Obfuscated malicious traffic detection based on data enhancement by Ke Ye, Tao Zeng, Yubing Duan, Jun Han, Guoxin Zhong, Zhi Chen, Yulong Wang

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SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection by M. Z. Naser, Ahmad Z. Naser

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Multi-scale ship detection algorithm in SAR images in complex scenes by He Shun, Wang Yuzhu, Yang Zhiwei

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Implementation of CNN Algorithm for Indonesian Hoax News Detection on Online News Portals by Clifansi Remi Siwi Hati, Heni Sulistiani

“…Therefore, an effective method is needed to detect it. The purpose of this research is to apply deep learning with the Convolutional Neural Network (CNN) algorithm in detecting text-based hoax news in Indonesian. …”
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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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Res-DNN based signal detection algorithm for end-to-end MIMO systems by Guoquan LI, Yonghai XU, Jinzhao LIN, Zhengwen HUANG

“…Deep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection, an end-to-end MIMO system signal detection scheme based on deep learning was proposed.The encoder and the decoder based on residual deep neural network replace the transmitter and the receiver of the wireless communication system respectively, and they were trained in an end-to-end manner as a whole.Firstly, the features of the input data were extracted by encoder, then the communication model was established and was sent to the zero forcing detector for preliminary detection.Finally, the detection signal was reconstructed through the decoder.Simulation results show that the proposed detection scheme is superior to the same type of algorithm, and the detection performance is significantly better than that of the MMSE detection algorithm at the expense of a certain time complexity.…”
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Res-DNN based signal detection algorithm for end-to-end MIMO systems by Guoquan LI, Yonghai XU, Jinzhao LIN, Zhengwen HUANG

“…Deep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection, an end-to-end MIMO system signal detection scheme based on deep learning was proposed.The encoder and the decoder based on residual deep neural network replace the transmitter and the receiver of the wireless communication system respectively, and they were trained in an end-to-end manner as a whole.Firstly, the features of the input data were extracted by encoder, then the communication model was established and was sent to the zero forcing detector for preliminary detection.Finally, the detection signal was reconstructed through the decoder.Simulation results show that the proposed detection scheme is superior to the same type of algorithm, and the detection performance is significantly better than that of the MMSE detection algorithm at the expense of a certain time complexity.…”
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Evaluation of Feature Transformation and Machine Learning Models on Early Detection of Diabetes Mellitus by Ahmed Ali Linkon, Inshad Rahman Noman, Md Rashedul Islam, Joy Chakra Bortty, Kanchon Kumar Bishnu, Araf Islam, Rakibul Hasan, Masuk Abdullah

“…The increasing prevalence of diabetes necessitates the development of effective early detection methods to mitigate its health impacts. This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. …”
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Research on defect perception model of distribution network based on big data analysis and waveform matching algorithm by LIN Kaifeng, LI Yiming, ZHANG Bo, YANG Changyu, ZHU Zeting, YANG Zhenda

“…Through utilizing the data acquisition capability of the distribution terminal unit (DTU) of the distribution line, the protection settings are reasonably set to collect fault flashover information without affecting FA functions, and analyze the signal waveform characteristics to extract fault waveform characteristics for equipment defect identification; The distribution network defect perception model is established based on the waveform matching algorithm, the identification of fault waveform is trained and learned, and the analytic hierarchy process (AHP) algorithm is used to quantify the risk assessment. …”
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Securing federated learning: a defense strategy against targeted data poisoning attack by Ansam Khraisat, Ammar Alazab, Moutaz Alazab, Tony Jan, Sarabjot Singh, Md. Ashraf Uddin

“…This paper investigates targeted data poisoning attacks in FL systems, where a small fraction of malicious participants corrupt the global model through mislabeled data updates. …”
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Hybrid Feature and Optimized Deep Learning Model Fusion for Detecting Hateful Arabic Content by Karim Gasmi, Ibtihel Ben Ltaifa, Alameen Eltoum Abdalrahman, Omer Hamid, Mohamed Othman Altaieb, Shahzad Ali, Lassaad Ben Ammar, Manel Mrabet

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scFocus: Detecting branching probabilities in single-cell data with SAC by Chunlin Chen, Zeyu Fu, Jiajia Yang, Huaqing Chen, Jiabao Huang, Shitian Qin, Chuhuai Wang, Xiaoqian Hu

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Diagnostics and Prognostics of Boilers in Power Plant Based on Data-Driven and Machine Learning by Achmad Widodo, Toni Prahasto, Mochamad Soleh, Herry Nugraha

“…The proposed method utilizes machine learning techniques through support vector machine (SVM) and random forest algorithm (RFA) for anomaly detection and similarity-based method of dynamic time warping (DTW) for RUL prediction. …”
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A Detection Method of Lentinus Edodes Based on Improved YOLOv4 Algorithm by HUANG Ying-lai, LI Da-ming, LU Xin, YANG Liu-song

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Infrared small target detection algorithm with U-shaped multiscale transformer network by DUAN Peipei, ZHANG Yan, LUO Mingshi, YAN Xiaoying

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Improved YOLOv8-Based Algorithm for Citrus Leaf Disease Detection by Zhengbing Zheng, Yibang Zhang, Luchao Sun

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Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…Finally, inspired by the RepVGG architecture, the original detection head was optimized to achieve a separation of detection and inference architecture, which not only accelerated the model's inference speed but also enhanced feature learning capability. …”
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Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection by Seyedeh Gol Ara Ghoreishi, Charles Boateng, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Kwangsoo Yang, Jinwoo Jang, Borko Furht, David Newman, Ruth Tappen, Monica Rosselli, Kelley L. Jackson

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Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms by Yogita Dubey, Yashraj Tarte, Nikhil Talatule, Khushal Damahe, Prachi Palsodkar, Punit Fulzele

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Optimizing Cancer Detection: Swarm Algorithms Combined with Deep Learning in Colon and Lung Cancer using Biomedical Images by HariKrishna Pathipati, Lova Naga Babu Ramisetti, Desidi Narsimha Reddy, Swetha Pesaru, Mashetty Balakrishna, Thota Anitha

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