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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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Recovering Packet Collisions below the Noise Floor in Multi-gateway LoRa Networks 42

Recovering Packet Collisions below the Noise Floor in Multi-...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Mao, Wenliang Zhao, Zhiwei Zheng, Kaiwen Min, Geyong University of Electronic Science and Technology of China School of Computer Science and Engineering China University of Exeter Department of Computer Science United Kingdom

ISBN: (纸本)9798350334142

LoRa has been widely applied in various vertical areas such as smart grids, smart cities, etc. Packet collisions caused by concurrent transmissions have become one of the major limitations of LoRa networks due to the ALOHA MAC protocol and dense deployment. The existing studies on packet recovery usually assume that the collided packet signals are above the noise floor. However, considering the large-scale deployment and low-power nature of LoRa communications, many collided packets are below the noise floor. Consequently, the existing schemes will suffer from significant performance degradation in practical LoRa networks. To address this issue, we propose CPR, a Cooperative Packet Recovery mechanism aiming at recovering the collided packets below the noise floor. CPR firstly employs the incoherence of signals and noises at multiple gateways to detect and extract the frequency features of the collided packets hidden in the noise. Then, CPR adopts a novel gateway selection strategy to select the most appropriate gateways based on their packet power domain features extracted from collision detection, such that the interference can be eliminated and the original packets can be recovered. Extensive experimental results demonstrate that CPR can significantly increase the symbol recovery ratio in low-SNR scenarios. © 2023 IEEE.

关键词： Recovery

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Mobile Application as Community Social Media 6

Mobile Application as Community Social Media

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Pahlevi, Teuku Ryan Haryanto, Johannes Adha, Rahmat Alfian, Bayu Warnars, Harco Leslie Hendric Spits Widiono, Rahayu Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350398496

Today's society is different from the past, where the speed of the internet is getting more sophisticated, and the existing gadget technology is getting faster, and of course, this is also changing the lifestyle of today's people, which of course almost half of their daily life is spent in front of gadgets. On the one hand, this is undoubtedly very good and supports humans in carrying out their daily life activities, but on the other hand, this raises new problems where humans start to lack time to communicate and socialize with each other and often making them stressed and anxious because they are already tired and do not know how to socialize anymore. This paper begins with a model and implementation of a very simple mobile application that behaves as a social media inspired by *** as a social media for academics. The design of this mobile application is done by using use case diagrams to describe existing processes and class diagrams to describe class relationships. © 2023 IEEE.

关键词： Social networking (online)

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Ultra-Wideband Technology for Healthcare Monitoring: A Machine Learning Approach

Ultra-Wideband Technology for Healthcare Monitoring: A Machi...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Raj, Raunak Awasthi, Avinash Arora, Komal Singh, Shreshth Miranka, Hemant Rakhra, Manik School of Computer Science Engineering Lovely Professional Univeristy Punjab India Malviya National Institute of Technology Department of Computer Science and Engineering Rajasthan India Visvesaraya National Institute of Technology Department of Computer Science and Engineering Maharashtra India Lakshmi Niwas Mittal Institute of Information Technology Department of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798331542344

Ultra-Wideband (UWB) technology has emerged as an emerging solution for a variety of applications in clinical establishments., offering significant advantages such as precise localization capabilities., high data rates., and low power consumption. The paper systematically examines the utilization of UWB technology within clinical settings., focusing on its applications in health care monitoring., medical imaging., indoor positioning., and wireless communication. By leveraging a comprehensive dataset from the UCI Machine Learning Repository., the authors provide detailed empirical insights into the performance and effectiveness of VWB-based systems in enhancing healthcare outcomes. The authors also identify and discuss specific challenges., such as integration complexities and regulatory hurdles., as well as opportunities., including potential advancements in patient care and health care delivery through the adoption of UWB technology. Additionally., the authors have built a model using a Random Forest classifier and achieved an accuracy of 91.8%. © 2024 IEEE.

关键词： Electronic health record

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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Appearance- and Orientation-aware Fine-grained Rotated Ship Detection in High-Resolution Satellite Imagery

Appearance- and Orientation-aware Fine-grained Rotated Ship ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yan Liu, Lingyi Bai, Yunpeng Li, Ying Shen, Qiang School of Software Northwestern Polytechnical University Xi'an China School of Computer Science Northwestern Polytechnical University Xi'an China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Ship detection using remote sensing imagery is a crucial research area with both military and civilian applications. However, it remains challenging due to limitations in current ship datasets, such as insufficient volume, incomplete annotations, and inaccuracies. Additionally, ships often exhibit arbitrary orientations, dense clustering, varying aspect ratios, and significant dimensional changes. To address these issues, this paper advances ship detection from both data and methodological perspectives. First, a new dataset, ORSISOD, is introduced. This dataset includes seven finely categorized ship types, annotated with rotated bounding boxes, which are more appropriate for ship detection than traditional horizontal boxes. Second, a novel rotated ship detection method is proposed, incorporating a Dynamic IOU Threshold Selection (DITS) module and a Positive Sample Quality Assessment (PSQA) module. DITS adjusts the IOU threshold based on ship size and shape, while PSQA assesses sample quality using ship aspect ratio and angle information. The ORSISOD dataset was tested on 12 object detection algorithms, providing benchmarks for ship detection. Furthermore, the proposed method was evaluated on both ORSISOD and DOTA datasets, demonstrating superior performance. © 2025 IEEE.

关键词： remote sensing images rotated object detection ship dataset ship detection

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A Comprehensive Study of OOP-Related Bugs in C++ Compilers

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IEEE Transactions on Software Engineering 2025年第6期51卷 1-21页

作者： Wang, Bo Chen, Chong Chen, Junjie Xu, Bowen Ye, Chen Lin, Youfang Dong, Guoliang Sun, Jun Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence China Tianjin University College of Intelligence and Computing Tianjin300350 China North Carolina State University Department of Computer Science NC27695 United States Singapore Management University School of Computing and Information Systems Singapore178902 Singapore

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from GCC and LLVM. Second, derived from the core concepts of OOP and C++, we manually identified a two-level taxonomy of the OOP-related features leading to compiler bugs, which consists of 6 primary categories (e.g., Abstraction & Encapsulation, Inheritance, and Runtime Polymorphism), along with 17 secondary categories (e.g., Constructors & Destructors and Multiple Inheritance). Third, we systematically analyze the root causes, symptoms, fixes, options, and C++ standard versions of these bugs. Our analysis yields 13 key findings, highlighting that features related to the construction and destruction of objects lead to the highest number of bugs, crashes are the most frequent symptom, and while the average time from bug introduction to discovery is 1856 days, fixing the bug once discovered takes only 174 days on average. Additionally, more than half of the bugs can be triggered without any compiler options. These findings offer valuable insights not only for developing new compiler testing approaches but also for improving language design and compiler engineering. Inspired by these findings, we developed a proof-of-concept compiler fuzzer OOPFuzz, specifically targeting OOP-related bugs in C++ compilers. We applied it against the newest release versions

关键词： C++ (programming language)

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A review on the energy efficient resource allocation schemes with improved quality of services in green cloud computing

A review on the energy efficient resource allocation schemes...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Fiaz, A.S. Syed Sonai, Veeramani Computer Science and Engineering Amrita Viswa Vidhyapeetham School of Computing Chennai India Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai India

ISBN: (纸本)9781032669663

The vast changes in the computing utilization have created a need with the improvised resource availability and reliability. The virtualization of servers and data centers to increase their efficiency has transformed how enormous data is stored and handled. Cloud computing has created a supreme and impressive approach to virtualize servers and data centers and to make them energy efficient in order to leverage on diverse IT resources. Massive power and energy consumption by IT resources leads to an energy crisis and a change in the planet’s climate. But because these IT resources use so much energy and power, they end up being a significant source of CO2 emissions. Due to this, green cloud computing is now required in order to make IT resources cost-and energy-effective. It is necessary to do a thorough analysis of the cloud’s power efficiency in order to arrive at the best options for green cloud computing. Green Cloud Computing is well known to be a broad domain and a popular field for research because of the increasing rise of enormous data storage and computational demand. It can generate solutions that not only make IT resources energy efficient but also reduce operational expenses. This review’s objective is to highlight some of the best practices for achieving green cloud computing, including workload clustering, Auction-Based Resource Optimization techniques, Hardware and Software controlled solutions, Minimize cooling requirements scheduling, clustering, virtualization and other energy-saving techniques. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Carbon footprint

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Design an optimization based ensemble machine learning framework for detecting rice leaf diseases

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Multimedia Tools and Applications 2024年第36期83卷 84401-84424页

作者： Rajasekhar, Veeramreddy Arulselvi, Gnanasekaran Babu, K. Suresh Department of Computer Science Engineering Annamalai University Tamilnadu Chidambaram India Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu Chidambaram India International School of Technology and Sciences for Women East Gonagudem Rajanagaram A.P Rajamahendravaram India

The agriculture industry is currently dealing with serious issues with rice plants as a result of illnesses that decrease the quantity and output of the harvest. Numerous fungi and bacteria diseases harm plants that affect the growth of yield. So identification of the detection of crop diseases is essential for enhancing the productivity of crops. Many techniques have been developed to improve disease detection in crops but there are still some issues such as noise, time consumption, delay, poor segmentation, and less accuracy. To overcome these issues, this paper, designed a Spider Monkey-based Random Forest (SMbRF) model for accurate detection and classification of rice leaf diseases and the designed model was implemented in the Python system. The main aim is to develop an optimization-based random forest technique for accurate prediction of rice leaf disease by exact segmentation. Initially, preprocessing is performed to enhance the data quality and remove errors. Furthermore, separate the lesions using mean shift image segmentation and extract the shape and color features to improve the detection results of the developed model. As well, spider monkey fitness is updated in the RF classification layer for accurate classification of diseases and improves accuracy with less execution time. The experiment results show the reliability and efficiency of the designed model, it attains better performance in accuracy, sensitivity, specificity, and precision. The designed model attained 99.29% accuracy, 99.52% sensitivity, 98.76% precision, and 99% specificity proving the scalability of the designed model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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Elevating Cystic Fibrosis Detection in Lungs using HRCT Images with a Cutting-Edge CNN-Based Approach 2

Elevating Cystic Fibrosis Detection in Lungs using HRCT Imag...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Ganjam, Amrutha Sriya Reddy C, Kishor Kumar Radhika, D. Ganjam, Karthik Singh.t, Monika Akuthota, Keerthi Sai Stanley College of Engineering and Technology for Women abids Department of Computer Science Hyderabad India University of Edinburgh Department School of Informatics MSc Data Science Edinburgh United Kingdom Stanley College of Engineering and Technology for Women Deptartment. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331519056

The creation of an algorithm for recognizing pathological abnormalities in cystic fibrosis is investigated in this paper using the CNN model with a modified psp-net. Currently, Decision Trees, Random Forests, PSP Nets, and Neural Networks are utilized in the diagnosis of cystic fibrosis. Since convolutional neural networks (CNNs) can process complicated picture data rapidly and efficiently, the goal of this study is to use CNNs for the detection of anomalies associated with cystic fibrosis. The method groups distinct annotated images into a simple and efficient structure, runs the set of images through a multiscale CNN procedure, and precisely locates the lung region affected by cystic fibrosis. The result of this paper demonstrated that differences in the training dataset can impact performance, but annotating CT images and categorizing them in terms of similar pathologies can improve the accuracy of the model. The proposed CNN model achieved an Accuracy of 84 %, Precision of 74%, Recall of 79%, F1-Score of 72%, error rate of 16% Which is better when compared with existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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