Development of data driven models to accurately estimate density of fatty acid ethyl esters by Walid Abdelfattah, Munthar Kadhim Abosaoda, Hardik Doshi, H. S. Shreenidhi, Manoranjan Parhi, Devendra Singh, Prabhjot Singh, Abdolali Yarahmadi Kandahari

“…Through extensive statistical evaluations and visualization techniques, SVR emerged as the most accurate model for density prediction. Sensitivity analysis confirms the influence of all input features, with SHAP analysis identifying temperature as the most dominant factor affecting density. …”
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Quantitatively exploring the influence of geographical conditions on ecological quality using a novel remote sensing model: a comparison between two geographical disparity regions... by Hanqiu Xu, Mengjing Lin, Yifan Wang, Huade Guan, Fei Tang

“…This study applied the novel Remote Sensing-based Ecological Index (RSEI) concurrently to two distinct provincial-level regions in China, Fujian and Ningxia, to quantitatively detect their ecological differences. These two regions possess contrasting geographical conditions, with Fujian having high forest coverage and abundant rainfall, while Ningxia features low forest coverage and extensive loess plateau and desert terrain. …”
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In-line inspection of pipeline crack based on biased AC field measurement by Shuangcheng FU FU, Bingfeng YU, Jiaxing XIN, Rong DING, Fan YANG, Hongwei LYU, Jinzhong CHEN, Xiaokang WAN

“…Objective The traditional magnetic flux leakage (MFL) testing technique for pipelines has been found to be insufficient in detecting crack defects with a small angle to the magnetic field lines. …”
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Stochastic algorithm for HDFS data theft detection based on MapReduce by Yuanzhao GAO, Binglong LI, Xingyuan CHEN

“…To address the problems of big data efficient analysis and insider theft detection in the data theft detection of distributed cloud computing storage,taking HDFS (hadoop distributed file system) as a case study,a stochastic algorithm for HDFS data theft detection based on MapReduce was proposed.By analyzing the MAC timestamp features of HDFS generated by folder replication,the replication behavior’s detection and measurement method was established to detect all data theft modes including insider theft.The data set which is suitable for MapReduce task partition and maintains the HDFS hierarchy was designed to achieve efficient analysis of large-volume timestamps.The experimental results show that the missed rate and the number of mislabeled folders could be kept at a low level by adopting segment detection strategy.The algorithm was proved to be efficient and had good scalability under the MapReduce framework.…”
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Modeling Dual-Task Performance: Identifying Key Predictors Using Artificial Neural Networks by Arash Mohammadzadeh Gonabadi, Farahnaz Fallahtafti, Judith Heselton, Sara A. Myers, Ka-Chun Siu, Julie Blaskewicz Boron

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PECULIARITIES OF COMMUNICATIVE BEHAVIOR OF A MASCULINE CULTURE TYPE "PRIVATE DETECTIVE" by Elena Kusaeva, Tatyana Tameryan

“…Using the method of continuous sampling, semantic analysis, and discourse and contextual analysis techniques we analyze features of non-verbal communicative behavior of the masculine culture type "private detective", represented by Sherlock Holmes, the fiction character of the English detective discourse of Conan Doyle. …”
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Cyberbullying Detection in Arabic Text Using Different Deep Learning Approaches by Humera Shaziya

“…Addressing this issue requires advanced emotional and sentiment analysis techniques. In this study, the ArCyC (Arabic Cyberbullying Corpus) were integrated with Twitter data to develop robust models for cyberbullying detection. …”
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Blockchain-Enabled Deep Recurrent Neural Network Model for Clickbait Detection by Abdul Razaque, Bandar Alotaibi, Munif Alotaibi, Fathi Amsaad, Ansagan Manasov, Salim Hariri, Banu B. Yergaliyeva, Aziz Alotaibi

“…The proposed BDRNN consists of three phases: analysis of clickbait and source rating, clickbait search process and multi-layered clickbait detection. …”
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Study on the Detection of Chlorophyll Content in Tomato Leaves Based on RGB Images by Xuehui Zhang, Huijiao Yu, Jun Yan, Xianyong Meng

“…This paper aims to explore an efficient and low-cost non-destructive method for detecting chlorophyll content (SPAD) and investigate the feasibility of smartphone image analysis technology in predicting chlorophyll content in greenhouse tomatoes. …”
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Flow cytofluorimetric detection and immunophenotyping of platelet-monocyte complexes in peripheral blood by O. V. Pavlov, S. V. Chepanov, A. V. Selutin, M. S. Zainulina, D. R. Eremeeva, S. A. Selkov

“…The aim of the study was to develop the method of quantitative determination of peripheral blood PMC, that preserve phenotypic features of platelets and monocytes, and to reveal their changes by ex vivo analysis. …”
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Multi-view adversarial attack defending method for host intrusion detection by WANG Fei, QIAN Kehan, LYU Mingqi, ZHU Tiantian, CHEN Honglong

“…Host-based intrusion detection (HID) aims to identify attack behaviors through the analysis of host logs. …”
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Light stress diagnosis of rapeseed seedling stage based on hyperspectral imaging technology by WANG Yitian, ZHANG Xiaomin, JIANG Haiyi, ZHANG Yanning, LIN Yangyang, RAO Xiuqin

“…Then successive projection algorithm was used to extract characteristic wavelengths, and the continuous wavelet transform-stepwise discriminant analysis method was used to extract wavelet features. …”
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A hybrid super learner ensemble for phishing detection on mobile devices by Routhu Srinivasa Rao, Cheemaladinne Kondaiah, Alwyn Roshan Pais, Bumshik Lee

“…By focusing on extracting features from URLs, including handcrafted features, transformer-based text embeddings, and other Deep Learning (DL) architectures, the proposed model offers several advantages: fast computation, language independence, and robustness against accidental malware downloads. …”
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Generative Adversarial Networks In Object Detection: A Systematic Literature Review by Anis Farihan Mat Raffei, Sinung Suakanto, Faqih Hamami, Mohd Arfian Ismail, Ferda Ernawan

“…The paper investigates three primary domains where GANs have demonstrated remarkable potential: data augmentation for addressing data scarcity, occlusion handling techniques designed to manage visually obstructed objects, and enhancement methods specifically focused on improving small object detection performance. Analysis reveals significant performance improvements resulting from these GAN applications: data augmentation methods consistently boost detection metrics such as mAP and F1-score on scarce datasets, occlusion handling techniques successfully reconstruct hidden features with high PSNR and SSIM values, and small object detection techniques increase detection accuracy by up to 10% Average Precision in some studies. …”
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Neuropathic Pain Detection: An EEG-Based Brain Functional Network Approach by Abdulyekeen T. Adebisi, Ho-Won Lee, Kalyana C. Veluvolu

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Gonorrhea in the Russian Federation: incidence, active case detection and laboratory diagnostics by Elena V. Bogdanova, Nikita Yu. Nosov, Xenia I. Plahova

“…The problem of gonococcal antimicrobial resistance remains actual. Aims. Analysis of incidence, circumstances of case detection, and methods of laboratory examination for gonococcal infection used in the Russian Federation. …”
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Potato Plant Leaf Disease Detection Using Deep Learning Method by Cemal İhsan Sofuoğlu, Derya Bırant

“…Improvements in the automated disease detection and analysis areas may provide important benefits for early action that would allow intervention at earlier stages for cure and preventing spread of the disease. …”
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A Driver’s Calling Behavior Detection Method Based on Deep Learning by XIONG Qunfang, LIN Jun, YUE Wei, LIU Shiwang, LUO Xiao, DING Chi

“…In order to prevent the driver from being distracted by the cell phone call, real-time monitoring of drivers’ behavior through video analysis is especially important. At present, driver’s calling behavior detection methods are prone to object occlusion, image rotation, illumination change and are difficult to extract deep features of the image, which degrade the detection accuracy. …”
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A Multimodal Semantic-Enhanced Attention Network for Fake News Detection by Weijie Chen, Yuzhuo Dang, Xin Zhang

“…The proliferation of social media platforms has triggered an unprecedented increase in multimodal fake news, creating pressing challenges for content authenticity verification. Current fake news detection systems predominantly rely on isolated unimodal analysis (text or image), failing to exploit critical cross-modal correlations or leverage latent social context cues. …”
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Quantitative Analysis of 3-Monochloropropane-1,2-diol in Fried Oil Using Convolutional Neural Networks Optimizing with a Stepwise Hybrid Preprocessing Strategy Based on Fourier Tra... by Xi Wang, Siyi Wang, Shibing Zhang, Jiping Yin, Qi Zhao

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