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Exploring Missing Value Handling Techniques for Optimized KNN Heart Disease Prediction

Exploring Missing Value Handling Techniques for Optimized KN...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

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

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376135

Heart disease prediction stands at the forefront of contemporary medical research, with machine learning models such as K-Nearest Neighbors (KNN) being increasingly employed for this purpose. A critical factor influencing the performance of these models is the presence of missing data, which, if not addressed appropriately, can severely hinder predictive accuracy. This study provides a comparative analysis of different missing value handling techniques and explores the impact of each method in predicting heart disease by applying the KNN model. Several imputation methods were employed in the existing literature and techniques such as mean imputation, median imputation, mode imputation, KNN imputation, list-wise deletion, and replacement with a constant value were scrutinized. The popular Framingham heart disease dataset is selected for the study and the missing values are addressed using the techniques mentioned. Subsequently, the KNN method is used to ascertain the predictive accuracy and this highlights the deviations in the results depending upon the chosen imputation method. Remarkably, the method of replacing missing values with a predefined constant value of zero achieved a more accurate result. The study focuses on the distinct characteristics, performance, suitable data type and limitations of each missing value handing technique and it provides valuable information for practitioners and researchers who aim to maximize the effectiveness of machine learning algorithms in cardiovascular disease prediction. © 2024 IEEE.

关键词： Nearest neighbor search

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Innovations in Agri-Tech: A Review of Artificial Intelligence applications and Challenges in Modern Agriculture 2

Innovations in Agri-Tech: A Review of Artificial Intelligenc...

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

作者： Agarwal, Rupanshi Bhardwaj, Isha Sharma, Ashish Kumar Sanghi, Akash Agarwal, Gaurav Invertis University Department of Computer Science and Engineering Bareilly India Kothiwal Institute of Technology and Professional Studeis Department of Applied Science and Humanities Moradabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Artificial intelligence (AI) is driving a massive shift in the agriculture industry, which is essential to both global food security and economic stability. This review article explores the use of AI technologies in agriculture, outlining their current and potential future applications while charting their evolution. We examine several AI applications such as computer vision, machine learning, robots and the (IoT) Internet of Things, highlighting their contributions to increasing efficiency, streamlining resource allocation, and strengthening decision-making. Machine learning techniques are utilized for predictive analytics in disease identification and crop yield estimation, while computer vision supports plant health monitoring and harvesting automation. Planting, weeding, and harvesting chores are revolutionized by robotics, and data collecting for precision agriculture is made easier by IoT devices in conjunction with sensors. Additionally, by offering indepth analyses of the soil and crops, drones and satellite imagery are essential to precision farming. The technological, financial, and social barriers to the broad adoption of AI are also covered in this study. These barriers include poor data quality, expensive implementation, and moral dilemmas with regard to labour displacement and environmental effects. In this paper, we propose that the future of global food security and sustainability depends on the strategic application of AI in agriculture. In order to fully realize the potential of artificial intelligence in agriculture Strategic implementation will determine the sustain ability and future of global food security. In this order, this study attempts to direct future research and policy development by offering a thorough overview of existing technology, applications, and difficulties. © 2024 IEEE.

关键词： Smart agriculture

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Attack Detection and Mitigation in IoT using SVM 2

Attack Detection and Mitigation in IoT using SVM

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Srivastava, Animesh Tiwari, Shweta Saini, Parveen Kumar Sawan, Vikash Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India University Institute of Engineering Chandigarh University Department Computer Science & Engineering Department Punjab Mohali India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Gla University Department of Computer Engineering and Applications Mathura India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350325799

As the Internet of Things (IoT) increases along with the cost of sensors decrease, more and more people are interested in the concept of the Internet of Things (IoT). Denial of service (DoS) and distributed denial of service (DDoS) attacks could be launched against these IoT devices. When there are many different types of IoT devices, security risks increase. There is currently no protocol in place to guarantee the security of IoT gadgets. However, resilience only works when there is constant observation and flexible planning. This can dynamically and flexibly deal with security threats to IoT devices without putting any extra load on the IoT devices themselves. This study proposes a strategy for quickly detecting and counteracting suspicious activity and malicious attacks. In Machine Learning, both Linear SVM and Non-Linear SVM techniques are employed, but the latter yields more accurate results. The accuracy of linear SVM is 93%. However, the accuracy increases to 97.8% when non-linear SVM is used. © 2023 IEEE.

关键词： Denial-of-service attack

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A Comparative Study in Image Fusion Using Orthogonal and Biorthogonal Wavelet 12th

A Comparative Study in Image Fusion Using Orthogonal and Bio...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Pandy, Saroj Kumar Kumar, Ankit Kumar, Gaurav Singh, Kamred Udham Singh, Teekam Srivastava, Sandeep Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

A method called picture fusion is largely concerned with improving photographs to enhance scene visualization. In order to produce a composite image that is more significant and instructive, it attempts to maintain the key elements from each image. This method has been used in a number of industries, including robots, satellite images, and medicine. Researcher decision-making and the success of scientific endeavors are greatly influenced by the quality of the fused image. In this article, a comparison of the wavelets Haar and Bior 2.2 is made. In order to compare the two wavelets, different photos are employed. Based on metrics like the Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Standard Deviation (SD), the performance of the wavelets is assessed. Based on the results, it can be said that Bior 2.2 performs somewhat better than Haar in terms of picture fusion quality. We use orthogonal and biological wavelets to compare how well image fusion works. To fuse pictures, we utilize the Discrete Wavelet Transform (DWT) and the maximum selection criteria. The performance of the fused pictures is assessed using a number of factors, such as visual quality measurements. The advantages of biorthogonal wavelets over orthogonal wavelets for image fusion tasks are highlighted in this research, especially when working with numerous sensors. It emphasizes how important the decomposition level is for producing high-quality fused pictures. The results help advance image fusion methodologies and offer insightful information to academics and industry professionals involved in picture processing and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Discrete wavelet transforms

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Moving Target Defense - Challenges, Problems and Suitability for Internet of Things 4

Moving Target Defense - Challenges, Problems and Suitability...

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4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Gurung, Dipendra Gurung, Sandeep Pradhan, Mohan Pratap Sikkim Manipal Institute of Technology Department of Computer Applications Majitar India Sikkim Manipal Institute of Technology Department of Computer Science and Engineering Majitar India Sikkim University Department of Computer Applications Gangtok India

ISBN: (纸本)9798350323771

Traditionally cyber attackers have always had an upper hand over defenders as the latter can only react in the event of an attack. Moving Target Defense (MTD) has emerged as a potential game changer by reversing the advantage from attackers to defenders. Together with traditional defenses, MTD is expected to take cyber security up a notch. MTD solutions have been proposed for various levels, namely operating system, network, runtime platform, etc. However, improvement of existing solutions and development of new solutions are required. This paper presents an overview of MTD, the various MTD approaches, attacks mitigated, open research problems in MTD and MTD for the Internet of Things (IoT). © 2023 IEEE.

关键词： Internet of things

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Deep Learning Based Automated Lip Reading for Deaf 3

Deep Learning Based Automated Lip Reading for Deaf

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Prathyakshini Prathwini Pratheeksha Hegde, N. Vaishali Rashmi, N. Kumar, Archana Praveen NMAM Institute of Technology Department of Information Science and Engineering Karkala Nitte India NMAM Institute of Technology Department of Master of Computer Applications Karkala Nitte India NMAM Institute of Technology Department of Computer Science and Engineering Karkala Nitte India Manipal Academy of Education Manipal Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350354171

Speech recognition systems play an integral role in numerous applications, from virtual assistants to accessibility tools. This paper offers a new viewpoint to speech recognition utilizing computer vision and deep learning techniques. The proposed system is trained on a sizable dataset comprising 700 video clips of individuals uttering predefined words, add up to approximately 3 GB of data. Leveraging TensorFlow and Keras, model architecture is designed incorporating convolutional and dense layers. The training process yielded promising results, with a training accuracy of 95.7% and a validation accuracy of 98.5%, indicative of robust classification performance. The integration of computer vision enriches the system's ability to extract meaningful features from audio-visual inputs, enhancing its overall recognition accuracy. Proposed method demonstrates significant potential for real-time speech recognition applications in different areas, like human-computer interaction and assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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Inspired Detection Model For Clustering Based Surveillance System: A Non-Conventional Review

Inspired Detection Model For Clustering Based Surveillance S...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Srm Institute of Science and Technology Department of Computer Science and Engineering NCR Campus Ghaziabad Uttar Pradesh Modinagar India Srm Institute of Science and Technology Department of Computer Applications NCR Campus Ghaziabad Uttar Pradesh Modinagar India Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350382723

in current years, there has been a growing hobby in growing superior surveillance systems for detecting and monitoring anomalous activities. Traditional surveillance systems depend upon handmade policies and features, which are frequently limited of their capacity to accurately become aware of complex anomalies. To conquer this problem, we suggest a better visible anomaly detection version for clustering-based surveillance structures the use of long brief-time period reminiscence (LSTM) Diffusion manner. The proposed version incorporates the temporal context of the CCTV photos with the aid of making use of the LSTM network, which has been proven to deal with sequential data effectively. The LSTM community is used to extract movement features from the video frames, which are then fed into the clustering set of rules. This allows for the identification of comparable styles of sports and grouping them into clusters. Furthermore, we combine the concept of the Diffusion technique into the LSTM version to decorate the clustering method. The Diffusion procedure entails the gradual propagation of information among nodes in a network, which is particularly beneficial in figuring out abrupt changes in activities. This permits the version to locate anomalies with better accuracy and decrease fake alarms. To assess the performance of our proposed version, we behavior experiments on a real-global surveillance dataset. The consequences display that our model outperforms current techniques in terms of accuracy and detection price. © 2024 IEEE.

关键词： Diffusion

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Enhanced-honey bee based load balancing algorithm for cloud environment

Enhanced-honey bee based load balancing algorithm for cloud ...

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

作者： Singhal, Saurabh Ali, Shabir Shukla, Dhirendra Kumar Dagur, Arvind Papalkar, Rahul Rathod, Vinod Awasthy, Mohan Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Engineering and Technology Maharastra Navi Mumbai India Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

Cloud computing is an emerging paradigm that brings services to consumers when needed at a lower cost. Adopting cloud computing causes major problems regarding costeffective load balancing. Effective load-balancing solutions in data centers prevent underloaded and overcrowded conditions. When certain services are full of tasks, to ensure Quality of Service (QoS) some of these tasks are transferred to underloaded resources in the same data center. This paper proposes a load-balancing algorithm using an improved honey bee colony algorithm. To balance the load between resources, the algorithm employs the foraging behavior of honey bees. Jobs in overloaded resources are referred to as beehives, while underloaded resources serve as food sources. The approach decreases both the makespan and response time. The experiment was carried out on the CloudSim simulator, and the result shows that makespan was reduced by 11% and response time by 13% when compared with other static and dynamic algorithms. © 2024 The Author(s).

关键词： Cloud computing

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Enhancing Heart Disease Prediction Through VGG16 and CapsNet 5

Enhancing Heart Disease Prediction Through VGG16 and CapsNet

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Hemalatha, S. Kavitha, T. Sivabalaselvamani, D. Naveena, N. Haripriya, M. Kongu Engineering College Department of Computer Applications Erode Perundurai India Presidency University School of Information Science Bengaluru India

ISBN: (纸本)9798331518097

This research investigates the application of deep learning algorithms, specifically Capsule Networks and Convolutional Neural Networks (CNNs) based on VGG16, for the early prediction of cardiac issues. By analyzing a diverse dataset of health-related information, we aim to improve the accuracy of binary classification in predicting the presence or absence of heart disease. The study compares the performance of CapsNets and VGG16 CNNs, with VGG16 demonstrating superior accuracy at 99.8% and CapsNets achieving 98% precision. This research contributes to the advancement of predictive analytics in healthcare, enabling more accurate and timely diagnosis of cardiac conditions and paving the way for improved patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring and Evaluating Energy-Efficient Clustering Schemes in AODV: An Implementation and Comparative Study 12th

Exploring and Evaluating Energy-Efficient Clustering Schemes...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Ankit Pandy, Saroj Kumar Kumar, Gaurav Singh, Kamred Udham Singh, Teekam Srivastava, Sandeep Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

Wireless sensor networks play an important part in the monitoring and analysis of the physical environment since they compile data from a wide range of sensors. In this research study, a clustering protocol is presented with the intention of establishing an automated framework for the organization of a wireless sensor network (WSN). The data gathering, processing, and decision-making capabilities of the network are all improved as a result of the implementation of the suggested protocol, which also contributes to the network's increased overall efficiency. In this paper, we analyse the security concerns surrounding MANETs and suggest a few promising new lines of study. Several key and trust management systems have been created and suggested in order to prevent attacks from occurring from outside the MANET system. Additionally, a number of secure MANET routing protocols have been developed in order to prevent attacks from occurring from inside the MANET system. Concerning the detection of intrusions, research has been conducted on a recently developed intrusion detection framework that was developed with MANET in mind especially. In order to address the issues about MANET's level of security, measures of both prevention and detection will be used. The clustering technique that was suggested in this research makes use of a hierarchical, scalable network architecture that is focused on energy optimization. The strategy that has been offered reduces the amount of energy that is wasted while simultaneously increasing the effectiveness of the network in terms of data collection and transmission. This is accomplished by intelligently grouping sensor nodes into clusters according to the amounts of energy they possess and the capabilities they possess. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Routing protocols

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