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2022 International Conference on Communication, computing and Internet of Things, IC3IoT 2022

作者： Roobini, M.S. Lakshmi, M. Sathyabama Institute of Science and Technology Chennai600127 India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665479950

Alzheimer and cognitive impairment are very dangerous disorders in the world. While cognitive decline is connected with Alzheimer. More researches have been carried out in recent days. Alzheimer is a kind of disease which is very much less in exposure. This paper gives a detailed explanation about the cognitive decline and its association with Alzheimer Disease and also some approaches. A conceptual framework for screening and therapy has been developed, as well as potential research requirements. This paper computes the model which gives better performance, worse, or nearly equal to another within the Bayesian technique. GridSearch revealed that CV 'rbf1' have 7.8% possibility to be worst condition than 'linear', and have 2.8 percent to be worse than '3 poly1'. Both have a percent chance of nearly comparable, but 'rbf1' and '3 poly1' only have a 12% chance. © 2022 IEEE.

关键词： Bayesian networks

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Boosting Robustness in Automatic Modulation Recognition for Wireless Communications

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1635-1648页

作者： Zhao, Yuhang Wang, Yajie Zhang, Chuan Li, Chunhai Xiong, Zehui Zhu, Liehuang Niyato, Dusit Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin541004 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Nanyang Technological University College of Computing and Data Science 639798 Singapore

In the radio frequency field, deep neural networks have been widely used for automatic modulation recognition tasks due to their superior accuracy. However, it has been shown that these models are susceptible to adversarial examples, which are the kinds of carefully crafted perturbations that can lead to model misclassification and raise security issues in applications. To solve this problem, we propose an Ultra-Fusion Adversarial Training method, which combines adversarial training and ensemble learning to enable the model robustness to withstand different attack strengths. We explore the number and distribution of ensembled attacks and introduce a Fermi-function-like distribution to optimally balance the performance of different attack strengths. Additionally, we investigate the effect of the signal-to-noise ratio (SNR) interval on the model accuracy and robustness, suggesting the effective SNR interval for training. Considering the demand for practical application scenarios of modulation recognition, we propose a comprehensive robustness metric based on weighted integral to evaluate the robustness of the trained models. Numerical experiments demonstrate that our method improves the model's robustness by 31.89% against white-box attacks, and achieves up to an 80.54% improvement in black-box scenarios. These results show that our method has the ability to resiliently resist potential attacks of various strengths and can be applied to spectrum application scenarios with high-security requirements. © 2015 IEEE.

关键词： Signal modulation

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Machine Learning-Based Timeseries Analysis for Cryptocurrency Price Prediction: A Systematic Review and Research 1

Machine Learning-Based Timeseries Analysis for Cryptocurrenc...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Kumar.a, Siva Pv, Gopirajan Jackson, Beulah SRM Institute of Science and Technology Kattankulathur Campus School of Computing Department of Data Science and Business Systems Chennai603203 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of ECE Chennai India

ISBN: (纸本)9798350336009

A virtual currency known as cryptocurrencies holds all business online. It's virtual money that wouldn't materialize like complicated conventional paper currency. Thus, this study emphasizes a distinction between distributed paper currency and cryptocurrencies, where these individuals may access information without outside interference. Because of its considerable market swings, such cryptocurrencies have an influence upon commerce as well as foreign diplomacy. Virtual currencies which are available in the market, such as Bitcoin (BTC), Ethereum (ETH), Terra (LUNA), Solana (SOL), Cardano (ADA), Tether (USDT), Binance coin (BNB), USD coin, XRP coin, Avalanche coin (AVAX) and Lite coin (LTC), etc. This study focussed on a detailed analysis of the literature about Machine Learning (ML) methods used for predictions. This proposed work also focused on implementing an efficient Machine Learning (ML)-based time series model for predicting BTC cryptocurrency prices. Long Short-Term Memory (LSTM) forecasting theory was established to accommodate the fluctuation of bitcoin prices and achieve great precision. The effectiveness of the LSTM in predicting the price of a cryptocurrency is demonstrated by this suggested study's comparison between it and comparable time-series models. © 2023 IEEE.

关键词： Long short-term memory

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An Improved Detection of Skin Melanoma Using Hybrid CNN-MobileNet V2 Approach with Multi-Scale Attention 2

An Improved Detection of Skin Melanoma Using Hybrid CNN-Mobi...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Maniraj, S.P. Thamizhamuthu, R. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai603203 India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331505745

Identifying melanoma in dermoscopic images is an immense challenge due to the varied appearances of the lesions. This research introduces a hybrid CNN-LSTM model augmented with a multi-scale attention mechanism to tackle this complexity, utilising CNNs for spatial feature extraction and LSTMs for temporal pattern recognition. Our model surpasses traditional CNN and MobileNet V2-LSTM models, with an accuracy of 96.98%, a recall of 96.65%, a specificity of 97.56%, and a JSI of 95.78%. These measurements demonstrate enhanced sensitivity and reliability compared to baseline models, signifying strong diagnostic capabilities. This study improves melanoma detection, aiding in more precise diagnoses for clinical environments and remote telemedicine applications. Future initiatives involve enhancing the model for more comprehensive skin lesion categorisation. © 2025 IEEE.

关键词： Feature Selection

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MedCare4Home: A Mobile Medical System for Families

MedCare4Home: A Mobile Medical System for Families

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2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023

作者： Wang, Mengting Le, Yen Yu, Chen-Hsiang School of Computing and Data Science Wentworth Institute of Technology BostonMA United States Multidisciplinary Graduate Engineering College of Engineering Northeastern University BostonMA United States

ISBN: (纸本)9798350302301

Managing healthcare data is essential for everyone, but it can be challenging and time-consuming. Although several Web and mobile applications existed for personal medical record management, there were few home-based, family-oriented solutions. In this research, we proposed to design an easy-to-use solution for a family. We designed and implemented an innovative Home Medical Care System, MedCare4Home, by deploying a MERN stack Web application to a minicomputer, Raspberry Pi 4, and enabling a local network to connect different devices at home. The users can access the Web application using either a desktop, tablet, mobile phone, or simply a 7-inch LCD touchscreen. MedCare4Home not only allows users to keep track of appointments and organize healthcare documents, but it can also help set up medication reminders and provide a self-report interface for symptom collection for family members easily and securely in one place. The final developed system demonstrated that we achieved the original goal and beyond. We expect to extend our current hardware design and software development to support physiological data collection with external sensors in the future. © 2023 IEEE.

关键词： Internet of things

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Anonymous Crime Reporting using Blockchain and Smart Contract

Anonymous Crime Reporting using Blockchain and Smart Contrac...

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Pervasive computing and Social Networking (ICPCSN), International Conference on

作者： Prince Kumar Chaurasia Darshnilsinh Rana Rajaram V S. Srividhya Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

The traditional crime reporting systems face challenges such as inefficiencies, security vulnerabilities, and witness reluctance. Despite recent advancements like mobile applications and web platforms, challenges persist in ensuring data security, transparency, and user anonymity. These include maintaining confidentiality, data integrity, and preventing unauthorized access. To address these issues, this paper proposes leveraging blockchain technology and smart contracts. The primary objective is to enhance the security, efficiency, and anonymity of crime reporting processes. By utilizing the decentralized and immutable nature of blockchain, the system aims to ensure data integrity, transparency, and decentralization. Smart contracts will automate functions such as complaint validation and evidence verification, streamlining the reporting process. By addressing challenges in maintaining confidentiality, data integrity, and access control, the proposed solution seeks to revolutionize crime reporting, promoting trust, transparency, and accountability.

关键词： Pervasive computing Technological innovation Law enforcement Social networking (online) data integrity Smart contracts Robustness

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Peak Evaluation for Liquid Chromatography-Mass Spectrometry data Using Neural Network on Statistical Quality Metrics 18

Peak Evaluation for Liquid Chromatography-Mass Spectrometry ...

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18th IEEE International Conference on Anti-Counterfeiting, Security, and Identification, ASID 2024

作者： Dong, Liheng Dai, Juehong Guan, Teo Bee Guo, Lei Wu, Caisheng Xu, Jingjing School of Computing and Data Science Xiamen University Malaysia Sepang43600 Malaysia Xiamen University Department of Electronic Science Xiamen361005 China Interdisciplinary Institute for Medical Engineering Fuzhou University Fuzhou350108 China School of Pharmaceutical Sciences Xiamen University Xiamen361005 China

ISBN: (纸本)9798331505639

Peak picking is a crucial step in the data analysis workflow of Liquid Chromatography-Mass Spectrometry (LCMS) in untargeted metabolomics. The process is challenging due to interference from background signals such as noise and matrix effects. To address this challenge, we first collected 5,447 ion peaks and manually scored them after alignment. Next, we calculated 12 quality metrics for each ion peak. Finally, we developed a Multilayer Perceptron (MLP) model to perform regression analysis, as well as binary and ternary classification, making it adaptable to various application scenarios. The results demonstrate that the MLP-based peak evaluation method is effective in accurately predicting peak quality. Researchers can quickly initiate their work by setting just one or two score thresholds based on their specific needs. This approach retains the flexibility to handle different datasets without the need to adjust thresholds for each peak quality metric, representing a step towards more flexible and efficient peak picking. © 2024 IEEE.

关键词： Regression analysis

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Prism: Privacy-Preserving Inter-Site MRI Harmonization via Disentangled Representation Learning 22

Prism: Privacy-Preserving Inter-Site MRI Harmonization via D...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Galada, Sarang Halder, Tanurima Deo, Kunal Krish, Ram P. Jadhav, Kshitij Indian Institute of Technology Trust Lab Bombay India School of Computing and Data Science Sai University Chennai India Koita Centre for Digital Health Indian Institute of Technology Bombay India

ISBN: (纸本)9798331520526

Multi-site MRI studies often suffer from site-specific variations arising from differences in methodology, hardware, and acquisition protocols, thereby compromising accuracy and reliability in clinical AI/ML tasks. We present PRISM (Privacy-preserving Inter-Site MRI Harmonization), a novel Deep Learning framework for harmonizing structural brain MRI across multiple sites while preserving data privacy. PRISM employs a dual-branch autoencoder with contrastive learning and variational inference to disentangle anatomical features from style and site-specific variations, enabling unpaired image translation without traveling subjects or multiple MRI modalities. Our modular design allows harmonization to any target site and seamless integration of new sites without the need for retraining or fine-tuning. Using multi-site structural MRI data, we demonstrate PRISM's effectiveness in downstream tasks such as brain tissue segmentation and validate its harmonization performance through multiple experiments. Our framework addresses key challenges in medical AI/ML, including data privacy, distribution shifts, model generalizability and interpretability. Code is available at https://***/saranggalada/PRISM. © 2025 IEEE.

关键词： Federated learning

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Enhancing data Accessibility in COVID Research - A Synthetic data Generator for the RADx data Hub 1

Enhancing Data Accessibility in COVID Research - A Synthetic...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Ahmad, Asiyah Roupe, Griffin Samsa, Leigh Ann Krishnamurthy, Ashok Hubal, Robert Computer Science University of North Carolina at Chapel Hill Chapel Hill United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel Hill United States Renaissance Computing Institute Chapel Hill United States

ISBN: (纸本)9798350371987

The NIH RADx data Hub serves as an access platform for COVID-19 research studies funded by the U.S. National institutes of Health (NIH). The RADx data Hub contains a range of patient-related information, including diagnostic test data, demographic details, and clinical records. Controlled access to data safeguards patient privacy, but also presents challenges for users unfamiliar with the platform, developers seeking to test processes, and curators aiming to showcase its features. Meanwhile, using actual patient data raises confidentiality concerns and timing challenges, such as needing to get requisite approval. To address these limitations, synthetic datasets support generation of simulated patients, enabling effective learning and testing of the system with data closely resembling real study data. This paper describes a dynamic synthetic data generator for COVID-19 research data based on RADx codebooks and external sources. The generator empowers RADx data Hub users to model diverse patient cohorts, generate hypotheses, and attain a better grasp of existing COVID-19 data that they may access through the RADx data Hub. ©2024 IEEE.

关键词： COVID-19

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on Big data computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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