Research on intelligent technology for broken chain monitoring on scraper conveyors by LI Lingfeng, ZHANG Jie, CHEN Zhuo, ZHA Tianren, YIN Rui

“…To address the issues of existing AI algorithm-based broken chain monitoring technologies for underground coal mine scraper conveyors, including poor online learning ability, low detection accuracy, instability, and inadequate adaptability and reliability in complex scenarios, an online sequential extreme learning machine (OSELM) network was developed by integrating incremental online training into the extreme learning machine (ELM). …”
Get full text

Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete by AGRAWAL Achal, CHANDAK Narayan

“…The present study utilizes advanced numerical evaluation techniques like Artificial Intelligence (AI), including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems with Genetic Algorithms (ANFIS-GA), Gene Expression Programming (GEP), and Multiple Linear Regression (MLR) to develop and compare the predictive models for determination of compressive and tensile strength. …”
Get full text

Hybrid POF-VLC Systems: Recent Advances, Challenges, Opportunities, and Future Directions by Rola Abdallah, Mohamed Atef, Nasir Saeed

“…Moreover, this paper presents several promising research directions, such as optimizing training algorithms, exploring deeper neural network architectures, and integrating POF-VLC systems with emerging technologies like beyond 5G, improving energy efficiency, and addressing scalability and complexity in real-time adaptive POF-VLC systems.…”
Get full text

Advancements in AI-Powered Electric Vehicle Routing: Multi-Constraint Optimization and Infrastructure Integration Approaches for Evolving EVs—A Survey by P. Anandha Prakash, R. Radha

Get full text

Intelligent recognition of “geological-engineering” sweet spots in tight sandstone reservoirs - an application to a tight gas reservoir in Ordos Basin, China by Kui Chen, Minghao Zhao, Minghao Zhao, Yansong Feng, Xiaoyan Fu, Yifei Wang, Hui Guo, Jingchen Ding, Qi Chen, Qi Chen

“…This study, based on an integrated geological-engineering perspective and utilizing data analysis and multiple machine learning methods, innovatively proposes a regression prediction model that integrates the Triangulation Topology Aggregation Optimizer (TTAO) algorithm, Random Forest (RF), and Multi-Head Self-Attention Mechanism (MSA), aiming to enhance the accuracy of oil and gas sweet spot identification. …”
Get full text

Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

“…This study introduces a novel data-driven approach utilizing a comprehensive suite of machine learning (ML) methods—including random forest, decision tree, adaptive boosting, ensemble learning, K-nearest neighbors, multilayer perceptron artificial neural networks, convolutional neural networks, and support vector machines—to provide robust predictions of permeability reduction. …”
Get full text

Automated Class Imbalance Learning via Few-Shot Multi-Objective Bayesian Optimization With Deep Kernel Gaussian Processes by Zhaoyang Wang, Shuo Wang, Damien Ernst, Chenguang Xiao

Get full text

Integration of Microarray Data and Single-Cell Sequencing Analysis to Explore Key Genes Associated with Macrophage Infiltration in Heart Failure by Rao J, Wang X, Wang Z

“…Additionally, we employed four integrated machine learning algorithms to identify macrophage-related genes with diagnostic value, and in vivo validation was performed. …”
Get full text

Energy Efficiency Optimization of Air Conditioning Systems Towards Low-Carbon Cleanrooms: Review and Future Perspectives by Xinran Zeng, Chunhui Li, Xiaoying Li, Chennan Mao, Zhengwei Li, Zhenhai Li

Get full text

Current state and prospects of development of energy-optimal control systems for 2ES6 electric locomotives by S. G. Istomin, K. I. Domanov, A. P. SHATOKHIN, I. N. Denisov

“…A tunable artificial recurrent neural network on long short-term memory in new or existing improved methods for energy-efficient train rolling stock control would improve the motion recorders used on locomotives. The developed algorithm may form the basis of a fundamentally new smart adaptive rolling stock control support system with machine learning and AI. …”
Get full text

Improving Multi-Class Classification for Recognition of the Prioritized Classes Using the Analytic Hierarchy Process by Algimantas Venčkauskas, Jevgenijus Toldinas, Nerijus Morkevičius

“…Machine learning (ML) algorithms are widely used in various fields, including cyber threat intelligence (CTI), financial technology (Fintech), and intrusion detection systems (IDSs). …”
Get full text

Multi-Step Prediction of TBM Tunneling Speed Based on Advanced Hybrid Model by Defu Liu, Yaohong Yang, Shuwen Yang, Zhixiao Zhang, Xiaohu Sun

“…The accurate prediction of tunneling speed in tunnel boring machine (TBM) construction is the basis for the timely adjustment of the operating parameters of TBM equipment to ensure safe and efficient tunneling. …”
Get full text

The spatiotemporal distribution patterns and impact factors of bird species richness: A case study of urban built-up areas in Beijing, China by Zheran Zhai, Siyao Liu, Zimeng Li, Ruijie Ma, Xiaoyu Ge, Haidong Feng, Yang Shi, Chen Gu

“…Additionally, this study employed three tree-based machine learning algorithms—Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—to investigate the influence of environmental factors on bird species distribution within urban built-up areas. …”
Get full text

Development and Evaluation of Neural Network Architectures for Model Predictive Control of Building Thermal Systems by Jevgenijs Telicko, Andris Krumins, Agris Nikitenko

Get full text

A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…The development of reliable and affordable sensors, together with improvements in processing power, has made data-intensive algorithms and real-time operational decision-making applications in the field of geothermal energy. …”
Get full text

Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture by Safa E. El-Mahroug, Ayman A. Suleiman, Mutaz M. Zoubi, Saif Al-Omari, Qusay Y. Abu-Afifeh, Heba F. Al-Jawaldeh, Yazan A. Alta’any, Tariq M. F. Al-Nawaiseh, Nisreen Obeidat, Shahed H. Alsoud, Areen M. Alshoshan, Fayha M. Al-Shibli, Rakad Ta’any

“…Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. …”
Get full text

Embedded Hardware-Efficient FPGA Architecture for SVM Learning and Inference by B. B. Shabarinath, Muralidhar Pullakandam

“…Edge computing allows to do AI processing on devices with limited resources, but the challenge remains high computational costs followed by the energy limitations of such devices making on-device machine learning inefficient, especially for Support Vector Machine (SVM) classifiers. …”
Get full text

Robust integral super-twisting controller for enhanced photovoltaic integration with hybrid battery and supercapacitor storage in DC microgrid by Ridha Benadli, Azeddine Houari, Nadia Ait-Ahmed, Saad Motahhir, Adrian Augustin Pop, Soumia El Hani

Get full text

Real-Time Wrist Motion Decoding With High Framerate Electrical Impedance Tomography (EIT) by Xiaodong Liu, Enhao Zheng, Qining Wang

Get full text

OMAL: A Multi-Label Active Learning Approach from Data Streams by Qiao Fang, Chen Xiang, Jicong Duan, Benallal Soufiyan, Changbin Shao, Xibei Yang, Sen Xu, Hualong Yu

“…The experimental results on ten benchmark multi-label datasets that were transformed into streams show that our proposed method is superior to several popular static multi-label active learning algorithms in terms of both the Macro-F1 and Micro-F1 metrics, indicating its specifical adaptions in the dynamic data stream environment.…”
Get full text