Encrypted traffic classification method based on convolutional neural network by Rongna XIE, Zhuhong MA, Zongyu LI, Ye TIAN

“…Aiming at the problems of low accuracy, weak generality, and easy privacy violation of traditional encrypted network traffic classification methods, an encrypted traffic classification method based on convolutional neural network was proposed, which avoided relying on original traffic data and prevented overfitting of specific byte structure of the application.According to the data packet size and arrival time information of network traffic, a method to convert the original traffic into a two-dimensional picture was designed.Each cell in the histogram represented the number of packets with corresponding size that arrive at the corresponding time interval, avoiding reliance on packet payloads and privacy violations.The LeNet-5 convolutional neural network model was optimized to improve the classification accuracy.The inception module was embedded for multi-dimensional feature extraction and feature fusion.And the 1*1 convolution was used to control the feature dimension of the output.Besides, the average pooling layer and the convolutional layer were used to replace the fully connected layer to increase the calculation speed and avoid overfitting.The sliding window method was used in the object detection task, and each network unidirectional flow was divided into equal-sized blocks, ensuring that the blocks in the training set and the blocks in the test set in a single session do not overlap and expanding the dataset samples.The classification experiment results on the ISCX dataset show that for the application traffic classification task, the average accuracy rate reaches more than 95%.The comparative experimental results show that the traditional classification method has a significant decrease in accuracy or even fails when the types of training set and test set are different.However, the accuracy rate of the proposed method still reaches 89.2%, which proves that the method is universally suitable for encrypted traffic and non-encrypted traffic.All experiments are based on imbalanced datasets, and the experimental results may be further improved if balanced processing is performed.…”
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Advancements and Challenges in Video-Based Deception Detection: A Systematic Literature Review of Datasets, Modalities, and Methods by Yeni Dwi Rahayu, Chastine Fatichah, Anny Yuniarti, Yusti Probowati Rahayu

“…This systematic literature review (SLR) aims to provide a comprehensive analysis of video-based deception detection research, with five distinct contributions: 1) an unprecedented analysis of 21 datasets, revealing critical gaps and opportunities in data resources; 2) a novel evaluation framework for assessing dataset quality and ecological validity; 3) a systematic comparison of multimodal integration approaches, identifying optimal strategies for combining visual, audio, and textual cues; 4) a critical examination of temporal modeling techniques for capturing the dynamic nature of deceptive behavior; and 5) a roadmap for addressing ethical challenges in deployment. …”
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Development and Validation of Approach for the Detection of Neutralizing Antibodies Against Insulin (Glargine) in Human Blood Plasma by N. B. Abramenko, P. I. Vnukova, E. S. Golovina, I. E. Makarenko, A. A. Mosikian, A. G. Nikiforova, P. V. Gremyakova, V. I. Kazey

“…Development and validation methods for detection of neutralizing antibodies against insulin in human plasma.Materials and methods. …”
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Optimized methods for the targeted surveillance of extended-spectrum beta-lactamase-producing Escherichia coli in human stool by Sarah Gallichan, Sally Forrest, Esther Picton-Barlow, Claudia McKeown, Maria Moore, Eva Heinz, Nicholas A. Feasey, Joseph M. Lewis, Fabrice E. Graf

“…In this study, we compared different laboratory methods to see which worked best for detecting extended-spectrum beta-lactamase (ESBL)-producing E. coli, a common cause of urinary tract or bloodstream infections, from human stool samples. …”
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Enhancing credit card fraud detection: the impact of oversampling rates and ensemble methods with diverse feature selection by Mohamed Akouhar, Abdallah Abarda, Mohamed El Fatini, Mohamed Ouhssini

“…The subject matter of this article is enhancing credit card fraud detection systems by exploring the impact of oversampling rates and ensemble methods with diverse feature selection techniques. …”
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Improving heat exchanger fouling detection for phosphoric acid concentration units: A hybrid inverse approach integrating genetic algorithms and the Levenberg-Marquardt technique by Ridha Zitouni, Ali Fguiri, Amine Aymen Assadi, Mohammod Hafizur Rahman, Abdetif Amrane, Juan Pedro Solano, Mohamed-Razak Jeday

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Study on Novel Surface Defect Detection Methods for Aeroengine Turbine Blades Based on the LFD-YOLO Framework by Wei Deng, Guixiong Liu, Jun Meng

“…This study proposes a novel defect detection method to address the low accuracy and insufficient efficiency encountered during surface defect detection on aeroengine turbine blades (ATBs). …”
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Review of Acoustic Emission Detection Technology for Valve Internal Leakage: Mechanisms, Methods, Challenges, and Application Prospects by Dongjie Zheng, Xing Wang, Lingling Yang, Yunqi Li, Hui Xia, Haochuan Zhang, Xiaomei Xiang

“…Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. …”
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Quantitative infrared detection methods for debonding in concrete-filled steel tubes during the hydration heat phase by Haonan Cai, Chongsheng Cheng, Hong Zhang, Jianting Zhou

“…Currently, there is a lack of an infrared detection method that can perform quantitative detection specifically during the construction phase of CFST. …”
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Self-Sensing of Piezoelectric Micropumps: Gas Bubble Detection by Artificial Intelligence Methods on Limited Embedded Systems by Kristjan Axelsson, Mohammadhossien Sheikhsarraf, Christoph Kutter, Martin Richter

“…Different types of sensors are used to detect gas bubbles: inline on the fluidic channels or inside the pump chamber itself. …”
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State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

“…This study contributes by reviewing how machine learning is applied to transformer fault detection, exploring hybrid methods that combine traditional techniques like DGA with advanced models for better accuracy. …”
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Comparison of Sample Preparation and Detection Methods for the Quantification of Synthetic Musk Compounds (SMCs) in Carp Fish Samples by Jungmin Jo, Eunjin Lee, Na Rae Choi, Ji Yi Lee, Jae Won Yoo, Dong Sik Ahn, Yun Gyong Ahn

“…This study deals with the separation and detection methods for 12 synthetic musk compounds (SMCs), which are some of the emerging contaminants in fish samples, are widely present in environmental media, and can be considered serious risks due to their harmful effects. …”
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Comparison of molecular with conventional methods for detection of genital tuberculosis in infertile women: A comparative prospective study by Anupama Hari, Anusha Reddy Thota, Sravanthi Katamreddy

“…Employing the best clinical practices for diagnosing and treating genital TB may help optimize fertility rate. The current study aimed to evaluate the molecular and conventional methods to diagnose genital TB in women with infertility. …”
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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

“…HAQ alone achieves about 40% of the performance improvements seen with SPACE images as input, allowing for fast and accurate, fully automated detection of small (< 1 cm) BMs. Relevance statement Training with higher-quality annotations, created using the SPACE sequence, improves the detection and delineation sensitivity of DL methods for the detection of brain metastases (BMs)on MPRAGE images. …”
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Optimized Fake News Classification: Leveraging Ensembles Learning and Parameter Tuning in Machine and Deep Learning Methods by Abubaker A. Alguttar, Osama A. Shaaban, Remzi Yildirim

“…In addition to using individual models for training like SVMs, Logistic Regression, and LSTMs, we investigated the combined power of these methods through Stacking and Delegation. This paper analyzes frequently used preprocessing techniques like counter-vectorizer and TF-IDF to understand their impact on detection effectiveness. …”
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Establishment and application of dual rpa-basic and rpa-lfd detection method for pasteurella multocida and actinobacillus pleuropneumoniae by Jingjing LI, Xiaobing WEI, Shuang LI, Mingcheng LIU, Qianlei ZHU, Kexin WANG, Lei WANG, Yingying CAO, Meinan CHANG, Chunling ZHU, Zhanwei TENG, Xuehan LIU, Huihui ZHANG, Xiaojing XIA, Ke DING

“…Based on recombinase polymerase amplification (RPA) detection technology (combined with (lateral flow dipstick, LFD)), it is aimed to establish a dual recombinase polymerase amplification method for the rapid identification of Pasteurella multocida (Pm) and Actinobacillus pleuropneumoniae (APP). …”
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A Robust Multi‐Objective Pressure Sensor Placement Method for Burst Detection in Water Distribution Systems by Kun Du, Jinxin Yu, Feifei Zheng, Wei Xu, Dragan Savic, Zoran Kapelan

“…These comparisons reveal the properties of each optimization method and show how detection performance is affected by various placement features. …”
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Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic Microscopy by Mingjing Li, Junshuai Wang, Shu Fang, Le Yang, Xinyang Liu, Haijiao Yun, Xiaoli Wang, Qingyu Du, Ziqing Han

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An abnormal traffic detection method for chain information management system network based on convolutional neural network by Chao Liu, Chunxiang Liu, Changrong Liu

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ADAM-DETR: an intelligent rice disease detection method based on adaptive multi-scale feature fusion by Hanyu Song, Xinyue Huang, Ziqiang Wang, Jianwei Hu, Huasheng Zhang, Hui Yang

“…Abstract Rice diseases pose a severe threat to global food security, while traditional detection methods suffer from low efficiency and dependence on manual expertise. …”
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