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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Jagadeesan, S. Ramalingam, Venkadeshan Abinash, P. Ilayaraja, V. Masood, Jafar Ali Ibrahim Syed Tamil Priya, D. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India UTAS-Shinas branch Department of Computing and Information Sciences Oman School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331542573

Stroke and related neurological disorders are difficult to classify accurately, hence new computational approaches are needed to enhance diagnosis. In order to transform the categorization of brain strokes, this study presents Neuro-Xception, a novel framework that combines the Xception architecture with Extreme Learning Machines (ELM). The rapid learning speed and enhanced generalisation capability of ELMs are combined with the feature extraction expertise of Xception to create Neuro-Xception. Through training on large neuroimaging datasets, Neuro-Xception learns complex patterns that are essential for differentiating between different types and intensities of stroke. By means of extensive testing on various datasets, Neuro-Xception exhibits notable gains in performance compared to traditional techniques, indicating its potential to revolutionise stroke detection in medical environments. Furthermore, Neuro-Xception's scalability and flexibility are enhanced by the combination of Xception and ELMs, which makes it a viable tool for real-time clinical decision support systems and personalised medicine. This study highlights how merging Xception and ELMs can revolutionise neurological healthcare and this development could lead to more accurate and efficient diagnostic techniques for treating stroke patients. Neuro-Xception offers a potential path for increasing diagnosis accuracy and expediting timely medical intervention. It is a major step forward in the use of artificial intelligence to enhance patient outcomes and advance the profession of stroke care. © 2024 IEEE.

关键词： Personalized medicine

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Using Local Gradient Compensation in Federated Learning for Improving Privacy and Performance 14th

Using Local Gradient Compensation in Federated Learning for ...

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14th International Conference on Frontier Computing, FC 2024

作者： Chang, Jia-Wei Chen, Wei-Cheng Hung, Chih-Chieh Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9789819623945

Federated learning (FL) has emerged as a leading approach for decentralized model training, preserving data privacy by exchanging only model parameters. However, recent studies have exposed vulnerabilities, revealing that attackers can reconstruct private data from shared model parameters, compromising user privacy. To address this, various defense methods have been proposed, with gradient perturbation being a lightweight yet effective strategy. However, it often incurs a trade-off with model performance. In this paper, we introduce a lightweight defense mechanism tailored for Federated Stochastic Gradient Descent (FedSGD). Our method leverages maximum gradient values to gauge noise scale, while also preserving and compensating for previous noise iterations to mitigate performance impacts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic

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computer systems Science & engineering 2022年第2期40卷 691-702页

作者： Mohammed Baz Sabita Khatri Abdullah Baz Hosam Alhakami Alka Agrawal Raees Ahmad Khan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our current health *** paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness,about public health and healthcare *** view of this unprecedented health crisis,distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages,and with the higher *** the implementation level,blockchain integration,early detection and avoidance of an outbreak,identity protection and safety,and a secure drug supply chain can be *** the opposite end of the continuum,artificial intelligence methods are used to detect corona effects until they become too serious,avoiding costly drug *** paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic *** paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational *** paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.

关键词： Artificial intelligence information privacy machine learning coronavirus(COVID-19) SARS-CoV-2 epidemic

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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A survey on hyperspectral image restoration: from the view of low-rank tensor approximation

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Science China(information Sciences) 2023年第4期66卷 33-63页

作者： Na LIU Wei LI Yinjian WANG Ran TAO Qian DU Jocelyn CHANUSSOT School of Information and Electronics and Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of Technology Department of Electrical and Computer Engineering Mississippi State University GIPSA-Lab University of Grenoble Alpes Aerospace Information Research Institute Chinese Academy of Sciences

The ability to capture fine spectral discriminative information enables hyperspectral images(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent the true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects, and sensors' hardware limitations. These degradations include but are not limited to complex noise, heavy stripes, deadlines,cloud/shadow occlusion, blurring and spatial-resolution degradation, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation(LRTA) is such an emerging technique, having gained much attention in the HSI restoration community, with an ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation(LRMA),LRTA characterizes more complex intrinsic structures of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: denoising, fusion,destriping, inpainting, deblurring, and super-resolution. For each topic, state-of-the-art restoration methods are introduced, with quantitative and visual performance assessments. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration, and application concerning the interpretation requirements.

关键词： hyperspectral image image restoration low-rank tensor approximation multisource fusion remote sensing

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Blockchain-Based Chain of Custody Evidence Management System for Digital Forensic Investigations 5

Blockchain-Based Chain of Custody Evidence Management System...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Bandara, Pasindu Bathiya Jayarathna, Oshada Dilshan Hewage, Dinetra Tharindu Bandara, Nuwan Pradeep Pandithage, Dinithi Siriwardana, Deemantha Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The demand for digital evidence is increasing worldwide. A reliable digital evidence management system is needed to ensure that justice is served in court. The chain of custody plays a vital role in this process, but people are the weakest link in any trust chain. Blockchain technology can be used to store and analyze digital evidence data in a secure and confidential manner, with proper access control. As digital evidence comes in various forms, blockchain can be dynamically changed to accommodate new data extraction methods. This research paper proposes and develops a blockchain-based digital evidence management system using Hyperledger Fabric. © 2023 IEEE.

关键词： Access control

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Secure Health Management and Prediction System for Chronic Diseases 7

Secure Health Management and Prediction System for Chronic D...

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7th IEEE Conference on information and Communication technology, CICT 2023

作者： Liyanarachchi, H.L.C.L. Kumara, K.K.K.P. Chaminda, P.I. Piyumal, W.R.A. Yapa, Kanishka Lokuliyana, Shashika Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Colombo Sri Lanka

ISBN: (纸本)9798350305173

The Secure Health Management and Prediction System for Chronic illnesses has transformed healthcare by predicting and controlling chronic illnesses using user-provided data. It lowers healthcare expenses by combining machine learning, data analytics, and cloud computing. With strong safeguards such as encryption and authentication, the system protects data privacy and security. Its HIPAA compliance and focus on patient privacy make it helpful in rural Sri Lanka, where experts are few. Using machine learning, data analytics, and cloud computing, the research study proposes a secure health management and prediction system for chronic diseases. Based on medical information, the algorithm forecasts chronic illnesses such as diabetes and cardiovascular disorders. For diabetes and cardiovascular disease prediction, it employs techniques such as Decision Tree, Linear Regression, Random Forest, Support V ector Machine, Logistic Regression, and Naive Bayes. Based on patient feedback, the system also assesses insurance costs and suggests providers. © 2023 IEEE.

关键词： Data Analytics

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QoS Evaluation of SDN Controllers: POX and RYU 5

QoS Evaluation of SDN Controllers: POX and RYU

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Lokuliyana, Shashika Wijesiri, Pipuni Jayakody, Anuradha Abeyagunawardhena, Pradeep Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Software Defined Network (SDN) proposes a network architecture approach which can be used to intelligently and centrally control the network environment using software applications by decoupling control plane from data plane. The control plane provides dynamic control over network elements, optimizes resource allocation, and increases adaptability. This research present a comprehensive and detailed evaluation of QoS mechanisms in SDN environment, focusing on two widely adapted SDN centralized controllers: POX and RYU, Python-based controllers. The QoS evaluation is being carried out with the throughput, Delay and Jitter variation for four different network topologies. Across all topologies, the RYU controller generally exhibits higher throughput than the POX controller with an average throughput is approximately 13.40 % higher than POX's throughput. The test analysis shows that on average, POX's delay is about 225.725% lower compared to RYU's delay. Similar to delay, the POX controller shows lower jitter values compared to RYU with an average of 38.33% lower than RYU's jitter. © 2023 IEEE.

关键词： Controllers

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