A Multispectral Automated Transfer Technique (MATT) for Machine-Driven Image Labeling Utilizing the Segment Anything Model (SAM) by James E. Gallagher, Aryav Gogia, Edward J. Oughton

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An artificial intelligence optimization of NOx conversion efficiency under dual catalytic mechanism reaction based on multi-objective gray wolf algorithm by Zhiqing Zhang, Zicheng He, Yuguo Wang, Feng Jiang, Weihuang Zhong, Bin Zhang, Yanshuai Ye, Zibin Yin, Dongli Tan

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The Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI by Fatih Atesoglu, Harun Bingol

“…The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. …”
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Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules by Lu Chen, Yan Wang, Yan Wang, Haoyu Jing, Haoyu Jing, Rui Bao, Bin Sun, Mingbo Zhang, Yukun Luo

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Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning by Kazim Kivanc Eren, Kerem Kucuk, Fatih Ozyurt, Omar H. Alhazmi

“…Our workflow emphasizes the importance of well-structured preprocessing pipelines missing data handling, categorical feature encoding, and multicollinearity reduction, paired with classical machine learning models. …”
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Mapping the distribution of pine wilt disease based on selected machine learning algorithms and high-resolution Gaofen-2/7 remote sensing by Yifan Wang, Xiaocheng Zhou, Chongcheng Chen, Xiaoqin Wang, Hao Wu, Fanglin Tan, Ruijiao Wu

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Advanced In Vitro Models for Preclinical Drug Safety: Recent Progress and Prospects by Dileep G. Nair, Ralf Weiskirchen

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Semi-Supervised Learning of Statistical Models for Natural Language Understanding by Deyu Zhou, Yulan He

“…The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). …”
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Tracking Poultry Drinking Behavior and Floor Eggs in Cage-Free Houses with Innovative Depth Anything Model by Xiao Yang, Guoyu Lu, Jinchang Zhang, Bidur Paneru, Anjan Dhungana, Samin Dahal, Ramesh Bahadur Bist, Lilong Chai

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Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam. by Raja Das, Pham Van Tien, Karl W Wegmann, Madhumita Chakraborty

“…The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. …”
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Prediction of reduced sound wave intensity in floor systems using machine learning methods by Hamid Mohammadnezhad, Fardin Jafari, Nahad Sedighi

“…The required data for machine learning methods were obtained by simulation of different floor systems with varying material and thickness in the INSUL software. …”
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Application of machine learning with gradient descent method for load forecasting: a performance analysis by Saroj Kumar Panda, Manoj Kumar Panda

“…One method of forecasting, short-term load forecasting (STLF) is used in this research, and machine learning like deep neural network (DNN) is the method used here for the analysis of STLF. …”
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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…These findings highlight the considerable potential of machine learning algorithms in predicting mixing ellipses and parameterizing eddy mixing processes within climate models.…”
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City-level total and sub-category energy intensity estimation using machine learning by Fei Shen, Xiwen Lin, Hao Chen, Jinji Ma, Kaifang Shi, Weidong Cao

“…This study proposes a city-level total and subcategories (coal, oil, gas) energy intensity estimation method based on multi-source remote sensing data and machine learning models. The performance of the machine learning models is validated using the four-fold cross-validation approach. …”
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Predictive Control for Steel Rib Bending Based on Deep Learning by Yijiang Xia, Jinhui Luo, Zhuolin Ou, Xin Han, Junlin Deng, Ning Wu

“…This study proposes control methods for cold bending machines based on deep learning models to address this challenge, including CNN and Transformer-CNN (T-CNN), to predict the elastic spring-back rate of cold-processed metal profiles and generate precise control pulses for achieving target bending angles. …”
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Scalable and robust machine learning framework for HIV classification using clinical and laboratory data by Qian Sui, Gaoxu Li, Yaqi Peng, Jiasheng Zhang, Yibo Zhang, Riyang Zhao

“…We evaluate five machine learning models, identifying the Random Forest Classifier (RFC) and Decision Tree Classifier (DTC) as the most effective, as they demonstrate higher classification performance compared to the other models. …”
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Machine Learning to Improve Resident Scheduling: Harnessing Artificial Intelligence to Enhance Resident Wellness by Aazad Abbas, Jay Toor, Denis Ariel Margalik, Jin Tong Du, Anne Versteeg, Nicholas J Yee, Joel A Finkelstein, Jihad Abouali, Markku T Nousiainen, Hans J Kreder, Jeremy Hall, Cari Whyne, Jeremie Larouche

“…While other industries have adopted machine learning models (MLMs) to optimize scheduling and employee well-being, medicine has lagged. …”
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A Bayesian-Optimized Surrogate Model Integrating Deep Learning Algorithms for Correcting PurpleAir Sensor Measurements by Masrur Ahmed, Jing Kong, Ningbo Jiang, Hiep Nguyen Duc, Praveen Puppala, Merched Azzi, Matthew Riley, Xavier Barthelemy

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Valuation of Urban Public Bus Electrification with Open Data and Physics-Informed Machine Learning by Upadhi Vijay, Soomin Woo, Scott J. Moura, Akshat Jain, David Rodriguez, Sergio Gambacorta, Giuseppe Ferrara, Luigi Lanuzza, Christian Zulberti, Erika Mellekas, Carlo Papa

“…We develop physics-informed machine learning models to evaluate energy consumption, carbon emissions, health impacts, and the total cost of ownership for each transit route. …”
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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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