Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction by Rui Wang, Xinxin Guo, Jiaxuan Liu, Yu Wan

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Detection of bulbar amyotrophic lateral sclerosis based on speech analysis by A. D. Gvozdovich, Y. N. Rushkevich, M. I. Vashkevich

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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

“…This performance profile indicates adept navigation of the delicate balance between missed diagnoses and unnecessary interventions. Feature importance analysis revealed a multifaceted risk landscape, where screening test results contributed substantial predictive power (approximately 60%), complemented by demographic and behavioral factors, including age, reproductive history, and contraceptive usage patterns. …”
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Image Processing Using Feature-Based Segmentation Techniques for the Analysis of Medical Images by Christodoss Prasanna Ranjith, Krishnamoorthy Natarajan, Sindhu Madhuri, Mahesh Thylore Ramakrishna, Chandrasekhar Rohith Bhat, Vinoth Kumar Venkatesan

“…Then, the detection of bone fractures is automatically carried out. …”
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Study of the metabolome features of patients with COVID-19 by Y. V. Russkikh, N. N. Sushentseva, O. S. Popov, S. V. Apalko, A. Yu. Anisenkova, S. V. Mosenko, A. M. Sarana, S. G. Shcherbak

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Analysis for Detecting Banana Leaf Disease Using the CNN Method by Nita Helmawati, Ema Utami

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VPN Traffic Analysis: A Survey on Detection and Application Identification by Yasameen Sajid Razooqi, Adrian Pekar

“…Based on a systematic review of the literature, we provide an in-depth analysis of the features, methodologies (including traditional and learning-based approaches), and datasets employed in recent studies. …”
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EEG Signal Acquisition and Analysis for Detecting and Classifying Mental Disorders by S Anshy Princella A., Kuresan Harisudha, M Anand

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PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network by Munira Islam, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…To this end, the study proposes a hybrid model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the detection of Parkinson’s disease. Certainly, CNNs are employed to extract spatial features from the extracted spectro-temporal characteristics of vocal data, while LSTMs capture temporal dependencies, accelerating a comprehensive analysis of the development of vocal patterns over time. …”
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Enhancing cerebral infarct classification by automatically extracting relevant fMRI features by Vitaly I. Dobromyslin, Wenjin Zhou, for the Alzheimer’s Disease Neuroimaging Initiative

“…The best-performing combination of biomarkers and classifiers achieved a cross-validation ROC score of 0.791, closely matching the accuracy of diffusion-weighted imaging methods used in acute stroke detection. Our proposed auto-ML fMRI infarct-detection technique demonstrated robustness across diverse imaging sites and scanner types, highlighting the potential of automated feature extraction to significantly enhance non-invasive infarct detection.…”
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Machine learning-assisted image analysis techniques for glaucoma detection by Vaibhav Yadav, Barnali Dey, Udayan Baruah, Saumya Das, Om Prakash

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Detecting Malicious .NET Executables Using Extracted Methods Names by Hamdan Thabit, Rami Ahmad, Ahmad Abdullah, Abedallah Zaid Abualkishik, Ali A. Alwan

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The abnormal traffic detection scheme based on PCA and SSH by Zhenhui Wang, Dezhi Han, Ming Li, Han Liu, Mingming Cui

“…At the same time, PCSS also combines feature fusion and SSH to enhance the feature extraction of unclear features data, and effectively improve the detection speed and accuracy. …”
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Plastid genomic features and phylogenetic placement in Rosa (Rosaceae) through comparative analysis by Hui Jiang, Shuilian He, Jun He, Yunjuan Zuo, Wenling Guan, Yan Zhao, Xuejiao Li, Jing Meng

“…Abundant SSRs (73–87) and long repeat sequences (36–52) were detected in Rosa plastomes, and most of these repeats could be found within the IGS region. …”
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LH Gene Cloning, Sequence Feature Analysis and Prokaryotic Expression in Conger myriaster by Yan CHEN, Bao SHI, Chenggang WANG, Wanjun BO, Kewen YAN, Meijun TAO, Xinyu ZHAO, Xiaodong MA

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Fractals and Independent Component Analysis for Defect Detection in Bridge Decks by Ikhlas Abdel-Qader, Fadi Abu-Amara, Osama Abudayyeh

“…Using Ground-Penetrating Radar (GPR) raw scans, this framework is composed of a feature extraction algorithm using fractals to detect defective regions and a deconvolution algorithm using banded-independent component analysis (ICA) to reduce overlapping between reflections and to estimate the radar waves travel time and depth of defects. …”
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A Review of Explainable AI for Android Malware Detection and Analysis by Maryam Tanha, Somayeh Kafaie

“…Recent advances in complex machine learning models have significantly enhanced Android malware detection and analysis. However, these models often operate as closed boxes, making it difficult to understand which aspects of the input data influence their decisions. …”
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The analysis of fraud detection in financial market under machine learning by Jing Jin, Yongqing Zhang

“…Abstract With the rapid development of the global financial market, the problem of financial fraud is becoming more and more serious, which brings huge economic losses to the market, consumers and investors and threatens the stability of the financial system. Traditional fraud detection methods based on rules and statistical analysis are difficult to deal with increasingly complex and evolving fraud methods, and there are problems such as poor adaptability and high false alarm rate. …”
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Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis by Jaskaran Singh Walia, Kavietha Haridass, L. K. Pavithra

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Automatic detection of non-convulsive seizures: A reduced complexity approach by Tazeem Fatma, Omar Farooq, Yusuf U. Khan, Manjari Tripathi, Priyanka Sharma

“…With the use of only one feature, all of the seizures under test were detected correctly, and hence the median sensitivity and specificity of 100% and 99.21% were achieved respectively.…”
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