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Detection of COVID-19 Using Machine Learning 1

引用

International Conference on computer Vision and Machine Intelligence, CVMI 2022

作者： Kumar, Saurav Tripathi, Rohit Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

In this paper, we aimed at experimenting and developing suitable machine learning algorithms along with some deep learning architectures, to achieve the task of COVID-19 classification. We experimented with various supervised learning algorithms for the classification of COVID-19 images and normal images, from X-ray images and radiography images. We evaluated the performance of our models on performance metrics like accuracy, precision, recall, F1 score, and Cohen Kappa score. The proposed model achieved the accuracy of 98.5%, F1 score of 97%, precision of 95%, and Cohen Kappa score of 0.88. In this experiment, we jotted down the values of performance of various performance metrics, that were obtained after tuning various parameters of the model and on different image resolutions of input image. We performed linear regression on the results to study the behavior of various performance metrics when we tuned our model with different parameters with different input image resolutions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Delivery Service Collaboration Using Blockchain Architecture

Delivery Service Collaboration Using Blockchain Architecture

引用

2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Gupta, Sheetal Agarwal, Mayank Tripathy, Somanath Indian Institute of Technology Department of Computer Science and Engineering Bihar Patna India

ISBN: (纸本)9798350313796

Due to huge developments in the e-commerce sector, the delivery service market has been growing by double digits every year. This growth has invited several collaborations on every regional level. Several third parties also join the market, partnering with the delivery services and acting as brokers. Also, most of these services are centralized, which makes them subject to single points of failure and privacy disclosure by both internal and external attackers. Moreover, they are vulnerable to denial of service (DoS) and sybil attacks launched by malicious users. As these companies have full control of the service and there is no guarantee of timely delivery, there are no provisions for penalties for delays. In this paper, we propose a blockchain-based delivery service collaboration that does not require any third or central party to control the service. The whole delivery process is implemented as a phase-lock system with smart contracts so that neither the user nor the delivery service can act maliciously. If any party tries to, then it is appropriately penalized by the blockchain. © 2023 IEEE.

关键词： Smart contract

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Trustworthiness of COVID-19 News and Guidelines 18th

Trustworthiness of COVID-19 News and Guidelines

引用

18th International Conference on Data Science and Artificial Intelligence, ICDSAI 2022

作者： Singh, Shubhanshu Nagar, Lalit Lal, Anupam Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (纸本)9783031161773

COVID-19 pandemic is a serious health concern issue over the past couple of years. It spreads mostly due to bio-contacts, which leads people to follow social distancing and stay away from social gatherings. It leads the people to bound themselves to stay with their family members at their home only, being at home, staying idle, or following work from home schedule by working online through the Internet over the electronic gadgets such as mobiles, laptops, desktops, etc. It leads the people to attach to online activities more for spending their time at their home, which enormously increases people interest in social media platforms such as Twitter, Facebook, etc. As it was a major pandemic period, it created panic and a fearful situation in society. It makes the people believe any news and guidelines spreading through social media platforms irrespective of checking their trustworthiness and truthiness of it. This pandemic period created a seriously bad impact on society’s emotional, physical, and mental health that is a great loss to a country even all over the world. Under this, many unwanted messages are spreading for one’s interest or a group to polarize their interest. In a panic situation, it is highly required of a solution that prevents the spread of these negative vibes to maintain the overall health of society. This chapter tries to implement an optimal solution using various kinds of layers and different optimization functions. It particularly gives better performance in the case of sequential data using machine learning (ML) and deep learning (DL) frameworks trained with the dataset for identifying the fake news and guidelines spread over on COVID-19. To train the model, a dataset was taken from the Twitter Application Programming Interface (API). Finally, the truthiness detection technique with social interaction is completed using Twitter dataset. The efficacy of the suggested method is demonstrated by the obtained results on a Twitter dataset. © 2023, T

关键词： COVID-19

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Comparative Analysis of TextRank and Latent Semantic Analysis Algorithms for Extractive News Summarization 3

Comparative Analysis of TextRank and Latent Semantic Analysi...

引用

3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Kodicherla, Pranathi Sathineni, Snigdha Reddy Sai, J. Shiva Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350302288

News summarization is an essential process that condenses news stories or collections of stories while retaining key details and main ideas. It offers readers a concise overview of significant events and essential takeaways from news stories, allowing them to quickly grasp the necessary information without reading the entire article. This paper presents a comparative analysis of two widely used algorithms, TextRank, a graph-based algorithm based on the PageRank algorithm, and Latent Semantic Analysis (LSA), a statistical method that captures latent semantic relationships using singular value decomposition for news summarization. The experiment results obtained using established evaluation metrics such as ROUGE-N and ROUGE-L suggest that the TextRank algorithm performs better than the LSA algorithm for all news categories and consistently demonstrates higher Precision, Recall, and F-Score values. The research aims to contribute to a deeper understanding of the strengths and weaknesses of TextRank and LSA in summarizing news by incorporating five news categories (Business, Politics, Sports, Entertainment, and technology) and using a significantly large dataset containing 2225 news articles and their summaries. This knowledge will empower researchers and practitioners to make informed decisions when selecting an algorithm for summarization tasks based on their requirements and constraints. © 2023 IEEE.

关键词： Singular value decomposition

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Optimizing Waste Management through Intelligent Machine Learning Systems: A Comparative Study 14

Optimizing Waste Management through Intelligent Machine Lear...

引用

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

作者： Shravani, Nandikante Vigneshwari, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

The purpose of this research study is to examine the use of intelligent machine learning systems in the process of optimising waste management practises. Several other approaches, such as Artificial Neural Networks, K-nearest neighbours, Logistic regression, Decision Trees, Support Vector Machine, and Random Forest, are being investigated as potential ways to improve the currently utilised waste management paradigm. On the other hand, with the implementation of algorithms for machine learning, these measures saw a huge improvement. The use of these methods brought in a remarkable increase in classification accuracy, which reached 92.349%, and a recall rate of 91.04%. This significant improvement in performance demonstrates the usefulness of machine learning in the process of optimising waste management. The Random Forest classifier proved to be the strategy that offered the best results after being compared to the other approaches investigated. In particular, the use of this classifier helped to improve the accuracy of estimates for the amount of time required to empty recycling containers. In a nutshell, the purpose of this study article was to demonstrate how effective intelligent machine learning systems can be in improving existing waste management procedures. © 2023 IEEE.

关键词： Neural networks

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A Comprehensive Review of Machine Learning Techniques for Malaria Diagnosis from Blood Samples

A Comprehensive Review of Machine Learning Techniques for Ma...

引用

2023 IEEE Conference on engineering informatics, EI 2023

作者： Muhammad Shameem, P. Malarvel, Muthukumaran Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350338522

ISBN: (纸本)9798350338522

This study paper provides an overview of the use of machine learning techniques for malaria diagnosis. The article looks at a variety of machine learning algorithms, such as supervised and unsupervised learning, deep learning techniques, and hybrid approaches, and how they might be used to diagnose malaria. In addition, widely used data sets and preprocessing methods for detecting malaria are discussed, as well as evaluation metrics for determining how well machine learning models work. The paper highlights how machine learning might improve patient outcomes and change the diagnosis of malaria, particularly in areas with limited resources. The paper concludes with a summary of key findings and contributions, limitations, and open research questions for future work. © 2023 IEEE.

关键词： Support vector machines

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Identification of Characteristics of Mewari Poems 6

Identification of Characteristics of Mewari Poems

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Jain, Shubham Dutta, Kamlesh National Institute of Technology Hamirpur Computer Science and Engineering Department Hamirpur India

ISBN: (纸本)9798350346961

Poetry is a type of literature that is used to convey a man's feeling, sentiments and imaginations. It is formed by a beautiful amalgamation of thoughts and notions in the form of words and tones. Mewari in particular is a major dialect of the Rajasthani language and is spoken by a vast majority of population in the Mewar region. It possess a great importance because many literature works created in Mewari glorifies about the great kings like Maharana Pratap, Maharana Kumbha, etc. Thus the cultural significance of this language increases exponentially. Despite having this huge significance very little work has been done in the field of Mewari language in the field of Natural Language Processing, be it identification, classification or extraction. Like Hindi, the Mewari language is written in Devanagari lipi. This paper aims to identify various elements of a Mewari literature like rhymic extent, negative tone, mood and number of a poem. The idea is to extract the data and match it with various parameters to find the hidden elements. Although, decent amount of work has been done in the field of poetry classification and analysis in different languages but this will be the first work being done on Mewari dialect. Various preprocessing operations were performed and it successfully predicted the Rhyme, Negative Tone, Mood and Number with accuracy of 83.75, 90,70 and 70.7 percentage respectively.. © 2023 IEEE.

关键词： Natural language processing systems

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Gait-Face Based Human Recognition from Distant Video 2

Gait-Face Based Human Recognition from Distant Video

引用

2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing, PCEMS 2023

作者： Mudliar, Gayatri Nath, Koushiki Saini, Yogesh Kumar, Praveen Visvesvaraya National Institute of Technology Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350310719

This paper presents a non-intrusive video-based identification system with a single camera view placed at 90° angle to the subject. The aim is to efficiently extract biometric features from a low-resolution video (acquired from a common CCTV camera) for recognizing individuals. The proposed method uses gait cues and face profile features for identification of individuals. A fusion rule is applied to these feature sets to obtain a new set of attributes. Thus, three different recognition models are developed using the gait, face, and fused feature sets. An ensemble technique is defined over the three classification models based on these sets of cues to identify an individual. This approach is experimentally validated on the CASIA-B dataset that achieves 99.33% identification accuracy. © 2023 IEEE.

关键词： Face recognition

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An Effective Fusion Model of Modified Wavelet Transform for Medical Diagnosis by using Wireless Optical Communications 1

An Effective Fusion Model of Modified Wavelet Transform for ...

引用

1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Poornima, D. Gowri, S. Jabez, J. Rajapriya, S.P. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Faster and more efficient data transfer made possible by the development of wireless optical communications has led to new possibilities in medical diagnosis. Medical diagnosis is crucial because it affects the quality of care provided to patients, the effectiveness of their treatments, and the cost-effectiveness of hospitals and clinics. This research provides a fusion model that makes use of Modified Wavelet Transform (MWT) and wireless optical communications to improve medical diagnostics, thus responding to the growing demand for better diagnostic tools. Accurate and quick analysis of clinical records, safe statistics switch, and effective use of available sources are all limitations in medical analysis. And there are unique problems with device layout, signal processing, and records protection while bringing cutting-edge technology like wi-fi optical communications into the healthcare area. This studies presents an included fusion version for greater clinical diagnostics (IFM-EMD) through fusing Modified Wavelet Transform (MWT) with wireless optical communications. The problems of useless records analysis, insecure facts transfer, and insufficient real-time diagnostics are all addressed by this technological advancement. The amassed functions are then despatched over a steady and real-time wi-fi optical communication machine. The performance of the model in phrases of precision, throughput, and safety is classified on this evaluation. The studies's rigorous simulations display the model's potential to improve scientific diagnostics and, with the aid of extension, strengthen healthcare offerings. Data accuracy, transmission velocity, and security are all measured through simulating and trying out the generation notably. © 2023 IEEE.

关键词： Wavelet transforms

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Predicting health insurance claim frauds using supervised machine learning technique 8

Predicting health insurance claim frauds using supervised ma...

引用

8th IEEE International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Veena, K. Supriya, S. Deepa, D. Cruz Antony, J. Karpura Dheepan, G.M. Pavan, Ayila Sri Dharma Siva Pagadalahariprasad Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347791

The healthcare sector is growing quickly, making it a complex system. At the same time, fraud in this sector is becoming a serious issue. Misuse of the medical insurance systems is one of the problems. The automated detection of healthcare scams uses data mining and machine learning approaches. In this work, an effort is made to provide a review of healthcare industry frauds and the methods for spotting them. A proposed solution to address issues in health insurance claims involves using a machine learning model that focuses on utilizing appropriate methods, identifying key data sources, and examining the properties of healthcare data. To gain insight into the patterns of the features, univariate and bivariate analysis are conducted on the data. Afterwards, proper data visualization is performed to determine which feature has the greatest impact. Based on the pre-processed data, a machine learning model is constructed and compared with four algorithms: Logistic Regression, Random Forest, Decision Tree Classifier, and Naive Bayes. The highest accuracy was achieved through the Decision Tree Classifier algorithm, with an accuracy of 97.03%. © 2023 IEEE.

关键词： Health insurance

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