A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

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Combating electricity fraud: Employing hybrid learning and computer vision for sustainable energy management by Jui-Sheng Chou, Nader Anwar Charaf, Dani Nugraha Limantono, Hoang-Minh Nguyen

“…Traditional methods for detecting fraudulent activities often depend on extensive data preprocessing, such as outlier removal, which can introduce biases and reduce practical effectiveness. …”
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ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11 by Zhe Zhang, Zhongyang Zhang, Gang Li, Chenxi Xia

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Analysis of the Influence of Image Resolution in Traffic Lane Detection Using the CARLA Simulation Environment by Aron Csato, Florin Mariasiu, Gergely Csiki

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Detection of Substation Pollution in District Heating and Cooling Systems: A Comprehensive Comparative Analysis of Machine Learning and Artificial Neural Network Models by Emrah ASLAN, Yıldırım ÖZÜPAK

“…The machine learning algorithms and the Convolutional Neural Network model are trained to perform fault detection at different contamination levels. …”
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A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization by Mădălina Duceac (Covrig), Călin Gheorghe Buzea, Alina Pleșea-Condratovici, Lucian Eva, Letiția Doina Duceac, Marius Gabriel Dabija, Bogdan Costăchescu, Eva Maria Elkan, Cristian Guțu, Doina Carina Voinescu

“…<b>Objective:</b> This study proposes a hybrid machine learning framework that integrates supervised classifiers, unsupervised anomaly detection, and decision threshold tuning to predict LDH recurrence using routine clinical data. …”
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Recurrent Neural Network Optimized by Grasshopper for Accurate Audio Data-Based Diagnosis of Parkinson's Disease by Saif Wali Ali Alsudani, Ghassan Khudair Saud

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CropsDisNet: An AI-Based Platform for Disease Detection and Advancing On-Farm Privacy Solutions by Mohammad Badhruddouza Khan, Salwa Tamkin, Jinat Ara, Mobashwer Alam, Hanif Bhuiyan

“…With the rise in global food security concern, the earliest detection of crop diseases has proven to be pivotal in agriculture industries to address the needs of the global food crisis and on-farm data protection, which can be met with a privacy-preserving deep learning model. …”
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Determination of disintegration time using formulation data for solid dosage oral formulations via advanced machine learning integrated optimizer models by Mohammed Ghazwani, Umme Hani

“…Utilizing a dataset of approximately 2,000 entries encompassing molecular properties, physical properties, excipient composition, and formulation characteristics, three ML models were evaluated: TabNet, Radial Basis Function Support Vector Regression (RBF-SVR), and Neural Oblivious Decision Ensembles (NODE). Data preprocessing involved Min-Max normalization, outlier detection via Elliptic Envelope, and feature selection using Conditional Mutual Information, with hyperparameters optimized through the Water Cycle Algorithm. …”
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FedHSP: A Robust Federated Learning Framework Coherently Addressing Heterogeneity, Security, and Performance Challenges by Divya G. Nair, C. V. Aswartha Narayana, K. Jaideep Reddy, Jyothisha J. Nair

“…Federated Learning (FL) is a machine learning training method that leverages local model gradients instead of accessing private data from individual clients, ensuring privacy. …”
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Deep learning models for detection of explosive ordnance using autonomous robotic systems: trade-off between accuracy and real-time processing speed by Vadym Mishchuk, Herman Fesenko, Vyacheslav Kharchenko

“…The study focuses on deep learning models for real-time explosive ordnance detection (EO). …”
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Research on highway road condition intelligent assessment and optimization system based on deep learning and internet of things by Tingquan He, Changhai Wang, Riyan Lan, Haiyu Luo

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MED-AGNeT: An attention-guided network of customized augmentation of samples based on conditional diffusion for textile defect detection by Jun Liu, Haolin Li, Hao Liu, Jiuzhen Liang

“…Fabric defect detection plays a vital role in ensuring the production quality of the textile manufacturing industry. …”
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DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s by Zhaomei Qiu, Weili Wang, Xin Jin, Fei Wang, Zhitao He, Jiangtao Ji, Shanshan Jin

“…Given the dearth of research on intelligent tuber-cutting machinery in China, particularly concerning the identification of bud eyes and defect detection, this study has developed a multi-target recognition approach for potato seed tubers utilizing deep learning techniques. …”
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Automated inflammatory bowel disease detection using wearable bowel sound event spotting by Annalisa Baronetto, Annalisa Baronetto, Sarah Fischer, Sarah Fischer, Markus F. Neurath, Markus F. Neurath, Oliver Amft, Oliver Amft

“…A deep-learning-based audio pattern spotting algorithm was trained to retrieve BS events. …”
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An Assessment of Land Use Land Cover Using Machine Learning Technique by V. Pushpalatha, H. N. Mahendra, A. M. Prasad, N. Sharmila, D. Mahesh Kumar, N. M. Basavaraju, G. S. Pavithra and S. Mallikarjunaswamy

“…Remote sensing imagery, Geographic Information System (GIS) tools, and machine learning algorithms are leveraged to process and interpret satellite data for accurate land-cover classification. …”
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Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding by Haoqing LI, Dian YU, Changchun PAN, Wenxian YU, Dongying LI

“…Modern radar systems face increasingly complex challenges in tasks such as detection, tracking, and identification. The diversity of task types, limited data resources, and strict execution time requirements make radar task scheduling a strongly NP-hard problem. …”
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Performance Analysis of Eye Movement Event Detection Neural Network Models with Different Feature Combinations by Birtukan Adamu Birawo, Pawel Kasprowski

“…Event detection is the most important element of eye movement analysis. …”
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Design of Intrusion Detection and Response Mechanism for Power Grid SCADA Based on Improved LSTM and FNN by Yu Huang, Liangyuan Su

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Structural health monitoring and evaluation method for an immersed tunnel based on deep learning by Yuhang Wei, Jinfang Hou, Linhu Yang, Bin Li, Zonghao Ma, Zhao Liu, Wenhao Sun, Binlei Wang

“…It also compares the efficacy of two deep learning algorithms, Long Short-Term Memory (LSTM) and Transformer, in predicting structural conditions. …”
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