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Securing Healthcare Data in IoMT Network Using Enhanced Chaos Based Substitution and Diffusion

computer Systems Science & engineering 2023年第11期47卷 2361-2380页

作者： Musheer Ahmad Reem Ibrahim Alkanhel Naglaa FSoliman Abeer D.Algarni Fathi E.Abd El-Samie Walid El-Shafai Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia

Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many *** modern-day applications,patient data usually holds clinical imagery,records,and other medical ***,the Internet of Medical Things(IoMT),equipped with cloud computing,has come out to be a beneficial paradigm in the healthcare ***,the openness of networks and systems leads to security threats and illegal ***,reliable,fast,and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT *** paper presents an image protection scheme for healthcare applications to protect patients’medical image data exchanged in IoMT *** proposed security scheme depends on an enhanced 2D discrete chaotic map and allows dynamic substitution based on an optimized highly-nonlinear S-box and diffusion to gain an excellent security *** optimized S-box has an excellent nonlinearity score of *** new image protection scheme is efficient enough to exhibit correlation values less than 0.0022,entropy values higher than 7.999,and NPCR values around 99.6%.To reveal the efficacy of the scheme,several comparison studies are *** comparison studies reveal that the novel protection scheme is robust,efficient,and capable of securing healthcare imagery in IoMT systems.

关键词： Secure communication healthcare data encryption Internet of Medical Things(IoMT) discrete chaotic map substitution box(S-box)

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Adding Photon Entanglement and Quantum Beating to Superradiance by Using Multilevel Atoms

Adding Photon Entanglement and Quantum Beating to Superradia...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Amir Sivan Meir Orenstein Andrew and Erna Faculty of Electrical & Computer Engineering Technion-Israel Institute of Technology Haifa Israel

Entanglement is added to the multiphoton state emanated by superradiance. It is achieved by employing multilevel atoms, via a multipath Dicke ladder. Additional quantum effects like beating between collective states a... 详细信息

ISBN: (纸本)9798350369311

关键词： Spontaneous emission Quantum entanglement Atom lasers Atoms Electro-optic effects

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Forgetting User Preference in Recommendation Systems with Label-Flipping

Forgetting User Preference in Recommendation Systems with La...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Alshehri, Manal A. Zhang, Xiangliang Computer Science Cemse Thuwal23955-6900 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Rabigh Saudi Arabia University of Notre Dame Department of Computer Science and Engineering Notre Dame United States

ISBN: (纸本)9798350324457

Recommendation systems play a crucial role in identifying users' preferences based on their historical interaction records and those of other users. However, the ability to 'forget' certain users' preferences is indispensable for ensuring user privacy and maintaining recommendation accuracy. It is essential to accommodate a user's request to exclude their behavioral data from the recommendation system when necessary. Likewise, if certain data corrupts the system, its impact should be removed to restore system performance. In this paper, we propose FlipRec, a general and efficient framework for recommendation models to 'forget' the preferences of specific users while retaining the model's performance for all other users. Our concept of forgetting user preferences is inspired by the label-flipping attack, a technique where the labels of some training samples are inverted to adversarially manipulate the weights of the trained model. FlipRec adjusts the recommendation model weights to forget the targeted users by flipping their interaction records $y \in \{ 0,1\}$. To preserve the model's performance for the remaining users, we augment the fine-tuning data with samples from users who have interacted with the same items as the targeted users. This ensures minimal impact on these users during the 'forgetting' process. FlipRec has been validated on both contentbased recommendation models and collaborative filtering models. The experimental results show that FlipRec outperforms stateof-the-art unlearning methods in terms of efficiency, the ability to forget targeted users, and the preservation of performance for the remaining users. © 2023 IEEE.

关键词： label-flipping attack machine unlearning Recommendation models

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MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descent 41

MADA: Meta-Adaptive Optimizers Through Hyper-Gradient Descen...

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41st International Conference on Machine Learning, ICML 2024

作者： Ozkara, Kaan Karakus, Can Raman, Parameswaran Hong, Mingyi Sabach, Shoham Kveton, Branislav Cevher, Volkan Department of Electrical and Computer Engineering University of California Los Angeles United States Amazon Web Services United States Department of Electrical and Computer Engineering University of Minnesota United States Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Israel LIONS IEM STI Ecole Polytechnique Fédérale de Lausanne Switzerland

Following the introduction of Adam, several novel adaptive optimizers for deep learning have been proposed. These optimizers typically excel in some tasks but may not outperform Adam uniformly across all tasks. In this work, we introduce Meta-Adaptive Optimizers (MADA), a unified optimizer framework that can generalize several known optimizers and dynamically learn the most suitable one during training. The key idea in MADA is to parameterize the space of optimizers and dynamically search through it using hypergradient descent during training. We empirically compare MADA to other popular optimizers on vision and language tasks, and find that MADA consistently outperforms Adam and other popular optimizers, and is robust against sub-optimally tuned hyper-parameters. MADA achieves a greater validation performance improvement over Adam compared to other popular optimizers during GPT-2 training and fine-tuning. We also propose AVGrad, a modification of AMSGrad that replaces the maximum operator with averaging, which is more suitable for hyper-gradient optimization. Finally, we provide a convergence analysis to show that parameterized interpolations of optimizers can improve their error bounds (up to constants), hinting at an advantage for meta-optimizers. Copyright 2024 by the author(s)

关键词： Deep learning

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Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

Energy-Optimized with Multi-Population Differential Annealed...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wu, Ruixuan Shi, Yuliang Yuan, Haitao Bi, Jing Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time. © 2023 IEEE.

关键词： computation offloading energy optimization Mobile edge computing multi-population optimization simulated annealing

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Smart Grid Prediction Using Federated Learning and Fully Homomorphic Encryption

Smart Grid Prediction Using Federated Learning and Fully Hom...

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International Conference on computer Theory and Applications (ICCTA)

作者： Islam M. Saad Mohamed Fakhr Tamer Abdelkader Nada M. Abdel Aziem Department of Computer Engineering Arab Academy for Science Technology & Maritime Transport Cairo Egypt Faculty of Computer Science and Engineering Galala University Cairo Egypt

ISBN: (数字)9798331529680

ISBN: (纸本)9798331529697

Electric load prediction is a crucial aspect of energy management. It can enhance grid stability and support renewable integration. Recently, there has been significant interest in privacy for smart grids that focus on the traditional model that is used in the smart grids, which concludes there is no privacy in training the model as it uses the raw data of the customers without any encryption or privacy preservation, this problem gives a significant contribution to looking for a tool to solve this problem, this tool is federated learning approach. This paper will explain using the federated learning approach in the smart grid, which allows decentralized model training to achieve privacy preservation and increase security by integrating fully homomorphic encryption in the proposed federated learning framework, which helps smart grid prediction without exposing sensitive data. The experiment results show that the proposed approach achieves a high prediction with high privacy preservation.

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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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EEG-based Brain Wave Recognition using GRU and LSTM 5

EEG-based Brain Wave Recognition using GRU and LSTM

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Shashidhar, R. Tippannavar, Sanjay S Bhat, Krishnakumar Balachandra Sharma, Neha Rashid, Mamoon Rana, Arti JSS Science and Technology University Department of Electronics and Communication Engineering Mysuru India Chitkara University Institute of Engineering and Technology Department of Computer Science & Engineering Chitkara University Punjab Rajpura India Vishwakarma University Faculty of Science & Technology Department of Computer Engineering Pune India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

The human brain is made up of millions of neurons, each of which plays a crucial part in directing the behaviour of the human body in response to internal/external motor-impulses. These neurons will act as data conduits between the brain and the body. Studying brain signals or images may help us better understand the cognitive behaviour of the brain. Motor and sensory states including eye movement, lip movement, memory, concentration, hand clutching, and others may be used to depict human behaviour. These states are associated with certain signal frequencies, which aids in understanding the functional behaviour of complex brain structures. In this study, the characterization of EEG signals in connection to various bodily states is the main topic of study. It also talks about the experimental design used for the EEG analysis. On a dataset of physiological signals, in this case the AMIGOS dataset, including electrocardiogram and galvanic skin reaction, this study uses a deep convolutional neural network. In order to characterize a person's emotional state, these physiological signals are combined with the dataset's arousal and valence data. In addition, a tool for detecting emotions based on traditional machine learning techniques is provided in order to extract the properties of physiological data in the time, frequency, and nonlinearity domains. For autonomous feature extraction of physiological inputs in this application, a convolutional neural network is used. Fully connected network layers are then used to predict emotions. Compared to the original authors of this dataset, the experimental results on the AMIGOS dataset show that the approach suggested in this study provides more accurate emotional state categorization. © 2022 IEEE.

关键词： Convolution

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Exploring household food security through institutional factors: A statistical and mathematical analysis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9179-9195页

作者： Khan, Younas Ashraf, Shahzaib Farman, Muhammad Abdallah, Suhad Ali Osman Department of Sociology Kohat University of Science and Technology Kohat Pakistan Institute of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Mathematics Faculty of Arts and Science Near East University Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Applied College Khamis Mushait King Khalid University Abha Saudi Arabia

Achieving household food security is the tumbling issue of the century. This article explores the factors affecting household food security and solutions by utilizing a synergy of statistical and mathematical models. The methodology section is divided into two portions namely sociological and mathematical methods. Sociologically, 379 household heads were interviewed through structured questions and further analyzed in terms of descriptive and binary logistic regression. The study found that 4 independent variables (poverty, poor governance, militancy, and social stratification) showed a significant association (P = 0.000) to explain variations in the dependent variable (household FS). The Omnibus test value (χ2= 102.386;P = 0.000) demonstrated that the test for the entire model against constant was statistically significant. Therefore, the set of predictor variables could better distinguish the variation in household FS. The Nagelkerke's R Square (R2 = .333) helps to interpret that the prediction variable and the group variables had a strong relationship. Moreover, 23% to 33% variation in FS was explained by the grouping variables (Cox and Snell R2 = 0.237 and Nagelkerke's R2 = 0.333). The significant value of Wald test results for each variable confirmed that the grouping variables (poor governance P = 0.004, militancy P = 0.000, social stratification P = 0.021 and poverty P = 0.000) significantly predicted FS at the household level. Mathematically, all the statistics were validated further through the application of spherical fuzzy mathematics (TOPIS and MADM) to explore what factors are affecting household FS. Thus, the study found that F3 (poverty) > F2 (militancy) > F4 (social stratification) > F1 (poor governance) respectively. Thus, it could be concluded from these findings that the prevalence of poverty dysfunctional all the channels of household FS at the macro and micro levels. Therefore, a sound and workable model to eradicate poverty in the study area by

关键词： Food supply

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Optimisation of plagiarism detection using vector space model on CUDA architecture

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International Journal of Innovative Computing and Applications 2022年第4期13卷 232-244页

作者： Abdul Cader, Jiffriya Mohamed Abdul Cader, Akmal Jahan Mohamed Gamaarachchi, Hasindu Ragel, Roshan G. Department of Information Technology Sri Lanka Institute of Advanced Technological Education Sammanthurai Sri Lanka Faculty of Applied Sciences South Eastern University of Sri Lanka Sri Lanka School of Computer Science and Engineering University of New South Wales Australia Faculty of Engineering University of Peradeniya Sri Lanka

Plagiarism is a rapidly rising issue among students during submission of assignments, reports and publications in universities and educational institutions, due to easy accessibility of abundant e-resources on the internet. Existing tools become inefficient in terms of time consumption when dealing with the prolific number of documents with large content. Therefore, we have focused on software-based acceleration for plagiarism detection using CPU/GPU. Initially serial version of vector space model was implemented on CPU and tested with 1,000 documents, which consumed 1,641 s. As processing time was a bottleneck of performance, we indented to develop parallel version of the model on the graphics processing units (GPUs) using compute unified device architecture (CUDA) and tested with the same dataset which consumed only 36 s and gained 45x speed up compared to the CPU. Then the version was optimised further and took only 4 s for the same dataset which was 389x faster than the serial implementation. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Graphics processing unit

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