Highway subgrade stability prediction model based on depth separation convolutional fusion network by Yubian Wang

“…To promote the ability of high-precision highway maintenance and detection and solve the situation of false detection or missing detection of road defects, it is necessary to establish a monitoring mechanism of multi-scale feature fusion. …”
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Hematological features of mine-blast trauma, accompanied by acubarotrauma, among servicemen - participants in high-intensity combat operations by S. A. Husieva, G. V. Osyodlo, I. P. Goncharov, O. Ya. Antonyuk, Yu. Ya. Kotyk, A. V. Gusev, М. Е. Krol, I. V. Malysh, Ya. M. Klimenko, S. V. Ткаchenko

“…All patients were fixed on the psychotraumatic circumstances of hostilities. Clinical blood analysis was performed on an automatic haematology analyser ABX Micros ES 60 of the company Horiba ABX. …”
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Comparative characteristics of the clinical and laboratory features of the primary Sjogren’s syndrome associated with anticentromere antibodies and the “classic” subtype of the dis... by B. D. Chaltsev, V. I. Vasiliev, S. G. Palshina, A. V. Torgashina, E. V. Sokol, Yu. I. Khvan, E. B. Rodionova, T. N. Safonova

“…A combination of pSS and SSc was diagnosed in 37 patients, and they were excluded from further analysis. We compared clinical and laboratory features in patients with pSS-ACA+ (n=82) and ACA-negative pSS (pSS-ACA–, n=64) and characterized lymphomas in the pSS-ACA+ (n = 14) and pSS-ACA– (n=10) groups.Results and discussion. …”
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Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers by Jawad Ashraf, Ghulam Musa Raza, Byung-Seo Kim, Abdul Wahid, Hye-Young Kim

“…In our research, we have developed and trained a real-time intrusion-detection system for IoT networks that can detect multiple modern and traditional threats with high accuracy. …”
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DTNLS: 3D Point Cloud Segmentation Based on 2D Image and 3D Point Cloud Double Texture Feature by Zhiguang Liu, Xiaoxiao Yan, Jiahui Zhao, Yong Shi, Fei Yu, Jian Zhao

“…The DTNLS method first uses the color texture feature of the image to segment the pixels and then obtains the clustering center and segmentation boundary outline. 3D LiDAR point cloud texture segmentation takes the clustering center obtained above as the diffusion source, diffuses outwards along the ring LiDAR line, finds the LiDAR point cloud texture boundary features near the image segmentation boundary contour, and finally realizes 3D point cloud segmentation. …”
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Wavelet Analysis-Based Texture Analysis of Ceramic Surface Images by Yanbing Liu, Bei Zhou, Xinghua Yang

“…The main research includes the extraction and evaluation of damage features of ceramic surface morphology by applying wavelet methods, the extraction of damage features in surface contours by using wavelet analysis, and the quantitative evaluation of damage degree by using damage rate and damage mean spacing. …”
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Emotional and Aesthetic Values as Basis for Popularity of TV Programs: Contrastive-Comparative Experience by E. S. Radiontseva

“…The article is devoted to the problem of identifying the competitive advantages of journalistic TV works. The analysis technique is based on a comparison of the format-forming features of two TV programs that are similar in terms of display subject, subject matter, functions, genres, and other features. …”
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A Dual-Stream Deep Learning Architecture With Adaptive Random Vector Functional Link for Multi-Center Ischemic Stroke Classification by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Raja Rizal Azman Bin Raja Aman, Nadia Fareeda Muhammad Gowdh, Izzah Amirah Binti Mohd Ahir, Mohd Salahuddin Bin Kamaruddin, Khairul Azmi Abdul Kadir, Filippo Molinari, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

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Application of the Genetic Algorithm in the Network Intrusion Detection System Using NSL-KDD Data by Naglaa Ibrahim, Hana Usman

“…For rules generation NSL-KDD Data Set is used which include, KDDTrain and KDDTest, 125973 and 22544 records respectively, each record  consists of 41 features and one class attribute for specifying   normal and abnormal connection (complete train and test data are used), In order to get rid of redundancy and inappropriate features Principal  Component Analysis (PCA) is used for selecting (5)  features. …”
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Enhanced Fetal Arrhythmia Classification by Non-Invasive ECG Using Cross Domain Feature and Spatial Differences Windows Information by Gede Angga Pradipta, Putu Desiana Wulaning Ayu, Made Liandana, Dandy Pramana Hostiadi

“…This study presents a new cross-domain feature extraction method that incorporates temporal relationships between consecutive windows, improving feature representation by examining the correlation between neighboring windows. …”
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Self-Supervised Multi-Task Learning for the Detection and Classification of RHD-Induced Valvular Pathology by Lorna Mugambi, Ciira wa Maina, Liesl Zühlke

“…Embedding visualisations, using both Uniform Manifold Approximation Projection (UMAP) and t-distributed Stochastic Neighbor Embedding (t-SNE), revealed distinct clusters for all tasks in both models, indicating the effective capture of the discriminative features of the echocardiograms. This study demonstrates the potential of using self-supervised multi-task learning for automated echocardiogram analysis, offering a scalable and efficient approach to improving RHD diagnosis, especially in resource-limited settings.…”
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Urgent natural foci infections transmitted by ticks in Saint-Petersburg by V. V. Nechaev, A. A. Yakovlev, A. N. Uskov, L. E. Boburina, N. V. Lavrova, M. N. Pogromskaya, B. I. Aslanov, A. O. Shapar, S. V. Pavlenko, L. N. Pozhidaeva, A. K. Ivanov, A. I. Kravtsova, S. A. Leppik, E. I. Vitovich, M. I. Fedunyak

“…Purpose: to conduct a comparative analysis and to identify the epidemiological and clinical features of tick-borne encephalitis (TBE) and Lyme borreliosis as a mono- and coinfections in St. …”
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The diagnostic value of convolutional neural networks in thyroid cancer detection using ultrasound images by Pei Zhang, Qijian Xu, Feng Jiang

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Non-Contact Laser Ultrasound Detection of Internal Gas Defects in Lithium-Ion Batteries by Dongxia Tang, Chenguang Xu, Guidong Xu, Sen Cui, Sai Zhang

“…Through both time-domain and frequency-domain analysis of the ultrasonic features, the results demonstrate that the signal amplitude attenuation characteristics of ultrasound in media with acoustic impedance mismatches can be used for precise detection and quantitative characterization of gas defect regions within the battery. …”
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Multitask Change-Aware Network and Semisupervised Enhanced Multistep Training for Semantic Change Detection by Yifei Si, Jie Jiang

“…Compared to binary change detection, which only predicts the location of changes, SCD provides detailed from-to change information, helping to gain a comprehensive understanding and analysis of land cover and land use. …”
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Detection of anthropogenic objects of aero-images based on geometric parameters of the edge of homogeneous areas by A. G. Shauchuk, V. Yu. Tsviatkou

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Incipient Fault Detection of Rolling Element Bearings Based on Deep EMD-PCA Algorithm by Huaitao Shi, Jin Guo, Zhe Yuan, Zhenpeng Liu, Maxiao Hou, Jie Sun

“…Due to the relatively weak early fault characteristics of rolling bearings, the difficulty of early fault detection increases. For unsolving this problem, an incipient fault detection method based on deep empirical mode decomposition and principal component analysis (Deep EMD-PCA) is proposed. …”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques by Hepseeba Kode, Khaled Elleithy, Laiali Almazaydeh

“…Machine Learning (ML) and Deep learning (DL) algorithms have emerged as powerful feature extraction and classification tools in EEG signal analysis. …”
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OVALYTICS: Enhancing Offensive Video Detection with YouTube Transcriptions and Advanced Language Models by Sneha Chinivar, Roopa M.S., Arunalatha J.S., Venugopal K.R.

“…In response, this work presents OVALYTICS (Offensive Video Analysis Leveraging YouTube Transcriptions with Intelligent Classification System), a comprehensive framework that introduces novel integrations of advanced technologies for offensive video detection. …”
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