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Visualizing the Full Spectrum Optimization of K-Nearest Neighbors From Data Preprocessing to Hyperparameter Tuning and K-Fold Validation for Cardiovascular Disease Prediction

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第2期49卷 355-374页

作者： Joseph, Jeena Kartheeban, K. Department of Computer Applications Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Computer Applications Marian College Kerala Kuttikkanam India Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India

Cardiovascular disease (CVD) is a prominent cause of death worldwide. This alarming need requires an accurate prediction model using machine learning that can detect and help prevent or mitigate the risk. This study focuses on this issue and has come up with new dimensional capabilities to enhance the K-Nearest Neighbors (KNN) algorithm to predict cardiovascular diseases at an early stage by incorporating various techniques for data preprocessing and feature selection thereby improving the efficiency of the model. The proposed model identifies the most relevant features using Principal Component Analysis. The main innovation revolves around fine tuning the hyperparameter of K-Nearest Neighbors, specifically the choice of neighbors (K), using a data driven approach to ensure accuracy across different datasets. The performance of the optimized K-Nearest Neighbors algorithm is evaluated using the Framingham heart disease dataset. This model achieved an impressive prediction accuracy of 92.46% and outperformed methods that solely rely on traditional K-Nearest Neighbors. As machine learning techniques plays an important role in the development of prediction models for early detection and prevention of cardiovascular disease, this model can be considered as a valuable tool for healthcare professionals and researchers. The core contribution of this study lies in offering a comprehensive optimization of the traditional K-Nearest Neighbors (KNN) algorithm. This includes robust data preprocessing using the Hampel filter for outlier removal, feature selection through Principal Component Analysis (PCA), and performance enhancement using grid search for hyperparameter tuning combined with 10-fold cross-validation. Unlike prior studies that apply KNN with minimal adjustments, this research emphasizes the importance of an end-to-end machine learning pipeline. This holistic refinement significantly improves the predictive performance and reliability of KNN for cardiovascular diseas

关键词： Nearest neighbor search

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Predicting Early Possibilities of Breast Cancer Detection Using Machine Learning Techniques 13

Predicting Early Possibilities of Breast Cancer Detection Us...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Khan, Rubina Liyakat Quraishi, Suhail Javed Singh, Anupam George, Remya P. Ahmad, Nazia Hussain, Sajithunisa Imam Abdulrahman Bin Faisal University Applied College Dammam Saudi Arabia Mriirs Computer Applications Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350380583

Breast cancer is one of the imperative health concerns affecting women globally, and early diagnosis can help to enhance the prognosis for the cases and patients. The main concern of the current study is to use machine learning algorithms to analyze the dataset of features of breast cancer and predict whether or not they are malignant. In the pre-processing step, we implemented basic steps and on classification steps, we applied several classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and kNearest Neighbor. Among all the classifiers, the Random Forest classifier had the highest estimated accuracy. The paper also brings into prominence the features which are selected and the affect of different sorts of machine learning etc in the diagnosis of the disease. © 2024 IEEE.

关键词： Adversarial machine learning

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Automated Detection of Language Disorders in Children Using NLP and Machine Learning

Automated Detection of Language Disorders in Children Using ...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Malathi, P. Legapriyadharshini, N. Nair, Shruthi S Sujatha, M.P. Sadaieswaran, R. Thirumalaikumari, T. Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Application Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Department of Computer Science and Engineering Chennai India Tagore College of Arts and Science Department of Computer Applications Chennai India Simats Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India

ISBN: (纸本)9798350309140

The purpose of the study is to address the essential need for a more efficient and objective technique for detecting language disorders in children, overcoming the constraints of standard manual evaluation systems. Existing systems, which utilize subjective evaluations and manual scoring, frequently result in lengthy processes and possible errors. To solve these issues, the proposed system automates the identification process by combining Natural Language Processing (NLP) and Machine Learning (ML). The system achieves a breakthrough in accuracy by normalizing text and extracting a comprehensive collection of linguistic features using advanced NLP techniques. The implementation involves difficult data preprocessing, extensive feature extraction, and the application of a cutting-edge Support Vector Machine (SVM) classifier. The proposed system achieves a 15% increase in accuracy, a 12% increase in precision, a 10% increase in recall, and an 11% increase in F1 score over existing systems. The confusion matrices show improved prediction skills, while accuracy across varied linguistic samples demonstrates the system's adaptability. Finally, the proposed system not only speeds up the detection of language disorders but also provides the groundwork for deeper insights into linguistic development, with revolutionary potential in educational and therapeutic settings. © 2024 IEEE.

关键词： Adversarial machine learning

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An IOT based Framework for Project Management 13

An IOT based Framework for Project Management

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13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Kumar, Devendra Tewari, Pragya ABES Engineering College Department of Computer Applications Uttar Pradesh Ghaziabad India School of Computing Science & Engineering Dept of Computer Science & Engineering Galgotias University India

ISBN: (纸本)9781713857938

The Internet of Things is transforming the world due to which organizations are facing many difficulties. The transformation due to these technologies is affecting several areas and project management is not an exception. The Internet of Things can help project managers to manage their projects as well as their types of equipment in an effective way. The connection between IoT devices and project management can allow businesses to achieve new innovations and services in a more effective manner while reducing the risk factor. In this paper, we proposed a framework for effective project management based on the Internet of Things (IoT). © Grenze Scientific Society, 2022.

关键词： Project management

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Discover Crypto-Jacker from Blockchain Using AFS Method 1

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4th International Conference on computer and Communication Technologies, IC3T 2022

作者： Subburaj, T. Shilpa, K. Sultana, Saba Suthendran, K. Karuppasamy, M. Arun Kumar, S. Jyothi Babu, A. Department of Computer Applications Rajarajeswari College of Engineering Bangalore India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of Information Technology Kalasalingam Academy of Research and Education Krishnan Koil Tamilnadu Srivilliputhur India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Koil TamilNadu Srivilliputhur India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of MCA Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9789811985638

ISBN: (纸本)9789811985621

The blockchain technology is used throughout the world for digital ledgers of transactions. To maintain all participant transactions on a blockchain, distributed ledger technology (DLT) is used. Blockchain technology has become a popular method of transferring huge amounts due to the pandemic situation. A hacker was able to exploit some part of a chain, smart contract, exchange, or stealing cryptocurrency, that is, a hack or a theft. Such attacks are referred to as crypto-Jack attacks. The Wormhole cryptocurrency platform was hacked in Feb 2022, resulting in a loss of $326 Millions. Many cryptocurrency Web pages are being hacked every day. Most of the attacks are focusing the financial purpose, so banking attackers are globally raised. To identify crypto-jackers and to trace out the hijackers, we proposed the new technology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Artistic Style Transfer Using Generative Adversarial Networks: A Pix2Pix Implementation

Artistic Style Transfer Using Generative Adversarial Network...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kumar, Dubasi Pavan Bhadula, Shuchi Al-Farouni, Mohammed Varshney, Neeraj Sharma, Vishal Saraswat, Manish National Institute Of Technology Integrated Master Of Science Mathematics Department Of Mathematics Patna India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This investigation explores the viability of four noticeable models, specifically Pix2Pix, Neural Style Transfer (NST), Fast Neural Style Transfer (FastNST), and CycleGAN, within the space of aesthetic style exchange. The think about envelops a fastidious assessment of these models, investigating their capabilities in generating outwardly engaging and elaborately reliable pictures. Through broad tests and quantitative evaluations, Pix2Pix developed as a strong choice, accomplishing a Crest Signal-to-Noise Ratio (PSNR) of 26.78 dB, a Basic Likeness List (SSIM) of 0.832, and a Fréchet Initiation Separate (FID) of 54.21. NST, exceeding expectations in style devotion, achieved a PSNR of 29.45 dB, an SSIM of 0.905, and an FID of 72.03. FastNST, known for its real-time preparation, illustrated an adjusted execution with a PSNR of 28.12 dB, an SSIM of 0.892, and an FID of 68.54. CycleGAN, outlined for unpaired picture interpretation, accomplished a PSNR of 27.65 dB, an SSIM of 0.874, and an FID of 63.72. The results give profitable experiences into the comparative qualities and weaknesses of these models, educating their appropriateness in different artistic assignments. © 2024 IEEE.

关键词： Generative adversarial networks

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Automatic Detection and Epidemiologic Trends in Head and Neck Cancer Using Fast-R-CNN and SVM Methods 7

Automatic Detection and Epidemiologic Trends in Head and Nec...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Sakthivel, M. Patil, Sonal Kamarunisha, M. Pokhariyal, Rajesh Pal, Subharun Tiwari, Mohit Malla Reddy College of Engineering Department of CSE-AIML Hyderabad India G H Raisoni Institute of Engineering and Business Management Jalgaon Department of CSE-IT Maharashtra India Department of Computer Applications Tamilnadu Perambalur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu & Kashmir India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350340600

H ead and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer in the world. Despite improvements in diagnosis and therapy, the five-year survival rate for patients with advanced HNSCC has risen only slowly. This is mostly due to the high number of patients who present with an advanced disease stage at diagnosis, as well as the high frequency of relapse and secondary tumors. Protein biomarkers that aid in the early diagnosis of primary HNSCC or relapse hold promise for improving clinical outcomes. Finding early changes in the mucosal linings previous to the development of invasive tumors and properly assessing the likelihood of malignant transformation, both currently tough activities, may be important. The proposed procedure makes use of preprocessing, feature extraction, segmentation, and model training. Anisotropic Diffusion Filter and Gaussian Filter are used in preprocessing. Extraction of the techniques follows preprocessing. The Grey Level Run Length matrix is used for feature extraction. Segmenting the nucleus is a highly recommended step in the segmentation process. Training is performed using an RCNN- SVM model. When compared to two other models, RCNN and SVM, the proposed method excels. © 2023 IEEE.

关键词： Support vector machines

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Comparative Analysis of Multilingual and Cross-Lingual Models for Aspect-Based Sentiment Analysis 13

Comparative Analysis of Multilingual and Cross-Lingual Model...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Hussain, Sajithunisa Khan, Rubina Liyakat Quraishi, Suhail Javed Singh, Anupam George, Remya P. Ahmad, Nazia Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Computer Applications Mriirs Haryana Faridabad India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350380583

Aspect-Based Sentiment Analysis (ABSA) is a finegrained sub-task of Natural Language Processing concerned with opinion bearing on certain aspects contained in text. The shift towards multicultural content on the digital platforms implies the need to have models which will perform the sentiment analysis of the multilingual content. In this study, a detailed comparison of ten developed methodologies of ABSA models is presented, including the theoretical background, data sets, and the assessment of performances of each model. We explore various techniques that are based on the lexical resources, statistical and machine learning, and state of the art deep learning networks. The study also sheds light on the potential strength and weakness of the proposed method and points out the possible further research directions that can be undertaken for improving the multilingual or cross-lingual sentiment analysis. © 2024 IEEE.

关键词： Contrastive Learning

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Autonomous Vehicle Navigation Systems: Machine Learning for Real-Time Traffic Prediction

Autonomous Vehicle Navigation Systems: Machine Learning for ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Hundekari, Sheela Parida, Prasanta Mittal, Tanusha Sehgal, Archana Bhavana, Munugapati LTIMindtree Limited Telangana Hyderabad India School of Computer Applications Pimpri Chinchwad University Pune India School of Rural Management KIIT University Odisha Bhubaneswar India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

By making transportation safer and more efficient, autonomous vehicle navigation systems are transforming the transportation industry. Autonomous cars are able to make route selections based on accurate, up-to-theminute traffic predictions, which help them avoid congestion and maximize their trip time. The use of machine learning algorithms to reliably forecast traffic conditions in real time for use in autonomous vehicle navigation is the focus of this research. Deep learning, support vector machines, and random forests are just a few examples of the machine learning models that can accurately forecast traffic conditions by mining massive datasets collected from GPS data, traffic sensors, and past trends. The suggested method incorporates these models into an autonomous vehicle navigation system that operates in real-time, enabling adaptive modifications according to the flow of traffic. We also look at how things like construction, road accidents, and weather affect the precision of traffic predictions. Based on the outcome of decreased trip time, increased safety and efficiency, results demonstrate how navigation systems supplemented with machine learning, far surpass conventional routing approaches. The study suggests that further research needs to be conducted on hybrid algorithms for better traffic prediction, and multi modal data integration to further optimize autonomous vehicle navigation and in general, machine learning presents great promise in this area. © 2025 IEEE.

关键词： Traffic congestion

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Smart Home Automation with IoT Sensor Systems for Energy Conservation using Zigbee and Wifi 5

Smart Home Automation with IoT Sensor Systems for Energy Con...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Jagannathan, Srinivasan Deepa, V. Sakthi Karthi Durai, B. Rajan, M. Sundar Celin, J. Jeya A. Dhamodaran, Sasikala Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India Tiruppur Kumaran College for Women Pg and Research Department of Computer Science Tamil Nadu Tiruppur India Kalasalingam Academy of Research and Education Computer Science and Engineering India Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Kalasalingam Academy of Research and Education Department of Information Technology Viruthunagar Krishnankoil626126 India Rajasthan Jaipur India

ISBN: (纸本)9798350379945

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency. © 2024 IEEE.

关键词： Smart homes

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