Estimation of physico-chemical properties of soil using machine learning by Patience Chizoba Mba, Opegbemi Matthias Busoye, John Temitope Ajayi, Judith Nkechinyere Njoku, Cosmas Ifeanyi Nwakanma, Senorpe Asem-Hiablie, Rammohan Mallipeddi, Tusan Park, Daniel Dooyum Uyeh

“…We evaluated different models, including Support Vector Regression (SVR), a Convolutional Neural Network (CNN), an optimized CNN using Grid Search, and a stacked ML-CNN approach. …”
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A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Using OCT: Integrating Inception Modules, SE Blocks, and ConvMixer by Elif Yusufoğlu, Hüseyin Fırat, Hüseyin Üzen, Salih Taha Alperen Özçelik, İpek Balıkçı Çiçek, Abdulkadir Şengür, Orhan Atila, Numan Halit Guldemir

“…Its effectiveness was evaluated on two datasets: a private dataset with 2316 images and the public Noor dataset. …”
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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…Picture selection, systematic time series feature engineering and model evaluation were all areas that showed a lot of promise in the systematic review for future research and practical applications.…”
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Deep learning-based cough classification using application-recorded sounds: a transfer learning approach with VGGish by Sanghoon Han, Yu-rim Lee, Ji-ho Lee, JinHee Jeon, Choongki Min, Kyungnam Kim, Donghoon Kim, Myung Pyo Kim, Young Mi Park, Uiri An, Kyoung Min Moon

“…To enhance interpretability, we applied Grad-CAM to visualize the features that influenced the model’s decision-making. …”
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A mobile hybrid deep learning approach for classifying 3D-like representations of Amazonian lizards by Arthur Gonsales da Silva, Arthur Gonsales da Silva, Roger Pinho de Oliveira, Caio de Oliveira Bastos, Caio de Oliveira Bastos, Elena Almeida de Carvalho, Bruno Duarte Gomes

“…We compared the effectiveness of four artificial neural networks (ANN) for feature extraction: (a) MobileNet; (b) MobileNetV2; (c) MobileNetV3-Small; and (d) MobileNetV3-Large. …”
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Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment by Yihao Zhao BSc, Cuiyun Yuan MSc, Ying Liang PhD, Yang Li MSc, Chunxia Li MSc, Man Zhao MSc, Jun Hu PhD, Ningze Zhong Bsc, Wei Liu PhD, Chenbin Liu PhD

“…Two groups of organ-at-risk (OAR) were generated. A ResNet-152 network was used as a feature extractor, and a one-class support vector machine (OC-SVM) was employed to classify contours as ‘high’ or ‘low’ quality. …”
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Gear Classification in Skating Cross-Country Skiing Using Inertial Sensors and Deep Learning by Antonio Pousibet-Garrido, Aurora Polo-Rodríguez, Juan Antonio Moreno-Pérez, Isidoro Ruiz-García, Pablo Escobedo, Nuria López-Ruiz, Noel Marcen-Cinca, Javier Medina-Quero, Miguel Ángel Carvajal

“…Convolutional neural networks combined with long short-term memory were utilized to classify and extract spatio-temporal features. …”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. …”
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MetaTrans-FSTSF: A Transformer-Based Meta-Learning Framework for Few-Shot Time Series Forecasting in Flood Prediction by Jiange Jiang, Chen Chen, Anna Lackinger, Huimin Li, Wan Li, Qingqi Pei, Schahram Dustdar

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SHERA: SHAP-Enhanced Resource Allocation for VM Scheduling and Efficient Cloud Computing by Ashwin Singh Slathia, Abhiram Sharma, P. B. Krishna, Saksham Anand, Ayush Rathi, Linda Joseph, Xiao-Zhi Gao

“…To improve the interpretability of the model Explainable Artificial Intelligence (XAI) techniques were applied, specifically SHapley Additive exPlanations (SHAP), to evaluate feature importance. The findings demonstrated that the most important variables which influence energy efficiency are CPU usage and memory consumption. …”
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An Operational Status Assessment Model for SF₆ High-Voltage Circuit Breakers Based on IAR-BTR by Ningfang Wang, Yujia Wang, Yifei Zhang, Ci Tang, Chenhao Sun

“…With the rapid advancement of digitalization and intelligence in power systems, SF₆ high-voltage circuit breakers, as the core switching devices in power grid protection systems, have become critical components in high-voltage networks of 110 kV and above due to their superior insulation performance and exceptional arc-quenching capability. …”
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Automatic Identification of High-Quality Channels in Distributed Acoustic Sensing Through Implementation of a Channel Quality Index by Tatiana Rodriguez, Aleix Segui, Arantza Ugalde, Melania Cubas Armas, Hugo Latorre, Sergi Ventosa, Tony Monfret

“…Trained on data from four offshore DAS experiments in the Spanish Mediterranean and Canary Islands, our model evaluates temporal, frequency, and energy-based features to assign quality scores to individual channels, reliably identifying high-quality channels with high precision (0.92–0.96) and strong performance in terms of area under the receiver operating characteristic curve (ROC–AUC, 0.92–0.98). …”
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Bridging the Gap: Limitations of Machine Learning in Real-World Prediction of Heavy Metal Accumulation in Rice in Hunan Province by Qing-Qian Peng, Xia Zhou, Hang Zhou, Ye Liao, Zi-Yu Han, Lu Hu, Peng Zeng, Jiao-Feng Gu, Rong Zhang

“…This study systematically evaluated the predictive performance of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Residual Neural Networks (ResNet), using a multi-source soil–rice dataset comprising 57,200 samples from Hunan Province. …”
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Multi-sensor fusion for AI-driven behavior planning in medical applications by Chang Jianming, Qin Yuanyuan, Qin Yuanyuan, Xu Yanling, Li Li, Li Li, Wu Mianhua, Wu Mianhua, Wang Lulu

“…By dynamically weighing sensor inputs and optimizing feature selection, APFN ensures superior decision-making under varying medical conditions.ResultsWe rigorously evaluate our approach on multiple large-scale medical datasets, comprising over one million trajectory samples across four public benchmarks. …”
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Enhancing Water Bodies Detection in the Highland and Coastal Zones Through Multisensor Spectral Data Fusion and Deep Learning by Xiaofei Han, Nazih Y. Rebouh, Yasmeen Ahmed, Muhammad Nasar Ahmad, Zainab Tahir, Yahia Said, Ishfaq Gujree

“…We developed and trained a simple U-Net in PyTorch and a shallow convolutional neural network in TensorFlow to evaluate model performance and architectural efficiency using the same dataset. …”
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Design of a randomized tobacco cessation trial among FDNY World Trade Center responders in a lung cancer screening program by David G. Goldfarb, Tyrone Moline, David J. Prezant, Matthew P. Bars, Rachel Zeig-Owens, Theresa Schwartz, Madeline F. Cannon, Brandon Vaeth, Julia H. Arnsten, Mayris P. Webber, Shadi Nahvi

“…Methods The trial includes retired Fire Department of the City of New York (FDNY) responders aged 50 years or older who have a smoking history that satisfies either the National Comprehensive Cancer Network criteria of at least 20 pack-years or a simplified criterion of at least 20 years of smoking. …”
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A multi-scene deep learning model for automated segmentation of acute vertebral compression fractures from radiographs: a multicenter cohort study by Hao Zhang, Genji Yuan, Ziyue Zhang, Xiang Guo, Ruixiang Xu, Tongshuai Xu, Xin Zhong, Meng Kong, Kai Zhu, Xuexiao Ma

“…For the development of the Positioning and Focus Network (PFNet), we used a training dataset consisting of 1071 participants from Hospitals A and B. …”
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Enhanced deep learning model for apple detection, localization, and counting in complex orchards for robotic arm-based harvesting by Tantan Jin, Xiongzhe Han, Pingan Wang, Zhao Zhang, Jie Guo, Fan Ding

“…Specifically, an optimized You Only Look Once (YOLO) v8n model was developed by integrating a dilation-wise residual–dilated re-parameterization block module, a generalized feature pyramid network, and the Scylla Intersection-over-Union loss function. …”
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YOLO-MECD: Citrus Detection Algorithm Based on YOLOv11 by Yue Liao, Lerong Li, Huiqiang Xiao, Feijian Xu, Bochen Shan, Hua Yin

“…Accurate quantification of the citrus dropped number plays a vital role in evaluating the disaster resistance capabilities of citrus varieties and selecting superior cultivars. …”
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Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM) by Yasser Alatawi, Palanisamy Amirthalingam, Narmatha Chellamani, Manimurugan Shanmuganathan, Mostafa A. Sayed Ali, Saleh Fahad Alqifari, Vasudevan Mani, Muralikrishnan Dhanasekaran, Abdulelah Saeed Alqahtani, Ahmed Aljabri

“…The primary objective of this research is to implement sensor devices/smart wearables powered by advanced deep learning (DL) techniques to evaluate complex data patterns effectively and make accurate predictions. …”
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