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Performance and Evaluation of Classification Algorithms Using Machine Learning with Comparative Study for Heart Disease Prediction

Performance and Evaluation of Classification Algorithms Usin...

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Hossain, Md. Manik Ashraful Alam, Md. Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

The leading cause of unseasonable death worldwide is heart complaints. Day by day the causes of heart disease are increasing at a rapid-fire rate and it's veritably important and concerning to prognosticate any such disease beforehand. Predicting how illness will affect a person is a delicate challenge. Machine learning is being applied in different fields around the world. In the healthcare sector, there is no exception. Data classification models and machine learning algorithms stoutly introduce diagnostic guidelines and enable experts to increase the effectiveness of the diagnostic process. The body's remaining organs are given advanced precedence over the nucleus. It provides the body with oxygen. The distribution of heart illnesses among medical practitioners can be estimated via data exploration. Medical facilities can examine various disorders and evaluate emerging diseases thanks to data collecting. Predicting the condition based on recent medical research has the biggest impact. To learn statistics more effectively, a variety of methods are being investigated in the scientific community. The renovation has had a big impact on the metropolitan community's way of life in addition to improving it. In this situation, it's crucial to offer a comprehensive tool that will enable medical professionals to foresee the sickness. Different machine learning applications suggest varying prediction precision. It should be analyzed with Logistic Regression, KNN, Decision tree, Random Forest, SVM, Gaussian NB, Ada Boost Classifier Gradient Boosting Classifier, Quadratic Discriminant Analysis, and MLP Classifier with comparative mean of three data sets. It will be better to investigate the accuracy of prediction from the most concerning algorithms of Machine learning with recall and f-score of the heart data and present it in the table with visual representation. This underpinning research has shown that multilayer perceptron with cross-validation has surpassed all other alg

关键词： Heart

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Reviewing the Landscape of Deep Learning Approaches for Breast Cancer Detection 4

Reviewing the Landscape of Deep Learning Approaches for Brea...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Maiti, Souvik Juneja, Sonam Goyal, Reema Chaudhry, Navneet Chandigarh University Dept. of Computer Science and Engineering Gharuan India

ISBN: (纸本)9798350306118

Rapid and precise identification of breast cancer is crucial in order to improve patient outcomes for this prevalent and life-Threatening disease. The emergence of AI and deep learning techniques has opened new avenues for enhancing breast cancer screening and diagnosis. CNNs and ensemble models, among other deep learning architectures, have demonstrated promising results in the analysis of medical pictures and the extraction of useful characteristics for precise diagnosis. Additionally, the use of transfer learning and pretrained CNN models has demonstrated superior performance in classifying breast tumor images. Studies investigating the usage of one-dimensional data conversion into pictures and utilizing stacked ensemble models for increased classification accuracy show that the use of deep learning extends beyond image-based research. These techniques offer potential solutions for addressing the challenges in breast cancer detection, such as distinguishing between benign and malignant tumors and predicting prognosis. By automating the diagnosis process, deep learning algorithms can assist medical professionals in making informed decisions and providing personalized treatment plans. Enhancing the accuracy and reliability of breast cancer prediction models necessitates further research into the incorporation of larger datasets, processing multi-modal data, and advancements in image generation methods. Overall, AI and deep learning hold great promise in revolutionizing breast cancer screening and diagnosis, ultimately leading to improved survival rates and better patient care. © 2023 IEEE.

关键词： Support vector machines

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Blockchain Unleashed: Empowering Information-centric Network Computing in Cloud, Fog, and Edge Services 14

Blockchain Unleashed: Empowering Information-centric Network...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Zambre, Pranav Panchal, Mitali Chouhan, Ankit Parul University Dept. of Computer Science and Engineering Vadodara India

ISBN: (纸本)9798350335095

Blockchain technology has been gaining a lot of attention due to its potential to improve data security, privacy, and decentralization. In this paper, we explore the use of blockchain in cloud, fog, and edge computing services. We analyze the unique characteristics of these services and how blockchain technology can enhance them. One of the benefits of blockchain in cloud computing is that it allows for secure and transparent data sharing among multiple parties. Blockchain can ensure the authenticity and integrity of data with smart contracts, which can automate processes and eliminate the need for intermediaries. This can result in cost savings and increased efficiency. In fog computing, blockchain can be used to enable secure and real-time data processing at the network edge. This can improve latency and reduce bandwidth consumption by processing data closer to the source. Additionally, blockchain-based fog computing can provide better data privacy and security for connected devices. Edge computing can benefit from blockchain by providing a trustless environment for data sharing and storage. Edge devices can act as nodes in a blockchain network, allowing for decentralized control and increased security. This can enable new use cases such as peer-to-peer transactions and autonomous decision making. Overall, blockchain has the potential to empower information-centric network computing in cloud, fog, and edge services by providing increased security, privacy, and efficiency. However, there are still challenges that need to be addressed such as scalability, interoperability, and governance. © 2023 IEEE.

关键词： Smart contract

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Enhancement of Accuracy for Iris Presentation Attack

Enhancement of Accuracy for Iris Presentation Attack

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Priyanka, V. Shyam, Gopal K. Presidency University Dept. Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350300826

Biometrics is the assessment and statistical examination of an individual's distinctive physical and behavioural traits. Identification, access control, and identifying those who are being monitored are the major areas where technology is focused. In security systems, biometric-based recognition has been substituting traditional recognition techniques. Iris recognition (IR) is more utilized in today's biometric technologies, which are applied to various devices for various security concerns. The effectiveness of IR systems has significantly improved during the past ten years. This is due to recent developments in deep convolutional neural networks (CNNs) and computer vision, as well as the availability of enormous training datasets. A presentation attack is a kind of attack where an imposter creates artificial or fake out of genuine. This paper proposed an effective approach to increase the accuracy in detecting iris presentation attacks and design the state-of-the-art of existing CNN techniques from 2015 to 2023. The proposed method is a Dual Channel Convolutional Neural Network Presentation Attack Detector(DC-CNNPAD) for increasing the accuracy of detecting real iris. The experiment was performed on the sample LivDet-2017 dataset to identify the effectiveness of the model in order to detect the artifact. The results obtained from the detection model on the sample dataset are extremely good. © 2023 IEEE.

关键词： Biometrics

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An Ensemble Learning Approach to Detect the Presence of Diabetic Retinopathy based on Inception Module 2

An Ensemble Learning Approach to Detect the Presence of Diab...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Devi, T. Aswini Yamana, Sathwik Harshankar Reddy, G. Sangita, Vedh Sivaji Kumar Kulkarni, Swapneel Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665456302

In recent times, Diabetic Retinopathy (DR) has become a complication of major degree for diabetic patients, in which the blood vessels present in the eye retina are severely damaged. This in most cases has lead to a loss of vision as observed from records of DR patients, and if left untreated, can cause blindness. As per the estimates of the World Health Organization, DR will impact around 224 million people by 2040. This research study presents a convolutional neural network (CNN) model, followed by employing ensemble learning over various ML algorithms through max voting, for the task of image classification for detecting Diabetic Retinopathy using color fundus images, with the goal of improving the accuracy and efficiency of the diagnostic process. The proposed model is designed to address the challenges of real-world data, such as variability in the appearance of images that belong to the same class and class imbalance. The dataset used for this study is EYEPACS, which is widely used for training and testing models for detecting Diabetic Retinopathy. The Metrics that are used for measuring the performance of the proposed model are accuracy, precision, and F1-S core. This study obtains an accuracy of 95.4 percent. © 2023 IEEE.

关键词： Blood vessels

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Enhancing Biomedical Literature Analysis to Predict Drug Interactions Using Multi Criteria Decision Making and LSTM-CNN Model

Enhancing Biomedical Literature Analysis to Predict Drug Int...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Sankaran, A. Sathiyamurthy, K. Puducherry Technological University Dept. of Computer Science and Engineering India Puducherry Technological University Faculty of Computer Science and Engineering India

ISBN: (纸本)9798350315127

The extraction of information from unstructured text has become a crucial task in the biomedical literature field. Specifically, the analysis of various relationships such as Drug Food, Drug Disease, Drug non-prescription and Drug Drug interactions has gained significant importance. To accomplish this, a combination of Multi Criteria Decision Making (MCDM) and machine learning techniques has been employed. A dataset has been curated and utilized to train a model based on Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). The trained model enables accurate predictions and identification of relevant information pertaining to drug interactions in biomedical literature. This approach enhances the understanding and knowledge extraction from vast amounts of unstructured text data, leading to improved insights and potential advancements in the field of biomedical research. © 2023 IEEE.

关键词： Long short-term memory

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Tagging Speech for Words in Low Resourced Monolingual Contexts of Sanskrit Shlokas

Tagging Speech for Words in Low Resourced Monolingual Contex...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Hegde, Thanmayi S. Vindhya, V. Badri Prasad, V.R. Dept. Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9798350325188

Natural Language Processing is widely used in Machine translation which helps in replacing many tedious and difficult tasks and producing qualitative and efficient results. Sanskrit is one of the primitive languages of India. It is also the oldest, purest and most systematic language in the world. It is a magical language which helps in generating different words from a single word by prefixing and suffixing the word with different helper words to same root word. Shloka in Sanskrit are phrases or words that represent a poem or a hymn. The famous texts written entirely in shlokas are the "Ramayana" and "Mahabharata." This paper aims to generate the POS tags for each word in Sanskrit Shlokas using various machine learning and deep learning algorithms thus helping in understanding the actual verbal beauty of Sanskrit Shlokas in widely spoken language English. The proposed work implements Parts of Speech Tagging for words in Sanskrit Shlokas using Conditional Random Field, Gaussian NB and Bi-directional LSTM models. Of the implemented approaches, Bi-directional LSTM gives the best result. © 2023 IEEE.

关键词： Bidirectional Long Short-Term Memory Networks Character Embeddings Conditional Random Fields Corpus building for Sanskrit Data Preprocessing Deep learning Gaussian Navie Bayes Machine translation systems Natural Language Translation Parts of speech tagging (POS)

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End-to-End Person Re-Identification for Security and Analysis 4

End-to-End Person Re-Identification for Security and Analysi...

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4th International Conference for Emerging Technology, INCET 2023

作者： Vishwas, R. Sathvik Rao, M.P. Siddhanth, M. Uthpal, P. Kanwal, Preet PES University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335750

There are millions of CCTVs deployed in public and private places which gather tremendous amounts of video data. It requires a huge amount of manpower to monitor this video data and get the required information extracted in a timely manner. To handle this problem the paper proposes a novel architecture to automate the Video surveillance system, capable of tracking each individual across a multi camera network in real time. Numerous important technologies are incorporated into the Video surveillance system, such as person detection, person re-identification and person *** effectiveness of person re-identification has been greatly improved by researchers, but majority of these methodologies use manually annotated bounding boxes for person detection and only concentrate on matching between query image and cropped gallery images with no regard to person tracking. The paper proposes the following pipeline of processes to build an end-to-end video surveillance system.•Collecting CCTV footage from a network of cameras•Person Detection using YOLOv4•Person Re-Identification using Locally Aware Transformer model for feature extraction•Person Tracking using Deep - SORT algorithm•Logs generation for further analysis and insightsThe Person Re-ID model trained using the aforementioned algorithms achieved an average Rank 1 accuracy of 81% when tested using the custom generated datasets. The resulting product is an End-to-End Person Re-ID Framework deployed as a Web portal which tracks persons across cameras and generates human readable logs for analysis. © 2023 IEEE.

关键词： Feature extraction

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Unlocking Efficiency in Agricultural Supply Chains: A Secure and Transparent Approach through Blockchain Technology

Unlocking Efficiency in Agricultural Supply Chains: A Secure...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Lohith, J.J. Shreya, S. Hamsa Priya, J.L. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India D.S.C.E Dept. of Information Science and Engineering Bangalore India B.M.S. College of Engineering Dept. of Electronics and Telecommunications Bangalore India

ISBN: (纸本)9798350372502

Supply Chain Management (SCM) involves orchestrating various processes that extend from acquiring raw materials to delivering finished products to end consumers. The key objectives of a proposed supply chain in agriculture, emphasizes information-driven processes, cost reduction, and customer impact. It creates a versatile system capable of managing the supply chain for diverse commodities within a country, while also addressing the crucial need for increased transparency and traceability in data throughout the chain of activities. In our proposed approach to agricultural supply chain management (SCM), the focus is on creating a secure, transparent, and traceable SCM. To achieve this, we leverage cutting-edge technologies like the Ethereum blockchain and the Interplanetary File System (IPFS). The resulting software is a Decentralized Application (DApp) that orchestrates a series of activities within the agricultural industry, using cryptocurrencies to establish a seamless and efficient supply chain management system. This software platform is fortified by the secure algorithms of blockchain, providing an added layer of reliability. Additionally, our application incorporates transparency and traceability, ensuring user-friendly functionality and making it more accessible for users. © 2024 IEEE.

关键词： Blockchain

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Active Learning Using Multifeature Kernel Sparse Representation Classifier for Hyperspectral Image Classification 3

Active Learning Using Multifeature Kernel Sparse Representat...

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3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

作者： Bortiew, Amos Patra, Swarnajyoti Tezpur University Dept. of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350325591

Active learning is a popular technique that mitigates the need for large training samples for hyperspectral image classification. The success of this technique is dependent on the query function designed to select informative samples for labelling. In the literature, most of the query functions are defined by exploiting spectral information. In this research, a novel query function for active learning is defined that considers both the spectral and spatial information of the hyperspectral image using multiple feature sets. The multiple feature sets provided complementary information for selecting those informative samples which are not only informative in spectral perspective but also from spatial perspective. Experiments conducted using two real hyperspectral datasets show that the proposed technique outperformed many state-of-the-art techniques. © 2023 IEEE.

关键词： Image classification

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