2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)最新文献_第4页

Voice Controlled Augmented Reality For Real Estate 语音控制增强现实房地产

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670978

Ghadekar Premanand Pralhad, S. Abhishek, Rushikesh Chounde, Tejas Kachare, Om Deshpande, Prachi Tapadiya

{"title":"Voice Controlled Augmented Reality For Real Estate","authors":"Ghadekar Premanand Pralhad, S. Abhishek, Rushikesh Chounde, Tejas Kachare, Om Deshpande, Prachi Tapadiya","doi":"10.1109/aimv53313.2021.9670978","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670978","url":null,"abstract":"As technology advances, augmented reality is becoming more prevalent in every business. The most frequent usage of AR is to project real things onto the user, which is usually done via an image target. In the real estate industry, no one can deny AR's capacity to improve the buying and selling experience. AR can help real estate developers expand their marketing methods and give clients a more memorable home experience. It has already been used in apps for house design and land hunting, and the industry's strike proves that Augmented Reality has a lot more to give. Everyone nowadays has a smartphone or tablet with which to access the internet, and the technology utilized in these devices is improving every day. As a result, using AR tools in everyday life and having a comfortable AR experience on mobile devices is becoming more convenient. The amount of time spent touring each site with consumers and not having appropriate resources to impress them is a common challenge that real estate developers confront. Augmented reality software is frequently the seal of approval that realtors receive in order to grow their business and overcome these obstacles. This paper proposes a method for projecting a home onto an image target and allowing the user to explore the interior of the house. Voice controllers incorporated into AR can control the interior.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123153180","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

COVID-19 pandemic deep learning implementations of prediction of disease with data analysis and real-time face-mask detection with camera 基于数据分析的疾病预测和基于摄像头的实时口罩检测的COVID-19大流行深度学习实现

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670960

Amrut Khatavkar, Namit Kharade, G. Navale, Tanaji Khadtare

{"title":"COVID-19 pandemic deep learning implementations of prediction of disease with data analysis and real-time face-mask detection with camera","authors":"Amrut Khatavkar, Namit Kharade, G. Navale, Tanaji Khadtare","doi":"10.1109/aimv53313.2021.9670960","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670960","url":null,"abstract":"In biomedical sciences, data mining skills are used to research and provide predictions to aid in the identification and classification of diseases. Controlling the spread of Corona Virus Disease requires screening a high number of reported cases for effective isolation and treatment (COVID-19). Infective laboratory testing (Pathogenic) is the benchmark in science, but it is time-consuming because of the high rate of false-negative? findings. To treat the illness, there is an urgent need for rapid and dependable diagnosis techniques.We wanted to create a deep learning system that could retrieve COVID-19 pictorial features from Computed tomography applying COVID-19 radiographic enhancements. In earlier study investigations, machine learning methods were employed in the prediction and categorization of COVID-19. This research, on the other hand, concentrates on the different effects of certain image processing techniques rather than on optimising these processes through the use of improved approaches. The CT image dataset benefits from the extraction of classified correctness. The DeTraC model, a previously published convolutional neural network architecture based on class decomposition, is used in this study to increase the performance of pre-trained models in detecting COVID-19 instances from chest X-ray pictures. This may be accomplished by including a class breakdown layer into the pre-trained models.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134220065","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

Biomarkers Identification for Parkinson’s Disease using Machine Learning 利用机器学习识别帕金森病的生物标志物

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670941

Archana C. Magare, Maulika S. Patel

{"title":"Biomarkers Identification for Parkinson’s Disease using Machine Learning","authors":"Archana C. Magare, Maulika S. Patel","doi":"10.1109/aimv53313.2021.9670941","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670941","url":null,"abstract":"Alzheimer’s disease or Parkinson’s disease is a neurodegenerative disease that starts developing at an early age however symptoms are demonstrated quite late.Parkinson’s disease is a neuro degenerative disease with a brain neuron loss causing shaking, stiffness and difficulty in motor movements. These symptoms worsen over time as the disease progresses. Computational biology and bioinformatics domains have witnessed advancement with the development of powerful methods to collect, process and analyze health informatics data such as molecular-genomic, proteomic, transcriptomic data revealing hidden patterns. Several machine learning techniques are widely used to mine the voluminous data with large feature space. Biomarkers identification process using machine learning helps to detect the minute changes that might have occurred at the molecular level. This paper presents preliminary work of identifying biomarkers using machine learning for Parkinson’s disease through differentially expressed genes. The dataset GSE54536 - Gene Expression Omnibus is obtained from Gene Expression Omnibus repository and pre-processed. This pre-processed data is used to construct a linear model indicating disease states. Then least square regression along with statistical tests such as t-test and fold change are used to find differentially expressed genes. Total 8 differentially expressed Parkinson’s disease genes-TLR10, OSBPL10, FCRLA, MS4A1,FOS, FOSB,EGR1,SLC11A2 are recognized.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132968230","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

Dementia Prediction Using OASIS Data for Alzheimer’s Research 使用OASIS数据预测阿尔茨海默病研究

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670900

Rahul B. Diwate, Ridhya Ghosh, Ritu Jha, Ishu Sagar, Saket Kumar Singh

引用次数: 0

Binary Classification of Melanoma Skin Cancer using SVM and CNN 基于SVM和CNN的黑色素瘤皮肤癌二值分类

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670894

Riya Tanna, Toshita Sharma

引用次数: 3

Edge, Fog and Cloud-based Smart Communications for IoT Network based Services & Applications 基于物联网网络的服务和应用的边缘，雾和基于云的智能通信

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670975

K. Bajaj, B. Sharma, Raman Singh

{"title":"Edge, Fog and Cloud-based Smart Communications for IoT Network based Services & Applications","authors":"K. Bajaj, B. Sharma, Raman Singh","doi":"10.1109/aimv53313.2021.9670975","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670975","url":null,"abstract":"The Internet of Things is increasing its span in our daily life, intelligent homes, agriculture, industries and smart cities are few popular fields among application areas. Use of smart devices connected over the network can be seen in the mentioned fields. Vast data is collected through the connected devices using wireless sensors and then transmitted over the network to the edge and cloud for the computation. The increase in sensory devices lead to more data generation thereby there is also raise in wireless terminals as now more data is generated. This brings some challenges that need to be resolved, like processing delay leading to more time consumption, data bandwidth issues affecting data transfer rate and computation capability. It has been identified that massive work needs to be researched on communication medium to provide IoT services among the applications. Various frameworks like TelcoFog, Edge framework, CoSMOS, ROUTER, FogFlow, Deep Learning and IoTecture were studied and their results were analysed. This paper aims to understand the role of different communication channels for edge/fog and cloud-based computing, and understand their role in different computation methodologies.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129386110","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

Analysis of Image Forgery Detection Using Canny Edge Detector 基于Canny边缘检测器的图像伪造检测分析

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670931

S. Jadhav, Neha Ramlal Shelot

引用次数: 1

An Efficient Spam Review Detection Using Active Deep Learning Classifiers 基于主动深度学习分类器的高效垃圾邮件审查检测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670966

Mehul Bhundiya, Maulik Trivedi

引用次数: 0

Deep-learning based helmet violation detection system 基于深度学习的头盔违章检测系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670937

Namit Kharade, Saiel Mane, Jitender Raghav, Neha Alle, Amrut Khatavkar, G. Navale

引用次数: 5

Intelligent Recruitment System Using NLP 基于NLP的智能招聘系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670958

Anushka Sharma, Smiti Singhal, Dhara Ajudia

{"title":"Intelligent Recruitment System Using NLP","authors":"Anushka Sharma, Smiti Singhal, Dhara Ajudia","doi":"10.1109/aimv53313.2021.9670958","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670958","url":null,"abstract":"India has the highest population of youths and unemployment is still a major problem. Even though a lot of job opportunities are coming in Pharmaceutical, Business Management, Information Technology, Instructors, Billing Counter, Accounts, Textile Business, Food Industries, Tourism, and many more fields, the number of applications is significantly higher. Eligible candidates and suitable jobs are the prime requirements of a recruiter and a candidate respectively. As per census 2011, 19.1% of the Indian population was constituted of Youth which was expected to become around 34% of the total population by the year 2020. Every day, thousands to lakhs of applications are being received for jobs against few vacancies. Recruiters generally screen the resumes manually for the selection of candidates. Going through every candidate’s resume in detail to evaluate them based on the skills, experience, and abilities they possess would take a long time for the recruiter. So, in the practical world, they would only be able to read limited resumes which would lead to organizations losing out on the quality of selection. The paper focuses on extracting data from resumes and performing the required analysis on the data to convert it into useful information for the recruiters. Thus, the Resume Parser would help the recruiters to select the best relevant candidates in a minimal amount of time, consequently saving their time and effort.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125031197","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