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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rajkumar, M. Madhan, E.S. Karthikeyan, D. Gopal, Mohanraj Nithish, G. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore Campus Tamil Nadu India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore632014 India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore Campus Tamil Nadu India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore632104 India Vellore Institute of Technology Vellore Campus Tamil Nadu India

ISBN: (纸本)9798350389609

Ensuring the well-being of foetuses during pregnancy is crucial for preventing child and maternal mortality, aligning with the United Nations' Sustainable Development Goals. Utilizing a dataset of 2126 records from Cardiotocography (CTG) exams, obtained from the Kaggle repository, the study employs machine learning ensemble techniques, specifically stacking with 10x10 validations, to increase the classification of fetal health's precision and dependability. To address class imbalance, the dataset is resampled to 150%, and filter-based feature selection using the chi-squared statistical test is applied. Base models, including Decision Trees (C4.5), Random Forest (100 trees), Logistic Regression, and Sequential Minimal Optimization (SMO), are utilized. With the help of the Weka Machine Learning software tool, the ensemble model, after hyper parameter tuning and employing diverse techniques, with 98.68% accuracy, the model effectively classifies fetal health, indicating robust performance across various evaluation metrics. © 2024 IEEE.

关键词： Electronic health record

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Comparative Study of Disease Prediction Through Data Mining 2

Comparative Study of Disease Prediction Through Data Mining

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Karthikeyan, D. Aameer Arshath, A. Gunasekaran, V. Uthra, B. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The growing occurrence of persistent liver disorder has been cited in current times. Similarly Diabetes, one of the frequently spreading disease is growing hastily in all of the Nations nowadays. According to WHO, approximately 422 million humans globally have diabetes and 1.5 million deaths are without delay associated with diabetes disorder. So the effective analysis should be made to detect the presence of disease before it kills the patient which reflects a plenty of studies have been made in the field of healthcare. In this paper, we are going to predict these two diseases through one of the data mining techniques called classification. The analysis is completed using Jupyter Notebook. One of the data mining techniques called supervised learning or the classification is applied in which we have used Decision tree, Logistic Regression, Random Forest, K-Nearest Neighbor. The data in this research paper is taken from University of California Irvine repository of machine learning. The main aim of this paper is to provide brief prediction on both diabetes and liver disease and the goal is to provide a better final results when compared to the other algorithms, for the disease prediction. In results we are predicting the presence of diabetes disease with 98% of accuracy with Random forest algorithm and prediction of liver disorder has 81.59% of accuracy with Random Forest algorithm along with accuracy we have attempted to use some parameters like precision, recall, F-Measure for the evaluation. © 2024 IEEE.

关键词： Forecasting

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Data Security Storage Mechanism Based on Blockchain Network

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computers, Materials & Continua 2023年第3期74卷 4933-4950页

作者： Jin Wang WeiOu Wenhai Wang RSimon Sherratt Yongjun Ren Xiaofeng Yu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China School of Control Science and Engineering Zhejiang UniversityHangzhou310058China School of Systems Engineering The University of ReadingReadingRG66AYUK School of Computer Engineering Research Center of Digital Forensics of Ministry of EducationNanjing University of Information Science&TechnologyNanjing210044China School of Business Nanjing UniversityNanjing210093China

With the rapid development of information technology,the development of blockchain technology has also been deeply *** performing block verification in the blockchain network,if all transactions are verified on the chain,this will cause the accumulation of data on the chain,resulting in data storage *** the same time,the security of data is also challenged,which will put enormous pressure on the block,resulting in extremely low communication efficiency of the *** traditional blockchain system uses theMerkle Tree method to store *** verifying the integrity and correctness of the data,the amount of proof is large,and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency,which will cause end-to-end communication delays and seriously affect the blockchain system’s stability,efficiency,and *** order to solve this problem,this paper proposes to replace the Merkle tree with polynomial commitments,which take advantage of the properties of polynomials to reduce the proof size and communication *** realizing the ingenious use of aggregated proof and smart contracts,the verification efficiency of blocks is improved,and the pressure of node communication is reduced.

关键词： Blockchain cryptographic commitment smart contract data storage

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Angle robust transmitted plasmonic colors with different surroundings utilizing localized surface plasmon resonance

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Chinese Physics B 2023年第7期32卷 226-233页

作者：高旭峰王琦张世杰洪瑞金张大伟 Shanghai Key Laboratory of Modern Optic Systems Engineering Research Center of Optical Instrument and SystemMinistry of Education and Shanghai Key Laboratory of Modern Optical SystemsSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Color filters in different surroundings inherently suffer from angular sensitivity,which hinders their practical ***,we present an angle-insensitive plasmonic filter that can produce different color responses to different surrounding *** color filters are based on a two-dimensional periodically and randomly distributed silver nanodisk array on a silica *** proposed plasmonic color filters not only produce bright colors by altering the diameter of the Ag nanodisk,but also achieve continuous color palettes by changing the surrounding *** to the weak coupling between the metallic nanodisks,the plasmonic color filters can enable good incident angle-insensitive properties(up to 30°).The strategy presented here could exhibit robust and promising applicability in anti-counterfeiting and imaging technologies.

关键词： plasmonic color filter color sensing high angular tolerance

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Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2269-2291页

作者： Zefeng Zheng Luyao Teng Wei Zhang Naiqi Wu Shaohua Teng the School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China the School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China the Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by ***,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of *** address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target *** analyses show that DDS avoids repeated sampling during the *** the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly *** addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA *** experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

关键词： Cross-domain risk dual density sampling intra-domain risk maximum mean discrepancy knowledge transfer learning resource-limited domain adaptation

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A Digital Forensic Analysis of Profiling and Avoidance of Websites Disseminating Disinformation 2

A Digital Forensic Analysis of Profiling and Avoidance of We...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Agrawal, Harsh Singh, Shaurya Pratap Dixit, Shashwat Nagdev, Parth Priya, V. Thaseen, Sumaiya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The internet is an essential source of data for individuals across the world. However, disinformation has emerged as a significant problem due to its rapid growth on social media and the prevalence of fallacious news. Conventional strategies for detecting disinformation sites, like manual examination, are tedious and time-wasting. As a result, it is necessary to develop an effective method to find such websites. The goal of this research is to find websites that spread false information and prevent its further spread. The methodology is unique and effective for detecting disinformation websites as it makes use of the OSINT tool Maltego and focuses on websites that spread news having a negative impact such as in politics, health, finance, etc. Thus, the outcome of this research helps users to detect the authenticity of news in an easy and user-friendly manner by footprinting mechanisms. © 2024 IEEE.

关键词： Websites

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Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2094-2107页

作者： Shaohua Teng Zefeng Zheng Naiqi Wu Luyao Teng Wei Zhang School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE

Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples *** approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.

关键词： Adaptive adjustment consistency and specificity domain adaptation graph embedding geometrical and semantic metrics

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India College of Computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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Exploring the scope of Blockchain-based Central Bank Digital Currencies (CBDCs) in Crowdfunding Platforms 7

Exploring the scope of Blockchain-based Central Bank Digital...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Ghosh, Satyajit Jaiswal, Rishabh Das, Tapan Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798331527822

This paper introduces an innovative crowdfunding platform utilizing Central Bank Digital Currency (CBDC) to tackle the challenges linked to conventional payment methods and the regulatory limitations of earlier cryptocurrency-based solutions. We present a practical prototype, demonstrating the feasibility of the CBDC approach. An extensive evaluation comparing CBDCs with Ethereum and traditional systems confirms its superior performance. This research marks the initial step toward understanding the potential of CBDCs in crowdfunding, setting the stage for future exploration and innovation in this evolving financial landscape. © 2024 IEEE.

关键词： Blockchain CBDC Cross-border Payment Crowdfunding Digital Currency

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Diabetic Retinopathy Detection using Deep Learning Framework and Explainable Artificial Intelligence Technique 14

Diabetic Retinopathy Detection using Deep Learning Framework...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Posham, Uppamma Bhattacharya, Sweta School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350344837

Diabetic retinopathy (DR) is a serious eye condition induced by diabetes that can result in eyesight. In order to overcome the vision impairment of diabetes mellitus patients, it is essential for early age prevention by medical practitioners. In the traditional approach, ophthalmologists conduct various screening tests to detect DR but fail to achieve accurate and conclusive diagnosis due to the associated time consuming processes. In order to eliminate such burden on the ophthalmologists, deep learning and machine learning techniques have evolved rapidly playing a significant role in the classification of diabetic and non diabetic patients. In this paper, the proposed framework implements a publicly available Benchmark APTOS 2019 Gaussian-filtered DR image dataset using a customized CNN model, yielding an enhanced accuracy of 98 %. In addition, the framework also employs a LIME explainer visualization model to provide enhanced transparency and interpretability to the generated predictions. This eliminates the 'blackbox' -ed nature of the predicted results from the traditional ML process and enables healthcare providers to take clinical decisions with confidence providing visual representations of the significant features that contribute towards an outcome. © 2024 IEEE.

关键词： Diagnosis

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