Detection and Classification of Dress Code Violations in Educational Environments Using Deep Learning by Aminu Suleiman Bashir, Ahmed Ibrahim Mahmud, Muhammad Yahya

“…The results showed how it is effective by combining powerful deep learning models with strong frameworks to solve problems of classification. …”
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UAV-based inspection of wind turbine blade surface defects detection technology by TAN Xingguo, ZHANG Gaoming

“…With the increasing scale of wind turbines, the damage probability of blades is also increasing. Aiming at the problems of high cost and poor working environment of large-scale wind turbine blade defect detection, a wind turbine blade surface defect detection method based on UAV image acquisition and digital image processing is proposed in this paper. …”
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An algorithm for road target detection of autonomous vehicles based on improved YOLOv8 by Jianping Gao, Haotian Li, Zhe Li, Chengwei Xie, Xiaolei Ji, Yuzhuo Zhang

“…Affected by the complex road traffic conditions and driving environment, the current autonomous vehicle’s ability to accurately identify road targets through on-board cameras still needs to be improved. Aiming at the problems of near-target error detection and remote target missing detection in road target detection of autonomous vehicles, an improved road target detection algorithm YOLOv8-RTDAV based on YOLOv8n was proposed. …”
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Predicting learners' engagement and help-seeking behaviors in an e-learning environment by using facial and head pose features by Guan-Yun Wang, Yasuhiro Hatori, Yoshiyuki Sato, Chia-Huei Tseng, Satoshi Shioiri

“…The current study succeeded in estimating when participants are engaging in solving a problem and when they need help. Features obtained from facial videos are useful in improving e-learning education.…”
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Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm by Earl Clarence San Diego, Seph Gerald Rodrin, Edwin Arboleda

“…Although cacao production aspired to be heightened to cope with the global trend, several difficulties were still needed to be addressed in crop propagation, mainly due to disruptive diseases and pests. In response to this problem, the study devised an algorithm based on k-Nearest Neighbors that can detect whenever a cacao pod was infected with the three most prominent diseases: black pod rot, Monilia, and pod borer infestations. …”
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18F-FDG PET and combined diffusion weighted MRI features in pleural infection: A case report by KyungAh Chun, MD, PhD

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Lightning Damage Detection Method Using Autoencoder: A Case Study on Wind Turbines with Different Blade Damage Patterns by Takuto Matsui, Kazuki Matsuoka, Kazuo Yamamoto

“…There have been numerous reported accidents of lightning strikes damaging wind turbine blades, which poses a serious problem. In certain accidents, the blades that were struck by lightning continued to rotate, resulting in breakage due to centrifugal force. …”
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An enhanced classification system of various rice plant diseases based on multi-level handcrafted feature extraction technique by Yasmin M. Alsakar, Nehal A. Sakr, Mohammed Elmogy

“…This paper proposes a new system for detecting and classifying rice plant leaf diseases by fusing different features, including color texture with Local Binary Pattern (LBP) and color features with Color Correlogram (CC). …”
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Clinical and psychological aspects of pregnancy and features of the course of labor in women with different types of psychological component of gestational dominant by V. M. Astakhov, O. V. Batsylieva, I. V. Puz

“…The results obtained indicate the need to organize a comprehensive medical and psychological support for pregnant women, during which a diagnosis of the psycho-emotional state in pregnant women with the detection of PCGD type is binding. This approach would not only help to prevent a number of clinical and psychological problems that may occur in women during pregnancy, but also to allow for the harmonious growth of an intrauterine fetus, effective women’s adaptation to the situation of impending motherhood and the birth of a healthy child.…”
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Hematological features of mine-blast trauma, accompanied by acubarotrauma, among servicemen - participants in high-intensity combat operations by S. A. Husieva, G. V. Osyodlo, I. P. Goncharov, O. Ya. Antonyuk, Yu. Ya. Kotyk, A. V. Gusev, М. Е. Krol, I. V. Malysh, Ya. M. Klimenko, S. V. Ткаchenko

“…A feature of the peripheral blood of patients with ear injury was a significant increase (p<0,05) in the absolute number of monocytes (0.7±0.36×10⁹/l) compared to the results of the control group (0.29±0.11×10⁹/l). …”
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Postural Orthostatic Tachycardia Syndrome as a Manifestation of Post-COVID-19 Syndrome by E. L. Trisvetova

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Emotional and Aesthetic Values as Basis for Popularity of TV Programs: Contrastive-Comparative Experience by E. S. Radiontseva

“…The article is devoted to the problem of identifying the competitive advantages of journalistic TV works. …”
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Self-attention-based graph transformation learning for anomaly detection in multivariate time series by Qiushi Wang, Yueming Zhu, Zhicheng Sun, Dong Li, Yunbin Ma

“…In this paper, we propose a self-attention based graph transformation learning (AT-GTL) method to solve this problem. AT-GTL uses a global self-attention graph pooling (GATP) module to aggregate all node features to obtain global features. …”
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RGB-D Image Saliency Detection Based on Multi-branch Backbone Supervised Network by WANG Wei-bing, ZHANG Xiao-zhuo, DENG Qiang

“…Aiming at the problem that the existing RGB-D image saliency detection technology is difficult to fully explore the effective information of depth image and can not effectively integrate RGB features and deep features, an RGB-D image saliency detection method under multi-branch backbone supervision network is proposed. …”
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GDSN-CD: Graph-Guided Diffusion Synergistic Network for Remote Sensing Change Detection by Yunfei Zhu, Jintao Song, Jinjiang Li

“…Remote sensing change detection often faces the challenge of accurately capturing discontinuous change features in complex scenes. …”
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Smart contract vulnerability detection method based on Bi-modal cross-attention mechanism by CHEN Jinfu, HU Xinyi, CAI Saihua, MIN Xirun

“…To address the problem that existing deep learning methods for smart contract vulnerability detection rely on single-modal feature extraction and insufficient contextual information capture, leading to relatively low detection accuracy, a smart contract vulnerability detection method based on the Bi-modal cross-attention mechanism was proposed. …”
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Multi-Scale Plastic Lunch Box Surface Defect Detection Based on Dynamic Convolution by Jing Yang, Gang Zhang, Yunwang Ge, Jingzhuo Shi, Yiming Wang, Jiahao Li

“…Firstly, this paper integrates the attention mechanism into Slim-neck, and enhances the model&#x2019;s ability to perceive multi-scale feature information. Secondly, a small target detection layer is added to Slim-neck to solve the semantic information loss problem of various defect features. …”
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Evaluating the Effectiveness of Image Processing in the Task of Detecting Moving Objects by an Optoelectronic Surveillance System by E. I. Mikhnionok, A. V. Khizniak

“…The problem considered by the authors, assessing the effectiveness of the developed image processing method in relation to the problem of detecting moving objects by an optoelectronic surveillance system, determines the need for its consideration in two directions. …”
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Highway subgrade stability prediction model based on depth separation convolutional fusion network by Yubian Wang

“…To promote the ability of high-precision highway maintenance and detection and solve the situation of false detection or missing detection of road defects, it is necessary to establish a monitoring mechanism of multi-scale feature fusion. …”
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Abnormal traffic detection method based on LSTM and improved residual neural network optimization by Wengang MA, Yadong ZHANG, Jin GUO

“…Problems such as a difficulty in feature selection and poor generalization ability were prone to occur when traditional method was exploited to detect abnormal network traffic.Therefore, an abnormal traffic detection method based on the long short term memory network (LSTM) and improved residual neural network optimization was proposed.Firstly, the features and attributes of network traffic were analyzed, and the variability of the feature values was reduced by preprocessing of network traffic.Then, a three-layer stacked LSTM network was designed to extract network traffic features of different depths.Moreover, the problem of weak adaptability of feature extraction was solved.Finally, an improved residual neural network with skipping connecting line was designed to optimize the LSTM.The defects of deep neural network such as overfitting and gradient vanishing were optimized.The accuracy of abnormal traffic detection was improved.Experimental results show that the proposed method has higher training accuracy and better visibility of data processing.The classification accuracy rates under two classifications and multiple classifications are 92.3% and 89.3%.It has the lowest false positive rate when the parameters such as precision rate and recall rate are optimal.Moreover, it has strong robustness when the sample is destroyed.Furthermore, better generalization ability can be achieved.…”
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