Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…Specifically, our precision reached 96.10%, sensitivity was 95.35%, specificity achieved 97.06%, and accuracy was 96.10%. This research enhances the precision of DR diagnosis by applying it to different publicly accessible datasets. …”
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Detection of Coffee Leaf Miner Using RGB Aerial Imagery and Machine Learning by Emerson Ferreira Vilela, Cileimar Aparecida da Silva, Jéssica Mayara Coffler Botti, Elem Fialho Martins, Charles Cardoso Santana, Diego Bedin Marin, Agnaldo Roberto de Jesus Freitas, Carolina Jaramillo-Giraldo, Iza Paula de Carvalho Lopes, Lucas de Paula Corrêdo, Daniel Marçal de Queiroz, Giuseppe Rossi, Gianluca Bambi, Leonardo Conti, Madelaine Venzon

“…A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). …”
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Review of Demand Response-based Optimal Scheduling of Electric and Thermal Integrated Energy Systems by Jingshan MO, Guangxian YAN, Na SONG, Mingyang YUAN

“…Based on this, the current research status of the three kinds of integrated electric and thermal energy system scheduling models, including the basic, flexibility, and stochastic models, classified based on differences in application scenarios, is elaborated. …”
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Expanded Design by Roberto Bottazzi

“…The application of such algorithmic models to design will be supported by the research developed by students part of Research Cluster 14 part of the Master in Urban Design at The Bartlett School of Architecture in London.  …”
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An optimized ensemble model with advanced feature selection for network intrusion detection by Afaq Ahmed, Muhammad Asim, Irshad Ullah, Zainulabidin, Abdelhamied A. Ateya

“…To address this challenge, our study presents the “Optimized Random Forest (Opt-Forest),” an innovative ensemble model that combines decision forest approaches with genetic algorithms (GAs) for enhanced intrusion detection. The genetic algorithms based decision forest construction offers notable benefits by traversing a wider exploration space and mitigating the risk of becoming stuck in local optima, resulting in the discovery of more accurate and compact decision trees. …”
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A Review of Modeling and Control Techniques for Unmanned Aerial Vehicles by Elisabeth Andarge Gedefaw, Nardos Belay Abera, Chala Merga Abdissa

“…The findings highlight that hybrid control strategies incorporating adaptive mechanisms, learning‐based algorithms, and quaternion‐based modeling offer significant potential for enhancing autonomy and control. …”
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A Collaborative Transit Signal Priority Method Considering Gaming among Traffic Participants by Bing Wu, Haishan Wang, Yanli Wang, Ben Zhai, TanXing Xue

“…A finite state machine-based algorithm is developed to estimate the average delays of nontransit vehicles. …”
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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. …”
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MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems by Abdul Jabbar, Marco Cocconcelli, Gianluca D’Elia, Davide Borghi, Luca Capelli, Jacopo Cavalaglio Camargo Molano, Matteo Strozzi, Riccardo Rubini

“…The primary objective is to advance research in machine health monitoring, predictive maintenance, and stochastic modeling by providing the first data set of its kind. …”
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Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies by Vahid Asadi, Akram Hasani Zonoozi, Hosein Haghi, Fatemeh Abedini, Atousa Kalantari, Marziye Jafariyazani, Nima Chartab

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Real‐Climatic Microcontroller‐in‐the‐Loop (RCMIL) Framework: A Novel, Rapid, and Cost‐Effective Approach for Verifying Photovoltaic Control Systems by Ambe Harrison, Idriss Dagal, Wulfran Fendzi Mbasso, Fritz Nguemo Kemdoum, Reagan Jean Jacques Molu, Hamid Belghiti, Pradeep Jangir

“…By pushing the frontiers of PV system research, this paper provides a vital resource that speeds up the transition from simulation to real‐world applications.…”
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Beam Scanning with Ultra‐Low Sidelobes and In‐Band Ultra‐Low Scattering Characteristics Empowered by Single Space‐Time‐Coding Radiation‐Scattering Metasurface by Lixin Jiang, Yongfeng Li, Hao Yang, Mingbao Yan, Jinming Jiang, Yunwei Zhang, Zhe Qin, Wanwan Yang, Hongya Chen, Yongqiang Pang, Zhihao Guo, Lin Zheng, Jiafu Wang, Shaobo Qu

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Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…Despite the rapid development of anomaly detection algorithms and deep-learning models for forecasting, their application to marine soundscapes remains unexplored. …”
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scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data by Qingfei Pan, Liang Ding, Siarhei Hladyshau, Xiangyu Yao, Jiayu Zhou, Lei Yan, Yogesh Dhungana, Hao Shi, Chenxi Qian, Xinran Dong, Chad Burdyshaw, Joao Pedro Veloso, Alireza Khatamian, Zhen Xie, Isabel Risch, Xu Yang, Jiyuan Yang, Xin Huang, Jason Fang, Anuj Jain, Arihant Jain, Michael Rusch, Michael Brewer, Junmin Peng, Koon-Kiu Yan, Hongbo Chi, Jiyang Yu

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A Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques by Mamunur Rashid, Tarek Elfouly, Nan Chen

“…The transition of the automotive sector to electric vehicles (EVs) necessitates research on charging demand forecasting for optimal station placement and capacity planning. …”
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