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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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Leveraging Recurrent Neural Networks for Diabetes Progression Monitoring 10

Leveraging Recurrent Neural Networks for Diabetes Progressio...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Badhoutiya, Arti Aswal, Upendra Singh Michaelson, Jacob Laxminarayanamma, K. Khan, Irfan Srivastava, Amit Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

This study investigates the use of recurrent neural networks, commonly referred to as RNNs, in tracking the development of diabetes with the goal of revolutionizing tailored medical intervention. A deductive strategy is used, supported by a descriptive research methodology, and is based on an interpretivist theoretical framework. An RNN-based model is created and trained to identify temporal relationships in the data by utilizing secondary data sources, such as persistent records of patients, medication the pasts and aspects of lifestyle. The regression neural network (RNN) model outperforms traditional monitoring techniques in its ability to forecast long-term as well as short-term patterns in the course of diabetes. Comparative analysis demonstrates its superiority to conventional methods and demonstrates its potential to revolutionize the treatment of diabetes. The combination of several data sources, including comorbidities as well as lifestyle factors, considerably improves prediction accuracy and offers a more complex picture of how diseases develop. The model's efficiency in practical problems healthcare settings has been validated through clinical studies. Patients who use the RNN-based approach benefit from better glucose control and greater managing one's own confidence. Clinicians report improved patient outcomes and faster decision-making procedures. The significance of meticulous verification of data and comprehension is highlighted by critical analysis. The incorporation of multimodal data sources, the dynamic adaptability provided by the RNN method, and focused on patients' outcomes research are suggested as areas for further investigation. This revolutionary method of tracking the advancement of diabetes must still be advanced while taking ethical and legal factors into account. © 2023 IEEE.

关键词： clinical evaluations model patient's sporadic measurements treatment

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Smart Aging Technology with IoT and Deep Learning Analytics for Elderly Activity Patterns and Health Outcomes 9

Smart Aging Technology with IoT and Deep Learning Analytics ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Srinivasan, V.Prasanna Pandey, Pramod Muthumarilakshmi, S. Konidhala, Jothibabu. Rajmohan, M. Murugan, S. R.M.D. Engineering College Department of Information Technology Kavaraipettai Tamil Nadu Chennai India Symbiosis institute of Technology Nagpur Campus Maharashtra Pune India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India University of Technology and Applied Sciences- Al Musannah Department of Electronics and Communication Engineering South Al Batina Musanna Oman Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350377972

Innovative healthcare solutions for older people have developed from the fast progress of the Internet of Things (IoT) technology and deep learning (DL). This research proposes a smart aging system that uses IoT devices and Long Short-Term Memory (LSTM) networks to track and evaluate geriatric behavior and health. The proposed approach collects real-time health and daily activity data using a wearable, smart home, and ambient sensors. A central platform uses LSTM networks to identify and forecast activity and health variables from collected data. LSTM can capture long-term relationships and patterns in time-series data, making it ideal. By evaluating these patterns, the system may detect abnormal behavior that may indicate health difficulties like falls, chronic diseases, or cognitive impairment. Preliminary findings show that LSTM-based analytics may provide accurate and timely insights to help caregivers and healthcare providers improve health outcomes. By encouraging mobility and preventive health management, the smart aging system improves older quality of life and decreases healthcare costs. To increase predicted accuracy and system dependability, LSTM models will be refined, the dataset expanded, and more health measures will be included. © 2024 IEEE.

关键词： Electronic health record

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BERT-Driven Natural Language Processing in Electronic Health Records for Improved Clinical Insights 1

BERT-Driven Natural Language Processing in Electronic Health...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Yadav, Kanchan Sharma, Manish Michaelson, Jacob Laxminarayanamma, K. Yadav, Vijay Kumar Krishna, Om Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

In order to improve clinical insights, this study investigates the incorporation of BERT-driven natural language processing (NLP) in electronic health records (EHRs). Utilizing a descriptive design, interpretivism philosophy, as well as a deductive approach, this research demonstrates the adaptability of BERT in deciphering intricate medical literature. The results show enhanced data extraction efficiency and accuracy, which has a significant impact on medication extraction and disease recognition. Among the practical ramifications are enhanced clinical decision support platforms. Ethical aspects such as prejudices and privacy issues generate attention to the necessity of thorough regulations. Future research on improving interpretability, dealing with ethical issues, and investigating the model's adaptability across healthcare institutions is advised by the study. This study adds to our comprehension of BERT in healthcare while highlighting the critical role that practical and ethical considerations play in ensuring its ethical integration. © 2023 IEEE.

关键词： BERT-driven NLP Clinical Insights Electronic Health Records (EHRs) Ethical Considerations information Extraction

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System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture

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IEEE Journal of Biomedical and Health informatics 2023年第7期27卷 3549-3558页

作者： Lin, Bor-Shyh Peng, Chih-Wei Lee, I-Jung Hsu, Hung-Kai Lin, Bor-Shing National Yang Ming Chiao Tung University Institute of Imaging and Biomedical Photonics Tainan71150 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei11031 Taiwan Taipei Medical University School of Gerontology and Long-term Care College of Nursing Taipei11031 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237303 Taiwan

Bedside falls and pressure ulcers are crucial issues in geriatric care. Although many bedside monitoring systems have been proposed, they are limited by the computational complexity of their algorithms. Moreover, most of the data collected by the sensors of these systems must be transmitted to a back-end server for calculation. With an increase in the demand for the Internet of Things, problems such as higher cost of bandwidth and overload of server computing are faced when using the aforementioned systems. To reduce the server workload, certain computing tasks must be offloaded from cloud servers to edge computing platforms. In this study, a bedside monitoring system based on neuromorphic computing hardware was developed to detect bedside falls and sleeping posture. The artificial intelligence neural network executed on the back-end server was simplified and used on an edge computing platform. An integer 8-bit-precision neural network model was deployed on the edge computing platform to process the thermal image captured by the thermopile array sensing element to conduct sleep posture classification and bed position detection. The bounding box of the bed was then converted into the features for posture classification correction to correct the posture. In an experimental evaluation, the accuracy rate, inferencing speed, and power consumption of the developed system were 94.56%, 5.28 frames per second, and 1.5 W, respectively. All the calculations of the developed system are conducted on an edge computing platform, and the developed system only transmits fall events to the back-end server through Wi-Fi and protects user privacy. © 2013 IEEE.

关键词： Feature extraction

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IPFS: An Off-Chain Storage Solution for Blockchain 5th

IPFS: An Off-Chain Storage Solution for Blockchain

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Manpreet Gupta, Shikha Kumar, Deepak Raboaca, Maria Simona Goyal, S.B. Verma, Chaman Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Apex Institute of Technology Chandigarh University Mohali India National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Râmnicu Vâlcea Romania Faculty of Information Technology City University London Malaysia Department of Media and Educational Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9789811998751

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Cloud-Powered Smart Rehabilitation and Assistive Robotics for Stroke Recovery with Recurrent Neural Networks 5

Cloud-Powered Smart Rehabilitation and Assistive Robotics fo...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Sampoornam, M. Maria Rajasekaran, Mothiram Visumathi, J. Pattanaik, Balachandra Kalaichelvi, S. Sujatha, S. J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Pine Candle Way Saint AugustineFL United States Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Wollega University Department of Electrical and Computer Engineering Nekemte Ethiopia Pg and Research Department of Computer Science and Applications Elayampalayam Tamil Nadu Tiruchengode India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

Innovative rehabilitation treatments are needed since stroke is a major cause of long-term impairment globally. In this paper, it presents a smart robotics-assisted rehabilitation system for stroke patients that uses cloud computing and is enhanced using Recurrent Neural Networks (RNNs). The system utilizes a combination of wearable sensors, robotic equipment, and cloud computing to create individualized rehabilitation plans for each patient. To quickly evaluate patients' progress and adjust rehabilitation programs, RNNs are used for real-time processing of continuous sensor data streams. Cloud computing allows for easy scaling, universal accessibility, and compatibility with preexisting healthcare systems. Initial results show promise in enhancing stroke recovery outcomes, highlighting the potential of the proposed method to transform rehabilitation techniques in the smart healthcare realm. The urgent need for effective and tailored stroke rehabilitation programs is something this framework intends to solve, which should lead to better results and a higher quality of life for patients. © 2024 IEEE.

关键词： Recurrent neural networks

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Public key-based search on encrypted data using blockchain and non-blockchain enabled cloud storage: a comprehensive survey, analysis and future research scopes

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Cluster Computing 2025年第6期28卷 1-43页

作者： Mallick, Debjani Das, Ashok Kumar Varri, Uma Sankararao Park, Youngho Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea Computer Science and Engineering SRM University AP Amaravati India School of Electronic and Electrical Engineering Kyungpook National University Daegu South Korea

Cloud-based storage service has emerged as a promising alternative to managing local storage, offering users additional features, such as storage, backup, and restoration of various resources, including software, applications, and sensitive private information, within a virtual database, while ensuring data confidentiality and security. However, the widespread use of cloud services by individuals and organisations raises concerns regarding user accessibility and data security due to increasing social threats and adversarial attacks. Searchable encryption (SE) has been introduced to address these issues, providing a secure environment for a convenient and effective way of data searching and sharing. SE models have been advanced by integrating blockchain functionalities to tackle certain existing vulnerabilities and enhance user accessibility, data privacy, and integrity. This survey work explores various research works on SE techniques leveraging cloud and blockchain functionalities, discussing and categorizing the diverse approaches based on different criteria. This study also discusses the different enhancements made to SE techniques over the years, including the underlying requirements that led to the inclusion of blockchain functionalities. Moreover, this study provides a comparative analysis of existing survey works done in this area, which highlights a lack of recent literature surveys that thoroughly explore blockchain-based public key encryption with keyword search (PEKS) schemes. Particularly, we delve into the technical aspects of PEKS by classifying, analyzing and comparing different functionalities based on various aspects. The survey concludes with a comparative analysis of existing PEKS solutions and a discussion on identified research gaps, aiming to improve future research on PEKS approaches in this emerging field.

关键词： Searchable encryption Public key cryptosystem Blockchain Cloud storage Security

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AI-Powered 6G Networks: Transforming Wireless Communication with Intelligence and Terahertz Waves

AI-Powered 6G Networks: Transforming Wireless Communication ...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Singh, Navdeep Devi, Runumi Singh, Jay Rajalakshmi, B. Mohammed, Mohammed Wael Thandra, Nagarjuna Vidyadhari, Chalasanvi Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Gl Bajaj Institute of Technology and Management Dept. of Eee U.P. Gr. Noida India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Griet Department of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

From 2027 to 2030, Sixth Generation (6G) wireless communication systems are expected to be launched to revolutionize human and machine interaction with ultra fast data transmission speeds and low, or virtually zero latency communication. In this paper, we investigate the transformative potential of 6G networks enabled by AI, and describe key 6G network technologies, including artificial intelligence (AI), machine learning (ML), and sub miiitter wave technology. In addition to being able to exceed 5G Maximum Throughput (MT), 6G is forecast to enable advanced applications including holographic communications, XR (extended reality), and ubiquitous M2M (machine to machine) interaction. Although significant obstacles remain with respect to security, energy efficiency, and spectrum management. In this paper, 5G development existing gaps are identified, and a comprehensive AI driven framework for resource allocation, latency reduction, and spectrum utilization optimization is proposed for 6G systems. This study further explores the position of edge computing, IoT integration, and AI powered network security protocols. Finally, we demonstrate real world applications, such as smart healthcare, autonomous vehicles, and space communications, discussing privacy and sustainability aspects of 6G technology. © 2025 IEEE.

关键词： Resource allocation

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Image Captioning with Reinforcement Learning 2

Image Captioning with Reinforcement Learning

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

Image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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