Location-Aware Ambient Sound Model Adaptation for On-Device Human Activity Recognition in Living Spaces by Cheolhwan Lee, Homin Kang, Soon Ju Kang

“…To mitigate domain shift, edge-based transfer learning fine-tunes a pre-trained model using locally collected data, improving classification accuracy across varying acoustic conditions. …”
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Advancements in epilepsy classification: Current trends and future directions by Rajani Rai B, Karunakara Rai B, Mamatha A S, Kavitha Sooda

“…The paper synthesizes cutting-edge techniques with the focus on the application of hybrid models that combine traditional signal processing techniques with machine learning algorithms. …”
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CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images by Tewodros Gizaw Tohye, Zhiguang Qin, Mugahed A. Al-antari, Chiagoziem C. Ukwuoma, Zenebe Markos Lonseko, Yeong Hyeon Gu

“…Cutting-edge deep learning techniques, such as vision transformers (ViTs), have been employed to tackle the challenge of early glaucoma detection. …”
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WGCAMNet: Wasserstein Generative Adversarial Network Augmented and Custom Attention Mechanism Based Deep Neural Network for Enhanced Brain Tumor Detection and Classification by Fatema Binte Alam, Tahasin Ahmed Fahim, Md Asef, Md Azad Hossain, M. Ali Akber Dewan

“…Brain tumor detection and categorization of its subtypes are essential for early diagnosis and improving patient outcomes. This research presents a cutting-edge approach that employs advanced data augmentation and deep learning methodologies for brain tumor classification. …”
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A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions by Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Yannis Stamatiou

“…With real-life case studies from several sectors to complement our exploration of cutting-edge methods in big data management, we present useful applications and results. …”
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The backpropagation algorithm implemented on spiking neuromorphic hardware by Alpha Renner, Forrest Sheldon, Anatoly Zlotnik, Louis Tao, Andrew Sornborger

“…It is competitive in accuracy with off-chip trained SNNs and achieves an energy-delay product suitable for edge computing. This implementation shows a path for using in-memory, massively parallel neuromorphic processors for low-power, low-latency implementation of modern deep learning applications.…”
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Development of Digital Training Twins in the Aircraft Maintenance Ecosystem by Igor Kabashkin

“…This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. …”
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Remote Sensing Applications in Ocean Observation (Second Edition) by Chung-Ru Ho

“…The articles presented in this Special Issue epitomize the convergence of cutting-edge sensor technologies, innovative data processing techniques, and advanced algorithmic approaches in ocean remote sensing. …”
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Advancements and Challenges in Microgrid Technology: A Comprehensive Review of Control Strategies, Emerging Technologies, and Future Directions by Ark Dev, Vineet Kumar, Gaurav Khare, Jayant Giri, Mohammad Amir, Furkan Ahmad, Prince Jain, Sumant Anand

“…Additionally, the paper examines the application of cutting‐edge technologies like machine learning, blockchain, reinforcement learning, neural networks, edge computing, and the internet of things (IoT) to mitigate concerns in expanding MG systems. …”
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Air quality prediction based on factor analysis combined with Transformer and CNN-BILSTM-ATTENTION models by Shuyuan Liu, Yang Hu

“…Abstract This study presents an innovative air quality prediction framework that integrates factor analysis with deep learning models for precise prediction of original variables. …”
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Proximal Policy Optimization-based Task Offloading Framework for Smart Disaster Monitoring using UAV-assisted WSNs by C.N. Vanitha, P. Anusuya, Rajesh Kumar Dhanaraj, Dragan Pamucar, Mahmoud Ahmad Al-Khasawneh

“…However, challenges such as limited energy resources, dynamic task allocation, and UAV trajectory optimization remain critical. This paper presents Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs (ETORL-UAV), a novel framework that integrates Proximal Policy Optimization (PPO) based reinforcement learning to intelligently manage UAV-assisted operations in edge-enabled WSNs. …”
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Controllability robustness of complex networks by Guanrong Chen

“…This article presents an overview on the state-of-the-art development in complex network controllability and its robustness against malicious attacks and random failures. …”
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E-Commerce Opportunities in the Web 5.0 Era of Innovative Entrepreneurship by Maria Cristina Enache

“…Global trade is being revolutionized by the integration of cutting-edge digital technologies such as blockchain, machine learning, artificial intelligence (AI), and the Internet of Things (IoT). …”
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LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology by Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan

“…This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. …”
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Insights into Galaxy Evolution from Interpretable Sparse Feature Networks by John F. Wu

“…Galaxy appearances reveal the physics of how they formed and evolved. Machine learning (ML) models can now exploit galaxies’ information-rich morphologies to predict physical properties directly from image cutouts. …”
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MEIS-YOLO: Improving YOLOv11 for efficient aerial object detection with lightweight design by Yicheng Liu, Jinsong Wu, Li Chen

“…With the advancement of aerial technologies like drones and satellites, deep learning-driven object detection has seen considerable improvements in the processing of aerial images. …”
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Accelerating AI performance with the incorporation of TVM and MediaTek NeuroPilot by Chao-Lin Lee, Chun-Ping Chung, Sheng-Yuan Cheng, Jenq-Kuen Lee, Robert Lai

“…The continuing prominence of machine learning has led to an increased focus on enhancing the inference performance of edge devices to reduce latency and improve efficiency. …”
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Classifying seismic event parameters using artificial intelligence by Abbas Maazallahi, Kameron Bustos, Mohammad Amir Salari, Eli Snir, Payam Norouzzadeh, Bahareh Rahmani

“…This paper addresses the critical need for advanced seismic event analysis by combining traditional data analysis with cutting-edge machine learning models. The primary objective is to develop models that classify seismic events into different types based on their geological and seismic characteristics and their magnitude. …”
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Real-time aerial fire detection on resource-constrained devices using knowledge distillation by Sabina Jangirova, Branislava Jankovic, Waseem Ullah, Latif U. Khan, Mohsen Guizani

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PlantNet: Scalable Convolutional Neural Network for Image-Based Plant Disease Detection by Sinha Anupa, Kumaraswamy Balasubramaniam

“…Furthermore, the system's scalability makes it suitable for deployment across various agricultural environments, including mobile-based and edge computing applications. Overall, PlantNet demonstrates the potential of deep learning-based image analysis in advancing automated plant disease diagnostics, offering a promising tool for precision agriculture and sustainable farming practices in both developed and developing regions.…”
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