Pub Date : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126346
Nilesh P. Sable, R. Bhimanpallewar, Rajhendra H Mehta, Sara Shaikh, Anay Indani, S. Jadhav
More than 2.1 billion people worldwide are shuddering from overweightness or obesity, which represents approximately 30% of the world’s population. Obesity is a serious global health problem. By 2030, 41% of people will likely be overweight or obese, if the current trend continues. People who show indications of weight increase or obesity run the danger of contracting life-threatening conditions including type 2 diabetes, respiratory issues, heart disease, and stroke. Some intervention strategies, like regular exercise and a balanced diet, might be essential to preserving a healthy lifestyle. Thus, it is crucial to identify obesity as soon as feasible. We have collected data from sources like schools and colleges within our organization to create our dataset. A vast range of ages is considered and the BMI value is examined in order to determine the level of obesity. The dataset of people with normal BMI and those at risk has an inherent imbalance. The outcomes are collected and showcased via a website which also includes various preventive measures and calculators. The outcomes are promising, and clock an accuracy of about 90%.
{"title":"A Machine Learning approach for Early Detection and Prevention of Obesity and Overweight","authors":"Nilesh P. Sable, R. Bhimanpallewar, Rajhendra H Mehta, Sara Shaikh, Anay Indani, S. Jadhav","doi":"10.1109/I2CT57861.2023.10126346","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126346","url":null,"abstract":"More than 2.1 billion people worldwide are shuddering from overweightness or obesity, which represents approximately 30% of the world’s population. Obesity is a serious global health problem. By 2030, 41% of people will likely be overweight or obese, if the current trend continues. People who show indications of weight increase or obesity run the danger of contracting life-threatening conditions including type 2 diabetes, respiratory issues, heart disease, and stroke. Some intervention strategies, like regular exercise and a balanced diet, might be essential to preserving a healthy lifestyle. Thus, it is crucial to identify obesity as soon as feasible. We have collected data from sources like schools and colleges within our organization to create our dataset. A vast range of ages is considered and the BMI value is examined in order to determine the level of obesity. The dataset of people with normal BMI and those at risk has an inherent imbalance. The outcomes are collected and showcased via a website which also includes various preventive measures and calculators. The outcomes are promising, and clock an accuracy of about 90%.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129799278","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126494
Digant Rastogi, Manika Jain, M. M. Rayguru, S. K. Valluru
This paper presents a framework to apply Reinforcement Learning control algorithm on benchmark nonlinear dynamical systems. This work focuses on a novel Artificial Neural Network (ANN) based dynamic programming approach using Value Iteration to obtain optimal control for continuous-time nonlinear system. In particular, Coupled Tank System has been chosen to represent benchmark nonlinear dynamical system. The proposed Artificial Neural Network-Reinforcement Learning (ANN-RL) algorithm, Naive Reinforcement Learning (Naive-RL) algorithm and traditional PID control schemes are investigated on coupled tank system. The ANN-RL algorithm performs better than the Naive-RL and PID controllers in terms of steady state error, stability, oscillations and overshoot.
{"title":"Design & Validation of ANN based Reinforcement Learning Control Algorithm for Coupled Tank System","authors":"Digant Rastogi, Manika Jain, M. M. Rayguru, S. K. Valluru","doi":"10.1109/I2CT57861.2023.10126494","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126494","url":null,"abstract":"This paper presents a framework to apply Reinforcement Learning control algorithm on benchmark nonlinear dynamical systems. This work focuses on a novel Artificial Neural Network (ANN) based dynamic programming approach using Value Iteration to obtain optimal control for continuous-time nonlinear system. In particular, Coupled Tank System has been chosen to represent benchmark nonlinear dynamical system. The proposed Artificial Neural Network-Reinforcement Learning (ANN-RL) algorithm, Naive Reinforcement Learning (Naive-RL) algorithm and traditional PID control schemes are investigated on coupled tank system. The ANN-RL algorithm performs better than the Naive-RL and PID controllers in terms of steady state error, stability, oscillations and overshoot.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129388493","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126479
Nivedita Kasturi, S. G. Totad, Goldina Ghosh
Contribution of social network is not only limited to inter-personal relationship, but there are increasing number of research works carried out towards other arena of commercial applications harnessing the potential of social network. Irrespective of decades of work being carried out in social networking, the idea of using social networking towards crowdsourcing has not received much attention owing to different levels of research challenges. Existing studies have no reported discussion about this and therefore, this paper contributes towards exploring the strength and weakness of existing approaches of building analytics on social networking in order to understand the possible challenges that crowdsourcing encounters while dealing massive and unstructured data. The paper also contributes towards illustrating research trends highlighting the possible limitations.
{"title":"Research Approaches for Building Analytics in Social Network towards Crowdsourcing","authors":"Nivedita Kasturi, S. G. Totad, Goldina Ghosh","doi":"10.1109/I2CT57861.2023.10126479","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126479","url":null,"abstract":"Contribution of social network is not only limited to inter-personal relationship, but there are increasing number of research works carried out towards other arena of commercial applications harnessing the potential of social network. Irrespective of decades of work being carried out in social networking, the idea of using social networking towards crowdsourcing has not received much attention owing to different levels of research challenges. Existing studies have no reported discussion about this and therefore, this paper contributes towards exploring the strength and weakness of existing approaches of building analytics on social networking in order to understand the possible challenges that crowdsourcing encounters while dealing massive and unstructured data. The paper also contributes towards illustrating research trends highlighting the possible limitations.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"289 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130536760","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126445
Gauthami Sreenivas, Kishan Minna Murthy, Kshitij Prit Gopali, Navya Eedula, Mamatha H R
Sentiment analysis is an important domain in Natural Language Processing (NLP) since it is an efficient way to extract features and user sentiments from textual data. Performing sentiment analysis of big data in the tourism industry is useful for businesses to understand the needs of their customers and improve hotel facilities to increase customer satisfaction. This paper aims to compare, analyze and employ different types of supervised, unsupervised, and pre-trained models. The supervised models - Decision Trees, XGBoost, Multinomial Naïve Bayes, Multinomial Logistic Regression, SVM, and Stochastic Gradient Descent were tested and the parameters were optimised using GridSearchCV. Two unsupervised models, K-means clustering and Latent Dirichlet Allocation were implemented with TF-IDF and Word2Vec embeddings. The pre-trained models, VADER and TextBlob were also implemented. The labelled dataset used for this study contains user reviews of hotels around the world, where each review is classified as positive, neutral, or negative. The SVM model resulted in the highest weighted F1 score of 0.8516.
{"title":"Sentiment Analysis of Hotel Reviews - a Comparative Study","authors":"Gauthami Sreenivas, Kishan Minna Murthy, Kshitij Prit Gopali, Navya Eedula, Mamatha H R","doi":"10.1109/I2CT57861.2023.10126445","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126445","url":null,"abstract":"Sentiment analysis is an important domain in Natural Language Processing (NLP) since it is an efficient way to extract features and user sentiments from textual data. Performing sentiment analysis of big data in the tourism industry is useful for businesses to understand the needs of their customers and improve hotel facilities to increase customer satisfaction. This paper aims to compare, analyze and employ different types of supervised, unsupervised, and pre-trained models. The supervised models - Decision Trees, XGBoost, Multinomial Naïve Bayes, Multinomial Logistic Regression, SVM, and Stochastic Gradient Descent were tested and the parameters were optimised using GridSearchCV. Two unsupervised models, K-means clustering and Latent Dirichlet Allocation were implemented with TF-IDF and Word2Vec embeddings. The pre-trained models, VADER and TextBlob were also implemented. The labelled dataset used for this study contains user reviews of hotels around the world, where each review is classified as positive, neutral, or negative. The SVM model resulted in the highest weighted F1 score of 0.8516.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130960093","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126451
J. Periasamy, Buvana S, J. P
The brain is the organ that governs all of the body's functions. A brain tumor is a malignant or noncancerous development of aberrant cells and tissues in the brain. The average survival rate for people with primary brain tumors is 75.2 percent, thus early detection is critical. The identification of brain tumors is a crucial but time-consuming procedure. Traditional procedures are time-consuming and prone to human error. Computer-assisted diagnosis of brain cancers is unavoidable to overcome these constraints. Automated Brain Tumor Recognition from Magnetic Resonance Images could be a good answer to this problem.This study uses Deep Learning models to diagnose a brain tumor based on MRI scan results. The Brain tumor detection system analyzes MRI data using image processing and deep learning algorithms to detect cancers. This study compares the VGG19, and ResNet50 models for processing and detecting brain cancers based on their accuracy while using the same dataset.
{"title":"Comparison of VGG-19 and RESNET-50 Algorithms in Brain Tumor Detection","authors":"J. Periasamy, Buvana S, J. P","doi":"10.1109/I2CT57861.2023.10126451","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126451","url":null,"abstract":"The brain is the organ that governs all of the body's functions. A brain tumor is a malignant or noncancerous development of aberrant cells and tissues in the brain. The average survival rate for people with primary brain tumors is 75.2 percent, thus early detection is critical. The identification of brain tumors is a crucial but time-consuming procedure. Traditional procedures are time-consuming and prone to human error. Computer-assisted diagnosis of brain cancers is unavoidable to overcome these constraints. Automated Brain Tumor Recognition from Magnetic Resonance Images could be a good answer to this problem.This study uses Deep Learning models to diagnose a brain tumor based on MRI scan results. The Brain tumor detection system analyzes MRI data using image processing and deep learning algorithms to detect cancers. This study compares the VGG19, and ResNet50 models for processing and detecting brain cancers based on their accuracy while using the same dataset.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124972184","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126179
N. VinaySeshu, A.G.K. SriHarsha, D. Shivareddy, K. Swaraja, N. Sreekanth, C. Sujatha
The primary causes of the detrimental effects on crops and plant life are majorly plant disease and leaf disease. For the agricultural unit, this is the main risk. Food scarcity is causing agony for millions of people. Farmers' ability to make a living is severely impacted by crop damage caused by damaged leaves. Crops are not receiving a good diagnosis, which has an impact on plant growth, due to ignorance about the type of illness and pesticide usage. Food security is seriously threatened by crop diseases. It might be difficult to diagnose a disease at an early stage in many places of the world. Early recognition and diagnosis of the disease is the solution to improve the overall health of the crop and thus reduce the scarcity of the food. To help farmers, a smart agricultural framework is designed by using CNN. In this paper a 10- DCNN is implemented for the identification and diagnosis of tomato leaf disease. The proposed framework attained 95.4% of training accuracy and 93.01% of testing accuracy.
{"title":"Recognition of Tomato Leaf Disease Using 10-Layered DCNN","authors":"N. VinaySeshu, A.G.K. SriHarsha, D. Shivareddy, K. Swaraja, N. Sreekanth, C. Sujatha","doi":"10.1109/I2CT57861.2023.10126179","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126179","url":null,"abstract":"The primary causes of the detrimental effects on crops and plant life are majorly plant disease and leaf disease. For the agricultural unit, this is the main risk. Food scarcity is causing agony for millions of people. Farmers' ability to make a living is severely impacted by crop damage caused by damaged leaves. Crops are not receiving a good diagnosis, which has an impact on plant growth, due to ignorance about the type of illness and pesticide usage. Food security is seriously threatened by crop diseases. It might be difficult to diagnose a disease at an early stage in many places of the world. Early recognition and diagnosis of the disease is the solution to improve the overall health of the crop and thus reduce the scarcity of the food. To help farmers, a smart agricultural framework is designed by using CNN. In this paper a 10- DCNN is implemented for the identification and diagnosis of tomato leaf disease. The proposed framework attained 95.4% of training accuracy and 93.01% of testing accuracy.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125291270","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126250
A. Deo, S. Shinde, Tejas Borde, Suraj Dhamak, Shreyas Dungarwal
This review paper focuses on different methods that are already in use for Grayscale Image Colorization. Image Colorization can be done using various methods. In today’s world, Convolutional Neural Networks(CNNs), Autoencoders, Generative Adversarial Networks, etc are the modern techniques that are used for Image Colorization. This paper gives a comparative study of the above methodologies/architectures. Along with this, a review of different Loss functions is categorized into three categories viz. Error-based, GAN-based, Distribution-based Loss functions are described in detail. We also discuss different methods for the evaluation of an image colorizer. Finally we summarize the results of different methodologies.
{"title":"A Comprehensive Review of Image Colorization Methods","authors":"A. Deo, S. Shinde, Tejas Borde, Suraj Dhamak, Shreyas Dungarwal","doi":"10.1109/I2CT57861.2023.10126250","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126250","url":null,"abstract":"This review paper focuses on different methods that are already in use for Grayscale Image Colorization. Image Colorization can be done using various methods. In today’s world, Convolutional Neural Networks(CNNs), Autoencoders, Generative Adversarial Networks, etc are the modern techniques that are used for Image Colorization. This paper gives a comparative study of the above methodologies/architectures. Along with this, a review of different Loss functions is categorized into three categories viz. Error-based, GAN-based, Distribution-based Loss functions are described in detail. We also discuss different methods for the evaluation of an image colorizer. Finally we summarize the results of different methodologies.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126860848","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126373
Priyanka Priyadarsinee, Sumit Swain, Satyabhama Dash, M. Tripathy
This paper, initially investigates a fractional-order bandpass filter using resistors and inductors. Where, it has been found out that, by incorporation of fractional-order devices in place of classical components, the centre-frequency and bandwidth of the filter can be increased to a very high extend, i.e., to microwave range. Now, in this study, a resistorless bandpass filter has been designed and the orders of the two fractional-inductors L1 & L2 and two fractional-capacitors C1& C2 are varied from 0.3 to 1.0 one at a time. It has been found that the exponents of the elements L1 and C2 play a vital role in improving the fractional bandpass filter’s bandwidth, as well as it increases the frequency range of the filter to 1010Hz to 1025Hz ranges that are probable frequency ranges that can be used for 5G applications.
{"title":"Performance Analysis of Fractional-Order Microwave Bandpass Filter for 5G Applications","authors":"Priyanka Priyadarsinee, Sumit Swain, Satyabhama Dash, M. Tripathy","doi":"10.1109/I2CT57861.2023.10126373","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126373","url":null,"abstract":"This paper, initially investigates a fractional-order bandpass filter using resistors and inductors. Where, it has been found out that, by incorporation of fractional-order devices in place of classical components, the centre-frequency and bandwidth of the filter can be increased to a very high extend, i.e., to microwave range. Now, in this study, a resistorless bandpass filter has been designed and the orders of the two fractional-inductors L1 & L2 and two fractional-capacitors C1& C2 are varied from 0.3 to 1.0 one at a time. It has been found that the exponents of the elements L1 and C2 play a vital role in improving the fractional bandpass filter’s bandwidth, as well as it increases the frequency range of the filter to 1010Hz to 1025Hz ranges that are probable frequency ranges that can be used for 5G applications.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127135335","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126180
Rachuri Sai Manasa, Jatoth Madhu, MD Sufiyanuddin, Patil Mounica
In today’s scenario traffic congestion is a serious issue to look after which has became a hectic issue to solve. There are some major consequences due to this traffic congestion like pollution, wastage of time due to the unnecessary stoppage at the signals due to the conventional time based signaling system and even it results in the loss of human life if the emergency vehicle like Ambulance got stuck in the traffic. So, to resolve these issues we have implemented a device which clears the traffic based on density as wells as when the ambulance arrives at the signal. This paper mainly focus on two important aspects 1. Clearing the traffic based on the density by using of IR sensors and Arduino UNO helps in collecting, processing and analyzing the information which monitors the signal accordingly 2. Controlling of traffic for ambulance by using IOT. Blynkapp is an IOT platforms used for the monitoring of the ambulance when it arrives near the traffic signals.
{"title":"Smart Traffic Signal Management System","authors":"Rachuri Sai Manasa, Jatoth Madhu, MD Sufiyanuddin, Patil Mounica","doi":"10.1109/I2CT57861.2023.10126180","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126180","url":null,"abstract":"In today’s scenario traffic congestion is a serious issue to look after which has became a hectic issue to solve. There are some major consequences due to this traffic congestion like pollution, wastage of time due to the unnecessary stoppage at the signals due to the conventional time based signaling system and even it results in the loss of human life if the emergency vehicle like Ambulance got stuck in the traffic. So, to resolve these issues we have implemented a device which clears the traffic based on density as wells as when the ambulance arrives at the signal. This paper mainly focus on two important aspects 1. Clearing the traffic based on the density by using of IR sensors and Arduino UNO helps in collecting, processing and analyzing the information which monitors the signal accordingly 2. Controlling of traffic for ambulance by using IOT. Blynkapp is an IOT platforms used for the monitoring of the ambulance when it arrives near the traffic signals.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127589803","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 : 2023-04-07DOI: 10.1109/I2CT57861.2023.10126241
Inam Ul Haq, Inzimam Ul Hassan, Hilal Ahmad Shah
Cricket is the most popular sport and most watched now a day. Test matches, One Day Internationals (ODI), and Twenty20 Internationals are the three forms in which it is played. Until the last ball of the last over, no one can predict who would win the match. Machine learning is a new field that uses existing data to predict future results. The goal of this study is to build a model that will predict the winner of a One-Day International Match before it begins. Machine learning techniques will be used on testing and training datasets to predict the winner of ODI match that will be based on the specified features. The data for the model is collected from Kaggle and some of the data are collected from the different cricket websites because the data obtained from Kaggle has only matches up until July 2021. Two algorithms were used for the prediction, K-Nearest and XGBoost, out of these two algorithms prediction accuracy of 91% was obtained by K-Nearest Neighbor Algorithm and prediction accuracy of 89% was obtained by XGBoost Algorithm
{"title":"Machine Learning Techniques for Result Prediction of One Day International (ODI)Cricket Match","authors":"Inam Ul Haq, Inzimam Ul Hassan, Hilal Ahmad Shah","doi":"10.1109/I2CT57861.2023.10126241","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126241","url":null,"abstract":"Cricket is the most popular sport and most watched now a day. Test matches, One Day Internationals (ODI), and Twenty20 Internationals are the three forms in which it is played. Until the last ball of the last over, no one can predict who would win the match. Machine learning is a new field that uses existing data to predict future results. The goal of this study is to build a model that will predict the winner of a One-Day International Match before it begins. Machine learning techniques will be used on testing and training datasets to predict the winner of ODI match that will be based on the specified features. The data for the model is collected from Kaggle and some of the data are collected from the different cricket websites because the data obtained from Kaggle has only matches up until July 2021. Two algorithms were used for the prediction, K-Nearest and XGBoost, out of these two algorithms prediction accuracy of 91% was obtained by K-Nearest Neighbor Algorithm and prediction accuracy of 89% was obtained by XGBoost Algorithm","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127272939","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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