Evaluation of Feature Transformation and Machine Learning Models on Early Detection of Diabetes Mellitus by Ahmed Ali Linkon, Inshad Rahman Noman, Md Rashedul Islam, Joy Chakra Bortty, Kanchon Kumar Bishnu, Araf Islam, Rakibul Hasan, Masuk Abdullah

“…The increasing prevalence of diabetes necessitates the development of effective early detection methods to mitigate its health impacts. This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. …”
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DBN-BAAE: Enhanced Lightweight Anomaly Detection Mechanism with Boosting Adversarial Autoencoder by Yanru Chen, Bei Wu, Wang Zhong, Yanru Guo, Dizhi Wu, Yi Ren, Yuanyuan Zhang

“…Although current research has advanced anomaly detection, it is still challenging for algorithms to be capable of effectively balancing the interplay between training speed, computational cost, and accuracy while simultaneously exhibiting robust stability and adaptability. …”
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Persian SMS Spam Detection using Machine Learning and Deep Learning Techniques by Roya Khorashadizade, Somayyeh Jafarali Jassbi, Alireza Yari

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Tongue Color Analysis and Diseases Detection Based on a Computer Vision System by Abdulghafor Khudhaer Abdullah, Saleem Lateef Mohammed, Ali Al-Naji, Mohammed Sameer Alsabah

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Low-Cost LiDAR Mapping on Bicycles for Urban Road and Sidewalk Detection by T. Bhatia, S. Goel, A. Medury

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Explainable few-shot learning workflow for detecting invasive and exotic tree species by Caroline M. Gevaert, Alexandra Aguiar Pedro, Ou Ku, Hao Cheng, Pranav Chandramouli, Farzaneh Dadrass Javan, Francesco Nattino, Sonja Georgievska

“…While few-shot learning algorithms can address the first problem, they still lack sufficient explanations for the results. …”
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SCoralDet: Efficient real-time underwater soft coral detection with YOLO by Zhaoxuan Lu, Lyuchao Liao, Xingang Xie, Hui Yuan

“…In recent years, climate change and marine pollution have significantly degraded coral reefs, highlighting the urgent need for automated coral detection to monitor marine ecosystems. However, underwater coral detection presents unique challenges, including low image contrast, complex coral structures, and dense coral growth, which limit the effectiveness of general object detection algorithms. …”
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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…This research focuses on detecting Bot-IoT activity using the Bot-IoT UNSW Canberra 2018 dataset. …”
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Early detection of bloodstream infection in critically ill children using artificial intelligence by Hye-Ji Han, Kyunghoon Kim, June Dong Park

“…Background Despite the high mortality associated with bloodstream infection (BSI), early detection of this condition is challenging in critical settings. …”
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Effects of Hybridizing the U-Net Neural Network in Traffic Lane Detection Process by Aron Csato, Florin Mariasiu

“…A vision-based lane detection system is difficult to implement in vehicles due to the unique characteristics of algorithms, neural network architectures, limitations, and strict hardware/software requirements. …”
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YOLO-RGDD: A Novel Method for the Online Detection of Tomato Surface Defects by Ziheng Liang, Tingting Zhu, Guang Teng, Yajun Zhang, Zhe Gu

“…The experimental results show that the average precision, recall, and F1-score of the proposed YOLO-RGDD model for tomato defect detection reach 88.5%, 85.7%, and 87.0%, respectively, surpassing advanced object recognition detection algorithms. …”
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Signals of propaganda-Detecting and estimating political influences in information spread in social networks. by Alon Sela, Omer Neter, Václav Lohr, Petr Cihelka, Fan Wang, Moti Zwilling, John Phillip Sabou, Miloš Ulman

“…Their methods include the use of synchronized or individual bots, multiple accounts operated by one social media management tool, or different manipulations of search engines and social network algorithms, all aiming to promote their ideology. While computational propaganda influences modern society, it is hard to measure or detect it. …”
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Advanced strategies for detecting acid sphingomyelinase deficiency type B with attenuated phenotypes by Thomas Villeneuve, Thibaut Jamme, Robin Schwob, Thierry Levade, Grégoire Prévot

“…This approach combining algorithmic filtering and clinical expertise, could enhance ASMD type B diagnosis.…”
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Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection by Iman Akour, Nour Mohamed, Said Salloum

“…The exponential growth of online payments necessitates more effective fraud detection mechanisms. Traditional fraud detection methods, including rule-based systems and basic statistical analyses, struggle to keep pace with the evolving strategies employed by fraudsters. …”
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Cybersecurity of smart grids: Comparison of machine learning approaches training for anomaly detection by S. V. Kochergin, S. V. Artemova, A. A. Bakaev, E. S. Mityakov, Zh. G. Vegera, E. A. Maksimova

“…The comparison of machine learning methods reveals the varying effectiveness of anomaly detection methods used to detect cyber threats and deviations in electrical systems. …”
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An Improved YOLOv9 and Its Applications for Detecting Flexible Circuit Boards Connectors by Gengjie Huang, Yinbing Huang, Haoyang Li, Ziwen Guan, Xuecong Li, Guidong Zhang, Wendong Li, Xiran Zheng

“…In automatic defect detection, FPC connectors present challenges such as minimal differences between oxidation defects and the background, easy degradation of Intersection over Union (IoU) scores, and significant variations in the shapes of black defect boundaries. …”
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The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection by Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq, Valentina Donzella

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Damage Scene Change Detection Based on Infrared Polarization Imaging and Fast-PCANet by Min Yang, Jie Yang, Hongxia Mao, Chong Zheng

“…Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. …”
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IRWT-YOLO: A Background Subtraction-Based Method for Anti-Drone Detection by Xueqi Cheng, Fan Wang, Xiaopeng Hu, Xinrong Wu, Min Nuo

“…To effectively separate low-contrast weak drone objects from complex backgrounds, the IRWT-YOLO model is proposed, in which image segmentation algorithms are leveraged to reduce background interference. …”
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Maximizing YOLOv2 efficiency: A study on multiclass detection of indoor objects by G Divya Deepak, Subraya Krishna Bhat

“…Therefore, it is crucial for object detection algorithms to incorporate optimized parameters to maximize efficiency, particularly in multiclass detection scenarios with substantial class and object size imbalances, as demonstrated in this work. …”
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