State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…To address this, deep learning algorithms such as long short-term memory (LSTM) have been focused on for SOC prediction. …”
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Research on recycling value grading and real-time perception of rock debris from TBM tunneling by Weiqi Yue, Weilin Su, Zhanfei Gu, Xiao Qu

“…Leveraging a comprehensive database that encompasses both tunnel rock debris performance indicators and TBM tunneling parameters, perception models are constructed using different machine learning algorithms. After Bayesian hyperparameter optimization, the perception models based on CART, SVM, KNN, and ANN demonstrate accuracies of 67.5%, 80.0%, 82.5%, and 83.8% respectively. …”
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Challenges of Water Resources Governance under the Impact of Climate Change and Water Transfer by Atabak Jaffari, Ahmad Sharafati, Amin reza Neshat, Seyed abbas Hosseini

“…Research Topic: The water resources management approach should be based on a water governance framework in different dimensions for optimal allocation in terms of time and space with a view to future conditions. …”
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Joint User Association and Resource Allocation for Hierarchical Federated Learning Based on Games in Satisfaction Form by Panagiotis Charatsaris, Maria Diamanti, Symeon Papavassiliou

“…Algorithms based on Reinforcement Learning (RL) and Best Response Dynamics (BRD) are, then, devised to conclude the SE and MESE points. …”
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A Federated Learning-Based Framework for Accurately Identifying Human Activity in the Environment by Nwadher Suliman Al-Blihed, Dina M. Ibrahim

“…Analysis of the data generated by HAR devices may involve deep learning models and algorithms of different kinds. These data are personal and may include some sensitive data. …”
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A comprehensive approach to Queue Waiting Time Prediction using Tree-Based Ensembles with Data Balancing and Explainable AI by Tapodhir Karmakar Taton, Bipin Saha, Md. Johirul Islam, Shaikh Khaled Mostaque

“…Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Autoencoders have been evaluated to compare the effectiveness of different dimensionality reduction techniques. …”
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Rethinking Technological Investment and Cost-Benefit: A Software Requirements Dependency Extraction Case Study by Gouri Ginde, Guenther Ruhe, Chad Saunders

“…Machine Learning (ML) is widely used for different purposes within Software Engineering. It can substantially improve the efficiency and effectiveness of organizations. …”
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AI-Driven Optimization of Breakwater Design: Predicting Wave Reflection and Structural Dimensions by Mohammed Loukili, Soufiane El Moumni, Kamila Kotrasova

“…Two datasets of 32,000 data points were generated for underwater and free-surface breakwaters, with an additional 10,000 data points for validation, totaling 42,000 data points per case. Five ML algorithms—Random Forest, Support Vector Regression, Artificial Neural Network, Decision Tree, and Gaussian Process—were applied and evaluated. …”
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Optimizing deep learning for accurate blood cell classification: A study on stain normalization and fine-tuning techniques by Mohammed Tareq Mutar, Jaffar Nouri Alalsaidissa, Mustafa Majid Hameed, Ali Almothaffar

“…BACKGROUND: Deep learning’s role in blood film screening is expanding, with recent advancements including algorithms for the automated detection of sickle cell anemia, malaria, and leukemia using smartphone images. …”
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False data injection attack dataset for classification, identification, and detection for IIoT in Industry 5.0Zenodo by A K M Ahasan Habib, Mohammad Kamrul Hasan, Rosilah Hassan, Shayla Islam, Huda Saleh Abbas

“…The dataset offers an accurate characterization of IoT environments by covering many different kinds of network configurations and scenarios. …”
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Empowering Community Health Workers With Scripted Medicine: Design Science Research Study by Dario Staehelin, Damaris Schmid, Felix Gerber, Mateusz Dolata, Gerhard Schwabe

“…We first developed the medical algorithms and ThinkLets based on insights from collaboration engineering and algorithmic management literature. …”
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Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images by Xiao-Lu Jin, Xue-Mei Li, Tie-Juan Liu, Ling-Yun Zhou

“…AIM: To develop different machine learning models to train and test diplopia images and data generated by the computerized diplopia test. …”
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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

“…In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. …”
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The Performance of an ML-Based Weigh-in-Motion System in the Context of a Network Arch Bridge Structural Specificity by Dawid Piotrowski, Marcin Jasiński, Artur Nowoświat, Piotr Łaziński, Stefan Pradelok

“…In civil and bridge engineering, they can facilitate the identification of specific patterns through the analysis of data acquired from structural health monitoring (SHM) systems. To evaluate the prediction capabilities of ML, this study examines the performance of several ML algorithms in estimating the total weight and location of vehicles on a bridge using strain sensing. …”
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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. …”
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Multi-objective optimization of HESS control for optimal frequency regulation in a power system with RE penetration by Ousama M.T. Ajami, Rodney H.G. Tan, Mithulan Nadarajah, Farah Adilah Jamaludin, Adlan Bagus Pradana

“…To optimize the control parameters with the best objectives, all possible sets of objectives with four different optimization algorithms are studied. The three control models considered for the HESS incorporate Virtual Synchronous Generator (VSG) or Virtual Inertia (VI) control with independent or simultaneous optimization of control parameters. …”
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Comprehensive analysis of total knee arthroplasty kinematics and functional recovery: Exploring full-body gait deviations in patients with knee osteoarthritis. by Xavier Gasparutto, Alice Bonnefoy-Mazure, Michael Attias, Katia Turcot, Stéphane Armand, Hermès H Miozzari

“…The knee was excluded from classification to focus on full-body kinematics. Differences between groups, with controls, as well as before and after surgery were evaluated for patients reported outcome measures, kinematic features, and spatio-temporal parameters. …”
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Incidence, Pathogenesis, Risk Factors, and Treatment of Cystoid Macula Oedema Following Cataract Surgery: A Systematic Review by Lorenzo Ferro Desideri, Kirupakaran Arun, Enrico Bernardi, Nicola Sagurski, Rodrigo Anguita

“…Further research is needed to establish optimal treatment algorithms and improve outcomes for patients with post-operative CMO…”
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Machine learning approaches for identifying and predicting voltage conditions in power system networks using network topology behavior input formulation by Tolulope David Makanju, Oluwole John Famoriji, Ali N. Hasan, Thokozani Shongwe

“…This paper developed a predictive model for determining the condition of voltage in a power system based on the network topology behavior as input formulation. Different machine learning algorithms random forest, support vector machine and gradient boosting were used to evaluate the input formulation approach for voltage condition prediction in power system networks. …”
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IMPACT OF SEASONALITY OF GENERATION AND LOAD ON THE OPTIMAL CAPACITY OF THE ENERGY STORAGE SYSTEM OF THE PROSUMER’S MICROGRID by O.В. Кулапін, A.В. Івахнов, С.O. Федорчук, К.В. Махотіло, Д.О. Данильченко, О.В. Булгаков

“…Development of a method for determining the optimal capacity of an energy storage system to maximize profit from interaction prosumer’s microgrid with the power grid. Two radically different controller algorithms of microgrid operation are considered: the first is focused on the maximum use of solar generation, and the second is on the balanced use of all elements of the prosumer’s microgrid system, including storage and energy consumption. …”
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