Exploring the potentialities and challenges of deep learning for simulation and prediction of urban sprawl features by Ange Gabriel Belinga, Stéphane Cedric Tékouabou Koumetio, Mohamed El Haziti

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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The study focuses on the optimization of predictive maintenance as a service on the industrial Internet of Things by machine learning algorithms. The main contribution of the study is the use of optimization techniques for feature selection and RNN-LSTM for improved accuracy.   …”
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Technological advances and the use of IoT in monitoring Diaphorina citri in citrus cultivation by Eduardo Goiano da Silva, Franciely da Silva Ponce, Silvia Graciele Hulse de Souza

“…Integrating data from satellite images, field sensors, and machine learning algorithms makes developing more comprehensive and predictive monitoring of agricultural conditions possible. …”
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Decoding PM_2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM by Minru Chen, Binglin Liu, Mingzhi Liang, Nini Yao

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Gait data generation using generative adversarial network based on human dynamics by Ryoya OBA, Yusuke OSAWA, Keiichi WATANUKI, Kazunori KAEDE

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Identification of the immune infiltration and biomarkers in ulcerative colitis based on liquid–liquid phase separation-related genes by Zhixing Hong, Shilin Fang, Haihang Nie, Jingkai Zhou, Yuntian Hong, Lan Liu, Qiu Zhao

“…We identified the hub LLPS-RGs (DE-LLPS-RGs) (HSPB3, SLC16A1, TRIM22, SRI, PLEKHG6, GBP1, PADI2) by machine learning algorithms. Hub genes were screened that displayed high prediction accuracy of UC patients. …”
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Adaptive Energy Management for MATTER-Enabled Smart Homes by Navodit Bhardwaj, Pallavi Joshi

“…This paper proposes a unified machine learning (ML) framework for dynamic energy optimization, integrating advanced prediction, anomaly detection, and reinforcement learning (RL) techniques. …”
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Dynamic Portfolio Return Classification Using Price-Aware Logistic Regression by Yakubu Suleiman Baguda, Hani Moaiteq AlJahdali, Altyeb Altaher Taha

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Fault Detection for Power Batteries Using a Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM) Hybrid Model by Shaofan Liu, Tianbao Xie, Yanxin Li, Siyu Liu

“…Then, the CNN-LSTM module directly processes raw multivariate time-series data, combining the capability of CNN in extracting local spatial patterns with the LSTM strength in modeling temporal dependencies, enabling accurate identification of battery faults. …”
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Psychophysiological changes during workspace virtualization by I. V. Tarasova, M. S. Nikitenko, O. A. Trubnikova, I. N. Kukhareva, D. S. Kupriyanova, A. S. Sosnina, O. L. Barbarash

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Analysis of soil salinization and land use change under water conservation retrofit in the Hetao irrigation district by Yi Zhao, Shuya Yang, Haibin Shi, Haoqi Han, Yunlei Dong, Xianyue Li, Jianwen Yan, Yan Yan, Xu Dou, Feng Tian, Qingfeng Miao

“…The soil salinity inversion model constructed using Random Forest demonstrates higher R2 values and lower MAE and RMSE compared to the Support Vector Machine and Gradient Boosting Tree, establishing it as the optimal model for soil salinity inversion. …”
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The Performance of an ML-Based Weigh-in-Motion System in the Context of a Network Arch Bridge Structural Specificity by Dawid Piotrowski, Marcin Jasiński, Artur Nowoświat, Piotr Łaziński, Stefan Pradelok

“…Machine learning (ML)-based techniques have received significant attention in various fields of industry and science. …”
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Survey on Backdoor Attacks on Deep Learning: Current Trends, Categorization, Applications, Research Challenges, and Future Prospects by Muhammad Abdullah Hanif, Nandish Chattopadhyay, Bassem Ouni, Muhammad Shafique

“…This dependence of DNN training on a large amount of computational and memory resources has increased the use of Machine Learning as a Service (MLaaS) or third-party resources for training large models for complex applications. …”
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Locomotion Joint Angle and Moment Estimation With Soft Wearable Sensors for Personalized Exosuit Control by Luying Feng, Lianghong Gui, Wenzhu Xu, Xiang Wang, Canjun Yang, Yaochu Jin, Wei Yang

“…Recent advancements in flexible sensing and machine learning have positioned soft sensors as promising alternatives to traditional methods for human posture detection. …”
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Development of a data-driven urban immunity assessment model: providing a new benchmark for urban governance under public health emergencies by Peng Cui, Peng Cui, Saiya Cao, Ruize Qin, Fan Zhang

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Energy Conversion in the Working Nozzle of a Pneumatic Abrasive-Jet Unit by Vadym Baha, Ján Piteľ

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Ensemble modeling of the climate-energy nexus for renewable energy generation across multiple US states by Joy Adul, Rohini Kumar, Renee Obringer

“…The effects of climate change on renewable energy generation are of growing concern, as shifts in weather patterns and extreme events can significantly impact energy production. …”
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Prediction and Assessment of Myocardial Infarction Risk on the Base of Medical Report Text Collection by Margaryta Prazdnikova

“…This paper explores an approach to predicting heart attack risk based on analysis of text data of medical reports using machine learning. The purpose of the article is to demonstrate how the application of machine learning, particularly the Naive Bayes classifier, can enhance the prediction of myocardial infarction risk through the analysis of extensive medical data. …”
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Advancing overbreak prediction in drilling and blasting tunnel using MVO, SSA and HHO-based SVM models with interpretability analysis by Yulin Zhang, Jian Zhou, Jialu Li, Biao He, Danial Jahed Armaghani, Shuai Huang

“…Empirical methods lack universal applicability due to their reliance on specific project conditions, while statistical models struggle with inconsistent patterns across different datasets. Furthermore, traditional AI models, including single machine learning algorithms, often overlook the multifaceted nature of overbreak, and hybrid models lack comprehensive evaluation standards. …”
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Integration of Deep Learning Neural Networks and Feature-Extracted Approach for Estimating Future Regional Precipitation by Shiu-Shin Lin, Kai-Yang Zhu, He-Yang Huang

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