An innovative efficiency of incubator to enhance organization supportive business using machine learning approach. by Xin Li, Qian Zhang, Hanjie Gu, Salwa Othmen, Somia Asklany, Chahira Lhioui, Ali Elrashidi, Paolo Mercorelli

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Enhancing Defect Classification in Solar Panels With Electroluminescence Imaging and Advanced Machine Learning Strategies by Fatih Demir

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MACHINE LEARNING AND DEEP LEARNING: A COMPARATIVE ANALYSIS FOR APPLE LEAF DISEASE DETECTION by Anupam Bonkra, Sunil Pathak, Amandeep Kaur

“…Accurate disease diagnosis depends on identifying the distinctive patterns that illnesses leave on foliage. Specialists or cultivators have frequently performed plant inspections, which may be costly and time-consuming. …”
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Occluded face recognition using optimum features based on efficient preprocessing and machine learning by Rajesh H. Khobragade, Dinesh B. Bhoyar, Ajay Paithane, Suresh Kurumbanshi

“…Optimum quality features are then extracted from the mean color and grayscale image using diverse descriptors such as Gabor, Linear Binary Patterns based on Haar Wavelet components, Histogram of Gaussian features, Statistical global features based on first order, wavelet components, and color histograms. …”
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Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

“…The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. …”
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Influence of the Level of Defective Insulation Resistance of Electrical Machine Windings on Parameters of no-Load Current by A. V. Isaev, Yu. V. Suhodolov, S. V. Sizikov, A. A. Lomtev, V. A. Lychkovsky

“…The purpose of the work was to consider and evaluate the possibility of a method that, based on the analysis of the parameters and characteristics of the no-load current, will allow to characterize the state of the current-carrying parts of the windings of diagnosed electric machines. The paper presents the results of experimental studies of the patterns of influence of the level of defective resistance of interturn insulation in the windings of electric machines on the parameters of the no-load current, including the features of changes in the parameters of its spectral components. …”
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Finding the original mass: A machine learning model and its deployment for lithic scrapers. by Guillermo Bustos-Pérez

“…It is commonly linked to lithic technological organization of past societies along with notions of stone tool general morphology, standardization through the reduction process, use life, and site occupation patterns. In order to obtain a prediction of original stone tool mass, previous studies have focused on attributes that would remain constant or unaltered through retouch episodes. …”
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Environmental impacts of economic growth: A STIRPAT analysis using machine learning algorithms by J. Krishnendu, Biswajit Patra

“…While the inverted U-shaped EKC is observed in most cases, more complex patterns, such as an M-shaped curve, emerge as countries progress economically. …”
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Chemical Space Exploration and Machine Learning-Based Screening of PDE7A Inhibitors by Yuze Li, Zhe Wang, Shengyao Ma, Xiaowen Tang, Hanting Zhang

“…The specific substructures that significantly enhance the activity of PDE7A inhibitors, including benzenesulfonamido, acylamino, and phenoxyl, were identified by an interpretable machine learning analysis. Subsequently, a machine learning model employing the Random Forest–Morgan pattern was constructed for the qualitative and quantitative prediction of PDE7A inhibitors. …”
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The Machine Learning-Based Task Automation Framework for Human Resource Management in MNC Companies by Suchitra Deviprasad, N. Madhumithaa, I. Walter Vikas, Archana Yadav, Geetha Manoharan

“…The ML-based task automation framework utilizes automation bots which can simulate all processes of HR management such as recruitment, time attendance, tracking employee records, scheduling calendar, and office administration tasks. The machine learning-based task automation framework utilizes predictive analytics to identify trends, patterns, behaviour, anomalies, and important insights from the large volumes of structured and unstructured data.…”
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Artificial Intelligence for Smoking Detection: A Review of Machine Learning and Deep Learning Approaches by Mohammed Al-Hayali, Fawziya Ramo

“…These technologies enable the analysis of diverse datasets to identify patterns that indicate smoking behavior By enhancing the effectiveness of smart smoking detection systems And so we can better protect public health and reduce exposure to secondhand smoke in public places. …”
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Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia. by Daniel R Schrider, Julien Ayroles, Daniel R Matute, Andrew D Kern

“…FILET works by combining information from a number of population genetic summary statistics, including several new statistics that we introduce, that capture patterns of variation across two populations. We show that FILET is able to identify loci that have experienced gene flow between related species with high accuracy, and in most situations can correctly infer which population was the donor and which was the recipient. …”
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Analyzing and Rewarding Credit Card Spending Habits in India: a Machine Learning Approach by Renuka Agrawal, Aryan Khanna, Safa Hamdare

“…The proposed work addresses this gap by leveraging machine learning techniques to analyze and assess credit card spending patterns and propose design targeted reward programs. …”
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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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Predicting Mobile Payment Behavior Through Explainable Machine Learning and Application Usage Analysis by Myounggu Lee, Insu Choi, Woo-Chang Kim

“…This study presents a comprehensive framework for predicting mobile payment behavior by integrating demographic, situational, and behavioral factors, focusing on patterns in mobile application usage. To address the complexity of the data, we use a combination of machine-learning models, including extreme gradient boosting, light gradient boosting machine, and CatBoost, along with Shapley additive explanations (SHAP) to improve interpretability. …”
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Identifying Human Factors in Aviation Accidents with Natural Language Processing and Machine Learning Models by Flávio L. Lázaro, Tomás Madeira, Rui Melicio, Duarte Valério, Luís F. F. M. Santos

“…The use of machine learning techniques to identify contributing factors in air incidents has grown significantly, helping to identify and prevent accidents and improve air safety. …”
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Flood prediction in urban areas based on machine learning considering the statistical characteristics of rainfall by Se-Dong Jang, Jae-Hwan Yoo, Yeon-Su Lee, Byunghyun Kim

“…By utilizing past rainfall data, 1D drainage system simulations, and 2D flood analyses, we trained the model to predict flood patterns for various rainfall events. To enhance prediction accuracy, statistical characteristics of rainfall, such as temporal distribution, were incorporated into the model. …”
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Domain Knowledge Preservation in Financial Machine Learning: Evidence from Autocallable Note Pricing by Mohammed Ahnouch, Lotfi Elaachak, Erwan Le Saout

“…The component-wise analysis reveals complex interactions between autocall mechanisms and higher-order sensitivities, particularly affecting vanna and volga patterns near barrier levels. These findings provide empirical evidence that financial machine learning benefits from domain-specific feature engineering principles that preserve economic relationships. …”
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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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Prediction of building subsidence in Vietnam using machine learning techniques based on leveling results by Dinh Trong Tran, Ngoc Dung Luong, Dinh Huy Nguyen

“…However, traditional prediction methods based on mathematical models have limitations in capturing complex subsidence patterns. Machine learning techniques have shown promise in enhancing subsidence prediction accuracy. …”
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