Accuracy-Aware MLLM Task Offloading and Resource Allocation in UAV-Assisted Satellite Edge Computing by Huabing Yan, Hualong Huang, Zijia Zhao, Zhi Wang, Zitian Zhao

“…This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. …”
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A Survey of Machine Learning Techniques Leveraging Brightness Indicators for Image Analysis in Biomedical Applications by Hajer Ghodhbani, Suvendi Rimer, Khmaies Ouahada, Adel M. Alimi

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UAVs and Blockchain Synergy: Enabling Secure Reputation-Based Federated Learning in Smart Cities by Syed M. Aqleem Abbas, Muazzam A. Khan Khattak, Wadii Boulila, Anis Kouba, M. Shahbaz Khan, Jawad Ahmad

“…To tackle these challenges, we present a differentially private federated learning framework based on Accumulative Reputation-based Selection (ARS) for the edge-aided UAV network that utilizes blockchains to prevent single-point failures where we switched from central control to decentralized control, Interplanetary File System (IPFS) for off-chain model storage and their respective hash-keys on-chain to ensure model integrity. …”
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Voice-activated home automation system for IoT edge devices using TinyML by Timothy Malche, Sandeep Budhani, Pramod Kumar Soni, Govind Murari Upadhyay

“…Deploying complex machine learning models on Internet of Things (IoT) devices with limited resources is still difficult. …”
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Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain by Yuzhou Chen, Gang Mao, Xue Yang, Mingqian Du, Hongqing Song

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Lightweight Deep Learning Model for Fire Classification in Tunnels by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…Tunnel fires pose a severe threat to human safety and infrastructure, necessitating the development of advanced and efficient fire detection systems. This paper presents a novel lightweight deep learning (DL) model specifically designed for real-time fire classification in tunnel environments. …”
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GAPO: A Graph Attention-Based Reinforcement Learning Algorithm for Congestion-Aware Task Offloading in Multi-Hop Vehicular Edge Computing by Hongwei Zhao, Xuyan Li, Chengrui Li, Lu Yao

“…Efficient task offloading for delay-sensitive applications, such as autonomous driving, presents a significant challenge in multi-hop Vehicular Edge Computing (VEC) networks, primarily due to high vehicle mobility, dynamic network topologies, and complex end-to-end congestion problems. …”
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IoT-Edge Hybrid Architecture with Cross-Modal Transformer and Federated Manifold Learning for Safety-Critical Gesture Control in Adaptive Mobility Platforms by Xinmin Jin, Jian Teng, Jiaji Chen

“…This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. …”
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On-Demand Gait-Synchronous Electrical Cueing in Parkinson's Disease Using Machine Learning and Edge Computing: A Pilot Study by Ardit Dvorani, Constantin Wiesener, Christina Salchow-Hommen, Magdalena Jochner, Lotta Spieker, Matej Skrobot, Hanno Voigt, Andrea Kuhn, Nikolaus Wenger, Thomas Schauer

“…This solution relies on edge computing on the devices' hardware. The wearable system consists of a single inertial sensor to control a stimulator and enables machine-learning-based FoG detection by classifying foot motion phases as either normal or FoG-affected. …”
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A review on machine learning applications in hydrogen energy systems by Zaid Allal, Hassan N. Noura, Ola Salman, Flavien Vernier, Khaled Chahine

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EnsembleEdgeFusion: advancing semantic segmentation in microvascular decompression imaging with innovative ensemble techniques by B. Dhiyanesh, M. Vijayalakshmi, P. Saranya, D. Viji

“…In response to this challenge, this study presents a meticulously curated dataset comprising 2003 RGB microvascular decompression images, each intricately paired with annotated masks. …”
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Multimodal Fusion and Cutting-Edge AI-Based Smart Vending Machines for Electronic Component Management by M. Karthiga, Syarifah Bahiyah Rahayu, E. Suganya, S. Sankarananth, S. Sountharrajan, K. Venkatesan

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10T SRAM Computing-in-Memory Macros for Binary and Multibit MAC Operation of DNN Edge Processors by Van Truong Nguyen, Jie-Seok Kim, Jong-Wook Lee

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Multitask Learning-Based Pipeline-Parallel Computation Offloading Architecture for Deep Face Analysis by Faris S. Alghareb, Balqees Talal Hasan

“…Therefore, this manuscript explores levels of parallelism techniques along with edge computation offloading to develop an innovative hardware platform that improves the efficacy of deep learning computing architectures. …”
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Embedded Hardware-Efficient FPGA Architecture for SVM Learning and Inference by B. B. Shabarinath, Muralidhar Pullakandam

“…Edge computing allows to do AI processing on devices with limited resources, but the challenge remains high computational costs followed by the energy limitations of such devices making on-device machine learning inefficient, especially for Support Vector Machine (SVM) classifiers. …”
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IoT device for detecting abnormal vibrations in motors using TinyML by Stalin Arciniegas, Dulce Rivero, Jefferson Piñan, Elizabeth Diaz, Francklin Rivas

“…We developed a system that integrates spectral analysis and deep learning on a resource-constrained edge device, enabling real-time monitoring and anomaly detection. …”
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RiceLeafClassifier‐v1.0: A Quantized Deep Learning Model for Automated Rice Leaf Disease Detection and Edge Deployment by Oluwaseun O. Martins, Christiaan C. Oosthuizen, Dawood A. Desai

“…Training enhancements included data augmentation, dropout, dynamic learning rate scheduling, and early stopping. Unlike previous transfer learning approaches, RiceLeafClassifier‐v1.0 was built from scratch to retain fine visual features while remaining efficient. …”
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Optimizing anomaly detection models for edge IIoT with an enhanced firefly algorithm-based hyperparameter tuning strategy by Mohemmed Yousuf Rahamathulla, Mangayarkarasi Ramaiah

“…By means of adaptive attack detection models, machine learning (ML) presents a potential solution. High-dimensional hyperparameter spaces, insufficient labeled data for different attack labels, limited computational resources on edge devices, and the need for real-time detection are just a few of the difficulties classical ML techniques confront, though. …”
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Block-Based Adaptive Compressed Sensing by Using Edge Information for Real-Time Reconstruction by V. Pavitra, V. B. S. Srilatha Indira Dutt

“…As part of this paper, we introduce a novel adaptive measurement allocation technique based on edge information with in the block. The algorithm is further improved by giving special considerations for edges present at the block boundary. …”
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A Novel Skin Cancer Detection Approach Using Deep Learning Algorithm with Image Segmentation Filters by Awf A. Ramadhan, Omer S. Kareem, Diyar Q. Zeebaree

“…This paper presents a deep learning model based on the convolutional neural network algorithm to provide automatic detection of skin cancer. …”
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