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Analysis and Prediction of COVID-19 Disease using Machine Learning 14

Analysis and Prediction of COVID-19 Disease using Machine Le...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Parmar, Shabnam Rani, Rinkle Kalra, Nidhi Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In this research, the symptoms and other factors of a patient are utilized to train machine learning algorithms to predict whether the patient would die from or recover from (COVID19). It is probable that the coronavirus (COVID19) will create the highly infectious Coronavirus illness COVID19 (SARSCoV2). By coughing, sneezing, speaking, or inhaling, this virus can be transmitted from an infected person's lips and nose to small liquid particles. The size of these tiny atoms' inhalation droplets and aerosols vary, with bigger droplets being larger than smaller atoms. COVID19 is transferred by inhalation or by touching your eyes, nose, or mouth with your fingers after meeting a contaminated surface. When a big number of people are present, the COVID-19 virus can spread rapidly. We will need to examine the COVID19 dataset to see which models are the most accurate in estimating fatality rates for the virus's most vulnerable victims. Machine learning is used to compute and evaluate the performance of a variety of prediction models. We have used K-nearest neighbor (KNN), Support Vector Machine (SVM) classifier, Gaussian naive Bayesian (GNB), Decision tree (DT), and Logistic regression (LR) for the prediction of death and recovery of symptomatic patients. In this research, a variety of feature selection and extraction strategies were used, and prediction accuracy for feature selection methods for the KNN model and feature extraction methods for the GNB model both reached up to 96 percent. The k-nearest neighbor has performed and predicted high accuracy of 96% in both feature selection and extraction techniques. © Grenze Scientific Society, 2023.

关键词： Support vector machines

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Candlestick Chart Based Stock Analysis System using Ensemble Learning 1

Candlestick Chart Based Stock Analysis System using Ensemble...

引用

1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Varghese, Angel Ann Krishnadas, J. Kumar, R. Satheesh Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350336009

The 18^{\mathrm{t}\mathrm{h}}-century candlestick charts, which were first utilized in the Japanese rice market, are now frequently used in trading tactics across all financial markets. Candlestick charts make it possible to comprehend an asset's opening, high, low, and closing values all in one image. In addition to these benefits, the abundance of candlestick chart patterns makes practical application challenging. A software framework that employs candlestick charts to forecast trend direction was built for this study. There are four stages of the project. A system that can identify candle patterns is developed in the first stage. The second stage involves executing training and testing procedures on data sets with labeled candlestick chart types and trend directions in order to assess the model's performance. During the machine learning phase, open-source techniques like xgboost were applied. In the project's final stage, it was discovered that the strategy focused solely on identifying candlestick patterns and taking positions in line with the trend based on the suggested methodology produced larger profits in 11 global indices than the Buy and Hold strategy. In comparison to the current accuracy of 53.8%, the model's average forecast accuracy is 59.42% © 2023 IEEE.

关键词： Commerce

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Machine Versus Human Intelligence: An Illustration from the CSES Problem Set

Machine Versus Human Intelligence: An Illustration from the ...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Dutta, Anurag Ray, Samrat Kumarasankaralingam, Lakshmanan Kumar, Pijush Kanti Ghosh, Soubhik Kumar, Padmanavan Government College of Engineering & Textile Technology Department of Computer Science Serampore India International Institute of Management Sciences Maharashtra Pune India Saint Joseph University Department of Mathematics Nagaland India Government College of Engineering & Textile Technology Department of Information Technology Serampore India Bengal College of Engineering And Technology Department of Computer Science Durgapur India Kalinga Institute of Industrial Technology Department of Computer Science Odisha Bhubaneswar India

ISBN: (纸本)9798350379716

Following the release of CHATGPT in November 2022, both textual and visual LLMs evolved a long way. Several comments have been made by experts on the expertise and intelligence possessed by these Large Language Models;Amongst all, the contest between Human and Machine Intelligence is that discussion that catches great heights. This research focuses on the analytical and reasoning abilities of LLMs and further compares them to those of Human Intelligence. As a benchmark for the same, the CSES Problem Set, a well-structured set of algorithmic problems, touches a wide spectrum of computational syllabi, like Data Structures, Mathematics, Decision Theory, etc. Our analysis of the contrast in abilities leads to the conclusion that LLMs need a long way to go, and thus LLM researchers rather than modeling more and more visually appealing LLMs (like Sora, Dall-e, etc.) should focus more on the abilities of the existing textual models like CHATGPT. Our research shows that the Machine Intelligence is ≈ 46 % lower than that of the Human Intelligence. © 2024 IEEE.

关键词： Problem oriented languages

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Automatic Document Summarization of Unilingual Documents: A Review 6th

Automatic Document Summarization of Unilingual Documents: A ...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Anan, Sabiha Islam, Nazneen Ali, Mohammed Nadir Bin Bhuiyan, Touhid Bijoy, Md.Hasan Imam Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Department of Computer Science and Engineering Daffodil International University Birulia Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9783031503269

Automatic summarization of documents is the creation of short and accurate summary of a source document. Due to the abundance of information on various platforms of our social life, shortening of that information is greatly needed for us. Thus, document summarization automatically provides short, precise and informative abstract of a large document. In this era of information, as the necessity of document summarization is increasing continuously, the techniques of summarizing document(s) are also evolved throughout the years. In this paper, a review on summarization of unilingual documents has been presented. A detailed description of some prominent approaches for document summarization and their performances are also analyzed in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Abstracting

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Advanced Machine Learning Techniques for Predicting Health Status in Smoke-Exposed Environments 2

Advanced Machine Learning Techniques for Predicting Health S...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Duraisamy, Premkumar Nitheshvaran, B. Kumar, Nalin S. Irshad, B. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350340235

S moker health status is a vital focus in public health and medical research due to smoking's impact on preventable diseases and premature death worldwide. Recognizing the diverse effects of smoking on health is crucial, spanning both immediate and long-term consequences across multiple physiological systems. This research underscores the pressing importance of comprehending smoking's far-reaching impacts on health and emphasizes the necessity of effective interventions and cessation strategies to mitigate i ts adverse effects. This study aims to utilize diverse machine learning algorithms to predict the health status of smokers accurately. The primary objective is to equip healthcare professionals with a reliable diagnostic tool for early and precise identifi cation of smoking-related health risks, ultimately saving lives. Leveraging a comprehensive dataset, various algorithms, such as Boost, LightGBM, CatBoost, Random Forest, AdaBoost, and others, are explored. Through rigorous experimentation, the Boost algorithm stands out, achieving an impressive 96.2% accuracy in predicting the health status of smokers. © 2023 IEEE.

关键词： Classification Kaggle dataset Machine learning Smoker health status Voting ensemble method

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Towards Enhancing Child Safety: A Deep Learning Approach to Detect Child Safe and Unsafe Objects

Towards Enhancing Child Safety: A Deep Learning Approach to ...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Khan, Fairuz Areefin Dey, Ashim Chittagong University of Engineering and Technology Department of Electrical and Electronic Engineering Chittagong4349 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong4349 Bangladesh

ISBN: (纸本)9798331535476

The safety and well-being of children are a paramount concern, and the accurate detection of potentially haz-ardous objects is crucial in ensuring their protection, especially in indoor situations. In the Artificial Intelligence (AI) age, the future belongs to domestic robots;they will be used for child education, surveillance, and healthcare support. Early identification of unsafe objects and intervention by these next-generation social family robots can play an essential role in providing a safer environment for children. Keeping these issues in mind, this study aims to tackle daily risks and accidents that endanger children by using computer vision to detect unsafe objects. To achieve this goal, a unique dataset namely ChildSUn is introduced containing approximately 5350 images of child-safe and unsafe objects of ten categories. Various CNN architectures, including custom CNN, VGG16, VGG19, ResNet50, Inception-V3, EfficientNet-B0, and MobileNetV2, are implemented on the prepared dataset. A deep dive analysis is performed on the implemented models. The EfficientNet-B0 model showed exceptional performance, reaching a remarkable level of accuracy of 99.63% on the test dataset. These findings underscore the potential of EfficientNet-B0 as an effective solution for real-world child safety applications and encourage further exploration of deep learning techniques to enhance child protection globally. We hope this work will pave the way for future social household robot development, which will ensure child safety in any place. © 2024 IEEE.

关键词： Deep learning

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SEOHFB: A Secure Environment for Organization based on Hyperledger Fabric Blockchain 14

SEOHFB: A Secure Environment for Organization based on Hyper...

引用

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

作者： Rahman, Arifur Moinuddin, Moinuddin Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350335095

Blockchain and hyperledger are decentralized approaches used for maintaining security, secrecy, and integrity of organization based business transactions. In our study we have built a permissioned blockchain using HyperLedger Fabric framework that able to handle data storage and secure intra-and inter-organizational communication. The key reason for which we have chosen HyperLedger Fabric framework is that, it uses RAFT consensus algorithm that provides permissioned blockchain network by creating a channel that was unavailable to the public blockchain network. We have made a smart contract for the permissioned blockchain by developing and writing the chaincode. We have used Fabric-CA server to generate crypto material. Then we have set up the network configuration, organizations, ordered peers, endorsing peers, anchor peers and developed client application for Hyperledger Fabric. We have integrated the web-front with fabric using WEB-API. © 2023 IEEE.

关键词： Blockchain

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Automated Exoskeleton Prosthetic Suit With Monitoring System 4

Automated Exoskeleton Prosthetic Suit With Monitoring System

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Jeeva, C. Sagar, Lalit Kumar Srivastava, Mudit P. Abinaya, R. Narmatha, M. Shanmuga Priya, M. Sri Sairam Engineering College Department of Electrical and Electronic Engineering Tamil Nadu India Srm Institute of Science and Technology Department of Computer Science Engineering Delhi India Srm Institute of Science and Technology Department of Physics Delhi India

ISBN: (纸本)9798331508845

The Automated Exoskeleton Prosthetic Suit with Monitoring System aims to develop a fully functional prosthetic suit with coordinated strength and response time. The suit is ameliorative and can be worn or removed as needed. Designed as a wearable inner suit, it operates autonomously, incorporating sensors and navigation systems to reduce inequality. In regions where the suit is worn, it analyzes blood flow using ultrasound Doppler principles and aids in improving circulation. The exoskeletons support or replace impaired body parts, allowing users to function properly. Electrical motors and precise position control form the core of its most common design. This suit explores the entire body using various actuators, tasks, and degrees of freedom. The product can be worn as an inner suit, featuring an autonomous sensor and navigation system to eliminate inequality. It measures blood flow in the regions covered by the suit, enhancing circulation. The primary goal of the product is to detect even small objects and prevent the wearer from falling. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Artificial Intelligence Techniques and Methodology Available for Lung Cancer Detection

Artificial Intelligence Techniques and Methodology Available...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Kanaga Priya, P. Sivakumar, T. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350333602

Cancer is a term used to describe the body's aberrant cells growing out of control, which can occur in various body parts. With over a hundred types of cancer known to exist, delays in treatment can lead to severe health issues and, in some cases, loss of life. This study reviews the current methods used to detect lung cancer through image processing. The study suggests that artificial intelligence (AI) is effective in processing large datasets, providing accurate and efficient results for cancer detection. However, implementing these systems on a large scale presents several challenges, such as the capture of images, preparation of images, segmentation, and information management. Classification strategies must also be developed to ensure compatibility with AI. This study examines various imaging techniques and methodologies available for detecting cancer using a computer-aided user interface, which is essential to improve healthcare and meet the growing patient population's needs. © 2023 IEEE.

关键词： Diseases

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Evaluating Security Risks and Protecting Conversational AI: An in-Depth Vulnerability Assessment and Security Testing of the ChatGPT Android Application 2nd

Evaluating Security Risks and Protecting Conversational AI: ...

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2nd International Conference on Advances in Computational Intelligence and Informatics, ICACII 2023

作者： Majeti, Srinadh Swamy Shyam Prasad, T. Thota, Sunil Kumar Department of Artificial Intelligence Anurag University Hyderabad India Department of Computer Science and Engineering Anurag University Hyderabad India Department of Computer Science and Engineering Keshav Memorial Institute of Technology Hyderabad India

ISBN: (纸本)9789819747269

Recent years have seen a surge in the popularity of conversational AI applications like ChatGPT, revolutionizing human–machine interactions. However, as these AI chat systems become more integrated into daily life, ensuring their security is paramount. This research paper conducts a thorough security assessment of ChatGPT, using static and dynamic testing. The study uncovers significant vulnerabilities, including potential data leaks and susceptibility to adversarial attacks, raising concerns about user privacy and potential exploitation. Recommendations for mitigation are provided to enhance ChatGPT's security. This research contributes to the growing body of knowledge on conversational AI security, stressing the importance of proactive measures to protect user data and maintain a secure conversational experience. It also highlights the necessity for ongoing security assessments as AI technology advances. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Differential privacy

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