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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Kalita, Swagat Subhash Mahajan, Parag Sharanya, S. Srm Institute Of Science And Technology Data Science And Business System School Of Computing Chennai India

ISBN: (纸本)9798350352689

This study examines computer-generated sculpture using a hybrid architecture of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). CNNs are essential for visual data analysis and processing, while GANs iteratively blur the line between synthesis and authenticity. A dynamically interacting generator and discriminator network form the GAN framework. The discriminator, trained to distinguish authentic sculptures from computer-generated ones, guides the generator toward greater realism and complexity with each iteration. This study analyzes the complicated dynamics of GAN-enabled sculpture production, examining several elements that affect the result. We intend to illuminate the complex relationship between composition, texture, and shape of digitally generated sculptures by analyzing sculptures using GANs and CNNs. We want to understand their formation methods as well as their aesthetic value. We also consider how GAN-generated sculptures may be transformed beyond traditional artistic usage. The sculptures offer unique opportunities for artistic expression, scholarly study, and practical use. They can enhance art, education, architectural design, and historical preservation. This thorough inquiry using GANs and CNNs aims to push the limitations of traditional art creation and explore unknown sculpture interpretation and analysis zones. © 2024 IEEE.

关键词： Generative adversarial networks

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Diabetic Retinopathy Detection 4

Diabetic Retinopathy Detection

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4th IEEE International Conference on data Engineering and Communication Systems, ICDECS 2024

作者： Sharanya, S. Rai, Arnav Vinod Vedanth, Sai Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems India

ISBN: (纸本)9798350393354

In this pivotal study, we delve into the imperative realm of Diabetic Retinopathy (DR), a sight-threatening eye disease, introducing a nuanced and comprehensive approach to its detection through cutting-edge deep learning techniques. Our focus centers on the meticulous comparison of four distinguished convolutional neural network (CNN) architectures: CNN, VGG16, ResNet50, and EfficientNetB5. The dataset comprises five meticulously curated image categories, delineating various DR stages. The discerning outcomes illuminate the consistent superiority of the CNN and VGG16 algorithms, showcasing a commendable edge in accuracy. To amplify DR detection precision, we introduce an innovative hybrid model amalgamating the robust attributes of both CNN and VGG16. This hybrid model triumphs with an extraordinary accuracy rate of 90.5%, surpassing individual performances of the four algorithms. In summation, our groundbreaking study unequivocally establishes the efficacy of a hybrid CNN-VGG16 model for Diabetic Retinopathy detection, underscoring its substantial superiority in accuracy when compared to each of the individual algorithms, advocating for the strategic integration of diverse deep learning models to fortify the accuracy and dependability of medical image analysis. The repercussions of our findings promise to revolutionize the landscape of early detection and holistic management of DR, fulfilling an unmet healthcare need. © 2024 IEEE.

关键词： Convolutional neural networks

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Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-27页

作者： Liu, Jiao Gupta, Abhishek Ong, Yew-Soon College of Computing and Data Science Nanyang Technological University Singapore Singapore School of Mechanical Sciences Indian Institute of Technology Goa Goa India

Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of tradeoff solutions is sought under tight evaluation budgets. In this article, we introduce a novel concept of inverse transfer in multiobjective optimization. Inverse transfer stands out by employing Bayesian inverse Gaussian process models to map performance vectors in the objective space to population search distributions in task-specific decision space, facilitating knowledge transfer through objective space unification. Building upon this idea, we introduce the first Inverse Transfer Evolutionary Multiobjective Optimizer (invTrEMO). A key highlight of invTrEMO is its ability to harness the common objective functions prevalent in many application areas, even when decision spaces do not precisely align between tasks. This allows invTrEMO to uniquely and effectively utilize information from heterogeneous source tasks as well. Furthermore, invTrEMO yields high-precision inverse models as a significant byproduct, enabling the generation of tailored solutions on-demand based on user preferences. Empirical studies on multi-and many-objective benchmark problems, as well as a practical case study, showcase the faster convergence rate and modeling accuracy of the invTrEMO relative to state-of-The-Art evolutionary and Bayesian optimization algorithms. The source code of the invTrEMO is made available at https://***/LiuJ-2023/invTrEMO. © 2024 Copyright held by the owner/author(s).

关键词： Budget control

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Large Graphs with Small Burning Numbers

Large Graphs with Small Burning Numbers

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International Conference on Computational Engineering, ICCE 2023

作者： Rajasingh, Angelin Jemima Parthiban, N. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

In this paper, we have determined the burning number of graphs with universal vertices and graphs that are generated using the graph operation namely ‘join of graphs’. © The Author(s), under exclusive license t... 详细信息

ISBN: (纸本)9783031777639

关键词： Burning number Join of graphs Universal vertex

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Big data analytics for privacy through ND-homomorphic encryption

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Journal of Control and Decision 2023年第1期10卷 64-71页

作者： S.Saravanan N.Poornima Department of CSE SRM Institute of Science and TechnologyChennaiIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathurIndia

Rapidly rising the quantity of Big data is an opportunity to flout the privacy of people. Whenhigh processing capacity and massive storage are required for Big data, distributed networkshave been used. There are several people involved in these activities, the system may contributeto privacy infringements frameworks have been developed for the preservation of privacy atvarious levels (e.g. information age, information the executives and information preparing) asfor the existing pattern of huge information. We plan to frame this paper as a literature surveyof these classifications, including the Privacy Processes in Big data and the presentation of theAssociate Challenges. Homomorphic encryption is particularised aimed at solitary single actionon the ciphered information. Homomorphic enciphering is restrained to an honest operation onthe encoded data. The reference to encryption project fulfils many accurate trading operationson coded numerical data;therefore, it protects the written in code-sensible information evenmore.

关键词： Security and confidentiality vast statistics data encryption nondeterministic fully homomorphic encryption

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From GPT to BERT: Benchmarking Large Language Models for Automated Quiz Generation 2024

From GPT to BERT: Benchmarking Large Language Models for Aut...

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1st ACM Virtual Global computing Education Conference V. 2, SIGCSE Virtual 2024

作者： Folajimi, Yetunde School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9798400706042

This study evaluates the effectiveness of four leading large language models (LLMs), GPT-3, GPT-4, GPT-4o, and BERT, in generating quiz questions for Java and Python programming courses. We aim to recognize how LLMs can effectively produce educationally valuable questions that meet specific pedagogical criteria, including technical precision, relevance to course objectives, linguistic clarity, and pedagogical appropriateness. Each model was prompted to generate 200 Java and 200 Python quiz questions, totaling 1600 unique questions. These questions are currently being evaluated based on both quantitative and qualitative assessments by a team of computer science educators. Preliminary findings suggest that GPT-4 outperforms BERT in terms of technical precision. Further analysis is ongoing to assess the performance of the models in generating contextually appropriate and educationally useful questions, offering insights into their potential integration into computer science curricula. This work seeks to contribute to the broader discourse on the utility of LLMs in educational settings, specifically within the scope of automated content creation to enhance teaching and assessment methodologies in computer science education. © 2024 Copyright is held by the owner/author(s).

关键词： Java programming language

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A Xgboost Optimized Ensemble Model for Remaining useful Life Prediction of Aircraft Turbofan Engines 6

A Xgboost Optimized Ensemble Model for Remaining useful Life...

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6th IEEE International Conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Sharma, Vaasudev Dagar, Riyansh Sharanya, S. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350306613

Proactive maintenance for the optimized lifecycle of equipment is now effortless via condition monitoring and fault diagnosis. Recognition and avoidance of a state of failure help to achieve solidity and reliability. Artificial Intelligence and Machine Learning (ML) forecast such situations and aid in the prevention of economic and safety emergencies. This work reviews such ML algorithms and ML models that help diagnose faults in Turbofan engines via measurement of the Remaining Useful Life parameter. This proposed an ensemble machine-learning model optimized by Extreme Gradient Boosting. The approach is then evaluated via different metrics and their performance is measured to current innovative methods. The system is useful for all processes that require high reliability quality and efficiency and reduces unplanned downtime, maintenance costs and asset quality. © 2024 IEEE.

关键词： CMAPSS Ensemble Model Machine Learning Predictive Maintenance Remaining useful life (RUL) Turbofan Engine

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Interactive E-Commerce: Addressing Cart Abandonment through Gamified User Engagement

Interactive E-Commerce: Addressing Cart Abandonment through ...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Sohanda, Mayank Kumar Gupta, Mihir Kalpana, A.V. Srm Institute Of Science And Technology School Of Computing Department Of Data Science And Business Systems Kattankulathur India

ISBN: (纸本)9798350352689

This paper addresses the pervasive issue of cart abandonment in online shopping, a phenomenon that significantly impacts e-commerce revenue streams. Through an investigation into the underlying causes, we identify the lack of social engagement as a critical factor contributing to this behavior. Our primary objective is to bridge the gap between item selection and purchase completion by enhancing the user experience and boosting conversion rates. To tackle this challenge, we propose a novel approach by integrating gamification strategies into the online shopping experience. Our methods include implementing real-time user activity indicators, such as visual representations of active shoppers and interactive leaderboards, to foster a sense of community and competition among users. Additionally, we introduce social shopping features, such as cart sharing and real-time feedback systems, to promote user collaboration and interaction. Preliminary data analysis reveals a significant increase in conversion rates following the implementation of these gamification strategies. Users are more inclined to complete purchases within a community-centric and competitive environment. Moreover, the introduction of social elements has enriched the overall shopping experience, as evidenced by user feedback and heightened engagement on the platform. In conclusion, our study demonstrates the effectiveness of integrating gamification elements into online shopping platforms to combat cart abandonment. By creating a more engaging and communal shopping environment, users are not only motivated to complete purchases, but also to enjoy a more fulfilling shopping experience. Our findings highlight the potential of gamification as a powerful tool for enhancing e-commerce platforms and offer valuable insights for future advancements in online retail strategies. © 2024 IEEE.

关键词： Sales

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An Intelligent Machine Learning Approach to Detect Spam in Social Media 5

An Intelligent Machine Learning Approach to Detect Spam in S...

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

作者： Mohandas, R. Prasanna, D.S. John Deva Meenakshi, N. Kaviyaraj, R. Nathiya, S. Arivazhagan, N. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350389609

Machine learning based intelligent approach is applied to finding spam in YouTube videos. Spam comments are ones that are promotional or unrelated. A growing number of users have been drawn to the idea of making money by placing advertisements on popular social networking platforms over time. These remarks damage a channel's reputation. YouTube has addressed the issue with a limited set of solutions that concentrate on preventing comments from including links. Such heuristics are now futile since spammers have found ways to get around them. Despite the fact that common machine learning classification techniques have demonstrated considerable success, new methods can still increase accuracy. Therefore, this research attempt to identify such statements using machine learning techniques like Random Forest, SVM, and Naive Bayes. In order to convey a clear concept, graphs are used to illustrate data cleaning, data analysis, text preparation, and model building in this research. © 2024 IEEE.

关键词： Adversarial machine learning

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