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An Efficient Approach to Data Clustering Using the K-Means Algorithm in Big Data Analytics 3

An Efficient Approach to Data Clustering Using the K-Means A...

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vani, S. Sandhiya Ramya, P. Nagendar, Y. Prakash, P. Arun Chanti, Malladi Atheeswaran, Athiraja Rajalakshmi Engineering College Department Of Computer Science and Engineering Thandalam Chennai India Anna University University College of Engineering Department of Computer Science and Engineering Dindigul India Sr University Department of School of Computer Science and Artificial Intelligence Warangal Telangana Hyderabad India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Maisammaguda Telangana Hyderabad India Telangana Hyderabad India

ISBN: (纸本)9798331515911

The large-scale, high-dimensionality of datasets poses major obstacles to effective and precise data clustering in the field of big data analytics. This work focuses on a well-known technique for dividing huge datasets into informative clusters: the K-Means algorithm. This approach to data clustering is efficient. However, because of scalability and computational complexity problems, K-Means is not as effective when managing large amounts of data. In order to reduce processing cost and improve clustering accuracy, this work provides an improved version of the K-Means algorithm that blends dimensionality reduction approaches and efficient initialization methods. Principal Component Analysis (PCA) is used as a preprocessing step in the methodology to reduce the dimensionality of huge datasets. An optimized K-Means algorithm with improved centroid initialization is then applied. These changes lead to shorter iteration durations and higher-quality clusters. Comparing the results to standard K-Means, experiments on many big data benchmarks showed a 15% gain in cluster accuracy and a 25% reduction in execution time. To sum up, the incorporation of optimized initialization techniques and dimensionality reduction greatly improves the scalability and performance of K-Means in big data analytics, with useful implications for sectors that depend on effective data analysis. © 2025 IEEE.

关键词： Data accuracy

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Camouflaged Variational Graph AutoEncoder against Attribute Inference Attacks for Cross-Domain Recommendation

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 3916-3932页

作者： Xiong, Yudi Guo, Yongxin Pan, Weike Yang, Qiang Ming, Zhong Zhang, Xiaojin Yu, Han Lin, Tao Tang, Xiaoying Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China School of Science and Engineering Shenzhen518172 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong WeBank Shenzhen China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Westlake University School of Engineering Hangzhou China

Cross-domain recommendation (CDR) aims to alleviate the data sparsity problem by leveraging the benefits of modeling two domains. However, existing research often focuses on the recommendation performance while ignores the privacy leakage issue. We find that an attacker can infer user attribute information from the knowledge (e.g., user preferences) transferred between the source and target domains. For example, in our experiments, the average inference accuracies of attack models on gender and age attributes are 0.8323 and 0.3897. The best-performing attack model achieves accuracies of 0.8847 and 0.4634, exceeding a random inference by 25.10% and 64.04%. We can see that the leakage of user attribute information may significantly exceed what would be expected from random inference. In this paper, we propose a novel recommendation framework named CVGAE (short for camouflaged variational graph autoencoder), which effectively models user behaviors and mitigates the risk of user attribute information leakage at the same time. Specifically, our CVGAE combines the strengths of VAEs in capturing latent features and variability with the ability of GCNs in exploiting high-order relational information. Moreover, to ensure against attribute inference attacks without sacrificing the recommendation performance, we design a user attribute protection module that fuses user attribute-camouflaged information with knowledge transfer during cross-domain processes. We then conduct extensive experiments on three real-world datasets, and find our CVGAE is able to achieve strong privacy protection while making little sacrifices in recommendation accuracy. © 1989-2012 IEEE.

关键词： Recommender systems

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Prospects of Deep Learning with Blockchain for Securing the Digital Radiography Data in Smart Healthcare 2

Prospects of Deep Learning with Blockchain for Securing the ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sudhakar, K. Mahaveerakannan, R. Saveetha School of Engineering Simats Saveetha University Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350382952

Blockchain knowledge is gaining traction in today's information and digital age, and it's expected to become extremely popular among the next generation for reasons that go well beyond bitcoin and other cryptocurrencies. Blockchain technology's potential use in healthcare may be observed through data-sharing, which enables users to pick certain data and limit data access based on user type, and is therefore crucial for the upkeep of EHRs. Despite the broad deployment of Internet of Things (IoT) integrated cloud for remote patient accuracy of sickness prediction remain important problems in today's health care system. Multiple machine learning techniques were used to successfully forecast illnesses at an early stage. Although it offers the possibility of precise categorizations, machine learning is time-consuming and inefficient. This research suggests combining Attribute based Searchable Honey Encryption with SqueezeNet to better analyse sickness and secure IoT-cloud healthcare data. To reduce variability and provide consistency across IoT data, means-mode normalisation is an essential first step. The final output was saved in view the prediction results and relevant patient evidence. We discovered that the projected model has higher accuracy (94%), sensitivity (94%), and specificity (94%), compared to the state-of-the-art models (also 94%). © 2024 IEEE.

关键词： Diseases

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Enhancing Early Exit Performance with Uncertainty-Aware Training in Convolutional Neural Networks for Image classification

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IEEE Access 2025年 13卷 91671-91681页

作者： Kurian, Thanu Thangam, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

Deep Neural Networks have become more and more complex over the past years delivering efficient results but demanding more requirements for resources. The traditional cloud-based DNN inference produces poor real-time performance due to its high latency requirement in the network. Nowadays, fog and edge have been popular, where computation occurs near the data source or between the source and the cloud. High usage of computational resources by DNN incurs limited room for its deployment in such resource-constrained devices. Deep Neural Networks with multiple exit architecture are an optimal approach to preserve time and computational resources by predicting results at an early stage using multiple early exits to support Edge and Fog Intelligence. Early exit, although lightweight and energy efficient, mostly exhibits degraded performance compared to later exits due to the count of samples that are misclassified. To overcome the downfall, we propose uncertainty estimation in the loss function as an additional parameter for classification. Observational results on CIFAR, MNIST, human lung CT scan, and brain MRI datasets using VGG-16, Lenet-5, and Resnet architectures exhibit that our proposed model is better than the existing models to improve performance as well as prediction confidence in Deep Learning Models with early exits. © 2013 IEEE.

关键词： Deep neural networks

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Comparative Analysis of MRT Jakarta Traction System Considering Electric Train Vehicles with Non-Regenerative and Regenerative Braking 6

Comparative Analysis of MRT Jakarta Traction System Consider...

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6th International Conference on Power engineering and Renewable Energy, ICPERE 2024

作者： Rukmana, Maman Rachmildha, Tri Desmana Haroen, Yanuarsyah Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Gunadarma University Department of Electrical Engineering Depok Indonesia

ISBN: (纸本)9798331504694

The development of electric train vehicles that can perform regenerative braking has an impact on reducing energy consumption in vehicles and traction substations. This paper presents the performance of the Jakarta MRT system with non-regenerative braking vehicles and regenerative braking vehicles. This study aims to determine how much regenerative braking impacts energy consumption. This paper uses the MRT Jakarta Phase 1 Lebak Bulus-Bundaran HI data. A single vehicle is modeled to obtain a single vehicle profile, including speed, power consumption, and energy consumption. The single vehicle profile is configured with headway and dwell time to obtain the catenary drop voltage and traction substation energy consumption. The calculation results show that regenerative vehicles decrease single-vehicle and total traction substation energy consumption, but catenary losses increase. Overall, total traction substation energy consumption decreases by 43.45%. © 2024 IEEE.

关键词： Rectifier substations

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Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

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computer Systems Science & engineering 2023年第7期46卷 659-670页

作者： Sanghyuck Nam Suhwan Kwak Jaehwan Lee Sangoh Park School of Computer Science and Engineering Chung-Ang UniversityDongjak-guSeoul06974Korea Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea

Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted *** autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing *** solve this problem,there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation ***,the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their *** are also challenging to process computation tasks within 100 ms which is the time limit for driving *** this paper,we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving *** proposed scheme can handle computation tasks by considering the communication bandwidth,delay,and amount of *** also conduct simulations in the highway environment to evaluate the existing scheme and the proposed *** result shows that our proposed scheme improves the utilization of nearby computing nodes,and the offloading tasks can be processed within the time for driving safety.

关键词： Task offloading vehicular edge computing vehicular ad-hoc network dedicated short-range communication autonomous platooning

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A new technology perspective of the Metaverse:Its essence,framework and challenges

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Digital Communications and Networks 2024年第6期10卷 1653-1665页

作者： Feifei Shi Huansheng Ning Xiaohong Zhang Rongyang Li Qiaohui Tian Shiming Zhang Yuanyuan Zheng Yudong Guo Mahmoud Daneshmand School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Jinzhong University Shanxi030619China Department of Business Intelligence and Analytics and the Department of Computer Science Stevens Institute of TechnologyHobokenNJ07030USA

The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical ***,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of ***,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical ***,we envision open issues and challenges of the Metaverse in the technical *** work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.

关键词： Metaverse Technical framework Ubiquitous connections Space convergence Virtuality and reality interaction Human-centered communication

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Stunting Program Classification in East Java, Indonesia from Internet News Using Location-Based and SVM 25

Stunting Program Classification in East Java, Indonesia from...

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25th International Electronics Symposium, IES 2023

作者： Ananta, Caesar Jalu Fariza, Arna Asmara, Rengga Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Stunting is a serious health problem for toddlers in Indonesia, especially in East Java, which makes the Indonesian government seriously try to overcome it to achieve the Golden Generation of Indonesia. To reduce the prevalence of stunting, programs related to stunting are held regularly. This research aims to compile and classify information regarding stunting programs in East Java from internet news. The solution is to create a website that visualizes the prevalence of stunting and stunting programs in East Java, Indonesia, in the form of a map that is divided into several districts in East Java. Stunting prevalence data is taken from SSGI data that has been collected by the Indonesian Ministry of Health, while information about the stunting program is taken from mined internet news, filtered using the SVM machine learning algorithm, and classified based on the location of the stunting program reported. The SVM filtering model created can classify and filter news with an accuracy rate of up to 87%. With this website, it is hoped that the public can easily get information about stunting programs in their location and that the government can conduct an interactive analysis of stunting data in East Java. © 2023 IEEE.

关键词： Location

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Performance Challenges and Solutions in Big Data Platform Hadoop

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Recent Advances in computer Science and Communications 2023年第9期16卷 27-41页

作者： Singh, Balraj Verma, Harsh K. Madaan, Vishu Department of Computer Science and Engineering Dr. B.R. Ambedkar NIT Jalandhar India School of Computer Science and Engineering Lovely Professional University Phagwara India

Background: The present era demands continuous support to bring improvements in executing complex analytics on large-scale data and to work beyond traditional systems. Objective: The need for processing diverse data types and solutions for different domains of the industry is rising. Such needs increase the requirement for sophisticated techniques and methods to enhance the existing platforms and mechanisms further. It provides an opportunity for the research community to investigate further into the existing systems, find potential issues, and propose new ways to improve the current systems. Hadoop is a popular choice to manage and process Big data. It is an open-source platform and a front-runner in the batch processing of large-scale jobs. The economy associated with the cluster in scaling is low as compared to other platforms. However, this popularity by no means guarantees high performance in all scenarios. With the continuous evolution in data development and industrial requirements, it is imperative to investigate and look into new methods and techniques to bring advancements to the existing system. Method: A systematic review is represented in this paper to have an insight into the current progress in this field. Research publications from various sources are taken and analyzed. The performance of a cluster largely depends upon the different job processing mechanisms and policies associated with it. Conclusion: While extensive studies and solutions are proposed, the performance bottlenecks in terms of load balancing, resource utilization, content management, and efficient processing prevail. Not many of the solutions are there on scheduling about the trade-off between different parameters, the process of content splitting and merging is not explored to a large extent and the skew mitigation solutions are more focused on Reduce side of the MapReduce while the Map side is not utilized much for load balancing. © 2023 Bentham Science Publishers.

关键词： Batch data processing

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Pneumonia Detection in Children from Chest X-Ray Images by Executing Network Surgery of Deep Neural Networks

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SN computer Science 2024年第2期5卷 215页

作者： Dash, Amiya Kumar Mohapatra, Puspanjali Ray, Niranjan Kumar Department of Computer science and Engineering IIIT Bhubaneswar Odisha Bhubaneswar 751003 India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar 751024 India

Pneumonia is an infection that is caused by bacteria, viruses, or fungi. It is a form of acute respiratory infection that affects the lungs and is a leading cause of death in developing countries. Pneumonia leads to one in three deaths as per WHO. However, with early detection and medication, it can be cured. Chest X-ray is one of the most common methods to detect pneumonia. Several deep learning models have been proposed to detect pneumonia using Chest X-rays, but these models come with their own set of limitations in terms of accuracy and *** challenge is to find the Deep Learning method which satisfies all the standard metrics of *** this paper, we compared six different deep learning models like VGG16, VGG19, ResNet50, ResNet152V2, MobileNetV2 and OpticNet by fine-tuning the model architecture. We replace the previously fully connected layers with newly initialized ones and subsequently training the current fully connected layers to predict our input classes. The results show that fine-tuning the pre-trained deep convolutional neural networks (CNN) proves to be effective in pneumonia detection compared to the state-of-the-art models with fewer parameters and less training data. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Fine-tuning Radiography images Transfer learning

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