Intelligent Machine Learning Techniques Research Articles

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

UTILIZAREA IMAGINILOR SATELITARE ÎN AGRICULTURĂ. STUDIU DE CAZ

All sizes of farms can benefit from satellite imagery, not only big producers. When paired with artificial intelligence (AI) and deep machine learning techniques, satellite photography becomes an effective tool for monitoring agricultural conditions and anticipating issues in the field. As a result, using satellite photos to guide crop farming choices can help determine when to apply nutrients and irrigation. This paper focuses on monitoring through satellite sensors with an emphasis on the facilities offered by the European Copernicus Program through Sentinel-2 satellites the crops from a farm from Calarasi County, Borcea commune.

Identifying necrotizing soft tissue infection using infectious fluid analysis and clinical parameters based on machine learning algorithms

BackgroundDetermining the presence of necrotizing soft tissue infection (NSTI) poses a significant hurdle. As of late, there has been a notable increase in the application of artificial intelligence (AI) machine learning techniques in identifying diseases, a shift that can be attributed to their exceptional efficiency, unbiased nature, and high precision. MethodsInformation was gathered from a cohort of 13 patients suffering from NSTI, alongside 12 patients with cellulitis. The construction of NSTI diagnostic machine learning models utilized four different algorithms, specifically random forest, k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression. These models were constructed based on 28 distinctive attributes identified through statistical examination. Following this, the diagnostic efficiency of each algorithms was evaluated. A novel random forest model, streamlined for clinical use, was later developed by focusing on 6 attributes that had the most pronounced influence on the accuracy of our initial random forest model. ResultsThe following data was noted regarding the sensitivity and specificity of the four NSTI diagnostic models:logistic regression displayed 78.2 % and 83.7 %, KNN presented 79.1 % and 87.1 %, SVM showed 83.5 % and 86.3 %, and random forest exhibited 89.6 % and 92.9 %, respectively. In comparison, lactate levels in fluid demonstrated 100 % sensitivity and 76.9 % specificity at an optimal cut-off point of 69.6 mg/dL. Among all four machine learning models, random forest outperformed the others and also showed better results than fluid lactate. A newly constructed random forest model, created using 6 of the 13 identified features, displayed promising results in diagnosing NSTI, having a sensitivity and specificity of 90.2 % and 92.2 %, respectively. ConclusionsDeveloping a diagnostic model for NSTI employing the random forest algorithm has resulted in a diagnostic technique that is more efficient, cost-effective, and expedient. This approach could provide healthcare practitioners with the tools to identify and manage NSTI with greater efficacy.

Computer-aided urban energy systems cyber attach detection and mitigation: Intelligence hybrid machine learning technique for security enhancement of smart cities

This paper introduces a sophisticated computer-aided system for enhancing the security of smart cities through urban cyber attack detection and mitigation. The proposed system employs a comprehensive mathematical framework that models the dynamics of cyber attacks, detection, and mitigation, capturing the intricate relationships within a smart city environment. Leveraging a hybrid approach combining Transfer Learning and Meta-Learning, the system adapts to evolving threats and tasks, showcasing superior performance in comparison to standalone methods. Simulation results demonstrate the system’s robustness, with low False Positive and False Negative Rates, high System Security, and effective Mitigation Strategies. This research contributes a state-of-the-art solution for securing smart cities, leveraging intelligent hybrid machine learning techniques.

Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model

BackgroundViral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications. Recently, the development of AVPs as an antiviral agent suggests an effective way to treat virus-affected cells. Recently, the involvement of intelligent machine learning techniques for developing peptide-based therapeutic agents is becoming an increasing interest due to its significant outcomes. The existing wet-laboratory-based drugs are expensive, time-consuming, and cannot effectively perform in screening and predicting the targeted motif of antiviral peptides.MethodsIn this paper, we proposed a novel computational model called Deepstacked-AVPs to discriminate AVPs accurately. The training sequences are numerically encoded using a novel Tri-segmentation-based position-specific scoring matrix (PSSM-TS) and word2vec-based semantic features. Composition/Transition/Distribution-Transition (CTDT) is also employed to represent the physiochemical properties based on structural features. Apart from these, the fused vector is formed using PSSM-TS features, semantic information, and CTDT descriptors to compensate for the limitations of single encoding methods. Information gain (IG) is applied to choose the optimal feature set. The selected features are trained using a stacked-ensemble classifier.ResultsThe proposed Deepstacked-AVPs model achieved a predictive accuracy of 96.60%%, an area under the curve (AUC) of 0.98, and a precision-recall (PR) value of 0.97 using training samples. In the case of the independent samples, our model obtained an accuracy of 95.15%, an AUC of 0.97, and a PR value of 0.97.ConclusionOur Deepstacked-AVPs model outperformed existing models with a ~ 4% and ~ 2% higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed Deepstacked-AVPs model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.

Discovery and learning of feature geometry in underwater acoustic sensing: Case studies and challenges in active sonar applications

Interpretable artificial intelligence (AI) and related machine learning (ML) techniques are gaining popularity for underwater acoustic sensing applications. However, interpretability of machine learnt features poses a fundamental challenge to successful application of these powerful data science techniques to underwater acoustics. Autonomous sonar target recognition is especially interesting from the data science perspective as beyond target-specific features, a sonar ping response typically includes significant interference from the environment, e.g., clutter, multipath scattering, etc. Such environmental interference, resulting from complex, dynamic, unpredictable, and often unknown factors, manifest as identifiable structures in acoustic color or alternate multi-dimensional feature representations that can lead to machine learning and classification errors. In this talk, we will discuss these challenges commonly encountered in underwater acoustics using case studies from field experiments in active sonar target recognition. Some results will also include physics-driven simulations in this domain to provide robust ground truths. In particular, we will posit how braided feature geometry and its representation, as well geometry of overlap between features can render a sonar target feature informative, explainable, and discoverable. [Work funded by the ONR Grant Nos. N000142112420 and N000142312503 and DoD Navy (NEEC) Grant No. N001742010016.]

Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning

Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advancement of artificial intelligence methods and machine learning techniques, the potential for accurate data-driven diagnoses and effective treatments has grown significantly. However, applying machine learning algorithms to real-world datasets presents diverse challenges at multiple levels. The scarcity of labeled data, especially in low regime scenarios with limited availability of real patient cohorts due to high costs of recruitment, underscores the vital deployment of scaling and transfer learning techniques. In this study, we explore a real-world pathology classification task to highlight the effectiveness of data and model scaling and cross-dataset knowledge transfer. As such, we observe varying performance improvements through data scaling, indicating the need for careful evaluation and labeling. Additionally, we identify the challenges of possible negative transfer and emphasize the significance of some key components to overcome distribution shifts and potential spurious correlations and achieve positive transfer. We see improvement in the performance of the target model on the target (NMT) datasets by using the knowledge from the source dataset (TUAB) when a low amount of labeled data was available. Our findings demonstrated that a small and generic model (e.g. ShallowNet) performs well on a single dataset, however, a larger model (e.g. TCN) performs better in transfer learning when leveraging a larger and more diverse dataset.

Composite Fins Subsonic Flutter Prediction Based on Machine Learning

In the present work, the potential application of machine learning techniques in the flutter prediction of composite materials missile fins is investigated. The flutter velocity data set required for different fin aerodynamic geometries and materials is generated using a hybrid data collection method: from the wind tunnel experiments at flows ranging from 5 to 30 m/s at Re = 300,000 to 500,000, whereas synthetic data is collected using modified NACA flutter boundary model. Once the flutter data are collected, different regression algorithms were investigated, and the results were compared in terms of accuracy (when compared to the experimentally obtained results and the numerical flutter models), training time (minimization), and R-squared values (maximization). The algorithms investigated and their performance analyzed are fast forest regression, SDCA regression (stochastic dual coordinate ascent), and the light GBM (light gradient boosting machine) regression algorithm that belongs to the gradient boosting regression algorithm family. It was found that the light GBM algorithm renders the most accurate results. Based on this research, it can be concluded that artificial intelligence (machine learning) techniques can be successfully deployed in the analysis of complex flutter phenomena.

Drug discovery for COVID-19 and related mutations using artificial intelligence

Researchers and scientists can transform interconnected data into valuable knowledge using computational-based models that can assist in disease diagnosis, inspection, and virus containment thanks to recent developments in the fields of artificial intelligence and machine learning. In this paper, we present a comprehensive analysis of how artificial intelligence and machine learning can contribute in the delivery of effective remedies and the fight against the COVID-19 pandemic, particularly in disease treatment and drug discovery. During the pandemic period, a large number of noteworthy studies were conducted in this direction by numerous academic and research communities from many fields. We explore the theoretical developments and practical applications of artificial intelligence algorithms and machine learning techniques that suggest potential solutions for accelerating the discovery of new drugs as well as repurposing existing ones, not only for COVID-19 but also for other related mutations and future pandemics, which unfortunately are highly predicted.

A Review, Synthesizing Frameworks, and Future Research Agenda: Use of AI & ML Models in Cardiovascular Diseases Diagnosis

Cardiovascular diseases (CVDs) continue to be a leading cause of morbidity and mortality worldwide. Early detection and accurate diagnosis of the initial phases of CVDs are crucial for effective intervention and improved patient outcomes. In recent years, advances in intelligent automation and machine learning (ML) techniques have shown promise in enhancing the accuracy and efficiency of CVD detection. This systematic review aims to comprehensively analyze and synthesize the existing literature on the application of intelligent automation and ML adaptive classifier models in the detection of the initial phase of cardiovascular disease within the realm of medical science. The review follows a rigorous systematic methodology, including comprehensive literature search, study selection, data extraction, and quality assessment. A wide range of scholarly articles from the reputed journal were searched to identify relevant studies published over a specified period. The selected studies were critically evaluated for methodological robustness and relevance to the research objective. The synthesis of findings reveals a diverse landscape of research endeavors focused on employing intelligent automation and ML adaptive classifier models for CVD detection. The review highlights the various types of ML algorithms utilized, such as neural networks, decision trees, and support vector machines, and their potential to enhance the accuracy of diagnosis by analyzing complex and heterogeneous data sources, clinical records, and omics data. Furthermore, the review discusses challenges and limitations encountered in implementing these models, including data quality, interpretability, and ethical considerations. It also underscores the importance of interdisciplinary collaboration between medical practitioners, data scientists, and domain experts to ensure the seamless integration of these innovative technologies into clinical practice. In conclusion, this systematic review underscores the significant advancements made in the field of intelligent automation and ML adaptive classifier models in the detection of the initial phase of cardiovascular disease. While acknowledging the potential of these approaches, it also emphasizes the need for further research, standardization, and validation to harness their full capabilities and contribute to more accurate, timely and personalized cardiovascular disease diagnosis and management.

Empirical Filtering-Based Artificial Intelligence Learning Diagnosis of Series DC Arc Faults in Time Domains

Direct current (DC) networks play a pivotal role in the growing integration of renewable energy sources. However, the occurrence of DC arc faults can introduce disruptions and pose fire hazards within these networks. In order to ensure both safety and optimal functionality, it becomes imperative to comprehend the characteristics of DC arc faults and implement a dependable detection system. This paper introduces an innovative arc fault detection algorithm that leverages current filtering based on the empirical rule in conjunction with intelligent machine learning techniques. The core of this approach involves the sampling and subsequent filtration of current using the empirical rule. This filtering process effectively amplifies the distinctions between normal and arcing states, thereby enhancing the overall performance of the intelligent learning techniques integrated into the system. Furthermore, this proposed diagnosis scheme requires only the signal from the current sensor, which reduces the complexity of the diagnosis scheme. The results obtained from the detection process serve to affirm the effectiveness and reliability of the proposed DC arc fault diagnosis scheme.

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges.

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques

The majority of energy sources being used today are traditional types. These sources are limited in nature and quantity. Additionally, they are continuously diminishing as global energy consumption increases as a result of population growth and industrial expansion. Their compensation is made from clean energy and renewable energy. Renewable energy is strongly dependent on climatic conditions; therefore, an aspect of energy management is needed, which is essential in distribution systems, because it enables us to calculate the precise energy used by the load as well as by its many components. It also helps us understand how much energy is required and its origin. The energy management aspect contains two main phases: forecasting and optimization. In this study, we are focused on the forecasting level using intelligent machine learning (ML) techniques. To ensure better energy management, it is very important to predict the production of renewable energy over a wide time period. In our work, several cases are proposed in order to predict the temperature, the irradiance, and the power produced by a PV system. The proposed approach is validated by an experimental procedure and a real database for a PV system. The big data from the sensors are noisy, which pose a major problem for forecasting. To reduce the impact of noise, we applied the multiscale strategy. To evaluate this strategy, we used different performance criteria, such as mean error (ME), mean absolute error (MAE), root mean square error (RMSE), nRMSE and the coefficient of determination (R2). The obtained experimental results show good performance with lower error. Indeed, they achieved an error for nRMSE criteria between 0.01 and 0.37.

A collection of read depth profiles at structural variant breakpoints

SWaveform, a newly created open genome-wide resource for read depth signal in the vicinity of structural variant (SV) breakpoints, aims to boost development of computational tools and algorithms for discovery of genomic rearrangement events from sequencing data. SVs are a dominant force shaping genomes and substantially contributing to genetic diversity. Still, there are challenges in reliable and efficient genotyping of SVs from whole genome sequencing data, thus delaying translation into clinical applications and wasting valuable resources. SWaveform includes a database containing ~7 M of read depth profiles at SV breakpoints extracted from 911 sequencing samples generated by the Human Genome Diversity Project, generalised patterns of the signal at breakpoints, an interface for navigation and download, as well as a toolbox for local deployment with user’s data. The dataset can be of immense value to bioinformatics and engineering communities as it empowers smooth application of intelligent signal processing and machine learning techniques for discovery of genomic rearrangement events and thus opens the floodgates for development of innovative algorithms and software.

Anomaly Detection Method for Unknown Protocols in a Power Plant ICS Network with Decision Tree

This study aimed to enhance the stability and security of power plant control network systems by developing detectable models using artificial intelligence machine learning techniques. Due to the closed system operation policy of facility manufacturers, it is challenging to detect and respond to security threats using standard security systems. With the increasing digitization of control systems, the risk of external malware penetration is also on the rise. To address this, machine learning techniques were applied to extract patterns from network traffic data produced at an average of 6.5 TB per month, and fingerprinting was used to detect unregistered terminals accessing the control network. By setting a threshold between transmission amounts and attempts using one month of data, an anomaly judgment model was learned to define patterns of data communication between the origin and destination. The hypothesis was tested using machine learning techniques if a new pattern occurred and no traffic occurred. The study confirmed that this method can be applied to not only plant control systems but also closed-structured control networks, where availability is critical, and other industries that use large amounts of traffic data. Experimental results showed that the proposed model outperformed existing models in terms of detection efficiency and processing time.

DeepFakes- The Digital Threat in the Real World

Objectives: Understanding and Tackling the alarming surge of Digital Imposters known as the world of DeepFakes. 
 Methods: Using Artificial Intelligence based Deep Machine Learning software; Cyber Forensics; Physical mind awareness and alertness! Deepfakes use Artificial Intelligence and Deep Machine Learning techniques to make fake images, of people and events, which are as attractive/ authentic as the original. Deepfakes is the next big Challenge 
 in Cyber Security, taking the Security mindset to the next high level. Findings: On date we have no Specialized High-Tech support at hand to handle such an extremity. We can’t even imagine that if it was a crisis. The impact might be ruinous; it can be anything. In one of the recent live impersonation cases, A Mayor of Berlin thought he was having an online meeting with former boxing champion and current mayor of Kyiv, Vital Klitschko, but later on turned out to be a conversation with a deepfake, an AI generated fake videotape, looking real and authentic. Previous DeepFake videos had some tell-tale signs that a commodity/ person/ event wasn’t real; edit or odd movements. Not presently. In recent Delhi Elections, the BJP Candidate Mr. Manoj Tiwari fabricated his old speech on “Citizenship Amendment Act” into a political campaign speech to reach different linguistic voter bases, especially Haryanvi voters. Tiwari did not have any knowledge about Haryanvi, but made his political campaign message reach Haryanvi voters through DeepFake Technology. 
 Novelty: Massive Online Videos on Internet!

SDN Application Plane Where Intelligence Meets Networking

The software-defined networking (SDN) paradigm has revolutionized the way we think about network management and control. The SDN application plane is a critical component of this paradigm, where intelligent applications are leveraged to drive network behavior and optimize network performance. In this abstract, we explore the role of the SDN application plane in bridging the gap between traditional network management and the intelligent, dynamic network of the future. We discuss the challenges and opportunities of deploying SDN applications, including the need for standardized APIs and the importance of intelligent analytics and machine learning techniques. Finally, we highlight the transformative potential of the SDN application plane, from enabling new applications and services to improving network security and resilience. By bringing intelligence and networking together in new and innovative ways, the SDN application plane promises to revolutionize the way we design, manage, and secure our networks.

Current Development, Challenges, and Future Trends in Cloud Computing: A Survey

Cloud computing is a new paradigm in information and communication technologies (ICTs) that provides the ability to access shared pools of different computing resources that are related to many cloud users within a pay-per-use or on-demand approach. It has transformed the delivery model of ICT from a product to a service. This provides several different advantages for institutions, companies and users based on savings and reduced capital expenditure through lower operating expenses. This paper provides a comprehensive survey of cloud computing. It first develops an understanding of cloud computing in general and discusses its advantages, current development, challenges and future trends. Subsequently, a detailed discussion on the cloud computing architectures, services models, fault tolerance mechanisms, services selection methods, adoption by industry, and scheduling of cloud-based resources is also presented. Nonetheless, cloud computing has many obstacles which expose it to a number of limitations. Some of these challenges include security of data, fault tolerance, and load balancing. A number of techniques in literature are proposed to cope with these challenges which are discussed and analyzed. Experimental data and usage drift validates the popularity of cloud computing and its adoption in recent years. Future trends in cloud computing support the use of intelligent machine learning (ML) techniques and new technologies to cope with some of the challenges and making cloud computing more efficient, secure and commercially viable to be widely accepted.

Mapping Homogeneous Response Areas for Forest Fuel Management Using Geospatial Data, K-Means, and Random Forest Classification

Accurate description of forest fuels is necessary for developing appropriate fire management strategies aimed at reducing fire risk. Although field surveys provide accurate measurements of forest fuel load estimations, they are time consuming, expensive, and may fail to capture the inherent spatial heterogeneity of forest fuels. Previous efforts were carried out to solve this issue by estimating homogeneous response areas (HRAs), representing a promising alternative. However, previous methods suffer from a high degree of subjectivity and are difficult to validate. This paper presents a method, which allows eliminating subjectivity in estimating HRAs spatial distribution, using artificial intelligence machine learning techniques. The proposed method was developed in the natural protected area of “Sierra de Quila,” Jalisco, and was replicated in “Sierra de Álvarez,” San Luis Potosí and “Selva El Ocote,” Chiapas, Mexico, to prove its robustness. Input data encompassed a set of environmental variables including altitude, average annual precipitation, enhanced vegetation index, and forest canopy height. Four, three, and five HRAs with overall accuracy of 97.78%, 98.06%, and 98.92% were identified at “Sierra de Quila,” “Sierra de Álvarez,” and “Selva El Ocote,” respectively. Altitude and average annual precipitation were identified as the most explanatory variables in all locations, achieving a mean decrease in impurity values greater than 52.51% for altitude and up to 36.02% for average annual precipitation. HRAs showed statistically significant differences in all study sites according to the Kruskal–Wallis test (p-value < 0.05). Differences among groups were also significant based on the Wilcoxon–Mann–Whitney (p-value < 0.05) for all variables but EVI in “Selva El Ocote.” These results show the potential of our approach to objectively identify distinct homogeneous areas in terms of their fuel properties. This allows the adequate management of fire and forest fuels in decision-making processes.

Wind turbine output power prediction and optimization based on a novel adaptive neuro-fuzzy inference system with the moving window

This study focuses on predicting the output power of wind turbines (WTs) using the wind speed and WT operational characteristics. The main contribution of this work is a model identification method based on an adaptive neuro-fuzzy inference system (ANFIS) through multi-source data fusion on a moving window (MoW). The proposed ANFIS-MoW-based approach was applied to data in different time series windows, namely the very short-term, short-term, medium-term and long-term time horizons. Data collected from a 30-MW wind farm on the west coast of Nouakchott (Mauritania) were used in the computational analysis. In comparison to nonparametric models from the literature and models employing artificial intelligence machine learning techniques, the proposed ANFIS-MoW model demonstrated superior predictions for the output power of the WT with the fusion of very few data collected from different WTs. Moreover, for various time series windows (TSW) and meteorological conditions, additional benchmarking demonstrated that the ANFIS-MoW-based method outperformed five existing ANFIS-based models, including grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), fuzzy c-means clustering (ANFIS-FCM), genetic algorithm (ANFIS-GA), and particle swarm optimization (ANFIS-PSO). The results indicated that the suggested methodology is a promising soft-computing tool for accurately estimating the WT output power for WTs' sustainability through better control of their operation.