As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user’s present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles.

Boltzmann machine

Restricted Boltzmann machine

10.3389/fenrg.2024.1401330

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Frequency Regulation of Two-Area Thermal and Photovoltaic Power System via Flood Algorithm

Results in Control and Optimization (2025)

Serdar Ekinci Davut İzci Cebrail Turkeri Aseel Smerat Absalom E. Ezugwu

10.1016/j.rico.2025.100539

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Optimization of Formic Acid-Formalin-Based Decalcification Protocol for Rat Calvarial Bone Histology

Journal of Experimental Biology and Agricultural Sciences (2024)

S. Amitha Banu Khan Sharun Merlin Mamachan Athira Subash Vadapalli Deekshita

Decalcification is crucial in histological processing, particularly for studying mineralized tissues like bone. The choice of decalcification method can significantly impact the quality of histological sections and the preservation of tissue morphology. This study aims to establish a standardized protocol for decalcifying rat calvarial bone using a formic acid-formalin-based decalcification solution. The protocol was systematically optimized and evaluated based on various parameters, including decalcification time, formic acid concentration, and tissue integrity preservation. The decalcification process was evaluated through comprehensive assessments, including gross physical examination, chemical analysis, and radiographic imaging techniques. Our result demonstrated that the 10% formic acid concentration proved most effective for decalcifying rat calvarial bone samples within eight days, excelling in mineral content removal while preserving specimen structural integrity. In contrast, the 5% concentration failed to complete decalcification within ten days, and the 15% compromised sample quality within eight days. Histological analyses confirmed the efficacy of the 10% formic acid concentration in maintaining tissue integrity and achieving optimal staining quality. The standardized protocol presented in this study provides an effective and reliable approach for achieving consistent and high-quality histological sections of rat calvarial bone. An ideal decalcification agent should effectively remove calcium salts, preserve structural integrity and molecular components, facilitate rapid yet minimally damaging decalcification, and ensure ease of handling for laboratory personnel. Further exploration of its applicability to different bone types or species is recommended to broaden its research utility.

Bone decalcification

Histology

10.18006/2024.12(2).218.225

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Regression Models for Predicting Gamified Educational Outcomes

2022 International Telecommunications Conference (ITC-Egypt) (2024)

Abdelhameed Ibrahim‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬Christos Gatzoulis Marwa M. Eid Faris H. Rizk Laith Abualigah

10.1109/itc-egypt61547.2024.10620545

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Optimal Integration of Machine Learning for Distinct Classification and Activity State Determination in Multiple Sclerosis and Neuromyelitis Optica

Technologies (2023)

Maha Gharaibeh Wlla Abedalaziz Noor Aldeen Alawad Hasan Gharaibeh Ahmad Nasayreh

The intricate neuroinflammatory diseases multiple sclerosis (MS) and neuromyelitis optica (NMO) often present similar clinical symptoms, creating challenges in their precise detection via magnetic resonance imaging (MRI). This challenge is further compounded when detecting the active and inactive states of MS. To address this diagnostic problem, we introduce an innovative framework that incorporates state-of-the-art machine learning algorithms applied to features culled from MRI scans by pre-trained deep learning models, VGG-NET and InceptionV3. To develop and test this methodology, we utilized a robust dataset obtained from the King Abdullah University Hospital in Jordan, encompassing cases diagnosed with both MS and NMO. We benchmarked thirteen distinct machine learning algorithms and discovered that support vector machine (SVM) and K-nearest neighbor (KNN) algorithms performed superiorly in our context. Our results demonstrated KNN’s exceptional performance in differentiating between MS and NMO, with precision, recall, F1-score, and accuracy values of 0.98, 0.99, 0.99, and 0.99, respectively, using leveraging features extracted from VGG16. In contrast, SVM excelled in classifying active versus inactive states of MS, achieving precision, recall, F1-score, and accuracy values of 0.99, 0.97, 0.98, and 0.98, respectively, using leveraging features extracted from VGG16 and VGG19. Our advanced methodology outshines previous studies, providing clinicians with a highly accurate, efficient tool for diagnosing these diseases. The immediate implication of our research is the potential to streamline treatment processes, thereby delivering timely, appropriate care to patients suffering from these complex diseases.

Neuromyelitis Optica

10.3390/technologies11050131

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Bald eagle search algorithm: a comprehensive review with its variants and applications

Systems Science & Control Engineering (2024)

M.A. El‐Shorbagy Anas Bouaouda Hossam A. Nabwey Laith Abualigah Fatma A. Hashim

Bald Eagle Search (BES) is a recent and highly successful swarm-based metaheuristic algorithm inspired by the hunting strategy of bald eagles in capturing prey. With its remarkable ability to balance global and local searches during optimization, the BES algorithm effectively addresses various optimization challenges across diverse domains, yielding nearly optimal results. This paper offers a comprehensive review of recent research on BES. Beginning with an introduction to BES's natural inspiration and conceptual optimization framework, it explores modifications, hybridizations, and applications of BES across various domains. Then, a critical evaluation of BES's performance is provided, offering an update on its effectiveness compared to recently published algorithms. Furthermore, the paper presents a meta-analysis of BES developments and outlines potential future research directions. As swarm-inspired metaheuristic algorithms become increasingly important in tackling complex optimization problems, this study is a valuable resource for researchers aiming to understand swarm-based algorithms, mainly focusing on BES comprehensively. It investigates BES's evolution, exploring its potential applications in solving intricate optimization challenges across diverse fields.

Bald eagle

10.1080/21642583.2024.2385310

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A Global Best-guided Firefly Algorithm for Engineering Problems

Journal of Bionic Engineering (2023)

Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi

Firefly Algorithm

Benchmark (surveying)

Robustness

Premature convergence

Global Optimization