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A novel medical image data protection scheme for smart healthcare system

CAAI Transactions on Intelligence Technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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Classification and Regression Trees (CART) Method in the Classification Process of Blood Control Candidates 9

Classification and Regression Trees (CART) Method in the Cla...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Ramdania, Diena Rauda Slamet, Cepy Lutfiyah, Hasna Fairuz Irfan, Mohamad Zulfikar, Wildan Budiawan Harika, Maisevli Uin Sunan Gunung Djati Department of Informatics Bandung Indonesia Politeknik Negeri Bandung Computer Engineering and Informatics Department Bandung Indonesia

ISBN: (纸本)9798350305029

The classification of blood donor candidates is a crucial process that requires thorough health checks and assessments. Implementing a reliable and efficient classification system for blood donor candidates is essential to ensure the safety and well-being of both the donor and the recipient. This study focuses on implementing the Classification and Regression Trees (CART) method in the classification process of blood control candidates. Given the complex series of health checks involved in the process, which includes age, weight, blood pressure, pulse rate, and blood tests (hemoglobin, blood type, and rhesus), a reliable and efficient classification system is necessary. This research demonstrates the successful implementation of the CART method in developing an expert classification system for blood donor candidates. The system achieved an accuracy value of 98.03%, making it a highly effective and reliable tool for improving blood control candidate classification accuracy and efficiency. This research has significant implications for enhancing the safety and success of blood transfusions. © 2023 IEEE.

关键词： Expert systems

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A Two-Pronged Approach to Security of Discrete-Event Systems

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Journal of Systems Science & Complexity 2025年第1期38卷 178-209页

作者： MATSUI Shoma RUDIE Karen CAI Kai Department of Electrical and Computer Engineering&the Ingenuity Labs Research Institute Queen's UniversityKingston K7L 3N6Canada Department of Core Informatics Osaka Metropolitan UniversityOsaka 545-0051Japan

This paper investigates a security problem of simultaneously addressing two types of attacks:Eavesdropping and *** authors model the target system as a discrete-event system(DES)with subsets of concealable events and protectable events,in order to make the proposed methodology applicable to various practical systems and employ two existing works of DES security:Degree of opacity and state ***,the authors consider that all protectable events are observable,and some observable events are *** addition,protectable events cannot be protected once they are *** such a constraint,the goal is to figure out which events to conceal and which transitions to protect so that the prescribed requirements of degree of opacity and state protection are *** this work the authors decide which events to conceal as all transitions of a given event label are concealed or not *** proposed problem formulation also requires a solution to only involve absolutely necessary protectable events in order for the system to avoid superfluous protection *** authors first examine a general version of our security problem with an intuitive algorithm to compute acceptable solutions,and then present a special version which results in a reduced computation time compared to the general version.

关键词： Degree of opacity discrete-event systems security state protection

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Utilizing Edge Device and Streamlit for Comparative Evaluation of CLAHE-Based Underwater Image Enhancement Techniques 13

Utilizing Edge Device and Streamlit for Comparative Evaluati...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Amalia, Nurul Asmi Afira, Aida Ulfa, Maria Muchtar, Kahlil Universitas Syiah Kuala Department of Informatics Aceh Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Indonesia

ISBN: (纸本)9798350355079

This study presents an edge-based system for underwater image enhancement using CLAHE and its fusion with various techniques. Utilizing the NVIDIA Jetson Orin Nano and the Streamlit framework, the system processes images in real-time. The enhancement methods include CLAHE, Fusion of CLAHE and USM, Fusion of CLAHE and HEF, and Blending CLAHE with Percentile Techniques. Tested on real underwater images, these methods demonstrate significant improvements in UIQM and UCIQE scores while reducing LOE. This combination boosts image quality while maintaining natural lightness, making it suitable for marine exploration, underwater robotics, and environmental monitoring. A demo of our image enhancement web app can be found here: https://***/EDJxPKv1LSs. © 2024 IEEE.

关键词： Underwater imaging

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Binary to Decimal Conversion and Subnetting Calculations using Augmented Reality 9

Binary to Decimal Conversion and Subnetting Calculations usi...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Ramdania, Diena Rauda Alam, Cecep Nurul Ghuswari, Afif Hani Irfan, Mohamad Atmadja, Aldy Rialdy Harika, Maisevli UIN Sunan Gunung Djati Department of Informatics Bandung Indonesia Politeknik Negeri Bandung Computer Engineering and Informatics Department Bandung Indonesia

ISBN: (纸本)9798350370126

This paper studies markerless augmented reality applications for displaying digital objects on different colored surfaces under varying lighting conditions and tracking angles. The study aimed to evaluate the application's effectiveness in accurately displaying digital objects. The testing was conducted in bright and dark light settings, with tracking angles of 45° and 90°. The results showed that surfaces with a texture in dark or bright colors, especially patterned surfaces, yielded optimal results in displaying digital objects in both lighting conditions. However, plain bright surfaces and surfaces without patterns were not practical for displaying digital objects. The angle of the tracking device and the presence of shadows significantly impacted the quality of the displayed objects. The tracking distance did not significantly affect the display quality. These findings provide valuable insights for enhancing the performance and usability of markerless augmented reality applications, guiding the selection of suitable surfaces and lighting conditions for optimal display outcomes. © 2023 IEEE.

关键词： Augmented reality

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Enhanced Sound Recognition and Classification Through Spectrogram Analysis, MEMS Sensors, and PyTorch: A Comprehensive Approach 19th

Enhanced Sound Recognition and Classification Through Spectr...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Spournias, Alexandros Nanos, Nikolaos Faliagka, Evanthia Antonopoulos, Christos Voros, Nikolaos Keramidas, Giorgos Electrical and Computer Engineering Department University of the Peloponnese Patra Greece School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783031545207

The importance of sound recognition and classification systems in various fields has led researchers to seek innovative methods to address these challenges. In this paper, the authors propose a concise yet effective approach for sound recognition and classification by combining spectrogram analysis, Micro-Electro-Mechanical Systems (MEMS) sensors, and the Pytorch deep learning framework. This method utilizes the rich information in audio signals to develop a robust and accurate sound recognition and classification system. The authors outline a three-stage process: data acquisition, feature extraction, and classification. MEMS sensors are employed for data acquisition, offering advantages such as reduced noise, low power consumption, and enhanced sensitivity compared to traditional microphones. The acquired audio signals are then preprocessed and converted into spectrograms, visually representing the audio data’s frequency, amplitude, and temporal attributes. During feature extraction, the spectrograms are analyzed to extract significant features conducive to sound recognition and classification. The classification task is performed using a custom deep learning model in Pytorch, leveraging modern neural networks’ pattern recognition capabilities. The model is trained and validated on a diverse dataset of audio samples, ensuring its proficiency in recognizing and classifying various sound types. The experimental results demonstrate the effectiveness of the proposed method, surpassing existing techniques in sound recognition and classification performance. By integrating spectrogram analysis, MEMS sensors, and Pytorch, the authors present a compact yet powerful sound recognition system with potential applications in numerous domains, such as predictive maintenance, environmental monitoring, and personalized voice-controlled devices. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Data acquisition

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Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

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computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

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A DNN Framework on Waveform Design for Over-the-Air Computation

A DNN Framework on Waveform Design for Over-the-Air Computat...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Evgenidis, Nikos G. Mitsiou, Nikos A. Tegos, Sotiris A. Diamantoulakis, Panagiotis D. Sarigiannidis, Panagiotis Karagiannidis, George K. Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece University of Western Macedonia Department of Informatics and Telecommunication Engineering Kozani50100 Greece

ISBN: (纸本)9798350368369

One of the main applications expected to be enabled by next-generation networks is computing. The goal-oriented nature of computing allows the use of different implementation techniques, with over-the-air (OTA) computation being one of the main proposed schemes due to its effective resource management and computational efficiency. In this work, we aim at optimizing the waveform of the system in the presence of intersymbol interference (ISI) and sampling error. To this end, we propose a deep neural network (DNN) framework that generates an optimal waveform that minimizes the mean square error (MSE) of the OTA computation system. To ensure that the generated waveform exhibits the same behavior as other common waveforms, weighted energy and spectrum constraints are included in the loss function of the training phase. To better mitigate ISI, the spectrum constraint integrates the roll-off factor of the waveform, allowing for the generation of different waveforms. Simulation results verify that the desired constraints are met and show a significant performance gain over state-of-the-art waveforms. © 2025 IEEE.

关键词： Deep neural networks

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Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm 5

Improving bot detection of fake followers on Twitter via a h...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Alzeyadi, Haider Duran, Fecir Informatics Institute Department of Computer Science Gazi University Ankara Turkey Gazi University Engineering Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665493604

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset. © 2023 IEEE.

关键词： Feature Selection

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Hyperparameter Optimization of MobileNet Architecture on Balinese Mask Classification 1

Hyperparameter Optimization of MobileNet Architecture on Bal...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Andika, I Gede Dirgayusari, Ayu Manik Arnawan, I Nyoman Wahyu Aristana, Made Dona Yudhi Ardiana, Dewa Putu Institut Bisnis Dan Teknologi Indonesia Computer Systems Engineering Faculty of Technology and Informatics Bali Denpasar Indonesia Institut Bisnis Dan Teknologi Indonesia Infomatics Engineering Faculty of Technology and Informatics Bali Denpasar Indonesia

ISBN: (纸本)9798350365191

This study utilized MobileN et to surpass the accuracy of previous research in the field of Balinese mask image classification. Balinese masks as one of the objects of Balinese cultural heritage must be preserved. Based on the data collected, most of young generation in Bali who has no interest in Balinese masks. The approach in the field of technology is known to have a very effective impact on the younger generation because of its rapid and efficient deployment. To improve accuracy, we analyze hyperparameter combinations such as learning rate, batch size, and dropout. Based on 16 scenarios, we got an accuracy of 98.57% for the combination of learning rate 0.00001, batch size 8, and dropout 40%. This value exceeds previous studies using the VGG16 architecture with an accuracy of 85%. © 2024 IEEE.

关键词： Image classification

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