Auto forensic detecting algorithms of malicious code fragment based on TensorFlow by Binglong LI, Jinlong TONG, Yu ZHANG, Yifeng SUN, Qingxian WANG, Chaowen CHANG

“…In order to auto detect the underlying malicious code fragments in complex，heterogeneous and massive evidence data about digital forensic investigation, a framework for malicious code fragment detecting algorithm based on TensorFlow was proposed by analyzing TensorFlow model and its characteristics.Back-propagation training algorithm was designed through the training progress of deep learning.The underlying binary feature pre-processing algorithm of malicious code fragment was discussed and proposed to address the problem about different devices and heterogeneous evidence sources from storage media and such as AFF forensic containers.An algorithm which used to generate data set about code fragments was designed and implemented.The experimental results show that the comprehensive evaluation index F₁of the method can reach 0.922, and compared with CloudStrike, Comodo, FireEye antivirus engines, the algorithm has obvious advantage in dealing with the underlying code fragment data from heterogeneous storage media.…”
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Deep Learning and Its Application in the Field of Rail Transit by XIONG Qunfang, LIN Jun, LIU Yue, YUAN Hao, YOU Jun

“…It introduced the current state of deep learning and its application in the field of rail transit, including identification, driver fatigue detection, lane detection and vehicle recognition equipment fault detection. …”
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FEATURES of NON-DESTRUCTIVE damage CONTROL OF aircraft parts made of composite MATERIALS at the enterprises of aviation industry by A. V. Kopylov, B. V. Zubkov

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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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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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FEATURES OF CHEMILUMINESCENT ACTIVITY OF NEUTROPHILIC GRANULOCYTES IN PATIENTS WITH CHRONIC GASTRITIS, CHRONIC ATROPHIC GASTRITIS AND GASTRIC CANCER by O. V. Smirnova, V. V. Tsukanov, A. A. Sinyakov

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Enhanced Image Processing and Fuzzy Logic Approach for Optimizing Driver Drowsiness Detection by Wasin AlKishri, Abdallah Abualkishik, Mahmood Al-Bahri

“…It is an enhanced approach for Viola–Jones to examine different visual signs to detect the driver's drowsiness level. It extracted eye blink duration and mouth features to detect driver drowsiness based on the desired facial feature image in a specific driver video frame. …”
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A hybrid Bi-LSTM and RBM approach for advanced underwater object detection. by Manimurugan S, Karthikeyan P, Narmatha C, Majed M Aborokbah, Anand Paul, Subramaniam Ganesan, Rajendran T, Mohammad Ammad-Uddin

“…Additionally, this architecture handles variable-length sequences, mitigates the vanishing gradient problem, and achieves enhanced significance by capturing complex patterns in the data. …”
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Features of short-term heart rate variability in internally displaced people with type 2 diabetes mellitus by V.A. Serhiyenko, V.B. Sehin, V.I. Pankiv, A.A. Serhiyenko

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Features of Development and Course of Disseminated Intravascular Coagulation Syndrome During Surgical Interventions in Children with Oncological Diseases by N. P. Leonov, V. V. Schukin, G. A. Novichkova, M. A. Maschan, F. I. Ataullakhanov, S. S. Yashin, A. M. Zeynalov, Е. A. Spiridonova

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Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 by Eiman Wahab, Wajeeha Shafique, Habiba Amir, Sameena Javed, Muhammad Marouf

“…We used the face detector algorithm called Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection with 99.8% accuracy. Further we have conducted feature extraction and pre-processing on our self-created dataset. …”
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A novel criterion for crack detection in beam structures by frequency response functions by Nguyen Tien Khiem, Tran Thanh Hai, Le Khanh Toan, Nguyen Thi Lan, Ho Quang Quyet

“…However, the complex feature of FRF has not been thoroughly employed for structural damage detection. …”
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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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Tea Disease Detection Method Based on Improved YOLOv8 in Complex Background by Junchen Ai, Yadong Li, Shengxiang Gao, Rongsheng Hu, Wengang Che

“…In order to solve the problems such as mutual occlusion of leaves, light disturbance, and small lesion area under complex background, YOLO-SSM, a tea disease detection model, was proposed in this paper. …”
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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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Cumulative confidence-driven task offloading for object detection in maritime Internet of Things by Yanglong Sun, Wenqian Luo, Weijian Xu, Qiang Mei, Haixia Peng, Linhai Wei

“…However, the dynamic marine network and environmental interference in feature extraction adversely affect detection accuracy and cause delay. …”
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Bayesian Neural Networks With Robust Feature Interpretation for Enhanced Compressive Strength Prediction of Ultra‐High‐Performance Concrete by Tao Zhang, Ji Hao, Jie Zhang, Lina Du, Yunhao Zhang, Wenbin Jiao

“…A novel dataset comprising 799 UHPC mix designs, featuring 15 input parameters and one target variable, was curated from the literature. …”
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YOLOv3-A: a traffic sign detection network based on attention mechanism by Fan GUO, Yongxiang ZHANG, Jin TANG, Weiqing LI

“…To solve the problem that the existing YOLOv3 algorithm had more false detections and missed detections for traffic sign detection task with small target problems and complex background, based on the YOLOv3, a channel attention method for target detection and a spatial attention method based on semantic segmentation guidance were proposed to form the YOLOv3-A (attention) algorithm.The detection features in the channel and spatial dimensions were recalibrated, allowing the network to focus and enhance the effective features, and suppress interference features, which greatly improved the detection performance.Experiments on the TT100K traffic sign data set show that the algorithm improves the detection performance of small targets, and the accuracy and recall rate of the YOLOv3 are improved by 1.9% and 2.8% respectively.…”
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CIDNet: A Maritime Ship Detection Model Based on ISAR Remote Sensing by Fei Liu, Boyang Liu, Hang Zhou, Song Han, Kunlin Zou, Wenjie Lv, Chang Liu

“…To solve these problems, this study proposes a new ISAR target detection model for ships based on deep learning—Complex ISAR Detection Net (CIDNet). …”
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Target Detection and Image Enhancement for Underwater Environment: Research on Improving YOLOv7 by Yang Luo, Wen Feng

“…Aiming at the common low accuracy and efficiency problems in underwater target detection, this paper designs an innovative algorithm based on the YOLOv7 framework. …”
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