Toward Cyborg: Exploring Long-Term Clinical Outcomes of a Multi-Degree-of-Freedom Myoelectric Prosthetic Hand by Yuki Kuroda, Yusuke Yamanoi, Hai Jiang, Yoshiko Yabuki, Yuki Inoue, Dianchun Bai, Yinlai Jiang, Jinying Zhu, Hiroshi Yokoi

“…Progress in this field has been directed toward employing machine-learning algorithms to process electromyogram patterns, enabling a broad spectrum of hand movements. …”
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A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification by Shravan Venkatraman, S. Kanthimathi, K. S. Jayasankar, T. Pranay Jiljith, R. Jashwanth

“…Traditional approaches, such as single Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) algorithms like Decision Trees (DTs) and Support Vector Machines (SVMs), struggle to extract the diverse features needed for precise classification, resulting in suboptimal performance. …”
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Telemedicine Screening for Diabetic Retinopathy Using Digital Technology: Foreign Experience by D. A. Andreev, N. N. Kamynina

“…Deep machine learning algorithms and other artificial intelligence-based approaches demonstrate excellent results. …”
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Collective behavior quantification on human odor effects against female Aedes aegypti mosquitoes-Open source development. by Abdul Halim Poh, Mahmoud Moghavvemi, Cherng Shii Leong, Yee Ling Lau, Alireza Safdari Ghandari, Alexlee Apau, Faisal Rafiq Mahamd Adikan

“…Although tracking large numbers of individual insects is hailed as one of the characteristics of an ideal automated image-based tracking system especially in 3D, it also is a costly method, often requiring specialized hardware and limited access to the algorithms used for mapping the specimens. The method proposed contributes towards (a) unlimited open source use, (b) a low-cost setup, (c) complete guide for any entomologist to adapt in terms of hardware and software, (d) simple to use, and (e) a lightweight data output for collective behavior analysis of mosquitoes. …”
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Tribological Performance Enhancement in FDM and SLA Additive Manufacturing: Materials, Mechanisms, Surface Engineering, and Hybrid Strategies—A Holistic Review by Raja Subramani, Ronit Rosario Leon, Rajeswari Nageswaren, Maher Ali Rusho, Karthik Venkitaraman Shankar

“…Further, the review highlights the growing use of finite element modeling, digital twins, and machine learning algorithms for predictive control of tribological performance at AM parts. …”
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Impact of occupancy behavior on building energy efficiency: What’s next in detection and monitoring technologies? by Wenjie Song, John Calautit

“…Particular attention is paid to data-driven methods, including probabilistic models such as Hidden Markov Models (HMMs), classical machine learning algorithms such as Support Vector Machines (SVMs) and K-Nearest Neighbors (KNN), and deep learning architectures such as Convolutional Neural Networks (CNNs), all of which have demonstrated high accuracy in both laboratory and real-world settings. …”
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Comparative review of intelligent structural safety in building seismic risk mitigation utilizing an integrated artificial intelligence controller by Normaisharah Mamat, Rawad Abdulghafor, Sherzod Turaev, Fitri Yakub

“…The research highlights the influence of integrated AI controllers on control systems, examining several AI controllers, including machine learning algorithms, neural networks, and evolutionary algorithms concerning structural safety. …”
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Conceptual Validation of High-Precision Fish Feeding Behavior Recognition Using Semantic Segmentation and Real-Time Temporal Variance Analysis for Aquaculture by Han Kong, Junfeng Wu, Xuelan Liang, Yongzhi Xie, Boyu Qu, Hong Yu

“…We provide the aquaculture industry with an efficient and reliable method for recognizing fish feeding behavior, offering new scientific support for intelligent aquaculture and delivering powerful solutions to improve aquaculture management and production efficiency. Although the algorithm proposed in this study has shown good performance in fish feeding behavior recognition, it requires certain lighting conditions and fish density, which may affect its adaptability in different environments. …”
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TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change by Juan Frausto Solís, Erick Estrada-Patiño, Mirna Ponce Flores, Juan Paulo Sánchez-Hernández, Guadalupe Castilla-Valdez, Javier González-Barbosa

“…The ensemble combines Long Short-Term Memory neural networks, Random Forest regression, and Support Vector Machines, optimizing their contributions using heuristic algorithms such as Particle Swarm Optimization. …”
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Serological proteomic characterization for monitoring liver fibrosis regression in chronic hepatitis B patients on treatment by Mengyang Zhang, Shuyan Chen, Xiaoning Wu, Jialing Zhou, Bingqiong Wang, Tongtong Meng, Rongxuan Hua, Yameng Sun, Hong You, Wei Chen

“…Our findings show that prolonged AVT drives progressive serological proteomic remodeling in fibrosis regressors, characterized by a temporal inversion in the activation of the complement and coagulation cascades. Using machine learning algorithms trained on the 4D-DIA-MS discovery cohort, we develop a logistic regression model incorporating a seven-protein panel for short-term AVT and a three-protein panel for long-term AVT, respectively, both of which demonstrate moderate discriminatory capabilities for fibrosis regression. …”
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A Piezoelectric Sensor Based on MWCNT-Enhanced Polyvinyl Chloride Gel for Contact Perception of Grippers by Qiyun Zhong, Qingsong He, Diyi Liu, Xinyu Lu, Siyuan Liu, Yuze Ye, Yefu Wang

“…By integrating PMPG with machine learning algorithms, soft robotic grippers gain advanced contact perception capabilities, enabling applications in medicine, rescue, exploration, and other fields requiring fine manipulation and adaptability. …”
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Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems by Hongming Yang, Hao Liu, Xin Yuan, Kai Wu, Wei Ni, J. Andrew Zhang, Ren Ping Liu

“…Finally, this review identifies critical open questions and promising future research paths, including ultra-lightweight models, robust algorithms for heterogeneity, machine unlearning, standardized benchmarks, novel FL paradigms, and next-generation security. …”
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Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

“…This research introduces a novel approach combining human-based metaheuristics—Teaching Learning Based Optimization (TLBO) and Cultural Algorithms (CA)—with non-parametric Decision Tree (DT) models. …”
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Global sorghum production dataset for temperate to subtropical regions at subnational scale over 2000–2020Zendo by Mohsen Davoudkhani, Nicolas Guilpart, David Makowski, Nicolas Viovy, Philippe Ciais, Ronny Lauerwald

“…The dataset can be used to develop crop models, machine learning algorithms, and statistical models for predicting sorghum yields under different climate scenarios. …”
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Advances in Sorghum Improvement for Climate Resilience in the Global Arid and Semi-Arid Tropics: A Review by Andekelile Mwamahonje, Zamu Mdindikasi, Devotha Mchau, Emmanuel Mwenda, Daines Sanga, Ana Luísa Garcia-Oliveira, Chris O. Ojiewo

“…In addition, recent advancements including new machine learning algorithms, gene editing, genomic selection, rapid generation advancement, and recycling of elite material, along with high-throughput phenotyping tools such as drone- and satellite-based images and other speed-breeding techniques, have increased the precision, speed, and accuracy of new crop variety development. …”
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Optimizing role assignment for scaling innovations through AI in agricultural frameworks: An effective approach by Sonia Bisht, Ranjana, Swapnila Roy

“…By leveraging advanced algorithms and machine learning techniques, the research aims to streamline the allocation of tasks and responsibilities among various stakeholders, including farmers, agronomists, technicians, and AI systems. …”
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Real-Time Polarimetric Imaging and Enhanced Deep Learning Model for Automated Defect Detection of Specular Additive Manufacturing Surfaces by Dingkang Li, Xing Peng, Hongbing Cao, Yuanpeng Xie, Shiqing Li, Xiang Sun, Xinjie Zhao

“…Defect detection technology remains a research focus in AM process monitoring. While machine learning and neural network algorithms have recently achieved significant advancements in innovative applications for AM defect detection, practical implementations still face challenges, including insufficient detection accuracy and poor system robustness. …”
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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

“…These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. …”
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Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies by Md. Kabin Hasan Kanchon, Mahir Sadman, Kaniz Fatema Nabila, Ramisa Tarannum, Riasat Khan

“…Furthermore, decision tree, random forest, support vector machine (SVM), logistic regression, XGBoost, blending ensemble, and convolutional neural network (CNN) algorithms with corresponding optimized hyperparameters and synthetic minority oversampling technique (SMOTE) have been applied for learning behavior classification. …”
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Enhancing energy efficiency of industrial boiler application by the integration of ground-source heat pumps and photovoltaic-thermal solar water collectors by Praveen Barmavatu, Baburao Gaddala, Sharun Mendonca, Sonali Anant Deshmukh, Marco Rosales-Vera, Hussein Togun, Ramalinga Viswanathan Mangalaraja, Vineet Singh Sikarwar

“…Optimization was achieved using a hybrid approach that combined Genetic Algorithms (GA) and machine learning (ML) techniques, which iteratively improved system design and operational strategies. …”
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