Classification of Anxiety Levels of IGD Patients at RSU Royal Prima Medan Using Support Vector Machine (SVM) Algorithm by Kharisma Gunanta Ginting, Nugroho Prasetyo, Al Vino Gunawan, Magdalena Sihombing, Adli Abdillah Nababan

“…This study aims to develop a patient anxiety level classification model in the ED using the Support Vector Machine (SVM) algorithm with the application of the Synthetic Minority Oversampling Technique (SMOTE) to address the class imbalance issue. …”
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Risk Classification of Food Incidents Using a Risk Evaluation Matrix for Use in Artificial Intelligence-Supported Risk Identification by Sina Röhrs, Sascha Rohn, Yvonne Pfeifer

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Random forest algorithm identifies miRNA signatures for breast cancer detection and classification from patient urine samples by Jochen Maurer, Matthias Rübner, Chao-Chung Kuo, Birgit Klein, Julia Franzen, Julia Wittenborn, Tomas Kupec, Laila Najjari, Peter Fasching, Elmar Stickeler

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Fault Classification in Power Transformers via Dissolved Gas Analysis and Machine Learning Algorithms: A Systematic Literature Review by Vuyani M. N. Dladla, Bonginkosi A. Thango

“…The survey also reveals the countries at the forefront of transformer fault diagnosis and a classification based on DGA using machine learning algorithms. …”
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CLASSIFICATION OF MICROCONTROLLER INTEGRATED CIRCUIT ON THE POCKET OF JEDEC TRAY USING CONVOLUTIONAL NEURAL NETWORK IN EMBEDDED MACHINE LEARNING SYSTEM by Mark M. Pallones, King Harold A. Recto

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Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households by Seok Min Ji, Jeewuan Kim, Kyu Min Kim

“…Abstract Background Despite the National Health Insurance (NHI) system implemented in South Korea, concerns persist regarding access to health coverage for low-income households. To address this issue, this study aims to use machine learning-based data mining techniques to classify whether such households will face catastrophic health expenditures (CHEs). …”
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A Multi-Task Learning Framework with Enhanced Cross-Level Semantic Consistency for Multi-Level Land Cover Classification by Shilin Tao, Haoyu Fu, Ruiqi Yang, Leiguang Wang

“…We addressed this issue with a deep multi-task learning (MTL) framework, named MTL-SCH, which enables collaborative classification across multiple semantic levels. …”
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A Convolutional Mixer-Based Deep Learning Network for Alzheimer’s Disease Classification from Structural Magnetic Resonance Imaging by M. Krithika Alias Anbu Devi, K. Suganthi

“…A hybrid sampling approach combining SMOTE (synthetic minority oversampling technique) with the ENN (edited nearest neighbors) effectively handles the complications of class imbalance issue inherent in the datasets. An explainable activation space occlusion sensitivity map (ASOP) pixel attribution method is employed to highlight the critical regions of input images that influence the classification decisions across different stages of AD. …”
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Dual Strategy Based Improved Swarm Intelligence and Stacked LSTM With Residual Connection for Land Use Land Cover Classification by Vinaykumar Vajjanakurike Nagaraju, Ananda Babu Jayachandra, Andrzej Stateczny, Swathi Holalu Yogesh, Raviprakash Madenur Lingaraju, Balaji Prabhu Baluvaneralu Veeranna

“…The LULC classification is a difficult task because of the high dimensional feature space, which affects the classification accuracy. …”
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Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches by Dipak Kumar Agrawal, Watcharin Jongpinit, Soodkhet Pojprapai, Wipawee Usaha, Pattra Wattanapan, Pornthep Tangkanjanavelukul, Timporn Vitoonpong

“…Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% ending up with amputations, resulting in high morbidity, mortality, and costs. …”
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Enhancing downwelling diffuse attenuation coefficient retrieval at 490 nm through a Neural network and water classification system by Tianle Yao, Tianjie Zhan, Jilin Men, Lan Zhang, Liqiao Tian

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A Semisupervised Classification Method for Riverbed Benthic Sediments Using Integrated Superpixel Segmentation and Confident Learning Sample Enhancement by Yaxue Wang, Yuewen Sun, Xiaodong Cui, Tianyu Yun, Xianhai Bu, Fanlin Yang

“…Comparison results show that the proposed method achieves a classification accuracy of 85.3%, an improvement of 2.9% to 9.9% compared to traditional classification methods, offering a new approach for large-scale benthic environment detection in river basins.…”
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Application of Hybrid Attention Mechanisms in Lithological Classification with Multisource Data: A Case Study from the Altay Orogenic Belt by Dong Li, Jinlin Wang, Kefa Zhou, Jiantao Bi, Qing Zhang, Wei Wang, Guangjun Qu, Chao Li, Heshun Qiu, Tao Liao, Chong Zhao, Yingpeng Lu

“…This resulted in a 7.99% improvement in classification accuracy compared with that of traditional models, significantly increasing the precision of lithological classification. (2) The combination of channel attention followed by spatial attention achieved the highest overall accuracy, 98.06%.…”
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Enhanced Fetal Arrhythmia Classification by Non-Invasive ECG Using Cross Domain Feature and Spatial Differences Windows Information by Gede Angga Pradipta, Putu Desiana Wulaning Ayu, Made Liandana, Dandy Pramana Hostiadi

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S²RCFormer: Spatial-Spectral Residual Cross-Attention Transformer for Multimodal Remote Sensing Data Classification by Yifei Xu, Lingming Cao, Jialu Li, Wenlong Li, Yaochen Li, Yingjie Zong, Aichen Wang, Yuan Rao, Shuiguang Deng

“…With the advancement of remote sensing technology, more and more modalities are becoming available for land cover classification tasks, helping to address the issue of insufficiency and incompleteness caused by modeling on single-source remote sensing images. …”
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Unlocking the Potential of XAI for Improved Alzheimer’s Disease Detection and Classification Using a ViT-GRU Model by S. M. Mahim, Md. Shahin Ali, Md. Olid Hasan, Abdullah Al Nomaan Nafi, Arefin Sadat, Shakib Al Hasan, Bryar Shareef, Md. Manjurul Ahsan, Md. Khairul Islam, Md. Sipon Miah, Ming-Bo Niu

“…The proposed model overcomes the class imbalance issue in the MRI image dataset and achieves superior accuracy and performance compared to existing methods. …”
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Impact of computing platforms on classifier performance in heart disease prediction by Beenish Ayesha Akram, Muhammad Irfan, Amna Zafar, Sidra Khan, Rubina Shaheen

“…Prediction and classification, a supervised learning technique in machine learning, addresses various challenges related to finding useful patterns present in data. …”
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Cardiovascular-kidney-metabolic syndrome — a new concept of the association of risk factors and metabolic disorders within the cardiovascular and renal continuum. View on the issue... by G. G. Arabidze, M. N. Mamedov

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GUARANTEES EQUIVALENT TO THE RIGHT TO A FAIR TRIAL IN THE MATTER OF CHALLENGES TO THE WITHDRAWAL OF SECURITY CERTIFICATES ISSUED BY THE OFFICE OF THE NATIONAL REGISTER OF STATE S... by Monica Cristina COSTEA

“…In the European countries, the situation regarding conditional access to classified documents from the point of view of lawyers and parties against whom such documents are issued is similar, in that it is recognised that the principle of adversarial proceedings and the rights of the defence must be limited because of the need to protect national security. …”
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Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations by Zohaib Khurshid, MRes, FDTFEd, FHEA, Maria Waqas, PhD, Shehzad Hasan, PhD, Shakeel Kazmi, PhD, Muhammad Faheemuddin, FCPS

“…Objectives: Dental health is integral to overall well-being, with early detection of issues critical for prevention. This research work focuses on utilizing artificial intelligence and deep learning–based object detection techniques for automated detection of common dental issues in orthopantomography x-ray images, including broken roots, periodontally compromised teeth, and the Kennedy classification of partially edentulous arches. …”
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