Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences by Yifan Sun, Chaozhong Wu, Hui Zhang, Yijun Zhang, Shaopeng Li, Hongxia Feng

“…Finally, we selected measurements calculated by IDBCPs that can distinguish drowsy driving to constitute individual drivers’ optimal drowsiness-detection measurement set. To verify the advantages of IDBCPs, the measurements calculated by UCPs and IDBCPs were, respectively, used to build driver-specific drowsiness-detection models: DF_U and DF_I based on the Fisher discriminant algorithm. …”
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Catalyzing early ovarian cancer detection: Platelet RNA-based precision screening by Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Hyejin Lee, Yeochan Kim, Sangick Park, Suyeon Lee, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, Cheol Lee, TaeJin Ahn, Yong-Sang Song

“…Summary: Early detection of ovarian cancer is crucial for successful treatment, yet most cases are diagnosed at advanced stages due to a lack of effective screening. …”
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The Design and Data Analysis of an Underwater Seismic Wave System by Dawei Xiao, Qin Zhu, Jingzhuo Zhang, Taotao Xie, Qing Ji

“…The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. …”
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Ship Detection Transformer in SAR Images Based on Key Scattering Points Feature Aggregation and Context Feature Refinement by Yifei Yin, Zhu Yang, Hao Shi, Fanyu Meng, Wei Li

“…In recent years, deep learning algorithms have demonstrated significant advancements in the field of ship detection using synthetic aperture radar (SAR). …”
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A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images by Sayed Jobaer, Xue-song Tang, Yihong Zhang, Gaojian Li, Foysal Ahmed

“…Current models are designed for sharp images, leading to potential detection failures in blurry images. Using image deblurring before object detection is an option, but it demands significant computing power and relies heavily on the accuracy of the deblurring algorithms. …”
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Deep Learning Models for Rotated Object Detection in Aerial Images: Survey and Performance Comparisons by Jiaying He, K. L. Eddie Law

“…Then we further carry out quantitative performance comparisons among the latest state-of-the-art rotated object detection algorithms using the benchmark dataset DOTA and computing facility. …”
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Automatic Selection of Machine Learning Models for Armed People Identification by Alonso Javier Amado-Garfias, Santiago Enrique Conant-Pablos, Jose Carlos Ortiz-Bayliss, Hugo Terashima-Marin

“…Furthermore, we use the YOLOv4 to detect people and weapons in each video frame. We developed a series of algorithms to create a dataset with the information extracted from the bounding boxes generated by YOLOv4 in real-time. …”
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AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review by Nusrat Jahan Sarna, Farzana Ahmed Rithen, Umme Salma Jui, Sayma Belal, Al Amin, Tasnim Kabir Oishee, A. K. M. Muzahidul Islam

“…In addition, it examines the role of cloud computing, edge AI, and distributed systems in enabling real-time transaction monitoring and fraud detection. Although AI significantly improves detection accuracy, reduces operational costs, and strengthens regulatory compliance, challenges such as model explainability, data privacy concerns, algorithmic bias, and the dynamic nature of fraud remain critical barriers to widespread adoption. …”
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Algorithmic Bias as a Core Legal Dilemma in the Age of Artificial Intelligence: Conceptual Basis and the Current State of Regulation by Gergely Ferenc Lendvai, Gergely Gosztonyi

“…We employed comparative legal analysis and reviewed pertinent regulatory documents to examine how the fragmented U.S. approaches and the EU’s user-centric legal frameworks, such as the GDPR, DSA, and AI Act, address the systemic risks posed by biased algorithms. The findings underscore persistent enforcement gaps, particularly concerning opaque black-box algorithmic design, which hampers bias detection and remediation. …”
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Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope by Ling Guo, PhD, Gregg S. Pressman, MD, Spencer N. Kieu, BS, Scott B. Marrus, MD, PhD, George Mathew, PhD, John Prince, PhD, Emileigh Lastowski, MS, Rosalie V. McDonough, MD, MSc, Caroline Currie, BA, John N. Maidens, PhD, Hussein Al-Sudani, MD, Evan Friend, BA, Deepak Padmanabhan, MD, Preetham Kumar, MD, Edward Kersh, MD, Subramaniam Venkatraman, PhD, Salima Qamruddin, MD

“…Recently, electrocardiogram-based algorithms have shown promise in detecting ALVSD. Objectives: The authors developed and validated a convolutional neural network (CNN) model using single-lead electrocardiogram and phonocardiogram inputs captured by a digital stethoscope to assess its utility in detecting individuals with actionably low ejection fractions (EF) in a large cohort of patients. …”
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SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n by Yue Fang, Chenbo Yang, Jie Li, Jingmin Tu

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A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals by Lei Liu, Ziyi Wang, Xiaohan Zhang, Yan Zhuang, Yongbo Liang

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Incremental Pyraformer–Deep Canonical Correlation Analysis: A Novel Framework for Effective Fault Detection in Dynamic Nonlinear Processes by Yucheng Ding, Yingfeng Zhang, Jianfeng Huang, Shitong Peng

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Adaptive Multi-Model Fault Diagnosis of Dynamic Systems for Motion Tracking by Annalena Daniels, Tommaso Benciolini, Dirk Wollherr, Marion Leibold

“…For most real-world systems, the exact description of possible faults is unknown, making these faults difficult to detect, and even more difficult to identify. The most promising way is to use multiple hypotheses for faults to find the best fitting fault model by comparing system measurements with the predictions of the multi-model algorithm. …”
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HIV testing algorithm using fourth generation reagents reduces the false positive rate of screening tests by Yu Gou, Zhengxiang Gao, Yifei Duan, Yu Wu, Leiwen Peng

“…Abstract To analyze the false-positive results of HIV screening in a women’s and children’s specialized hospital and discuss the improved effect of the testing algorithm that retesting with two fourth-generation HIV assays to reduce false-positive results. …”
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Algorithm for Analyzing Computer Images in Radiation Diagnostics of Changes in the Cervical-Cranial Region in Dysfunction of the Temporomandibular Joints by I. U. Samuilau, I. N. Baradina, Yu. F. Vasilyeva, V. Ch. Neden

“…To improve the results of diagnostics of patients with diseases of the temporomandibular joints, an algorithm for analyzing a computer image of the cervicocranial region is proposed, which has diagnostic reproducibility, allows determining the asymmetry of the mutual arrangement of anatomical structures, detecting degenerative-dystrophic and other changes in the articulating surfaces. …”
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Sentiment Modeling of Instagram Users Towards Traditional and Modern Body Scrubs Using the Naive Bayes Algorithm by Mardiana Mardiana, Kusrini Kusrini

“…The results indicate that neutral sentiment dominates, followed by negative and positive sentiments. The Naive Bayes algorithm demonstrated strong performance, particularly in detecting negative and neutral sentiments, but exhibited a lower recall rate for positive sentiments. …”
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Restoration of Degraded Images Using Pupil-Size Diversity Technology With Stochastic Parallel Gradient Descent Algorithm by Zongliang Xie, Haotong Ma, Bo Qi, Ge Ren, Yufeng Tan, Bi He, Hengliang Zeng, Chuan Jiang

“…Furthermore, images detected by long-distance imaging schemes also suffer blurring induced by atmospheric turbulence. …”
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Recognition of Handwritten Persian Two-digit Numerals Using a Novel Hybrid SVM/HMM algorithm by Mahsa Aliakbarzadeh, Farbod Razzazi, Alireza Behrad

“…In contrast, we used HMM to detect the location of the boundary for two-digit numerals. …”
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Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library by Muhamad Maulana Yulianto, Riza Arifudin, Alamsyah Alamsyah

“…The accuracy of Levenshtein algorithm is 86% based on 1055 source case and 100 target case.…”
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