DO-MDS&DSCA: A New Method for Seed Vigor Detection in Hyperspectral Images Targeting Significant Information Loss and High Feature Similarity by Liangquan Jia, Jianhao He, Jinsheng Wang, Miao Huan, Guangzeng Du, Lu Gao, Yang Wang

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Vision-based approach to knee osteoarthritis and Parkinson’s disease detection utilizing human gait patterns by Zeeshan Ali, Jihoon Moon, Saira Gillani, Sitara Afzal, Muazzam Maqsood, Seungmin Rho

“…Several researchers proposed different methods in this area, including gait detection utilizing sensor-based data and vision-based systems that include both marker-based and marker-free techniques. …”
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Dual-hybrid intrusion detection system to detect False Data Injection in smart grids. by Saad Hammood Mohammed, Mandeep S Jit Singh, Abdulmajeed Al-Jumaily, Mohammad Tariqul Islam, Md Shabiul Islam, Abdulmajeed M Alenezi, Mohamed S Soliman

“…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…”
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Establishment and assessment of an early screening model for cervical cancer based on single-cell Raman spectroscopy combined with machine learning algorithms by MA Dongmei, ZHAO Wenjie, LIU Shihai, XU Haicang, CAI Duo, JI Yuetong, XU Jian, GUO Cancan, MA Bo, PAN Huazheng

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Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review by Jie Zhang, Xinchun Zhu, Yigong Xie, Guo Chen, Shuangquan Liu

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Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm by Bihui Zhang, Zhuqi Li, Bingjie Li, Jingbo Zhan, Songtao Deng, Yi Fang

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Impact of Data Distribution and Bootstrap Setting on Anomaly Detection Using Isolation Forest in Process Quality Control by Hyunyul Choi, Kihyo Jung

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Predicting Rolling Element Bearings’ Deterioration Vibration Trend based on Limited Historical Data for a Desired Confidence Level using Machine Learning Algorithms by Mohammad Reza Seif, Somaye Mohammadi, Parham Rahimi, Mehdi Behzad

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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Abstract Diabetes Mellitus (DM) is a global health challenge, and accurate early detection is critical for effective management. The study explores the potential of machine learning for improved diabetes prediction using microarray gene expression data and PIMA data set. …”
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Using deep learning for detecting BotCloud by Guang KOU, Guang-ming TANG, Shuo WANG, Hai-tao SONG, Yuan BIAN

“…Finally, in order to detect BotCloud, it utilized CNN algorithm to learn and extract characteristics that were more abstract to express the hidden model and structural relationship in the network data flow. …”
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Cluster Workload Allocation: A Predictive Approach Leveraging Machine Learning Efficiency by Leszek Sliwko

“…This research investigates how Machine Learning (ML) algorithms can assist in workload allocation strategies by detecting tasks with node affinity operators (referred to as constraint operators), which constrain their execution to a limited number of nodes. …”
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Incremental Pyraformer–Deep Canonical Correlation Analysis: A Novel Framework for Effective Fault Detection in Dynamic Nonlinear Processes by Yucheng Ding, Yingfeng Zhang, Jianfeng Huang, Shitong Peng

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Deep Learning-Based Atmospheric Visibility Detection by Yawei Qu, Yuxin Fang, Shengxuan Ji, Cheng Yuan, Hao Wu, Shengbo Zhu, Haoran Qin, Fan Que

“…With advancements in data science and computing, deep learning-based visibility detection technologies have rapidly emerged as a research hotspot in atmospheric science. …”
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Tackling the Optimal Phasor Measurement Unit Placement and Attack Detection Problems in Smart Grids by Incorporating Machine Learning by Ramzi Al-Sharawi, Abdelfatah Ali, Mostafa Shaaban, Nasser Qaddoumi, Mohamed S. Abdalzaher

“…Existing research primarily addresses cybersecurity by focusing on the optimal placement of phasor measurement units (PMUs) to ensure topological observability and minimize system costs, followed by developing AI-based attack detection algorithms. However, these studies fail to simultaneously consider system cost, loss in system observability, and false data injection attack (FDIA) detection performance. …”
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Transition state structure detection with machine learningś by Yitao Si, Yiding Ma, Tao Yu, Yifan Wu, Yingzhe Liu, Weipeng Lai, Zhixiang Zhang, Jinwen Shi, Liejin Guo, Oleg V. Prezhdo, Maochang Liu

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Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions by Teerath Kumar, Rob Brennan, Alessandra Mileo, Malika Bendechache

“…Deep learning algorithms have exhibited impressive performance across various computer vision tasks; however, the challenge of overfitting persists, especially when dealing with limited labeled data. …”
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Attack resilient IoT security framework using multi head attention based representation learning with improved white shark optimization algorithm by Jawhara Aljabri

“…Therefore, identifying numerous anomalies or cyberattacks in a network and constructing an effectual intrusion detection system (IDS) becomes more significant. Artificial intelligence (AI), mostly machine learning (ML) and deep learning (DL), has been employed to construct a data-driven intelligent IDS. …”
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A Scoring Algorithm for the Early Prediction of Academic Risk in STEM Courses by Vanja Čotić Poturić, Sanja Čandrlić, Ivan Dražić

“…Educational data mining (EDM) and learning analytics (LA) are widely applied to predict student performance, particularly in determining academic success or failure. …”
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A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite by S. Zhao, S. Zhao, Y. Zhang, Y. Zhang, S. Zhao, S. Zhao, X. Wang, X. Wang, D. J. Varon

“…Here, we propose a novel deep-transfer-learning-based methane plume detection framework. It consists of two components: an adaptive artifact removal algorithm (low-reflectance artifact detection, LRAD) to reduce artifacts in methane retrievals and a deep subdomain adaptation network (DSAN) to detect methane plumes. …”
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Detection of network anomaly based on hybrid intelligence techniques by Shahbaa Khaleel, Karam mahdi saleh

“…The first method implemented by applying traditional clustering algorithm of KM in a way Kmeans on KDDcup99 data to detect attacks, in the way the second hybrid clustering algorithm HCA method was used where the Kmeans been hybridized with GA. …”
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