Hypertension Detection Using Passive-Aggressive Algorithm With The PA-I And PA-II Methods by M. Hafidz Ariansyah, Sri Winarno

“…Researchers get detection results using a branch of AI technology, namely machine learning to find new knowledge from data and find patterns to make diagnoses. …”
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Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm by Rudy Chandra, Tegar Arifin Prasetyo, Heni Ernita Lumbangaol, Veny Siahaan, Johan Immanuel Sianipar

“…To overcome this problem, research will use machine learning with the Convolutional Neural Network (CNN) algorithm to build a mobile application that can accurately classify four types of Caladiums. …”
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An improved Red-billed blue magpie feature selection algorithm for medical data processing. by Chenyi Zhu, Zhiyi Wang, Yinan Peng, Wenjun Xiao

“…Feature selection is a crucial preprocessing step in the fields of machine learning, data mining and pattern recognition. …”
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Prophetic Energy Assessment with Smart Implements in Hydroelectricity Entities Using Artificial Intelligence Algorithm by Abdullah Saleh Alqahtani, Pravin R. Kshirsagar, Hariprasath Manoharan, Praveen Kumar Balachandran, C. K. Yogesh, Shitharth Selvarajan

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Modeling Stock Price Changes Based on Microstructural Market Data by N. A.  Bilev

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Leader election of dynamic wireless intelligent control machine in sensor network distributed processing by Eko Sediyono, Ferry Wahyu Wibowo, Hindriyanto Dwi Purnomo

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BiAF: research on dynamic goat herd detection and tracking based on machine vision by Yun Hou, Mingjuan Han, Wei Fan, Xinyu Jia, Zhuo Gong, Ding Han

“…Traditional methods, such as manual tracking and wearable monitoring, often disrupt the natural movement and feeding behaviors of grazing livestock, posing significant challenges for in-depth studies of grazing patterns. In this paper, we propose a machine vision-based grazing goat herd detection algorithm that enhances the streamlined ELAN module in YOLOv7-tiny, incorporates an optimized CBAM attention mechanism, refines the SPPCSPC module to reduce the parameter count, and improves the anchor boxes in YOLOv7-tiny to enhance target detection accuracy. …”
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Clustering and Interpretability of Residential Electricity Demand Profiles by Sarra Kallel, Manar Amayri, Nizar Bouguila

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Detecting Fraudulent Transaction in Banking Sector Using Rule-Based Model and Machine Learning by Cut Dinda Rizki Amirillah

“…This research aims to develop an effective fraud detection model in banking transactions using the rule-based model (RBM) approach and the isolation forest (IF) machine learning algorithm. Based on data from the Ministry of Communication and Information Technology, there were more than 405,000 online fraud cases during the 2019–2022 period, indicating the need for a reliable fraud detection system to protect customers. …”
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Planning and layout of tourism and leisure facilities based on POI big data and machine learning. by Shifeng Wu, Jiangyun Wang, Yinuo Jia, Jintian Yang, Jixiu Li

“…Drawing on POI and demographic data, and considering the distribution patterns of existing tourism and leisure facilities, this research applies machine learning to quantitatively simulate the optimal siting of such amenities. …”
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Analysis of 19.9 million publications from the PubMed/MEDLINE database using artificial intelligence methods: approaches to the generalizations of accumulated data and the phenomen... by I. Yu. Torshin, O. A. Gromova, L. V. Stakhovskaya, N. P. Vanchakova, A. N. Galustyan, Zh. D. Kobalava, T. R. Grishina, A. N. Gromov, I. A. Ilovaiskaya, V. M. Kodentsova, A. G. Kalacheva, O. A. Limanova, V. A. Maksimov, S. I. Malyavskaya, E. V. Mozgovaya, N. I. Tapilskaya, K. V. Rudakov, V. A. Semenov

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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain by Ali Asghar Rostami, Mohammad Taghi Sattari, Halit Apaydin, Adam Milewski

“…In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). …”
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Defect Detection and Error Source Tracing in Laser Marking of Silicon Wafers with Machine Learning by Hsiao-Chung Wang, Teng-To Yu, Wen-Fei Peng

“…Machine learning has been successfully applied to improve the classification accuracy, and we propose a random forest algorithm with a training database to not only detect the defect but also trace its cause. …”
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An interpretable machine learning approach for predicting and grading hip osteoarthritis using gait analysis by Qing Yang, Xinyu Ji, Yuyan Zhang, Shaoyi Du, Bing Ji, Wei Zeng

“…Second, a support vector machine (SVM) is used to classify gait patterns between unilateral hip OA patients and HCs. …”
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Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) by Ahmad Fauzi, Annisya E Chandra, Sofyah Imammah, Malvin Zapata, Marza I Marzuki, Soni Prayogi

“…This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. …”
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Effectiveness of machine learning methods in detecting grooming: a systematic meta-analytic review by Marcelo Leiva-Bianchi, Nicolas Castillo, César A. Astudillo, Francisco Ahumada-Méndez

“…Multilayer Perceptron (MLP) demonstrated the highest accuracy (ACC=92%, p<0.001) and precision (P=81%, p<0.001), excelling in capturing complex, nonlinear patterns essential for analyzing nuanced online interactions. …”
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Analyzing the Impact of Organic Food Consumption on Citizens Health Using Unsupervised Machine Learning by Giulio Angiolini, Giovanna Maria Dimitri

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Immune Microenvironment Characterization and Machine Learning-Guided Identification of Diagnostic Biomarkers for Ulcerative Colitis by Zheng Q, Wang L, Zhang Y, Peng J, Hou J, Wang H, Ma Y, Tang P, Li Y, Li H, Chen Y, Li J, Chen Y

“…It employs machine learning algorithms to construct diagnostic models, including an optimal 8-gene model (GATA2, IL8, LAT, NOLC1, SMARCA5, SMC3, STX10, ZMIZ1), which demonstrates high predictive performance (AUC of 0.964 in training datasets and 0.884 in testing datasets). …”
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Machine Learning and Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring by Sehat Ullah, Sangeen Khan, David Vanecek, Inam Ur Rehman

“…Furthermore, health carers cannot forecast abnormalities based on health data. Machine Learning (ML) can analyze massive amounts of data and perceive patterns to anticipate anomalous health conditions. …”
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Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning by ShengHui Miao, YiJing Liu, Min Li, Jing Yan

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