Planning and Scheduling System for Electric Vehicle Charging by Piotr Palka, Tomasz Sliwinski, Przemyslaw Kaszynski, Marta Kuta, Bogdan Ruszczak, Marcin Malec, Piotr Saluga, Jacek Kaminski

“…Unlike other existing methods, the combination of the aforementioned mixed-integer mathematical optimization model and machine learning predictors allows for consideration of several factors and adaptation to actual data. …”
Get full text

Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction by Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia, Honglei Liu

“…Both deep learning (TabNet, Multi-Layer Perceptron) and machine learning (Random Forest, XGboost) models were employed in constructing model. …”
Get full text

A deep learning model to predict glioma recurrence using integrated genomic and clinical data by Jessica A. Patricoski-Chavez, Seema Nagpal, Ritambhara Singh, Jeremy L. Warner, Ece D. Gamsiz Uzun

“…Currently, no widely available models exist for reliably predicting early glioma recurrence, which is critical for optimizing patient outcomes. …”
Get full text

OPTIMIZING LONG TEXT CLASSIFICATION PERFORMANCE THROUGH KEYWORD-BASED SENTENCE SELECTION: A CASE STUDY ON ONLINE NEWS CLASSIFICATION FOR INDONESIAN GDP GROWTH-RATE DETECTION by Dinda Pusparahmi Sholawatunnisa, Lya Hulliyyatus Suadaa

Get full text

A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in M... by Abhijeet Das

“…To tackle this issue, we developed a new tool that harnesses optimization models, enhancing the reliability and accuracy of water quality assessments. …”
Get full text

Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model by Muhammad Kashif Saeed, Alanoud Al Mazroa, Bandar M. Alghamdi, Fouad Shoie Alallah, Abdulrhman Alshareef, Ahmed Mahmud

“…Earlier disease diagnosis can significantly reduce the risk of fatality. Machine learning (ML) and deep learning (DL) models are used to hasten these cancer analyses, allowing researcher workers to analyze a considerable proportion of patients in a limited time and at a low price. …”
Get full text

Low-light image enhancement method for underground mines based on an improved Zero-DCE model by WANG Yiwei, LI Xiaoyu, WENG Zhi, BAI Fengshan

“…An Asymmetric Convolution Block (ACB) was incorporated into the shallow network to optimize the model's learning of local image features and its ability to represent fine details. …”
Get full text

Degradation modeling of polymer electrolyte membrane water electrolyzers for hydrogen production: motivation, status, and strategies by Thomas Waite, Mohammad Yazdani-Asrami

“…This paper studies the emerging trends in degradation modeling of PEMWEs. The ability to predict durability and degradation in PEMWEs is key to optimizing their control, design, maintenance, safety monitoring, performance, and lifespan. …”
Get full text

Supply Chains Problem During Crises: A Data-Driven Approach by Farima Salamian, Amirmohammad Paksaz, Behrooz Khalil Loo, Mobina Mousapour Mamoudan, Mohammad Aghsami, Amir Aghsami

“…This study addresses these challenges by proposing a novel bi-objective optimization framework. The model integrates a Mixed-Integer Linear Programming (MILP) approach with advanced machine learning techniques to simultaneously minimize total costs and maximize patient satisfaction. …”
Get full text

Multi-step prediction of greenhouse crop growth based on the SVR_Seq2Seq model by Chao Wu, Zijing Zhou, Hong Qiu, Guowei Duan, Yeping Peng

“…However, the challenge of obtaining accurate predictions over extended periods persists due to inherent complexities such as the influence of diverse environmental factors. The Support Vector Machine Sequence to Sequence (SVR_Seq2Seq) model introduced in this paper offers a multi-step prediction method for greenhouse crop growth. …”
Get full text

Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT. by Haoran Zhang, Jinlong Liu, Danyang Su, Zhen Bai, Yan Wu, Yuanbo Ma, Qiuju Miao, Mingyue Wang, Xiaopeng Yang

“…This comprehensive model comparison provides a broader perspective for determining the optimal model for liver fat diagnosis. …”
Get full text

Research on rock fracture evolution prediction model based on Adam-ConvLSTM and transfer learning by Runze Liu, Ziwei Wang, Yanbo Zhang, Xulong Yao, Shaohong Yan, Zhiyuan Chen, Shuai Wang, Hua Li, Qi Wang

“…We generated a rock fracture dataset through numerical simulation and then incorporated it into a machine-learning imagework to produce a predictive model. …”
Get full text

Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection by Li-Chin Chen, Kuo-Hsuan Hung, Yi-Ju Tseng, Hsin-Yao Wang, Tse-Min Lu, Wei-Chieh Huang, Yu Tsao

“…Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. …”
Get full text

Performance Prediction of the Gearbox Elastic Support Structure Based on Multi-Task Learning by Chengshun Zhu, Zhizhou Lu, Jie Qi, Meng Xiang, Shilong Yuan, Hui Zhang

Get full text

Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds by Mengdie Fan, Chenhui Sang, Hua Li, Yue Wei, Bin Zhang, Yang Xing, Jing Zhang, Jie Yin, Wei An, Bing Shao

“…Finally, by training the optimal quantitative structure–retention relationship (QSRR) models for each category of compounds and selecting the best-fitting model for prediction via discriminant analysis during the prediction period, a novel and universal high-throughput retention time prediction model was established. …”
Get full text

Interpretable prediction model for hand-foot-and-mouth disease incidence based on improved LSTM and XGBoost by Xiao LI, Shuyu HE, Yan PENG, Rongxin YANG, Lu TAO, Tingqi LOU, Wenqi HE

“…The experimental results demonstrate that the ARIMA–LSTM–XGBoost model achieves a significantly improved prediction accuracy compared to other machine learning prediction models. …”
Get full text

Clinical-genomic characteristics of homologous recombination deficiency (HRD) in breast cancer: application model for practice by Jinsui Du, Lizhe Zhu, Chenglong Duan, Nan Ma, Yudong Zhou, Danni Li, Jianing Zhang, Jiaqi Zhang, Yalong Wang, Xi Liu, Yu Ren, Bin Wang

“…Methods A total of 93 breast cancer patients who underwent HRD genetic testing were included in the study. According to the machine learning model called genomic scar (GS) HRD was defined as a genomic scar score (GSS) ≥ 50 or with deleterious mutation in the BRCA. …”
Get full text

An advanced direct torque control for doubly fed induction motor using evolutionary computational techniques by Said Mahfoud, Najib El Ouanjli, Aziz Derouich, Abderrahman El Idrissi, Elmostafa Chetouani, Azeddine Loulijat, Shimaa A. Hussien, Mohamed I. Mosaad

“…Abstract The doubly-fed induction machine is progressively supplanting the cage machine owing to its superior efficiency in variable-speed applications and improved performance in renewable energy systems. …”
Get full text

Advanced Heart Disease Prediction Through Spatial and Temporal Feature Learning with SCN-Deep BiLSTM by Vivek Pandey, Umesh Kumar Lilhore, Ranjan Walia

“…Abstract Heart disease prediction using machine learning methods faces various challenges, such as low data quality, missing irrelevant values, and underfit and overfit problems, which increase the time complexity and degrade the model's prediction performance. …”
Get full text

Improving distributed systems failure prediction via multi-objective feature selection and deep forest by Zhidan Yuan, Yikai Zhang, Yingqi Yu, Taizhi Lv, Tao Huang

“…Distributed systems failure prediction uses Key Performance Indicator (KPI) metrics to train machine learning models to identify potential system failures. …”
Get full text