Grapes leaf disease dataset for precision agricultureMendeley Data by Madhuri Dharrao, Nilima Zade, R. Kamatchi, Rakesh Sonawane, Rabinder Henry, Deepak Dharrao

“…This High-quality annotated image dataset can help to design standard advanced AI models for automated disease detection, classification, and prediction. The dataset was validated through a transfer learning approach using the ResNet-18 algorithm and demonstrated the remarkable classification accuracy of 96 %. …”
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External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data by Anja Braune, René Hosch, David Kersting, Juliane Müller, Frank Hofheinz, Ken Herrmann, Felix Nensa, Jörg Kotzerke, Robert Seifert

“…The performance of this algorithm has so far only been clinically evaluated on patient data featuring limited scan statistics and unknown actual activity concentration. …”
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EFCNet: Expert Feature-Based Convolutional Neural Network for SAR Ship Detection by Zheng Chen, Yuxiang Zhang, Jing Bai, Biao Hou

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Advanced fault detection in photovoltaic panels using enhanced U-Net architectures by Khalfalla Awedat, Gurcan Comert, Mustafa Ayad, Abdulmajid Mrebit

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Evaluating the feasibility of automating dataset retrieval for biodiversity monitoring by Alexandre Fuster-Calvo, Sarah Valentin, William C. Tamayo, Dominique Gravel

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Strawberry Disease Detection Using Multispectral UAV Imagery by A. F. Cheshkova, V. S. Riksen

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A comprehensive survey of imbalanced learning methods for bankruptcy prediction by Tuong Le

“…Abstract In practical datasets used for supervised learning, the uneven distribution of the amounts of data between classes is known as the class imbalance problem, and can reduce the performance of basic classifiers. …”
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Face recognition in dense crowd using deep learning approaches with IP camera by Sobhana Mummaneni, Venkata Chaitanya Satya Ramaraju Mudunuri, Sri Veerabhadra Vikas Bommaganti, Bhavya Vani Kalle, Novaline Jacob, Emmanuel Sanjay Raj Katari

“…The detected faces are then fed as input to a model, where they are compared with data from the database. …”
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A Multimodal Approach of Machine and Deep Learnings to Enhance the Fall of Elderly People by Saleh Al meraikhi, Murad Al-Rajab

“…The purpose of this study is to contribute to the field of Machine Learning and Fall Detection by investigating the optimal ways to apply common machine and deep learning algorithms trained on multimodal fall data. …”
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Optimized ensemble learning for non-destructive avocado ripeness classification by Panudech Tipauksorn, Prasert Luekhong, Minoru Okada, Jutturit Thongpron, Chokemongkol Nadee, Krisda Yingkayun

“…Grid Search achieved the best classification performance, reaching an accuracy of 82.5% and an F1-score of 85.3%, highlighting the benefits of weight-optimized ensemble learning compared to single classifiers. This study offers a scalable and clear method for non-destructive ripeness detection. …”
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Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data by Masahiro Hata, Yuki Miyazaki, Kohji Mori, Kenji Yoshiyama, Shoshin Akamine, Hideki Kanemoto, Shiho Gotoh, Hisaki Omori, Atsuya Hirashima, Yuto Satake, Takashi Suehiro, Shun Takahashi, Manabu Ikeda

“…A total of 102 patients, both with and without AD-related biomarker changes (amyloid beta and phosphorylated tau), were recorded using a 2-minute resting-state portable EEG. A machine-learning algorithm then analyzed the EEG data to identify these biomarker changes. …”
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Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning by Brianna J. Pickstone, Hugh A. Graham, Andrew M. Cunliffe

“…This study aims to compare the performance of three machine learning algorithms (Multiple Linear Regression (MLR), Random Forest (RF), and Convolutional Neural Networks (CNN)) when using PlanetScope and Sentinel-2 imagery to improve the accuracy of height predictions. …”
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Early Detection of Soil Salinization by Means of Spaceborne Hyperspectral Imagery by Giacomo Lazzeri, Robert Milewski, Saskia Foerster, Sandro Moretti, Sabine Chabrillat

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Land cover classification using Land Parcel Identification System (LPIS) data and open source Eo-Learn library by Fatih Fehmi Şimşek, Süleyman Savaş Durduran

“…In the classification process, the Light Gradient Boosting Machines (LightGBM) algorithm in the Eo-Learn library and the physical blocks produced within the scope of the Land Parcel Identification System (LPIS) project were used as ground truth data; arable land, bare land, forest, artificial surface, shrubland, tree crops and water classes were created. …”
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Federated Learning Framework for Real-Time Activity and Context Monitoring Using Edge Devices by Rania A. Alharbey, Faisal Jamil

“…The smartphones continuously collect real-time data as the elderly individuals go about their daily routines. …”
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Algorithm for Recognition of Small Air Targets by Trajectory Features in Passive Bistatic Radar by Dao Van Luc, A. A. Konovalov, Le Minh Hoang

“…Aim. Development of an algorithm for recognizing small air targets by trajectory features based on machine learning. …”
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Optimized Two-Stage Anomaly Detection and Recovery in Smart Grid Data Using Enhanced DeBERTa-v3 Verification System by Xiao Liao, Wei Cui, Min Zhang, Aiwu Zhang, Pan Hu

“…The increasing sophistication of cyberattacks on smart grid infrastructure demands advanced anomaly detection and recovery systems that balance high recall rates with acceptable precision while providing reliable data restoration capabilities. …”
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Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept by Eli Garlick, Nourhan Hesham, MD. Zoheb Hassan, Imtiaz Ahmed, Anas Chaaban, MD. Jahangir Hossain

“…Cognitive tactical wireless networks (TWNs) require spectrum awareness to avoid interference and jamming in the communication channel and assure quality-of-service in data transmission. Conventional supervised machine learning (ML) algorithm’s capability to provide spectrum awareness is confronted by the requirement of labeled interference signals. …”
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Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors by Sunnia Ikram, Imran Sarwar Bajwa, Amna Ikram, Isabel de la Torre Diez, Carlos Eduardo Uc Rios, Angel Kuc Castilla

“…To enhance detection accuracy, multiple machine learning algorithms including Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF) and Gaussian Naïve Bayes (GNB) are utilized. …”
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Target Detection and Image Enhancement for Underwater Environment: Research on Improving YOLOv7 by Yang Luo, Wen Feng

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