Pub Date : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315735
Samarth Garg, Divyansh Singh Panwar, Aakansha Gupta, R. Katarya
Sentiment analysis refers to the active field of Natural language processing that extracts the attitude and emotion of a human being. With the growth of social media, more people are using online platforms such as Twitter, Facebook, Y ouTube, etc. to express their opinions. Twitter is considered to be the purest platform to express one's views. Mostly all personalities from diverse backgrounds use twitter. Therefore, it becomes a need of the hour to study public opinion. This provides us valuable information and helps organizations and governments to contemplate mass public opinion and take better decisions accordingly. In this review paper, an extensive and exhaustive guide to the subfield of Natural language processing (NLP), focusing precisely on sentiment analysis on twitter dataset, has been presented. It highlights three main approaches to analyze the sentiment. We have summarized and compared the approaches on different metrics opted by various researchers in the field of sentiment analysis using the twitter dataset. With so much active work in this field, this review paper would assist all future researchers.
{"title":"A Literature Review On Sentiment Analysis Techniques Involving Social Media Platforms","authors":"Samarth Garg, Divyansh Singh Panwar, Aakansha Gupta, R. Katarya","doi":"10.1109/PDGC50313.2020.9315735","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315735","url":null,"abstract":"Sentiment analysis refers to the active field of Natural language processing that extracts the attitude and emotion of a human being. With the growth of social media, more people are using online platforms such as Twitter, Facebook, Y ouTube, etc. to express their opinions. Twitter is considered to be the purest platform to express one's views. Mostly all personalities from diverse backgrounds use twitter. Therefore, it becomes a need of the hour to study public opinion. This provides us valuable information and helps organizations and governments to contemplate mass public opinion and take better decisions accordingly. In this review paper, an extensive and exhaustive guide to the subfield of Natural language processing (NLP), focusing precisely on sentiment analysis on twitter dataset, has been presented. It highlights three main approaches to analyze the sentiment. We have summarized and compared the approaches on different metrics opted by various researchers in the field of sentiment analysis using the twitter dataset. With so much active work in this field, this review paper would assist all future researchers.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127544732","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315853
B. Kumar, U. Tiwari, Santosh Kumar
A collection of sensor nodes are available in wireless sensor network for gathering the distinguish data from environment. This sensing process consumes more energy of the network which effects the whole network life time. So energy usage in efficient manner is the main issue to maintaining the network. Clustering is the process used for reducing the energy consumption. K-means is the post popular clustering algorithm to form the clusters. In this paper, propose energy efficient clustering i.e quad clustering based on K-means algorithm. This approach improves the performance of wireless sensor network in terms of network lifetime. As simulation shows the proposed work is better than single cluster in case of distance coverage as well as energy consumption.
{"title":"Energy Efficient Quad Clustering based on K-means Algorithm for Wireless Sensor Network","authors":"B. Kumar, U. Tiwari, Santosh Kumar","doi":"10.1109/PDGC50313.2020.9315853","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315853","url":null,"abstract":"A collection of sensor nodes are available in wireless sensor network for gathering the distinguish data from environment. This sensing process consumes more energy of the network which effects the whole network life time. So energy usage in efficient manner is the main issue to maintaining the network. Clustering is the process used for reducing the energy consumption. K-means is the post popular clustering algorithm to form the clusters. In this paper, propose energy efficient clustering i.e quad clustering based on K-means algorithm. This approach improves the performance of wireless sensor network in terms of network lifetime. As simulation shows the proposed work is better than single cluster in case of distance coverage as well as energy consumption.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126836511","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315821
Nidhi Kundu, Geeta Rani, V. Dhaka
Crop diseases are a major cause of degrading the quality and reducing the number of agricultural products. Hence, there is a strong need for the early diagnosis of the disease. The effectiveness of deep learning techniques in pattern matching and image processing motivated the authors to design an automatic tool for the detection of diseases in bell pepper plants. In this manuscript, the authors present the comparative analysis of different deep learning models applied for plant disease classification. They applied the deep learning models namely VGG16, VGG19, ResNet50, ResNet101, ResNet152, InceptionResNetV2, DenseNet121 on the publicly available dataset of the bell pepper plant. The experimental results prove that the model ‘DenseNet’ requires less training time and gives the highest validation accuracy among all the above-stated models. It achieves a training accuracy of 97.49% and the testing accuracy of 96.87% in classifying the bell pepper plants into healthy and diseased categories.
{"title":"A Comparative Analysis of Deep Learning Models Applied for Disease Classification in Bell Pepper","authors":"Nidhi Kundu, Geeta Rani, V. Dhaka","doi":"10.1109/PDGC50313.2020.9315821","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315821","url":null,"abstract":"Crop diseases are a major cause of degrading the quality and reducing the number of agricultural products. Hence, there is a strong need for the early diagnosis of the disease. The effectiveness of deep learning techniques in pattern matching and image processing motivated the authors to design an automatic tool for the detection of diseases in bell pepper plants. In this manuscript, the authors present the comparative analysis of different deep learning models applied for plant disease classification. They applied the deep learning models namely VGG16, VGG19, ResNet50, ResNet101, ResNet152, InceptionResNetV2, DenseNet121 on the publicly available dataset of the bell pepper plant. The experimental results prove that the model ‘DenseNet’ requires less training time and gives the highest validation accuracy among all the above-stated models. It achieves a training accuracy of 97.49% and the testing accuracy of 96.87% in classifying the bell pepper plants into healthy and diseased categories.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125664591","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315848
Nishant Agnihotri, A. Sharma
Lately the trend of the internet is taking a front seat for different applications. Organizations are collecting and processing and then sharing the data using the internet. Sharing using public network will invite various security lapses in the data. Security will remain the maj or thrust in the area for providing enough level of security for the data. Encryption is the best way to provide security for the data. There are two different types of approaches for ensuring data security. These techniques are symmetric and asymmetric. The symmetric technique includes different approaches with variation in the time and space complexity. In this research paper five different techniques of the symmetric approaches are compared for three different length strings. AES is the best performing in all the three cases. The time comparison for the AES with different techniques is comparatively better than the other four techniques like IDEA, RC6, Two Fish, MARS.
{"title":"Comparative Analysis of Different Symmetric Encryption Techniques Based on Computation Time","authors":"Nishant Agnihotri, A. Sharma","doi":"10.1109/PDGC50313.2020.9315848","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315848","url":null,"abstract":"Lately the trend of the internet is taking a front seat for different applications. Organizations are collecting and processing and then sharing the data using the internet. Sharing using public network will invite various security lapses in the data. Security will remain the maj or thrust in the area for providing enough level of security for the data. Encryption is the best way to provide security for the data. There are two different types of approaches for ensuring data security. These techniques are symmetric and asymmetric. The symmetric technique includes different approaches with variation in the time and space complexity. In this research paper five different techniques of the symmetric approaches are compared for three different length strings. AES is the best performing in all the three cases. The time comparison for the AES with different techniques is comparatively better than the other four techniques like IDEA, RC6, Two Fish, MARS.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124856644","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315788
Shikhar Vaish, Shreyam, Sunita Singhal
A* algorithm performs well as a Best First Search method, which would not give the shortest path in certain scenarios. Its accuracy depends on the heuristic function and has slow processing speed in the real world. RRT performs slower than A* and Dijkstra's algorithm gives correct output but shows us a slow runtime performance unsuitable for the real-world. This paper uses Dijkstra's algorithm using the priority queue for testing and proposes an approach that can be applied to any path planning algorithm. Experimental results show that the proposed approach performs 51% faster than A* on game datasets and 14% faster on extremely dense map datasets.
{"title":"Segmented Approach to Path Planning","authors":"Shikhar Vaish, Shreyam, Sunita Singhal","doi":"10.1109/PDGC50313.2020.9315788","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315788","url":null,"abstract":"A* algorithm performs well as a Best First Search method, which would not give the shortest path in certain scenarios. Its accuracy depends on the heuristic function and has slow processing speed in the real world. RRT performs slower than A* and Dijkstra's algorithm gives correct output but shows us a slow runtime performance unsuitable for the real-world. This paper uses Dijkstra's algorithm using the priority queue for testing and proposes an approach that can be applied to any path planning algorithm. Experimental results show that the proposed approach performs 51% faster than A* on game datasets and 14% faster on extremely dense map datasets.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130513023","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315756
Gaurav Gambhir, J. K. Mandal
The paper presents shared memory implementation of chaotic RGB LSB steganography technique, The proposed technique involves hiding the secret information into RGB components of the cover image. Chaotic logistic map has been used to generate highly random numbers for enhancing the security of embedded information. Encryption and decryption process is parallelized using OpenMP API in multicore environment, and results show significant speed up and highly scalable results even with large amount of data.
{"title":"Multi-core Implementation of Chaotic RGB-LSB Steganography Technique","authors":"Gaurav Gambhir, J. K. Mandal","doi":"10.1109/PDGC50313.2020.9315756","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315756","url":null,"abstract":"The paper presents shared memory implementation of chaotic RGB LSB steganography technique, The proposed technique involves hiding the secret information into RGB components of the cover image. Chaotic logistic map has been used to generate highly random numbers for enhancing the security of embedded information. Encryption and decryption process is parallelized using OpenMP API in multicore environment, and results show significant speed up and highly scalable results even with large amount of data.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134279819","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315755
Randhir Kumar, Rakesh Tripathi
Today healthcare industries are maintaining COVID-19 patients' information electronically which includes patients' diagnostic reports, patients' private information, and doctor prescriptions. However, the COVID-19, patient sensitive information is currently stored in centralized or third-party storage model. One of the key challenge of centralized storage model is the preserving privacy of patient information and transparency in the system. The privacy risk include illegitimate access to sensitive information of patient such as identification details access and misutilization of patient information and their clinical records. To overcome this challenge, we proposed a distributed on-chain and off-chain storage model using consortium blockchain and interplanetary file systems (IPFS). The proposed framework though maintaining patient privacy makes it easier for legitimate entities like healthcare providers (e.g., physicians and clinical staffs) to access clinical data of COVID-19 patients'.
{"title":"A Secure and Distributed Framework for sharing COVID-19 patient Reports using Consortium Blockchain and IPFS","authors":"Randhir Kumar, Rakesh Tripathi","doi":"10.1109/PDGC50313.2020.9315755","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315755","url":null,"abstract":"Today healthcare industries are maintaining COVID-19 patients' information electronically which includes patients' diagnostic reports, patients' private information, and doctor prescriptions. However, the COVID-19, patient sensitive information is currently stored in centralized or third-party storage model. One of the key challenge of centralized storage model is the preserving privacy of patient information and transparency in the system. The privacy risk include illegitimate access to sensitive information of patient such as identification details access and misutilization of patient information and their clinical records. To overcome this challenge, we proposed a distributed on-chain and off-chain storage model using consortium blockchain and interplanetary file systems (IPFS). The proposed framework though maintaining patient privacy makes it easier for legitimate entities like healthcare providers (e.g., physicians and clinical staffs) to access clinical data of COVID-19 patients'.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125015864","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315781
Deepak Sarvate, A. Bhati, Rahul Srivastava, VS Choudhary, RV Raghavan
The article aims to investigate the effect of shifting of spectral reflectance on colorimetric parameters due to solar exposure of the commercially available artificial fabric-based vegetation. The spectral reflectance of the control (samples at time t0) and exposed samples (time t0+t) are measured and analyzed in the visible region using a spectrophotometer. The CIE XYZ color coordinates are computed from the measured spectral reflectance. The XYZ represents the area under the multiplied spectral reflectance, illuminant and observer function. The XYZ parameters are computed for D65 illuminant and 10o observer function. The change in the XYZ with wavelength is discussed to correlate the deviation of the XYZ with color fading. The L*a*b and sRGB values are derived from the XYZ to visualize the color change. The work finds a range of applications in color based process automation, object discrimination and remote sensing for change analysis.
{"title":"Color Fading: Variation of Colorimetric Parameters with Spectral Reflectance","authors":"Deepak Sarvate, A. Bhati, Rahul Srivastava, VS Choudhary, RV Raghavan","doi":"10.1109/PDGC50313.2020.9315781","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315781","url":null,"abstract":"The article aims to investigate the effect of shifting of spectral reflectance on colorimetric parameters due to solar exposure of the commercially available artificial fabric-based vegetation. The spectral reflectance of the control (samples at time t0) and exposed samples (time t0+t) are measured and analyzed in the visible region using a spectrophotometer. The CIE XYZ color coordinates are computed from the measured spectral reflectance. The XYZ represents the area under the multiplied spectral reflectance, illuminant and observer function. The XYZ parameters are computed for D65 illuminant and 10o observer function. The change in the XYZ with wavelength is discussed to correlate the deviation of the XYZ with color fading. The L*a*b and sRGB values are derived from the XYZ to visualize the color change. The work finds a range of applications in color based process automation, object discrimination and remote sensing for change analysis.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114489039","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315817
Gitanjali Wadhwa, Mansi Mathur
Most common cancer in females is found to be Breast cancer which is a widespread disease. One out of eight females worldwide are affected by this cancer only. We can detect this cancer by detecting malignancy from breast tissues. There are various types of computer-aided techniques and approaches which are used by doctors for detecting cancer. The major objective of this paper is to build a well-defined model for the recognition of breast cancer by expending various parameters. Different types of machine learning and deep learning methodologies are used for the classification of malignant and benign tissues. In this we are using a dataset that obtains 569 samples with 30 features, this dataset is majorly called the Wisconsin dataset. Many techniques are implemented on this dataset we are using deep convolutional neural network (CNN) and Machine learning methodology (KNN) for the diagnosis and training purpose and then compare the results of both the techniques. Deep convolutional NN is implemented on the google platform called the Google Colab on the other side KNN is implemented on the Anaconda Spyder platform. The best accuracy achieved from KNN is 96.49%. To improve the performance and accuracy we implemented CNN on the same dataset and then achieved 99.41% accuracy. Deep learning is extensively useful in getting the best and optimal results in other performance matrics such as precision, recall, F1-score and AVC-ROC - 98.64%,97.61 %, 98.08%, 97.61% respectively.
{"title":"A Convolutional Neural Network Approach for The Diagnosis of Breast Cancer","authors":"Gitanjali Wadhwa, Mansi Mathur","doi":"10.1109/PDGC50313.2020.9315817","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315817","url":null,"abstract":"Most common cancer in females is found to be Breast cancer which is a widespread disease. One out of eight females worldwide are affected by this cancer only. We can detect this cancer by detecting malignancy from breast tissues. There are various types of computer-aided techniques and approaches which are used by doctors for detecting cancer. The major objective of this paper is to build a well-defined model for the recognition of breast cancer by expending various parameters. Different types of machine learning and deep learning methodologies are used for the classification of malignant and benign tissues. In this we are using a dataset that obtains 569 samples with 30 features, this dataset is majorly called the Wisconsin dataset. Many techniques are implemented on this dataset we are using deep convolutional neural network (CNN) and Machine learning methodology (KNN) for the diagnosis and training purpose and then compare the results of both the techniques. Deep convolutional NN is implemented on the google platform called the Google Colab on the other side KNN is implemented on the Anaconda Spyder platform. The best accuracy achieved from KNN is 96.49%. To improve the performance and accuracy we implemented CNN on the same dataset and then achieved 99.41% accuracy. Deep learning is extensively useful in getting the best and optimal results in other performance matrics such as precision, recall, F1-score and AVC-ROC - 98.64%,97.61 %, 98.08%, 97.61% respectively.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114373337","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315320
Vidisha, Vandana Bhatia
Cricket is one of the most watched sports in the world. Analyzing the factors, activities that affect the aftermath of the matches is of interest to many cricket lovers. Machine learning (ML) has demonstrated favorable outcomes in various fields like medical diagnosis, image processing, prediction, classification, regression etc. and is evidenced to be accurate. The ingenious frameworks built on ML have scope to learn from past experiences. The cricket pitch is the prime segment along with home game advantage, coin toss, innings, day/night match, physical fitness etc. A compendious examination of the cricket pitch, if done with imminent objectives, will be useful to precisely speculate outcomes match. This paper focuses on the existing research for analyzing the cricket pitch, predicting the performance of players and outcomes of the matches. A comparison among machine learning techniques has also been done and Naïve Bayes gave the best results due to its capability of producing accurate results with small samples while in other researches random forest turned out to be the precise classifier as it gave more accuracy than any other approach used.
{"title":"A review of Machine Learning based Recommendation approaches for cricket","authors":"Vidisha, Vandana Bhatia","doi":"10.1109/PDGC50313.2020.9315320","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315320","url":null,"abstract":"Cricket is one of the most watched sports in the world. Analyzing the factors, activities that affect the aftermath of the matches is of interest to many cricket lovers. Machine learning (ML) has demonstrated favorable outcomes in various fields like medical diagnosis, image processing, prediction, classification, regression etc. and is evidenced to be accurate. The ingenious frameworks built on ML have scope to learn from past experiences. The cricket pitch is the prime segment along with home game advantage, coin toss, innings, day/night match, physical fitness etc. A compendious examination of the cricket pitch, if done with imminent objectives, will be useful to precisely speculate outcomes match. This paper focuses on the existing research for analyzing the cricket pitch, predicting the performance of players and outcomes of the matches. A comparison among machine learning techniques has also been done and Naïve Bayes gave the best results due to its capability of producing accurate results with small samples while in other researches random forest turned out to be the precise classifier as it gave more accuracy than any other approach used.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"96 3 Pt 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129650014","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：