How can we make robot dance expressive and responsive? A survey of methods and future directions by Benedikte Wallace, Benedikte Wallace, Kyrre Glette, Kyrre Glette, Alex Szorkovszky

“…Our analysis covers various approaches, including traditional control systems, machine learning algorithms, and hybrid systems that aim to imbue robots with the capacity for responsive, expressive movement. …”
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Shape Penalized Decision Forests for Imbalanced Data Classification by Rahul Goswami, Aindrila Garai, Payel Sadhukhan, Palash Ghosh, Tanujit Chakraborty

“…The proposed approach enhances predictive performance and generalization by leveraging ensemble learning strategies such as bagging and adaptive boosting. We evaluate the method on twenty benchmark tabular imbalanced datasets, spanning diverse sample sizes and imbalance ratios, and demonstrate its superiority over several state-of-the-art data-level and algorithmic-level methods. …”
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A novel approach for breast cancer detection using a Nesterov accelerated adam optimizer with an attention mechanism by Abeer Saber, Tamer Emara, Samar Elbedwehy, Esraa Hassan

“…Abstract Image-based automatic breast tumor detection has become a significant research focus, driven by recent advancements in machine learning (ML) algorithms. Traditional disease detection methods often involve manual feature extraction from images, a process requiring extensive expertise from specialists and pathologists. …”
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Digital Empowerment in Social Work: Leveraging AI to Enhance Educational Access in Developing Nations by Zvinodashe Revesai, Benjamin Tungwa, Telson Anesu Chisosa, Vanessa Runyararo Meki

“…This study aims to examine the potential of emerging Artificial Intelligence (AI) technologies in empowering social work students by enhancing access to information through machine translation and intelligent search tools, improving resource availability via virtual simulations and adaptive learning platforms, and integrating AI-powered self-help tools into the curriculum. …”
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IMPLEMENTATION OF LEARNING MANAGEMENT SYSTEMS WITH GENERATIVE ARTIFICIAL INTELLIGENCE FUNCTIONS IN THE POST-PANDEMIC ENVIRONMENT by Denis-Cătălin Arghir

“…To demonstrate the system's effectiveness, a curriculum was crafted for a specialized field of study - Artificial Intelligence (AI), with a specific focus on the practical application of Machine Learning algorithms. This curriculum incorporates theoretical and practical application components, complemented by a suite of assessment tools and assignments tailored to the proposed subject. …”
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Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies by Mohamed Ahwiadi, Wilson Wang

“…Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. …”
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AI-Driven Accounting and Sensing Applications for Investment Management by Foziljonov Ibrohimjon, Yuldashev Jahongir, Turgunov Jasurbek, Khujamurodov Askarjon, Rozhkova Elena

“…Rather, their professional judgment and domain expertise are integrated into the adaptive learning processes of AI systems. The article concludes by identifying critical implementation challenges, reflecting on the application of machine learning and sensor fusion in the field of investment analytics, and proposing suggestions for future interdisciplinary research. …”
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AI in biomedical science: innovations, challenges, and ethical perspectives by Aynur Aliyeva

“…Key technologies such as generative AI platforms, machine learning algorithms, and mobile-based auditory training systems are highlighted. …”
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Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization by Huaitao Shi, Yangyang Li, Peng Zhou, Shenghao Tong, Liang Guo, Baicheng Li

“…The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. …”
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Increment of Academic Performance Prediction of At-Risk Student by Dealing With Data Imbalance Problem by Nguyen Giap Cu, Thi Lich Nghiem, Thi Hoai Ngo, Manh Tuong Lam Nguyen, Hong Quan Phung

“…Studies on automatically predicting student learning outcomes often focus on developing and optimizing machine learning algorithms that fit the data captured from different education systems. …”
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Optimization of IoT-Based Motion Intelligence Monitoring System by Jian Qiao, Zhendong Zhang, Enqing Chen

“…For active pattern recognition, two types of classification algorithms with different complexity are proposed based on Support Vector Machine (SVM) and deep neural networks, respectively, to adapt to scenarios with different computational capabilities. …”
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Optimized CNN-LSTM with hybrid metaheuristic approaches for solar radiation forecasting by İrem Fatma Şener, İhsan Tuğal

“…To improve the accuracy of the model, hyperparameter optimization is applied to the CNN-LSTM model using three metaheuristic algorithms: Particle Swarm Optimization, Grey Wolf Optimization, and Starfish Optimization Algorithm. …”
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Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects by Ma Yuhan

“…Although these factors limit the widespread application of FL in medical settings, this paper also proposes potential solutions, such as improving algorithm interpretability through interpretable tools, and utilizing transfer learning and domain adaptation methods to enhance model effectiveness across different datasets. …”
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Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models by Sami Naouali, Oussama El Othmani

“…This study introduces a novel framework leveraging Rough Set Theory (RST)-based feature selection—MLReduct, MLSpecialReduct, and MLFuzzyRoughSet—to enhance machine learning performance on uncertain data. Applied to a private cardiovascular dataset, our MLSpecialReduct algorithm achieves a peak Random Forest accuracy of 0.99 (versus 0.85 without feature selection), while MLFuzzyRoughSet improves accuracy to 0.83, surpassing our MLVarianceThreshold (0.72–0.77), an adaptation of the traditional VarianceThreshold method. …”
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Optimal Dual Control Strategy for Multi-Interconnected Microgrids Under Unintentional Islanding and Fault Scenarios by H. K. Shaker, Ines Mahmoud, Magdi A. Mosa, K. M. Abdel-Latif, A. A. Ali, H. E. Keshta

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A data management system for precision medicine. by John J L Jacobs, Inés Beekers, Inge Verkouter, Levi B Richards, Alexandra Vegelien, Lizan D Bloemsma, Vera A M C Bongaerts, Jacqueline Cloos, Frederik Erkens, Patrycja Gradowska, Simon Hort, Michael Hudecek, Manel Juan, Anke H Maitland-van der Zee, Sergio Navarro-Velázquez, Lok Lam Ngai, Qasim A Rafiq, Carmen Sanges, Jesse Tettero, Hendrikus J A van Os, Rimke C Vos, Yolanda de Wit, Steven van Dijk

“…LogiqSuite is used for multi-centre research study data registrations and monitoring, data analytics in interdisciplinary consortia, design of new machine learning / artificial intelligence (AI) algorithms, development of new or updated prediction models, integration of care with advanced therapy production, and real-world data monitoring in using CDS tools. …”
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DEVELOPMENT OF THE METHODS FOR RESOURCE REALLOCATION IN CLOUD COMPUTING SYSTEMS by Viacheslav Davydov, Daryna Hrebeniuk

“…The goal of the work is to increase the efficiency of available resources usage in cloud computing systems (such as RAM, disk space, CPU, network) by developing the model for adaptive management of resource reallocation. This will allow new virtual machines to be launched with minimal performance degradation for already running applications. …”
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Analysis of autonomous UAV navigation methods in GPS dead zones by A.R. Kravchuk, A.H. Tkachuk, O.O. Dobrzhanskyi, Yu.V. Bogoyavlenska, D.Yu. Tkachuk

“…Separately, modern trends are analyzed, namely the growing role of adaptive systems with sensor fusion and the involvement of machine learning methods. …”
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Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery by Xiaoyu Hu, Xiuyuan Zhao, Wenhe Liu

“…This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. …”
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A Comprehensive Review on Sensor-Based Electronic Nose for Food Quality and Safety by Teodora Sanislav, George D. Mois, Sherali Zeadally, Silviu Folea, Tudor C. Radoni, Ebtesam A. Al-Suhaimi

“…To address these gaps, our review recommends solutions such as the adoption of adaptive machine learning models to reduce calibration needs and enhance drift resilience and the implementation of standardized protocols for data acquisition and model validation. …”
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