Artificial Intelligence-based Rice Variety Classification: A State-of-the-art Review and Future Directions by Md. Masudul Islam, Galib Muhammad Shahriar Himel, Md. Golam Moazzam, Mohammad Shorif Uddin

“…By synthesizing findings from various studies, this review proposes future directions for advancing rice variety classification, focusing on improved feature extraction techniques, enhanced dataset management, and integrating innovative machine learning paradigms. …”
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Transferring Learned ECG Representations for Deep Neural Network Classification of Atrial Fibrillation with Photoplethysmography by Jayroop Ramesh, Zahra Solatidehkordi, Raafat Aburukba, Assim Sagahyroon, Fadi Aloul

“…This results in the <i>student</i> model yielding average improvements in accuracy, sensitivity, F1 score, and Matthews correlation coefficient of 2.0%, 15.05%, 11.7%, and 9.85%, respectively, across both PPG datasets. …”
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Balancing conflicting objectives in pre-salt reservoir development: A robust multi-objective optimization framework by Auref Rostamian, Amir Davari Malekabadi, Marx Vladimir De Souda Miranda, Vinicius Edurado Botechia, Denis José Schiozer

“…However, by employing the proposed framework, a 3 % improvement in EMV and a 28 % enhancement in the NPVeco of RM4 is achieved compared to the single objective optimization of EMV, which highlights the strength and robustness of the framework.…”
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Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network by Lu Xu, Yixin Ma, Rui Shi, Juanjuan Li, Yijia Zhang

“…The proposed approach enhances the acquisition of features from soft-decision sequence information, leading to improved recognition performance without necessitating prior knowledge of coding parameters, sequence starting positions, or other metadata. …”
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STATE-OF-THE-ART OF MACHINE LEARNING IN NEURO DEVELOPMENT DISORDER: A SYSTEMATIC REVIEW by Lilian Yen Wei Lee, Ag Asri Ag Ibrahim, Rayner Alfred

“…Results: The findings of this review shed light on prevalent ML and DL methodologies, the challenges encountered, and potential avenues for future research aimed at enhancing services for the NDD community through improved prediction and detection techniques. [JJCIT 2025; 11(1.000): 33-53]…”
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Toward automatic and reliable evaluation of human gastric motility using magnetically controlled capsule endoscope and deep learning by Xueshen Li, Yu Gan, David Duan, Xiao Yang

“…Abstract In this paper, we develop a combination of algorithms, including camera motion detector (CMD), deep learning models, class activation mapping (CAM), and periodical feature detector for the purpose of evaluating human gastric motility by detecting the presence of gastric peristalsis and measuring the period of gastric peristalsis. …”
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A General Framework for CFAR Detection in PolSAR Imagery Based on Quadratic Statistics by Ziyuan Yang, Liguo Liu, Xiaoyang Hou, Yinghui Quan, Xian Zhang, Tao Liu

“…In the field of target detection in polarimetric synthetic aperture Radar (PolSAR) imagery, the constant false alarm rate (CFAR) algorithm is renowned for its operability and high interpretability. …”
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Depression Analysis and Detection Using Machine Learning: Incorporating Gender Differences in a Comparative Study by Marina Galanina, Anna Rekiel, Anna BaCzyk, Bozena Kostek

“…This is achieved through the implementation of a tailored feature vector methodology. In most models, this approach led to measurable improvements: SVM improved by 1.43% (p =0.044), MLP-CNN by 7.25% (p =0.008), and Perceptron by 7.39% (p =0.05). …”
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Mental health evaluation during internet blackouts: A case study of Bangladesh Quota Movement by Rafi Mohammad Ariful Islam, Islam Tahidul

“…Results revealed varying stress levels, from minimal to extreme, reflecting widespread distress. Machine learning models were employed to classify the stress levels, with the Decision Tree model achieving 55% accuracy, the Random Forest model improving to 67%, and XGBoost performing better than both with over 94% accuracy. …”
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Accelerating Ray Tracing-Based Wireless Channels Generation for Real-Time Network Digital Twins by Claudio Modesto, Lucas Mozart, Pedro Batista, Andre Cavalcante, Aldebaro Klautau

“…Ray tracing (RT) simulation is a widely used approach to enable modeling wireless channels in applications such as network digital twins. …”
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Carbonate Seismic Facies Analysis in Reservoir Characterization: A Machine Learning Approach with Integration of Reservoir Mineralogy and Porosity by Papa Owusu, Abdelmoneam Raef, Essam Sharaf

“…Amid increasing interest in enhanced oil recovery and carbon geological sequestration programs, improved static reservoir lithofacies models are emerging as a requirement for well-guided project management. …”
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Forecasting the volatility of Financial Time Series by Tree Ensembles by O. S. Vidmant

“…The constant volatility of the world indices forces all financial market players to improve risk management models and, at the same time, to revise the policy of capital investment. …”
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High-resolution surface soil moisture retrieval: A hybrid machine learning framework integrating change detection and downscaling for precision water management by Zihao Wang, Qi Gao, Michele Crosetto, Maria Jose Escorihuela

“…A novel backscatter difference variable, derived from the change detection method, improves model performance by addressing vegetation. …”
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A Device for the Rapid Detection of Benzodiazepines and Synthetic Cannabinoids via Fluorescence Spectroscopy and Machine Learning by A. Power, M. Gardner, C. Pudney

“…Trials of the device in UK drug hotspots are imminent and will result in a significant data collection of the scans performed and the predictions that the model made per scan. This will provide us with an unprecedented insight into the pervasiveness of illegal drug use in the UK and drive improvements for future iterations of the device. …”
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AI-Enhanced Disaster Management: A Modular OSINT System for Rapid Automated Reporting by Klaus Schwarz, Kendrick Bollens, Daniel Arias Aranda, Michael Hartmann

“…ODET enables the implementation of AI-enhanced algorithms to respond to various complex disaster scenarios. …”
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Machine learning Techniques for Spondylolisthesis Diagnosis: a review by Rahma Rabee Aziz, Mohammed S. Jarjees, Mohammad R. Aziz, Ali Asim Hameed

“…This review highlights how deep learning and machine learning models can improve spondylolisthesis diagnosis, enabling enhanced diagnosis and treatment methods. …”
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Spaceborne GNSS Reflectometry for Vegetation and Inland Water Monitoring: Progress, Challenges, Opportunities, and Potential by Jiaxi Xie, Jinwei Bu, Huan Li, Qiulan Wang

“…The article also mentioned that the accuracy and efficiency of parameter retrieval can be significantly improved by improving models and algorithms, such as using neural networks and data fusion technology. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…This approach provides a reliable method for the real-time monitoring of thermal insulation integrity in liquefied natural gas storage tanks, contributing to improved industrial safety and operational efficiency.…”
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A new machine learning method for rainfall classification: temporal random tree by Kokten Ulas Birant, Bita Ghasemkhani, Özlem Varlıklar, Derya Birant

“…Traditional classification algorithms usually assume that all samples in a dataset contribute equally to the training of a machine learning model, which is not always the case. …”
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Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation by Alice Finocchi, Silvia Campagnini, Andrea Mannini, Stefano Doronzio, Marco Baccini, Bahia Hakiki, Donata Bardi, Antonello Grippo, Claudio Macchi, Jorge Navarro Solano, Michela Baccini, Francesca Cecchi

“…Machine learning (ML) improves prognostics but integrating it with Multiple Imputation (MImp) for dealing missingness is an unexplored field. …”
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