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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1563-1570页

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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QG-SKI: Question Classification and MCQ Question Generation Using Sequential Knowledge Induction 22nd

QG-SKI: Question Classification and MCQ Question Generation ...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Dhanvardini, R. Deepak, Gerard Santhanavijayan, A. Health Care Informatics Domain Optum-United Health Groups Hyderabad India Department of Computer Science Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274084

E-Learning has emerged as the most effective way of getting information in a range of sectors in the contemporary age. The utilisation of electronic content to provide education and development is referred to as e-learning. While the broadening internet has a plethora of e-learning tools, knowledge acquisition is not just an aspect that adds to an individual's enrichment. Assessment and evaluation are crucial parts of every learning system. Due to more complex assessments and quicker inspection, multiple choice questions are becoming extremely prevalent in current evaluations. However, establishing a diversified pool of MCQs relevant to a certain subject matter presents a hurdle. Manually creating high-quality MCQ exams is a time-consuming and arduous procedure that demands skill. As a result, research has concentrated on the automated construction of well-structured MCQ-based tests. This paper presents a paradigm using natural language processing based on semantic similarity and dynamic ontology. The proposed QG-SKI model uses LOD Cloud and Wiki Data to generate Ontologies dynamically and Knowledge reservoir is performed. The dataset is analysed using TF-IDF algorithm and the Semantic Similarity and Semantic Dissimilarity are computed using Shannon’s entropy, Jaccard Similarity and Normalised Google Distance. These algorithms are executed for a multitude of degrees and levels to generate a similar of similar instances. The suggested model has a 98.15% accuracy and outperforms previous baseline models by dampening resilience. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： E-learning

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Integrated Sensing and Communications With Reconfigurable Intelligent Surfaces: From signal modeling to processing

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IEEE Signal Processing Magazine 2023年第6期40卷 41-62页

作者： Chepuri, Sundeep Prabhakar Shlezinger, Nir Liu, Fan Alexandropoulos, George C. Buzzi, Stefano Eldar, Yonina C. Indian Institute of Science Department of Electrical and Computer Engineering Bengaluru560012 India Ben-Gurion University of the Negev School of Electrical and Computer Engineering Be'er Sheva84105 Israel Southern University of Science and Technology Shenzhen518055 China School of Sciences National and Kapodistrian University of Athens Department of Informatics and Telecommunications Athens15784 Greece University of Cassino and Lazio Meridionale Cassino03043 Italy Weizmann Institute of Science Department of Mathematics and Computer Science Rehovot7610001 Israel

Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mm-wave) and terahertz (THz) frequency bands. However, establishing wireless connections at these high frequencies is quite challenging, mainly due to the penetrating path loss that prevents reliable communication and sensing. Another emerging technology for next-generation wireless systems is reconfigurable intelligent surface (RIS), which refers to hardware-efficient planar structures capable of modifying harsh propagation environments. In this article, we provide a tutorial-style overview of the applications and benefits of RISs for sensing functionalities in general, and for ISAC systems in particular. We highlight the potential advantages when fusing these two emerging technologies, and identify for the first time that 1) joint sensing and communications (S&C) designs are most beneficial when the channels referring to these operations are coupled, and that 2) RISs offer the means for controlling this beneficial coupling. The usefulness of RIS-aided ISAC goes beyond the obvious individual gains of each of these technologies in both performance and power efficiency. We also discuss the main signal processing challenges and future research directions that arise from the fusion of these two emerging technologies. © 1991-2012 IEEE.

关键词： Signal processing

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Biernacki, Piotr Ziebinski, Adam Syu, Jia-Hao Lin, Jerry Chun-Wei Silesian University of Technology Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Gliwice Poland Silesian University of Technology Institute of Informatics Gliwice Poland National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors. © 2023 IEEE.

关键词： machine learning sensors calibration single-beam distance sensors

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Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

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Digital Communications and Networks 2024年第2期10卷 404-415页

作者： Mohamed Abdel-Basset Hossam Hawash Nour Moustafa Imran Razzak Mohamed Abd Elfattah Faculty of Computers and Informatics Zagazig UniversityZagazigSharqiyah44519Egypt School of Engineering and Information Technology University of New South Wales@ADFACanberraACT2600Australia Deakin University Geelong Waurn Ponds CampusAustralia Computer Science Department Misr Higher Institute for Commerce and ComputersMansoura35511Egypt

With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber *** complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning *** Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacypreserved intelligence in IoT *** though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d *** is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other *** paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d ***-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each *** PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 *** empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8%as accuracy improvements.

关键词： Privacy preservation Federated learning Deep learning Fog computing Smart cities

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Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality 46

Optimizing Magnetic Induction Sensors for Non-Obtrusive Vita...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Momeni, Mahdi Radomski, Adrian Guler, Ulkuhan Teichmann, Daniel University of Southern Denmark Sdu Health Informatics and Technology The Maersk Mc-Kinney Moller Institute Denmark Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350371499

This paper investigates the advancement of magnetic induction-based heart and respiration rate sensing by actively controlling the coil current. This is realized through the implementation of a current-starved inverter mechanism. Experiments show a notable level of accuracy of the proposed circuit in measuring heart and respiration activity when compared to a reference sensor. The direct manipulation of current levels was found to have a direct impact on the signal strength. Incrementing the overall current within the proposed circuit from 60 mA to 100 mA resulted in an augmentation of the output amplitude of the heart rate signal from 8.5 mV to 27 mV, accompanied by a marginal enhancement in beat-to-beat interval accuracy. Moreover, the proposed sensor demonstrates noteworthy precision in monitoring the respiratory rate when compared with the reference sensor under different current values, exhibiting the same trend in signal strength. This finding offers valuable insight for the development of future power-optimized magnetic induction sensors with enhanced robustness. © 2024 IEEE.

关键词： Contact sensors

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Survey on Cryptoprocessors Advances and Technological Trends 3rd

Survey on Cryptoprocessors Advances and Technological Tren...

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3rd International Conference on Innovations in Computing Research, ICR 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Paphos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031655210

Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors, encompassing their historical development, architectural features, and various types based on their applications. The paper explores the crucial security considerations surrounding cryptoprocessors, covering both physical and logical aspects, as well as potential threats and countermeasures. Furthermore, the survey navigates through the latest technological trends and advancements in cryptoprocessor design. This includes discussions on post-quantum cryptography, secure enclaves, and mitigating side-channel attacks, all of which are shaping the future of cryptoprocessors. Real-world use cases and applications in domains such as secure communications, the Internet of Things (IoT), and data centers are presented, showcasing the practical significance of cryptoprocessors. However, the paper also highlights the persistent challenges faced by cryptoprocessor designers and provides insights into future research directions aimed at addressing emerging security and performance issues. As the digital landscape continues to evolve, the role of cryptoprocessors becomes increasingly vital in safeguarding sensitive information and fortifying the foundations of cybersecurity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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An automated speech analysis system for the detection of cognitive decline in elderly

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International Journal of Speech technology 2023年第2期26卷 337-353页

作者： Loizou, Christos P. Pantzaris, Marios Faculty of Engineering & Technology Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology 30 Arch. Kyprianos Str. Office 420 Limassol3036 Cyprus Cyprus Institute of Neurology and Genetics P. O. Box 23462 Nicosia1683 Cyprus

The goal of this study is to develop and test an automated integrated speech analysis system for detecting mild cognitive impairment (MCI) and dementia in spontaneous free speech. During the years 2010–2016, speech recordings (N = 2800) were obtained from 200 Greek Cypriots over the age of 65. These were divided into three groups (G1, G2, and G3) based on the results of their Mini-mental state examination (MMSE): G1:95 normal (NOR) individuals with an MMSE greater than 26;G2:65 MCI subjects with 20 ≤ MMSE ≤ 26;G3:40 dementia subjects with 0 ≤ MMSE © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Neurodegenerative diseases

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Urban Pluvial Flood Prediction Using Machine Learning Models 4

Urban Pluvial Flood Prediction Using Machine Learning Models

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Waseemullah Hassan, Tahir Majeed, Sajid Memon, Muhammad Qasim NED University of Engineering and Technology Department of Computer Science & Information Technology Karachi Pakistan Institute of Space Technology Department of Remote Sensing & Geo-Informatics Karachi Pakistan University of Sufism and Modern Sciences Department of Information and Computing Bhitshah Pakistan

ISBN: (纸本)9798331504342

Urban pluvial flooding causes threats to human lives, economy, infrastructure, and ecosystems around the world, particularly in developing countries. Pakistan is among those developing countries that have become vulnerable to urban flooding due to global warming, climate change risks, rapid urbanization, and the occurrence of extreme and unusual rainfall events in the monsoon season. To effectively mitigate the adverse effects of pluvial flooding, there is a pressing need for an advanced and accurate predictive model that is capable of predicting urban pluvial flooding. The aim of this research is to utilize the potential of machine learning (ML) techniques and algorithms to accurately predict the occurrence of urban pluvial flooding, for this study we have worked on the monsoon rainfall data of multiple cities of Sindh province in Pakistan. We made an attempt to use multiple ML models especially logistic regression (LR), random forest (RF), k nearest neighbor (KNN), and support vector machine (SVM) to propose the best and accurate predictive model. The developed models were evaluated and assessed using various performance evaluation metrics which includes accuracy, recall, precision and F1 score. The best among proposed models was created using LR which achieved an accuracy of 97%, while the accuracy of other models which were created using SVM, KNN and RF were 96%, 93%, and 92% respectively. © 2024 IEEE.

关键词： Prediction models

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Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Kasif, Gibran Thondilege, Comganesha University of Westminster London School of Computer Science and Engineering United Kingdom Informatics Institute of Technology Department of Computing Colombo06 Sri Lanka

ISBN: (纸本)9798350341454

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model's performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues. © 2023 IEEE.

关键词： Music

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