An integrated machine learning and fractional calculus approach to predicting diabetes risk in women by David Amilo, Khadijeh Sadri, Evren Hincal, Muhammad Farman, Kottakkaran Sooppy Nisar, Mohamed Hafez

“…We employ seven machine learning algorithms: Decision Tree, Logistic Regression, Support Vector Machine (SVM), Random Forest, Bagged Trees, Naive Bayes, and XGBoost, to identify key risk factors, with XGBoost demonstrating higher performance. …”
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A Synergy Between Machine Learning and Formal Concept Analysis for Crowd Detection by Anas M. Al-Oraiqat, Oleksandr Drieiev, Sattam Almatarneh, Mohammadnoor Injadat, Karim A. Al-Oraiqat, Hanna Drieieva, Yassin M. Y. Hasan

“…Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. …”
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Monitoring and Modeling Urban Temperature Patterns in the State of Iowa, USA, Utilizing Mobile Sensors and Geospatial Data by Clemir Abbeg Coproski, Bingqing Liang, James T. Dietrich, John DeGroote

“…Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m² aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R² coefficients ranging from 0.879 to 0.997 with the majority exceeding an R² of 0.95, all with <i>p</i>-values < 0.001. …”
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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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Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field by Rasif Nahari, Utama Widya, Ardhya Garini Sherly, Fitri Indriani Rista, Pratama Novian Putra Dhea

“…To address missing data, machine learning algorithms, like gradient boosting, provide an effective solution. …”
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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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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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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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CONSTRUCTING METAMORPHOSIS OF IMAGES FOR THE OBJECTS ON THE BASIS OF SOLVING EULER-POINCARE EQUATIONS by S. V. Leichter

“…The considered problem of comparing two images can be used for constructing optimal metamorphosis of images, when there is no exact correspondence between the target image and the final image of the diffeomorphism. The designed algorithms can be used through a biometrical system, in images and subjects classification systems, machine vision systems, images and patterns recognition, tracking systems.…”
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Commonalities and differences in gene expression patterns in major depressive disorder and chronic spontaneous urticaria: implications for comorbidity by Yibo Jiang, Wen Li, Lihao Zheng, Nan Ba

“…Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to characterize putative pathophysiological mechanisms. Subsequently, machine learning algorithms were implemented via 10-fold cross-validation to build transcriptomic classifiers for MDD in a sex-stratified manner. …”
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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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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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Modeling Stock Price Changes Based on Microstructural Market Data by N. A.  Bilev

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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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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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Deep Learning-Based Dzongkha Handwritten Digit Classification by Yonten Jamtsho, Pema Yangden, Sonam Wangmo, Nima Dema

“…With the advancement in deep learning technology, many machine learning algorithms were developed to tackle the problem of pattern recognition. …”
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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

“…Key findings include: Firstly, compared with the existing tourism and leisure facilities, the fitting degree tested by the machine learning algorithm is 83.5%, suggests that the proposed method is highly feasible. …”
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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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