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5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Vijayalakshmi, V. Selvakumar, A. Panimalar, K. Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Simats School of Engineering Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665474672

Medical expenses are increasing day by day due to unexpected epidemic diseases. People have awareness about the medical insurance and claiming processes which is helping in critical situations. Predicting the medical insurance cost is very important work in health care sectors. Many researcheis applied different machine learning algoiithms to predict the insurance premium in Kaggle data set with seven attributes such as age, sex, bmi, children, smoker, region and charges. But these features only not suitable for prediction. In this paper, the dataset with 24 features including all relevant attributes needed for prediction of insurance cost was used. The implementation done using regression algorithms such as Linear Regression, Decision Tree Regression, Lasso Regression, Ridge Regression, Random Forest Regression, ElasticNet Regression, Support Vector Regression, K Nearest Neighbor Regression and Neural Network Regression in R Programming. There were seven metiices applied to measure the performance of the model namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), RSquared (R2), Adjusted R-Squared (Adj. R2), and Explained Variance Score (EVS). Random Forest Regression outperformed with 0.9533 as RS quared value. This prediction system will reduce the manual work and give the prediction accurately in medical field. © 2023 IEEE.

关键词： Forecasting

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Analyzing and Mitigating Common Vulnerabilities in Smart Contracts in Web3 Ecosystem: A Comprehensive Study 2

Analyzing and Mitigating Common Vulnerabilities in Smart Con...

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2nd IEEE International Conference on data, Decision and systems, ICDDS 2023

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

ISBN: (纸本)9798350314168

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. © 2023 IEEE.

关键词： Smart contract

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Grey Wolf Algorithm with Rat Swarm Optimizer for Constrained Optimization and engineering Design Problems

Grey Wolf Algorithm with Rat Swarm Optimizer for Constrained...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Kathiroli, Panimalar Kanmani, S. Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu District603203 India Department of Information Technology Puducherry Technological University Puducherry India

ISBN: (纸本)9789819746491

This paper proposes a new hybrid optimization algorithm that combines the grey wolf optimization (GWO) algorithm with rat swarm optimizer (RSO) to improve the exploration and exploitation capabilities of the search space. The proposed algorithm, named GRO, is evaluated using benchmark functions and engineering design problems. The GRO algorithm leverages the social hierarchy and hunting strategies of wolf packs and incorporates the swarm intelligence of rats and the diversity-enhancing effects of GWO to guide the search process. Experimental results demonstrate that the GRO algorithm outperforms existing state-of-the-art optimization algorithms in terms of convergence speed, accuracy, and robustness for most benchmark functions. The hybrid algorithm’s ability to balance exploration and exploitation is shown to be superior to other metaheuristic algorithms. The algorithm is further validated by solving two well-known engineering design problems, where it demonstrates its efficacy in solving practical constrained problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Constrained optimization

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Boruta Feature Selection for Prediction of Coronary Artery Disease

Boruta Feature Selection for Prediction of Coronary Artery D...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Yuvalatha, S. Nithyapriya, S. Kuzhali, R. Tamizh Savitha, S. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science C K college of Engineering and Technology Chennai India Department of Computer Science nad Engineering PSR Engineering College Sivakasi India

ISBN: (纸本)9798350305708

Day by day cardio vascular disease death cases increasing. It mainly affects the human heart and blood vessels and it is difficult to diagnosis it. In this paper various machine learning algorithm is used to predict the cardio vascular disease. The data set of 14 attributes is used for prediction. The irrelevant features are handled using Boruta algorithm of 100 iterations. The proposed work uses Adaboosting with different hyper parameter and the multiple decision trees are built for the wrongly classified feature. At finally the Adaboosting algorithm achieve 93.74% accuracy in predicting the disease. © 2023 IEEE.

关键词： Adaboosting Boruta Algorithm Cardio vascular Hyper parameter

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Retinal Image Analysis for Early Deduction of Diabetic Retinopathy 9

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

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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

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

ISBN: (纸本)9798350397376

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. © 2023 IEEE.

关键词： Mean square error

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Masking of Colposcopy Images- A New Approach

Masking of Colposcopy Images- A New Approach

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

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

ISBN: (纸本)9798350371567

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. © 2024 IEEE.

关键词： Diseases

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Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

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International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Rajakani, M. Kavitha, R.J. Rajesh, S. Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Electronics and Communication Engineering University College of Engineering Panruti Panruti 607106 India Department of Information Technology Mepco Schlenk Engineering College Sivakasi India

Multispectral image classification has received significant attention among research communities and academicians. Recently, several artificial intelligence (AI) models can be used for the extraction of prominent features. Besides, deep learning (DL) methods become more familiar and gained interest in the remote sensing community for the classification of multispectral and hyperspectral images. With this motivation, this paper presents an optimized feature learning with deep transfer learning enabled multispectral image classification (OFLDTL-MSIC) technique. The proposed OFLDTL-MSIC technique aims to categorize the different class labels of the multispectral images. Besides, the OFLDTL-MSIC technique applies multi-level discrete wavelet transform (DWT) based image decomposition technique. Moreover, the EfficientNet technique is applied as a feature extractor to generate a collection of features. Furthermore, selecting optimal features extracted by a EfficientNet involves identifying and retaining the most relevant and informative features The improved chaotic satin bowerbird optimization (CSBO) algorithm which uses dynamic population size is proposed for feature selection and kernel extreme learning machine (KELM) model is applied for the classification process. In order to ensure the enhanced performance of the OFLTDL-MSIC technique, a wide range of simulations take place using the Madurai LISS IV multispectral images and the results are examined under several aspects. The extensive comparative study highlighted the better performance of the OFLTDL-MSIC technique over the recent methods. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Deep learning Image classification Multispectral images Parameter tuning Remote sensing

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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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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Risk and Potential:An Asset Allocation Framework with Applications to Robo-Advising

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Journal of the Operations Research Society of China 2022年第3期10卷 529-558页

作者： Xiang-Yu Cui Duan Li Xiao Qiao Moris S.Strub School of Statistics and Management Shanghai University of Finance and EconomicsShanghai200437China School of Data Science City University of Hong KongHong KongChina School of Data Science and Hong Kong Institute for Data Science City University of Hong KongHong KongChina Department of Information Systems and Management Engineering Southern University of Science and TechnologyShenzhen518055GuangdongChina

We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance *** utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility ***,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth *** naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and *** proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal *** establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its ***,we discuss applications of this framework to robo-advisors.

关键词： Mean-risk optimization Mean-variance Expected utility maximization Portfolio choice Risk Potential Robo-advising FinTech

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