Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models by Bin Xie, Mingda Mo, Haidong Cui, Yijie Dong, Hongping Yin, Zhe Lu

“…<b>Objectives:</b> Lung cancer is one of the most prevalent cancers worldwide. Accurately determining lung cancer subtypes and identifying high-risk patients are helpful for individualized treatment and follow-up. …”
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Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence by Wadi’ Othmani, Arthur Coste, Dimitri Papathanassiou, David Morland

“…In this study we aimed to develop a Convolutional Neural Network (CNN) able to predict the outcome of the full examination based on the first acquired projection, and reliably detect normal patients. …”
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Short-term wind power forecasting method for extreme cold wave conditions based on small sample segmentation by Lin Lin, Jinhao Xu, Jianfei Liu, Hao Zhang, Pengchen Gao

“…Cold waves, as one of the most common extreme weather events, cause significant fluctuations in wind power over short periods, greatly increasing the difficulty of wind power forecasting. …”
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Toward Adaptive Unsupervised and Blind Image Forgery Localization with ViT-VAE and a Gaussian Mixture Model by Haichang Yin, KinTak U, Jing Wang, Wuyue Ma

“…Most image forgery localization methods rely on supervised learning, requiring large labeled datasets for training. …”
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A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter by Serpil Aslan, Muhammed Yildirim

“…Twitter has emerged as one of the most widely used platforms for sharing information and updates. …”
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AI-Based Forecasting in Renewable-Rich Microgrids: Challenges and Comparative Insights by Martins Osifeko, Josiah Lange Munda

“…Classical ML models outperformed most DL architectures, including Transformer and Convolutional Neural Network (CNN)-LSTM, which underperformed despite their complexity. …”
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Mathematical Modeling of Cyberattack Defense Mechanism Using Hybrid Transfer Learning With Snow Ablation Optimization Algorithm in Critical Infrastructures by Mohamad Khairi Ishak

“…Industrial control methods are one of the most vital aspects of the cybersecurity of critical infrastructures. …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…Statistical tests, including the Friedman and Nemenyi post-hoc test, identified the CNN model trained with MHMXAI-selected features as the most robust choice for CKD stage prediction. These findings demonstrate that the proposed MHMXAI method effectively integrates metaheuristic algorithms and XAI tools, improving CKD stage prediction accuracy and clinical interpretability.…”
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Photoplethysmogram (PPG)-Based Biometric Identification Using 2D Signal Transformation and Multi-Scale Feature Fusion by Yuanyuan Xu, Zhi Wang, Xiaochang Liu

“…To address these issues, this paper proposes an improved MSF-SE ResNet50 (Multi-Scale Feature Squeeze-and-Excitation ResNet50) model based on 2D PPG signals. Unlike most existing methods that directly process one-dimensional PPG signals, this paper adopts a novel approach based on two-dimensional PPG signal processing. …”
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Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines by Ruifeng Chen, Ke Zhang, Min Luo, Ye An, Lixiang Guo

“…Moreover, the Shapley additive explanations (SHAP) interpretation is utilized to reveal the most significant features influencing structural responses. …”
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Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network by Zemzem Mohammed Megersa, Abebe Belay Adege, Faizur Rashid

“…Maize is one of the most widely grown crops in Ethiopia and is a staple crop around the globe; however, common rust maize disease (CRMD) is becoming a serious problem and severely impacts yields. …”
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Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN by Siti Khairunniza-Bejo, Mohd Firdaus Ibrahim, Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad, Mohammad Aufa Mhd Bookeri

“…The datasets were subjected to data augmentation and utilised to train four convolutional object detection models based on transfer learning. …”
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Predicting wheat yield using deep learning and multi-source environmental data by Muhammad Ashfaq, Imran Khan, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…The Google Earth Engine platform was used to process and integrate remote sensing, climate, and soil data. CNN emerged as the most effective model, achieving an R2 value of 0.77 and a forecast accuracy of 98% one month before harvest. …”
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A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods by Xinhua Liu, Kaiyi Yang, Lisheng Zhang, Wentao Wang, Sida Zhou, Billy Wu, Mengyu Xiong, Shichun Yang, Rui Tan

“…Machine learning methods have in recent years shown considerable potential for accelerating research efforts. However, most approaches are limited to specific properties of particular devices. …”
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Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving by Ambati Pravallika, Mohammad Farukh Hashmi, Aditya Gupta

“…This work investigates the most recent 3D object detection methods for self-driving cars, emphasizing the importance of advanced deep learning models and multi-sensor fusion methods. …”
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High-Quality Sample Generation for Power System Transient Stability Assessment Based on Data-Driven Methods by Baoqin Li, Pengfei Fan, Qixin Chen, Rong Li, Kaijun Lin

“…Finally, a deep convolutional generative adversarial network (DCGAN) is constructed to mitigate the class imbalance problem. …”
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Enhanced Brain Tumor Classification Using MobileNetV2: A Comprehensive Preprocessing and Fine-Tuning Approach by Md Atiqur Rahman, Mohammad Badrul Alam Miah, Md. Abir Hossain, A. S. M. Sanwar Hosen

“…<b>Background:</b> Brain tumors are among the most difficult diseases to deal with in modern medicine due to the uncontrolled cell proliferation, which causes grave damage to the nervous system. …”
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Cloud-edge collaborative data anomaly detection in industrial sensor networks. by Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

“…However, existing research on sensor data anomaly detection for industrial sensor networks still has several inherent limitations. First, most detection models usually consider centralized detection. …”
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Research on Multi-Objective Pedestrian Tracking Algorithm Based on Full-Size Feature Fusion by Yihuai Zhu, Zhandong Liu, Ke Li, Yong Li, Xiangwei Qi, Nan Ding

“…In the field of Multi-Object Tracking (MOT), the current mainstream approach is the tracking by detection paradigm, which heavily relies on the accuracy of the detector, the comprehensiveness of feature extraction, and the superiority of the data association matching algorithm. Most existing pedestrian re-identification methods are based on convolutional neural networks (CNNs), which struggle to balance both local and global features of pedestrians. …”
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Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data by Byeongcheol Kang, Kyungbook Lee

“…The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). After simulated production data are used to train the classification model, the most possible TI can be selected when the observed production responses are put into the trained model. …”
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