2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)最新文献_第7页

Hand Gesture Identification System Using Convolutional Neural Networks 基于卷积神经网络的手势识别系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670906

S. Arjaria, Riya Sahu, Sejal Agrawal, Suyash Khare, Yashi Agarwal, Gyanendra Chaubey

{"title":"Hand Gesture Identification System Using Convolutional Neural Networks","authors":"S. Arjaria, Riya Sahu, Sejal Agrawal, Suyash Khare, Yashi Agarwal, Gyanendra Chaubey","doi":"10.1109/aimv53313.2021.9670906","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670906","url":null,"abstract":"Recognition of hand movements is a key to conquering several difficulties and building warmth for human life. In an enormous number of applications, human actions and their significance are used in an array of applications to grasp the flexibility of machines. Sign language interpretation is one particular area of interest. Following paper describes a practical and interactive procedure for hand gesture detection by making use of a Convolutional Neural Network. The techniques are suitably graded into various stages during the process, such as the data acquisition, pre-processing, segmentation, extraction of features, and classification. The different algorithms that have done their task at each location are elaborated, along with their merits. Challenges and limitations faced during the process are discussed. Overall, it is hoped that the analysis might provide a detailed introduction into the sector of machine-driven gesture and signing acknowledgment and further facilitation of future research efforts in this sector. The proposed methodology has been tested over the 8700 images, and it classifies the images with an approximate accuracy of above 95%.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"6 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":"130735473","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

Modelling Veracity of Football Player Trade Rumours on Twitter Using Naive Bayes Algorithm 利用朴素贝叶斯算法对推特上足球运动员交易谣言的准确性建模

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670932

Nishant Rajadhyaksha

引用次数: 1

Daily Stock Price Direction Prediction using Random Multi-Layer Perceptron 基于随机多层感知机的日股价走势预测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670927

A. Naik, V. Gaikwad, R. Jalnekar, M. Rane

{"title":"Daily Stock Price Direction Prediction using Random Multi-Layer Perceptron","authors":"A. Naik, V. Gaikwad, R. Jalnekar, M. Rane","doi":"10.1109/aimv53313.2021.9670927","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670927","url":null,"abstract":"The stock market has always been a quick income source but involved great risks for its high uncertainty. Stock analysts use various fundamental techniques to predict its nature but the results haven't always been profitable. It is mandatory to have a secure prediction method to gain maximum benefits. In this era of automation, machine learning in data science is a valuable tool to predict the nature of the stock market conditions. The literature provides a variety of machine learning techniques such as SVM, AdaBoost, Regression, etc. This study proposes a novel technique called Random MultiLayer Perceptron (RMLP) Classifier which divides the dataset into subsets and applies MLP on them individually. It predicts whether the closing price of the stocks of a particular firm will increase or decrease on the next day by considering the historical data of the firm's stocks as input. This technique gives an accuracy of about 78% which is greater than normal multilayer perceptron in predicting the direction of the stock prices. The proposed method of RMLP is also compared with other existing methods of predicting the direction of the stock prices and promising results are obtained in favor of the proposed method.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"7 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":"125536259","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

Stock Market Prediction through Artificial Intelligence, Machine Learning and Neural Networks 通过人工智能、机器学习和神经网络进行股市预测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670919

Ambarish Shashank Gadgil, Aditya Fakirmohan Desity, Prasanna Hemant Asole, Harsh Shailesh Dandge, S. Shinde

{"title":"Stock Market Prediction through Artificial Intelligence, Machine Learning and Neural Networks","authors":"Ambarish Shashank Gadgil, Aditya Fakirmohan Desity, Prasanna Hemant Asole, Harsh Shailesh Dandge, S. Shinde","doi":"10.1109/aimv53313.2021.9670919","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670919","url":null,"abstract":"Stock prices and their fluctuations have a major impact on our daily lives. Therefore, it is necessary to discuss this forum today and study its various aspects. he use of machine learning(ML) and artificial intelligence(AI) in this field can bring us new insights, and the use of computers to predict prices can give us significant advantages in this field. In this paper, there is a significant attempt to achieve this stock market forecasting with the help of two techniques as follows: The first technique uses neural networking :It is used to collect and analyse the data to calculate a price by finding a suitable balance of past information that equals the present information. The final report which is generated by the above process is then upgraded by combining the actual prices in the past associated with the market. The next technique which is being involved here is linear regression. Linear regression is used to forecast prices that will involve the coming price having a calculated and nearly accurate probability. This model uses the previous data available and gives accurate results for the stock price for the next day. The model will further assist in the future research and will be useful for the growing scientific community in this field.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"9 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":"130518341","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

ExOpSum: An Extractive Opinion Summarization methodology based on aspect-sentence-review ranking ExOpSum:一种基于方面-句子-评论排序的抽取意见总结方法

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670917

S. C., V. G

引用次数: 0

Automatic Mask and Temperature Detection System using Deep Learning and Bus sanitization module for Covid-19 基于深度学习和总线消毒模块的新型冠状病毒自动口罩和温度检测系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670989

Ranjana S. Jadhav, Tanay P. Vartak, Rutvik R. Deshmukh, Tasmiya Tamreen N. Kankurti, Shailesh N. Kadam, Kalyani Vidhate

引用次数: 0

Simulating the Cognitive Thought Process with a Modified Genetic Algorithm 用改进的遗传算法模拟认知思维过程

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670980

Pooja Gadekar, S. Tikhe

{"title":"Simulating the Cognitive Thought Process with a Modified Genetic Algorithm","authors":"Pooja Gadekar, S. Tikhe","doi":"10.1109/aimv53313.2021.9670980","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670980","url":null,"abstract":"Cognitive Computing is a developing paradigm with its applications found in almost every field. It aims to develop computing methodologies and systems inspired by mind’s capabilities. A thought is the most fundamental capability of the mind. Hence it is important in the cognitive computing to understand the Thought Process. The purpose of this paper is to develop a computer model that simulates the Thought Process. The paper proposes that the Genetic Algorithm (GA) can be used for the same. Both cognitive model and computer model of the thought process with the help of GA has been given. In the computer model, a modification of GA has been implemented, which consists of a new crossover operator called Learning Crossover operator. The new crossover operator is not a replacement but is a supplement to the existing crossover operators. The GA is implemented over the Travelling Salesperson Problem (TSP), which is a classical NP problem and hence the algorithm is possible to be implemented on any other problem. The modification to the GA aims to improve the Thought Process Simulation. But it can also improve the performance of GA, when further explored.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"95 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":"116633624","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 Deep Neural Network Machine Vision Application for Preventing Wildlife-Human Conflicts 深度神经网络机器视觉在预防野生动物与人类冲突中的应用

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9671013

Vivek Bharati

{"title":"A Deep Neural Network Machine Vision Application for Preventing Wildlife-Human Conflicts","authors":"Vivek Bharati","doi":"10.1109/aimv53313.2021.9671013","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9671013","url":null,"abstract":"Most wildlife-human conflicts can be prevented if humans, who could potentially be affected, can be alerted about the presence of wildlife nearby so that they can take avoidance measures. The alerts must be accurate and timely so that such measures can be taken. We propose a Deep Neural Network consisting of two stages, that we call ‘WildlifeNet’, to automatically detect the presence of specific wildlife. WildlifeNet is optimized for low power and low memory so that it can be embedded in edge devices such as surveillance cameras or low cost special-purpose cameras. The first stage in WildlifeNet is an object detection system using the MobileNet model in TensorFlow that detects animals in an image. This is followed by our custom Convolutional Neural Network classification system that identifies specific animal species from the animals detected in the first stage. WildlifeNet uses images from surveillance cameras or low cost cameras placed near typical animal paths to detect the presence of wildlife. The components surrounding WildlifeNet in the machine vision system presented in this paper can quickly alert those living near the specific location where detections occur via their mobile phones. The custom Convolutional Neural Network model in WildlifeNet’s second stage was trained using a large number of coyote images from the Caltech wildlife image dataset to demonstrate its usefulness in detecting specific wildlife. We observed a consistently high accuracy of coyote detection with a potential towards even higher accuracies with user feedback. Therefore, this system is a viable candidate for consideration as an effective, fast, low-cost technology to assist in preventing wildlife-human conflicts.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"8 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":"130323469","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

Dangerous Object Detection for Visually Impaired People using Computer Vision 基于计算机视觉的视障人士危险目标检测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670992

Harsh Shah, Rishil Shah, Shlok Shah, Paawan Sharma

{"title":"Dangerous Object Detection for Visually Impaired People using Computer Vision","authors":"Harsh Shah, Rishil Shah, Shlok Shah, Paawan Sharma","doi":"10.1109/aimv53313.2021.9670992","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670992","url":null,"abstract":"In this contemporary world, Artificial Intelligence and Machine Learning are one of the leading technologies creating an impact in the world by mimicking human behaviour to solve a particular problem. Hence, these technologies are widely used to aid different obstacles encountered by humans. One such problem widely faced by the mankind is visual impairment. According to World Health Organization, approximately 285 million people suffer with vision impairment. Therefore, applications of machine learning and computer vision can be applied to guide the people with such problems. This paper presents the idea of using object detection to aid the visually impaired people. In this paper, an experiment has been proposed which uses a custom-built image dataset of various dangerous objects. The objects have been categorized into 5 broad categories: Sharp objects, Danger signs, Broken glass, Manhole and Fires. A number of different algorithms have been trained on this custom image dataset containing the menacing objects and their performances have been evaluated. The evaluation indicators for the models are the validation error in terms mean Average Precision (mAP) and the processing time for each model. The models have also been tested in real world scenario by evaluating on a custom video to gauge their performance in terms of accuracy in detection of different objects as well as their ease in deployment by suggesting their frame rate handling capacity. The results are discussed and the most robust and balanced model is suggested at the end of the paper.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"62 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":"130754782","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

Analysis of Effort Estimation for Test Suite using Control Graph 基于控制图的测试套件工作量估算分析

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670952

Babita Pathik, Meena Sharma

引用次数: 0