Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks by Saad Said Alqahtany, Asadullah Shaikh, Ali Alqazzaz

“…Therefore, Enhanced Grey Wolf Optimization (EGWO) for Feature Selection (FS) is implemented. The function of EGWO is to remove unnecessary features from datasets used for intrusion detection. …”
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A Drilling Debris Tracking and Velocity Measurement Method Based on Fine Target Feature Fusion Optimization by Jinteng Yang, Yu Bao, Zumao Xie, Haojie Zhang, Zhongnian Li, Yonggang Li

“…Specifically, we enhance the multi-scale feature fusion capability of the YOLOv11 detection head by incorporating a lightweight feature extraction module, Ghost Conv, and a feature-aligned fusion module, FA-Concat, resulting in an improved model named YOLOv11-Dd (drilling debris). …”
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Developing and Implementing an Artificial Intelligence (AI)-Driven System For Electricity Theft Detection by Nwamaka Georgenia Ezeji, Kingsley Ifeanyi Chibueze, Nnenna Harmony Nwobodo-Nzeribe

“…To address this issue, this study aims to develop and implement an artificial intelligence (AI)-driven system for electricity theft detection. Methodology used are data collection, data analysis, feature selection with Chi-Square, feature transformation with Principal Component Analysis (PCA), Support Vector Machine (SVM) and model for electricity theft detection.   …”
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Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles by Naif Almakayeel

“…Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. …”
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PLY-SLAM: Semantic Visual SLAM Integrating Point–Line Features with YOLOv8-seg in Dynamic Scenes by Huan Mao, Jingwen Luo

“…On this basis, accurate 3D line-segment fitting is realized in combination with the RANSAC algorithm. Subsequently, we introduce Delaunay triangulation to construct the geometric relationships between map points, detect dynamic feature points by matching changes in the topological structure of feature points in adjacent frames, and combine them with the instance labels provided by the YOLOv8-seg to accurately remove dynamic feature points. …”
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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment by Alanoud Al Mazroa, Fahad R. Albogamy, Mohamad Khairi Ishak, Samih M. Mostafa

“…In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. …”
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Efficient sepsis detection using deep learning and residual convolutional networks by Ahmed S. Almasoud, Ghada Moh Samir Elhessewi, Munya A. Arasi, Abdulsamad Ebrahim Yahya, Menwa Alshammeri, Donia Badawood, Faisal Mohammed Nafie, Mohammed Assiri

“…In this article, we present a new deep learning model to detect the occurrence of sepsis and the African vulture optimization algorithm (AVOA) to enhance the model performance. …”
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Non-Destructive Detection of Silage pH Based on Colorimetric Sensor Array Using Extended Color Components and Novel Sensitive Dye Screening Method by Kai Zhao, Haiqing Tian, Jue Zhang, Yang Yu, Lina Guo, Jianying Sun, Haijun Li

“…Extended color components, a novel sensitive dye screening method, and a feature screening method were integrated and applied to enhance pH detection. …”
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Features of video information processing in differential correlator of pilotless flying machine by V. P. Gerasimov, A. Ju. Darzhanija, V. D. Kovalyov

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Real-time Detection and Tracking for Operating Vehicles in Complex Mining Environments by KANG Gaoqiang, LIN Jun, LIU Shiwang, YUE Wei, XIONG Qunfang, TONG Hao

“…Aiming at the problems of poor detection effect and low tracking stability of multi-type vehicles in complex mining environment due to the similarity of operating vehicles and background images, this paper proposes a multi-category and multi-target real-time detection and tracking algorithm for operating vehicles in complex mining environments. …”
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SH-SDS: a new static-dynamic strategy for substation host security detection by Yang Diao, Hui Chen, Wei Liu, Abdur Rasool

“…To reduce the subjectivity in manually selecting features, we combine classified protection for cybersecurity-related standards and construct the requirement generation algorithm to construct a network security detection standard library for the substation host. …”
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A Statistical Framework to Detect and Quantify Operator-Learning Curves in Medical Device Safety Evaluation by Ssemaganda HC, Davis SE, Govindarajulu US, Koola JD, Mao J, Westerman DM, Perkins AM, Speroff T, Ramsay CR, Sedrakyan A, Ohno-Machado L, Matheny ME, Resnic FS

“…Correctly attributing safety signals to learning or device effects allows for appropriate corrective actions and recommendations to improve patient safety.Objective: To develop and assess the statistical performance of an analytic framework to detect the presence of LE and quantify the learning curve (LC).Design and Setting: We generated synthetic datasets based on observed clinical distributions and complex feature correlations among patients hospitalized at US Department of Veterans Affairs facilities. …”
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Singular Value Decomposition (SVD) Method for LiDAR and Camera Sensor Fusion and Pattern Matching Algorithm by Kaiqiao Tian, Meiqi Song, Ka C. Cheok, Micho Radovnikovich, Kazuyuki Kobayashi, Changqing Cai

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Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques by Amit Gupta, Rohan Raju Dhanakshirur, Kshitiz Jain, Sanil Garg, Neel Yadav, Amlesh Seth, Chandan J. Das

“… Objective The aim of this study was to explore an innovative approach for developing deep learning (DL) algorithm for renal cell carcinoma (RCC) detection and subtyping on computed tomography (CT): clear cell RCC (ccRCC) versus non-ccRCC using two-dimensional (2D) neural network architecture and feature consistency modules.…”
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Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection by Onur Polat, Ali Ayid Ahmad, Saadin Oyucu, Enes Algul, Ferdi Dogan, Ahmet Aksoz

“…This research presents a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model developed for malware classification from IoT devices in the SCADA system and for detecting anomalies in the network. The developed model identifies complex attacks in the network by taking advantage of the strengths of CNNs that reveal spatial features and LSTMs that detect temporal dependency. …”
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AI-driven UAV with image processing algorithm for automatic visual inspection of aircraft external surface by Mohammed A. H. Ali, Muhammad Zamil A. Zulkifle, Nik Nazri Nik Ghazali, Retna Apsari, M. M. F. Meor Zulkifli, Mohammad Alkhedher

“…Meanwhile, the two stages of AI-algorithm demonstrate a good capability on classifying the extracted features by image processing into possible defect or noises which yields to accuracy rates of 86.67%, 66.67%, 80.0%, and 76.67% for cracks, dents, scratches, and rust, respectively. …”
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SH-YOLO: Enhanced Real-Time Detection of Laparoscopic Surgical Instruments in Computer-Aided Surgery Based on Star Operation and Hybrid Attention Mechanisms by Yiping Shao, Zhilong Xu, Qicong Zhu

“…To meet the real-time detection requirements of laparoscopic surgical instruments, a dataset for laparoscopic surgery is established, and an enhanced YOLOv5 algorithm named SH-YOLO is proposed. …”
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A method for detecting the rate of tobacco leaf loosening in tobacco leaf sorting scenarios by Yansong Wang, Yansong Wang, Chunjie Zhang, Chunjie Zhang, Mingjie Wu, Mingjie Wu, Ruilin Luo, Lin Lu, Zaiqing Chen, Zaiqing Chen, Lijun Yun, Lijun Yun

“…Subsequently, modifications were made to YOLOv8 to improve its multi-scale object detection capabilities. This was achieved by adding layers for detecting smaller objects and integrating a weighted bi-directional feature pyramid structure to reconstruct the feature fusion network. …”
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Smartphone sensor-based depression detection in campus environments: a proof-of-concept study with small-sample behavioral analysis by Yichen Bai, Yueze Liu, Yang Zhang, Amr Tolba

“…Feature selection was conducted using Pearson correlation, and model validation was performed using leave-one-out cross-validation with common classification algorithms.ResultsThe results yielded accuracy rates between 73.11% and 88.24%. …”
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A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA) by Charmayne Mary Lee Hughes, Yan Zhang, Ali Pourhossein, Terezia Jurasova

“…Multi-class classification was more challenging, with Gradient Boosting emerging as the top model, achieving the highest recall (0.666) and precision (0.663) on the external validation set, with a strong F1-score (0.664) and reasonable calibration (Brier Score = 0.223).ConclusionMachine learning algorithms show promise for the detection of current frailty status, particularly in binary classification. …”
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