Automatic Card Fraud Detection Based on Decision Tree Algorithm by Elena Flondor, Liliana Donath, Mihaela Neamtu

“…Its aim is to gain a comprehensive understanding of fraud in the banking sector and explore effective detection techniques. The paper examines advanced techniques such as data analysis, automatic learning algorithms, and real-time monitoring systems to detect suspicious patterns, anomalies, and deviations from normal behavior with precision. …”
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Novel Multimodal Fusion Algorithm for Non-Intrusive Anxiety Detection by Mahir Shadid, Mushfiqus Salehin Afnan, Rashed Mustafa, M. Jamshed Alam Patwary

“…The study utilizes six advanced machine learning algorithms (Gaussian Naive Bayes, XGB Classifier, K-Neighbors, SVM, Decision-Tree, and RandomForest) for data classification, pattern recognition, and predictive accuracy. …”
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GastroEndoNet: Comprehensive endoscopy image dataset for GERD and polyp detectionMendeley Data by Abu Kowshir Bitto, Md. Hasan Imam Bijoy, Kamrul Hassan Shakil, Aka Das, Khalid Been Badruzzaman Biplob, Imran Mahmud, Syed Md. Minhaz Hossain

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Research on Fire Detection of Cotton Picker Based on Improved Algorithm by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song

“…The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…A well-known outlier detection algorithm is applied on the gathered data to assess the data reliability before model development. …”
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Presenting the detection algorithm of all types of vessels in the Persian Gulf region using radar images in order to improve the security of navigation by Ali Hanafi, Mehdi Keykhaee

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Identification of Mattic Epipedon Degradation on the Northeastern Qinghai–Tibetan Plateau Using Hyperspectral Data by Junjun Zhi, Hong Zhu, Jingwen Yang, Qiuchen Yan, Dandan Zhi, Zhongbao Sun, Liangwei Ge, Chengwen Lv

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YOLOX-S-TKECB: A Holstein Cow Identification Detection Algorithm by Hongtao Zhang, Li Zheng, Lian Tan, Jiahui Gao, Yiming Luo

“…Therefore, this paper proposes a cow identification method based on YOLOX-S-TKECB. (1) Based on the characteristics of Holstein cows and their breeding practices, we constructed a real-time acquisition and preprocessing platform for two-dimensional Holstein cow images and built a cow identification model based on YOLOX-S-TKECB. (2) Transfer learning was introduced to improve the convergence speed and generalization ability of the cow identification model. (3) The CBAM attention mechanism module was added to enhance the model’s ability to extract features from cow torso patterns. (4) The alignment between the apriori frame and the target size was improved by optimizing the clustering algorithm and the multi-scale feature fusion method, thereby enhancing the performance of object detection at different scales. …”
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HCRP-YOLO: A lightweight algorithm for potato defect detection by Haojie Liao, Guanping Wang, Siyu Jin, Yan Liu, Wei Sun, Sen Yang, Lu Wang

“…Therefore, it is an urgent need for precise and efficient automated detection technologies. In recent years, deep-learning based object detection algorithms have gained significant attention in agriculture. …”
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Federated knowledge distillation for enhanced insulator defect detection in resource‐constrained environments by Xiaohu Huang, Minghui Jia, Xianghua Tai, Wei Wang, Qi Hu, Dongping Liu, Peiheng Guo, Shengxiang Tian, Dequan Yan, Haishan Han

“…However, the existing high‐quality insulator defect detection models still face problems such as relying on massive‐labelled data and huge model parameters. …”
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Vision-based detection algorithm for monitoring dynamic change of fire progression by Yongyoon Suh

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Machine Learning Model for Early Detection of COVID-19 by Heart Rhythm Abnormalities by M. S. Mezhov, V. O. Kozitsin, Iu. D. Katser

“…The article describes the problem of detecting covid anomalies in rhythm strips. The work aims at creating a mathematical model based on machine learning algorithms to automate the process of detecting covid abnormalities in the heart rhythm. …”
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Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding by Ibrahim Almubark

“…This paper explored the utilization of machine learning models, with a particular emphasis on ensemble methods, to advance the detection of CC fraud. …”
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Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows by Rui Guo, Yongqiang Dai

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An Improved Machine Learning-Based Method for Unsupervised Characterisation for Coral Reef Monitoring in Earth Observation Time-Series Data by Zayad AlZayer, Philippa Mason, Robert Platt, Cédric M. John

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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. …”
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A Comprehensive Analysis of Fake News Detection Models: A Systematic Literature Review and Current Challenges by Alok Mishra, Halima Sadia

“…Our study focuses on how machine learning and deep learning algorithms are used to detect fraudulent data. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…This article presents a novel review of Machine Learning (ML) and Deep Learning (DL) techniques for PD detection and progression monitoring, offering new perspectives by integrating diverse data sources. …”
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Pavement condition detection using acceleration data collected by smartphones based on 1D convolutional neural network by Yudong Han, Zhaobo Li, Jiaqi Li

“…Five types of 1D-CNN with different activation functions and network structures were designed to classify the data and were compared with machine learning algorithms, including support vector machine (SVM) and radial basis function (RBF) neural networks. …”
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Machine learning classifiers to detect data pattern change of continuous emission monitoring system: A typical chemical industrial park as an example by Zhefeng Xu, Xiahong Shi, Wei Shu, Yilu Xin, Xuan Zan, Zhaonian Si, Jinping Cheng

“…By categorizing outlets into 12 datasets based on monitoring parameters, 17 machine learning models were evaluated to identify emission patterns and detect potential data anomalies. …”
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