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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ravikumar, R. Sasipriyaa, N. Thilagaraj, T. Hareesh Raj, R. Abishek, A. Gokula Kannan, G. The Erode College of Pharmacy Tamilnadu India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu India School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus India Kongu Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350348217

The importance of diagnosing Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI), its prodromal form, for patient care, has made it a new study focus because of AD's high prevalence and huge social impact on health. Existing research have used machine learning approaches to diagnose AD early by looking at numerous biomarkers. However, in the bulk of current research, the diagnosis performance is hampered by the inability to efficiently extract high-level brain features that indicate the primary abnormalities of anatomical brain regions connected to AD. In order to diagnose AD brain pictures, the suggested model combines a deep convolutional neural network (CNN) and a conditional generative adversarial network (cGAN).The dataset is preprocessed to create a clear representation of the brain's grey matter, and the cGAN is utilised to create more training instances for the model to improve accuracy. According to the limitations of the discriminator, the generator in cGAN-ADD learns to include the illness category feedback from the classifier into the 2D-brain slice image reconstruction process for image improvement in the training phase. In the prediction phase, the stacked CNN layers extract high-level brain properties from category-enhanced 2D-brain slice pictures. The classifier gets the collected brain features in order to output the posterior probability of disease conditions (Normal, AD, Mild and Very Mild). Tensorflow is used to train the model, and Keras is used to evaluate classification accuracy. The model is trained and tested on ADNI dataset. © 2023 IEEE.

关键词： Brain

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Comparative study of IoT-and AI-based computing disease detection approaches

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Data Science and Management 2025年第1期8卷 94-106页

作者： Wasiur Rhmann Jalaluddin Khan Ghufran Ahmad Khan Zubair Ashraf Babita Pandey Mohammad Ahmar Khan Ashraf Ali Amaan Ishrat Abdulrahman Abdullah Alghamdi Bilal Ahamad Mohammad Khaja Shaik Department of Computer Application Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationGunturAndhra Pradesh522502India School of Computing Science and Engineering VIT Bhopal UniversityKothrikalanSehore466114India Babasaheb Bhimrao Ambedkar University Lucknow226025India College of Commerce and Business Administration Dhofar UniversitySalalah211Oman Faculty of Computer Studies Arab Open University18211Bahrain Department of Computer Application Shri Ramswaroop Memorial UniversityLucknow225003India College of Computing and Information Technology Shaqra UniversityShaqra11961Saudi Arabia

The emergence of different computing methods such as cloud-,fog-,and edge-based Internet of Things(IoT)systems has provided the opportunity to develop intelligent systems for disease *** to other machine learning models,deep learning models have gained more attention from the research community,as they have shown better results with a large volume of data compared to shallow ***,no comprehensive survey has been conducted on integrated IoT-and computing-based systems that deploy deep learning for disease *** study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection,using the most recent papers on IoT-based disease detection systems that include computing approaches,such as cloud,edge,and *** analysis focused on an IoT deep learning architecture suitable for disease *** also recognizes the different factors that require the attention of researchers to develop better IoT disease detection *** study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.

关键词： Deep learning Internet of Things(IoT) Cloud computing Fog computing Edge computing

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Blockchain and Machine Learning for Data-Driven Insights in Consumer Behavior Analytics 13

Blockchain and Machine Learning for Data-Driven Insights in ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Rawat, Bhupender Singh Thapliyal, Nitin Pal, Sunil Kumar Mishra, Nikhil Verma, Rohan College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Axis Colleges Department of Computer Science & Application Kanpur India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305463

Comparing with neural network method to more known methods, this article assesses their simplicity and effectiveness. Our approach uses new network pruning, interpretability-based regularization, and gradient-based sensitivity analysis. Numerous dataset trials show that the suggested method is more accurate, faster, and more durable than others. Also easier to understand. It turns out that small neural networks may simplify models without compromising accuracy. This comparative study shows how neural networks can handle complex data and make good predictions. These networks will be increasingly accessible in daily life. © 2024 IEEE.

关键词： Sensitivity analysis

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Ensuring Privacy and Integrity in IoT Supply Chains through Blockchain and Homomorphic Encryption

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IEEE Internet of Things Journal 2025年第13期12卷 25547-25558页

作者： Din, Ikram Ud Almogren, Ahmad Han, Zhu Guizani, Mohsen The University of Haripur Department of Information Technology Haripur22620 Pakistan King Saud University College of Computer and Information Sciences Department of Cyber Security Department of Computer Science Riyadh11633 Saudi Arabia University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle. © 2014 IEEE.

关键词： Data integrity

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Software design and FPGA implementation of optimized medical image fusion techniques

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Multimedia Tools and Applications 2025年 1-31页

作者： Elshazly, E.A. El-Shafai, Walid El-Hoseny, Heba El-Rabaie, El-Sayed M Zahran, O. Abdelwahab, Safey A. S. El-Halawany, M.M. Fikry, R.M. Elaraby, S.M. Faragallah, Osama S. Mohamed, Wael A. Mahmoud, Korany R. Abd El-Samie, Fathi E. Cairo Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Faculty of Computer Studies Arab Open University 11681 Riyadh Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Benha University Benha Egypt Electronics and Communications Engineering Department Faculty of Engineering Helwan University Helwan Egypt Giza Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Nowadays, medical image fusion plays a crucial role in enhancing the diagnosis accuracy and the clinical decision-making process in various healthcare applications. This research work presents a comprehensive study of the design and implementation of optimized medical image fusion techniques using a combination of software and Field-Programmable Gate Array (FPGA) technologies. The proposed medical image fusion strategy is based on the utilization of Discrete Wavelet Transform (DWT) and Modified Central Force Optimization (MCFO). The implementation of the proposed technique as well as the traditional medical image fusion techniques is considered using an appropriate software design and FPGA. The presented techniques aim to overcome the limitations of traditional fusion techniques by integrating advanced image processing algorithms, optimization algorithms, and parallel computing capabilities offered by FPGA platforms. The first step in the proposed framework is to match the histogram of one of the images with that of the other, so that both images will have the same dynamic range. After that, the DWT is used to decompose the images that should be fused together. Based on some constraints, the MCFO optimization algorithm is used to evaluate the optimum level of decomposition and the optimum parameters for the best fusion quality. Finally, to improve the obtained visual quality and reinforce the information in the fusion result, an additional contrast enhancement step using adaptive histogram equalization is applied to the fusion result. Comparative study between the proposed optimized DWT-based fusion framework, the traditional Principal Component Analysis (PCA), Additive Wavelet Transform (AWT), and DWT-based fusion techniques is presented. Various metrics of fusion quality are considered, including average gradient, standard deviation, local contrast, entropy, edge strength, Peak Signal-to-Noise Ratio (PSNR), Qab/f, and processing time. The proposed optimized DWT-ba

关键词： MATLAB

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A Two-Step SAR Capacitance-to-Digital Converter 66

A Two-Step SAR Capacitance-to-Digital Converter

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Castro, Alexander Abshire, Pamela University of Maryland Alexander Castro Is with the Electrical and Computer Engineering Department College ParkMD20740 United States University of Maryland Electrical and Computer Engineering Department College ParkMD20740 United States

ISBN: (纸本)9798350302103

The design of a two-step successive approximation register (SAR) capacitance-to-digital converter (CDC) is presented. First the coarse conversion stage performs a capacitive offset compensation down to within 10fF. The fine conversion stage converts the amplified residue voltage with a resolution of 200aF. Repeated fine conversions occur until an out-of-range condition arises. These bits are communicated off chip on an I2C bus. The effective number of bits (ENOB) is compared for sinusoidally varying capacitance inputs. The circuit achieves 9 ENOB with a 28μs conversion time under nominal conditions and 8.2 ENOB with 14μ s conversion time when overclocked. This equates to an ENOB throughput of 321kbits/s and 585kbits/s, respectively. © 2023 IEEE.

关键词： Capacitance-to-digital converter (CDC) offset compensation successive approximation register (SAR)

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X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

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computer Systems Science & engineering 2022年第11期43卷 671-682页

作者： Shabana Habib Saleh Alyahya Aizaz Ahmed Muhammad Islam Sheroz Khan Ishrat Khan Muhammad Kamil Department of Information Technology College of ComputerQassim UniversityBuraidah52571Saudi Arabia Department of Electrical Engineering College of Engineering&Information TechnologyOnaizah CollegesOnaizah51911Saudi Arabia Digital Image Processing Lab Islamia CollegePeshawar25120KPKPakistan Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electrical Engineering National University of Computer and Emerging SciencesPeshawarPakistan

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic *** the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected *** COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain *** paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral *** approach includes preprocessing,feature extraction,and ***,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature ***,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is *** results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic *** is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

关键词： Image pre-processing detection classification x-ray images filter

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A Comparison of Object Detection and Phrase Grounding Models in Chest X-Ray Abnormality Localization Using Eye-Tracking Data 22

A Comparison of Object Detection and Phrase Grounding Models...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ghelichkhan, Elham Tasdizen, Tolga Scientific Computing and Imaging Institute University of Utah UT United States Kahlert School of Computing University of Utah UT United States University of Utah Department of Electrical and Computer Engineering UT United States

ISBN: (纸本)9798331520526

Chest diseases rank among the most prevalent and dangerous global health issues. Object detection and phrase grounding deep learning models interpret complex radiology data to as-sist healthcare professionals in diagnosis. Object detection locates abnormalities for classes, while phrase grounding lo-cates abnormalities for textual descriptions. This paper inves-tigates how text enhances abnormality localization in chest X-rays by comparing the performance and explainability of these two tasks. To establish an explainability benchmark, we proposed an automatic pipeline to generate image regions for report sentences using radiologists' eye-tracking data1. The better performance - mIoU = 36% vs. 20% - and ex-plainability - Containment ratio 48% vs. 26% - of the phrase grounding model infers the effectiveness of text in enhancing chest X-ray abnormality localization. © 2025 IEEE.

关键词： Deep learning

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A Novel Internet of Medical Thing Cryptosystem Based on Jigsaw Transformation and Ikeda Chaotic Map

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computer Systems Science & engineering 2023年第9期46卷 3017-3036页

作者： Sultan Almakdi Mohammed SAlshehri Yousef Asiri Mimonah Al Qathrady Anas Ibrar Jawad Ahmad Department of Computer Science Najran UniversityNajran61441Saudi Arabia Department of Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical Engineering Wah Engineering CollegeUniversity of WahWah Cantt47040Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Image encryption has attracted much interest as a robust security solution for preventing unauthorized access to critical image *** picture encryption is a crucial step in many cloud-based and healthcare *** this study,a strong cryptosystem based on a 2D chaotic map and Jigsaw transformation is presented for the encryption of medical photos in private Internet of Medical Things(IoMT)and cloud storage.A disorganized three-dimensional map is the foundation of the proposed *** dispersion of pixel values and the permutation of their places in this map are accomplished using a nonlinear encoding *** suggested cryptosystem enhances the security of the delivered medical images by performing many *** validate the efficiency of the recommended cryptosystem,various medical image kinds are used,each with its unique *** measures are used to evaluate the proposed cryptosystem,which all support its robust *** simulation results confirm the supplied cryptosystem’s ***,it provides strong robustness and suggested protection standards for cloud service applications,healthcare,and *** is seen that the proposed 3D chaotic cryptosystem obtains an average entropy of 7.9998,which is near its most excellent value of 8,and a typical NPCR value of 99.62%,which is also near its extreme value of 99.60%.Moreover,the recommended cryptosystem outperforms conventional security systems across the test assessment criteria.

关键词： Jigsaw transformation cryptosystem image encryption medical images Ikeda map chaotic system

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