A Survey of Machine Learning Techniques for Optimal Capacitor Placement and Sizing in Smart Distribution Networks by Kwabena Addo, Katleho Moloi, Musasa Kabeya, Evans Eshiemogie Ojo

“…Traditional optimization techniques, while effective, often struggle with dynamic system behaviors, nonlinear loads, and real-time operational constraints. This paper presents a comprehensive review of machine learning (ML)-based methodologies for optimal capacitor placement and sizing, focusing on their ability to enhance voltage stability, minimize power losses, and improve overall grid efficiency in smart distribution networks. …”
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Learning Constitutive Relations From Soil Moisture Data via Physically Constrained Neural Networks by Toshiyuki Bandai, Teamrat A. Ghezzehei, Peishi Jiang, Patrick Kidger, Xingyuan Chen, Carl I. Steefel

“…The limited degrees of freedom of such soil hydraulic models constrain our ability to extract soil hydraulic properties from soil moisture data via inverse modeling. We present a new free‐form approach to learning the constitutive relations using physically constrained neural networks. …”
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Multi-Objective Optimization of Machine Learning-Based Nonlinear Equalizers for Digital Coherent Optical Interconnects by Lucas C. Dantas, Hildo Guillardi Junior, Plinio Dester, Rafael A. Penchel, Jose Augusto de Oliveira, Marcelo L. F. Abbade, Leandra I. Abreu, Jinlong Wei, Ivan Aldaya

“…Traditional nonlinear compensation techniques, such as the Inverse Volterra Series Transfer Function (IVSTF) and Digital Back Propagation (DBP), are computationally expensive and require oversampling. Machine learning-based equalizers offer reduced complexity and improved adaptability. …”
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Multi-Class Intrusion Detection in Internet of Vehicles: Optimizing Machine Learning Models on Imbalanced Data by Ágata Palma, Mário Antunes, Jorge Bernardino, Ana Alves

“…The Internet of Vehicles (IoV) presents complex cybersecurity challenges, particularly against Denial-of-Service (DoS) and spoofing attacks targeting the Controller Area Network (CAN) bus. …”
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Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides by I. Garberis, V. Gaury, C. Saillard, D. Drubay, K. Elgui, B. Schmauch, A. Jaeger, L. Herpin, J. Linhart, M. Sapateiro, F. Bernigole, A. Kamoun, A. Filiot, O. Tchita, R. Dubois, M. Auffret, L. Guillou, I. Bousaid, M. Azoulay, J. Lemonnier, M. Sefta, S. Everhard, A. Sarrazin, J-F Reboud, F. Brulport, J. Dachary, B. Pistilli, S. Delaloge, P. Courtiol, F. André, V. Aubert, M. Lacroix-Triki

“…Our deep learning model, RlapsRisk BC, independently predicts MFS and provides significant prognostic value beyond traditional clinico-pathological variables (C-index 0.81 vs 0.76, p < 0.05). …”
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Deep Reinforcement Learning-Based Resource Allocation for QoE Enhancement in Wireless VR Communications by Georgios Kougioumtzidis, Vladimir K. Poulkov, Pavlos I. Lazaridis, Zaharias D. Zaharis

“…This research paper presents a novel approach to address the challenge of enhancing QoE by incorporating deep reinforcement learning (DRL) techniques in the resource allocation process. …”
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Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images by Md Abdullah All Mahmud, Sadia Afrin, M. F. Mridha, Sultan Alfarhood, Dunren Che, Mejdl Safran

“…Abstract Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. …”
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Chemical Composition-Driven Machine Learning Models for Predicting Ionic Conductivity in Lithium-Containing Oxides by Yudai IWAMIZU, Kota SUZUKI, Michiyo KAMIYA, Naoki MATSUI, Kuniharu NOMOTO, Satoshi HORI, Masaaki HIRAYAMA, Ryoji KANNO

“…Thus, new models demonstrating improved prediction ability must be developed. This study presents the development of machine learning models for the accurate prediction of ionic conductivity in lithium-containing materials based solely on their chemical composition. …”
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Optimizing navigation and chemical application in precision agriculture with deep reinforcement learning and conditional action tree by Mahsa Khosravi, Zhanhong Jiang, Joshua R. Waite, Sarah E. Jones, Hernan Torres Pacin, Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh, Soumik Sarkar

“…This paper presents a novel reinforcement learning (RL)-based planning scheme for optimized robotic management of biotic stresses in precision agriculture. …”
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Computationally Efficient Transfer Learning Pipeline for Oil Palm Fresh Fruit Bunch Defect Detection by Yang Luo, Anwar P. P. Abdul Majeed, Zaid Omar, Saad Aslam, Yi Chen

“…The present study addresses the inefficiencies of the manual classification of oil palm fresh fruit bunches (FFBs) by introducing a computationally efficient alternative to traditional deep learning approaches that require extensive retraining and large datasets. …”
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Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides by Ching-Wei Wang, Nabila Puspita Firdi, Yu-Ching Lee, Tzu-Chiao Chu, Hikam Muzakky, Tzu-Chien Liu, Po-Jen Lai, Tai-Kuang Chao

“…Traditional TMB prediction methods, such as sequencing exomes or whole genomes, are costly and often unavailable in clinical settings. We present the first TR-MAMIL deep learning framework to predict TMB status and classify the EC cancer subtype directly from H&E-stained WSIs, enabling effective personalized immunotherapy planning and prognostic refinement of EC patients. …”
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On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces by Yingkun Zhu, Haomin Song, Ruiying Liu, Yunyun Mu, Murali Gedda, Abdullah N. Alodhay, Lei Ying, Qiaoqiang Gan

“…Abstract The global surge in opioid misuse, particularly fentanyl, presents a formidable public health challenge, highlighted by increasing drug-related mortalities. …”
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Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning by Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, Dake Chen

“…This paper presents an advanced graph convolutional network model, enhanced with Wasserstein distance-based adversarial learning (WD-ACGN), addressing the limitations of existing single-station and less explored multi-station water level forecasting approaches. …”
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Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques by Althaf Ali A, Rama Devi K, Syed Siraj Ahmed N, Ramchandran P, Parvathi S

“…The proliferation of malicious webpages presents a growing threat to online security, necessitating advanced detection methods to mitigate risks. …”
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Challenges of manufacturing for energy efficiency: towards a systematic approach through applications of machine learning by Elaheh Gholamzadeh Nabati, Maria Teresa Alvela Nieto, Dennis Bode, Thimo Florian Schindler, André Decker, Klaus-Dieter Thoben

“…Main findings The main result is a 5-step approach for increasing the energy efficiency of manufacturing processes through machine learning. Essential applications and technical challenges for data mapping, integrating, modelling, implementing, and deploying machine learning algorithms in manufacturing processes for increasing energy efficiency are presented. …”
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Detection of Transformer Faults: AI-Supported Machine Learning Application in Sweep Frequency Response Analysis by Hakan Çuhadaroğlu, Yılmaz Uyaroğlu

“…Six different machine learning algorithms were applied to detect these conditions. …”
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A dual scheduling framework for task and resource allocation in clouds using deep reinforcement learning by Jiahui Pan, Yi Wei, Lei Meng, Xiangxu Meng

“…Accordingly, we propose a dual scheduling approach based on deep reinforcement learning, which consists of two layers of Deep Q-Network algorithms to realize these functions. …”
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Machine Learning for Anomaly Detection in Blockchain: A Critical Analysis, Empirical Validation, and Future Outlook by Fouzia Jumani, Muhammad Raza

“…This study comprehensively analyzes various machine learning (ML) methods to detect anomalies in blockchain networks. …”
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Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data. by Dominik Schaack, Markus A Weigand, Florian Uhle

“…Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). …”
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Adaptive Reconfigurable Learning Algorithm for Robust Optimal Longitudinal Motion Control of Unmanned Aerial Vehicles by Omer Saleem, Aliha Tanveer, Jamshed Iqbal

“…This study presents the formulation and verification of a novel online adaptive reconfigurable learning control algorithm (RLCA) for improved longitudinal motion control and disturbance compensation in Unmanned Aerial Vehicles (UAVs). …”
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