Future Smart Grids Control and Optimization: A Reinforcement Learning Tool for Optimal Operation Planning by Federico Rossi, Giancarlo Storti Gajani, Samuele Grillo, Giambattista Gruosso

“…AI methods offer powerful tools for planning electrical systems, including electrical distribution networks. This study presents a methodology based on reinforcement learning (RL) for evaluating optimal power flow with respect to various cost functions. …”
Get full text

Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…Additionally, a novel graph learning module is designed to adaptively capture potential correlations between nodes during training. …”
Get full text

Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing by Nada Alasbali, Jawad Ahmad, Ali Akbar Siddique, Oumaima Saidani, Alanoud Al Mazroa, Asif Raza, Rahmat Ullah, Muhammad Shahbaz Khan

“…Most existing automated detection/classification approaches that utilize machine learning or deep learning poses privacy issues, as they involve centralized computing and require local storage for data training.MethodsKeeping the privacy of sensitive patient data as a primary objective, in addition to ensuring accuracy and efficiency, this paper presents an algorithm that integrates Federated learning techniques into an IoT-based edge-computing environment. …”
Get full text

Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning by Pengpai Li, Bowen Shao, Guoqing Zhao, Zhi-Ping Liu

“…Results In this study, we examined the biases inherent in ML models during the learning and prediction of protein-molecular interactions, particularly those arising from the scale-free property of biological networks—a characteristic where in a few nodes have many connections while most have very few. …”
Get full text

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions by Oumayma Jouini, Kaouthar Sethom, Abdallah Namoun, Nasser Aljohani, Meshari Huwaytim Alanazi, Mohammad N. Alanazi

“…The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.…”
Get full text

Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications by Saroj Mali, Feng Zeng, Deepak Adhikari, Inam Ullah, Mahmoud Ahmad Al-Khasawneh, Osama Alfarraj, Fahad Alblehai

“…Similarly, on the IID EMNIST dataset, the proposed D4PG model attains an F1-score of 0.82 and an accuracy of 82%, surpassing other Reinforcement Learning-based approaches. Additionally, for edge server power consumption, the hybrid offloading algorithm reduces fluctuations compared to existing methods, ensuring more stable energy usage across edge nodes. …”
Get full text

EasyHypergraph: an open-source software for fast and memory-saving analysis and learning of higher-order networks by Bodian Ye, Min Gao, Xiu-Xiu Zhan, Xinlei He, Zi-Ke Zhang, Qingyuan Gong, Xin Wang, Yang Chen

“…At the same time, they carry out hypergraph learning studies to learn better node representations by designing hypergraph neural network models. …”
Get full text

Multi-Task Learning for Real-Time BSIM-CMG Parameter Extraction of NSFETs With Multiple Structural Variations by Seunghwan Lee, Seungjoon Eom, Jinsu Jeong, Junjong Lee, Sanguk Lee, Hyeok Yun, Yonghwan Ahn, Rock-Hyun Baek

“…We present a novel multi-task learning (MTL) approach with shared representation for the real-time extraction of Berkeley Short-channel IGFET Model-Common Gate (BSIM-CMG) parameters in nanosheet field-effect transistors (NSFETs) with multiple structural variations. …”
Get full text

Developing a Predictive Model for Stroke Disease Detection Using a Scalable Machine Learning Approach by Assefa Senbato Genale, Tsion Ayalew Dessalegn

“…To address this issue, a scalable stroke disease prediction model for a multinode distributed environment, which was developed by combining big data analytics concepts with machine learning to handle extensive healthcare datasets, an aspect not seen in the prior literature on stroke disease detection, is presented in this work. …”
Get full text

Algorithm for link prediction in self-regulating network with adaptive topology based on graph theory and machine learning by Evgeny Y. Pavlenko

Get full text

Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times by Mikel Barrena-Herrán, Itziar Modrego-Monforte, Olatz Grijalba

“…The results demonstrate that by analyzing geotemporal data, we can classify urban nodes according to their hourly activity patterns. …”
Get full text

Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions by R. Vijay Anand, G. Magesh, I. Alagiri, Madala Guru Brahmam, Balamurugan Balusamy, Chithirai Pon Selvan, Haya Mesfer Alshahrani, Masresha Getahun, Ben Othman Soufiene

“…We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. …”
Get full text

Research on Citrus Leaf Disease Recognition Using Class-Agnostic Contrastive Learning and Supervised Organizational Mapping by Wen Xiao, Jinxia Shang, Fan Li, Ou Ao, Xin Wang, Shiyi Tian

Get full text

EADRL: Efficiency-aware adaptive deep reinforcement learning for dynamic task scheduling in edge-cloud environments by J. Anand, B. Karthikeyan

“…In dynamic edge–cloud environments, task scheduling must adapt to fluctuations in workload and resource conditions. This paper presents Efficiency-Aware Adaptive Deep Reinforcement Learning (EADRL), a framework that introduces two key mechanisms: an adaptive learning rate and a dynamic confidence-aware reward adjustment to enable intelligent context-aware scheduling decisions. …”
Get full text

A Framework Integrating Federated Learning and Fog Computing Based on Client Sampling and Dynamic Thresholding Techniques by Dang van Thang, Artem Volkov, Ammar Muthanna, Ibrahim A. Elgendy, Reem Alkanhel, Dushantha Nalin K. Jayakody, Andrey Koucheryavy

“…In this paper, we introduce a novel gradient-driven client-sampling framework that tightly couples Federated Learning with Fog Computing. By dynamically adjusting per-round thresholds based on local gradient change rates, our method selects only the most informative clients and leverages fog nodes for partial aggregation, thereby minimizing redundant transmissions, accelerating convergence under heterogeneous data, and offloading the central server. …”
Get full text

Federated XAI IDS: An Explainable and Safeguarding Privacy Approach to Detect Intrusion Combining Federated Learning and SHAP by Kazi Fatema, Samrat Kumar Dey, Mehrin Anannya, Risala Tasin Khan, Mohammad Mamunur Rashid, Chunhua Su, Rashed Mazumder

“…In this article, we propose a novel framework, FEDXAIIDS, converging federated learning and explainable AI. The proposed approach enables IDS models to be collaboratively trained across multiple decentralized devices while ensuring that local data remain securely on edge nodes, thus mitigating privacy risks. …”
Get full text

Efficient hardware implementation of interpretable machine learning based on deep neural network representations for sensor data processing by J. Schauer, P. Goodarzi, A. Schütze, T. Schneider

“…For this purpose, this paper presents an approach to represent trained interpretable machine learning algorithms, consisting of a stack of feature extraction, feature selection, and classification/regression algorithms, as deep neural networks. …”
Get full text

Prediction of weld quality in laser welding of hardmetal and steel using high-speed imaging and machine learning methods by Mohammadhossein Norouzian, Mahan Khakpour, Marko Orosnjak, Atal Anil Kumar, Slawomir Kedziora

“…Laser welding of steel and hardmetal presents significant challenges due to their differing material properties. …”
Get full text

A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care by Kunal Chandnani, Suryakant Tripathy, Ashutosh Krishna Parbhakar, Kshitij Takiar, Urvi Singhal, P. Sasikumar, S. Maheswari

“…Microcontrollers like Raspberry Pi and NodeMCU use intelligent machine-learning algorithms to process the collected data and trigger adaptive responses. …”
Get full text

Network-based analyses of multiomics data in biomedicine by Rachit Kumar, Joseph D. Romano, Marylyn D. Ritchie

“…Encoding such data in a network-based or graph-based representation allows the explicit incorporation of such relationships into various biomedical big data tasks, including (but not limited to) disease subtyping, interaction prediction, biomarker identification, and patient classification. This review will present various existing approaches in using network representations and analysis of data in multiomics in the framework of deep learning and machine learning approaches, subdivided into supervised and unsupervised approaches, to identify benefits and drawbacks of various approaches as well as the possible next steps for the field.…”
Get full text