AgriFusionNet: A Lightweight Deep Learning Model for Multisource Plant Disease Diagnosis by Saleh Albahli

“…This paper proposes AgriFusionNet, a lightweight and efficient deep learning model designed to diagnose plant diseases using multimodal data sources. …”
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Federated learning with tensor networks: a quantum AI framework for healthcare by Amandeep Singh Bhatia, David E Bernal Neira

“…In today’s context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning while effectively managing critical concerns regarding data privacy and governance. …”
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Deep learning time-series modeling for assessing land subsidence under reduced groundwater use by Chih-Yu Liu, Cheng-Yu Ku, Chuen‑Fa Ni

“…Abstract Intensive groundwater extraction and a severe 2021 drought have worsened land subsidence in Taiwan’s Choshui Delta, highlighting the need for effective predictive modeling to guide mitigation. In this study, we develop a machine learning framework for subsidence analysis using electricity consumption data from pumping wells as a proxy for groundwater extraction. …”
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Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks by C. Christy, A. Nirmala, A. Mary Odilya Teena, A. Isabella Amali

“…A new method for improving VANET security, a multi-stage Lightweight IntrusionDetection System Using Random Forest Algorithms (MLIDS-RFA), focuses on feature selection and ensemble models based on machine learning (ML). A multi-step approach is employed by the proposed system, with each stage dedicated to accurately detecting specific types of attacks. …”
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Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study by Qi Huang, Xiantong Zou, Zhouhui Lian, Xianghai Zhou, Xueyao Han, Yingying Luo, Shuohua Chen, Yanxiu Wang, Shouling Wu, Linong Ji

“…Both the trajectory and machine learning algorithm contributed significantly to the enhancement of model performance. …”
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Robust ConvLSTM Model With Deep Reinforcement Learning for Stealth Attack Detection in Smart Grids by Ahmad N. Alkuwari, Abdullatif Albaseer, Saif Al-Kuwari, Marwa Qaraqe

“…In response, anomaly detection models have been tested and evaluated against machine-generated adversarial attacks, such as the fast gradient sign method (FGSM) and Carlini and Wagner (C&W). …”
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Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes by Sheng Du, Li Jin, Zixin Huang, Xiongbo Wan

“…These studies collectively contribute to the body of knowledge on hybrid intelligent modeling technology and optimization strategy, offering practical solutions and theoretical frameworks to address energy conservation and consumption reduction.…”
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Temporally-consistent koopman autoencoders for forecasting dynamical systems by Indranil Nayak, Ananda Chakrabarti, Mrinal Kumar, Fernando L. Teixeira, Debdipta Goswami

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Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking by Harry Hoa Huynh, Cheol-Hong Min

“…Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. …”
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Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization by Prabhat Kumar Sahu, Taiyaba Fatma

“…This study presents a novel and optimized breast cancer classification system using machine learning models enhanced through advanced hyperparameter tuning techniques and statistical validation methods. …”
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Deep learning-based approach for extracting inflorescence morphology features in cut chrysanthemum by Shanpeng Xu, Jingshan Lu, Yin Wu, Huahao Liu, Fadi Chen, Fei Zhang, Sumei Chen, Weimin Fang, Zhiyong Guan

“…To address these limitations, we developed a lightweight deep learning and machine learning pipeline for automated trait extraction in over 30 chrysanthemum cultivars. …”
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An Empirically Validated Framework for Automated and Personalized Residential Energy-Management Integrating Large Language Models and the Internet of Energy by Vinícius Pereira Gonçalves, Andre Luiz Marques Serrano, Gabriel Arquelau Pimenta Rodrigues, Matheus Noschang de Oliveira, Rodolfo Ipolito Meneguette, Guilherme Dantas Bispo, Maria Gabriela Mendonça Peixoto, Geraldo Pereira Rocha Filho

“…The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. …”
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Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites by Ghazaleh Ramezani, Ixchel Ocampo Silva, Ion Stiharu, Theo G. M. van de Ven, Vahe Nerguizian

“…The findings, supported by machine learning optimization, have significant implications for flexible electronics, smart packaging, and biomedical applications, paving the way for future research on scalability, long-term stability, and advanced modeling techniques for these sustainable, multifunctional materials.…”
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Explainable light-weight deep learning pipeline for improved drought stress identification by Aswini Kumar Patra, Aswini Kumar Patra, Lingaraj Sahoo

“…Sensor-based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis that aims to identify drought stress. …”
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Predicting Early Outcomes of Prostatic Artery Embolization Using <i>n</i>-Butyl Cyanoacrylate Liquid Embolic Agent: A Machine Learning Study by Burak Berksu Ozkara, David Bamshad, Ramita Gowda, Mert Karabacak, Vivian Bishay, Kirema Garcia-Reyes, Ardeshir R. Rastinehad, Dan Shilo, Aaron Fischman

“…Nevertheless, a proportion of patients undergoing PAE fail to demonstrate clinical improvement. Machine learning models have the potential to provide valuable prognostic insights for patients undergoing PAE. …”
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Reducing Defense Vulnerabilities in Federated Learning: A Neuron-Centric Approach by Eda Sena Erdol, Hakan Erdol, Beste Ustubioglu, Guzin Ulutas, Iraklis Symeonidis

“…Federated learning is a distributed machine learning approach where end users train local models with their own data and combine model updates on a reliable server to create a global model. …”
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The Influence of Running Technique Modifications on Vertical Tibial Load Estimates: A Combined Experimental and Machine Learning Approach in the Context of Medial Tibial Stress Syn... by Taylor Miners, Jeremy Witchalls, Jaquelin A. Bousie, Ceridwen R. Radcliffe, Phillip Newman

“…This study investigated whether changes to speed, cadence, stride length, and foot-strike pattern influence vGRF and TA. Additionally, machine-learning models were evaluated for their ability to estimate vGRF metrics. …”
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Connecting climate science and society: reflections from early and mid-career researchers at the World Climate Research Programme Open Science Conference 2023 by Leandro B. Díaz, Leandro B. Díaz, Leandro B. Díaz, Carla Gulizia, Carla Gulizia, Carla Gulizia, Shipra Jain, Shipra Jain, Gaby S. Langendijk, Hindumathi Palanisamy, Valentina Rabanal, Valentina Rabanal, Candice S. Charlton, Alima Dajuma, Felix K. Donkor, Temitope S. Egbebiyi, Temitope S. Egbebiyi, Ghafi Kondi Akara, Ghafi Kondi Akara, Amna Jrrar, Md Rezwan Siddiqui, Yassine Ait Brahim, Yassine Ait Brahim, Bapentire D. Angnuureng, Anna-Adriana Anschütz, Founi M. Awo, Founi M. Awo, Alejandro Builes-Jaramillo, Alison Cobb, Mohamadou A. Diallo, Mohamadou A. Diallo, Houda Er-Retby, Houda Er-Retby, Houda Er-Retby, Thierry C. Fotso-Nguemo, Benjamin Gwinneth, Masilin Gudoshava, Franck E. Kemgang Ghomsi, Franck E. Kemgang Ghomsi, Franck E. Kemgang Ghomsi, Inés M. Leyba, Inés M. Leyba, Redouane Lguensat, Redouane Lguensat, Roché Mahon, Shailendra K. Mandal, Lucia Mumo, Meshel Muzuva, Meshel Muzuva, Rodric M. Nonki, Juan A. Rivera, Juan A. Rivera, Alcade C. Segnon, Alcade C. Segnon, Laouali I. Tanimoune, Erlich-Honest E. I. Zauisomue, Gilbert Siame

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Data-Driven Computational Methods in Fuel Combustion: A Review of Applications by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…This review article provides a comprehensive analysis of the recent advancements in combustion science and engineering, focusing on the application of machine learning and genetic algorithms from 2015 to 2024. …”
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Cortical Adaptation Dynamics in Human-Exoskeleton Interaction Using Multi-Model AMICA by Jasim Naeem, Seongmi Song, Michael Nonte, Courtney A. Haynes, J. Cortney Bradford

“…The human-machine interface is a crucial component of exoskeleton design, and understanding how the human nervous system adapts to and learns to coordinate with wearable robotic systems is essential for optimizing assistive device functionality. …”
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