Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control by Rodrigo Gutiérrez-Moreno, Rafael Barea, Elena López-Guillén, Felipe Arango, Fabio Sánchez-García, Luis M. Bergasa

“…Specifically, the authors address the Decision Making problem by employing a Partially Observable Markov Decision Process formulation and offer a solution through the use of Deep Reinforcement Learning algorithms. Furthermore, an additional control module to execute the decisions in a safe and comfortable way through a hybrid architecture is presented. …”
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Securing the road ahead: Machine learning-driven DDoS attack detection in VANET cloud environments by Himanshu Setia, Amit Chhabra, Sunil K. Singh, Sudhakar Kumar, Sarita Sharma, Varsha Arya, Brij B. Gupta, Jinsong Wu

“…Additionally, it leverages machine learning techniques for classification and predictive analytics with an accuracy of 99.59%. …”
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Deep learning-based natural language processing in human–agent interaction: Applications, advancements and challenges by Nafiz Ahmed, Anik Kumar Saha, Md. Abdullah Al Noman, Jamin Rahman Jim, M.F. Mridha, Md Mohsin Kabir

“…Human–Agent Interaction is at the forefront of rapid development, with integrating deep learning techniques into natural language processing representing significant potential. …”
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A multicenter bladder cancer MRI dataset and baseline evaluation of federated learning in clinical application by Kangyang Cao, Yujian Zou, Chang Zhang, Weijing Zhang, Jie Zhang, Guojie Wang, Chu Zhang, Jiegeng Lyu, Yue Sun, Hongyuan Zhang, Bin Huang, Lei Deng, Shuiqing Yang, Jianpeng Li, Bingsheng Huang

“…One emerging machine learning paradigm that addresses this concern is federated learning (FL). …”
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Parameter estimation of submarine power cables in offshore applications using machine learning-based methods by Felipe P. de Albuquerque, Rafael Nascimento, Gabriel de Castro Biage, Rooney R.A. Coelho, Ronaldo F. Ribeiro Pereira, Eduardo C. Marques da Costa, Mario L. Pereira Filho, Cassio G. Lopes, José R. Cardoso

“…Monitoring electrical parameters of power transmission systems is essential to ensure reliability and optimal operating conditions. This research presents an accurate methodology for estimation of the sequence parameters of submarine power cables using a data-driven approach based synchrophasor measurements. …”
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Screening the grading markers and their application in the grade discrimination of Gastrodiae Rhizoma using metabolomics and machine learning by Shuang Liu, Hongjing Dong, Yanling Geng, Yuzhang Mi, Quanli Wang, Xiao Wang

“…The grades of GR are closely related to its biological activity, and are mainly identified by their appearance characteristics, mainly individual weight. At present, few studies focus on the content difference of chemical components in different grades of GR. …”
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Multi-objective optimization of low moisture food extrusion processing through active learning and robotics by Deborah Becker, Jean-Vincent Le Bé, Cornelia Rauh, Christoph Hartmann

“…To overcome these limitations, this study presents a closed-loop framework that links automated product characterization with multi-objective optimization to configure the extruder’s operating variables for achieving specific product characteristics. …”
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Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach by Martina Srutova, Vaclav Kremen, Lenka Lhotska

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A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction by Ching-Heng Lin, Zhi-Yong Liu, Pao-Hsien Chu, Jung-Sheng Chen, Hsin-Hsu Wu, Ming-Shien Wen, Chang-Fu Kuo, Ting-Yu Chang

“…We present a novel multi-task deep learning model, the ECG-MACE, which predicts the one-year first-ever major adverse cardiovascular events (MACE) using 2,821,889 standard 12-lead ECGs, including training (n = 984,895), validation (n = 422,061), and test (n = 1,414,933) sets, from Chang Gung Memorial Hospital database in Taiwan. …”
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A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence by Abdullah Alabdulatif

“…This study presents a novel, hybrid ensemble learning-based intrusion detection framework that integrates deep learning and traditional ML algorithms with explainable artificial intelligence for real-time cybersecurity applications. …”
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Fog-Enabled Machine Learning Approaches for Weather Prediction in IoT Systems: A Case Study by Buket İşler, Şükrü Mustafa Kaya, Fahreddin Raşit Kılıç

“…To address this limitation, the present study aims to improve temperature forecasting by collecting temperature, pressure, and humidity data through IoT sensor networks. …”
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Development and validation of machine learning models predicting hospitalizations of hypertensive patients over 12 months by A. E. Andreychenko, A. D. Ermak, D. V. Gavrilov, R. E. Novitsky, O. M. Drapkina, A. V. Gusev

“…To develop models for predicting hospitalizations of hypertensive (HTN) over 12 months using machine learning algorithms and to validate them using real-world practice data.Material and methods. …”
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Using Machine Learning to Develop a Surrogate Model for Simulating Multispecies Contaminant Transport in Groundwater by Thu-Uyen Nguyen, Heejun Suk, Ching-Ping Liang, Yu-Chieh Ho, Jui-Sheng Chen

“…Recent advances in artificial intelligence (AI) offer promising alternatives, particularly data-driven machine learning techniques, for accelerating such simulations. …”
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The Impact of Travel Behavior Factors on the Acceptance of Carsharing and Autonomous Vehicles: A Machine Learning Analysis by Jamil Hamadneh, Noura Hamdan

“…This study employs machine learning techniques to model transport mode choice, with a focus on traffic safety perceptions of people towards CS and privately shared autonomous vehicles (PSAVs). …”
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Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning by Shuang Yang, Yuanbo Yang, Yufeng Zhou

“…The fusion of ultrasound echo features with machine learning presents a promising non-invasive approach for the monitoring of cerebral edema.…”
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Deep learning based predictive models for real time accident prevention in autonomous vehicle networks by Ahmed Almutairi, Abdullah Faiz Al Asmari, Fayez Alanazi, Tariq Alqubaysi, Ammar Armghan

“…Within the realm of autonomous vehicle networks, this study presents an innovative accident prediction and prevention model that is referred to as A-LAPPM (Attention-based Long- and Short-Term Memory Autoencoder). …”
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Performance Evaluation of Various Deep Learning Models in Gait Recognition Using the CASIA-B Dataset by Nakib Aman, Md. Rabiul Islam, Md. Faysal Ahamed, Mominul Ahsan

“…Additionally, identifying individuals from different viewpoints presents a significant challenge in HGR. Numerous conventional and deep learning techniques have been introduced in the literature for HGR, but traditional methods are not well suited to handling large datasets. …”
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Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations by Liuhui Quan, Minjie Wang, Lyu Baihang, Zhang Ziwen, Zhang Ziwen, Zhang Ziwen

“…This railway big data offers immense opportunities for advancing safety, efficiency, and sustainability in transportation but presents significant analytical challenges due to its heterogeneity, high-dimensionality, and temporal dependencies. …”
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The Use of Machine Learning Algorithms for Water Quality Index Prediction in the Sai Gon River, Vietnam by Thuy Nguyen Thi Diem, Mai Nguyen Thi Huynh, Tra Tran Quang

“…Recent and accelerated advances in machine learning have led to various promising applications in water quality assessment. …”
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Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles by Naif Almakayeel

“…The deep learning (DL) model is an efficient tool for detecting various malware variants. …”
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