The majority of the existing unsupervised methods for detecting shilling attacks are based on user rating patterns, ignoring the differences in rating behavior between legitimate users and attack users. These methods have low accuracy in detecting different shilling attacks without having any prior knowledge of the attack types. We provide a novel unsupervised shilling assault detection technique based on an examination of user rating behavior in order to overcome these constraints. By first examining the deviation of rating tendencies on each item, we are able to determine the target item(s) and the accompanying goals of the attack users. Based on the results of this study, a group of suspicious users is then created. Second, we examine the users' rating behaviors in terms of their rating and interest preferences. Finally, using measurements of user rating behavior, we determine the suspicious degree and identify attack users within the collection of suspicious users. The Movie Lens 1M dataset, the sampled Amazon review dataset, and the Netflix dataset all show how good the suggested detection model.
{"title":"Shilling Attack Detection in User Based Recommendation System","authors":"S. Poornima, M. Geethanjali","doi":"10.46632/daai/3/2/17","DOIUrl":"https://doi.org/10.46632/daai/3/2/17","url":null,"abstract":"The majority of the existing unsupervised methods for detecting shilling attacks are based on user rating patterns, ignoring the differences in rating behavior between legitimate users and attack users. These methods have low accuracy in detecting different shilling attacks without having any prior knowledge of the attack types. We provide a novel unsupervised shilling assault detection technique based on an examination of user rating behavior in order to overcome these constraints. By first examining the deviation of rating tendencies on each item, we are able to determine the target item(s) and the accompanying goals of the attack users. Based on the results of this study, a group of suspicious users is then created. Second, we examine the users' rating behaviors in terms of their rating and interest preferences. Finally, using measurements of user rating behavior, we determine the suspicious degree and identify attack users within the collection of suspicious users. The Movie Lens 1M dataset, the sampled Amazon review dataset, and the Netflix dataset all show how good the suggested detection model.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124092279","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}
Technology development has brought so many threats and hazards at a very high rate in the recent years. The development of application, software tools and its usage in all the fields has brought the awareness about the security. Many mechanisms are used as the security tool such as firewalls, antivirus, spam filters and anti-malware for the security purposes to protect their system and network. Intrusion detection system is a very powerful security system to detect any abnormal or unauthorised access to the system and to the network. This paper is about the study of the importance of intrusion detection, classification of intrusion detection system (IDS), its datasets and usage in various applications. The intrusion detection system has got many developments through its datasets, new technologies and methods but as the technologies increases, the threats of attacking the system and data breaches also increases, so in order to overcome this problem a hybrid framework for the intrusion detection has to be developed to detect the intrusions from the intruder.
{"title":"A Review on Intrusion Detection System and its Techniques","authors":"A. Kalaivani, R. Pugazendi","doi":"10.46632/daai/3/2/24","DOIUrl":"https://doi.org/10.46632/daai/3/2/24","url":null,"abstract":"Technology development has brought so many threats and hazards at a very high rate in the recent years. The development of application, software tools and its usage in all the fields has brought the awareness about the security. Many mechanisms are used as the security tool such as firewalls, antivirus, spam filters and anti-malware for the security purposes to protect their system and network. Intrusion detection system is a very powerful security system to detect any abnormal or unauthorised access to the system and to the network. This paper is about the study of the importance of intrusion detection, classification of intrusion detection system (IDS), its datasets and usage in various applications. The intrusion detection system has got many developments through its datasets, new technologies and methods but as the technologies increases, the threats of attacking the system and data breaches also increases, so in order to overcome this problem a hybrid framework for the intrusion detection has to be developed to detect the intrusions from the intruder.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130363443","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}
Software defect prediction provides development groups with observable outcomes while contributing to industrial results and development faults predicting defective code areas can help developers identify bugs and organize their test activities. The percentage of classification providing the proper prediction is essential for early identification. Moreover, software- defected data sets are supported and at least partially recognized due to their enormous dimension.
{"title":"Software Defect Prediction Using Machine Learning Techniques","authors":"G. Cauvery, D. DhinaSuresh","doi":"10.46632/daai/3/2/7","DOIUrl":"https://doi.org/10.46632/daai/3/2/7","url":null,"abstract":"Software defect prediction provides development groups with observable outcomes while contributing to industrial results and development faults predicting defective code areas can help developers identify bugs and organize their test activities. The percentage of classification providing the proper prediction is essential for early identification. Moreover, software- defected data sets are supported and at least partially recognized due to their enormous dimension.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125861494","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}
Educational Data mining (EDM) in e-Learning systems is a rapidly growing phenomenon. It’s used to improve education by monitoring student performance and trying to understand the students’ learning. Past two decades the teachers collecting the feedback from their students through a written words. This method is very time consuming. Now a dayse-Learning system familiar for the virtual class room environment and learners free to learn at their own pace and to define personal learning path based on their individual needs and interests. This system provides the learning support through explanation, examples, interactive and feedback. The data of feedback is an essential part of effective learning. It helps students understood the subject being studied and gives us idea to give how to improve their learning. The data of feedback can be collected in different ways, such as chart window, SMS, e-Mail, Voice mail and Social Media like twitter, whatsapp. This kind of data should be collected from student in the mode of audio, video and text through social media. This data should be realised the positive and negative or emotion of the students. This paper presents a survey on the analyse of the students feedback by using sentiment analysis methodologies.
{"title":"A Study on Emotion Analysis for Online Learning Based on Students' Feedback via Social Networks","authors":"","doi":"10.46632/daai/3/2/2","DOIUrl":"https://doi.org/10.46632/daai/3/2/2","url":null,"abstract":"Educational Data mining (EDM) in e-Learning systems is a rapidly growing phenomenon. It’s used to improve education by monitoring student performance and trying to understand the students’ learning. Past two decades the teachers collecting the feedback from their students through a written words. This method is very time consuming. Now a dayse-Learning system familiar for the virtual class room environment and learners free to learn at their own pace and to define personal learning path based on their individual needs and interests. This system provides the learning support through explanation, examples, interactive and feedback. The data of feedback is an essential part of effective learning. It helps students understood the subject being studied and gives us idea to give how to improve their learning. The data of feedback can be collected in different ways, such as chart window, SMS, e-Mail, Voice mail and Social Media like twitter, whatsapp. This kind of data should be collected from student in the mode of audio, video and text through social media. This data should be realised the positive and negative or emotion of the students. This paper presents a survey on the analyse of the students feedback by using sentiment analysis methodologies.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125346061","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}
Arun K Talawar, N. K. Honnagoudar, Prabhu Y Avaradi
It is only the farmer who faithfully plants seeds in the spring, who reaps a harvest in the autumn. The goal of this study is to create a useful classification method using the Random Forest (RF) algorithm. Different crops, namely brinjal, carrot, and onion, were examined, and many features have been derived dependent on the design, color, and texture. A preparation stage is described that uses image analysis to enhance the vegetables images dataset in order to minimize their color index. The features of the vegetable images are then retrieved. Finally, Random Forests (RF), a newly generated pattern recognition method, used in the vegetable’s classification process. The proposed method achieved higher accuracy in terms of identification and classification of the vegetables
{"title":"A Novel Method for Identification and Classification of Indian Vegetables Using Random Forest Algorithm","authors":"Arun K Talawar, N. K. Honnagoudar, Prabhu Y Avaradi","doi":"10.46632/daai/3/2/1","DOIUrl":"https://doi.org/10.46632/daai/3/2/1","url":null,"abstract":"It is only the farmer who faithfully plants seeds in the spring, who reaps a harvest in the autumn. The goal of this study is to create a useful classification method using the Random Forest (RF) algorithm. Different crops, namely brinjal, carrot, and onion, were examined, and many features have been derived dependent on the design, color, and texture. A preparation stage is described that uses image analysis to enhance the vegetables images dataset in order to minimize their color index. The features of the vegetable images are then retrieved. Finally, Random Forests (RF), a newly generated pattern recognition method, used in the vegetable’s classification process. The proposed method achieved higher accuracy in terms of identification and classification of the vegetables","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124015710","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}
Data security has been the major issue in information technology. People from all over the world put all kinds of information like public, private and confidential information in the cloud. Also data in the cloud are from various storage devices like servers, PC, mobile devices, smart phones and wireless sensor networks. So data security becomes a serious issue in the cloud environment. Protecting the data from unauthorized access is tedious. There are a lot of issues related to data security and privacy in cloud computing technology. This paper focuses on understanding the security and privacy issues in the cloud environment and comprehending compliance management in cloud computing
{"title":"A Quick Review of Data Security, Privacy in Cloud Computing","authors":"J. Sathya","doi":"10.46632/daai/3/2/8","DOIUrl":"https://doi.org/10.46632/daai/3/2/8","url":null,"abstract":"Data security has been the major issue in information technology. People from all over the world put all kinds of information like public, private and confidential information in the cloud. Also data in the cloud are from various storage devices like servers, PC, mobile devices, smart phones and wireless sensor networks. So data security becomes a serious issue in the cloud environment. Protecting the data from unauthorized access is tedious. There are a lot of issues related to data security and privacy in cloud computing technology. This paper focuses on understanding the security and privacy issues in the cloud environment and comprehending compliance management in cloud computing","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127334324","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}
Traditionally, websites are hosted viacentralized network, where the web servers distribute the website files to the clients. However a server failure can prevent the web applications from being used until the server goes live again. Due to the fact that websites rely entirely on the integrity of a single organization, it enables larger companies or government to decide what content is shown, this put into question the freedom that the internet originally brought. The block chain hosting service, also known as decentralized web hosting, is not controlled by a single organization or a third party organization. The architecture relies on peer-to-peer communication rather than client-to-server communication. Due to the decentralized nature of this system, all clients do not rely on a single server for their data, but instead data is sent directly between the clients across the network. It is possible to achieve this with the help of IPFS and Block chain. IPFS is a protocol for distributed file storage that allows computers to store and serve files in a peer-to-peer network by distributing them across computers and with the block chain technology, it is possible to ensure both authenticity as well as confidentiality.
{"title":"A Study On Decentralized Web Hosting Using Peer-To-Peer Architecture","authors":"D. Santhi Jeslet, M. Shanmugam","doi":"10.46632/daai/3/2/6","DOIUrl":"https://doi.org/10.46632/daai/3/2/6","url":null,"abstract":"Traditionally, websites are hosted viacentralized network, where the web servers distribute the website files to the clients. However a server failure can prevent the web applications from being used until the server goes live again. Due to the fact that websites rely entirely on the integrity of a single organization, it enables larger companies or government to decide what content is shown, this put into question the freedom that the internet originally brought. The block chain hosting service, also known as decentralized web hosting, is not controlled by a single organization or a third party organization. The architecture relies on peer-to-peer communication rather than client-to-server communication. Due to the decentralized nature of this system, all clients do not rely on a single server for their data, but instead data is sent directly between the clients across the network. It is possible to achieve this with the help of IPFS and Block chain. IPFS is a protocol for distributed file storage that allows computers to store and serve files in a peer-to-peer network by distributing them across computers and with the block chain technology, it is possible to ensure both authenticity as well as confidentiality.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122299268","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}
{"title":"An Empirical study on designing a security framework for legacy system evolution towards SOA","authors":"T. Lavanya","doi":"10.46632/daai/3/2/3","DOIUrl":"https://doi.org/10.46632/daai/3/2/3","url":null,"abstract":"","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128290284","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}
This paper aims to investigate certain factors that hide outliers in two dimensions such as boundary partitioning and space angular parameters. In this proposed algorithm, boundary representation of clusters, the data points that lie on the cluster boundary is stored geometrically as coordinate values such as i_bound (inliers) and o_bound(outliers). Outliers that present in dataset are investigated by boundary fitness over centroid stability. In this paper we focus to examine whether the data point lie on the boundary is treated as inliers or outliers. Several iterations are manipulated to fix the outlier point deeply. Using fuzzy clustering, data points are clustered and boundary is fixed. If the space occupied by the cluster varies for every iteration, the distance from inlier to outlier between the boundaries is calculated. After calculation, if the data point is below the threshold value, it is treated as outlier. Our proposed method shows efficiency over evaluation metrics of outlier detection performance.
{"title":"Certain Investigation on Perpetualistic Fuzzy Outlier Data for Efficiency Evaluation of Centroid Stability with Cluster Boundary Fitness","authors":"S. Rajalakshmi, P. Madhubala","doi":"10.46632/daai/3/2/4","DOIUrl":"https://doi.org/10.46632/daai/3/2/4","url":null,"abstract":"This paper aims to investigate certain factors that hide outliers in two dimensions such as boundary partitioning and space angular parameters. In this proposed algorithm, boundary representation of clusters, the data points that lie on the cluster boundary is stored geometrically as coordinate values such as i_bound (inliers) and o_bound(outliers). Outliers that present in dataset are investigated by boundary fitness over centroid stability. In this paper we focus to examine whether the data point lie on the boundary is treated as inliers or outliers. Several iterations are manipulated to fix the outlier point deeply. Using fuzzy clustering, data points are clustered and boundary is fixed. If the space occupied by the cluster varies for every iteration, the distance from inlier to outlier between the boundaries is calculated. After calculation, if the data point is below the threshold value, it is treated as outlier. Our proposed method shows efficiency over evaluation metrics of outlier detection performance.","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134196776","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}
The use of a fast evolving artificial intelligence technology (AIT) to forecast the vibration response of maritime soil based on geocells is explored in this research. The vibration response is represented by an indicator called peak particle velocity (PPV). For the purpose of predicting PPV, the artificial intelligence techniques Artificial Neural Network (ANN) and Modified Gravitational Search Algorithm (MGSA) are employed. To create the dataset for the model, a number of field vibration tests were first performed over the geocell-reinforced beds. PPV variation was investigated by varying the test variables—footing embedment, dynamic load, infill material modulus, width, and depth of geocell mattress placement—during the test performed. The various statistical indicators were determined in order to evaluate the prediction performance of a constructed model. Plate load results on geocell-reinforced foundation beds have been used to validate the proposed hybrid ANN-MGSA model. High accuracy and consistency were found when the findings of the ANN-EHO, JSA, MOA, and RNN method were compared, particularly at predicted and actual resolution levels. A parametric sensitivity has also been examined in order to better understand the behaviour of geocell-reinforced structures
{"title":"Performance of Artificial Neural Network and Modified Gravitational Search Algorithm Models to Predict Vibration Response of Geocell Reinforced Based Weak Sand","authors":"","doi":"10.46632/daai/2/3/38","DOIUrl":"https://doi.org/10.46632/daai/2/3/38","url":null,"abstract":"The use of a fast evolving artificial intelligence technology (AIT) to forecast the vibration response of maritime soil based on geocells is explored in this research. The vibration response is represented by an indicator called peak particle velocity (PPV). For the purpose of predicting PPV, the artificial intelligence techniques Artificial Neural Network\u0000(ANN) and Modified Gravitational Search Algorithm (MGSA) are employed. To create the dataset for the model, a number of field vibration tests were first performed over the geocell-reinforced beds. PPV variation was investigated by varying the test variables—footing embedment, dynamic load, infill material modulus, width, and depth of geocell mattress placement—during the test performed. The various statistical indicators were determined in order to evaluate the prediction performance of a constructed model. Plate load results on geocell-reinforced foundation beds have been used to validate the proposed hybrid ANN-MGSA model. High accuracy and consistency were found when the findings of the ANN-EHO, JSA, MOA, and RNN method were compared, particularly at predicted and actual resolution levels. A parametric sensitivity has also been examined in order to better understand the behaviour of geocell-reinforced structures","PeriodicalId":226827,"journal":{"name":"Data Analytics and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121663244","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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