A Deep Learning Framework for the Classification of Brazilian Coins by Debabrata Swain, Viral Rupapara, Amro Nour, Santosh Satapathy, Biswaranjan Acharya, Shakti Mishra, Ali Bostani

“…At the same time, image-based methods rely on other properties like color, shape, and edge. This paper presents a novel deep-learning framework tailored to classify Brazilian coins. …”
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Overview of Modern Technologies for Acquiring and Analysing Acoustic Information Based on AI and IoT by Sabina Szymoniak, Łukasz Kuczyński

“…Thanks to the development of Internet of Things (IoT) and artificial intelligence (AI) technologies, it has become possible to create distributed, intelligent acoustic systems used in medicine, industry, cities, and the natural environment. The article presents an overview of modern methods of acquiring and analysing sound data, from MEMS sensors and microphones, signal processing, and feature extraction to machine learning algorithms. …”
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Adaptive high frequency data streaming for Soft Real-Time Industrial AI: A scalable microservices based architecture with dynamic downsampling by Telmo Fernández De Barrena, Alcides Fernandes, Juan Luis Ferrando, Ander García, Hugo Landaluce, Ignacio Angulo

“…While high-frequency data streams provide valuable insights, they also introduce network congestion, storage limitations, and computational overhead. This paper presents a novel IoT architecture wherein an Edge server dynamically down-samples sensor data before transmission to a Fog (central) server equipped with upsampling, feature extraction, machine learning (ML) inference, and network latency analysis services. …”
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Learning Graph Structures With Autoregressive Graph Signal Models by Kyle Donoghue, Ashkan Ashrafi

“…This paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals with propagation delay. …”
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Quality medical data management within an open AI architecture – cancer patients case by Mirjana Ivanovic, Serge Autexier, Miltiadis Kokkonidis, Johannes Rust

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Deep learning-driven IoT solution for smart tomato farming by Akshit Saxena, Aayushi Agarwal, Bhavya Nagrath, Carmel Sanjana Jayavanth, Shamita Thulasidoss, S. Maheswari, P. Sasikumar

“…Abstract The rising food demand and challenges with respect to the climate have made precision agriculture (PA) vital for sustainable crop production. This study presents an IoT-based smart greenhouse platform tailored for tomato farming, integrating environmental sensing and deep learning. …”
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Enhancing Air Quality Forecasting Using Machine Learning Techniques by Zeinab Shahbazi, Zahra Shahbazi, Slawomir Nowaczyk

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Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning by Shaowei Wang, Sufen Zeng, Jin Li, Shaozheng Huang, Yuyang Chen

“…In decentralized scenarios without fully trustable parties (e.g., in mobile edge computing or IoT environments), the shuffle model has recently emerged as a promising paradigm for differentially private federated learning. …”
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Artificial Intelligence in Healthcare A Review of Machine Learning Applications by Karajgi Santosh, R Vijaya Prakash, Kumar K. Gagan, Selvi P Tamil, Shankar Bhukya, S Jeyanthi

“…Despite its potential, however, there are significant barriers to widespread adoption, such as data privacy issues, high computational costs, AI bias, lack of standardized evaluation, regulatory barriers, and integration with legacy healthcare systems. At present, the challenges explored highlight the need for federated learning as a new way to train AI without exposing sensitive patient data, bias-aware models which promote equitable and fair healthcare decisions for all patients, cloud and edge AI to ensure that processing is cost effective and appropriate, and Explainable AI (XAI) to promote trust and transparency to patients and communities. …”
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Reservoir direct feedback alignment: deep learning by physical dynamics by Mitsumasa Nakajima, Yongbo Zhang, Katsuma Inoue, Yasuo Kuniyoshi, Toshikazu Hashimoto, Kohei Nakajima

“…Abstract The rapid advancement of deep learning has motivated various analog computing devices for energy-efficient non-von Neuman computing. …”
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An In-Memory-Computing Binary Neural Network Architecture With In-Memory Batch Normalization by Prathamesh Prashant Rege, Ming Yin, Sanjay Parihar, Joseph Versaggi, Shashank Nemawarkar

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Acceleration of Urdu Optical Character Recognition on Zynq UltraScale+ MPSoC Using Deep Convolutional Neural Network by Fauzia Yasir, Majida Kazmi

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Lightweight Stereo Matching for Real-Time Applications With 2D Cost Volume Aggregation by Thai la, Linh Tao, Dai Watanabe

“…Despite the significant advancements in learning-based stereo matching algorithms, a significant challenge remains: the high computational cost and memory demands of 3D convolutions, which hinder real-time deployment on resource-constrained platforms like edge devices. …”
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Enhancing the cognitive load theory and multimedia learning framework with AI insight by Khanyisile Twabu

“…By bridging the gap between traditional cognitive theories and cutting-edge AI technologies, this framework supports adaptive cognitive load management, AI-mediated schema creation, and human-AI collaborative learning. …”
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Machine Learning Meets Communication Networks: Current Trends and Future Challenges by Ijaz Ahmad, Shariar Shahabuddin, Hassan Malik, Erkki Harjula, Teemu Leppanen, Lauri Loven, Antti Anttonen, Ali Hassan Sodhro, Muhammad Mahtab Alam, Markku Juntti, Antti Yla-Jaaski, Thilo Sauter, Andrei Gurtov, Mika Ylianttila, Jukka Riekki

“…Important future research challenges are identified and presented to help stir further research in key areas in this direction.…”
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Deep dive into deep learning methods for cervical cancer detection and classification by Pooja Patre, Dipti Verma

“…This review presents a thorough analysis of deep learning methods utilized for cervical cancer diagnosis, with an emphasis on critical approaches, evaluation metrics, and the ongoing challenges faced in the field. …”
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Deep-Learning Framework for Efficient Real-Time Speech Enhancement and Dereverberation by Tomer Rosenbaum, Emil Winebrand, Omer Cohen, Israel Cohen

“…Deep learning has revolutionized speech enhancement, enabling impressive high-quality noise reduction and dereverberation. …”
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Modern Deep Learning Techniques for Big Medical Data Processing in Cloud by Mohammed Y. Shakor, Mustafa Ibrahim Khaleel

“…Finally, it concludes by discussing the trends and future directions that are upcoming, including federated learning, explainable AI, and edge computing. Additionally, an experimental case study is presented to validate our approach, demonstrating its effectiveness in real-world healthcare scenarios through improved diagnostic accuracy and reduced computational time. …”
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A neuromorphic processor with on-chip learning for beyond-CMOS device integration by Hugh Greatorex, Ole Richter, Michele Mastella, Madison Cotteret, Philipp Klein, Maxime Fabre, Arianna Rubino, Willian Soares Girão, Junren Chen, Martin Ziegler, Laura Bégon-Lours, Giacomo Indiveri, Elisabetta Chicca

“…The processor provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research.…”
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Adaptive deep feature representation learning for cross-subject EEG decoding by Shuang Liang, Linzhe Li, Wei Zu, Wei Feng, Wenlong Hang

“…Specifically, we first minimize the distribution discrepancy between the source and target domains by employing maximum mean discrepancy (MMD) regularization, which aids in learning the shared feature representations. We then utilize the instance-based discriminative feature learning (IDFL) regularization to make the learned feature representations more discriminative. …”
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