Geometric Distribution Weight Information Modeled Using Radial Basis Function with Fractional Order for Linear Discriminant Analysis Method by Wen-Sheng Chen, Chu Zhang, Shengyong Chen

“…Fisher linear discriminant analysis (FLDA) is a classic linear feature extraction and dimensionality reduction approach for face recognition. It is known that geometric distribution weight information of image data plays an important role in machine learning approaches. …”
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Thermal and carbonation resistance of tunnel concrete: Experimental evaluation and hybrid ANN–GPR modeling under fire–CO₂ exposure by Amirhossein Fatemi, Ahmad Ganjali, Reza Babaei Semiromi, Pejman Aminian

“…Ultrasonic pulse velocity (UPV) measurements showed strong correlation with both strength loss and carbonation depth, supporting UPV as a reliable non-destructive evaluation method. A hybrid machine learning model combining artificial neural networks (ANN) and Gaussian process regression (GPR) was developed to predict residual compressive strength and carbonation depth based on UPV and exposure parameters. …”
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FastColitisDetector-XAI: An efficient AI model utilizing sparse Autoencoder with explainable AI for ulcerative colitis diagnosis by Sumedh Vithalrao Dhole, Sangeeta R. Chougule

“…Features so extracted are passed to a machine learning classifier for classification for detection of UC presence. …”
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On signal encryption at MapReduce and collaborative attribute-based access with ECAs for a preprocessed data set with ML in a privacy-preserving health 4.0 by Arnab Mitra, Anabik Pal

“…To support the cost-effectiveness modeling of data security and privacy in Healthcare 4.0 scenarios, the Privacy-Preserving Health 4.0 (PPH 4.0) framework was proposed by integrating Machine Learning (ML) and Elementary Cellular Automata (ECAs). …”
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DenseNet-Enabled Intelligent Ship Energy Consumption Prediction Method with Navigation Information and Environmental Factors by Gulashan Tang, Rui Luo, Bin Huang, Xiang Li, Hui Ma

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Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

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Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model by Xiaoai Dai, Junying Cheng, Shouheng Guo, Chengchen Wang, Ge Qu, Wenxin Liu, Weile Li, Heng Lu, Youlin Wang, Binyang Zeng, Yunjie Peng, Shuneng Liang

“…Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. …”
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A multi-level classification model for corrosion defects in oil and gas pipelines using meta-learner ensemble (MLE) techniques by Adamu Abubakar Sani, Mohamed Mubarak Abdul Wahab, Nasir Shafiq, Kamaludden Usman Danyaro, Nasir Khan, Adamu Tafida, Arsalaan Khan Yousafzai

“…It provides vital information for improving pipeline safety and optimizing predictive maintenance practices by providing an in-depth assessment of various machine learning models, especially when real-time monitoring systems are integrated.…”
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Harnessing Data-mining Algorithms to Model and Evaluate Factors Influencing Distortion Product Otoacoustic Emission Variations in a Mining Industry by Sajad Zare, Reza Esmaeili, Mojtaba Nakhaei pour

“…In the second phase, the weight of the factors affecting OAEs was investigated using deep learning (DL) and support vector machine (SVM) algorithms. …”
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A Novel Evolutionary Deep Learning Approach for PM_2.5 Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran by Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh

“…The performance of the proposed OA-LSTM model is compared to five advanced machine learning (ML) algorithms. …”
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HiViT-IDS: An Efficient Network Intrusion Detection Method Based on Vision Transformer by Hai Zhou, Haojie Zou, Wei Li, Di Li, Yinchun Kuang

“…Nevertheless, IDS relying on traditional Machine Learning (ML) technologies demonstrate limited efficacy in classifying malicious traffic. …”
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Elevation Error Prediction of Continuous Beam Cantilever Construction Phase Based on LS-SVM by Xilong Zheng, Yunfeng Hu, Baitao Sun

“…I compare the Least Squares-Support Vector Machines (LS-SVM) prediction value with the measured value, the SVM model predictions, the BP neural network model predictions and the dimensionality reduction model predictions, so that predict elevation errors during cantilever construction phases by established models. …”
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Spatio-temporal dynamics of urbanization and environmental sustainability: A predictive modelling approach to forecasting land use transitions in Vellore, India by Sai Saraswathi Vijayaraghavalu, Kumaraguru Arumugam, Sakshi Dange

“…The integration of Remote Sensing, Geographical Information System, and machine learning provides a scientifically rigorous framework for monitoring, analysing, and forecasting LULC and climate trends with precision. …”
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A Systematic Review of Model Predictive Control for Robust and Efficient Energy Management in Electric Vehicle Integration and V2G Applications by Camila Minchala-Ávila, Paul Arévalo, Danny Ochoa-Correa

“…Future research should focus on integrating digital modeling, real-time optimization, and machine learning techniques to improve predictive accuracy and operational resilience. …”
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Modeling impacts of climate-induced yield variability and adaptations on wheat and maize in a sub-tropical monsoon climate - using fuzzy logic by Md. Abdul Kaium, Md. Sharif Ahmed, Muhammad Habib-Ur-Rahman, Md. Saidul Islam, Yeasmin Akter Ratry, Md Mostofa Uddin Helal, Muhammad Ali Fardoush Siddquy, Most. Moslema Haque, Ahsan Raza, Fatma Mansour, Majed Alotaibi, Ayman El Sabagh, Reimund P. Roetter

“…This study aims to assess yield impacts of extreme temperatures and rainfall variability on wheat, and winter and summer season-planted maize in northwestern Bangladesh. Utilizing a machine learning approach, future yield patterns were predicted for these crops under various climate change scenarios. …”
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Feature representation ‎via‎ graph-regularized ‎entropy-‎weighted nonnegative matrix factorization by Hazhir Sohrabi, Shahrokh Esmaeili, Parham Moradi

“…Feature extraction plays a crucial role in dimensionality reduction in machine learning applications. Nonnegative Matrix Factorization (NMF) has emerged as a powerful technique for dimensionality reduction; however, its equal treatment of all features may limit accuracy. …”
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Geospatial dataset on deforestation and urban sprawl in Dhaka, Bangladesh: A resource for environmental analysisMendeley Data by Md. Fahad Khan, Md. Rakibul Islam, Shanto Kumar Basak, Ahmed Imtiaz, Abhijit Bhowmik, Dip Nandi, Mashiour Rahman, Debajyoti Karmaker

“…Utilizing the annotations and masks enables the training of machine learning models to identify and forecast vegetation changes, aiding environmental monitoring and conservation initiatives. …”
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Amplification of Higher-Order Salivary Gland Volume Effects from External Beam Radiotherapy in Normal Tissue Complication Probability Modeling of Radiopharmaceutical Therapy by Chunming Gu, Robert F. Hobbs, Ana P. Kiess, Junghoon Lee, Todd McNutt, Harry Quon, Zhuoyao Xin, Tahir I. Yusufaly

“…The goal of this work is to combine machine learning of EBRT dose–outcome data with stylized small-scale RPT dosimetry to discover more reliable normal tissue complication probability (NTCP) models of xerostomia across both modalities. …”
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Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes by Neha Irfan, Sherin Zafar, Kashish Ara Shakil, Mudasir Ahmad Wani, S. N. Kumar, A. Jaiganesh, K. M. Abubeker

“…This paper proposes a comprehensive approach to predicting and monitoring psychological health risks in pregnant women using advanced machine learning techniques. The study employs a systematic methodology including data collection, preprocessing, feature selection, and model implementation. …”
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Classification of Chicken Carcass Breast Blood-Related Defects Using Hyperspectral Imaging Combined with Convolutional Neural Networks by Liukui Duan, Juanfang Bao, Hao Yang, Liuqian Gao, Xu Zhang, Shengjie Li, Huihui Wang

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