Leveraging machine learning for data-driven building energy rate prediction by Nasim Eslamirad, Mehdi Golamnia, Payam Sajadi, Francesco Pilla

“…This paper presents a novel, data-driven approach for predicting Building Energy Ratings (BER) in urban environments, using advanced Machine Learning (ML) algorithms. …”
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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…This review critically examines the integration of machine learning (ML) with conventional water quality monitoring and treatment methods, presenting a systematic comparison of their capabilities, limitations, and synergies. …”
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An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection by Mohammad Rifat Ahmmad Rashid, Md. AL Ehtesum Korim, Mahamudul Hasan, Md Sawkat Ali, Mohammad Manzurul Islam, Taskeed Jabid, Raihan Ul Islam, Maheen Islam

“…To address this challenge, we present an Ensemble Learning Framework with Explainable AI (XAI) tailored to disease detection, using cucumber leaf diagnosis as a key use case. …”
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Multi-dimensional flux balance analysis to optimized resources and energy efficiency in MEC aided 5G networks by Rachit Patel, Rajeev Arya

“…Reinforcement learning is utilized to optimize the Signal’s signal-to-noise ratio (SNR), decreasing transmission power between the helper, relay, and edge server. …”
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A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage by Jonathan Gregory, Scott M. Miehls, Jesse L. Eickholt, Daniel P. Zielinski

“…This research presents a case study of a novel application that utilizes deep machine learning to detect partially dewatered fish exiting an Archimedes Screw Fish Lift (ASFL). …”
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Generalized domain prompt learning for accessible scientific vision-language models by Qinglong Cao, Yuntian Chen, Lu Lu, Hao Sun, Zhengzhong Zeng, Xiaokang Yang, Dongxiao Zhang

“…Extensive experiments across diverse domains—including remote sensing, medical imaging, geology, synthetic aperture radar, and fluid dynamics—demonstrate the effectiveness of the framework, achieving state-of-the-art domain recognition performance within a prompt learning structure. Our work presents a pathway for inclusive and sustainable research that bridges the gap between academia and industry, empowering smaller research groups and promoting broader access to cutting-edge advancements in the context of future sustainable development. …”
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Enhanced YOLOv8 Ship Detection Empower Unmanned Surface Vehicles for Advanced Maritime Surveillance by Abdelilah Haijoub, Anas Hatim, Antonio Guerrero-Gonzalez, Mounir Arioua, Khalid Chougdali

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Smart Elderly Healthcare Services in Industry 5.0: A Survey of Key Enabling Technologies and Future Trends by Xiaoling Wang, Li Huang, Dongni Wang, Linyu Liu, Peng Guo

“…This survey presents a comprehensive analysis of the transformative role of Industry 5.0 technologies in advancing smart elderly healthcare services, focusing on China’s evolving digital landscape. …”
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Zero Trust Strategies for Cyber-Physical Systems in 6G Networks by Abdulrahman K. Alnaim, Ahmed M. Alwakeel

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Incoherent dictionary learning and sparse representation for single-image rain removal by Hong-zhong TANG, Xiang WANG, Xiao-gang ZHANG, Xiao LI, Li-zhen MAO

“…The incoherent dictionary learning and sparse representation algorithm was present and it was applied to single-image rain removal.The incoherence of the dictionary was introduced to design a new objective function in the dictionary learning,which addressed the problem of reducing the similarity between rain atoms and non-rain atoms.The divisibility of rain dictionary and non-rain dictionary could be ensured.Furthermore,the learned dictionary had similar properties to the tight frame and approximates the equiangular tight frame.The high frequency in the rain image could be decomposed into a rain component and a non-rain component by performing sparse coding based learned incoherent dictionary,then the non-rain component in the high frequency and the low frequency were fused to remove rain.Experimental results demonstrate that the learned incoherent dictionary has better performance of sparse representation.The recovered rain-free image has less residual rain,and preserves effectively the edges and details.So the visual effect of recovered image is more sharpness and natural.…”
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Incoherent dictionary learning and sparse representation for single-image rain removal by Hong-zhong TANG, Xiang WANG, Xiao-gang ZHANG, Xiao LI, Li-zhen MAO

“…The incoherent dictionary learning and sparse representation algorithm was present and it was applied to single-image rain removal.The incoherence of the dictionary was introduced to design a new objective function in the dictionary learning,which addressed the problem of reducing the similarity between rain atoms and non-rain atoms.The divisibility of rain dictionary and non-rain dictionary could be ensured.Furthermore,the learned dictionary had similar properties to the tight frame and approximates the equiangular tight frame.The high frequency in the rain image could be decomposed into a rain component and a non-rain component by performing sparse coding based learned incoherent dictionary,then the non-rain component in the high frequency and the low frequency were fused to remove rain.Experimental results demonstrate that the learned incoherent dictionary has better performance of sparse representation.The recovered rain-free image has less residual rain,and preserves effectively the edges and details.So the visual effect of recovered image is more sharpness and natural.…”
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Asynchronous Real-Time Federated Learning for Anomaly Detection in Microservice Cloud Applications by Mahsa Raeiszadeh, Amin Ebrahimzadeh, Roch H. Glitho, Johan Eker, Raquel A. F. Mini

“…In our approach, edge clients perform real-time learning with continuous streaming local data. …”
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Spatial omics-based machine learning algorithms for the early detection of hepatocellular carcinoma by Mengjun Wang, Stephane Grauzam, Muhammed Furkan Bayram, James Dressman, Andrew DelaCourt, Calvin Blaschke, Hongyan Liang, Danielle Scott, Gray Huffman, Alyson Black, Shaaron Ochoa-Rios, David Lewin, Peggi M. Angel, Richard R. Drake, Lauren Ball, Jennifer Bethard, Stephen Castellino, Yuko Kono, Naoto Kubota, Yujin Hoshida, Lisa Quirk, Adam Yopp, Purva Gopal, Amit Singal, Anand S. Mehta

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Potato Leaf Disease Detection Based on a Lightweight Deep Learning Model by Chao-Yun Chang, Chih-Chin Lai

“…The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. …”
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Privacy Issues, Attacks, Countermeasures and Open Problems in Federated Learning: A Survey by Blessing Guembe, Sanjay Misra, Ambrose Azeta

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SapotaNet: Paving the Way for Efficient Deep Learning with Lightweight Network Architecture by Anita Bhatt, Maulin Joshi

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Federated Reinforcement Learning in Stock Trading Execution: The FPPO Algorithm for Information Security by Haogang Feng, Yue Wang, Shida Zhong, Tao Yuan, Zhi Quan

“…Stock trading execution is a critical component in the complex financial market landscape, and the development of a robust trade execution framework is essential for financial institutions pursuing profitability. This paper presents the Federated Proximal Policy Optimization (FPPO) algorithm, an adaptive trade execution framework that leverages joint reinforcement learning. …”
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Biological Motion-Based Emotion Recognition Through a Deep Learning Approach by Amjaad T. Alotaibi, Suhare Solaiman

“…In this study, biological motion was employed to attain cutting-edge results in the field of emotion recognition tasks, highlighting its importance in various affective computing applications.…”
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Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges by Márton Bendegúz Bankó, Szymon Dyszewski, Michaela Králová, Márton Bertalan Limpek, Maria Papaioannou, Gaurav Choudhary, Nicola Dragoni

“…This paper presents a systematic literature review based on the PRISMA model on machine learning-based Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks. …”
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Comparison of Deep Learning Techniques for RF-Based Human Posture Detection Systems by Eugene Casmin, Miriam Rodrigues, Americo Alves, Rodolfo Oliveira

“…We further show the slight edge that VAE-based solutions have over plain DL solutions in terms of accuracy.…”
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