Pub Date : 2023-02-01DOI: 10.1080/1206212X.2020.1738665
Rongqing Zhuo, Zhongxian Bai
We, the Editor and Publisher of the International Journal of Computers and Applications, have retracted the following article which was part of the Special Issue on Advanced Security Techniques for Cloud Computing and Big Data - New Directions: Rongqing Zhuo & Zhongxian Bai (2020) Key technologies of cloud computing-based IoT data mining, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2020.1738665 Since publication, it came to our attention that the articles published in this Special Issue were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the Editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract. The concerns raised have been shared with the authors and they have been given the opportunity to respond. The authors have been informed about the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.
{"title":"Retracted Article: Key technologies of cloud computing-based IoT data mining","authors":"Rongqing Zhuo, Zhongxian Bai","doi":"10.1080/1206212X.2020.1738665","DOIUrl":"https://doi.org/10.1080/1206212X.2020.1738665","url":null,"abstract":"We, the Editor and Publisher of the International Journal of Computers and Applications, have retracted the following article which was part of the Special Issue on Advanced Security Techniques for Cloud Computing and Big Data - New Directions: Rongqing Zhuo & Zhongxian Bai (2020) Key technologies of cloud computing-based IoT data mining, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2020.1738665 Since publication, it came to our attention that the articles published in this Special Issue were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the Editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract. The concerns raised have been shared with the authors and they have been given the opportunity to respond. The authors have been informed about the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"37 1","pages":"196 - 203"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74120024","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 : 2023-02-01DOI: 10.1080/1206212X.2020.1730070
Pengcheng Li, Hui-Ping Cheng
Face recognition technology is an important branch based on biometrics technology, and has broad application prospects in the fields of law, business and security. The purpose of this paper is to p...
{"title":"Retracted Article: Track recognition algorithm based on neural network for rail transit","authors":"Pengcheng Li, Hui-Ping Cheng","doi":"10.1080/1206212X.2020.1730070","DOIUrl":"https://doi.org/10.1080/1206212X.2020.1730070","url":null,"abstract":"Face recognition technology is an important branch based on biometrics technology, and has broad application prospects in the fields of law, business and security. The purpose of this paper is to p...","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"36 1","pages":"144 - 150"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73841272","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 : 2023-02-01DOI: 10.1080/1206212X.2020.1738664
Caixia Chen, Sheng-yi Zhou, Qingqing Chang
We, the Editor and Publisher of the International Journal of Computers and Applications, have retracted the following article which was part of the Special Issue on Advanced Security Techniques for Cloud Computing and Big Data - New Directions: Caixia Chen, Sheng Zhou & Qingqing Chang (2020) The role of computer security management in preventing financial technology risks, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2020.1738664 Since publication, it came to our attention that the articles published in this Special Issue were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the Editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract. The concerns raised have been shared with the authors and they have been given the opportunity to respond. The authors have been informed about the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.
{"title":"Retracted Article: The role of computer security management in preventing financial technology risks","authors":"Caixia Chen, Sheng-yi Zhou, Qingqing Chang","doi":"10.1080/1206212X.2020.1738664","DOIUrl":"https://doi.org/10.1080/1206212X.2020.1738664","url":null,"abstract":"We, the Editor and Publisher of the International Journal of Computers and Applications, have retracted the following article which was part of the Special Issue on Advanced Security Techniques for Cloud Computing and Big Data - New Directions: Caixia Chen, Sheng Zhou & Qingqing Chang (2020) The role of computer security management in preventing financial technology risks, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2020.1738664 Since publication, it came to our attention that the articles published in this Special Issue were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the Editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract. The concerns raised have been shared with the authors and they have been given the opportunity to respond. The authors have been informed about the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"10 1","pages":"187 - 195"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76254182","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}
We, the Editor and Publisher of the International Journal of Computers and Applications, have retracted the following article which was part of the Special Issue on Advanced Security Techniques for Cloud Computing and Big Data - New Directions: Hongxiang Zhang, Shaojie Shi, Yongkai Wu & Tong Feng (2020) Development of computer-based agricultural remote intelligent information monitoring system, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2020.1730567 Since publication, it came to our attention that the articles published in this Special Issue were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the Editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract. The concerns raised have been shared with the authors and they have been given the opportunity to respond. The authors have been informed about the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.
{"title":"Retracted Article: Development of computer-based agricultural remote intelligent information monitoring system","authors":"Hongxiang Zhang, Shaojie Shi, Yongkai Wu, Tong Feng","doi":"10.1080/1206212X.2020.1730567","DOIUrl":"https://doi.org/10.1080/1206212X.2020.1730567","url":null,"abstract":"We, the Editor and Publisher of the International Journal of Computers and Applications, have retracted the following article which was part of the Special Issue on Advanced Security Techniques for Cloud Computing and Big Data - New Directions: Hongxiang Zhang, Shaojie Shi, Yongkai Wu & Tong Feng (2020) Development of computer-based agricultural remote intelligent information monitoring system, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2020.1730567 Since publication, it came to our attention that the articles published in this Special Issue were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the Editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract. The concerns raised have been shared with the authors and they have been given the opportunity to respond. The authors have been informed about the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"68 1","pages":"151 - 160"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78256439","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 : 2023-01-04DOI: 10.1080/1206212X.2022.2162671
Amina Kchaou, Sonda Ammar Bouhamed
The performance of an Automatic iris Identification System is impacted by both the poor quality of iris images and the uncertainty of information. Assessing image quality and rejecting poor-quality images can substantially improve the performances of the current biometric systems. The main idea behind our proposed Image Quality Assessment approaches is to take advantage, firstly, of the texture of iris images and, secondly, of the uncertainty of these information. This is achieved by defining a set of Contextual Quality Indicators extracted from the image texture and transforming them into Quality Assessment Criteria in the evidential framework, taking into account the information uncertainty degree. The Contextual Quality Indicators are defined based on a priori analysis of the context of the application. We use ‘iris’ as the context of application. Generally, only the normalized iris image is saved, i.e. the acquired iris image is not always available. So, the main advantage of our approaches over other related methods is that it can act in the normalization level of the processing chain to reject poor-quality images. So that, the subsequent Automatic iris Identification System can process only good-quality images, which result in better recognition rate performance. The functioning of our evidential approaches is illustrated using image samples from CASIA 1.0 database. The performance of over the proposed image quality assessment approaches is compared with the standard iris identification system without an image quality assessment step. A statistical test, based on 95% confidence interval, is used to assess if there is a statistically significant difference between the performances of the proposed approaches. The CASIA 1.0 has been used to make the comparison. The comparison results highlight the effectiveness of the proposed approaches for iris domain of applications. The source code of our paper is available at https://github.com/Sonda09/IIQA
{"title":"Two image quality assessment methods based on evidential modeling and uncertainty: application to automatic iris identification systems","authors":"Amina Kchaou, Sonda Ammar Bouhamed","doi":"10.1080/1206212X.2022.2162671","DOIUrl":"https://doi.org/10.1080/1206212X.2022.2162671","url":null,"abstract":"The performance of an Automatic iris Identification System is impacted by both the poor quality of iris images and the uncertainty of information. Assessing image quality and rejecting poor-quality images can substantially improve the performances of the current biometric systems. The main idea behind our proposed Image Quality Assessment approaches is to take advantage, firstly, of the texture of iris images and, secondly, of the uncertainty of these information. This is achieved by defining a set of Contextual Quality Indicators extracted from the image texture and transforming them into Quality Assessment Criteria in the evidential framework, taking into account the information uncertainty degree. The Contextual Quality Indicators are defined based on a priori analysis of the context of the application. We use ‘iris’ as the context of application. Generally, only the normalized iris image is saved, i.e. the acquired iris image is not always available. So, the main advantage of our approaches over other related methods is that it can act in the normalization level of the processing chain to reject poor-quality images. So that, the subsequent Automatic iris Identification System can process only good-quality images, which result in better recognition rate performance. The functioning of our evidential approaches is illustrated using image samples from CASIA 1.0 database. The performance of over the proposed image quality assessment approaches is compared with the standard iris identification system without an image quality assessment step. A statistical test, based on 95% confidence interval, is used to assess if there is a statistically significant difference between the performances of the proposed approaches. The CASIA 1.0 has been used to make the comparison. The comparison results highlight the effectiveness of the proposed approaches for iris domain of applications. The source code of our paper is available at https://github.com/Sonda09/IIQA","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"214 1","pages":"254 - 268"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88068038","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 : 2023-01-02DOI: 10.1080/1206212X.2019.1706031
Dahai Yu, Juan Zhu
We, the Editor and Publisher of the International Journal of Computers and Applications, have retracted the following article which was part of the Special Issue on Advanced Security Techniques for Cloud Computing and Big Data - New Directions: Dahai Yu & Juan Zhu (2019) Research on digital image wavelet transform filtering optimization processing method based on DSP Internet of Things, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2019.1706031 Since publication, it came to our attention that the articles published in this Special Issue were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the Editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract. The concerns raised have been shared with the authors and they have been given the opportunity to respond. The authors have been informed about the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.
{"title":"Retracted Article: Research on digital image wavelet transform filtering optimization processing method based on DSP Internet of Things","authors":"Dahai Yu, Juan Zhu","doi":"10.1080/1206212X.2019.1706031","DOIUrl":"https://doi.org/10.1080/1206212X.2019.1706031","url":null,"abstract":"We, the Editor and Publisher of the International Journal of Computers and Applications, have retracted the following article which was part of the Special Issue on Advanced Security Techniques for Cloud Computing and Big Data - New Directions: Dahai Yu & Juan Zhu (2019) Research on digital image wavelet transform filtering optimization processing method based on DSP Internet of Things, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2019.1706031 Since publication, it came to our attention that the articles published in this Special Issue were not reviewed fully in line with the journal's peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the Editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract. The concerns raised have been shared with the authors and they have been given the opportunity to respond. The authors have been informed about the retraction of the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"93 7 1","pages":"77 - 87"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83499544","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 : 2022-12-31DOI: 10.1080/1206212X.2022.2161147
Aarthi Paramasivam, S. Nirmala
The concept of a medical intelligent system has steadily garnered attention as modern technology advances. An intelligent medical system is a medical system that develops a certain amount of intelligence and performs a task without the assistance of a human. A chatbot can be thought of as a medical intelligent consultation system. The chatbot's question generation quality can be improved by creating more relevant questions depending on the patient's demands. Question generation, in addition to chatbots, is used to assess a learner's comprehension. This paper proposes a two-step approach to question generation. The first stage generates the entailment for the sentence that the question should be generated for. The generated entailed sentences are used to create questions in the second step. By generating questions from the original sentence, one can discover relevant information about the sentence. Furthermore, to increase the size of the entailment dataset, a data augmentation approach is used in this paper. The proposed work in this paper focuses on the importance of entailment in question generation and also studies the influence of entailment on the questions generated. Since data augmentation is employed, the overall effectiveness of data augmentation on the model is also investigated.
{"title":"Exploring question generation in medical intelligent system using entailment","authors":"Aarthi Paramasivam, S. Nirmala","doi":"10.1080/1206212X.2022.2161147","DOIUrl":"https://doi.org/10.1080/1206212X.2022.2161147","url":null,"abstract":"The concept of a medical intelligent system has steadily garnered attention as modern technology advances. An intelligent medical system is a medical system that develops a certain amount of intelligence and performs a task without the assistance of a human. A chatbot can be thought of as a medical intelligent consultation system. The chatbot's question generation quality can be improved by creating more relevant questions depending on the patient's demands. Question generation, in addition to chatbots, is used to assess a learner's comprehension. This paper proposes a two-step approach to question generation. The first stage generates the entailment for the sentence that the question should be generated for. The generated entailed sentences are used to create questions in the second step. By generating questions from the original sentence, one can discover relevant information about the sentence. Furthermore, to increase the size of the entailment dataset, a data augmentation approach is used in this paper. The proposed work in this paper focuses on the importance of entailment in question generation and also studies the influence of entailment on the questions generated. Since data augmentation is employed, the overall effectiveness of data augmentation on the model is also investigated.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"14 1","pages":"248 - 253"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86310581","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 : 2022-12-20DOI: 10.1080/1206212X.2022.2149117
Ritu Garg, R. K. Singh
Software Configuration Management (SCM) involves tracking similarities/changes during software evolution. Efficient comparison for tracking requires two perspectives—Granularity: comparing the entities at file level, class level, and method level. Second, Robustness should be prominent to detect renaming and shifting that occur as a part of restructuring. Even GIT repository, which is widely used, allows such comparison with renaming and shifting details but is limited to file level only, along with its own limitation of default similarity criteria of above 50%. In this study, the proposed technique named SBCDetector detects similarity/change status with both perspectives that is lacking in the existing literature. Result shows that one-fourth of entities have been found renamed/shifted at three granularities for eight subject systems improving tracking, understandability, and onboarding. Hybrid technique involving fuzzy logic derives classification model with .99 f-score to detect first- and second-order similarity/change.
{"title":"SBCDetector: a hybrid approach to detect second-order similarity or change","authors":"Ritu Garg, R. K. Singh","doi":"10.1080/1206212X.2022.2149117","DOIUrl":"https://doi.org/10.1080/1206212X.2022.2149117","url":null,"abstract":"Software Configuration Management (SCM) involves tracking similarities/changes during software evolution. Efficient comparison for tracking requires two perspectives—Granularity: comparing the entities at file level, class level, and method level. Second, Robustness should be prominent to detect renaming and shifting that occur as a part of restructuring. Even GIT repository, which is widely used, allows such comparison with renaming and shifting details but is limited to file level only, along with its own limitation of default similarity criteria of above 50%. In this study, the proposed technique named SBCDetector detects similarity/change status with both perspectives that is lacking in the existing literature. Result shows that one-fourth of entities have been found renamed/shifted at three granularities for eight subject systems improving tracking, understandability, and onboarding. Hybrid technique involving fuzzy logic derives classification model with .99 f-score to detect first- and second-order similarity/change.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"23 1","pages":"238 - 247"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86351191","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 : 2022-11-08DOI: 10.1080/1206212X.2022.2143026
Touseef Iqbal, Shaima Qureshi
Anomaly detection is a method of categorizing unexpected data points or events in a dataset. Variational Auto-Encoders (VAEs) have proved to handle complex problems in a variety of disciplines. We propose a technique for detecting anomalies based on the reconstruction probability of VAEs. The proposed method trains VAEs on three different datasets. The reconstruction probability is a much more principled and realistic anomaly score than the reconstruction error utilized by auto-encoders and other data compression methods because of the theoretical background and by including the concept of variability. The paper describes recent deep learning models for anomaly detection, as well as a comparison to other methodologies. Variational auto-encoders are trained on three different datasets, in an unsupervised setup to classify the anomalies, based on reconstruction probability. Further, the in-depth study of anomaly detection techniques is presented in this paper. The data are reconstructed using the VAEs generative characteristics to investigate the root cause of the anomalies.
{"title":"Reconstruction probability-based anomaly detection using variational auto-encoders","authors":"Touseef Iqbal, Shaima Qureshi","doi":"10.1080/1206212X.2022.2143026","DOIUrl":"https://doi.org/10.1080/1206212X.2022.2143026","url":null,"abstract":"Anomaly detection is a method of categorizing unexpected data points or events in a dataset. Variational Auto-Encoders (VAEs) have proved to handle complex problems in a variety of disciplines. We propose a technique for detecting anomalies based on the reconstruction probability of VAEs. The proposed method trains VAEs on three different datasets. The reconstruction probability is a much more principled and realistic anomaly score than the reconstruction error utilized by auto-encoders and other data compression methods because of the theoretical background and by including the concept of variability. The paper describes recent deep learning models for anomaly detection, as well as a comparison to other methodologies. Variational auto-encoders are trained on three different datasets, in an unsupervised setup to classify the anomalies, based on reconstruction probability. Further, the in-depth study of anomaly detection techniques is presented in this paper. The data are reconstructed using the VAEs generative characteristics to investigate the root cause of the anomalies.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"81 1","pages":"231 - 237"},"PeriodicalIF":0.0,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83977033","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 : 2022-10-18DOI: 10.1080/1206212X.2022.2133805
Aafaq Mohi ud din, Shaima Qureshi
The study of graph neural networks has revealed that they can unleash new applications in a variety of disciplines using such a basic process that we cannot imagine in the context of other deep learning designs. Many limitations limit their expressiveness, and researchers are working to overcome them to fully exploit the power of graph data. There are a number of publications that explore graph neural networks (GNNs) restrictions and bottlenecks, but the common thread that runs through them all is that they can all be traced back to message passing, which is the key technique we use to train our graph models. We outline the general GNN design pipeline in this study as well as discuss solutions to the over-smoothing problem, categorize the solutions, and identify open challenges for further research. Abbreviations: CGNN: Continuous Graph Neural Networks; CNN: Convolution NeuralNetwork; DeGNN: Decomposition Graph Neural Network; DGN: Directional GraphNetworks; DGN: Differentiable Group Normalization; DL: Deep Learning; EGAI:Enhancing GNNs by a High-quality Aggregation of Beneficial Information; GAT: GraphAttention Network; GCN: Graph Convolutional Network; GDC: Graph Drop Connect; GDR: Group Distance Ratio; GNN: Graph Neural Network; GRAND: GraphRandom Neural Networks; IIG: Instance Information Gain; MAD: Man AverageDistance; PDE-GCN: Partial Differential Equations-GCN; PTDNet: ParameterizedTopological Denoising network; TDGNN: Tree Decomposition Graph NeuralNetwork;
图神经网络的研究表明，它们可以在各种学科中释放新的应用，使用这样一个基本的过程，我们无法想象在其他深度学习设计的背景下。许多限制限制了它们的表现力，研究人员正在努力克服它们，以充分利用图形数据的力量。有许多出版物探讨了图神经网络(gnn)的限制和瓶颈，但贯穿它们的共同点是，它们都可以追溯到消息传递，这是我们用来训练图模型的关键技术。在本研究中，我们概述了通用GNN设计流程，并讨论了过度平滑问题的解决方案，对解决方案进行了分类，并确定了进一步研究的开放挑战。CGNN:连续图神经网络;CNN:卷积神经网络;DeGNN:分解图神经网络;DGN:定向图网络;DGN:可微群归一化;DL:深度学习;EGAI:通过高质量的有益信息聚合来增强gnnGAT: GraphAttention Network;GCN:图卷积网络;GDC: Graph Drop Connect;GDR:组距离比;图神经网络;GRAND: GraphRandom神经网络;IIG:实例信息增益;MAD:男子平均距离;偏微分方程;gcn;PTDNet:参数化拓扑去噪网络;树分解图神经网络;
{"title":"A review of challenges and solutions in the design and implementation of deep graph neural networks","authors":"Aafaq Mohi ud din, Shaima Qureshi","doi":"10.1080/1206212X.2022.2133805","DOIUrl":"https://doi.org/10.1080/1206212X.2022.2133805","url":null,"abstract":"The study of graph neural networks has revealed that they can unleash new applications in a variety of disciplines using such a basic process that we cannot imagine in the context of other deep learning designs. Many limitations limit their expressiveness, and researchers are working to overcome them to fully exploit the power of graph data. There are a number of publications that explore graph neural networks (GNNs) restrictions and bottlenecks, but the common thread that runs through them all is that they can all be traced back to message passing, which is the key technique we use to train our graph models. We outline the general GNN design pipeline in this study as well as discuss solutions to the over-smoothing problem, categorize the solutions, and identify open challenges for further research. Abbreviations: CGNN: Continuous Graph Neural Networks; CNN: Convolution NeuralNetwork; DeGNN: Decomposition Graph Neural Network; DGN: Directional GraphNetworks; DGN: Differentiable Group Normalization; DL: Deep Learning; EGAI:Enhancing GNNs by a High-quality Aggregation of Beneficial Information; GAT: GraphAttention Network; GCN: Graph Convolutional Network; GDC: Graph Drop Connect; GDR: Group Distance Ratio; GNN: Graph Neural Network; GRAND: GraphRandom Neural Networks; IIG: Instance Information Gain; MAD: Man AverageDistance; PDE-GCN: Partial Differential Equations-GCN; PTDNet: ParameterizedTopological Denoising network; TDGNN: Tree Decomposition Graph NeuralNetwork;","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"105 1","pages":"221 - 230"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85734756","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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