2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)最新文献_第3页

FID Automatic Bus Ticketing System FID自动巴士售票系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670957

Trupti Khedekar, Vaishnavi Jamdar, Snehal Waghmare, M. Dhore

{"title":"FID Automatic Bus Ticketing System","authors":"Trupti Khedekar, Vaishnavi Jamdar, Snehal Waghmare, M. Dhore","doi":"10.1109/aimv53313.2021.9670957","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670957","url":null,"abstract":"Public transport is one of lowest cost and therefore most dependable mass transit systems in India. Bus services are a common mode of public transportation. Throughout India, travel is extremely important. During emergence within recent years, the use of public transportation has become more prevalent in daily life. People are increasingly relying on public transportation to go about to workplace, schools, clinics etc. The far more common issue in bus services is indeed the distribution of bus tickets, this further frequently results in a dispute as between the rider and the conductor. Taking that into consideration, we're going to build an automatic bus ticketing system in which RFID cards were used to improve the process. This would be an easy-to-use device that automatically deducts the traveler's payment depending on the distance travelled. The traveler is authenticated via a Radio Frequency Identification (RFID) card, which allows for extremely exact transactions. When comparing paper- based vs RFID-based systems, RFID cards are frequently a superior alternative because they are rechargeable. RFID cards is issued to the general public. By obtaining data and information, a distinct profile will then be generated. RFID cards is being used to grant ID to each individual. As a result of having access to this database, this becomes feasible to detect and verify your passenger's profile and make a withdrawal. The android-based application is used to get the count of passenger travelling in a day which will help the conductor to check total tickets costing in a day.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"32 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":"125310833","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

3D Modelling using Sequential and Convolutional Generative Adversarial Networks 使用顺序和卷积生成对抗网络的3D建模

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670945

Apoorv Kakade, Mihir Deshpande, Suyash Sardeshpande, Varad Thokal

引用次数: 0

A Review on Human Activity Recognition 人体活动识别研究进展

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670915

Jigar Shah, M. S. Shaikh, Samir Patel

引用次数: 4

Early Fire Detection Using Deep Learning 利用深度学习进行早期火灾探测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670963

Akshad Jha, Saurabh Vedak, Kapil Mundada, Raj Walnuskar, Utkarsh Chopade, Anand Iyer

引用次数: 4

Recommendation and Prediction of Solar energy consumption for smart homes using machine learning algorithms 使用机器学习算法的智能家居太阳能消耗推荐和预测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670909

Anish Dhage, Apoorv Kakade, Gautam Nahar, Mayuresh Pingale, S. Sonawane, Archana Ghotkar

{"title":"Recommendation and Prediction of Solar energy consumption for smart homes using machine learning algorithms","authors":"Anish Dhage, Apoorv Kakade, Gautam Nahar, Mayuresh Pingale, S. Sonawane, Archana Ghotkar","doi":"10.1109/aimv53313.2021.9670909","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670909","url":null,"abstract":"Solar Energy is a principal source of renewable energy generation. Solar intensity is directly proportionate to solar power generation and it is highly reliant on the weather. A model is proposed that predicts the amounts of solar radiation produced using weather information implemented using various machine learning techniques such as Gradient boosting, SVM, etc. The results allow us to make effective energy consumption plans for smart homes with efficient utilization of solar energy which may provide several economic benefits. Additionally, accurate forecasts would make users more prepared to switch between conventional and renewable sources as required. A comparison study is performed with various machine learning models to determine the best method for building a prediction model. The groundwork for constructing models that could be dispatched to various regions is laid out that will incorporate that geographic location’s weather data, and output accurate solar intensity predictions for that area. Furthermore, a recommendation system is proposed for the consumption of thus predicted energy.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"43 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":"122662642","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

COVID-19 Outbreak from the Experience of Wave 1 and start of Wave 2: Comparison and Analysis 从第一波爆发的经验和第二波爆发的开始:比较与分析

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670988

Akshay Vijay Munot, Eshaa Niteen Mohod, Sahil Raviraj Hemnani, Babita Sonare

引用次数: 0

Real Time Emotion Analysis (RTEA) 实时情绪分析(RTEA)

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670908

D. Joshi, Anant Dhok, Anuj Khandelwal, Sonica Kulkarni, Srivallabh Mangrulkar

引用次数: 0

Machine Intelligence-Based Reference Evapotranspiration Modelling: An application of Neural Networks 基于机器智能的参考蒸散发建模:神经网络的应用

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670999

K. Reddy

{"title":"Machine Intelligence-Based Reference Evapotranspiration Modelling: An application of Neural Networks","authors":"K. Reddy","doi":"10.1109/aimv53313.2021.9670999","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670999","url":null,"abstract":"After inventing Artificial Neural Networks, a deep learning algorithm, simulation of hydrology and water resource-related problems become more efficient. The investigation aimed to discover an efficient Artificial Neural Networks (ANN) model for obtaining weekly reference evapotranspiration (ET0) in the Tirupati region. Air temperature (T), Sunshine hours (S), Wind speed (W) and Relative Humidity (RH) are among the climate variables commonly utilized to evaluate the ET0. Multiple and partial correlation analyses were performed between the ET0 calculated by the Penman-Monteith (PM) method (PMET0) and these variables by deleting one variable each time to determine the most impacting variable, RH, W, S, and T were found to be impacting variables in the order of lowest to highest. As a result, the most desirable ANN model (ANN ET0) was created using all the variables as inputs and eliminating one of the least influential variables each time to assess ET0. The ANN models are developed and validated using climatic data from 1992 to 2001. The model's ability was evaluated using numerical indicators and scatter & comparison plots by matching the PM ET0 to the ANN ET0. The numerical indexes are employed to validate the usefulness of the generated models. The ANN (1-5-1) considering one input variable (T), ANN (2-5-1) considering two input variables (T & S), ANN (3-4-1) considering three input variables (T, S, & W), and ANN (4-3- 1) considering four input variables (T, S, W, & RH), were found to have 83.53%, 89.85%, 94.21%, and 99.30% efficiency during the validation, respectively. Therefore, the ANN models may accurately predict the weekly ET0 in the research area and elsewhere in climatological situations similar to the study area.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"100 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":"130581300","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

Assistive Navigation System for Visually Impaired and Blind People: A Review 视障及盲人辅助导航系统研究进展

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670951

Noopur Tyagi, Deepika Sharma, Jaiteg Singh, B. Sharma, Sushil Narang

{"title":"Assistive Navigation System for Visually Impaired and Blind People: A Review","authors":"Noopur Tyagi, Deepika Sharma, Jaiteg Singh, B. Sharma, Sushil Narang","doi":"10.1109/aimv53313.2021.9670951","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670951","url":null,"abstract":"The emergence of modern technologies in healthcare systems like the Internet of Things, Wireless Sensor Network, Machine Learning, etc. has ameliorated the cognitive abilities of humans. The increased accessibility of healthcare data and the exponential growth of advanced analytics can be attributed to the innovative amalgamation of these technologies. These technologies have adaptive and self-correcting capabilities to enhance accuracy depending on the information. Assistive technology enables independence and attainment of quality of life for blind and visually impaired people. With the support of guided navigation tools, assistive technologies aid the people with the facility to move across inside as well as the outside environment. The major concern of a visually challenged and blind person is to live a life with quality and safety. This study contributes information about distinctive wearable and portable assistive tools and devices which are designed to provide support to visually impaired people. Also, it was revealed that traditional navigation devices lacked a few features that are crucial for independent navigation. To overcome those navigation deficiencies, IoT technology is exploited to provide better solutions. Global Positioning System (GPS) tracker can assist to discover several opportunities in numerous areas such as location detection, mapping, healthcare, security, etc. Navigation gadgets embedded with sensors have a huge variety of programs and benefits. The major objective of this comprehensive study is to showcase a clearer perspective about the wearable or embedded devices used by visually impaired or blind persons.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"4 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":"130511639","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

Diabetes Prediction, using Stacking Classifier 基于堆叠分类器的糖尿病预测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670920

Vinay O. Khilwani, Vasu Gondaliya, Shreya Patel, Jaya T. Hemnani, Bhuvan Gandhi, S. Bharti

{"title":"Diabetes Prediction, using Stacking Classifier","authors":"Vinay O. Khilwani, Vasu Gondaliya, Shreya Patel, Jaya T. Hemnani, Bhuvan Gandhi, S. Bharti","doi":"10.1109/aimv53313.2021.9670920","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670920","url":null,"abstract":"Diabetes is a disease, which occurs due to excessive blood sugar. It has become very common nowadays. It is dependent on various factors of the human body such as Blood Sugar Level, Weight, etc. We have used one benchmark dataset, PIMA Indians Diabetes Dataset, for training and testing our model. For predicting diabetes at an early stage using the risk-based features of a person’s health, we have developed a stacking classifier, and for the same, we have stacked 6 classifiers, namely Support Vector Machine, Artificial Neural Network Classifier, Logistic Regression Classifier, Decision Tree Classifier, Random Forest Classifier and Gaussian Naive Bayes Classifier, into a single model, which as a whole, uses Logistic Regression Classification on these 6 basic hyperparameter tuned models. Also, we have compared these 6 basic models with the stacked model in terms of performance. The results obtained are satisfactory and effective in comparison to the results of already proposed methods. We have achieved accuracy of 82.68%. The results of this model will add value to additional reports, because studies on prediction of diabetes using Stacking doesn’t seem to be common, in comparison with other Machine Learning Techniques.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"216 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":"121111395","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