Lightweight remote sensing change detection with progressive multi scale difference aggregation by Yinghua Fu, Haifeng Peng, Tingting Zhao, Yize Li, Jiansheng Peng, Dawei Zhang

“…Deep learning is becoming increasingly popular for change detection tasks in remote sensing due to its significant advantages in deep feature representation and nonlinear problem modeling. …”
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Deep Convolutional Generative Adversarial Network and Convolutional Neural Network for Smoke Detection by Hang Yin, Yurong Wei, Hedan Liu, Shuangyin Liu, Chuanyun Liu, Yacui Gao

“…To improve the performance of smoke detection and solve the problem of too few datasets in real scenes, this paper proposes a model that combines a deep convolutional generative adversarial network and a convolutional neural network (DCG-CNN) to extract smoke features and detection. …”
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A Novel Lightweight U-Shaped Network for Crack Detection at Pixel Level by Zhong Luo, Xinle Li, Yanfeng Zheng

“…Therefore, a highly efficient lightweight network with only 0.78M parameters is proposed for the pixel-level pavement crack detection task. In this paper, adaptive enhancement module (AEM) is designed and added to the encoder network in order to avoid the problem of insufficient model learning capability due to the use of depth-wise separable convolutions. …”
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Lightweight construction safety behavior detection model based on improved YOLOv8 by Kan Huang, Mideth B. Abisado

“…Traditional YOLO models often have problems of missed detection and insufficient feature processing when dealing with complex scenes, especially when facing large-scale data sets. …”
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RP-DETR: end-to-end rice pests detection using a transformer by Jinsheng Wang, Tao Wang, Qin Xu, Lu Gao, Guosong Gu, Liangquan Jia, Chong Yao

“…Owing to its high efficiency, deep learning is now the favored approach for detecting plant pests. In this regard, the paper introduces an effective rice pest detection framework utilizing the Transformer architecture, designed to capture long-range features. …”
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FUR-DETR: A Lightweight Detection Model for Fixed-Wing UAV Recovery by Yu Yao, Jun Wu, Yisheng Hao, Zhen Huang, Zixuan Yin, Jiajing Xu, Honglin Chen, Jiahua Pi

“…Secondly, an adaptive multiscale feature pyramid network (AMFPN) is designed, which effectively integrates different scales of information through multi-level feature fusion and information transmission mechanism, alleviates the problem of information loss in small-target detection, and improves the detection accuracy in complex backgrounds. …”
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Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning by Ashraf Abdulmunim Abdulmajeed, Tawfeeq Mokdad Tawfeeq, Marwa Adeeb Al-jawaherry

“…The methodology is by using machine learning, which is characterized by a HOG (histogram orientation gradient) for extraction of features, then an SVM(support vector machine) for classification, as it can contribute to the literature and enhance mask detection accuracy. …”
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DDoS attack detection and defense based on hybrid deep learning model in SDN by Chuanhuang LI, Yan WU, Zhengzhe QIAN, Zhengjun SUN, Weiming WANG

“…Software defined network (SDN) is a new kind of network technology,and the security problems are the hot topics in SDN field,such as SDN control channel security,forged service deployment and external distributed denial of service (DDoS) attacks.Aiming at DDoS attack problem of security in SDN,a DDoS attack detection method called DCNN-DSAE based on deep learning hybrid model in SDN was proposed.In this method,when a deep learning model was constructed,the input feature included 21 different types of fields extracted from the data plane and 5 extra self-designed features of distinguishing flow types.The experimental results show that the method has high accuracy,it’s better than the traditional support vector machine (SVM) and deep neural network (DNN) and other machine learning methods.At the same time,the proposed method can also shorten the processing time of classification detection.The detection model is deployed in SDN controller,and the new security policy is sent to the OpenFlow switch to achieve the defense against specific DDoS attack.…”
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Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures by Ismahani Ismail, Sulaiman Mohd Nor, Muhammad Nadzir Marsono

“…Malware detection done at the network infrastructure level is still an open research problem ,considering the evolution of malwares and high detection accuracy needed to detect these threats. …”
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Whispers in the air: Designing acoustic classifiers to detect fruit flies from afar by Alia Khalid, Muhammad Latif Anjum, Salman Naveed, Wajahat Hussain

“…Detecting weak wingbeats of a flying bug is a challenging problem in uncontrolled outdoor settings. …”
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Tomato leaf disease detection method based on improved YOLOv8n by Ming Chen, Chunping Wang, Chengwei Liu, Ying Yu, Yuan Yuan, Jiaxuan Ma, Kaisheng Zhang

“…To better address the bounding box regression problem in object detection, we incorporate the GIoU loss function. …”
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Adaptive gate residual connection and multi-scale RCNN for fake news detection by QunHui Zhou, Tijian Cai

“…Detection of false news based on text classification technology has significant research significance and practical value in the current information age. …”
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A novel motion key frame extraction and video stream classification based on reinforcement learning and feature fusion by Hongbo Cui, Tao Feng, Jinhui Zheng

“…In order to solve the problem of missing detection and false detection caused by the inaccuracy of motion feature extraction in the existing video key frame extraction algorithms, a reinforcement learning and feature fusion for key frame extraction algorithm and video stream classification is proposed. …”
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RGB-D salient object detection based on BC2 FNet network by WANG Feng, CHENG Yongmei

“…Aiming at this problem, a boundary-driven cross-modal and cross-layer fusion network (BC2FNet) for RGB-D salient object detection is proposed in this paper, which preserves the boundary of the object by adding the guidance of boundary information to the cross-modal and cross-layer fusion, respectively. …”
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A novel approach for detecting malicious hosts based on RE-GCN in intranet by Haochen Xu, Xiaoyu Geng, Junrong Liu, Zhigang Lu, Bo Jiang, Yuling Liu

“…For malicious host detection, this paper proposes the Relational-Edge Graph Convolutional Network (RE-GCN) model, which can directly aggregate and learn features on edges and use them to accurately classify nodes, compared to other GNN models. …”
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Tracing truth: dynamic temporal networks for multi-modal fake news detection by Jiaen Hu, Juan Zhang, Zichen Li

“…However, the proliferation of fake news has emerged as a critical problem, presenting major challenges to the integrity of the information ecosystem. …”
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UnidecNMR: automatic peak detection for NMR spectra in 1-4 dimensions by Charles Buchanan, Gogulan Karunanithy, Olga Tkachenko, Michael Barber, Michael T. Marty, Timothy J. Nott, Christina Redfield, Andrew J. Baldwin

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Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID-19 in CT Images by Ariyo Oluwasanmi, Muhammad Umar Aftab, Zhiguang Qin, Son Tung Ngo, Thang Van Doan, Son Ba Nguyen, Son Hoang Nguyen

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The Induction and Detection Method of Angry Driving: Evidences from EEG and Physiological Signals by Lixin Yan, Ping Wan, Lingqiao Qin, Dunyao Zhu

“…This study focuses on the problem of inducing and detecting driving anger based on the simulation and on-road experiments. …”
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Remote Sensing Image Detection Method Combining Dynamic Convolution and Attention Mechanism by Yunfei Zhang, Ming Chen, Cong Chen

“…To address the resolution loss issue in small object detection, we adopted the CARAFE (Content-Aware ReAssembly of FEatures) upsampling operator. …”
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