The VGG16 Method Is a Powerful Tool for Detecting Brain Tumors Using Deep Learning Techniques by Sarthak Raghuvanshi, Sumit Dhariwal

“…A brain tumor diagnosis is a complex and difficult task that requires accurate and efficient data analysis. …”
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Improved fish swarm algorithm based on chaos and its application in abnormal detection of industrial control network by Yaping WU, Hongzhao DONG, Yingying LIN, Jing LIU

“…Artificial fish swarm algorithm as a new type of bionic swarm intelligence optimization algorithm has been successfully used in a variety of optimization problems and practical engineering field,but when faced with the complex optimization problems,especially the multiple extreme value of peak and multimodal function optimization problems,due to the objective function has many local minima,inevitably there are defects such as premature and slow convergence speed.The random and ergodic theory was introduced into the basic artificial fish swarm algorithm,and an improved algorithm was constructed to make artificial fish swarm search avoid possible local extremum.The effectiveness of the algorithm was validated in the simulation application in industrial control network anomaly detection.Compared with the basic artificial fish school algorithm,the chaos improved artificial fish swarm algorithm can effectively avoid the long-term search of the algorithm near the local extreme value.The algorithm has better performance in terms of global convergence and the search efficiency is more prominent.…”
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ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments by Fei Gao, Yang Tian, Yongliang Wu, Yunxia Zhang

“…Synthetic Aperture Radar (SAR) image ship detection faces challenges such as distinguishing ships from other terrains and structures, especially in specific marine complex environments. …”
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Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Firstly, considering the multidimensional complexity of textual features, we integrate comprehensive feature engineering, i.e., encompassing word frequency, statistical metrics, sentiment analysis, and comment tree structure features, as well as advanced feature selection methodologies, particularly lassonet, i.e., a neural network with feature sparsity, to effectively address dimensionality challenges while enhancing model interpretability and computational efficiency. …”
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Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning by Jun Tan, Raoof Mohammed Radhi, Kimia Shirini, Sina Samadi Gharehveran, Zamen Parisooz, Mohsen Khosravi, Hossein Azarinfar

“…Abstract Hydropower systems face significant challenges in load control and fault detection due to their complex operational dynamics. …”
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Optimized Ensemble Deep Learning for Real-Time Intrusion Detection on Resource-Constrained Raspberry Pi Devices by Muhammad Bisri Musthafa, Samsul Huda, Tuy Tan Nguyen, Yuta Kodera, Yasuyuki Nogami

“…Deep learning techniques offer promising solutions for such detection due to their superior complex pattern recognition and anomaly detection capabilities in large datasets. …”
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Advances in the Application of CRISPR-Cas-Based Detection Systems for Safety Monitoring and Control in the Food Supply Chain by WAN Wen-yuan, ZHANG Feng, LIU Zhi-yong, ZHAO Yu

“…CRISPR-Cas-based detection methods significantly improve detection efficiency, providing valuable technical support for food industry monitoring. …”
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Detection Model for 5G Core PFCP DDoS Attacks Based on Sin-Cos-bIAVOA by Zheng Ma, Rui Zhang, Lang Gao

“…A 5G core network DDoS attack detection model is been proposed which utilizes a binary improved non-Bald Eagle optimization algorithm (Sin-Cos-bIAVOA) originally designed for IoT DDoS detection to select effective features for DDoS attacks. …”
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Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections by Gideon Asare Owusu, Ashutosh Dumka, Adu-Gyamfi Kojo, Enoch Kwasi Asante, Rishabh Jain, Skylar Knickerbocker, Neal Hawkins, Anuj Sharma

“…The model was tested in real-world scenarios at a single-lane and a complex multi-lane stop-controlled intersection in Iowa. …”
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YOLOv8-MFD: An Enhanced Detection Model for Pine Wilt Diseased Trees Using UAV Imagery by Hua Shi, Yonghang Wang, Xiaozhou Feng, Yufen Xie, Zhenhui Zhu, Hui Guo, Guofeng Jin

“…However, existing remote sensing-based detection models often struggle with performance degradation in complex environments, as well as a trade-off between detection accuracy and real-time efficiency. …”
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Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification by Heba G. Mohamed, Fadwa Alrowais, Fahd N. Al-Wesabi, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Abdelwahed Motwakel

“…Furthermore, the attention bidirectional gated recurrent unit (ABiGRU) method is implemented for disease detection and classification. Finally, the hyperparameter selection of the ABiGRU method is performed by utilizing the modified coati optimization algorithm (MCOA) method. …”
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Mixed image detection method of belt coal blockage and leakage based on improved RetinaNet mode by Qingjun Fu, Xiang Liu, Yinqiang Yan, Zhibin Guo

“…The test results show that this method can comprehensively collect the images of coal conveying by belt, and accurately identify the detection of coal blockage and coal leakage. This method is applied to the actual production environment, which can monitor the situation of belt coal transportation in real time and accurately detect coal blockage and leakage, which is of great significance to improve the production efficiency of coal transportation, reduce economic losses and ensure production safety.…”
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gamUnet: designing global attention-based CNN architectures for enhanced oral cancer detection and segmentation by Jinyang Zhang, Hongxin Ding, Hongxin Ding, Runchuan Zhu, Weibin Liao, Weibin Liao, Junfeng Zhao, Junfeng Zhao, Min Gao, Xiaoyun Zhang

“…Additionally, we introduce an extended model, gamResNet, to further improve OSCC detection performance. Both architectures show significant improvements in handling the unique challenges of oral cancer images.ResultsExtensive experiments on public datasets show that our GAM-enhanced architecture significantly outperforms conventional models, achieving superior accuracy, robustness, and efficiency in OSCC diagnosis.DiscussionOur approach provides an effective tool for clinicians in diagnosing OSCC, reducing diagnostic variability, and ultimately contributing to improved patient care and treatment planning.…”
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The advantages of k-visibility: A comparative analysis of several time series clustering algorithms by Sergio Iglesias-Perez, Alberto Partida, Regino Criado

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Forced Oscillation Detection via a Hybrid Network of a Spiking Recurrent Neural Network and LSTM by Xiaomei Yang, Jinfei Wang, Xingrui Huang, Yang Wang, Xianyong Xiao

“…Deep learning (DL) holds significant potential for detecting forced oscillations correctly. However, existing artificial neural networks (ANNs) face challenges when employed in edge devices for timely detection due to their inherent complex computations and high power consumption. …”
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An optimized anomaly detection framework in industrial control systems through grey wolf optimizer and autoencoder integration by Muhammad Muzamil Aslam, Liyanage Chandratilak De Silva, Rosyzie Anna Awg Haji Mohd Apong, Ali Tufail

“…The method operates in two stages: (1) Optimizing GWO for feature selection to identify relevant features and reduce feature errors, and (2) Utilizing AE for efficient anomaly detection. …”
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Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach by Osama F. Hassan, Ahmed F. Ibrahim, Ahmed Gomaa, M. A. Makhlouf, B. Hafiz

“…This represents a significant advancement over existing methods, demonstrating the effectiveness of transformer-based architectures in capturing complex spatial dependencies and extracting relevant features for drowsiness detection. …”
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FDIA Attack Detection Technique for Smart Grids Based on Graph Reconstruction and Spatio-Temporal Joint Modeling by Gao Yuzhang, Xia Jing

“…With the widespread application of smart grids, the false data injection attack (FDIA) has become a major threat to power grid security. Traditional detection methods often have difficulty in effectively identifying such attacks, especially in complex environments. …”
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SDFSD-v1.0: A Sub-Meter SAR Dataset for Fine-Grained Ship Detection by Peixin Cai, Bingxin Liu, Peilin Wang, Peng Liu, Yu Yuan, Xinhao Li, Peng Chen, Ying Li

“…In the field of target detection, a prominent area is represented by ship detection in SAR imagery based on deep learning, particularly for fine-grained ship detection, with dataset quality as a crucial factor influencing detection accuracy. …”
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Enhanced YOLOv8-based method for space debris detection using cross-scale feature fusion by Yang Guo, Xianlong Yin, Yao Xiao, Zhengxu Zhao, Xu Yang, Chenggang Dai

“…The experimental results show that the detection accuracy and speed of the method are improved, and that they can meet the requirements of space debris detection in complex backgrounds.…”
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