Pub Date : 2020-12-04DOI: 10.1109/SMART50582.2020.9337089
A. Suman, C. Kumar, Preetam Suman
Transport is one of the pillars of the economy of any country: every year government and car/vehicle manufacturers spending a lot of money on road safety. But unfortunately, crashes on roads cause various problems for the government. The main factors that cause fatal crashes, grievous injuries, and deaths are of varied nature. The type of roads, the structure of ways, weather conditions, and behaviour of the driver are a few pertinent factors that are studied and relationship identified. This paper describes a device that is capable to locate crashes early. In case if a crash occurs, the device can recognize and send information to the nearest hospital and police station. There are two acoustic signals analysed, vehicle crash sound and human distress call (generated during crashes/accidents). For recognition of both the sounds, this paper describes an algorithm based on acoustic signal processing. The algorithm was tested in the lab and it is robust and the efficiency of the algorithm 94.7% to detect a collision.
{"title":"An Approach to Prevent Road Accident using Intelligent Device","authors":"A. Suman, C. Kumar, Preetam Suman","doi":"10.1109/SMART50582.2020.9337089","DOIUrl":"https://doi.org/10.1109/SMART50582.2020.9337089","url":null,"abstract":"Transport is one of the pillars of the economy of any country: every year government and car/vehicle manufacturers spending a lot of money on road safety. But unfortunately, crashes on roads cause various problems for the government. The main factors that cause fatal crashes, grievous injuries, and deaths are of varied nature. The type of roads, the structure of ways, weather conditions, and behaviour of the driver are a few pertinent factors that are studied and relationship identified. This paper describes a device that is capable to locate crashes early. In case if a crash occurs, the device can recognize and send information to the nearest hospital and police station. There are two acoustic signals analysed, vehicle crash sound and human distress call (generated during crashes/accidents). For recognition of both the sounds, this paper describes an algorithm based on acoustic signal processing. The algorithm was tested in the lab and it is robust and the efficiency of the algorithm 94.7% to detect a collision.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121522439","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}
Pub Date : 2020-12-04DOI: 10.1109/SMART50582.2020.9337123
S. Bamal, M. Gupta, Nidhi Sewal, Amit Kumar Sharma
Diabetes is an incessant illness and a significant general wellbeing challenge worldwide and adds to nerve harm, visual deficiency, coronary illness, expands the dangers of creating kidney sickness and coronary illness and vein harm. The fundamental goal of this work is to plan a classification model by utilizing the machine learning methods. Counts are done to anticipate diabetes in patients at a beginning phase with most extreme exactness by utilizing machine learning classification algorithm specifically SVM, Naive Bayes, Decision tree, Random Forest, Linear Regression, and K-NN, Neural Network. Dataset is taken from UCI (Machine Learning Repository) and calculations and tests are done on the dataset and result got shows Neural Net, improved k-NN, and improved Random Forest beats with most elevated precision of (96%) and (93%) and (78.8%) nearly different calculations.
{"title":"Performance Comparison of Classification Models for Diabetes Prediction","authors":"S. Bamal, M. Gupta, Nidhi Sewal, Amit Kumar Sharma","doi":"10.1109/SMART50582.2020.9337123","DOIUrl":"https://doi.org/10.1109/SMART50582.2020.9337123","url":null,"abstract":"Diabetes is an incessant illness and a significant general wellbeing challenge worldwide and adds to nerve harm, visual deficiency, coronary illness, expands the dangers of creating kidney sickness and coronary illness and vein harm. The fundamental goal of this work is to plan a classification model by utilizing the machine learning methods. Counts are done to anticipate diabetes in patients at a beginning phase with most extreme exactness by utilizing machine learning classification algorithm specifically SVM, Naive Bayes, Decision tree, Random Forest, Linear Regression, and K-NN, Neural Network. Dataset is taken from UCI (Machine Learning Repository) and calculations and tests are done on the dataset and result got shows Neural Net, improved k-NN, and improved Random Forest beats with most elevated precision of (96%) and (93%) and (78.8%) nearly different calculations.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129107502","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}
Pub Date : 2020-12-04DOI: 10.1109/SMART50582.2020.9337108
M. I. Khan, Sarvesh Tanwar, A. Rana
A major part of the global economic activity is now constituting small to medium sized enterprises (SMEs) and under the current business globalization scenario Information Security Management (ISM) is crucial. As the size of an organization grows along with it multiplies the amount of sensitive information the organization is storing in their databases. It has been reported that small and medium business accounts for 80-90% of the market share, but most of the ISM effort is concentrated towards large business, as they provide around 50% of the turnover. SMEs are now becoming dependent on technology to provide better and more efficient services; this is a cause of concern as not all SMEs are taking the necessary steps to ensure information security. This paper explores the limitations faced by SMEs regarding ISM and how they can overcome it.
{"title":"The Need for Information Security Management for SMEs","authors":"M. I. Khan, Sarvesh Tanwar, A. Rana","doi":"10.1109/SMART50582.2020.9337108","DOIUrl":"https://doi.org/10.1109/SMART50582.2020.9337108","url":null,"abstract":"A major part of the global economic activity is now constituting small to medium sized enterprises (SMEs) and under the current business globalization scenario Information Security Management (ISM) is crucial. As the size of an organization grows along with it multiplies the amount of sensitive information the organization is storing in their databases. It has been reported that small and medium business accounts for 80-90% of the market share, but most of the ISM effort is concentrated towards large business, as they provide around 50% of the turnover. SMEs are now becoming dependent on technology to provide better and more efficient services; this is a cause of concern as not all SMEs are taking the necessary steps to ensure information security. This paper explores the limitations faced by SMEs regarding ISM and how they can overcome it.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129199549","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}
Pub Date : 2020-12-04DOI: 10.1109/SMART50582.2020.9337100
Dr. Manish Saraswat, Dr. R. C. Tripathi
The cloud computing refers to network that enables to distribute processing, application, storage capabilities among many remote located computer systems. In cloud computing platform the IT resources are utilized and released as per the requirement by using internet. It is a better option to organizations and ordinary users to utilize services (IaaS, PaaS, SaaS, DaaS etc) as provided by cloud service providers and need pay as use. Currently a large number of service providers in market and due to diversity of features and services, it very difficult to find suitable provider for s for long term needs. As per market share top three providers are Amazon, Microsoft and Google. In this paper we will analysis some of the tools such as compute, storage space management and performance offered by AWS, Azure and GCP which are the top three market leaders in cloud computing technology. In this paper, we will summarize and compare the features of AWS, Azure & GCP to provide help to organizations and users to choose the suitable features which will fulfill the long term requirements of the users.
{"title":"Cloud Computing: Comparison and Analysis of Cloud Service Providers-AWs, Microsoft and Google","authors":"Dr. Manish Saraswat, Dr. R. C. Tripathi","doi":"10.1109/SMART50582.2020.9337100","DOIUrl":"https://doi.org/10.1109/SMART50582.2020.9337100","url":null,"abstract":"The cloud computing refers to network that enables to distribute processing, application, storage capabilities among many remote located computer systems. In cloud computing platform the IT resources are utilized and released as per the requirement by using internet. It is a better option to organizations and ordinary users to utilize services (IaaS, PaaS, SaaS, DaaS etc) as provided by cloud service providers and need pay as use. Currently a large number of service providers in market and due to diversity of features and services, it very difficult to find suitable provider for s for long term needs. As per market share top three providers are Amazon, Microsoft and Google. In this paper we will analysis some of the tools such as compute, storage space management and performance offered by AWS, Azure and GCP which are the top three market leaders in cloud computing technology. In this paper, we will summarize and compare the features of AWS, Azure & GCP to provide help to organizations and users to choose the suitable features which will fulfill the long term requirements of the users.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128664766","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}
Pub Date : 2020-12-04DOI: 10.1109/SMART50582.2020.9337072
Vivek Kumar, D. Sharma, V. Mishra
The visualization helps understand the data. It is a technique to show outliers, noise and worthy data with the help of charts, graphs, plots, and various other techniques. The size of data is changing with time and becoming big data. The visualization of big data is becoming a challenge. This paper explores various state-of-the-art techniques and implements these techniques on Unity3D as a virtual reality (VR) application. The paper concludes that VR, AR and MR visualization techniques are better techniques to understand the big data with a 3D visualization and real time interaction.
{"title":"Visualizing Big Data with Mixed Reality","authors":"Vivek Kumar, D. Sharma, V. Mishra","doi":"10.1109/SMART50582.2020.9337072","DOIUrl":"https://doi.org/10.1109/SMART50582.2020.9337072","url":null,"abstract":"The visualization helps understand the data. It is a technique to show outliers, noise and worthy data with the help of charts, graphs, plots, and various other techniques. The size of data is changing with time and becoming big data. The visualization of big data is becoming a challenge. This paper explores various state-of-the-art techniques and implements these techniques on Unity3D as a virtual reality (VR) application. The paper concludes that VR, AR and MR visualization techniques are better techniques to understand the big data with a 3D visualization and real time interaction.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133024625","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}
Pub Date : 2020-12-04DOI: 10.1109/smart50582.2020.9337086
{"title":"System Modeling & Advancement in Research Trends","authors":"","doi":"10.1109/smart50582.2020.9337086","DOIUrl":"https://doi.org/10.1109/smart50582.2020.9337086","url":null,"abstract":"","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132682170","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}
Pub Date : 2020-12-04DOI: 10.1109/SMART50582.2020.9337117
M. Jagtap, R. Tripathi, D. Jawalkar
Selecting the proper aspect ratio and managing the image depth can improve the visual quality of image in terms of lowering or minimizing the depth distortion score. The technology enhanced by observing the people habit to carry the small mobile handheld devices and their interest in watching the contents over it. So, the technology improved by retargeting the images with different sizes and aspect ratio by still preserving the image contents without cropping or scaling the originality. The popular stereo image retargeting method is proposed to visualize the 3D images in better aspect for human stereo visual experience to viewer. We conduct the experimental result that shows the adjustment of aspect ratio in order to achieve the better visualization effect and less depth distortion in the image. During the retargeting processing, the usability of depth similarity is used. The depth similarity can be applied before and after the retargeting. The Disparity Map Acquisition (DMA) along with its modified version will give the better 3D visual effects.
{"title":"Depth Accuracy Determination in 3-D Stereoscopic Image Retargeting using DMA","authors":"M. Jagtap, R. Tripathi, D. Jawalkar","doi":"10.1109/SMART50582.2020.9337117","DOIUrl":"https://doi.org/10.1109/SMART50582.2020.9337117","url":null,"abstract":"Selecting the proper aspect ratio and managing the image depth can improve the visual quality of image in terms of lowering or minimizing the depth distortion score. The technology enhanced by observing the people habit to carry the small mobile handheld devices and their interest in watching the contents over it. So, the technology improved by retargeting the images with different sizes and aspect ratio by still preserving the image contents without cropping or scaling the originality. The popular stereo image retargeting method is proposed to visualize the 3D images in better aspect for human stereo visual experience to viewer. We conduct the experimental result that shows the adjustment of aspect ratio in order to achieve the better visualization effect and less depth distortion in the image. During the retargeting processing, the usability of depth similarity is used. The depth similarity can be applied before and after the retargeting. The Disparity Map Acquisition (DMA) along with its modified version will give the better 3D visual effects.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116184574","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}
Pub Date : 2020-12-04DOI: 10.1109/SMART50582.2020.9337153
Shubhi Goel, R. K. Dwivedi, Anu Sharma
The objective of this paper is to build a model to understand how “opinions” about a certain topic get formed. In our model of the world, an opinion has two elements: Abstraction: What the opinion is about, for e.g. an opinion on demonetisation can be ‘about a topic’ such as “Digital India”, Corruption, PM Modi, etc. Expression: The “sentiment” of the opinion, i.e. positive, negative or neutral. Further, we say that when multiple opinions are shared among people, similar opinions start teaming up, reinforce other similar opinions, and thus become stronger. In other words, people start supporting other people having similar opinions, and as a result, opinions turn into narratives.
{"title":"Analysis of Social Network using Data Mining Techniques","authors":"Shubhi Goel, R. K. Dwivedi, Anu Sharma","doi":"10.1109/SMART50582.2020.9337153","DOIUrl":"https://doi.org/10.1109/SMART50582.2020.9337153","url":null,"abstract":"The objective of this paper is to build a model to understand how “opinions” about a certain topic get formed. In our model of the world, an opinion has two elements: Abstraction: What the opinion is about, for e.g. an opinion on demonetisation can be ‘about a topic’ such as “Digital India”, Corruption, PM Modi, etc. Expression: The “sentiment” of the opinion, i.e. positive, negative or neutral. Further, we say that when multiple opinions are shared among people, similar opinions start teaming up, reinforce other similar opinions, and thus become stronger. In other words, people start supporting other people having similar opinions, and as a result, opinions turn into narratives.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121340471","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}
Pub Date : 2020-12-04DOI: 10.1109/SMART50582.2020.9337111
Prakhar Srivastava, V. Tiwari
In this paper, Brushless DC motor speed has been controlled using the Particle Swarm Optimization (PSO) technique. Appropriate parameters i.e. Kp and Ki for the PI controller can be found using the PSO technique. The system is supplied from a Solar PV Array along with the MPPT technique to fetch its maximum efficiency from the solar array. The end result shows that the Solar fed controller based on PSO can control the BLDC motor speed.
{"title":"Speed Control of BLDC Motor fed from Solar PV Array using Particle Swarm Optimization","authors":"Prakhar Srivastava, V. Tiwari","doi":"10.1109/SMART50582.2020.9337111","DOIUrl":"https://doi.org/10.1109/SMART50582.2020.9337111","url":null,"abstract":"In this paper, Brushless DC motor speed has been controlled using the Particle Swarm Optimization (PSO) technique. Appropriate parameters i.e. Kp and Ki for the PI controller can be found using the PSO technique. The system is supplied from a Solar PV Array along with the MPPT technique to fetch its maximum efficiency from the solar array. The end result shows that the Solar fed controller based on PSO can control the BLDC motor speed.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"356 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120836208","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}
Pub Date : 2020-12-04DOI: 10.1109/SMART50582.2020.9337155
Nguyen Thi Mai Trang, M. Shcherbakov
A question answering (QA) system based on natural language processing and deep learning gets more attention from AI communities. Many companies and organizations are interested in developing automated question answering systems which are being researched widely. Recently, the new model named Bidirectional Encoder Representation from Transformer (BERT) was proposed to solve the restrictions of NLP tasks. BERT achieved the best results in almost tasks that include QA tasks. In this work, we tried applying the multilingual BERT models (multilingual BERT [1], DeepPavlov multilingual BERT, multilingual BERT fine-tuned on XQuAD) and the language-specific BERT model for Vietnamese (PhoBERT). The obtained result has shown that the monolingual model outperforms the multilingual models. We also recommend multilingual BERT fine-tuned on XQuAD model as an option to build a Vietnamese QA system if the system is built from a multilingual BERT based model.
{"title":"Vietnamese Question Answering System f rom Multilingual BERT Models to Monolingual BERT Model","authors":"Nguyen Thi Mai Trang, M. Shcherbakov","doi":"10.1109/SMART50582.2020.9337155","DOIUrl":"https://doi.org/10.1109/SMART50582.2020.9337155","url":null,"abstract":"A question answering (QA) system based on natural language processing and deep learning gets more attention from AI communities. Many companies and organizations are interested in developing automated question answering systems which are being researched widely. Recently, the new model named Bidirectional Encoder Representation from Transformer (BERT) was proposed to solve the restrictions of NLP tasks. BERT achieved the best results in almost tasks that include QA tasks. In this work, we tried applying the multilingual BERT models (multilingual BERT [1], DeepPavlov multilingual BERT, multilingual BERT fine-tuned on XQuAD) and the language-specific BERT model for Vietnamese (PhoBERT). The obtained result has shown that the monolingual model outperforms the multilingual models. We also recommend multilingual BERT fine-tuned on XQuAD model as an option to build a Vietnamese QA system if the system is built from a multilingual BERT based model.","PeriodicalId":129946,"journal":{"name":"2020 9th International Conference System Modeling and Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129422970","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：