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A Survey on Privacy-Preserving data Publishing Methods and Models in Relational Electronic Health Records 1

3rd International Conference on Sustainable Advanced computing, ICSAC 2021

作者： Jayapradha, J. Prakash, M. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu603203 India

ISBN: (数字)9789811690129

ISBN: (纸本)9789811690112

The collections of various digital information from corporations, public sectors, private sectors, and individuals have formed remarkable opportunities for analysis and decision-making. The electronic health record is highly constructive for various study purposes;however, it should be carried in a way that preserves the identity and privacy of the patients. Due to the digital transformation, the flow of data increases that lead to the need for big data. Protecting privacy cannot be straightforward as there are several privacy threats such as membership disclosure, identity disclosure, and attribute disclosure. The privacy methods such as generalization, suppression, pseudonymization, bucketization, slicing are illustrated with examples. The study focused on various privacy models such as k-anonymity, l-diversity, t-closeness, differential privacy, and cryptographic technique. The models are discussed with the sample dataset and workflow. The paper has discussed the security and privacy concerns on data publishing, the need for big data scope, an overview of threats, samples, privacy methods, models, and challenges in privacy-preserving data publishing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Records management

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Measuring COVID-19 Opinion in the Online Debate using an Unsupervised Model 1

Measuring COVID-19 Opinion in the Online Debate using an Uns...

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1st IEEE International Conference on Automation, computing and Renewable systems, ICACRS 2022

作者： Priyadarsini, K. Jeba Sonia, J. Vijayalakshmi, K. Anbazhagu, U.V. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computational Intelligence School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9781665460842

A large quantity of potentially threatening COVID-19 false information is available online. In this article, machine learning approach is adopted to assess COVID-19 materials in online health advice adversaries, particularly those who oppose immunizations like (anti-vaccine). Pro-vaccination (pro-vaccine) group is emerging a more attentive conversation regarding COVID-19 above its corresponding portion, the anti-vaccine group. However, the anti-vaccine group presents a wide series of flavors of COVID-19-relatedtopics, andas a result, can demandto a wider cross-section of entities searching for COVID-19 assistance online, such as those who may be wary of receiving a COVID-19 vaccine as a condition of employment or those looking for alternative medications. Later, the anti-vaccine group appears to be better positioned than the pro-vaccine side to obtain complete support moving forward. This is important because if the COVID-19 vaccine is not widely used, the world will not be able to produce herd immunity, parting countries exposed to a COVID-19 comeback in the future. An automatic supervision machine learning model is provided that clarifies these results andcan be used to evaluate the efficacy of intervention efforts. Our method is adaptable and capable of addressing the crucial problem that social media platforms face when analyzing the vast amounts of online health misinformation. © 2022 IEEE

关键词： COVID-19

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Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

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Computers, Materials & Continua 2023年第3期74卷 5431-5446页

作者： Muhammad Umair Zafar Saeed Faisal Saeed Hiba Ishtiaq Muhammad Zubair Hala Abdel Hameed Faculty of Information Technology University of Central PunjabLahore54590Pakistan DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirmingham B47XGUK Faculty of Computer and Information Systems Fayoum University63514Egypt Khaybar Applied College Taibah UniversitySaudi Arabia

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud *** a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG *** of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are ***’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent *** research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load *** our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

关键词： Big data data analysis feature engineering genetic algorithm machine learning

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Distributed and Localized Model Predictive Control - Part II: Theoretical Guarantees

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IEEE Transactions on Control of Network systems 2023年第3期10卷 1113-1123页

作者： Alonso, Carmen Amo Li, Jing Shuang Matni, Nikolai Anderson, James California Institute of Technology Computing and Mathematical Sciences Department PasadenaCA91106 United States University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States Columbia University Department of Electrical Engineering and the Data Science Institute New YorkNY10027 United States

Engineered cyberphysical systems are growing increasingly large and complex. These systems require scalable controllers that robustly satisfy state and input constraints in the presence of additive noise: such controllers should also be accompanied by theoretical guarantees on feasibility and stability. In our companion paper, we introduced distributed and localized model predictive control (DLMPC) for large-scale linear systems;DLMPC is a scalable closed-loop MPC scheme in which subsystems need only exchange local information in order to synthesize and implement local controllers. In this article, we provide recursive feasibility and asymptotic stability guarantees for DLMPC. We leverage the system level synthesis framework to express the maximal positive robust invariant set for the closed-loop system and its corresponding Lyapunov function in terms of the closed-loop system responses. We use the invariant set as the terminal set for DLMPC, and show that this guarantees feasibility with minimal conservatism. We use the Lyapunov function as the terminal cost, and show that this guarantees stability. We provide fully distributed and localized algorithms to compute the terminal set offline, and also provide necessary additions to the online DLMPC algorithm to accommodate coupled terminal constraint and cost. In all algorithms, only local information exchange is necessary, and computational complexity is independent of the global system size: we demonstrate this analytically and experimentally. This is the first distributed MPC approach that provides minimally conservative yet fully distributed guarantees for recursive feasibility and asymptotic stability in the nominal and robust settings. © 2014 IEEE.

关键词： Lyapunov methods

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AstroFusion: A GAN-Augmented Approach for Exoplanet Detection 4

AstroFusion: A GAN-Augmented Approach for Exoplanet Detectio...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Suresh, Aakash Lakshmi, C. Gopirajan, P.V. Dhanasekaran, K. School of Computing Srm Institute of Science and Technology Chengalpattu Chennai TN Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Chengalpattu Chennai TN Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Chennai TN Kattankulathur603203 India

ISBN: (纸本)9798350387063

Humanity's exploration of the cosmos spans billions of years, prompting contemplation of our position in the universe. This study advocates for proactive communication with potential intelligent species, driven by uncertainty about their existence. The focus is on exploring Generative Adversarial Networks (GANs) to enhance exoplanet detection through data augmentation. Utilizing the extensive Kepler dataset, the study evaluates the accuracy of conventional machine learning techniques: Logistic Regression, Decision Trees, and Random Forests. (RF)-trained on original data. Subsequently, it assesses the effectiveness of models trained solely on GAN-generated synthetic data and compares their performance with that of models trained on original data. Lastly, it merges original and synthetic data to create a composite dataset, evaluating the ML model's accuracy. The results indicate that models trained solely on synthetic data achieve comparable accuracy to those trained on original data. Furthermore, the combined dataset yields improved accuracy-87%, 95%, and 96% in Logistic Regression, Decision Tree, and RF, respectively-highlighting the potential of GAN-based data augmentation in enhancing exoplanet detection. This study advances space exploration by integrating technology with astronomical research, facilitating more precise and reliable exoplanet detection methods. Leveraging GANs for data augmentation promises novel insights and discoveries about the universe. © 2024 IEEE.

关键词： Generative adversarial networks

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Grade It: A Quantitative Essay Grading System 6th

Grade It: A Quantitative Essay Grading System

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6th International Conference on Innovative computing and Communication, ICICC 2023

作者： Vanga, Roopchand Reddy Bharath, M.S. Sindhu, C. Vadivu, G. Hsu, Hsiu Chun Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur603203 India Department of Information Management National Chung Cheng University Minxiong Taiwan

ISBN: (纸本)9789819930098

Automated writing evaluation system that employs automated essay scoring technologies generates a rating to the writings which helps students with self-assessment. It provides an efficient and easy way for grading essays, which usually takes abundance of time to be evaluated by human graders;this can be greatly useful for educational institutions like schools and colleges. It provides ratings for essays based on the grammatical errors and topic relevancy by using specific tools implemented in the auto grading system. The generated result will help students grading essays which helps students in self-assessment, this will be useful for the student to understand the mistakes that he makes which will be pinpointed by the system in matter of time, not only that the system also displays the strengths of the user, but also the system will improve the accuracy compared to already existing automated grading system. The system uses scores from multiple features (handcraft features, coherence score, prompt-relevant score, and semantic score). These key features illustrated above will provide enough information on the essay for the student. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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science China(Information sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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Blockchain-Enabled Quality Assurance and Traceability in the Indian Confectionery Market

Blockchain-Enabled Quality Assurance and Traceability in the...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Subashini, B. Shanthi, P. Venkatesh, K. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamilnadu kattankulathur India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai119 India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu kattankulathur India

ISBN: (纸本)9798331543617

India, whose population growth is its greatest asset cannot afford to let its people eat confectionery that is either tainted or somehow polluted, since this would lead to extensive malnutrition. Traditional food supply chains are characterized by centralization and the problems associated with it such as having a single point of failure and producing inconsistent products while compromising the quality, along with data loss. With the need for daily reports in all regions of India regarding food fraud, contamination, and adulteration, a clearly advanced decentralized model is sought after for the creation of a supply chain. This paper introduces the supply chain network for the Indian confectionery market based on blockchain to better address quality claims, contamination, and transparency. This enables real-time traceability within the ecosystem, by using IoT strategies along with the Ethereum blockchain leading to better product authentication and resulting in higher consumer confidence. Blockchain creates a secure data tracking function, while smart contracts can be used to define the process - such as IoT monitoring of temperature. The united method enables verified data to be available to the consumer, energy efficiency, and trust across the supply chain. © 2024 IEEE.

关键词： Smart contract

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2024年 1-27页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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