YOLO-IRS: Infrared Ship Detection Algorithm Based on Self-Attention Mechanism and KAN in Complex Marine Background by Limin Guo, Yuwu Wang, Muran Guo, Xiaohai Zhou

“…Compared to mainstream detection algorithms, YOLO-IRS achieves higher detection accuracy while requiring relatively fewer computational resources, verifying the superiority of the proposed algorithm and enhancing the detection performance of infrared ship targets.…”
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Inside the Black Box: Detecting and Mitigating Algorithmic Bias Across Racialized Groups in College Student-Success Prediction by Denisa Gándara, Hadis Anahideh, Matthew P. Ison, Lorenzo Picchiarini

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Intrusion Detection Framework for Internet of Things with Rule Induction for Model Explanation by Kayode S. Adewole, Andreas Jacobsson, Paul Davidsson

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Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm by G. Ayappan, S. Anila

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Tuberculosis Detection using Gray Level Co-Occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN) Algorithms by Fuad Anwar, Mohtar Yunianto*, Rahmanisya Fani Aisha Putri

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AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation by Shaoliang Fang, Lu Lu, Zhu Lin, Zhanyu Yang, Shaosheng Wang

“…To address these issues, this paper proposes a crack detection model based on adaptive feature quantization, which primarily consists of a maximum soft pooling module, an adaptive crack feature quantization module, and a trainable crack post-processing module. …”
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Identification of fresh leaves of Anji White Tea: S-YOLOv10-ASI algorithm fusing asymptotic feature pyra-mid network. by Chunhua Yang, Wenxia Yuan, Qiang Zhao, Zejun Wang, Bowu Song, Xianqiu Dong, Yuandong Xiao, Shihao Zhang, Baijuan Wang

“…The algorithm improves the partial structure of the YOLOv10 network through space-to-depth convolution. …”
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Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms by S. Jayanthi, Swathi Sowmya Bavirthi, P. Murali, K. Vijaya Kumar, Hend Khalid Alkahtani, Mohamad Khairi Ishak, Samih M. Mostafa

“…Initially, the data normalization stage leverages linear scaling normalization (LSN) for converting input data into a beneficial format. Furthermore, the feature selection process uses the RIME optimization algorithm (ROA) model to select the most relevant features from the data. …”
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A Lightweight YOLOv8s Algorithm for Ceiling Fan Blade Defect Detection With Optimized Pruning and Knowledge Distillation by Qinyuan Huang, Chen Fan, Yuqi Sun, Jiaxiong Huang, Wengziyang Jiang

“…While industrial ceiling fans play a vital role in maintaining efficient airflow and system stability within factory settings, surface defects on their blades that arise over time can disrupt their functionality and create safety hazards. Detecting these surface defects is crucial; however, accurate and rapid detection typically involves complex machine vision algorithms, such as You Only Look Once (YOLO) networks, that require considerable computing resources, which contradicts the industry’s preference for simpler algorithms that can be deployed using low-cost computing power. …”
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Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study by Sabire Kılıçarslan, Meliha Merve Hız Çiçekliyurt, Serhat Kılıçarslan

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Mangrove Extraction Algorithm Based on Orthogonal Matching Filter-Weighted Least Squares by Yongze Li, Jin Ma, Dongyang Fu, Jiajun Yuan, Dazhao Liu

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MST-YOLO: Small Object Detection Model for Autonomous Driving by Mingjing Li, Xinyang Liu, Shuang Chen, Le Yang, Qingyu Du, Ziqing Han, Junshuai Wang

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Assessing the quality of whole slide images in cytology from nuclei features by Paul Barthe, Romain Brixtel, Yann Caillot, Benoît Lemoine, Arnaud Renouf, Vianney Thurotte, Ouarda Beniken, Sébastien Bougleux, Olivier Lézoray

“…To address these challenges, this article introduces a straightforward, interpretable, and computationally efficient quality control module to ensure optimal algorithmic performance. Methods: The proposed quality control module ensures algorithmic performance by representing an algorithm by a reference whole slide image preparation protocol validated on it. …”
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StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images by Evren Ekingen, Ferhat Yildirim, Ozgur Bayar, Erhan Akbal, Ilknur Sercek, Abdul Hafeez-Baig, Sengul Dogan, Turker Tuncer

“…In the DFE pipeline, StrokeNeXt extracts features from fixed‑size patches, iterative neighborhood component analysis (INCA) selects the top features, and a t algorithm-based k-nearest neighbors (tkNN) classifier has been utilized for classification. …”
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Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance by Fauzi Adi Rafrastara, Wildanil Ghozi, Ramadhan Rakhmat Sani, Lekso Budi Handoko, Abdussalam Abdussalam, Elkaf Rahmawan Pramudya, Faizal M. Abdollah

“…Recent studies have shown that this challenge can be addressed by employing machine learning algorithms for detection. Some studies have also implemented various feature selection methods to optimize detection efficiency. …”
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A Human Pose Estimation Algorithm with Scale Invariant by SUN Ruiyang, YANG Huixin, ZHAO Lanfei

“…Due to the existing issues in current human pose estimation algorithms, which struggle with accurately detecting small and large-sized human keypoints as well as having lower precision, this paper proposes a scale invariant convolution neural network to estimate human pose. …”
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Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm by Mosleh Hmoud Al-Adhaileh, Mosleh Hmoud Al-Adhaileh, Sultan Ahmad, Alhasan A. Alharbi, Mohammed Alarfaj, Mohammed Alarfaj, Mukta Dhopeshwarkar, Theyazn H. H. Aldhyani

“…An essential step that may help clinicians identify and treat epileptic seizures is the differentiation between epileptic and non-epileptic signals by use of epileptic seizure detection categorization.MethodsIn this work, we investigated Machine learning algorithms including Random Forest, Gradient Boosting, and K-Nearest Neighbors, alongside advanced DL architectures such as Long Short-Term Memory networks and Long-term Recurrent Convolutional Networks for detecting epileptic seizures in terms of difficulties and procedures evolved depending on EEG data. …”
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Improvement of signal detection based on using machine learning by Bassam Abd

“…The paper strongly emphasized extracting the dataset's most essential features, which improved Support Vector Machines’ capacity to detect signals in noisy and complicated situations. …”
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A Feature-Driven Inception Dilated Network for Infrared Image Super-Resolution Reconstruction by Jiaxin Huang, Huicong Wang, Yuhan Li, Shijian Liu

“…This allows the network to focus on learning the reconstruction of the target areas only, reducing the interference of background areas in the target areas’ reconstruction. Additionally, a feature-driven module is cascaded at the end of the IDSR network to guide the high-resolution (HR) image reconstruction with feature prior information from a detection backbone. …”
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The "Low Slow and Small" UAV target detection and tracking algorithm based on improved YOLOv7 and DeepSort by JIAN Yuhong, YANG Huiyue, WANG Xinggang, RONG Yisheng, ZHU Yukun

“…The results show that the detection part mAP@0.5 of the improved algorithm is improved by 8.6%, and the detection accuracy of small-size and weak-feature targets is improved by about 21%. …”
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