2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)最新文献_第8页

Big Data Analytics with Deep Learning based Intracranial Haemorrhage Diagnosis and Classification Model 基于深度学习的颅内出血诊断和分类模型的大数据分析

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010826

Munirathinam. T, Shrikant Upadhyay, R. Babitha Lincy, Jency Rubia J, R. Beaulah Jeyavathana, Anandbabu Gopatoti

{"title":"Big Data Analytics with Deep Learning based Intracranial Haemorrhage Diagnosis and Classification Model","authors":"Munirathinam. T, Shrikant Upadhyay, R. Babitha Lincy, Jency Rubia J, R. Beaulah Jeyavathana, Anandbabu Gopatoti","doi":"10.1109/ICAISS55157.2022.10010826","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010826","url":null,"abstract":"Owing to the rapid growth of medical imaging technologies, medical image analysis is entered the period of big data for proper diagnosis of diseases. Intracranial hemorrhage (ICH) means a pathological disorder that requires quick decision making and diagnosis. Computed tomography (CT) can be accurate and extremely dependable diagnosis method for detecting hemorrhages. Automatic recognition of ICH from CT scans by using a computer-aided diagnosis (CAD) method was helpful in detecting and classifying various grades of ICH. Due to the recent development of deep learning (DL) methods in image processing applications, numerous medical imaging approaches use it. This article introduces innovative Big Data Analytics with Deep Learning based Automated Brain Intracranial Haemorrhage Diagnosis and Classification Model (BDDL-IHDCM). The presented BDDL-IHDCM model examines the computed tomography (CT) scans for detecting and classifying ICH. To accomplish this, the presented BDDL-IHDCM model applies bilateral filtering (BF) technique for noise elimination procedure. The BDDL-IHDCM method employs neural architectural search network (NASNet) feature extractor with Bayesian optimization (BO) based hyperparameter tuning. For ICH detection and classification, a grey wolf optimizer (GWO) with auto encoder (AE) model is utilized in this study. To handle big data, Hadoop MapReduce will be used. To exhibit the significant performance of the presented BDDL-IHDCM method, a comprehensive simulation analysis is made by making use of benchmark dataset. The experimental outcomes indicate the betterment of the presented BDDL-IHDCM method to other DL models with maximum accuracy of 96.22%.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127608876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Review on the Pre-owned Car Price Determination using Machine Learning Approaches 利用机器学习方法确定二手车价格的研究综述

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010958

Punitha Ponmalar P, Angelin Christinal C

引用次数: 2

AI Based Third Eye for Alzheimer Patient 基于AI的阿尔茨海默病患者的第三只眼

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010763

S. Mahaboob Basha, S. Vaishali, B. Vinothini, S. Shalini

引用次数: 0

Blockchain Technology Based Crowdfunding Using Smart Contracts 基于区块链技术的众筹使用智能合约

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010749

P. Shelke, Shreyash Zanjal, Rishikesh Patil, Digvijay Desai, Harish Chavan, Varad Kulkarni

{"title":"Blockchain Technology Based Crowdfunding Using Smart Contracts","authors":"P. Shelke, Shreyash Zanjal, Rishikesh Patil, Digvijay Desai, Harish Chavan, Varad Kulkarni","doi":"10.1109/ICAISS55157.2022.10010749","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010749","url":null,"abstract":"An innovative and modern avenue for startup fundraising is crowdfunding. There are many different forms of crowdfunding, but the fundamental goal is to raise enough money to produce or provide services. The internet and social media are essential for nonprofit organizations and entrepreneurs to raise money. Blockchain is a decentralised, immutable, and secure technology that maintains the transparency of all interactions between parties. The foundation of crowdfunding is the mutual trust between investors and stakeholders. Blockchain based smart contracts has enormous promise for both crowdfunding organisations and individual users. Blockchain-based crowdfunding platforms boost the legitimacy of various initiatives and endeavours, attracting large amounts of funds from donors and investors. The basics of crowdfunding, its existing limits, and the role of blockchain technology in crowdfunding will all be covered in the first section of the paper. Then it talks about other crowdfunding systems built on blockchain that have appeared recently. The explanation of the proposed system and its implementation and outcomes follows.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130425379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Efficient Smart Wearables for the Advance Ubiquitous Applications in the era of Smart Health using Big Data 节能智能可穿戴设备在大数据智能健康时代的先进无处不在的应用

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010956

Upinder Kaur, Murali Dhar M S, M. Reka, S. Vidya Priya Darcini, Tulasi Radhika Patnala, Muzeeb Khan Patan

{"title":"Energy Efficient Smart Wearables for the Advance Ubiquitous Applications in the era of Smart Health using Big Data","authors":"Upinder Kaur, Murali Dhar M S, M. Reka, S. Vidya Priya Darcini, Tulasi Radhika Patnala, Muzeeb Khan Patan","doi":"10.1109/ICAISS55157.2022.10010956","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010956","url":null,"abstract":"For the IoT to succeed, every conceivable item must be linked together (healthcare sensors, cameras, household appliances, smart phones, wearable sensors, etc.). As a result, dependable ways of data storage and processing are required to cope with the vast amounts of information created by all of these channels. Cloud computing is a new method being developed to solve this issue. However, some applications (healthcare) necessitate minimal latency and delay since they must process data in real time to increase performance. One of the promising technologies that help the healthcare sector is fog computing since it helps with things like multihop data connection delays, resource distribution, and service flexibility. This research proposes a fog-based IoT healthcare system to reduce the power needs of individual fog nodes. The experimental outcomes demonstrate the effectiveness of the suggested framework with regards to network delay and power consumption. In addition, the authors discussed and made suggestions for key services of big data infrastructure that should be available in fog devices for healthcare big data analytics.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"807 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131858579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Learning Framework for Pronoun Resolution Using Convolution Neural Network 基于卷积神经网络的代词解析学习框架

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011124

Shilpa Kamath, Sagar F Honnabindagi, K. Karibasappa

{"title":"Learning Framework for Pronoun Resolution Using Convolution Neural Network","authors":"Shilpa Kamath, Sagar F Honnabindagi, K. Karibasappa","doi":"10.1109/ICAISS55157.2022.10011124","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10011124","url":null,"abstract":"The task of understanding grammatical entities in sentences is a very important part of preprocessing in natural language processing tasks. Named entity recognition is a subtask of information extraction. The fundamental goal of NER is to induce and classify the defined categories such as person names, organizations, locations, and other entities which might be the requirement of the application. Parts of speech are another important aspect of preprocessing required by many tasks in NLP. The main challenge in most reference resolution systems is having a pre-labeled dataset that has entities that are rich in features such as NER and POS tags.This research study proposes a learning-based outcome resolution of grammatical entities in a self-curated data set in this study. The suggested model, which includes six classes, learns from a hand-annotated corpus and determines different classes of input entities. This system is the first learning-based model for the provided corpus to approach comparable performance. It pledges and achieves a high performance as compared to a non-learning outcome. The proposed model's the main challenge is to anticipate the references among the entities which are the base for understanding the relationship between each other. Towards this, the proposed model curate sentences that have entities that require references to be resolved to help build the mentions for coreference resolution systems. To test the proposed hypothesis, a learning-based approach with word embedding and position embedding techniques is proposed to classify various types of nouns and pronouns. The pronouns in the dataset are linked with the nouns and their types by following the approach of the binding theory. The evaluation of the model's accuracy is robust, with an F1 score of 97.45, recall of 99.20, and precision of 95.75, and it identifies the correct references and compare it to a state-of-the-art model.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134242127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Search Engine Optimization by using Machine Learning for Web Page Classification 利用机器学习进行网页分类的搜索引擎优化

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011123

S. Shaffi, I. Muthulakshmi

{"title":"Search Engine Optimization by using Machine Learning for Web Page Classification","authors":"S. Shaffi, I. Muthulakshmi","doi":"10.1109/ICAISS55157.2022.10011123","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10011123","url":null,"abstract":"This study presents an original way for classifying websites into three groups depending on how well their content adheres to search engine optimization (SEO) standards. It is based on expert knowledge and machine learning algorithms. Here the classifiers developed, then trained to separate a sample of unknown (a web page) into 1 of 3 categories which recognize critical traits that influence the level of page alteration. By hand, subject-matter experts manually label the data in the training set. The experimental findings demonstrate that machine learning may be used to anticipate how much a web page will be adjusted to the SEO recommendations. The proposed technique is valuable because it paves the way for agents in software and system expert that automatically identify web-pages or particular web pages area, that need to be changed to adhere to SEO best practices, perhaps obtain improved search engine ranks as a result. The findings of this study also advance the topic of determining the best positions for ranking variables that search engines use to rank webpages. Earlier research’ results indicated the page title, meta description, and H1 tag (header), and body text should all be considered when constructing a web page are supported by the experiments conducted for this study. The creation of a fresh data set of manually annotated web pages as a result of this study is another output that might be applied to future research.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123000728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Social Network based Netnographic Approach for Disruptive Behaviour Analysis 基于社会网络的破坏性行为分析网络学方法

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010970

Aliff Nawi, Nurfatin Syahirah Norsaidi, Masturah Sabri, Nur Auni Safirah Mat Sapinie, Fatin Athirah Zameran

{"title":"Social Network based Netnographic Approach for Disruptive Behaviour Analysis","authors":"Aliff Nawi, Nurfatin Syahirah Norsaidi, Masturah Sabri, Nur Auni Safirah Mat Sapinie, Fatin Athirah Zameran","doi":"10.1109/ICAISS55157.2022.10010970","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010970","url":null,"abstract":"Social media has become a major favorite platform for teenagers these days. Starting with just social activities, social media has grown in popularity as a mean of communication, entertainment, information, and even education. Today, social media is also a cause for the formation of adolescent behavior today. This study, therefore, aims to explore in-depth about teenagers' behavior in social media. A netnographic approach was used over three months to understand the behaviours of nine teenagers while on social media. All of the participants' posts, whether they were text, photos, or videos were taken into account. Instagram stories through the participants account that have been posted, shared, and reposted are also recorded in Nvivo for thematic analysis and coding. The study's findings revealed that five key themes of behaviour were identified: entertainment, popularity, morals and ethics, relationships, and new contacts. This study can be concluded with some implications and a detailed discussion of teenage behaviour on social media. At the end of the study, several recommendations were made.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123905438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Network Optimized Medical Image Classification with a Deep Comparison 基于深度比较的神经网络优化医学图像分类

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10011109

Mohd Faizaan Khan, Runku Nikhil Sai Kumar, Tanishka Patil, A. Reddy, V. Mane, Sneha Santhoshkumar

{"title":"Neural Network Optimized Medical Image Classification with a Deep Comparison","authors":"Mohd Faizaan Khan, Runku Nikhil Sai Kumar, Tanishka Patil, A. Reddy, V. Mane, Sneha Santhoshkumar","doi":"10.1109/ICAISS55157.2022.10011109","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10011109","url":null,"abstract":"Clinical care and educational assignments both heavily rely on the categorization of medical images. However, the performance of the conventional approach has peaked. Additionally, employing them requires extensive time and effort to extract and choose categorization characteristics. An innovative machine learning technique called the deep neural network has demonstrated its potential for many categorization problems. On several picture classification tasks, the convolutional neural network stands out with the greatest results. Clinical care and therapy are greatly aided by accurate medical picture classification. For instance, the analysis X-ray is the best method for diagnosing pneumonia, which kills over 50,000 people annually in the US, but identifying pneumonia from chest X-rays requires qualified radiologists, which can be difficult and expensive in some areas. Medical image categorization has traditionally used standard machine learning techniques like support vector machines (SVMs). The main motive of the authors seem to be optimizing the medical image classification using Deep learning neural networks such as DNN, ANN and CNN.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124182011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Analysis on Artificial Intelligence Technologies and its Application in FinTech 人工智能技术及其在金融科技中的应用比较分析

2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) Pub Date : 2022-11-24 DOI: 10.1109/ICAISS55157.2022.10010573

Anany Kumar Singh, Priyanshu Sharma, Margi Bhatt, Akansh Choudhary, Shivang Sharma, Sanchari Sadhukhan

{"title":"Comparative Analysis on Artificial Intelligence Technologies and its Application in FinTech","authors":"Anany Kumar Singh, Priyanshu Sharma, Margi Bhatt, Akansh Choudhary, Shivang Sharma, Sanchari Sadhukhan","doi":"10.1109/ICAISS55157.2022.10010573","DOIUrl":"https://doi.org/10.1109/ICAISS55157.2022.10010573","url":null,"abstract":"Universal technology has advanced with time, and as they say, “Necessity is the Mother of Invention,” this is impacting people's attitudes. As a result, people's attitudes regarding online networks have shifted from those of cable networks, which has steadily influenced the world's adoption of new technologies. Computerized Intelligence (AI). AI will enable financial businesses to totally rethink their business models, launch cutting-edge goods and services, and, most crucially, influence customer experience initiatives. In this second machine age, banks will face competition from nimble fintech startups that use cutting-edge technologies to supplement or even replace human labor with complex algorithms. Banking businesses will need to accept AI and use it into their business plan in order to keep a strong competitive advantage. As human selection is expensive, AI and ML are replacing human analysts in corporate tasks. Since AI is built on ML, it learns over time and provides the utmost accuracy in calculations and data analysis. AI also has the ability to introduce process automation to many industries as needed, as well as smart analytics and clear thought. AI-powered Chat-Bots have established themselves as a viable customer service tool, and because they save businesses time and money, they appear to be an uncomposable resource. The investments are a major part of the finance sector and taking in account of the investments prediction by the artificial intelligence algorithms and a comparison between them is the main motive behind this paper.","PeriodicalId":243784,"journal":{"name":"2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128947180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0