Artificial Intelligence Research and Applications
Artificial Intelligence Research and Applications (AIra) is not a profit organization and scholarly, peer-reviewed, open-access academic journal disseminating cutting-edge research in artificial intelligence (AI). As AI continues to evolve and impact numerous fields, AIra is an important platform for researchers, academicians, and industry professionals to present their latest findings, theories, methodologies, and applications in AI. The journal aims to promote innovation, encourage interdisciplinary collaboration, and support responsible and ethical development of AI technologies. AIra embraces a broad scope, covering diverse areas of artificial intelligence. We welcome original research, review articles, case studies, and technical reports that explore theoretical advancements, algorithmic developments, and practical AI applications across various domains. The topics of interest include, but are not limited to: Machine Learning & Deep Learning: Supervised, unsupervised, reinforcement learning techniques, neural networks, and optimization strategies. Neural Networks & Fuzzy Systems: Artificial and spiking neural networks, adaptive learning mechanisms, and fuzzy logic-based AI models. Natural Language Processing (NLP): Text mining, sentiment analysis, machine translation, speech recognition, large language models, and generative AI. Computer Vision & Image Processing: Object detection, image classification, facial recognition, AI-driven video analysis, and multimodal learning. AI in Robotics & Autonomous Systems: Intelligent control, human-robot interaction, motion planning, swarm intelligence, and soft robotics. AI Ethics & Fairness: Bias detection, interpretability, transparency, privacy preservation, and the responsible use of AI in society. Reinforcement Learning & Optimization: Multi-agent systems, game theory, AI-driven decision-making models, and real-world applications of reinforcement learning. AI in Healthcare, Finance, and Other Domains: AI-driven medical diagnostics, personalized medicine, financial modeling, fraud detection, AI solutions for smart cities, and AI in legal and governance systems. Hybrid AI Models & Explainable AI (XAI): Neuro-symbolic AI, hybrid machine learning techniques, interpretable AI models, and AI model trustworthiness. AI Applications in Industry 4.0 & Smart Systems: AI-powered automation, predictive maintenance, digital twins, IoT-integrated AI solutions, and AI-driven cybersecurity. AI for Sustainable Development & Climate Change: AI applications in renewable energy, climate modeling, environmental monitoring, disaster prediction, and sustainable smart grids. Quantum AI & Next-Generation Computing: AI algorithms for quantum computing, neuromorphic computing, and advanced AI computation frameworks. Human-centered AI & Augmented Intelligence: AI-assisted creativity, collaborative AI, human-in-the-loop learning, and AI for education. Edge AI & Distributed AI Systems: AI at the edge, federated learning, decentralized AI systems, and AI in embedded systems. AI in Smart Grids & Energy Management: AI-driven demand response, energy consumption forecasting, grid stability enhancement, and decentralized energy systems. AI in Power Electronics: Intelligent control strategies for power converters, fault detection, efficiency optimization, and AI-integrated power systems. AI in Renewable Energy: Predictive maintenance for solar and wind farms, optimization of energy storage, AI-driven grid integration, and sustainable energy solutions. AI in Engineering: AI solutions for engineering problems, including advanced models and applications. AI in Education & E-Learning: AI applications related to education and e-learning methods. AI in Cybersecurity: Applying artificial intelligence and machine learning techniques to enhance the security of computer systems, networks, and data from various cyber threats. Journal Contact Mailing Address Senol Caddesi, 130/4, Gazi Mahallesi, Yenimahalle, 06560, Ankara/Turkey Principal Contact Professor ilhami COLAK Istinye University, Istanbul/Turkiye Phone: +905325996112 Email: editoraira@gmail.com Support Contact Professor ilhami Colak Email: ilhcol@gmail.com; editoraira@gmail.com; editor@ijaira.org https://www.ijaira.org Online ISSN: 3108-3889 Publisher:INSMARTGRID https://www.ilhamicolak.org/english.htm
Licensing All articles published in the Artificial Intelligence Research and Applications, AIRA, are licensed under an open access Creative Commons CC BY 4.0 license, meaning that anyone may download and read the paper for free. In addition, the article may be reused and quoted provided that the original published version is cited. These conditions allow for maximum use and exposure of the work, while ensuring that the authors receive proper credit. In exceptional circumstances articles may be licensed differently. If you have specific condition (such as one linked to funding) that does not allow this license, please mention this to the editorial office of the journal at submission. Exceptions will be granted at the discretion of the publisher.
Benchmarking Deep Learning Architectures for 24-Hour Energy Forecasting in Smart Buildings Using Real-World IoT Data
Accurate 24-hour energy forecasting in smart buildings remains a challenging real-world problem due to the highly non-linear, dynamic, and heterogeneous nature of IoT sensor data. This study extensively expands upon previous research that demonstrated the effectiveness of Bi-LSTM as a univariate predictor. To address existing gaps, we make two primary contributions: (1) developing a multivariate forecasting framework that incorporates eight diverse IoT sensor streams, including HVAC (heat pump) energy consumption and indoor environmental comfort metrics, and (2) benchmarking advanced attention-based architectures, such as the Temporal Fusion Transformer (TFT), optimized for interpretable, high-performance multidimensional time-series forecasting. Using real-world datasets from a multifunctional smart building in France (Pôle Culturel), experiments were conducted over approximately four months (October 23, 2024, to February 27, 2025) at 10-minute intervals. The preprocessing pipeline includes timestamp harmonization, Akima interpolation for non-linear data gaps, outlier correction, and feature scaling. Using MAE, RMSE, MAPE, and R2 as evaluation metrics, the experiments compare a multivariate Bi-LSTM against standard models such as LSTM, GRU, and the TFT. The results indicate that the multivariate Bi-LSTM (R2 = 0.9656) significantly reduces errors compared to univariate approaches, accurately capturing daily trends and peak loads. Furthermore, while the TFT effectively models complex multivariate dependencies (R2 = 0.8130), it demonstrates that attention-based models require specific architectural tuning for medium-scale IoT datasets, providing practical guidance for sustainable, data-driven energy management.
Copyright (c) 2026 Artificial Intelligence Research and Applications
ML Models for Predicting Remaining Useful Life-Time of EV's Batteries for Stakeholders
Proper forecasting of remaining useful life (RUL) of lithium-ion batteries is critical for safety, operational stability, and cost-effectiveness in modern electric vehicles. Because battery degradation arises from complex electrochemical and thermal processes, data-driven approaches have become a practical alternative to physics-based modeling. This study develops and critically compares supervised machine learning models, including Random Forest, Gradient Boosting, Linear Regression, Ridge Regression, K-Nearest Neighbors, Deep Neural Networks (DNN), and Convolutional Neural Networks (CNN), for cycle-level RUL estimation using a publicly available lithium-ion battery dataset. The dataset comprises engineered voltage-time features, capacity, and charge–discharge indicators, which are preprocessed using Min–Max normalization to support stable training and fair model comparison. Among the evaluated methods, the Random Forest regressor achieved the best overall performance, reaching an R2 of 0.9998 with lower MAE and RMSE than both traditional regressors and deep learning architectures. The results indicate that ensemble tree-based models are especially effective at capturing nonlinear degradation behavior when trained on engineered tabular features rather than raw time-series signals. While the DNN and CNN achieved high R2 values, their error metrics were comparatively larger, suggesting that deep architectures may benefit from higher-resolution time-series inputs or alternative sequence modeling formulations. Finally, the top-performing model was integrated into an end-to-end prediction system using FastAPI, SQLite, and a responsive web dashboard to demonstrate real-world applicability. The deployment allows estimating RUL in real time and at lowlatency and provides stakeholders with an accessible tool for battery health monitoring. Overall, the study shows that ensemble learning techniques can deliver strong performance for battery condition monitoring and provides a deployable architecture to practical RUL prediction in electric-vehicle battery management systems.
Frequency Stability Control in Low-Inertia Microgrids Using Optimized Virtual Damping Stabilizers
Inverter-based Generators generally lack natural inertia required for ascertaining the frequency stability of Electrical Microgrid Power Systems. A robust virtual inertia emulation technique is required in ensuring that frequency stability of low-inertia systems is greatly improved. This paper presents a hybrid control strategy that combines Fractional Derivative Inertia Control (FDIC) and a Virtual Damping Stabilizer (VDS) modelled as lead-lag compensator, and then as FOPID, implemented via a Battery Energy Storage System (BESS) to address these challenges. The FDIC is a BESS-based virtual inertia controller that leverages fractional calculus to emulate inertia and damping, while the VDS generates the appropriate compensating power that helps dampen out oscillations. A comprehensive MATLAB/Simulink model of an islanded microgrid comprising a diesel generator, photovoltaic (PV), and wind generation is developed to evaluate the proposed approach. Control parameters are optimized using the Beluga Whale Optimization Algorithm (BWO), ensuring robust performance under various disturbances and its performance is evaluated by comparison with Particle Swarm Optimization (PSO) based VDS design. Simulation results demonstrate that the FDIC+VDS scheme significantly improves frequency deviation compared to traditional virtual inertia. The findings highlight the efficacy of advanced BESS-based control strategies in enhancing the resilience and stability of low-inertia, renewable-rich microgrids.
Neural Networks in Control Systems for Multi-Drive Facilities of Metallurgical Enterprises
Metallurgical plants use mechanisms with multiple motors, and their control systems must account for the specifics of the process and the impact of external and internal disturbances. Standard controllers don't always achieve the desired results, especially when systems contain various uncertainties, such as signal, parametric, and structural ones. Therefore, it has been proposed to integrate neural network-based control systems to account for and minimize nonlinearities and disturbances present in the systems. The study developed an algorithm for creating a nonlinear control system that takes into account processes occurring in the mechanical part of the object, as well as random disturbances. An algorithm for creating neural controllers for multi-drive systems was developed. Compared to a standard neural controller, its use allowed for a reduction in the number of neurons in the inner layer and the number of internal layers, reducing the training set by approximately 28 percent. The proposed universal structure of neural controllers and observers enables the creation of stable control systems for multi-drive mechanisms, while maintaining the operational capability of the devices and ensuring high-quality process control even when unmeasured parameters or distorted signals are involved in their formation.
Deep Reinforcement Learning for Voltage Stability: DDQN-Based Control with Real-Time Hardware Validation
This study proposes an autonomous voltage control framework based on the Double Deep Q-Network (DDQN) algorithm to enhance voltage stability in power distribution systems with high renewable penetration. The proposed controller learns adaptive voltage regulation policies by interacting with dynamic grid environments and observing voltage deviations, power flows, and generator-load dynamics. The method is evaluated on both IEEE 14-bus and IEEE 124-bus test systems and benchmarked against state-of-the-art DRL agents, including DQN, PPO, DDPG, and SAC. The results demonstrate that DDQN provides a favorable balance between control performance and computational efficiency, particularly in large-scale systems. Furthermore, the proposed approach is implemented and validated using an OPAL-RT real-time hardware-in-the-loop platform, confirming its practical applicability for real-time voltage control in next-generation smart grids.
Copyright (c) 2025 Artificial Intelligence Research and Applications
Implementation of Robust Fractional-Order Neural Modified Sliding Mode Controls for Managing the Power Output of Doubly Fed Induction Generators
This paper presents a robust fractional-order neural-modified sliding mode control (FONMSMC) strategy for enhancing the performance of dual-rotor wind turbines (DRWTs) driven by doubly-fed induction generators (DFIGs). Conventional direct power control methods often exhibit unstable power output, high electrical noise, and limited adaptability under variable wind conditions. The proposed FONMSMC integrates fractional calculus for precise dynamic tuning, neural networks for adaptive adjustment, and a modified sliding mode control framework to improve robustness, combined with pulse-width modulation for efficient power conversion. Simulation results demonstrate significant performance improvements, including reductions of up to 94% in active power steady-state error, 87.2% in active power fluctuations, and 71.79% in total harmonic distortion. The controller also maintains stable operation across a wide range of wind speeds, ensuring enhanced grid stability and reduced mechanical stress. The proposed method offers a reliable and efficient control solution for DFIG-based DRWT systems, contributing to improved sustainability and robustness in modern wind energy applications.
fractional-order neural modified sliding mode approach
Neural Networks-Based 12-Sector Direct Torque Control of Asynchronous Machine Drives: Experimental Results
Achieving optimal performance from industrial asynchronous motors requires precise control of both torque and magnetic flux. The conventional six-sector direct torque control (6-DTC) method is widely used for this purpose, but it suffers from several drawbacks, including significant torque and flux ripple and excessive switching activity in the power converter, which leads to energy losses. This research addresses these issues by introducing an enhanced control method that integrates neural networks (NNs) with a refined twelve-sector direct torque control (12-DTC) scheme. Unlike traditional approaches that depend on hysteresis comparators and fixed lookup tables, the proposed NN-based controller intelligently determines voltage vectors in real time, enabling smoother and more adaptive motor operation. Simulation studies verified the method’s effectiveness, showing substantial reductions in torque ripple, improved control tracking, and lower total harmonic distortion (THD). Experimental validation on a dSPACE DS1104 hardware platform confirmed these improvements in practice. The 12-sector NN-DTC achieved THD reductions of 30.87%, 42.22%, and 49.73% across different test conditions, along with 47% faster dynamic speed response. Overall, the proposed neural network–based 12-sector DTC represents a significant advancement over the traditional 6-DTC. It delivers smoother performance, more rapid response, higher efficiency, and robust real-world applicability—offering a more precise and energy-efficient solution for controlling industrial asynchronous machines.
AI and Explainable AI for Automatic Detection and Grading of Diabetic Retinopathy and Related Ophthalmic Diseases: A Review
The management of retinal diseases within a national health service is greatly improved by the availability and development of good automatic systems for eye disease identification and grading. Identifying the characteristics of retinal diseases, particularly diabetic retinopathy, is a challenging and lengthy process. Artificial intelligence (AI) and explainable AI (XAI) are reshaping the landscape of automated ophthalmic diagnostics, with diabetic retinopathy (DR) emerging as a central benchmark for such innovations. This review paper offers a thorough and critical examination of AI and XAI methodologies in this domain, with DR as the primary core focus area. Artificial intelligence, and especially deep learning, now facilitates precise and swift identification of retinal lesions and disease classification, while explainable AI enhances interpretability, transparency, and clinical confidence. This review paper synthesizes recent AI and XAI developments for DR and other ophthalmic diseases, covering image pre-processing, deep learning architectures and feature extraction as well. In order to contextualize the discussion, relevant background information on diabetic retinopathy and related retinal diseases is presented, including a detailed overview of the characteristic pathological features and the screening procedures commonly adopted by healthcare services. Furthermore, this article examines the publicly available eye fundus image datasets that serve as valuable resources for research in this domain. In view of the inherent challenges in developing automated screening systems, the paper concludes with a critical evaluation of current limitations and an exploration of prospective directions for future developments on AI and XAI for diabetic retinopathy and other ophthalmic diseases.
Integrated Solar Geometry Modelling and Real-Time Environmental Monitoring for Photovoltaic Performance Optimisation
Photovoltaic (PV) systems are important for advancing renewable energy generation; however, their operational efficiency is strongly dependent on fluctuating environmental conditions. Variations in solar irradiance, ambient temperature, wind speed, and angle of incidence interact to influence PV cell temperature and, consequently, power output. While these factors have been examined individually in previous studies, integrated approaches that combine continuous environmental monitoring with real-time performance modelling remain limited. This study presents a year-long analysis in which theoretical solar geometry calculations were coupled with high-resolution empirical measurements. Solar position parameters were derived dynamically, enabling real-time cell temperature and efficiency estimation. The results show that efficiency decreased from the nominal 15% under standard test conditions to an average of 13.0% under field conditions, reflecting the effect of elevated temperatures and variable irradiance. Wind speed provided a measurable cooling effect, reducing cell temperature by up to 12 °C at 15–17 m/s and thereby mitigating thermal losses. Furthermore, variations in the angle of incidence were identified as a key factor driving short-term efficiency fluctuations. Validation of the predictive efficiency model (Eq. 11) against measured data demonstrated strong consistency (deviations < 8%). These findings underscore the importance of incorporating adaptive orientation mechanisms and effective thermal management strategies into PV system design. The integrated modelling–measurement approach proposed here offers a more accurate basis for site-specific performance prediction and system optimisation.
Development of a Blockchain Platform for Protection and Security of Medical Data and Patient Identification in Kazakhstan
Data breaches in healthcare are increasingly caused by insider threats, yet traditional logging systems remain vulnerable to tampering. This paper proposes a hybrid security framework for medical data in Kazakhstan, combining blockchain for immutable log storage with machine learning for automated anomaly detection. We introduce a dual-backend architecture where a private Ethereum network secures access logs, while a separate analysis module utilizes supervised learning algorithms to identify suspicious behaviour patterns in real-time. To validate the system, we generated a region-specific synthetic dataset compliant with Kazakhstani national identification standards (IIN). Experimental results demonstrate that the system effectively secures audit trails against modification and detects anomalous access patterns with high accuracy. The proposed solution addresses the critical gap between data integrity and proactive threat detection, offering a scalable architecture compliant with data privacy regulations.
Exploring Gender Classification Performance on AI-Generated Facial Datasets Using Transfer Learning Models
The demand for gender classification has increased significantly with the growth of smart and automated security systems. Artificial Intelligence (AI), particularly Deep Learning (DL), has emerged as a promising approach for building reliable classification systems. Despite advancements in classification techniques, the availability of high-quality datasets for training and testing remains a notable challenge. In this work, we propose utilizing a synthetic facial dataset to train several well-known classification models. We evaluate different Convolutional Neural Network (CNN) architectures, including transfer learning approaches, on a set of unreal face images generated entirely using a Generative Adversarial Network (GAN). To the best of our knowledge, this is among the few works that investigate gender classification trained exclusively on fully synthetic GAN-generated datasets, highlighting its novelty. The models achieved strong performance on the synthetic dataset, with up to 99% training accuracy and 95% test accuracy. However, as a limitation, the generalization of these results to real-world datasets remains uncertain, since synthetic images may not capture all demographic and natural variations. This study demonstrates the viability of using artificially generated data when real data is scarce or difficult to obtain.
Multilingual Speech Transcription System for Kazakh, Russian and English Languages
This paper presents a novel multilingual speech transcription system designed for Kazakh, Russian, and English languages. Unlike existing solutions such as OpenAI Whisper and Kaldi-based offline models, the proposed system introduces three key innovations: (1) a specialized preprocessing pipeline optimized for Kazakh phonetic characteristics, (2) dynamic language detection with confidence scoring, and (3) fine-tuned acoustic models trained on a comprehensive trilingual dataset of 450 hours. The system achieves a 23% better Word Error Rate (WER) for Kazakh (8.7% vs. 11.3%) compared to Whisper and a 15% improvement for code-switched utterances. Statistical significance testing using paired t-tests (p < 0.001) and 95% confidence intervals confirm the superiority of the proposed approach across all target languages. Unlike existing solutions such as OpenAI Whisper which achieves 11.3% WER for Kazakh, the proposed system demonstrates 23% improvement with 8.7% WER (p < 0.001, 95% CI: [8.1%, 9.3%]), while maintaining complete offline functionality and specialized optimization for Central Asian linguistic patterns. However, OpenAI Whisper supports 99 languages, while proposed system supports 3 languages, and has better zero‑shot performance.
Structural Damage Detection in Beam Structures Using the Pathfinder Algorithm
This paper presents an approach to structural health monitoring (SHM) using the Pathfinder Algorithm (PFA), a metaheuristic method for detecting damage in beam-type structures. The damage identification task is formulated as an unconstrained optimization problem that estimates both the location and severity of damage by maximizing the correlation between simulated and measured modal data. The Multiple Damage Location Assurance Criterion (MDLAC) is employed as the objective function. A 15-element cantilever beam model is analyzed under different damage scenarios. The performance of PFA is compared with that of the Genetic Algorithm (GA). Results show that PFA identifies damage locations and severities more accurately and efficiently, with improved convergence speed and robustness.
Artificial Intelligence for Evaluation of PM2.5 and PM10 Levels in Urban Streets
Atmospheric pollution, especially due to fine particles (PM2.5) and larger particles (PM10), constitutes a major risk to public health and the long-term viability of urban areas. This research focuses on measuring the presence of PM2.5 and PM10 across selected city streets, investigating the variables that affect their concentration, and identifying possible solutions to limit their impact. The study utilizes continuous air quality monitoring, data interpretation through statistical tools, and a review of urban planning measures to offer a thorough understanding of particulate contamination in metropolitan settings. Findings reveal significant patterns in how these pollutants spread, their links to vehicular traffic and weather conditions, and suggest practical approaches to lower human exposure. The contribution of artificial intelligence starts in collecting raw data, converting in Matlab’s matrices and help the operator identify correlations between the taken measurements and the the influence of environmental parameters, which allow particulate matter to vary daily even without following consolidated trends in the same time period.
Enhanced Parameter Identification of Solar Photovoltaic Models Using a Novel Two-Stage Improved Sea Lion Optimization Algorithm
This study introduces a novel two-stage metaheuristic framework for parameter identification (PI) of the single diode model (SDM) in solar cells. The Sea Lion Optimizer (SLO) is, for the first time, applied to SDM parameter estimation and compared with five other classical and recent metaheuristic algorithms. Results demonstrate SLO's superior performance. An improved SLO (ISLO) is then developed, incorporating Lévy flight and personal best information to enhance accuracy. To further improve the accuracy, a two-stage methodology is proposed, using mathematical analysis of the SDM to derive initial parameter estimates and refine search ranges. This approach significantly enhances parameter estimation accuracy and robustness. ISLO shows enhanced accuracy, with an overall RMSE improvement of approximately 59.7 % compared to SLO. The two-stage ISLO further improves estimation, reducing the mean and standard deviation of RMSE by 89.6 % and 91.3 %, respectively. The proposed two-stage methd can also be integrated with other metaheuristic algorithms for similar gains.
Fuzzy Logic-Driven Reliability Improvement in Power Systems under Environmental Conditions
The Energy Not Supplied (ENS) index is an important indicator for determining the economic performance of power systems. This index analyzes the losses sustained as a result of network faults that prevent the delivery of electrical energy, as well as the economic impact on consumers during power outages. As a direct indication of system reliability, ENS sheds light on the resilience of distribution networks. However, a significant issue in dependability assessments is a lack of adequate statistical data. To address these concerns, fuzzy logic has evolved as a reliable method for modeling uncertainty in engineering parameters. Distribution networks, which operate in a wide range of environmental and geographic conditions—from urban to rural—are frequently vulnerable to natural disasters such as storms, heavy rainfall, and snow, as well as contaminants such as salt, dust, and humidity. Using fuzzy logic, this study analyzes how these ambient and natural elements affect the ENS index. The ENS index is calculated using MATLAB simulations on the RBTS-bus2 network. Results show that by addressing environmental and operational uncertainties, up to 31% improvement in both ENS and SAIFI indices can be achieved, with an overall enhancement of 41.88% in system reliability. These findings demonstrate the value of fuzzy logic in enhancing reliability evaluations even when statistical data is limited.
Thermography Inspection with Machine Learning for Malfunction Prediction in Power System Equipment
This paper presents a machine learning-based approach for early failure detection in power system equipment using thermographic image analysis. While existing studies often rely on complex deep learning models, our method introduces a lightweight yet effective solution combining image preprocessing, unsupervised segmentation, and classical classifiers. The key innovation lies in the integration of automated Region of Interest (RoI) detection and the evaluation of its impact on model performance. A dataset of 624 RGB thermographic images from motors and transformers is used to benchmark various machine learning algorithms, including KNN, SVM, Decision Tree, Random Forest, Naive Bayes, and Neural Networks. Performance metrics such as precision, recall, and F1-score are analyzed and show that RoI segmentation significantly improves classification accuracy (up to 32% for SVM and 15% for Neural Networks). The proposed method is resource-efficient and achieves strong results without requiring temperature vector data. These findings highlight the practical value of the approach for predictive maintenance and early fault diagnosis in industrial environments.
Segmentation of Brain MRI Based on Modified Type-2 Fuzzy Compactness
One of the important aspects in many algorithms for image analysis, object representation and visualization is the image thresholding that is usually applied as an initial step. Another issue in image analysis is the segmentation since is an important image-processing step by which regions of an image are classified according to the presence of relevant anatomic features. The success of image segmentation depends on object-background intensity difference, object size and noise measurement, however is unaffected by location of the object on that image. In this context, this paper proposes a new automatic thresholding method to accurately segment the brain in magnetic resonance images (MRI) on the axial plane. The proposed automatic method is based on a new algorithm that utilizes a modified type-2 fuzzy compactness algorithm, allowing it to solve the global thresholding for brain MRI in the axial plane. Associated with this method it was used has metrics the Uniformity measure (UM) and Shape measure (SM) with the purpose to analyse their effectiveness. Experiment using real-image data of MR medical images demonstrated the accuracy and robustness of the proposed method. With the purpose to analyse the capability of the proposed method, it was realized a comparison with other two approaches, namely with the Otsu and Kittler methods. The obtained results of the proposed method achieved state-of-the-art results in which UM reaches 0.998 and SM reaches 1.000.
PDF Deep Learning in Tumor Detection: Advancing Accuracy and Efficiency in Cancer Diagnosis
Precise early identification of cerebral neoplasms can markedly enhance patient prognosis. Conventional diagnostic techniques, including manual analysis of medical pictures, are labor-intensive and susceptible to subjective bias. This study employs deep learning methodologies, specifically Convolutional Neural Networks (CNNs) and the YOLO (You Only Look Once) model, to improve the precision of brain tumor diagnosis in MRI scans. A dataset including 961 labeled MRI images was employed, resized to 416 × 416 pixels, and partitioned into training, validation, and test sets. A multi-class CNN model was created for classification, and YOLOv11 was employed for real-time detection and classification. Performance evaluation utilized mean Average Precision (mAP), precision, and recall, yielding 95.4% mAP, 91.1% precision, and 93.2% recall using YOLOv11, indicating substantial clinical potential. Challenges remain, including data variability and model interpretability, underscoring the need for models that improve transparency and strengthen NLP techniques. The NLP integration involves using the Gemini large language model (LLM) to automatically generate comprehensive diagnostic reports. Specifically, the NLP system analyzes outputs from the CNN-based image classification, combined with patient notes, to produce detailed, context-aware medical reports, thereby enhancing clinical diagnostics. These findings endorse the advancement of refined deep-learning models for expedited and precise tumor detection, facilitating real-time clinical applications.
Optimized Type-2 Fuzzy Logic Controller for Load-Frequency Control in Multi-Area Power Systems Using the HBB-BC Algorithm
In this paper, an optimizedType-2 fuzzy logic controller (T2FLC) is developed for load-frequency control (LFC) of multi-area interconnected power systems. In particular, theproposed controller employs the Hybrid Big Bang-Big Crunch (HBB-BC) algorithm to optimize the membership functions, which considers system uncertainties and nonlinearities. Thecomparison results under two different load disturbances using simulation show that the optimized T2FLC is superior in frequency oscillation and transmission line poweras compared to conventional PI, PID, and Type-1 fuzzy controllers. The settling time is reduced by 40% and the frequency fluctuation amplitude is decreased by 70% compared to the PI controller; the ITAE is the lowest, indicating that the T2FLC is the bestin terms of damping oscillations and guaranteeing system stability.
Neural modified super-twisting filed-oriented approach of asynchronous machine drives
This paper proposes a novel field-oriented command (FOC) strategy with a neural-modified super-twisting algorithm (NMSTA) controller for a category of nonlinear systems. This designed control is used to adjust the speed and torque of the asynchronous machine (AM) to enhance the dynamic response, tracking error and robustness. Also, to reduce the torque, stream, and flux undulations of the AM. The FOC-NMSTA technique of the AM has been simulated in MATLAB and compared with the FOC technique. The comparative results that were obtained under different operating conditions prove the superior performance and robustness of this proposed new intelligent nonlinear technique (FOC-NMSTA). In all four tests, the current harmonic distortion value was improved by 85.96% and 77.91%. Also, the FOC-NMSTA provided a better ripple value of flux than the FOC, where the reduction rates in all tests were 87.76% and 86%. Torque overshoot in all tests was also reduced compared to the FOC with percentages estimated at 41.02% and 44%. These relatively high percentages indicate the extent of FOC-NMSTA's competence and ability to get better the features of the control system.
Field-oriented control, neural modified super-twisting algorithm, dynamic responses, and asynchronous machine.