findings emphasise the importance of thoroughly understanding (a priori) the functionalities and capabilities of each potentially beneficial AI-based methodology, along with the knowledge of the physical characteristics of the territory under investigation, to optimise GWL predictive models. [ABSTRACT FROM AUTHOR]

The surging global population strains resources, escalating pollution, and exacerbating water scarcity. Sustainable water management necessitates alternative sources such as abandoned mine water. However, these sources often contain hazardous heavy metals, like lead, copper, iron, and manganese, posing grave health and environmental risks. Conventional methods struggle to effectively treat these heavy metals in abandoned mining ponds, urging the search for cost‐efficient and sustainable solutions. Biochar, particularly from spent mushroom compost (SMC), emerges as a potent adsorbent due to its high surface area and binding groups. Yet, the variability in its efficiency remains a challenge. Conventional empirical models fail to capture the dynamic nature of adsorption processes accurately. Adopting machine learning, specifically an adaptive neuro‐fuzzy inference system (ANFIS), shows potential in predicting adsorption efficiency. This study aims to employ ANFIS to forecast SMC biochar's performance in a lab‐scale metal retention pond, providing design charts for diverse initial metal concentrations and pH levels. Validation through real cases aims to enhance accuracy and establish a framework for future heavy metal adsorption capacities. This research offers a sustainable approach to removing heavy metals from abandoned mining ponds whereas the computational modeling in optimizing SMC biochar introduces a novel approach for practical applications. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have assumed prominence due to their enhanced performance, efficiency, and zero carbon emission. This paper proposes an efficient adaptive neuro-fuzzy inference system (ANFIS) based fractional order PID (FOPID) controller for an EV speed tracking control driven by a DC motor. The optimal controller parameters of the FOPID controller are found via an Ant Colony Optimization (ACO) method. The ANFIS controllers are well trained, tested, and validated using the data set sextracted from the fuzzy-based controllers. The performance and accuracy of the ANFIS model are evaluated using statistical parameters such as mean square error (MSE), coefficient of correlation (R), and root mean square error (RMSE). The controller performance, energy consumption, and robustness are tested using the new European drive cycle (NEDC) test. The efficacy of the ANFIS-based controller is demonstrated by comparing its performance with properly tuned fuzzy-based controllers. The proposed controller shows robustness towards external disturbances and offers promising EV speed regulation control. The comparative results illustrate the superior performance of ANFIS-based FOPID controller with high prediction and low error rates. MATLAB- Simulink platform is used for system modeling, controller design, and numerical simulation.

College students are learning a foreign language must know how to translate the spoken or written content from the respective language into English. These approaches do not help the college students to develop the capacity for rational thinking and adequate the motivation for the English translation. The educational principles are not in line with the qualities of the students in the typical English translation classroom teaching, and the teaching methods are out-dated. In the older process of the teaching English translation, many unreliable, vague aspects need to be considered, such as recognizing students' fundamental English knowledge, unique circumstances, language proficiency, cultural differences, and the ambiguity of the source language. The main issue with the current English translation evaluation methodology is that it cannot be easily to deal with thecomplex fuzzy indices when judging the accuracy of the student translations. An algorithm named FCAM-AHP-ANFIS is proposed to provide an effective and accurate method for evaluating and predicting students' English translation outcomes to overcome the traditional shortcomings. According to the proposed approach, students can learn about passive translation, but they may struggle to actively improve their translation skills. College students can benefit from the decision-making aid provided by the extensive evaluation technique due to its high availability and precision. The fundamental benefit of the fuzzy technique over more traditional forms of the assessment is that it accounts for the ambiguity and uncertainty of the making judgments at the human level and provides a coherent framework that includes the indistinct findings of the several steps in evaluating an English translation. The Fuzzy Comprehensive Assessment Model (FCAM) is a decision-making method that uses the fuzzy logic to assess the quality of English translations among the college students. The Analytic Hierarchy Process (AHP) is employed to calculate each criterion's relative importance and determine the optimal weighting for each criterion utilized in the assessment model. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to analyze the translated data and generate predictions for the students' translation outcomes. The experimental outcomes show the accuracy of the English translation assessment scores are 95.6% with 97% precision, 96% recall, and 96.5% of f1-score metric in addition to Root Mean Square (RMSE) and Mean Absolute Error (MAE). [ABSTRACT FROM AUTHOR]

Context: Electroporation is a technique that creates electrically generated pores in the cell membrane by modifying transmembrane potential. In this work, the finite element method (FEM) was used to examine the induced transmembrane voltage (ITV) of a spherical-shaped MCF-7 cell, allowing researchers to determine the stationary ITV. A greater ITV than the critical value causes permeabilization of the membrane. Furthermore, the present study shows how a specific surface conductivity can act as a stand-in for the thin layer that constitutes a cell membrane as the barrier between extracellular and intracellular environments. Additionally, the distribution of ITV on the cell membrane and its maximum value were experimentally evaluated for a range of applied electric fields. Consequently, the entire cell surface area was electroporated 66% and 68% for molecular dynamics (MD) simulations and FEM, respectively, when the external electric field of 1500 V/cm was applied to the cell suspension using the previously indicated numerical methods. Furthermore, the lipid bilayers' molecular structure was changed, which led to the development of hydrophilic holes with a radius of 1.33 nm. Applying MD and FEM yielded threshold values for transmembrane voltage of 700 and 739 mV, respectively. Method: Using MD simulations of palmitoyloleoyl-phosphatidylcholine (POPC), pores in cell membranes exposed to external electric fields were numerically investigated. The dependence on the electric field was estimated and developed, and the amount of the electroporated cell surface area matches the applied external electric field. To investigate more, a mathematical model based on an adaptive neuro-fuzzy inference system (ANFIS) is employed to predict the percent cell viability of cancerous cells after applying four pulses during electroporation. For MD simulations, ArgusLab, VMD, and GROMACS software packages were used. Moreover, for FEM analysis, COMSOL software package was used. Also, it is worth mentioning that for mathematical model, MATLAB software is used. [ABSTRACT FROM AUTHOR]

In the era of artificial cognizance, context-aware decision-making problems have attracted significant attention. Contextual bandit addresses these problems by solving the exploration versus exploitation dilemma faced to provide customized solutions as per the user's liking. However, a high level of accountability is required, and there is a need to understand the underlying mechanism of the black box nature of the contextual bandit algorithms proposed in the literature. To overcome these shortcomings, an explainable AI (XAI) based FuzzyBandit model is proposed, which maximizes the cumulative reward by optimizing the decision at each trial based on the rewards received in previous observations and, at the same time, generates explanations for the decision made. The proposed model uses an adaptive neuro-fuzzy inference system (ANFIS) to address the vague nature of arm selection in contextual bandits and uses a feedback mechanism to adjust its parameters based on the relevance and diversity of the features to maximize reward generation. The FuzzyBandit model has also been empirically compared with the existing seven most popular art of literature models on four benchmark datasets over nine criteria, namely recall, specificity, precision, prevalence, F1 score, Matthews Correlation Coefficient (MCC), Fowlkes--Mallows index (FM), Critical Success Index (CSI) and accuracy. [ABSTRACT FROM AUTHOR]

The manufacturing industry constantly aims to improve product quality while improving production speed and lowering production costs. Resistance spot welding (RSW) is widely used in the automotive industry to join thin sheets of coated and uncoated materials. Manufacturers measure weld quality by performing destructive tests like peel and with the help of metallographic examination, which is time-consuming. Further, critical welding parameters need to be optimized to achieve consistent and predictable weld quality. This work addresses the effects of the three critical welding parameters: welding current, welding time, and electrode force on RSW of 1.40-mm-thick DP780 steel sheets. The weld quality indicators studied are nugget diameter (from the peel test), peel strength, tensile shear strength, and the mean dynamic contact resistance. Artificial neural network and adaptive neuro-fuzzy inference system models were used to predict the weld quality indexes, and the prediction accuracy was found to be 99.36 and 99.98%, respectively. A mathematical model was developed using regression analysis to correlate the welding parameters and weld quality indicators. The multi-objective optimization of the welding parameters was done using the genetic algorithm, and its results were validated experimentally. It was found that the welding current had the most significant impact on the weld quality, followed by the electrode force and the welding time. [ABSTRACT FROM AUTHOR]

The removal of reactive red M-2BE dye textile from aqueous solution was performed using multi-walled carbon nanotubes (MWCN) and powdered activated carbon (PAC). Kinetic adsorption modeling has been performed using machine learning (ML) algorithms of artificial neural networks, adaptive-neuro fuzzy inference system (ANFIS), random forest, gradient boosting, and support vector machine. Although ML models are more accurate, they often fail to interpret the reasoning behind predictions. Therefore, the SHapley Additive exPlanations (SHAP) were used to understand the effect of each feature on the adsorption capacity. The ANFIS has presented the best statistical metrics with R = 0.9993 , R M S E = 0.0214 , and S A E = 7.1172 . A higher adsorption capacity was observed for MWCN compared to PAC; while the first peaked at 300 mg L−1, the second approached 230 mg L−1. Temperature was found to have the smallest contribution in describing adsorption capacity. This novel application of ML with SHAP can provide important insights for adsorption researchers. [ABSTRACT FROM AUTHOR]

There is an imperative need to eliminate pharmaceutical residues from aquatic environments due to their hazardous properties, including toxicity, mutagenicity, and carcinogenicity, particularly when present in water sources. Conventional water treatment methods have proven insufficient in addressing nano-pollutants such as pharmaceutical residues. Consequently, the ongoing quest for economically viable, sustainable, and environmentally friendly removal mechanisms persists. In this particular study, we employed Giant Macroporous Silica (GMS) derived from marine sponges as a promising biosorbent. GMS exhibits commendable characteristics, including a high specific surface area, swift mass transfer capabilities, and non-discriminatory adsorption qualities. The efficacy of GMS in adsorbing carbamazepine (CBZ), a common drug residue, was scrutinized under diverse experimental conditions, including a sorbate/sorbent ratio ranging from 0.005 to 1.500 weight ratio, contact times spanning from 0 to 240 min, and initial pH values ranging from 5 to 9. Remarkably, at a concentration of 1000 µg L−1, GMS demonstrated an attractive adsorption rate (98.88%) of carbamazepine at pH 7.07, within 90 min. To enhance our understanding, we developed an ANFIS model utilizing the experimental parameters as inputs. The developed model exhibited a high correlation coefficient of 0.9944% and a root mean square error (RMSE) of 1.6693, indicating its dependability in accurately predicting the adsorption of CBZ on GMS. The results of our study highlight the efficacy of GMS in adsorbing CBZ, suggesting its considerable potential for adsorbing other pharmaceutical residues and nano-pollutants. Furthermore, we propose the possibility of developing a solid-phase extraction cartridge from GMS. [ABSTRACT FROM AUTHOR]

Dynamic parameters are the most important geotechnical data used to understand the behavior of soil media under dynamic loads and to recognize the seismic response of the soil. Several in-situ and laboratory geophysical tests, such as the down-hole test, are used to determine these parameters. Since this experiment is costly and time-consuming and the preparation of appropriate boreholes is not easy, it is preferable to estimate the results of this test with the help of empirical correlations or experimental models. The main output of the down-hole test is the shear wave velocity (VS) of soils, which can be used to obtain the dynamic shear modulus (Gs) indirectly. The relationship between physical properties and mechanical specifications of soils is a well-known principle of geotechnical engineering. Utilizing the results of 19 down-hole experiments and available geotechnical data in the southern regions of Tehran, as well as the inputs of an adaptive neuro-fuzzy inference system (ANFIS). This study attempts to provide practical models to predict shear wave velocity of fine-grained soils in Tehran. Two new models have been proposed as a result of preprocessing and smart modeling. The independent variables of the first suggested model included the moisture content, plasticity index (PI), liquid limit (LL), depth of test, and grain size distribution of soils. In the second model, the number of standard penetration test (NSPT) is also used in addition to the mentioned independent variables. The proposed models had coefficients of determination (R²) of 0.74 and 0.8 for the total training and validation data, respectively. [ABSTRACT FROM AUTHOR]

Landslides pose significant risks to human lives and infrastructure. The Medea region in Algeria is particularly susceptible to these destructive events, which result in substantial economic losses. Despite this vulnerability, a comprehensive landslide map for this region is lacking. This study aims to develop a novel hybrid metaheuristic model for the spatial prediction of landslide susceptibility in Medea, combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) with four novel optimization algorithms (Genetic Algorithm—GA, Particle Swarm Optimization—PSO, Harris Hawks Optimization—HHO, and Salp Swarm Algorithm—SSA). The modeling phase was initiated by using a database comprising 160 landslide occurrences derived from Google Earth imagery; field surveys; and eight conditioning factors (lithology, slope, elevation, distance to stream, land cover, precipitation, slope aspect, and distance to road). Afterward, the Gamma Test (GT) method was used to optimize the selection of input variables. Subsequently, the optimal inputs were modeled using hybrid metaheuristic ANFIS techniques and their performance evaluated using four relevant statistical indicators. The comparative assessment demonstrated the superior predictive capabilities of the ANFIS-HHO model compared to the other models. These results facilitated the creation of an accurate susceptibility map, aiding land use managers and decision-makers in effectively mitigating landslide hazards in the study region and other similar ones across the world. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned for its challenging features, supports the framework's resilience and adaptability. The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and random forest (RF), for data training. In contrast, a DL-based model utilizes one dimensional convolution neural network (1dCNN) to automate feature extraction. Furthermore, the recursive feature elimination (RFE) algorithm, which drives an ML-based estimator to eliminate low-participation features, helps identify the optimal features for enhancing model performance. The best accuracies of the ANFIS, SVM, RF, and 1dCNN models with meticulous featuring process achieve around 90%, 96%, 91%, and 93%, respectively. Comparative analysis using the MHEALTH dataset showcases the 1dCNN model's remarkable perfect accuracy (100%), while the RF, SVM, and ANFIS models equipped with selected features achieve accuracies of 99.8%, 99.7%, and 96.5%, respectively. Finally, when applied to the WISDM dataset, the DL-based and ML-based models attain accuracies of 91.4% and 87.3%, respectively, aligning with prior research findings. In conclusion, the proposed framework yields HAR models with commendable performance metrics, exhibiting its suitability for integration into the healthcare services system through AI-driven applications. [ABSTRACT FROM AUTHOR]

Introduction: Modeling plays a crucial role in understanding wastewater treatment processes, yet conventional deterministic models face challenges due to complexity and uncertainty. Artificial intelligence offers an alternative, requiring no prior system knowledge. This study tested the reliability of the Adaptive Fuzzy Inference System (ANFIS), an artificial intelligence algorithm that integrates both neural networks and fuzzy logic principles, to predict effluent Biochemical Oxygen Demand. An important indicator of organic pollution in wastewater. Materials and Methods: The ANFIS models were developed and validated with historical wastewater quality data for the Kauma Sewage Treatment Plant located in Lilongwe City, Malawi. A Self Organizing Map (SOM) was applied to extract features of the raw data to enhance the performance of ANFIS. Cost-effective, quicker, and easier-to-measure variables were selected as possible predictors while using their respective correlations with effluent. Influents' temperature, pH, dissolved oxygen, and effluent chemical oxygen demand were among the model predictors. Results and Discussions: The comparative results demonstrated that for the same model structure, the ANFIS model achieved correlation coefficients (R) of 0.92, 0.90, and 0.81 during training, testing, and validation respectively, whereas the SOM-assisted ANFIS Model achieved R Values of 0.99, 0.87 and 0.94. Overall, despite the slight decrease in R-value during the testing stage, the SOM- assisted ANFIS model outperformed the traditional ANFIS model in terms of predictive capability. A graphic user interface was developed to improve user interaction and friendliness of the developed model. Integration of the developed model with supervisory control and data acquisition system is recommended. The study also recommends widening the application of the developed model, by retraining it with data from other wastewater treatment facilities and rivers in Malawi. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have assumed prominence due to their enhanced performance, efficiency, and zero carbon emission. This paper proposes an efficient adaptive neuro-fuzzy inference system (ANFIS) based fractional order PID (FOPID) controller for an EV speed tracking control driven by a DC motor. The optimal controller parameters of the FOPID controller are found via an Ant Colony Optimization (ACO) method. The ANFIS controllers are well trained, tested, and validated using the data set sextracted from the fuzzy-based controllers. The performance and accuracy of the ANFIS model are evaluated using statistical parameters such as mean square error (MSE), coefficient of correlation (R), and root mean square error (RMSE). The controller performance, energy consumption, and robustness are tested using the new European drive cycle (NEDC) test. The efficacy of the ANFIS-based controller is demonstrated by comparing its performance with properly tuned fuzzy-based controllers. The proposed controller shows robustness towards external disturbances and offers promising EV speed regulation control. The comparative results illustrate the superior performance of ANFIS-based FOPID controller with high prediction and low error rates. MATLAB- Simulink platform is used for system modeling, controller design, and numerical simulation. [ABSTRACT FROM AUTHOR]

This study employs an adaptive neuro-fuzzy inference system (ANFIS) to identify critical success factors (CSFs) crucial for the success of pavement construction projects. Challenges such as construction cost delays, budget overruns, disputes, claims, and productivity losses underscore the need for effective project management in pavement projects. In contemporary construction management, additional performance criteria play a vital role in influencing the performance and success of pavement projects during construction operations. This research contributes to the existing body of knowledge by comprehensively identifying a multidimensional set of critical success performance factors that impact pavement and utility project management. A rigorous literature review and consultations with pavement experts identified sixty CSFs, categorized into seven groups. The relative importance of each element and group is determined through the input of 287 pavement construction specialists who participated in an online questionnaire. Subsequently, the collected data undergo thorough checks for normality, dependability, and independence before undergoing analysis using the relative importance index (RII). An ANFIS is developed to quantitatively model critical success factors and assess the implementation performance of construction operations management (COM) in the construction industry, considering aspects such as clustering input/output datasets, fuzziness degree, and optimizing five Gaussian membership functions. The study confirms the significance of three primary CSFs (financial, bureaucratic, and governmental) and communication-related variables through a qualitative structural and behavioral validation process, specifically k-fold cross-validation. The outcomes of this research hold practical implications for the management and assessment of overall performance indices in pavement construction projects. The ANFIS model, validated through robust testing methodologies, provides a valuable tool for industry professionals seeking to enhance the success and efficiency of pavement construction endeavors. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) research on video games primarily focused on the imitation of human-like behavior during the past few years. Moreover, to increase the perceived worth of amusement and gratification, there is an enormous rise in the demand for intelligent agents that can imitate human players and video game characters. However, the agents developed using the majority of current approaches are perceived as rather more mechanical, which leads to frustration, and more importantly, failure in engagement. On that account, this study proposes an imitation learning framework to generate human-like behavior for more precise and accurate reproduction. To build a computational model, two learning paradigms are explored, artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). This study utilized several variations of ANN, including feed-forward, recurrent, extreme learning machines, and regressions, to simulate human player behavior. Furthermore, to find the ideal ANFIS, grid partitioning, subtractive clustering, and fuzzy c-means clustering are used for training. The results demonstrate that ANFIS hybrid intelligence systems trained with subtractive clustering are overall best with an average accuracy of 95%, followed by fuzzy c-means with an average accuracy of 87%. Also, the believability of the obtained AI agents is tested using two statistical methods, i.e., the Mann–Whitney U test and the cosine similarity analysis. Both methods validate that the observed behavior has been reproduced with high accuracy.

This study presents a comprehensive analysis of predicting the temperature in a vacuum membrane distillation (VMD) process. The simulation was carried out via computational fluid dynamics (CFD) as well as machine learning. CFD was performed for obtaining temperature distribution in the feed solution, and the calculated temperature was used for training several machine learning models. The dataset comprises over 13,000 observations, which provide a rich source for modeling complex relationships. Three sophisticated regression models were employed: Adaptive Neuro-Fuzzy Inference System (ANFIS), Kernel Ridge Regression (KRR), and Multi-Layer Perceptron (MLP). ANFIS was chosen for its hybrid nature, combining neural networks and fuzzy logic, effectively capturing intricate non-linear relationships in data. ANFIS performance in fitting the data was compared with the other models. Hyper-parameter optimization for these models was conducted using the Tabu Search algorithm to ensure optimal performance. The ANFIS model demonstrated superior performance with an R2 score of 0.9964513 on the training set and 0.9964507 on the test set, alongside a MSE of 0.037655 and a MAE of 0.168272. The robustness of ANFIS was further confirmed by a 3-fold cross-validation mean R2 score of 0.9964579 and a standard deviation of 3.3619616e−05.

Bipolar plates (BPPs) play an important role in PEM fuel cells in terms of weight and cost points of view. In this paper, the manufacturing of titanium BPPs with parallel flow field was experimentally and numerically studied. In this regard, a stamping die with a parallel pattern is conducted to perform the experiments. Then, the process was modeled via the finite element (FE) simulation. By comparing simulation and experiment results, it was found that the results are in good agreement and hereupon, the accuracy of the FE model was verified. To evaluate the sheet formability, a set of FE experiments was designed through the response surface methodology (RSM). The die clearance, forming velocity, and friction coefficient were considered input parameters, and the maximum thickness reduction (MTR) of the sheet was assumed to be the output. The results revealed that a lower friction coefficient causes an increase in thickness reduction and finally tearing in the formed BPPs. Moreover, changing the forming velocity has no remarkable influence on the MTR. Afterward, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was trained for predicting the output of the MTR with the three mentioned inputs.

Background and Objective: Ergonomic climate reflects employees' understanding of organizational emphasis on job design and modification in order to improve "operational performance" and "employee well-being. Job stress is a growing problem worldwide, affecting employee well-being and organizational productivity". The present study aimed to provide a model to explain job stress centered on ergonomic climate using the adaptive neuro-fuzzy inference system approach. Materials and Methods: This research was conducted based on an applied design. The statistical population of the current research is the healthcare workers of one of the hospitals in Urmia. To this end, 376 questionnaires were collected from this population. Cronbach's alpha was used to determine reliability. Adaptive Neuro-Fuzzy Inference System (ANFIS) is a suitable method for solving nonlinear problems, ambiguities, and uncertainties. ANFIS was used to model the present research. The model was validated through Root Mean Squared Error (RMSE), mean absolute error, and R2 evaluation criteria. Results: The mean scores of hospital ergonomic climate and job stress were obtained at 122.69 ±34.41 and 96.13 ±18.35, respectively. the correlation coefficient between ergonomic climate and job stress for 376 data was calculated at -0.63. The RMSE for the training data in the fuzzy c-means clustering method was 0.02. Conclusion: Ergonomic climate and its dimensions, including operational performance and employees' well-being, have an inverse relationship with employees' job stress. Compared to the evaluation criteria of the model, the presented model can predict employees' job stress using ergonomic climate and its related dimensions with a lower mean error, indicating the accuracy and reliability of the model. We would like to express our gratitude to all the dear ones who helped us in carrying out this research.

Abstract Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller’s performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm’s performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.

This study proposes a combined system for salinity management of reservoirs in which the lake ecosystem simulation is integrated with the reservoir operation optimization. A finite volume-based depth-averaged model is applied for simulating salinity in the reservoir for a long-term period. Then, a surrogate model is developed by applying outputs of the fluid dynamic model using adaptive neuro-fuzzy inference system. The surrogate model is used in the structure of the optimization model to estimate the average salinity concentration in the reservoir. Two objectives are defined in the reservoir operation optimization including minimizing water supply loss and mitigating salinity impacts on the aquatic habitats in the lake ecosystem. According to case study results, the fluid dynamic model is reliable for simulating salinity distribution in the reservoir, which means it is recommendable for simulating salinity distribution of reservoirs. Moreover, The Nash–Sutcliff coefficient of surrogate model is 0.79, which implies it is reliable for applying in the optimization model as a surrogate model of salinity. Based on the environmental considerations, 0.55 ppt was defined as the average threshold of habitat suitability. Average optimal salinity during the simulated period is 0.52 ppt, which implies the optimization model is able to reduce salinity impacts properly. We recommend using the proposed method for the case studies in which increasing salinity is an environmental challenge for the aquatic species those living in the artificial lakes of large dams. [ABSTRACT FROM AUTHOR]

ABSTRACTSThe spontaneous discharge of pollutants or toxicants into the ecosystem as a result of various anthropogenic activities is alarming, this necessitates a drastic cheap, and eco-friendly clean-up approach. Out of all the methods, adsorption has proven to be the most effective. The batch adsorptive removal of Thymol Blue (TB) dye from aqueous solutions is examined in this study utilizing calcined snail shells (CalSS). With the aid of SEM, EDS, XRD, and FTIR, the synthesized adsorbent was examined for different physicochemical characteristics. A machine learning model namely Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed to assess the TB adsorption process while taking into account the important adsorption parameters contact time, temperature, pH, adsorbent dosage, then initial adsorbate concentration. Adsorbent pHpzc was also evaluated. EDX and FTIR confirm the formation of CaO with sharp peaks at 547 cm−1 and C–O and O–H are present. SEM and XRD show an irregularly shaped highly crystalline adsorbent material having a typical particle size of 65 ± 2.81 nm and lattice parameter value of 8.611617 Å. The pHpzc value is 11.04, indicating basic surface characteristics. The pH of 3.0, an adsorbent dose of 10 mg and the highest achievable adsorption efficiency were measured to be 98.75% at 20 °C. The findings from the study fit nicely onto Brouser Sotolongo with q BS = 259.4887 mg/g and R2 = 0.9888. The pseudosecond order model (PSOM) recorded the least error value of 0.3336 and R2 = 0.9952. This indicates chemisorption and multilayer adsorption processes. The thermodynamic parameters ΔH° and ΔG° demonstrate the exothermic and spontaneous nature of the adsorption process. The ANFIS model for the TB sequestration was evaluated using relevant statistical metrics, giving Root Mean Square Error (RMSE) value of 1.4644, Mean Absolute Deviation (MAD) value of 0.576, Mean Absolute Error (MAE) value of 1.2974 and Mean Absolute Percentage Error (MAPE) value of 1.5141. This outcome revealed that the ANFIS model and experimental findings are in good agreement. The efficacy of calcined snail shell in removing Thymol Blue dye from aqueous solution was also examined using in-silico technique which was in accordance to the value of the calculated descriptors obtained from the adsorbent as well as the calculated binding affinity. The elimination of TB-polluted wastewater using calcined snail shells is demonstrated in this study to be a successful and environmentally benign process. [ABSTRACT FROM AUTHOR]

Efficiency measurement plays an increasingly important role in the regulation and management of railway organizations. Despite its proven usefulness in efficiency measurement, data envelopment analysis (DEA) lacks predictive capability. In order to benefit from their learning and mapping capabilities, machine learning (ML) algorithms have been used as a complementary method to DEA, recently. However, the majority of the existing ML-DEA studies focused on efficiency estimation while disregarding the prediction of DEA projected inputs/outputs toward better performance. This study proposes a novel framework using the adaptive neuro-fuzzy inference system (ANFIS) and the support vector machines (SVM) models in conjunction with the context-dependent DEA model to predict efficiency scores and the best input/output levels for 37 railway companies of OECD countries. Despite drawing on a small sample size, the proposed DEA-ANFIS and DEA-SVM models successfully predicted the efficiency scores and the best output levels of the organizations via approximating the efficient frontiers. [ABSTRACT FROM AUTHOR]

In this study, a sensorimotor controller is designed to characterize the required muscle force to enable a robotics system to perform a human-like circular movement. When the appropriate muscle internal forces are chosen, the arm end-point tracks the desired path via joint-space feedback. An objective function of the least-change rate of muscle forces is determined to find suitable feedback gains. The parameter defining the muscle force is then treated as a learning parameter through an adaptive neuro-fuzzy inference system, incorporating the rate of change of muscle forces. In experimental section, the arm motion of healthy subjects is captured using the inertial measurement unit sensors, and then the image of the drawn path is processed. The inertial measurement unit sensors detect each segment motion's orientation using quaternions, and the image is employed to identify the exact end-point position. Experimental data on arm movement are then utilized in the control parameter computation. The proposed brain–motor control mechanism enhances motion performance, resulting in a more human-like movement. [ABSTRACT FROM AUTHOR]

A micro grid system with renewable source operation control is a complex part as each source operates at different parameters. This renewable micro grid with multiple sources like solar plants, wind farm, fuel cell, battery backup has to be operated in both grid connected and standalone condition. During grid connection the micro grid, inverter has to inject power to the grid and compensate load in synchronization to the grid voltages. And during standalone condition the inverter is controlled with droop control module which stabilizes the voltage and frequency of the system even during grid disconnection. The droop control module is further updated with new advanced controllers like fuzzy inference system (FIS) and adaptive neurofuzzy inference system (ANFIS) replacing the traditional proportional integral derivative (PID) and proportional integral (PI) controllers improving the response rate and for achieving better stabilization. This paper has comparative analysis of the micro grid system with different droop controllers under various operating conditions. Parameters like voltage magnitude (Vmag), frequency (F), load and inverter powers (Pload and Pinv) of the test system are compared with different controllers. A numeric comparison table is given to determine the optimal controller for the inverter operation. The analysis is carried out in MATLAB/Simulink software with graphical and parametric validations. [ABSTRACT FROM AUTHOR]

Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller’s performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm’s performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method. [ABSTRACT FROM AUTHOR]

The viscosity properties of GNP-alumina hybrid nanofluids are of significant importance in various engineering applications. This study compares the predictive performance of response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) for the viscosity (µrel) and relative viscosity (µrel) of GNP-alumina hybrid nanofluid at varying mixing ratio (0–3) and temperature (15–55 °C). The ANN and ANFIS models were optimised by varying the number and type of neurons and membership functions (MFs), respectively. In contrast, the RSM model was optimised by varying the source model. The efficacy of the models was assessed using various measures of performance metrics, including residual sum of squares, root mean square error, mean absolute error, and mean absolute percentage error (MAPE). The ANN architecture with 4 neurons exhibited exceptional proficiency in forecasting the µnf, achieving an R2 value of 0.9997 and a MAPE of 0.3100. Meanwhile, the best ANN architecture for the µrel was achieved with 5 neurons, resulting in an R2 of 0.9817 and MAPE of 0.2588. Furthermore, the ANFIS model with the difference of two sigmoidal MFs and the product of two sigmoidal MFs for µnf and Generalized Bell MFs for µrel exhibited the best performance with (3 5) and (4 5) input membership functions, respectively. An R2 value of 0.9999 and 0.9872, with a corresponding MAPE value of 0.0945 and 0.1214, were reported for the optimal ANFIS architecture of µnf and µrel, respectively. The RSM model also produced its most accurate prediction with the quadratic model for both µnf and µrel, with an R2 value of 0.9986 and 0.8835, respectively. Thus, comparative analysis across various models indicated that the ANFIS model outperformed others regarding performance metrics for both µnf and µrel. This study underscores the potential of ANN and ANFIS models in accurately forecasting the viscosity properties of GNP-alumina hybrid nanofluids, thus offering reliable tools for future applications. [ABSTRACT FROM AUTHOR]

Waste heat recovery stands out as a promising technique for tackling both energy shortages and environmental pollution. Currently, this valuable resource, generated through processes like fuel combustion or chemical reactions, is often dissipated into the environment, despite its potential to significantly contribute to the economy. To harness this untapped potential, a traveling-wave thermo-acoustic generator has been designed and subjected to comprehensive experimental analysis. Fifty-two data corresponding to different working conditions of the system were extracted to build ANN, ANFIS, and ANN-PSO models. Evaluation of performance metrics reveals that the ANN-PSO model demonstrates the highest predictive accuracy ( R 2 = 0.9959 ), particularly in relation to output voltage. This research demonstrates the potential of machine learning techniques for the analysis of thermo-acoustic systems. In doing so, it is possible to obtain an insight into nonlinearities inherent to thermo-acoustic systems. This advancement empowers researchers to forecast the performance characteristics of alternative configurations with a heightened level of precision. [ABSTRACT FROM AUTHOR]

In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming and labor-intensive, prompting the exploration of novel computational strategies. This paper illustrates the development and application of machine learning techniques—multivariate linear regression (MLR), artificial neural networks (ANN), and the adaptive neuro-fuzzy inference system (ANFIS)—to indirectly predict the CBR based on the soil type, plasticity index (PI), and maximum dry density (MDD). Our study analyzed 2191 soil samples for parameters including PI, MDD, particle size distribution, and CBR, leveraging theoretical calculations and big data analysis. The ANFIS demonstrated superior performance in CBR prediction with an R2 value of 0.81, surpassing both MLR and ANN. Sensitivity analysis revealed the PI as the most significant parameter affecting the CBR, carrying a relative importance of 46%. The findings underscore the potent potential of machine learning and neuro-fuzzy inference systems in the sustainable management of non-renewable urban resources and provide crucial insights for urban planning, construction materials selection, and infrastructure development. This study bridges the gap between computational techniques and geotechnical engineering, heralding a new era of intelligent urban resource management. [ABSTRACT FROM AUTHOR]

In this study, the power draw (P) of several grizzly feeders used in the Turkish Mining Industry (TMI) is investigated by considering the classification and regression tree (CART), random forest (RF) and adaptive neuro-fuzzy inference system (ANFIS) algorithms. For this purpose, a comprehensive field survey is performed to collect quantitative data, including power draw (P) of some grizzly feeders and their working conditions such as feeder width (W), feeder length (L), feeder capacity (Q), and characteristic feed size (F80). Before applying the soft computing methodologies, correlation analyses are performed between the input parameters and the output (P). According to these analyses, it is found that W and L are highly associated with P. On the other hand, Q is moderately correlated with P. Consequently, numerous soft computing models were run to estimate the P of the grizzly feeders. Soft computing analysis results demonstrate no superiority between the performances of RF and CART models. The RF analysis results indicate that the W is necessary for evaluating P for grizzly feeders. On the other hand, the ANFIS-based predictive model is found to be the best tool to estimate varying P values, and it satisfies promising results with a correlation of determination value (R2) of 0.97. It is believed that the findings obtained from the present study can guide relevant engineers in selecting the proper motors propelling grizzly feeders. [ABSTRACT FROM AUTHOR]

The carbon dioxide emissions associated with the production of conventional Portland cement may be mitigated through the utilization of ground granulated blast furnace slag (GGBFS). The consideration of compressive strength (fc) is essential in the design and construction of concrete structures, as it is an essential demand in concrete mixtures. The primary objective of this study is to establish an effective approach for conducting a comprehensive evaluation of machine learning algorithms in predicting the fc of concrete that incorporates GGBFS. The work focuses on using the adaptive neuro‐fuzzy inference system (ANFIS) to create predictive models for fc. The fc of the collected datasets ranged from 6.3 to 101.3 MPa. The study included the artificial rabbit optimization (ARO) and Bald Eagle search algorithm (BES) to improve the effectiveness of the ANFIS approaches. The novelty of this study is attributed to several factors, including the utilization of the ARO and BES methodologies, the incorporation of GGBFS in the evaluation of fc, the comparison with previous research findings, and the utilization of a substantial dataset encompassing multiple input variables. These factors offer a novel method for improving the effectiveness of prediction models and advance our understanding of forecasting the mechanical characteristics of concrete. The integrated ANF‐AR and ANF‐BA systems showed good estimating skills, according to the findings, which showed R2 values of 0.9961 and 0.9967 for the ANF‐AR's training and testing components and 0.9916 and 0.9946 for the ANF‐BA, respectively. Overall, the ANFIS optimized with ARO model is recognized as the outperformed system for prediction purposes. [ABSTRACT FROM AUTHOR]

The automation of the manufacturing processes of thermoplastic composite laminates has become dependent on open mold processes such as automated tape placement (ATP), which couples tape layering with in situ consolidation. The manufacturing parameters of ATP open mold processes, which comprise processing time, consolidation pressure and temperature, affect the bond strength between the plies and the quality of the laminates produced. Therefore, the effect of the manufacturing parameters should be characterized. This work experimentally evaluates the feasibility of fabricating thermoplastic laminates using an open mold process that reasonably models that of ATP. Glass fiber-reinforced polypropylene laminates are fabricated from unidirectional tapes under different consolidation periods, pressures, and temperatures. The bond quality in the produced laminates is assessed by measuring their interlaminar shear strength, which is measured using a short beam standardized shear test in conjunction with digital image correlation. Results show that consolidation can occur at temperatures slightly below the composite tapes' complete melting temperature, and consolidation times between 7 and 13 min can result in acceptable bond strengths. The results confirmed the feasibility of the process and highlighted its limitations. Analysis of variance and machine learning showed that the effect of process parameters on interlaminar shear strength is nonlinear. [ABSTRACT FROM AUTHOR]

This study addressed the critical problem of repairing cracks in aging aircraft structures, a safety concern of paramount importance given the extended service life of modern fleets. Utilized a finite element (FE) method enhanced by the design of experiments (DOE) and adaptive neuro-fuzzy inference system (ANFIS) approaches to analyze the efficacy of piezoelectric actuators in mitigating stress intensity factors (SIF) at crack tips—a novel integration in structural repair strategies. Through simulations, we examined the impact of various factors on the repair process, including the plate, actuator, and adhesive bond size and characteristics. In this work, initially, the SIF estimation used the FE approach at crack tips in aluminum 2024-T3 plate under the uniform uniaxial tensile load. Next, numerous simulations have been performed by changing the parameters and their levels to collect the data information for the analysis of the DOE and ANFIS approach. The FE simulation results have shown that changing the parameters and their levels will result in changing of SIF. Several DOE and ANFIS optimization cases have been performed for the depth analysis of parameters. The current results indicated that optimal placement, size, and voltage applied to the piezoelectric actuators are crucial for maximizing crack repair efficiency, with the ability to significantly reduce the SIF by a quantified percentage under specific conditions. This research surpasses previous efforts by providing a comprehensive parameter optimization of piezoelectric actuator application, offering a methodologically advanced and practically relevant pathway to enhance aircraft structural integrity and maintenance practices. The study innovation lies in its methodological fusion, which holistically examines the parameters influencing SIF reduction in aircraft crack repair, marking a significant leap in applying intelligent materials in aerospace engineering.

Shortage and environmental threats posed by fossil fuel have become a critical issue requiring searching for alternative energy sources. This study utilized clay-doped barium chloride and ionic liquid (CD-BaCl-IL) as a catalyst for optimizing biodiesel production from shea butter. 10% barium chloride and ionic liquid were blended with clay, dried, and then calcined for 4 h at 600 °C to develop the catalyst. The synthesized catalyst and the biodiesel were suitably characterized. Response surface methodology (RSM) implementing Central Composite Design (CCD) and Genetic Algorithm (GA) were employed to model and optimize the effect of the process parameters on the response. The model's capabilities were evaluated using coefficient of determination (R2) and mean square error (MSE). The second-order polynomial model is shown in the Analysis of Variance (ANOVA) with an (R2 -0.9945, Adjusted R2-0.9846, Predicted R2-0.8694) demonstrating the model's acceptance. Artificial Neural Network (ANN) and Adaptive Neurofuzzy Inference System (ANFIS) were used to assess the model's capability. The obtained statistical results - (R2 = 0.8694, MSE = 0.7035), ANN (R2 = 0.999, MSE = 0.0001026), ANFIS (R2 = 0.99, MSE = 0.000041), showed that ANFIS had the best prediction with the lowest MSE. ANFIS integrated with GA (GA-ANFIS) gave the best optimization with a biodiesel yield (96.72%) at a catalyst concentration of 4 wt%, a methanol/mol ratio of 10:1, a time of 2.5 h, a temperature of 70 °C, and an agitation speed of 400 rpm. The optimal developed biodiesel properties were successfully evaluated within the ASTM D 6751 standards.

Introduction: Modeling plays a crucial role in understanding wastewater treatment processes, yet conventional deterministic models face challenges due to complexity and uncertainty. Artificial intelligence offers an alternative, requiring no prior system knowledge. This study tested the reliability of the Adaptive Fuzzy Inference System (ANFIS), an artificial intelligence algorithm that integrates both neural networks and fuzzy logic principles, to predict effluent Biochemical Oxygen Demand. An important indicator of organic pollution in wastewater.Materials and Methods: The ANFIS models were developed and validated with historical wastewater quality data for the Kauma Sewage Treatment Plant located in Lilongwe City, Malawi. A Self Organizing Map (SOM) was applied to extract features of the raw data to enhance the performance of ANFIS. Cost-effective, quicker, and easier-to-measure variables were selected as possible predictors while using their respective correlations with effluent. Influents’ temperature, pH, dissolved oxygen, and effluent chemical oxygen demand were among the model predictors.Results and Discussions: The comparative results demonstrated that for the same model structure, the ANFIS model achieved correlation coefficients (R) of 0.92, 0.90, and 0.81 during training, testing, and validation respectively, whereas the SOM-assisted ANFIS Model achieved R Values of 0.99, 0.87 and 0.94. Overall, despite the slight decrease in R-value during the testing stage, the SOM- assisted ANFIS model outperformed the traditional ANFIS model in terms of predictive capability. A graphic user interface was developed to improve user interaction and friendliness of the developed model. Integration of the developed model with supervisory control and data acquisition system is recommended. The study also recommends widening the application of the developed model, by retraining it with data from other wastewater treatment facilities and rivers in Malawi.

The evaluation of power demand is fundamental in the Economic Load Dispatch problem, ensuring that the generated power meets the needs of consumers reliably and efficiently in planning system operations. This paper presented two approaches using an Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Artificial Neural Network (ANN) to evaluate the power demand. The modified IEEE 57-Bus system is considered the thermal units that incorporate renewables. The ANFIS and ANN are implemented using MATLAB online version R2023b. The results show that the ANN and ANFIS techniques are suitable for evaluating power demand. A comparison of both methods indicates that ANFIS is relatively superior to the ANNs techniques, considering the coefficient of determination of the ANNs and ANFIS were equal. The accuracy of its results in terms of prediction RMSE for the ANN and ANFIS of 10.147e-05 and 5.2177e-05 for the training and 14.639e-05 and 5.2177e-05 for the testing, respectively. Finally, the prediction accuracy of the ANFIS can be observed to be higher than that of the ANN, but the ANFIS takes longer to process. ANFIS is the method that can be appropriately applied to evaluate the power demand in this research. However, it could not guarantee for other research topics that ANFIS would be better than ANN for the RMSE. It depends on input and output data complexity and the training function type.

An exponential growth and complexity in diverse distribution systems have contributed to protection coordination challenges. Initially, protection coordination schemes were achieved by means of conventional techniques; however, the utilisation of such methods is based on trial-and-error principles and laborious. Consequently, current studies have adopted the utilisation of particle swarm optimization, artificial intelligence models, and genetic algorithms to optimise overcurrent relay selectivity and operational speed. Particle swarm optimization, artificial intelligence, and genetic algorithms are optimization techniques that at times converges prematurely due to poor selection of control parameters and lack of optimal values, which results in increased computational time. Therefore, this paper presents a comprehensive review of recent developments in terms of parametric sensitivity analysis, selection of artificial intelligence models based on data availability, and the likelihood of solving overcurrent relay coordination problems. The reviewed literature shows that particle swarm optimization performance is greatly influenced by inertia weight and swarm size, while the number of iterations has insignificant effect. The findings also indicate that crossover rate, mutation probability, and population size affect genetic algorithms behaviour. Artificial intelligence models lack sensitivity study for parametric tuning, that is, number of hidden layers, membership functions, epsilon in support vector machine, and number of fuzzy rules affects the models’ performance.