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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Janet, S. Shaji, Ayda Sara D'Cruz, Brian Cedric Abraham, Eldhose Philip, Abin Oommen Kamal, Leda Toc H Institute of Science and Technology Department of Computer Science and Engineering Kochi India Indian Institute of Information Technology Kottayam IIIT-K Department of Computer Science and Engineering Cybersecurity India

ISBN: (纸本)9798350314090

A metaverse is a rapidly evolving concept in the realm of virtual reality that refers to a network of three-dimensional virtual environments centered on social interaction. The word is widely used in futurism and science fiction to depict a potential iteration of the internet as a single, universal virtual environment assisted by the usage of virtual and augmented reality headgear. There are several Metaverse currently available, such as Sandbox and Decentraland, each offering unique and immersive experiences for users. Non-Fungible Tokens (NFTs) are a novel form of digital assets that are stored on a blockchain, a decentralized digital ledger that keeps track of transactions. NFTs can prove ownership and validity for these digital assets via a blockchain, increasing their value and collectability. NFTs can be linked to digital assets that can be reproduced, such as images, movies, and audio. To give a public certificate of authenticity or proof of ownership, NFTs use a digital ledger. As part of our ongoing project, the creation of the Metarealm, a metaverse for Kochi city, promises to provide an engaging and cutting-edge platform for users to buy, sell, and trade virtual land and assets. This Metaverse will enable users to explore the city and its landmarks in a fully immersive 3D environment, fostering a sense of community and engagement among its users. As the Metaverse and NFTs continue to evolve, the possibilities for virtual experiences and digital ownership are limitless. © 2023 IEEE.

关键词： Blockchain

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Precision Cardiac Risk Evaluation through Flexible Classification Mining Strategies 3

Precision Cardiac Risk Evaluation through Flexible Classific...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Das, Neeraj Ganorkar, Akshaya M Jayaprakash, B. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350343274

Accurate methods of measuring cardiac risk are desperately needed, as cardiovascular diseases remain a major global health concern. The aim of a newly released study titled 'Precision Cardiac Risk Evaluation through Flexible Classification Mining Strategies' was to establish new standards in the field of cardiac risk assessment. This work suggests a novel method to increase the accuracy and precision of cardiac risk assessment by utilizing flexible category mining algorithms. The study included a proposed strategy. There are clear limitations to traditional risk assessment methods, especially with regard to how accurately they can identify those who are at risk. As a result of this work, innovative new methods for improving cardiac risk assessment without imposing these limitations have been created. Each of these enhancements is supported by the research's conclusions. Combining state-of-the-art data analysis and machine learning technology allows the recommended strategy to enhance forecast precision and adjust to changing risk indicators. The most significant and remarkable outcome of this study is the development of a prediction model with enhanced AUC-ROC values over previous methods and exceptional sensitivity, specificity, and accuracy. The suggested approach shows a higher ability to recognize individuals who may have cardiovascular diseases. By reducing the likelihood of missed diagnoses and the frequency of false negatives, this is achieved. The research also emphasizes the model's interpretability, which gives medical practitioners a better understanding of the variables that affect the predictions. This interpretability increases the technique's potential impact in clinical settings by ensuring that medical practitioners can provide individualized treatment and make correct diagnoses. To put it another way, interpretability ensures that health care providers can provide each patient with the individualized attention they require. © 2023 IEEE.

关键词： Diagnosis

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A Novel Time Resource Allocation Configuration for Multi-Task Offloading in Mobile Cloud Computing (MCC)

A Novel Time Resource Allocation Configuration for Multi-Tas...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kumar, M. Praveen Meena, M. Kumar, S. P. Santhosh Saravanan, B. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Vivekanandha College of Technology for Women Department of Information Technology Tiruchengode India Rathinam Technical Campus Department of Information Technology Coimbatore India

ISBN: (纸本)9798350300857

The main objective of this research study revolves around reducing remote cloud communication latency and alleviating system load through the implementation of a 'cloudlet.' However, it is important to acknowledge that while cloudlets offer advantages in reducing communication delays, matching the formidable system performance of remote clouds fall short in terms. The cloud dynamically allocates resources to cater to the changing user population, but the vulnerability of cloudlet services leads to interruptions in user access during task execution. Consequently, this renders cloud computing ineffective, as users fail to receive the expected computational outcomes. In response to these challenges, this article introduces a comprehensive framework for real-time resource allocation, enabling users to efficiently distribute essential resources. To overcome issues related to task failures and real-time resource allocation, the paper recommends the adoption of the Particle Swarm Optimization-Motion Log (PSO-ML) method. Remarkably, the PSO-ML method surpasses the traditional PSO method in terms of delivering more robust and impactful results. Keywords: MCC, PSO-ML, PSO, Assets allocation, Task Offloading © 2023 IEEE.

关键词： Resource allocation

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Maximizing a κ-Submodular Maximization Function under an Individual Knapsack Constraint 12

Maximizing a κ-Submodular Maximization Function under an In...

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12th International Symposium on information and Communication technology, SOICT 2023

作者： Tran, Tan Pham, Canh Ha, Dung Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam ORLab Faculty of Computer Science Phenikaa University Hanoi Viet Nam Faculty of Information Technology Vnu University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9798400708916

In this work, we consider a novel problem of maximizing monotone k-submodular functions under the individual knapsack constraint () over the ground set V, which has been found numerous applications in machine learning, including data summarization and information propagation. We propose an approximation algorithm that has approximation ratio and takes O(nklog (n)/μ) query complexity, where μ is an input parameter. Alongside theoretical analysis, we conduct extensive experiments on our proposed algorithm via some applications, such as Influence Maximization and Sensor Placement. The experimental results demonstrate that our algorithm gives competitive solution quality with state-of-the-art techniques but significantly reduces the required queries. © 2023 ACM.

关键词： Combinatorial optimization

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Ensemble Learning for Multi-Class Classification in Cervical Cancer Risk Factor Analysis: An Empirical Computational Approach 3

Ensemble Learning for Multi-Class Classification in Cervical...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Chaudhuri, Sumantra Suneetha, K. Yadav, Rakesh Kumar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350343274

One cancer that affects women of all ages frequently and is also particularly fatal is cervical cancer. It is fatal because it cannot be anticipated in the early stages when the patient could be treated properly. When cervical cancer cells begin to migrate to nearby organs including the liver, lymph nodes, and lungs, they block the tube that takes urine from the bladder and cause symptoms. Leg discomfort, exhaustion, weight loss, vaginal bleeding, bone fractures, and pelvic pain are among the symptoms in this latter stage. Human papillomavirus (HPV), smoking, numerous pregnancies, using hormonal contraception, and other risk factors include cervical cancer. Screening is a crucial step in the detection of cervical cancer. This study suggests a technique for categorizing cervical cancer risk variables into four separate classes using a multi-class approach. The ensemble learning approach aims to increase the suggested model's accuracy. This technique aids in enhancing the study of risk factors used to determine the likelihood of developing cervical cancer. A multi-layer feed-forward network is used to examine the suggested Artificial Bee colony (ABC) along with a deep learning method known as the LST Model for various screening methods. The gradient boosting method with Multi-Layer Perceptron (MLP) is found to produce a higher % for all performance measures using the suggested model. © 2023 IEEE.

关键词： Learning systems

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Detection and Classification of Agricultural Pest and Vermin's using Full-Convolutional Neural Network 7

Detection and Classification of Agricultural Pest and Vermin...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Abdulrazzaq, Ali Raad Türkben, Ayça Kurnaz Kurnaz, Sefer Altinbas University Information Technology Istanbul Turkey Altinbas University Electrical and Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350383065

This paper comprehensively explores the development and implementation of Full-Convolutional Neural Networks (FCNs) for precise agricultural pest and vermin detection and classification. Precision farming, a cornerstone of modern agriculture, relies heavily on effective crop health management, with pest and rodent identification being a critical aspect. FCN, a specialized deep learning technique, exhibits unique capabilities by facilitating precise pest localization within crop images, regardless of input *** ensure the research's credibility and efficacy, a diverse array of data sources, encompassing drones, stationary cameras, and handheld devices, is tapped. High-quality images with superior resolution and ideal lighting conditions are essential for maximizing model performance. Additionally, dataset diversity and rigorous professional-grade annotation processes significantly bolster model robustness. Annotation tools such as Labelbox are harnessed for accurate pest and vermin delineation, employing advanced techniques like bounding boxes and segmentation masks, with multiple reviewers engaged to minimize *** paper presents a comprehensive roadmap for developing and deploying FCNs tailored to agricultural pest and vermin detection and classification. It offers invaluable insights into the seamless integration of cutting-edge technology into precision farming practices. The research thereby contributes to improving crop health management and promoting sustainable agricultural approaches, underlining its significance in the contemporary agricultural landscape. © 2023 IEEE.

关键词： Convolutional neural networks

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An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

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computer Systems Science & engineering 2023年第3期44卷 2457-2468页

作者： Mahesh B.Shelke Jeong Gon Lee Sovan Samanta Sachin N.Deshmukh G.Bhalke Daulappa Rahul B.Mannade Arun Kumar Sivaraman Department of Computer Science and Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabadMaharashtra431004India Division of Applied Mathematics Wonkwang University460Iksan daeroIksan SiJeonbuk54538Korea Department of Mathematics Tamralipta MahavidyalayaTamlukWest Bengal721636India Department of Electronics and Telecommunication Engineering AISSMSCOEPuneMaharashtra411001India Department of Information Technology Govemment College of EngineeringAurangabadMaharashtra431005India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

In today’s digital world,millions of individuals are linked to one another via the Internet and social *** opens up new avenues for information exchange with *** analysis(SA)has gotten a lot of attention during the last *** analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as *** also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral *** addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was *** contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%.

关键词： Sentiment analysis machine learning lexical resource ensemble classifier

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Unsupervised Normative Learning for Quality Assessment on Diffusion MRI 22

Unsupervised Normative Learning for Quality Assessment on Di...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yu, Jiahao Wang, Tenglong He, Yifei Pan, Yiang He, Jianzhong Wu, Ye School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Information Engineering Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798331520526

Quality assessment of diffusion MRI (dMRI) aims to ensure that the presence of image artifacts does not affect the results of subsequent image analysis. Recently, a variety of deep learning methods have been proposed to enhance the quality and utility of these acquisitions. Still, they require a large amount of training data with artifact labeling. In this work, we developed an Unsupervised Quality Assessment tool (UNL-QA) for dMRI data that can handle a variety of artifacts such as ghosting, spike, noise and swap. Our method uses a vector quantized variational autoencoder to train typical patterns on a standard dMRI dataset, achieving 99.732% accuracy in detecting artifacts and standard data and 95% sensitivity in detecting artifacts on the training dataset. Performance across datasets is also excellent. It has also been demonstrated to be replicable on other datasets with different acquisition parameters. Without labeled artifact images necessary, UNL-QA allows fast and paves the way for efficient and effective artifact detection in large datasets. The code is available at https://***/yjh200319/***. © 2025 IEEE.

关键词： Damage detection

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Efficient Clustered Mean Forecasting for Time Series with Head-Based Aggregation 5th

Efficient Clustered Mean Forecasting for Time Series with He...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Anand, Piyush Ramesh, T. Agarwal, Trapty Preethi, D. Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Luknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Green Clustered mean Forecasting (ECMF) for Time series is a novel forecasting approach that is capable of efficaciously aggregating the underlying additives of a time collection through combining the top-primarily based Aggregation (HBA) technique alongside the mean Forecasting (MF) method. ECMF may be utilized to detect and become aware of companies of time collection intently associated with every other, therefore taking into account extra correct and reliable forecasts. By way of counting on HBA to discover any patterns in the information and then imposing MF to generate forecasts from the ones styles, ECMF can offer more accurate and reliable forecasts than the prevailing strategies. Moreover, this technique is computationally green, due to the hierarchical shape of the data, which reduces the quantity of forecasting experiments wanted. The ensuing forecasts are also easier to examine, as they may be generated from a standard head-based totally version. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Investigating Time Series Clustering Algorithms for Hyper Spectral Image Analysis 5th

Investigating Time Series Clustering Algorithms for Hyper Sp...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mahalakshmi, R. Agarwal, Trapty Kudari, Jayashree M. Mehra, Ritika School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Time collection clustering algorithms (TSCAs) are of increasing significance within the evaluation and interpretation of hyper spectral image (HSI) facts. TSCAs are being used to become aware of spatiotemporal styles of spectral characteristics in HSI records by identifying areas of interest (ROIs) which can be associated with physical techniques and mapping the ones ROIs to clusters. One-of-a-kind clustering algorithms may be used to analyze HSI statistics and tease out diffused spatiotemporal modifications. This paper investigates the software of two exceptional TSCAs, particularly okay-method clustering and Self-Organizing Maps (SOMs) to HSI *** SOM set of rules changed into additionally capable of phase the hyperspectral facts into meaningful clusters primarily based on their spectral traits. The accuracy of the SOM results was also assessed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self organizing maps

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