2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)最新文献_第8页

ChaDRaL: RGB Image Encryption based on 3D Chaotic Map, DNA, RSA and LSB ChaDRaL:基于三维混沌图、DNA、RSA和LSB的RGB图像加密

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670921

Nirali Parekh, L. D'mello

引用次数: 1

Chatbot User Interface for Customer Relationship Management using NLP models 聊天机器人用户界面的客户关系管理使用NLP模型

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670914

Jash Doshi

引用次数: 6

Land Cover Classification from Satellite Data using Machine Learning Techniques 利用机器学习技术从卫星数据中进行土地覆盖分类

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9671016

Nisarg Vora, Arushi Patel, Kathan Shah, P. Saikia

{"title":"Land Cover Classification from Satellite Data using Machine Learning Techniques","authors":"Nisarg Vora, Arushi Patel, Kathan Shah, P. Saikia","doi":"10.1109/aimv53313.2021.9671016","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9671016","url":null,"abstract":"This work attempts automatic land cover classification of different parts of India into forest, built-up, agricultural land and water bodies using temporal remote sensing data. Data from Agra district, Uttar Pradesh has been used to train different models - k-nearest neighbours, decision trees, support vector machines and convolutional neural networks. These models are then tested in Ahmedabad and Gandhinagar, Gujarat. Google Earth Engine has been used to obtain data from Landsat 8 satellite images. For the purpose of classification, Normalized Difference Vegetation Index (NDVI) values are calculated by masking all other light bands except near-infrared and red light bands. Temporal images with NDVI labels are fed as input to train the models and subsequently, the performance of these models is compared. A convolutional neural network based on the U-Net architecture is found to produce the most accurate results, improving upon traditional machine learning techniques. The models implemented can be used to produce land cover maps for any region, with good accuracy, which can then be used for various applications like natural resource management, urban expansion etc.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"112 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":"133148005","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

Improved Face Gender Identification Using Fusion of Global Thepade’s SBTC and Local OTSU Thresholding Features 基于全局页面SBTC和局部OTSU阈值特征融合的人脸性别识别改进

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670898

Sudeep D. Thepade, Arati R. Dhake

引用次数: 2

An Effective Keypoints Extraction Scheme for Image Tampering Detection 一种有效的图像篡改检测关键点提取方法

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9671014

N. Maya, V. R. Bindu, M. Greeshma

引用次数: 0

Deep Hybrid Learning Method for Classification of Fetal Brain Abnormalities 胎儿脑异常分类的深度混合学习方法

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670994

Kavita Shinde, A. Thakare

{"title":"Deep Hybrid Learning Method for Classification of Fetal Brain Abnormalities","authors":"Kavita Shinde, A. Thakare","doi":"10.1109/aimv53313.2021.9670994","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670994","url":null,"abstract":"In recent years, lot of work has been carried out to develop a computer automated system to identify brain disorders. In the study and research of fetal brain disorders MRI images plays vital role. From the study of several literatures it is observed that existing machine learning techniques for the classification of fetal brain MRI are complex, time consuming and facing the problem of over-fitting. In the proposed system Deep Hybrid Learning (DHL) method is used for classification of fetal brain abnormality. In this work, the fusion of Deep Learning technique with the conventional machine learning method has been carried out in order to obtain the good classification results. The aim of this research study is to make more acceptable results in the classification of fetal brain abnormality using MRI images. The classification layer of Deep Neural Network (DNN) architecture is replaced by Random Forest (RF) machine learning classifier. The experimental results obtained from DNN+RF model are compared with the results of simple DNN and DNN+SVM framework. It shows that the proposed system achieves the good classification result. The DNN+RF has an Area Under Curve (AUC) of 94% and 87% for training and validation respectively which is better than the state-of-arts method. The paper is concluded with challenges and possible future directions.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"65 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":"124944794","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: 3

Real-Time Sign Language Converter for Mute and Deaf People 实时手语转换为哑巴和聋人

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670928

Akshit J. Dhruv, Santosh Kumar Bharti

{"title":"Real-Time Sign Language Converter for Mute and Deaf People","authors":"Akshit J. Dhruv, Santosh Kumar Bharti","doi":"10.1109/aimv53313.2021.9670928","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670928","url":null,"abstract":"Deaf people may get irritated due to the problem of not being able to share their views with common people, which may affect their day-to-day life. This is the main reason to develop such system that can help these people and they can also put their thoughts forward similar to other people who don’t have such problem. The advancement in the Artificial intelligence provides the door for developing the system that overcome this difficulty. So this project aims on developing a system which will be able to convert the speech to text for the deaf person, and also sometimes the person might not be able to understand just by text, so the speech will also get converted to the universal sign language. Similarly, for the mute people the sign language which they are using will get converted to speech. We will take help of various ML and AI concepts along with NLP to develop the accurate model. Convolutional neural networks (CNN) will be used for prediction as it is efficient in predicting image input, also as lip movements are fast and continuous so it is hard to capture so along with CNN, the use of attention-based long short-term memory (LSTM) will prove to be efficient. Data Augmentation methods will be used for getting the better results. TensorFlow and Keras are the python libraries that will be used to convert the speech to text. Currently there are many software available but all requires the network connectivity for it to work, while this device will work without the requirement of internet.Using the proposed model we got the accuracy of 100% in predicting sign language and 96% accuracy in sentence level understanding.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"22 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":"117130594","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

Data-science to predict Entrepreneurial Skills based on Profession 基于职业的数据科学预测创业技能

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670947

Dhvani M Vaidya, Akshit J. Dhruv

引用次数: 1

Hate Speech Detection using ML algorithms 使用ML算法进行仇恨语音检测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670987

Aditya Razdan, S. S

引用次数: 1

Dyslexia Prediction Using Machine Learning 使用机器学习预测阅读障碍

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9671004

Ghadekar Premanand Pralhad, Anshul Joshi, Mukul Chhipa, Sumant Kumar, Gourav Mishra, M. Vishwakarma

引用次数: 2