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9th IEEE International Conference on Smart Structures and systems, ICSSS 2023

作者： Birundadevi, M. Premalatha, G. Nalini, M. Iyyanar, Chelladurai Arul, Vettrivel Sri Sairam Siddha Medical College and Research Center Department of Maruthuvam Tamilnadu Chennai India S R M Institute of Science and Technology Department of Data Science and Business Systems Tamilnadu Chennai India Panimalar Engineering College Department of Ece Tamilnadu Chennai India Siddha Central Research Institute Literary Research and Documentation Department Cum Library Tamilnadu Chennai India Department of Community Medicine Tamilnadu Salem India

ISBN: (纸本)9798350384208

To gain a more comprehensive understanding of the complex correlation between millets and the management of pediatric obesity, this research employs machine learning techniques. We meticulously assess the impact of a nutritional intervention utilizing millet on the body mass index (BMI), dietary patterns, and physical activity levels among children. Employing feature engineering, data preprocessing, and model training, this study enhances its outcomes via regression and classification analyses. The findings should shed light on concealed patterns, ascertain crucial predictors, and expand our comprehension of the potential of millets in mitigating childhood obesity. This methodology presents a novel outlook on the complex issues surrounding childhood obesity and the immense potential of millets in fostering a healthy lifestyle through the integration of machine learning techniques and nutritional research. © 2023 IEEE.

关键词： Nutrition

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Analyzing and Mitigating Common Vulnerabilities in Smart Contracts in Web3 Ecosystem: A Comprehensive Study

Analyzing and Mitigating Common Vulnerabilities in Smart Con...

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data, Decision and systems (ICDDS), IEEE International Conference on

作者： Rajendra Hegadi Syam Sankar Karthik Akella Koushik Reddy Pavan Kumar C Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka India Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka India

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology.

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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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Diabetic Retinopathy Imaging: Network Exudate Detection to Classify Abnormalities

Diabetic Retinopathy Imaging: Network Exudate Detection to C...

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Intelligent Computing and Control for engineering and Business systems (ICCEBS)

作者： A. Chinnasamy P. Selvakumari Department of Data Science and Business Systems school of computing SRMIST kattankulathur campus Chennai Department of Computer Science and Engineering Chennai Institute of Technology Chennai Anna University

The DR becomes increasingly common, there is a need to automatically extract and classify disease severity. Due to diabetes problems, about 2% of people with this disease become completely blind, and DR complications make him 10% visually impaired if he has diabetes for 15 years. Furthermore, it plays an important role in the progression of blindness in middle-aged and older adults. If the disease is not recognized as soon as possible, patients can experience a severe stage of irreversible blindness. The shortage of ophthalmologists is a serious problem for the growing number of diabetic patients. Hard exudates must be found to screen and assist in disease monitoring and diagnosis. Therefore, Lloyd's clustering technique was used in this work.

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Spatial flood susceptibility mapping using an explainable artificial intelligence(XAI)model

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Geoscience Frontiers 2023年第6期14卷 8-19页

作者： Biswajeet Pradhan Saro Lee Abhirup Dikshit Hyesu Kim Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydneyNSW 2007Australia Earth Observation Center Institute of Climate ChangeUniversiti Kebangsaan Malaysia43600 UKMBangiSelangorMalaysia Geoscience Data Center Korea Institute of Geoscience and Mineral Resources(KIGAM)124 Gwahang-noYuseong-guDaejeon 34132South Korea Department of Resources Engineering Korea University of Science and Technology217 Gajeong-roYuseong-guDaejeon 34113South Korea Department of Astronomy Space Science and GeologyChungnam National University99 Daehak-roYuseong-guDaejeon 34134South Korea

Floods are natural hazards that lead to devastating financial losses and large displacements of *** susceptibility maps can improve mitigation measures according to the specific conditions of a study *** design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning *** these models have achieved better accuracy than traditional models,they are not widely used by stakeholders due to their black-box *** this study,we propose the application of an explainable artificial intelligence(XAI)model that incorporates the Shapley additive explanation(SHAP)model to interpret the outcomes of convolutional neural network(CNN)deep learning models,and analyze the impact of variables on flood susceptibility *** study was conducted in Jinju Province,South Korea,which has a long history of flood *** performance was evaluated using the area under the receiver operating characteristic curve(AUROC),which showed a prediction accuracy of 88.4%.SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study *** light of these findings,we recommend the use of XAIbased models in future flood susceptibility mapping studies to improve interpretations of model outcomes,and build trust among stakeholders during the flood-related decision-making process.

关键词： Flood susceptibility Explainable AI Deep learning South Korea

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Retinal Image Analysis for Early Deduction of Diabetic Retinopathy

Retinal Image Analysis for Early Deduction of Diabetic Retin...

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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： A. Chinnasamy P. Selvakumari Department of Data Science and Business Systems School of Computing SRMIST Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Anna University Chennai India

The prevalence of DR is steadily rising, which necessitates the automatic disease severity extraction and classification. About 2% of those with this illness are entirely blind because of the diabetic mellitus problem and 10% get vision impairment after 15 years of diabetes as a result of the DR complication. It is also a significant contributor to blindness in both middle aged and older age groups. The patient may develop to severe stages of irreversible blindness if the condition is not detected early. The growing number of diabetic patients face a major issue due to a lack of ophthalmologists. It is suggested that an automated DR screening system be created to aid the ophthalmologist in making decisions. One of the primary symptoms of the DR is hard exudates. The detection of hard exudates is crucial for screening purposes and aids in disease monitoring and diagnosis. Thus, utilising Lloyd’s clustering technique, this work offered a unique techniques to segment the exudates and irregularities in DR were found.

关键词： Support vector machines Image segmentation Visual impairment Blindness Blood vessels Retina Time measurement

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An Innovation of Algebric Mathamatical Based Statistical Analysis Model for Complex Number Theory

An Innovation of Algebric Mathamatical Based Statistical Ana...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Pattanaik, Balachandra Kumari, Sukhavasi Santha Kumar C, Kiran Pattnaik, Manjula Ali, Syed Ibad Kumarasamy, M. Wollega University Department of Ece Nekemte Ethiopia Koneru Lakshmaiah Education Foundation Commerce Department AP Vaddeswaram India Codecraft Technologies Practice Lead Data Science Bangalore Karnataka India Princess Nourah Bint Abdulrahman University Department of Accounting Riyadh Saudi Arabia Gh Raisoni Institute of Engineering & Technology Department of Etc MH Nagpur India

ISBN: (纸本)9798350323887

In general, statistical analysis is an advanced method of evaluating analytical components based on mathematical data. The important parts in statistical analysis are the collecting statistics, organizing and analyzing data. Every part of this process is tested. Statistical techniques are also used in other areas of knowledge. Although it is seen as a field based on statistical predictive functions, its structure and calculation methods are based on probability. It also adds advanced mechanisms to regulate its randomness as its improved methods predict the probability of occurrence. In this paper an algebric mathematical based statistical analysis model was proposed. It is important to sample statistics because a typical sample of people should be selected. Mathematical statistics involves a basic calculation of events. These estimates are used to determine the mean, variance, and skewness of a probability distribution. Statistical data processing is impossible without their sorting, generalization and analysis. Any results should be put in such a format that one can get the most useful information first. If the data is large, then they should be grouped or spread. © 2023 IEEE.

关键词： Statistics

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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5th International Conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities

arXiv

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arXiv 2023年

作者： Lin, Zheng Qu, Guanqiao Chen, Qiyuan Chen, Xianhao Chen, Zhe Huang, Kaibin The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong HKU Musketeers Foundation Institute of Data Science University of Hong Kong Pok Fu Lam Hong Kong The School of Electronics and Information Engineering Harbin Institute of Technology Guangdong Shenzhen China The School of Computer Science Fudan University Shanghai China

Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, the status quo cloud-based deployment faces some critical challenges: 1) long response time;2) high bandwidth costs;and 3) the violation of data privacy. 6G mobile edge computing (MEC) systems may resolve these pressing issues. In this article, we explore the potential of deploying LLMs at the 6G edge. We start by introducing killer applications powered by multimodal LLMs, including robotics and healthcare, to highlight the need for deploying LLMs in the vicinity of end users. Then, we identify the critical challenges for LLM deployment at the edge and envision the 6G MEC architecture for LLMs. Furthermore, we delve into two design aspects, i.e., edge training and edge inference for LLMs. In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs. This article serves as a position paper for thoroughly identifying the motivation, challenges, and pathway for enabling LLMs at the 6G edge. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing

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Masking of Colposcopy Images- A New Approach

Masking of Colposcopy Images- A New Approach

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Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： Priyadarshini Chatterjee Surajit Das Shadab Siddiqui Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak Telengana India Department of Artificial Intelligence and Data Science B V Raju Institute of Technology Narsapur Medak Telengana India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Masking, in the context of image processing and computer vision, refers to the process of isolating specific regions or objects of interest within an image while suppressing or removing irrelevant information. It involves the creation of a mask, which is essentially a binary image that defines which pixels in the original image belong to the region of interest and which do not. The mask typically consists of black and white pixels, where white pixels represent the areas to be preserved or highlighted, and black pixels represent the areas to be ignored or masked out. This paper presents a novel approach aimed at enhancing the accuracy of masking algorithms for colposcopy images, a crucial component in the diagnosis of cervical cancer among women. While previous research has demonstrated promising outcomes in this domain, our study introduces a novel masking algorithm tailored specifically to improve segmentation accuracy. The colposcopy dataset utilized in this research is sourced from the International Agency for Research on Cancer in France.

关键词： Training Technological innovation Accuracy Convolution Object detection Computer architecture Streaming media

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