2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)最新文献_第9页

Parts-of-Speech Tagger for Gujarati Language using Long-short-Term-Memory 基于长短期记忆的古吉拉特语词性标注器

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670996

Charmi Jobanputra, Nihit Parikh, Vishwa Vora, S. Bharti

引用次数: 0

Blockchain-Based Crowdfunding: A Trust Building Model 基于区块链的众筹:一种信任构建模式

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9671003

Sayyam Gada, Akash Dhuri, Denish Jain, Smita Bansod, Dhanashree Toradmalle

{"title":"Blockchain-Based Crowdfunding: A Trust Building Model","authors":"Sayyam Gada, Akash Dhuri, Denish Jain, Smita Bansod, Dhanashree Toradmalle","doi":"10.1109/aimv53313.2021.9671003","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9671003","url":null,"abstract":"Blockchain-based crowdfunding is one of the new, upcoming alternatives to the traditional centralized approach to crowdfunding. Traditional crowdfunding platforms are vulnerable to data leaks, high transaction and platform fees, and rampant frauds which happens due to the anonymity of user’s identity i.e., users cannot be identified when they commit cybercrimes. As blockchain is immutable and decentralized, it can reduce the possibility of data breaches. This brings in transparency as there is no central authority over the blockchain-based crowdfunding system. This paper attempts to solve these existing issues with the aid of a digital identity management system with an underlying Blockchain system. By implementing blockchain in a digital identity management system, malicious users can be identified and action can be taken against them. This paper explores donation-based crowdfunding using Ethereum as a framework and has been tested on the Rinkeby Test Network. This system can conduct several crowdfunding campaigns simultaneously. This paper explains the smart contract written in Solidity language in detail.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"28 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":"127827403","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: 5

A Comprehensive study of Machine Learning Techniques used for estimating State of Charge for Li-ion Battery 用于估算锂离子电池充电状态的机器学习技术的综合研究

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9671010

C. Mehta, Paawan Sharma, A. Sant

引用次数: 1

Automatic Satellite Image Stitching Based on Speeded Up Robust Feature 基于加速鲁棒特征的卫星图像自动拼接

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670954

V. Megha, K. Rajkumar

引用次数: 5

Integrated Database Management System for Emergency Services 应急服务综合数据库管理系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670946

Badal Parmar, Nishant Doshi, A. Gandhi, Hansal Shah, Devam Jariwala

{"title":"Integrated Database Management System for Emergency Services","authors":"Badal Parmar, Nishant Doshi, A. Gandhi, Hansal Shah, Devam Jariwala","doi":"10.1109/aimv53313.2021.9670946","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670946","url":null,"abstract":"India is the epitome of many emergencies where there arises a need for emergency services frequently. Vehicular accidents, thefts, injuries, riots, fire - breakouts, floods are some of the typical adverse situations that happen in India. According to Indian Healthcare statistics, 25.8% of victim deaths occur due to delayed response to the emergency or inappropriate management of the emergency services. In the medical field, there is a commonly used term, called the ‘golden hour’. It is the first ‘60 minutes’ following an emergency of a victim. Appropriate and fast response to the situation within this period decreases the chances of death and can save the life of the victim. It is a very critical period. Also, to overcome this issue of the late or inappropriate response of emergency services our IDMS can be used in any type of emergency to call the required services. It acts as a bridge between all the three pillars of emergency services namely police, medical, and fire. No such system is currently established in India which can keep track of all data related to all emergency services. The file management system cannot handle integrated data so, using IDMS for this data management can provide an easy and reliable method for processing data.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"74 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121012541","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

Human Action Recognition in Dark Videos 黑暗视频中的人类动作识别

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670923

H. Patel, Jash Tejaskumar Doshi

{"title":"Human Action Recognition in Dark Videos","authors":"H. Patel, Jash Tejaskumar Doshi","doi":"10.1109/aimv53313.2021.9670923","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670923","url":null,"abstract":"Image processing and action recognition in images are one of the most researched topics in Deep learning. Combining these two concepts for action recognition in lowlight footage is useful in a variety of applications, including night surveillance and self-driving at night. Due to the low photon count and SNR, video in low light is difficult. Short exposures videos are prone to noise, while long exposures can result in blur and are often impractical. To get a better understanding of the presented Action Recognition in Dark(ARID) dataset, which has low light videos divided into it’s action, making it an image classification problem. We examined it in depth and demonstrated it’s utility using simulated dark images. On this dataset, we also benchmarked the performance of existing action recognition models and investigated possible strategies for improving their performance. We introduce a novel pipeline for low-light images using RenNets and statistical image processing methods to identify the human’s actions in it to support the development of learning-based pipelines for human actions recognition in dark videos. We present promising findings from the latest dataset improving the top-1 accuracy by 3.8%. We also examined performance-related causes, and identify areas for potential research.","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":"128914394","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 and Application of Regularized Neural Networks in Smart Agriculture 正则化神经网络在智慧农业中的分析与应用

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670902

Rajni Jindal, Ashutosh Raturi, Aditya Kulraj Kunwar, Abhinav Thapper

{"title":"Analysis and Application of Regularized Neural Networks in Smart Agriculture","authors":"Rajni Jindal, Ashutosh Raturi, Aditya Kulraj Kunwar, Abhinav Thapper","doi":"10.1109/aimv53313.2021.9670902","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670902","url":null,"abstract":"Crop related services like fisheries, sericulture hubs, animal husbandry, and agriculture, that is, traditional farming methods, play a highly vital role in the progression of the economies of the developing third world countries and are also responsible, to some extent, for the current status of the so-called developed countries. Good crop choice is a vital parameter that is directly proportional to the amount of yield of a particular crop a farmer gets in an agricultural year. Poor crop selection patterns that are not per external factors like rainfall, temperature, humidity, etc. lead to detrimental outputs and yields, which may even be a factor to some length, in the increasing debts that the Indian farmers are in for the past 8 years. Thus there are direct consequences of bad crop selection and poor yield to the social, economic, and mental wellbeing of the farmer. The Indian agriculture industry is heavily at the mercy of climate in different parts of the year. To this view, over the past years, many different Artificial Intelligence-based techniques have been introduced to try to revolutionize the farming industry in some way. These techniques come under the banner of Precision Agriculture. Concepts used in precision agriculture include Ensemble models, KNN based models, Similarity-based frameworks and many other techniques to mitigate traditional problems in farming. Along the same lines of thinking, we discuss in this paper, a regularized ANN-based method to better recommend crops based on selective factors like rain and temperature.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"114 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":"132260478","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

Analysis of Various Clustering Algorithms to Enhance Bag-of-Visual-Words for Drowsiness Prediction 增强视觉词袋预测睡意的各种聚类算法分析

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670943

V. Vijayan, P. P

引用次数: 0

Application of an Expert System for Reverse Parking Sensor in an Electrical Vehicle 电动汽车倒车传感器专家系统的应用

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670930

Harshvardhan Gaikwad, Hema Gaikwad

{"title":"Application of an Expert System for Reverse Parking Sensor in an Electrical Vehicle","authors":"Harshvardhan Gaikwad, Hema Gaikwad","doi":"10.1109/aimv53313.2021.9670930","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670930","url":null,"abstract":"It is a widely accepted fact that electric vehicles will be replacing the contemporary vehicles in less than three decades. Artificial intelligence (AI) is also a technology of the future. Thus, an integration of both is necessary. Expert System (ES), Fuzzy Logic (FL) and Neural Network (NN) are some very powerful tools of AI that enable the machine to take decisions on its own and improve on its previous mistakes without an external help from a human. An ES manages to take decisions on its own based on the knowledge database that it possesses. AI powered automatic vehicles comprise of several intelligent components which provide a better control of the vehicle to the driver. Reverse parking sensors are an example. These are the devices that aid the driver in parking the vehicle by acting as a warning system. This paper proposes a novel design of the Reverse Parking Sensor for Electric Vehicles (RPSEV) using an Arduino board and an ultrasonic distance sensor. These components together act as an ES that will assist the driver while parking by displaying Light Emitting Diodes (LEDs) and thus human involvement will be greatly reduced.","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":"127853016","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

A Survey on the Role of IoT in Agriculture for Smart Farming 物联网在智慧农业中的作用调查

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670901

Deepali Deshpande, Swapnil Jadhav, Rushikesh Chounde, Tejas Kachare, Kaustubh Bhale, Pratik Waso

引用次数: 2