Pub Date : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00085
Zili Niu
A novel platform with motion video recognition for the intelligent sport monitoring application is studied in this manuscript. The action markers of human target action images in sports videos are random. Combining image preprocessing and specific part recognition technology, dynamic tracking and recognition of human target action markers in sports videos is an important application scenario. This paper proposes the novel system of processing, which contains the image preprocessign, arm recognition, whole body recognition and the data storage. The proposed is efficiency in processing the real-time video images and the tests show the performance.
{"title":"A Novel Platform with Motion Video Recognition for Intelligent Sport Monitoring Application","authors":"Zili Niu","doi":"10.1109/ICSMDI57622.2023.00085","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00085","url":null,"abstract":"A novel platform with motion video recognition for the intelligent sport monitoring application is studied in this manuscript. The action markers of human target action images in sports videos are random. Combining image preprocessing and specific part recognition technology, dynamic tracking and recognition of human target action markers in sports videos is an important application scenario. This paper proposes the novel system of processing, which contains the image preprocessign, arm recognition, whole body recognition and the data storage. The proposed is efficiency in processing the real-time video images and the tests show the performance.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129403881","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00043
Devi reddy Manasa, Mgvn Sai Kalyani, P. Hemalatha, A. Sreeja, M. M Yamuna Devi
Cloud computing leverages wide range of services (storage, servers, databases, analytics, intelligence, networking, and software) via internet to speed up innovation, provide flexible resources, and forecast economic scalability. Examples include, Netflix (for streaming video) and Gmail (for backup needs). Cloud simulation utilizes computer services in simulation and is nothing more than infrastructure and software that researchers employ as a service. Data centres, virtual machines (VM), and other items can all be managed through CloudSim's administration interfaces. Cloud simulators are used to simulate many kinds of cloud applications. A product can be evaluated by using simulations and make unrestricted fixes to issues prior to the actual launch. Therefore, cloud simulator is an economical tool used to analyse the working of cloud components to operate under various conditions and workloads. Over the years, several cloud simulators have been created; this article provides an evaluation study of most of the existing techniques. This study discusses about the typical architecture in computer simulators. This study has covered cloud simulation operation of the Internet Simulator.
{"title":"Cloud Computing and Security using CloudSim","authors":"Devi reddy Manasa, Mgvn Sai Kalyani, P. Hemalatha, A. Sreeja, M. M Yamuna Devi","doi":"10.1109/ICSMDI57622.2023.00043","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00043","url":null,"abstract":"Cloud computing leverages wide range of services (storage, servers, databases, analytics, intelligence, networking, and software) via internet to speed up innovation, provide flexible resources, and forecast economic scalability. Examples include, Netflix (for streaming video) and Gmail (for backup needs). Cloud simulation utilizes computer services in simulation and is nothing more than infrastructure and software that researchers employ as a service. Data centres, virtual machines (VM), and other items can all be managed through CloudSim's administration interfaces. Cloud simulators are used to simulate many kinds of cloud applications. A product can be evaluated by using simulations and make unrestricted fixes to issues prior to the actual launch. Therefore, cloud simulator is an economical tool used to analyse the working of cloud components to operate under various conditions and workloads. Over the years, several cloud simulators have been created; this article provides an evaluation study of most of the existing techniques. This study discusses about the typical architecture in computer simulators. This study has covered cloud simulation operation of the Internet Simulator.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126913574","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00050
Angelin Florence A, Narendra Choudhary, Sarvesh Rane, R. Kothari, Himanshu Chavan
Flight insurance has always been an element of air travel because the aviation sector has always been vulnerable to risks and uncertainties. There is a chance to upgrade aviation insurance processes and make them more secure, transparent, and effective with the development of blockchain technology. In this paper, potential of blockchain technology in flight industry is explored, including its advantages and challenges, and how it can be applied to create a decentralized and secure system for flight insurance. The application will allow users to purchase Insurance on the go and will make claim procedures hassle-free with less documentation. The service can be accessible to users by visiting the website.
{"title":"A Decentralized Flight Insurance Smart Contract Application using Blockchain","authors":"Angelin Florence A, Narendra Choudhary, Sarvesh Rane, R. Kothari, Himanshu Chavan","doi":"10.1109/ICSMDI57622.2023.00050","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00050","url":null,"abstract":"Flight insurance has always been an element of air travel because the aviation sector has always been vulnerable to risks and uncertainties. There is a chance to upgrade aviation insurance processes and make them more secure, transparent, and effective with the development of blockchain technology. In this paper, potential of blockchain technology in flight industry is explored, including its advantages and challenges, and how it can be applied to create a decentralized and secure system for flight insurance. The application will allow users to purchase Insurance on the go and will make claim procedures hassle-free with less documentation. The service can be accessible to users by visiting the website.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"26 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114116956","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00026
Ch. Vyshnavi, B. Vasavi, M. Bhavana, Nookala Sai Homitha, Suneetha Bulla
The primary cause of competitive speech in TV news debates is disagreement among panel members. In competitive situations, panel members frequently interrupt the active speaker to emphasize their point of view. In most cases, neither the active speaker nor the interrupters stop speaking. As a result, extended periods of continuous speech overlap occur. Speaker conflicts are unpleasant for the active speaker and can provoke aggressive responses. In most cases, induced aggression manifests as shouted speech. As a result, the presence of shouted and overlapped speech in TV news debates may be regarded as critical cues for detecting competitive speech. As a result, an in-depth understanding of the acoustics of shouted and overlapped speech is required. Previous speech researchers attempted to understand various aspects of shouted, overlapping, and competitive speech as separate tasks. The current thesis was motivated by the limitations of the available literature.
{"title":"A Review on Shouted Speech Detection Technique","authors":"Ch. Vyshnavi, B. Vasavi, M. Bhavana, Nookala Sai Homitha, Suneetha Bulla","doi":"10.1109/ICSMDI57622.2023.00026","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00026","url":null,"abstract":"The primary cause of competitive speech in TV news debates is disagreement among panel members. In competitive situations, panel members frequently interrupt the active speaker to emphasize their point of view. In most cases, neither the active speaker nor the interrupters stop speaking. As a result, extended periods of continuous speech overlap occur. Speaker conflicts are unpleasant for the active speaker and can provoke aggressive responses. In most cases, induced aggression manifests as shouted speech. As a result, the presence of shouted and overlapped speech in TV news debates may be regarded as critical cues for detecting competitive speech. As a result, an in-depth understanding of the acoustics of shouted and overlapped speech is required. Previous speech researchers attempted to understand various aspects of shouted, overlapping, and competitive speech as separate tasks. The current thesis was motivated by the limitations of the available literature.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125145527","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00098
L. Rahunathan, D. Sivabalaselvamani, E.S. Elakkiya, M. Madhumitha, K. Kumaresh
Plant leaf diseases have increased in prevalence recently, making it necessary to conduct accurate research. Bean leaf diseases can be prevented from spreading by earlier detection and accurate identification. Diseases of bean leaves have negatively affected bean yield and quality. There are two types of diseases predicted in bean leaves: angular leaf spots and rust. This work includes a contrast between deep learning algorithms and machine learning algorithms-based approaches such as CNN (Convolutional Neural Networks/ConvNet) and its predefined models, K-Nearest Neighbor in short KNN, Support Vector Machine can also have written as SVM, Multinomial Logistic Regression that automate the identification of leaf diseases in bean plant species. As far as known, no one has offered a comparison study for identifying bean leaf disease.
{"title":"Recognition of Bean Leaf Diseases Using Neural Network and Machine Learning Techniques","authors":"L. Rahunathan, D. Sivabalaselvamani, E.S. Elakkiya, M. Madhumitha, K. Kumaresh","doi":"10.1109/ICSMDI57622.2023.00098","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00098","url":null,"abstract":"Plant leaf diseases have increased in prevalence recently, making it necessary to conduct accurate research. Bean leaf diseases can be prevented from spreading by earlier detection and accurate identification. Diseases of bean leaves have negatively affected bean yield and quality. There are two types of diseases predicted in bean leaves: angular leaf spots and rust. This work includes a contrast between deep learning algorithms and machine learning algorithms-based approaches such as CNN (Convolutional Neural Networks/ConvNet) and its predefined models, K-Nearest Neighbor in short KNN, Support Vector Machine can also have written as SVM, Multinomial Logistic Regression that automate the identification of leaf diseases in bean plant species. As far as known, no one has offered a comparison study for identifying bean leaf disease.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125286650","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00079
S. D, Aravinda Cv, Roheet Bhatnagar
In a video, distance estimate refers to calculating the distance between an object and the camera. The camera records the live video of a person walking in front of it. Live video is collected when a human stands in front of the camera and begins to walk in front of it. A sequence of video frames is derived from the collected live footage. These frames are handled separately. Each frame is subjected to a face detection method. The detected face is surrounded by a rectangle. The rectangle created around the detected face is used to calculate height and breadth. This is known as the perspective width. The focal length is calculated using the perspective width. The proposed system is using the focal length to determine distance once it has been calculated. The user can now walk in front of the system, which is now ready for distance estimation. The basic goal is to recognize a moving face and calculate its distance from the camera. In the realm of research, distance estimate is useful. For execution, the initiative makes use of cutting-edge technologies such as machine learning.
{"title":"Distance Estimation in Video using Machine Learning","authors":"S. D, Aravinda Cv, Roheet Bhatnagar","doi":"10.1109/ICSMDI57622.2023.00079","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00079","url":null,"abstract":"In a video, distance estimate refers to calculating the distance between an object and the camera. The camera records the live video of a person walking in front of it. Live video is collected when a human stands in front of the camera and begins to walk in front of it. A sequence of video frames is derived from the collected live footage. These frames are handled separately. Each frame is subjected to a face detection method. The detected face is surrounded by a rectangle. The rectangle created around the detected face is used to calculate height and breadth. This is known as the perspective width. The focal length is calculated using the perspective width. The proposed system is using the focal length to determine distance once it has been calculated. The user can now walk in front of the system, which is now ready for distance estimation. The basic goal is to recognize a moving face and calculate its distance from the camera. In the realm of research, distance estimate is useful. For execution, the initiative makes use of cutting-edge technologies such as machine learning.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132793055","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00062
Rajat Dang, Vamsi Krishna, Riya Sharma, M. Kowsigan
This study explains the concept of deadlock by focusing on prevention and avoidance with the help of one of the emerging technologies, Augmented Reality (AR). With the help of AR, people can easily understand this concept. This can be easily explained with the help of real time example of traffic system on the road. To solve this issue, this study has implemented the Bankers Algorithm, which is a Deadlock Prevention Algorithm. The Bankers Algorithm can use its calculation and prevent the occurrence of Deadlock or incase a deadlock is happening it can help to resolve it.
{"title":"An Optimal Visualization of Traffic System by using Augmented Reality and Virtual Reality","authors":"Rajat Dang, Vamsi Krishna, Riya Sharma, M. Kowsigan","doi":"10.1109/ICSMDI57622.2023.00062","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00062","url":null,"abstract":"This study explains the concept of deadlock by focusing on prevention and avoidance with the help of one of the emerging technologies, Augmented Reality (AR). With the help of AR, people can easily understand this concept. This can be easily explained with the help of real time example of traffic system on the road. To solve this issue, this study has implemented the Bankers Algorithm, which is a Deadlock Prevention Algorithm. The Bankers Algorithm can use its calculation and prevent the occurrence of Deadlock or incase a deadlock is happening it can help to resolve it.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115566381","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00073
J. Guntaka, Velangi Joseph Karunakar Reddy Gade, RamPrakash Yallavula, A.Dinesh Kumar, P. Sagar
The stock exchange has grown to be one of most important events in today's financial world. The current state of the stock market has a significant impact on the global economy. People from many walks of life, whether they come from business or academic backgrounds, have been drawn to the stock market with great success. The stock market's nonlinear character has made research on it among the most important and popular topics worldwide. People choose to make investments in the stock market based on their predictions or knowledge from earlier studies. In terms of forecasting, people frequently seek out instruments or strategies that would reduce their risks and maximize their earnings; as a result, stock price forecasting assumes a significant position in the always competitive stock market industry. Adopting conventional methods like fundamental and technical analysis doesn't seem to guarantee the predictability's consistency and accuracy. As a result, machine learning technologies have emerged as the most recent trend in stock market forecasting, with predictions based on current market values because of training on earlier values. In order to forecast the present trend of the stock market, this article focuses upon Machine Leaning, Analysis and LSTM (Long Short Term Memory) technology.
{"title":"Stock Market Prediction using Machine Learning Technique","authors":"J. Guntaka, Velangi Joseph Karunakar Reddy Gade, RamPrakash Yallavula, A.Dinesh Kumar, P. Sagar","doi":"10.1109/ICSMDI57622.2023.00073","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00073","url":null,"abstract":"The stock exchange has grown to be one of most important events in today's financial world. The current state of the stock market has a significant impact on the global economy. People from many walks of life, whether they come from business or academic backgrounds, have been drawn to the stock market with great success. The stock market's nonlinear character has made research on it among the most important and popular topics worldwide. People choose to make investments in the stock market based on their predictions or knowledge from earlier studies. In terms of forecasting, people frequently seek out instruments or strategies that would reduce their risks and maximize their earnings; as a result, stock price forecasting assumes a significant position in the always competitive stock market industry. Adopting conventional methods like fundamental and technical analysis doesn't seem to guarantee the predictability's consistency and accuracy. As a result, machine learning technologies have emerged as the most recent trend in stock market forecasting, with predictions based on current market values because of training on earlier values. In order to forecast the present trend of the stock market, this article focuses upon Machine Leaning, Analysis and LSTM (Long Short Term Memory) technology.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114247027","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00086
P. Cerna, Charisma S. Ututalum, R. S. Evangelista, Aldaruhz T. Darkis, Masnona Sabdani Asiri, Jehana A. Muallam-Darkis
Automatic Speech Recognition (ASR) aims to establish communication between humans and computers in a more natural way. The main aim of this study is to build hardware-based automatic speech recognition for Indigenous People (IP)'s ancestral dialects, in particular for Manobo, Mandaya, and B'laan using Raspberry Pi. Jasper is an open source toolkit used for creating voice-activated, always-on applications. The researcher recording audio data from research participants, the study's participants will be located in Davao Occidental and Sarangani for B'laan, Agusan Del Sur for Manobo, and Davao Oriental for Mandaya. A functional microphone and raspberry pi boards serve as the experiment's hardware where audi o input is being fine-tuned from a raspberry pi-powered device that records audio in waveform format, which includes Mandaya, Manobo, and Malita words and phrases. The Tensorflow STFT technique will be used to analyze, generate, transform, and characterize audio signals. JiWER plugins for Similarity measures will also be used The WER output is 98.53%, an acceptable percentage for the number of datasets used
自动语音识别(ASR)旨在以一种更自然的方式建立人与计算机之间的交流。本研究的主要目的是建立基于硬件的原住民(IP)祖先方言的自动语音识别，特别是使用树莓派的Manobo, Mandaya和B'laan。Jasper是一个开源工具包，用于创建语音激活的、永远在线的应用程序。研究人员记录了研究参与者的音频数据，该研究的参与者将位于B'laan的Davao Occidental和Sarangani, Manobo的Agusan Del Sur和Mandaya的Davao Oriental。一个功能麦克风和树莓派板作为实验的硬件，在这里，从树莓派供电的设备上输入的音频被微调，该设备以波形格式记录音频，包括Mandaya, Manobo和Malita单词和短语。Tensorflow STFT技术将用于分析、生成、转换和表征音频信号。我们还将使用JiWER插件进行相似性度量。WER输出为98.53%，对于所使用的数据集数量来说，这是一个可以接受的百分比
{"title":"An IOT-based Language Recognition System for Indigenous Languages using Integrated CNN and RNN","authors":"P. Cerna, Charisma S. Ututalum, R. S. Evangelista, Aldaruhz T. Darkis, Masnona Sabdani Asiri, Jehana A. Muallam-Darkis","doi":"10.1109/ICSMDI57622.2023.00086","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00086","url":null,"abstract":"Automatic Speech Recognition (ASR) aims to establish communication between humans and computers in a more natural way. The main aim of this study is to build hardware-based automatic speech recognition for Indigenous People (IP)'s ancestral dialects, in particular for Manobo, Mandaya, and B'laan using Raspberry Pi. Jasper is an open source toolkit used for creating voice-activated, always-on applications. The researcher recording audio data from research participants, the study's participants will be located in Davao Occidental and Sarangani for B'laan, Agusan Del Sur for Manobo, and Davao Oriental for Mandaya. A functional microphone and raspberry pi boards serve as the experiment's hardware where audi o input is being fine-tuned from a raspberry pi-powered device that records audio in waveform format, which includes Mandaya, Manobo, and Malita words and phrases. The Tensorflow STFT technique will be used to analyze, generate, transform, and characterize audio signals. JiWER plugins for Similarity measures will also be used The WER output is 98.53%, an acceptable percentage for the number of datasets used","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123143325","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 : 2023-03-01DOI: 10.1109/ICSMDI57622.2023.00076
P. R., A. Choudhary, Pulak Jain, Om Kajave
The performance analysis and efficiency of a vehicle play a prominent role and a very necessary step to do in today's scenario. There are various instances when the user feels reluctant to discard the vehicle. In such cases where the user is ignorant of the fact to discard the car, the concerned authorities must come forward to check whether the user is using the car beyond the limit. Therefore, there is an increasing need to save the environment and nature to live a sustainable life. The performance analysis of the car is based on the engine type, number of engine cylinders, fuel type, etc. This study predicts the mpg value by using machine learning models like Random Forest (RF), K-Nearest Neighbors (KNN), XG-Boost, Ridge Regression, Lasso Regression, etc. and based on that it is compared with the optimum value of mpg and hence one can reach to a decision to discard the vehicle.
{"title":"Vehicle Efficiency Prediction using Machine Learning Algorithms","authors":"P. R., A. Choudhary, Pulak Jain, Om Kajave","doi":"10.1109/ICSMDI57622.2023.00076","DOIUrl":"https://doi.org/10.1109/ICSMDI57622.2023.00076","url":null,"abstract":"The performance analysis and efficiency of a vehicle play a prominent role and a very necessary step to do in today's scenario. There are various instances when the user feels reluctant to discard the vehicle. In such cases where the user is ignorant of the fact to discard the car, the concerned authorities must come forward to check whether the user is using the car beyond the limit. Therefore, there is an increasing need to save the environment and nature to live a sustainable life. The performance analysis of the car is based on the engine type, number of engine cylinders, fuel type, etc. This study predicts the mpg value by using machine learning models like Random Forest (RF), K-Nearest Neighbors (KNN), XG-Boost, Ridge Regression, Lasso Regression, etc. and based on that it is compared with the optimum value of mpg and hence one can reach to a decision to discard the vehicle.","PeriodicalId":373017,"journal":{"name":"2023 3rd International Conference on Smart Data Intelligence (ICSMDI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122013541","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}
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