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Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques

Artificial Intelligence Review 2025年第8期58卷 1-41页

作者： Islam, Saidul Rjoub, Gaith Elmekki, Hanae Bentahar, Jamal Pedrycz, Witold Cohen, Robin Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Department of Computer Science 6 G Research Center Khalifa University Abu Dhabi United Arab Emirates Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Research Center of Performance and Productivity Analysis Istinye University Sariyer/Istanbul Turkey David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR), marking a paradigm shift from conventional, manually driven resuscitation practices to intelligent, data-driven interventions. It examines the evolution of CPR through the lens of predictive modeling, AI-enhanced devices, and real-time decision-making tools that collectively aim to improve resuscitation outcomes and survival rates. Unlike prior surveys that either focus solely on traditional CPR methods or offer general insights into ML applications in healthcare, this work provides a novel interdisciplinary synthesis tailored specifically to the domain of CPR. It presents a comprehensive taxonomy that classifies ML techniques into four key CPR-related tasks: rhythm analysis, outcome prediction, non-invasive blood pressure and chest compression modeling, and real-time detection of pulse and Return of Spontaneous Circulation (ROSC). The paper critically evaluates emerging ML approaches-including Reinforcement Learning (RL) and transformer-based models-while also addressing real-world implementation barriers such as model interpretability, data limitations, and deployment in high-stakes clinical settings. Furthermore, it highlights the role of eXplainable AI (XAI) in fostering clinical trust and adoption. By bridging the gap between resuscitation science and advanced ML techniques, this survey establishes a structured foundation for future research and practical innovation in ML-enhanced CPR. It offers clear insights, identifies unexplored opportunities, and sets a forward-looking research agenda identifying emerging trends and practical implementation challenges aiming to improve both the reliability and effectiveness of CPR in real-world emergencies. © The Author(s) 2025.

关键词： Reinforcement learning

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Real-World Implementation of Offline Model-Free Reinforcement Learning for Thermostat Control

Real-World Implementation of Offline Model-Free Reinforcemen...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Ghane, Sara Elmaz, Furkan Jacobs, Stef Huybrechts, Thomas Verhaert, Ivan Mercelis, Siegfried Mannens, Erik University of Antwerp - imec IDLab - Faculty of Applied Engineering Antwerp Belgium University of Antwerp EMIB Faculty of Applied Engineering Antwerp Belgium University of Antwerp - imec IDLab Department of Computer Science Antwerp Belgium

ISBN: (纸本)9798331533892

This study introduces a model-free, offline Reinforcement Learning (RL) approach for optimizing the thermostat control in heating systems. Specifically, historical data from a real-world building was used to train the RL agent that is based on the Dueling Double Deep Q-Network (DQN) algorithm. The RL agent's objective is to balance thermal comfort with energy savings, by controlling indoor temperature set points. Traditional rule-based thermostat control methods often depend on fixed schedules and lack adaptability to varying conditions, resulting in energy inefficiencies. Training the RL model offline eliminates the requirement for live interaction, thereby preventing potential disruptions to occupant comfort. During evaluation, the RL model achieved an 18.66% reduction in energy use compared to a rule-based controller, while maintaining recommended indoor temperature levels. © 2025 IEEE.

关键词： Reinforcement learning

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5G Technology and Impact on Modern engineering 4

5G Technology and Impact on Modern Engineering

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kate, Priya Pacharney, Utkarsha Datta Meghe Institute of Higher Education and Research Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Department of Computer Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

5G technology represents a progressive leap in Wi-Fi conversation, providing unheard-of pace, connectivity, and capacity. Its deployment has profound implications for modern engineering, impacting industries such as telecommunications, healthcare, transportation, and clever towns. With speeds up to one hundred instances quicker than 4G and latency as little as 1 millisecond, 5G permits real-time facts trade, fostering improvements like independent vehicles, far-flung surgeries, and the Internet of Things (IoT). Currently, Engineers are challenged to design infrastructure that will assist the dense community of gadgets related to 5G, necessitating advancements in antenna design, power performance, and statistics security. The era also quickens the adoption of part computing, reducing reliance on centralised facts centres and ensuring quicker processing near the date supply. The transformative impact of 5G extends beyond connectivity. It reshapes the layout and capability of present-day engineering structures, emphasising adaptability, resilience, and scalability. As 5G continues to conform, its function in driving innovation and redefining engineering paradigms becomes increasingly glaring. © 2025 IEEE.

关键词： 5G mobile communication systems

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A Whispering Widgets and Wireless Wonders Pioneering Change in the World of IoT 7th

A Whispering Widgets and Wireless Wonders Pioneering Change ...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Tripathi, Amit Kumar Islam, Baharul Murugan, R. Adhvaryu, Rachit Department of Electronical and Electronics Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Engineering Assam Down Town University Assam Guwahati India Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9789819753369

The IoT has made it possible to connect and control more devices than ever before, opening up new avenues for efficiency and creativity. However, new problems have emerged as a result of this explosion of linked devices, notably concerning the security and privacy of personal information. To meet these difficulties, we provide a new approach within the context of "Whispering Widgets and Wireless Wonders: Ushering in a New Era for the Internet of Things." Our suggested approach safeguards data during transit and storage by using state-of-the-art cryptographic methods including Elliptic Curve Cryptography (ECC) and the Advanced Encryption Standard (AES). Furthermore, we use blockchain technology to guarantee the immutability and transparency of data by creating a public and unchangeable ledger of all data exchanges. Our research shows that our suggested strategy for securing IoT data is superior to more conventional methods. We test how well our system protects sensitive information, keeps data intact, keeps users anonymous, and how efficient it is overall. When compared to conventional approaches, the "Proposed Method" regularly performs better, demonstrating its strength and applicability for protecting IoT data. High security is guaranteed by the combination of ECC and AES, and data integrity is improved by blockchain technology. Furthermore, our approach places a premium on user privacy, giving people more say over their data inside the IoT ecosystem. Finally, we believe that our suggested solution is a major advancement in achieving more data security and privacy in the IoT context. To the problems caused by the proliferation of linked gadgets, it provides a thorough and effective answer. Our approach prioritizes security, data integrity, and user privacy to let people and businesses get the benefits of the Internet of Things (IoT) without compromising on data privacy or security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025

关键词： Data integrity

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of computer science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

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Multimedia Tools and Applications 2025年 1-22页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia

Accurate segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans presents notable challenges. This particularly in differentiating tumors from surrounding tissues with similar intensity. This study utilizes the DeepLabV3Plus model with an Xception encoder to address these challenges. We evaluated the model on a dataset of 3064 MR images, which included meningioma, glioma, and pituitary tumors. The model achieved high segmentation performance, with an accuracy of 0.9991, Dice Similarity Coefficient (DSC) of 0.9529, and Jaccard Index (JI) of 0.9133. It also demonstrated substantial boundary precision, recording an Average Symmetric Surface Distance (ASSD) of 0.84 mm and a Hausdorff Distance (HD) of 2.3 mm. These metrics surpass those of existing state-of-the-art models. While the model performed well with well-defined tumor boundaries, it faced challenges segmenting tumors with ambiuous edges. These findings suggest significant potential for enhancing diagnostic accuracy in neuro-oncology. Future work will focus on enhancing the model’s sensitivity to subtle tumor features and expanding the dataset to include a broader range of tumor characteristics. Additionally, incorporating multimodal imaging data could further improve segmentation precision. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Magnetic resonance imaging

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An Empirical Comparison of Machine and Deep Learning Algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

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5th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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Big Data Healthcare Monitoring System: Improved Recurrent Neural Network for Disease Classification with Apache Spark

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Sensing and Imaging 2025年第1期26卷 1-52页

作者： Prabhugouda, Amruta Asra, Syeda Department of Computer Science and Engineering Faculty of Engineering and Technology (Exclusively for Women) Sharnbasva University Karnataka Kalaburagi 585102 India Department of Computer Science and Engineering Faculty of Engineering and Technology Sharnbasva University Karnataka Kalaburagi 585102 India

Big data analytics has increasingly penetrated the medical industry due to the swift growth of Internet technology along with medical data digitalization, hospital information systems, a huge count of Electronic Health Records (EHR) and other emerging data. The big data’s potential in healthcare is mainly based on its capability of detecting patterns and turning the high volume of data into actionable knowledge for decision-makers. Considering the applications of big data analytics in the medical industry, we have introduced an improved RNN-based big data healthcare monitoring system including the following working stages. Firstly, acquired data gets pre-processed by an outlier detection process. Afterwards, Improved SMOTE (Synthetic Minority Oversampling Technique) based class imbalance processing is performed to get the balanced data. This balanced data is handled using the Spark framework, with the master node carrying out an improved Deep Fuzzy Clustering (DFC) based clustering process and the slave node handling feature extraction and an enhanced Support Vector Machine Recursive Feature Elimination (SVM-RFE) based feature selection process. To divide the data according to the patient’s condition, an enhanced Deep Autoencoder-based Fuzzy C Means Clustering (DAE-FCM) is suggested in the improved DFC. Features including statistics, enhanced entropy, and mutual information are extracted throughout the feature extraction process. Ultimately, an Improved Recurrent Neural Network (RNN) is used to classify diseases using the chosen feature from the slave node. The implementation outcomes proved that the proposed big data healthcare monitoring system can provide effective and accurate disease classification. The Improved RNN method demonstrated the highest accuracy, achieving an impressive rate of 0.945 for dataset 1 and 0.947 for dataset 2 at training data 80%, while the conventional methods acquired the least accuracy ratings. By integrating advanced techniques, the s

关键词： Bid data analytics Deep learning Healthcare data Improved DAE-FCM Improved DFC Improved entropy Improved RNN Improved SMOTE Mutual information Outlier detection

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Detection of Mobile Phone Usage Among Motorcycle Riders: A computer Vision Approach 5th

Detection of Mobile Phone Usage Among Motorcycle Riders: A C...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Charef, Ayoub Jarir, Zahi Quafafou, Mohamed LAMIGEP EMSI Moroccan School of Engineering Marrakesh Marrakesh Morocco Department of Computer Science Faculty of Science Cadi Ayyad University Marrakech Marrakesh Morocco Department of Computer Science University of Aix-Marseille Marseille Marseille France

ISBN: (纸本)9789819791118

This study examines the prevalence of mobile phone use among motorcycle riders at signalized intersections in Marrakech, utilizing advanced computer vision techniques. Employing the YOLOv8 object detection model and the DeepSORT tracking algorithm, our approach effectively detects and tracks mobile phone usage in real-time traffic scenarios. By analyzing data from strategically placed cameras at high-traffic intersections, the research identifies patterns in mobile phone usage among motorcyclists and evaluates the impact on traffic safety. The findings highlight significant times of day for phone usage and offer insights into behavioral trends, providing a foundation for developing targeted interventions to enhance road safety. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motorcycles

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FDSOI-Based Reconfigurable FETs: A Ferroelectric Approach

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IEEE Transactions on Nanotechnology 2025年 24卷 277-281页

作者： Lee, Donghyeok Das, Suprem R. Kwon, Jiseok Chang, Jiwon Yonsei University Department of System Semiconductor Engineering Department of Materials Science and Engineering Seoul03722 Korea Republic of Kansas State University Department of Industrial and Manufacturing Systems Engineering Department of Electrical and Computer Engineering ManhattanKS66506 United States The Catholic University of Korea School of Information Communications and Electronic Engineering Gyeonggi-do14662 Korea Republic of

In this study, we propose ferroelectric-based reconfigurable field-effect transistors (FeRFETs) that utilizes the structure of a fully depleted silicon-on-insulator field-effect transistors (FDSOI FETs). In FeRFETs, the non-volatile and reconfigurable electrostatic doping facilitated by ferroelectric enables type conversion. Through the TCAD simulations calibrated with the experimental data, we confirm a reconfigurable high doping level (> 1×1021 cm-3), a clear type conversion and highly tunable performance in FeRFETs. It is also found that carefully tailoring coercive field (EC) is important to maximize the performance of FeRFETs. © 2002-2012 IEEE.

关键词： Reconfigurable hardware

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