Application of machine learning with gradient descent method for load forecasting: a performance analysis by Saroj Kumar Panda, Manoj Kumar Panda

“…This is because accurate forecasting models provide the foundation for the majority of system planning and operation tasks. …”
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Deployment and Operation of Battery Swapping Stations for Electric Two-Wheelers Based on Machine Learning by Yu Feng, Xiaochun Lu

“…This study developed a data-driven optimization model based on machine learning algorithms using Beijing’s battery swapping stations and point of interest (POI) dataset. …”
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Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil by Binghui Xu, Tzu-Chia Chen, Danial Ahangari, S. M. Alizadeh, Marischa Elveny, Jeren Makhdoumi

“…The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. …”
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Optimizing PID Controller Design for Rotor Systems Suspended by Active Magnetic Bearings by Ibrahim Abubakar, Atte Putkonen, Marek Rehtla, Tuomo Lindh, Stijn Derammelaere, Niko Nevaranta

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A machine-learning-enabled approach for bridging multiscale simulations of CNTs/PDMS composites by Yu Lingjie, Zhi Chao, Sun Zhiyuan, Guo Hao, Chen Jianglong, Dong Hanrui, Zhu Mengqiu, Wang Xiaonan

“…By comparing the AGAT model with the original multiscale simulation results, the data-driven strategy is shown to be promising with high accuracy, demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.…”
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Transformer-Based Amharic-to-English Machine Translation With Character Embedding and Combined Regularization Techniques by Surafiel Habib Asefa, Yaregal Assabie

“…This study proposes a Transformer-based Amharic-to-English neural machine translation model that leverages character-level embeddings and integrates advanced regularization techniques, including dropout, L1, L2, and Elastic Net. …”
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Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine by Shuhan Yang, Shufang Tian

“…The ideal parameters of the Kernel Extreme Learning Machine model were successfully acquired by utilizing the sparrow optimization method for continuous search and iteration, avoiding the blindness and arbitrariness associated with parameter selection by humans. …”
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Development of an mPBPK machine learning framework for early target pharmacology assessment of biotherapeutics by Krutika Patidar, Nikhil Pillai, Saroj Dhakal, Lindsay B. Avery, Panteleimon D. Mavroudis

“…In the present work, we propose a machine learning-based target pharmacology assessment framework that utilizes minimal physiologically based pharmacokinetic (mPBPK) modeling and machine learning (ML) to infer optimal physicochemical properties of antibodies and their targets. …”
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Classification of psychiatry clinical notes by diagnosis: a deep learning and machine learning approach by Sergio Rubio-Martín, María Teresa García-Ordás, Antonio Serrano-García, Clara Margarita Franch-Pato, Arturo Crespo-Álvaro, José Alberto Benítez-Andrades

“…In this study, we compare the performance of various artificial intelligence models, including both traditional machine learning approaches (random forest, support vector machine, K-nearest neighbors, decision tree, and eXtreme Gradient Boost) and deep learning models (DistilBERT and SciBERT), to classify clinical notes into these two diagnoses. …”
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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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Machine learning approaches for predicting the link of the global trade network of liquefied natural gas. by Pei Zhao, Hao Song, Guang Ling

“…This study demonstrates that machine learning algorithms-specifically random forest and decision tree-outperform in predicting links within the global LNG trade network based on local information proximity, while the graph attention network, a deep learning model, exhibits stable optimization and effective feature learning. …”
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Learning to Drive in the NGSIM Simulator Using Proximal Policy Optimization by Yang Zhou, Yunxing Chen

“…The built NGSIM simulator was used to train a model using the proximal policy optimization method. …”
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Machine learning enables legal risk assessment in internet healthcare using HIPAA data by Shixian Liu, Hailing Liu, Siyu Fan, Leming Song, Zeyu Wang

“…The research methods include data collection and processing, construction and optimization of ML models, and the application of a risk assessment framework. …”
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Chaos Time Series Prediction Based on Membrane Optimization Algorithms by Meng Li, Liangzhong Yi, Zheng Pei, Zhisheng Gao, Hong Peng

“…This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m) and least squares support vector machine (LS-SVM) (γ,σ) by using membrane computing optimization algorithm. …”
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Design on a Hybrid Drive Feeding Mechanism of High-speed Industrial Sewing Machine by Zheng Ji, Qiu Weiming, Cui Lintao, Ke Xianglin, Yang Shu

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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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Prediction of laser welding qualities of Al alloys using regression and machine learning techniques by Hemant Kumar, Soumyabrata Chakravarty, Nitesh Kuamr, Nikhil Kumar

“…This work compared different machine learning models such as linear regression, polynomial regression and XG-Boost for the prediction of laser welding qualities in aluminum alloys. …”
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Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review by Matheus Lima Diniz Araujo, Trevor Winger, Samer Ghosn, Carl Saab, Jaideep Srivastava, Louis Kazaglis, Piyush Mathur, Reena Mehra

“…Background: Obstructive sleep apnea (OSA) is a prevalent and potentially severe sleep disorder characterized by repeated interruptions in breathing during sleep. Machine learning models have been increasingly applied in various aspects of OSA research, including diagnosis, treatment optimization, and developing biomarkers for endotypes and disease mechanisms. …”
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kMoL: an open-source machine and federated learning library for drug discovery by Romeo Cozac, Haris Hasic, Jun Jin Choong, Vincent Richard, Loic Beheshti, Cyrille Froehlich, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima, Hiroaki Iwata, Aki Hasegawa, Takao Otsuka, Yasushi Okuno

“…Its key features include state-of-the-art model architectures, Bayesian optimization, explainability, and federated learning mechanisms. …”
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Improving CNN predictive accuracy in COVID-19 health analytics by Tae-hoon Kim, Asadi Srinivasulu, Ravikumar Chinthaginjala, Dhakshayani J, Xin Zhao, Safia Obaidur Rab, Sivarama Prasad Tera

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