2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)最新文献_第5页

IoT in Automobile Industry - A Smart Sensor Based Collision Avoidance Parking system 汽车工业中的物联网——基于智能传感器的防撞停车系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9671001

Vishva Jani, Kush Sutaria, Samir Patel

{"title":"IoT in Automobile Industry - A Smart Sensor Based Collision Avoidance Parking system","authors":"Vishva Jani, Kush Sutaria, Samir Patel","doi":"10.1109/aimv53313.2021.9671001","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9671001","url":null,"abstract":"The automobile industry is one of the most thriving and rapidly growing industries in the modern world and with the recent advancement in technologies, it is inevitable to avoid the merger of different technological sectors for creating more efficient systems. Automobile manufacturers have understood the plethora of features and advancements IoT provides and especially in the safety of the vehicle and its passengers and as the cars become bigger and longer, the safety of the vehicle has become a major concern for the manufacturers as bigger support pillars introduce more hindrance in the driver’s field of view. It becomes even more difficult for amateur drivers and geriatric people to handle such vehicles, as they are not able to get a proper judgment of the size of the car making them unable to maneuver the car properly. So far, only a few cost-effective systems are available in the market which assists the driver in avoiding a front-head collision. In this paper, an android application has been built that incorporates all of the above-mentioned components and helps to avoid head-on accidents utilising the Raspberry Pi, motioneyeOS, cameras, Arduino, ultrasonic sensor, and LCD screen. The cameras are placed in front of the vehicle which will assist the driver in avoiding the obstacles which are in front of the car. The prototype system has shown promising results and the components and the technology implemented are quite feasible and cost-effective.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"79 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":"126336352","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

A stealthy evasive information invasion using covert channel in mobile phones 一种利用移动电话中的隐蔽信道进行的秘密躲避信息入侵

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670998

Ketaki Pattani, S. Gautam

{"title":"A stealthy evasive information invasion using covert channel in mobile phones","authors":"Ketaki Pattani, S. Gautam","doi":"10.1109/aimv53313.2021.9670998","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670998","url":null,"abstract":"The proliferation of mobile devices and widening technological advancements have led the world to potential repercussions of insecurities. This brings in the most intrinsic requirement of security in mobile devices that may have crucial information like contacts, messages or payment passwords. However, the rapid advancements and technological vulnerabilities have created a space for these threats to get in unnoticed from detection mechanisms like reverse engineering. Covert channels that either disrupt the information flow or thwart the flow in order to sidestep the detection mechanisms and leak sensitive information have been discovered in mobile devices also. The paper depicts an attack PCEII utilizing one of such covert channels and evasive mechanism to bypass the detection mechanisms like reverse engineering, data and control flow tracking a malware detection tools. The current research discusses the malicious approaches of such covert channels based evasive attacks, their operation, research gap and its solution in detail. Also, it open up an area for defense against covert channels to be incorporated in state-of-art tools.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"13 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":"127833401","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

Predicting the Presence of Amphibians Near Road Construction Sites Using Emerging Machine Learning Algorithms 使用新兴机器学习算法预测道路建设工地附近两栖动物的存在

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670972

Ipsita Goel, Siddharth Rajesh Goradia, Anil Kumar Kakelli

引用次数: 0

Smart Indoor Navigation, Shopping Recommendation & Queue less billing based shopping assistant using AI 使用人工智能的智能室内导航，购物推荐和基于排队计费的购物助手

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670948

Shreya Kothavale, Shivam Pawar, Sanket Kankarej, Sonali Patil, R. Raut

{"title":"Smart Indoor Navigation, Shopping Recommendation & Queue less billing based shopping assistant using AI","authors":"Shreya Kothavale, Shivam Pawar, Sanket Kankarej, Sonali Patil, R. Raut","doi":"10.1109/aimv53313.2021.9670948","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670948","url":null,"abstract":"This paper presents a queue-less billing system feasibly integrated with existing systems for easy and fast checkout at supermarkets. This system includes RFID-based automatic billing checkout as well as a supporting mobile application for customers. The application will cater to additional features like personalized shopping recommendations, product search and indoor navigation, live cart, and voice-based shopping assistant. The user will add products to the cart while shopping. The mobile application connected to the cart will display the list of products and billing information. The mobile application will have an option to pay for the products in the cart. After payment, users can simply walk out with the purchase. Users can also search for amenities and products and navigate to the desired location. While the user is shopping, he can see the recommendations on the mobile application of the products which the user is most likely to buy based on his previous shopping history and current items in the cart. This system will save time and resources for the supermarkets. And improve efficiency along with giving freedom from long waiting queues.","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":"132072491","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

Histopathological Cancer Detection using Deep Learning 使用深度学习的组织病理学癌症检测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670991

P. Ghadekar, A. Khandelwal, Prateek Roy, Abhijeet Gawas, C. Joshi

引用次数: 0

Enhanced Demand Forecasting System For Food and Raw Materials Using Ensemble Learning 基于集成学习的食品和原材料需求预测系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9671005

K. Harshini, Padmini Kousalya Madhira, S. Chaitra, G. Reddy

引用次数: 1

Forecasting Electrical Demand for the Residential Sector at the National Level Using Deep Learning 使用深度学习在全国范围内预测住宅用电需求

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670956

Pavan Kumar Dharmoju, Karthik Yeluripati, Jahnavi Guduri, Kowstubha Palle

{"title":"Forecasting Electrical Demand for the Residential Sector at the National Level Using Deep Learning","authors":"Pavan Kumar Dharmoju, Karthik Yeluripati, Jahnavi Guduri, Kowstubha Palle","doi":"10.1109/aimv53313.2021.9670956","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670956","url":null,"abstract":"A fundamental element of power-system planning is estimating electricity demand at the national level. However, given the residential sector's trend of rapidly fluctuating energy consumption, it’s challenging to achieve these targets in the residential sector, which is the main source of demand. While deep learning methods have lately demonstrated success in a variety of time series studies, its relevance to forecasting monthly household energy demand has yet to be thoroughly investigated. The forecasting model for this paper used is long short-term memory (LSTM); it has proven itself to be successful in deep learning-based time series forecasting problems. A compilation of data on social and weather variables spanning 42 years in the United States of America was used to validate the proposed model. In addition, the performance of this model was compared to the performance of three benchmark models. According to all of the metrics used, the proposed model performed exceptionally well. This model will make power-system planning effective and improve grid efficiency by properly anticipating the future energy demands.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"39 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":"115477741","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

Deep Learning model for diagnosis and report generation of lethal chest diseases using X-rays 使用x射线进行致命性胸部疾病诊断和报告生成的深度学习模型

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670907

P. Ghadekar, Anuj Jevrani, Sanjana Dumpala, Sanchit Dass, Aman Pandey, Raksha Bansal

{"title":"Deep Learning model for diagnosis and report generation of lethal chest diseases using X-rays","authors":"P. Ghadekar, Anuj Jevrani, Sanjana Dumpala, Sanchit Dass, Aman Pandey, Raksha Bansal","doi":"10.1109/aimv53313.2021.9670907","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670907","url":null,"abstract":"Viewing medical images, diagnosing and summarizing them is a challenging task. An expert in this field gives a description of the X Ray in the form of a radiology report by distinguishing between the usual and unusual findings and provide an overview for their decision. Research shows that because of the inadequate number of experts, which in turn increases patient volumes, and the nature of human perception, radiology practice sometimes results in error. To lessen the volume of analytical errors and to assuage the job of radiologists, there is a necessity for a computer-assisted diagnosis and create a radiology report when an X Ray is given as an input. In the proposed model, chest X Rays are used for the diagnosis of diseases. Additionally, VGG16 has been used to classify the images resulting in an accuracy of 88%. For summarizing the X-rays, Encoder Decoder model has been used along with the Xception model. To further evaluate the reports, Bilingual evaluation has been used which has given a score of 96 percent for the proposed model.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"434 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":"116329120","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

Smart IoT Based People Counting System 基于智能物联网的人员计数系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670970

Ashish Lalchandani, Samir Patel

引用次数: 1

RecNN: A Deep Neural Network based Recommendation System RecNN:基于深度神经网络的推荐系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670990

Shaanya Singh, Maithili Lohakare, Keval Sayar, Shivi Sharma

{"title":"RecNN: A Deep Neural Network based Recommendation System","authors":"Shaanya Singh, Maithili Lohakare, Keval Sayar, Shivi Sharma","doi":"10.1109/aimv53313.2021.9670990","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670990","url":null,"abstract":"Deep learning’s breakthrough in speech recognition, image analysis and natural language processing has helped it gain a considerable amount of recognition in today’s highly modernized world. As it is known, collaborative filtering and content-based filtering are two incredibly desired memory-based methods used for recommending new products to the targeted users, but it does happen to have certain restrictions and it thus fails to provide the intended user with effectual recommendations as primarily required. In this paper, we evaluate the performance of a revised version of a deep learning-based recommender system for movies and books using multiple fully connected dense layered neural net embeddings-based structures that primarily ensembles deep neural networks integrated alongside embedding layers and their dot product values. We develop and test the recommendation systems using the data provided by Wikipedia book dataset and MovieLens 100k dataset for books and movies respectively. To recommend books or movies to a particular user, the inputs are converted to an embedding layer and then passed through dense layers for obtaining recommendations. The same process was applied to 2,3 and 4 layer architecture for obtaining recommendations for both books and movies. The results that we’ve obtained shows that our approach is a promising solution when compared with the independent memory-based collaborative filtering methods or content-based methods. It leads us to conclude that our comparative study of multiple layered architectures provides probable research directions for deep learning-based recommender systems in the near future.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"25 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":"133427525","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