Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. by Xiaohua Zeng, Changzhou Liang, Qian Yang, Fei Wang, Jieping Cai

“…Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. …”
Get full text

A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. …”
Get full text

Early Prediction of Sepsis in the Intensive Care Unit Using the GRU-D-MGP-TCN Model by Seunghee Lee, Geonchul Shin, Jeongseok Hwang, Yunjeong Hwang, Hyunwoo Jang, Ju Han Park, Sunmi Han, Kyeongmin Ryu, Jong-Yeup Kim

“…However, a state-of-the-art model has not yet been developed. In this study, we developed a predictive model for the early detection of sepsis by leveraging advanced machine learning techniques, specifically the Gated Recurrent Unit (GRU-D) and Multitask Gaussian Process-Temporal Convolutional Network (MGP-TCN) models. …”
Get full text

Data Augmentation Framework for Improved Classification in Object Detectors by Ioan-Alexandru Herdea, Divya Tiwari, John Oyekan, Ashutosh Tiwari

“…Deep Learning techniques have been classified as a significant advancement for data-driven industries such as electrical machine manufacturing. …”
Get full text

A Novel Approach to Pine Nut Classification: Combining Near-Infrared Spectroscopy and Image Shape Features with Soft Voting-Based Ensemble Learning by Yueyun Yu, Xin Huang, Danjv Lv, Benjamin K. Ng, Chan-Tong Lam

Get full text

Accelerating multi-objective optimization of concrete thin shell structures using graph-constrained GANs and NSGA-II by Zhichun Fang, Xiuhong Wang, Yuyong Sun, M. A. Adibhashimi

“…The implementation of the system in a concrete thin shell structure at the Shenzhen Qianhai Smart Community resulted in significant performance improvements: a 33.3% reduction in total weight, a 50% decrease in maximum deflection, and a 20% reduction in strain energy compared to baseline models. …”
Get full text

Principal component analysis and fine-tuned vision transformation integrating model explainability for breast cancer prediction by Huong Hoang Luong, Phuc Phan Hong, Dat Vo Minh, Thinh Nguyen Le Quang, Anh Dinh The, Nguyen Thai-Nghe, Hai Thanh Nguyen

“…In the final phase, SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, and Gradient-weighted Class Activation Mapping were used for the interpretability and explainability of machine-learning models, aiding in understanding the feature importance and local explanations, and visualizing the model attention. …”
Get full text

Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method by Y. Zhu, Y. Zhu, P. Burlando, P. Y. Tan, C. Geiß, C. Geiß, S. Fatichi

“…Accordingly, this study underscores the practical value of machine learning techniques that leverage publicly available global datasets to generate DEMs that allow for the enhancement of flood simulations.…”
Get full text

WiPy-RT: A Fast Ray Tracing Modeling Platform for RIS-Assisted Wireless Channels by Mohammadreza Farashahi, Boon-Chong Seet, Xue Jun Li

“…The interdisciplinary nature of WiPy-RT, combining techniques from machine learning and computational electromagnetics, represents a significant step forward in addressing the challenges of modeling next-generation wireless systems.…”
Get full text

GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection by Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki

“…Federated learning preserves user privacy by training the model across a distributed network. …”
Get full text

Dynamic weighted ensemble model for predictive optimization in green sand casting: Advancing industry 4.0 manufacturing by Rajesh V․ Rajkolhe, Dr. Sanjay S․ Bhagwat, Dr. Priyanka V․ Deshmukh

“…To overcome the limitations of individual machine learning models and static ensemble strategies, a novel Dynamic Weighted Ensemble (DWE) model is introduced. …”
Get full text

Optimization of engine parameters and emission profiles through bio-additives: Insights from ANFIS Modeling of Diesel Combustion by Abbas Rohani, Javad Zareei, Kourosh Ghadamkheir, Seyed Alireza Farkhondeh

“…Various machine learning configurations and training algorithms were employed to optimize the model's performance. …”
Get full text

Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach by Rasool Jader, Sadegh Aminifar

“…Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. …”
Get full text

Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…Precise vegetation productivity monitoring and forecasting are crucial for the global carbon cycle. Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. …”
Get full text

Evaluation of net-zero materials in mortar bricks with predictive modelling using random forest and gradient boosting techniques by Golla Uday kiran, Nakkeeran Ganesan, Dipankar Roy, Sumant Nivarutti Shinde, Mamillapalli Indumathi, George Uwadiegwu Alaneme, Val Hyginus Udoka Eze, Kuzmin Anton

“…Additionally, a significant reduction in water absorption was recorded. Machine learning models, including Random Forest and Gradient Boosting, were employed to predict mechanical behavior. …”
Get full text

Towards Automated Quality Control in Industrial Systems: Developing Markov Decision Process Model for Optimized Decision-Making by Katerina Mitkovska-Trendova, Robert Minovski, Verica Bakeva, Simeon Trendov, Dimitar Bogatinov

“…The study focuses on improving the sub-key element of quality-accuracy within a Performance Measurement System (PMS) framework, specifically targeting scrap minimization and cost reduction. The research employs a mathematical model that integrates vector random processes, each representing critical factors such as machine condition, operator behaviour, tools, and materials. …”
Get full text

End-to-end neural automatic speech recognition system for low resource languages by Sami Dhahbi, Nasir Saleem, Sami Bourouis, Mouhebeddine Berrima, Elena Verdú

“…The rising popularity of end-to-end (E2E) automatic speech recognition (ASR) systems can be attributed to their ability to learn complex speech patterns directly from raw data, eliminating the need for intricate feature extraction pipelines and handcrafted language models. …”
Get full text

Psychological distress in adolescence and later economic and health outcomes in the United States population: A retrospective and modeling study. by Nathaniel Z Counts, Noemi Kreif, Timothy B Creedon, David E Bloom

“…<h4>Methods and findings</h4>This analysis estimated the relationship between psychological distress in those aged 15 to 17 years in 2000 and economic and health outcomes approximately 10 years later, accounting for an array of explanatory variables using machine learning-enabled methods. The cohort was from the National Longitudinal Study of Youth 1997 and nationally representative of those aged 12 to 18 years in 1997. …”
Get full text

A novel multi-source data-driven energy consumption prediction model for Venlo-type greenhouses in China by Yangda Chen, Aiqun Bao, Yapeng Li, Yingfeng Xiang, Wanlong Cai, Zhaoqiang Xia, Jialei Li, Mingyang Ning, Jing Sun, Haixi Zhang, Xianpeng Sun, Xiaoming Wei

“…Optimizing energy strategies and implementing predictive models for energy consumption are essential for more efficient management and reduction of greenhouse operational energy costs. …”
Get full text

BiCrossNet: resource-efficient cross-view geolocalization with binary neural networks by Federico Fontana, Thomas Jantos, Jan Steinbrener, Luigi Cinque, Gian Luca Foresti, Bernhard Rinner

Get full text