Pub Date : 2021-12-10DOI: 10.1109/SMART52563.2021.9676254
Keshav Kaushik, Susheela Dahiya
While Bitcoin is legal, hackers, narcotics smugglers, and other dubious persons that have to be prosecuted are still utilizing it. Bitcoin is used in various industries due to its wide range of applications but it is also on the radar of malicious people and they are performing various types of cybercrimes in the dark web. Future conflicts will be cyber wars, with crimes combining cryptography and malware to manipulate information technology and compromise their security. Cyber-attacks are made easier by the rapid development of the Internet. Loss of private information and degradation of customer trust in e-commerce are two examples of web threats. In this paper, the authors have implemented an automated process for investigating the bitcoins balances and wallet addresses. The authors have also highlighted the use of bitcoin in various cybercrimes. The tool used in investigating the Bitcoin balances and the bitcoin wallets is SpiderFoot. The results are generated in our paper are the form of hashes of bitcoin balances and wallet addresses that are investigated properly to check for any cyber fraud in the dark web.
{"title":"An Automated Abstract Approach for Investigating Bitcoin Balances and Wallet Addresses","authors":"Keshav Kaushik, Susheela Dahiya","doi":"10.1109/SMART52563.2021.9676254","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676254","url":null,"abstract":"While Bitcoin is legal, hackers, narcotics smugglers, and other dubious persons that have to be prosecuted are still utilizing it. Bitcoin is used in various industries due to its wide range of applications but it is also on the radar of malicious people and they are performing various types of cybercrimes in the dark web. Future conflicts will be cyber wars, with crimes combining cryptography and malware to manipulate information technology and compromise their security. Cyber-attacks are made easier by the rapid development of the Internet. Loss of private information and degradation of customer trust in e-commerce are two examples of web threats. In this paper, the authors have implemented an automated process for investigating the bitcoins balances and wallet addresses. The authors have also highlighted the use of bitcoin in various cybercrimes. The tool used in investigating the Bitcoin balances and the bitcoin wallets is SpiderFoot. The results are generated in our paper are the form of hashes of bitcoin balances and wallet addresses that are investigated properly to check for any cyber fraud in the dark web.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124407416","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676269
Amit Jain
In modern times, the use of Visual assistance including mixed media makes contributions undoubtedly in order to didactic fee of in-magnificence and e-learning based schooling. In this paper, a visualization device has been tested to mark its impact at the ratings and with aid of implication, the information tiers inside data structures path have been presented.Importance of handling the core subject like Data Structure and Algorithms constitutes a vital basis of subject matter in technological era but so many college graduates fail to understand it properly as because of the huge and complex number of principles and hypothesis. Hence, the expectation is that the software shall help all users including college graduates or higher, to recognize the knowledge of code and pseudo codes referring to sorting algorithms.Visualizer is an interactive on-line platform that presents graphical view of algorithms from code [2]. In this paper, set of rules related to sorting represents how the detail in an array are taken care of and this envision allows the human mind to recognize the unique sorting algorithms in preference to going through the lengthy codes. This is a computer software; therefore, individual can effectively use it and research the sorting set of rules and examine the principles on every situation. The modern-day software is applied for illustration of array factors, sorting of factors and controlling the velocity of sorting the factors along with the dimensions of array.
{"title":"Realizing Algorithms Using GUI","authors":"Amit Jain","doi":"10.1109/SMART52563.2021.9676269","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676269","url":null,"abstract":"In modern times, the use of Visual assistance including mixed media makes contributions undoubtedly in order to didactic fee of in-magnificence and e-learning based schooling. In this paper, a visualization device has been tested to mark its impact at the ratings and with aid of implication, the information tiers inside data structures path have been presented.Importance of handling the core subject like Data Structure and Algorithms constitutes a vital basis of subject matter in technological era but so many college graduates fail to understand it properly as because of the huge and complex number of principles and hypothesis. Hence, the expectation is that the software shall help all users including college graduates or higher, to recognize the knowledge of code and pseudo codes referring to sorting algorithms.Visualizer is an interactive on-line platform that presents graphical view of algorithms from code [2]. In this paper, set of rules related to sorting represents how the detail in an array are taken care of and this envision allows the human mind to recognize the unique sorting algorithms in preference to going through the lengthy codes. This is a computer software; therefore, individual can effectively use it and research the sorting set of rules and examine the principles on every situation. The modern-day software is applied for illustration of array factors, sorting of factors and controlling the velocity of sorting the factors along with the dimensions of array.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115187717","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676333
Bhaskar Majumdar, Md. RafiuzzamanBhuiyan, Md. Arid Hasan, Md. Sanzidul Islam, S. R. H. Noori
Nowadays the spread of fake news or information is having a detrimental effect on society. Due to the widespread spread of fake news, we sometimes believe a lot of fake news is true. As a result, we face issues and deprive ourselves of a lot of good and realistic news. To protect people’s lives from these various problems, we need to work to automatically detect fake news. Fake news detection is very complex task. In this paper we present our approach to address multi class fake news detection using Deep Learning. We used a Long Short Term Memory (LSTM) model for multi class fake news detection using data provided by the task organizers. Our best performing model on the training data achieved an accuracy of 0.98. Our trained model gave an accurate response to the detection of fake news.
{"title":"Multi Class Fake News Detection using LSTM Approach","authors":"Bhaskar Majumdar, Md. RafiuzzamanBhuiyan, Md. Arid Hasan, Md. Sanzidul Islam, S. R. H. Noori","doi":"10.1109/SMART52563.2021.9676333","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676333","url":null,"abstract":"Nowadays the spread of fake news or information is having a detrimental effect on society. Due to the widespread spread of fake news, we sometimes believe a lot of fake news is true. As a result, we face issues and deprive ourselves of a lot of good and realistic news. To protect people’s lives from these various problems, we need to work to automatically detect fake news. Fake news detection is very complex task. In this paper we present our approach to address multi class fake news detection using Deep Learning. We used a Long Short Term Memory (LSTM) model for multi class fake news detection using data provided by the task organizers. Our best performing model on the training data achieved an accuracy of 0.98. Our trained model gave an accurate response to the detection of fake news.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129094525","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 : 2021-12-10DOI: 10.1109/smart52563.2021.9676242
{"title":"TRACK VIII: Industry 4.0 [Breaker page]","authors":"","doi":"10.1109/smart52563.2021.9676242","DOIUrl":"https://doi.org/10.1109/smart52563.2021.9676242","url":null,"abstract":"","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122501698","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676214
Sharik Ali Ansari, Rahul Nijhawan, Ishan Bansal, Shlok Mohanty
This paper proposes a deep learning and traditional machine learning based automatic fusion detection method for Spasmodic Torticollis (the most common type of Cervical dystonia), a neurological disorder. The proposed method utilizes videos of subjects where all of the subjects will be tested if they have Cervical Dystonia or not. For Neurological disorders, generally, very less data is available in public domain due to patient anonymity issue. The paper focused on training Cervical dystonia detection model on very less dataset. Deep learning in the methodology is used to detect the features providing information to traditional ML models for classification task. Methodology developed can be also be extended to grade the severity of disorder. The proposed model achieves video classification accuracy of 90.00% using SVM as final traditional machine learning classifier. We also contribute the first publicly available dataset for Cervical dystonia.
{"title":"Cervical Dystonia Detection using Facial and Eye Feature","authors":"Sharik Ali Ansari, Rahul Nijhawan, Ishan Bansal, Shlok Mohanty","doi":"10.1109/SMART52563.2021.9676214","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676214","url":null,"abstract":"This paper proposes a deep learning and traditional machine learning based automatic fusion detection method for Spasmodic Torticollis (the most common type of Cervical dystonia), a neurological disorder. The proposed method utilizes videos of subjects where all of the subjects will be tested if they have Cervical Dystonia or not. For Neurological disorders, generally, very less data is available in public domain due to patient anonymity issue. The paper focused on training Cervical dystonia detection model on very less dataset. Deep learning in the methodology is used to detect the features providing information to traditional ML models for classification task. Methodology developed can be also be extended to grade the severity of disorder. The proposed model achieves video classification accuracy of 90.00% using SVM as final traditional machine learning classifier. We also contribute the first publicly available dataset for Cervical dystonia.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126803027","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676317
D. Rastogi, P. Johri, Varun Tiwari
Segmentation of brain tumors is a difficult task because of the enormous variation in the intensity and size of gliomas. The tumor type Glioma is the highly prevalent malignant tumor in the brain, with a high death rate and a morbidity rate of more than 3%. In the clinic, MRI is the most common way of detecting brain cancers. Automatic segmentation is difficult because of the overlapping area between the intensity distributions of healthy, enhancing, non-enhancing and edema regions. Segmenting brain tumour areas utilising multi-modal MRI scan pictures can help with treatment observation, post-diagnosis monitoring, and patient impacts evaluation. Manual segmentation, on the other hand, is still the most common procedure in clinical brain tumour segmentation, which takes time and results in significant performance variations across operators. For this reason, the development of accurate and consistent automatic segmentation technology is required. Convolutional neural networks (CNNs), have shown promise in brain tumor segmentation due to their powerful learning capacity. This article suggests an 2D-VNet model for brain tumor segmentation and enhancing the prediction. The presented model was successfully segmented brain tumors and predict the result in enhancing tumor and real enhancing tumor. Experiment with BRATS2020 benchmarks dataset, we found that Loss (for Training: .0025, Testing: .0032 and Validation: .0031), Dice Coefficient (for Training: .9974, Testing: .9967 and Validation: .9968) and Accuracy (for Training: .9971 Testing: .9963 and Validation: .9964).
{"title":"Brain Tumor Segmentation and Tumor Prediction Using 2D-VNet Deep Learning Architecture","authors":"D. Rastogi, P. Johri, Varun Tiwari","doi":"10.1109/SMART52563.2021.9676317","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676317","url":null,"abstract":"Segmentation of brain tumors is a difficult task because of the enormous variation in the intensity and size of gliomas. The tumor type Glioma is the highly prevalent malignant tumor in the brain, with a high death rate and a morbidity rate of more than 3%. In the clinic, MRI is the most common way of detecting brain cancers. Automatic segmentation is difficult because of the overlapping area between the intensity distributions of healthy, enhancing, non-enhancing and edema regions. Segmenting brain tumour areas utilising multi-modal MRI scan pictures can help with treatment observation, post-diagnosis monitoring, and patient impacts evaluation. Manual segmentation, on the other hand, is still the most common procedure in clinical brain tumour segmentation, which takes time and results in significant performance variations across operators. For this reason, the development of accurate and consistent automatic segmentation technology is required. Convolutional neural networks (CNNs), have shown promise in brain tumor segmentation due to their powerful learning capacity. This article suggests an 2D-VNet model for brain tumor segmentation and enhancing the prediction. The presented model was successfully segmented brain tumors and predict the result in enhancing tumor and real enhancing tumor. Experiment with BRATS2020 benchmarks dataset, we found that Loss (for Training: .0025, Testing: .0032 and Validation: .0031), Dice Coefficient (for Training: .9974, Testing: .9967 and Validation: .9968) and Accuracy (for Training: .9971 Testing: .9963 and Validation: .9964).","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130645561","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676221
Akhilesh Kumar Singh
Sentiment Analysis or we can say the study of any person’s attitudes or their emotions for an event, discussion on any random thing. It has been evolving over the recent decades; mainly the work done in the past decades is in the area of text sentiment analysis with many text mining methods. But Voice sentimental analysis remains during a growing stage within the research communities. In this paper, we had performed a sentimental analysis on user’s voice to detect the emotions of the users. By using the Librosa python library we will perform speaker discrimination and sentiment analysis. Interpreting the mood of any person is very useful. Like, if computers gain the power to recognize and replying to human non-verbal discussion such as human feelings. In this situation, after recognizing a person’s feelings, the machine can change its own settings in accordance with user’s mood or emotion and preferences.
{"title":"Prediction of Voice Sentiment using Machine Learning Technique","authors":"Akhilesh Kumar Singh","doi":"10.1109/SMART52563.2021.9676221","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676221","url":null,"abstract":"Sentiment Analysis or we can say the study of any person’s attitudes or their emotions for an event, discussion on any random thing. It has been evolving over the recent decades; mainly the work done in the past decades is in the area of text sentiment analysis with many text mining methods. But Voice sentimental analysis remains during a growing stage within the research communities. In this paper, we had performed a sentimental analysis on user’s voice to detect the emotions of the users. By using the Librosa python library we will perform speaker discrimination and sentiment analysis. Interpreting the mood of any person is very useful. Like, if computers gain the power to recognize and replying to human non-verbal discussion such as human feelings. In this situation, after recognizing a person’s feelings, the machine can change its own settings in accordance with user’s mood or emotion and preferences.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133702876","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676231
Md. Ruhul Amin, Sharmin Akter, Md Abu Taher Dulal, A. Sattar
The main purpose of this study has to conduct an online survey to get feedback from Daffodil International University(DIU), Bangladesh faculty and students on their perceptions and experiences with Blended Learning Center classrooms. In the midst of the present pandemic crisis, the DIU education system has made a recent change by delivering classes via online BLC (Blended Learning Center) platform. Additionally, this survey analyses the perspectives and considerations of university teachers and students about attending online programs, which have become mandatory because of COVID19. The survey included 9 teachers and 133 students from university. For the aim of data gathering, an online survey method has been used. The study reveals that excellent and regular interaction among students and professors, technical support accessibility, organized online educational modules, and adjustments to allow the conduct of practical lessons are all significant for teachers and students sense of accomplishment using online courses.
{"title":"Blended Learning Center Management During COVID-19 Pandemic","authors":"Md. Ruhul Amin, Sharmin Akter, Md Abu Taher Dulal, A. Sattar","doi":"10.1109/SMART52563.2021.9676231","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676231","url":null,"abstract":"The main purpose of this study has to conduct an online survey to get feedback from Daffodil International University(DIU), Bangladesh faculty and students on their perceptions and experiences with Blended Learning Center classrooms. In the midst of the present pandemic crisis, the DIU education system has made a recent change by delivering classes via online BLC (Blended Learning Center) platform. Additionally, this survey analyses the perspectives and considerations of university teachers and students about attending online programs, which have become mandatory because of COVID19. The survey included 9 teachers and 133 students from university. For the aim of data gathering, an online survey method has been used. The study reveals that excellent and regular interaction among students and professors, technical support accessibility, organized online educational modules, and adjustments to allow the conduct of practical lessons are all significant for teachers and students sense of accomplishment using online courses.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133721759","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676298
M. Sneh, A. Bhandari
Adopting the Internet of Things (IoT) in multi-functional domains has led to management, operational, and security challenges. As a foundation stone, researchers have invested sincere efforts towards classifying traffic, thereby categorizing the devices. However, the classification solutions are missing the vital attributes of the state-of-the-art high-performance real-time framework. This paper provides the taxonomy of the techno-functional application areas of the IoT characterization. The article also inferences empirically investigated IoT traffic attributes leveraging an Australian dataset collected from 28 IoT devices over six months. Based on the forensics of IoT traffic, the characteristics of IoT-based traffic are listed, which paves the grounds of the security, operational, and management solutions for IoT devices. The paper also details the research gaps in the implemented solutions by exploring additional research dimensions of a trailblazing real-time classification solution, which the researchers often ignore. Lastly, the paper offers recommendations and prospects.
{"title":"Empirical Investigation of IoT Traffic in Smart Environments: Characteristics, Research Gaps and Recommendations","authors":"M. Sneh, A. Bhandari","doi":"10.1109/SMART52563.2021.9676298","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676298","url":null,"abstract":"Adopting the Internet of Things (IoT) in multi-functional domains has led to management, operational, and security challenges. As a foundation stone, researchers have invested sincere efforts towards classifying traffic, thereby categorizing the devices. However, the classification solutions are missing the vital attributes of the state-of-the-art high-performance real-time framework. This paper provides the taxonomy of the techno-functional application areas of the IoT characterization. The article also inferences empirically investigated IoT traffic attributes leveraging an Australian dataset collected from 28 IoT devices over six months. Based on the forensics of IoT traffic, the characteristics of IoT-based traffic are listed, which paves the grounds of the security, operational, and management solutions for IoT devices. The paper also details the research gaps in the implemented solutions by exploring additional research dimensions of a trailblazing real-time classification solution, which the researchers often ignore. Lastly, the paper offers recommendations and prospects.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134553923","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 : 2021-12-10DOI: 10.1109/SMART52563.2021.9676307
Anurag Gupta, R. Shukla, Abhishek Bhola, A. Sengar
Software Bug prediction or defect prediction is very important for the organizations to detect the bugs in the early stage of Software development process because software developers can know the vulnerable areas where the defects may be present.In this research paper we have compared different statistical techniques like Linear Regression, Naïve Bayes, Random Forest, Decision Tree, Artificial Neural Networks etc. and come up with the best among them for the Bug prediction. Comparison is made using Performance Measures like Accuracy, precision, recall and F-measure.
{"title":"Comparative Analysis of Supervised Learning Techniques of Machine Learning for Software Defect Prediction","authors":"Anurag Gupta, R. Shukla, Abhishek Bhola, A. Sengar","doi":"10.1109/SMART52563.2021.9676307","DOIUrl":"https://doi.org/10.1109/SMART52563.2021.9676307","url":null,"abstract":"Software Bug prediction or defect prediction is very important for the organizations to detect the bugs in the early stage of Software development process because software developers can know the vulnerable areas where the defects may be present.In this research paper we have compared different statistical techniques like Linear Regression, Naïve Bayes, Random Forest, Decision Tree, Artificial Neural Networks etc. and come up with the best among them for the Bug prediction. Comparison is made using Performance Measures like Accuracy, precision, recall and F-measure.","PeriodicalId":356096,"journal":{"name":"2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"4 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134604760","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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