Pub Date : 2023-04-03DOI: 10.1080/1206212X.2023.2193778
A. Pathan
The legal status of cryptocurrency varies from country to country. The intent of this article is to investigate the key reasons behind some IT Freelancers’ preference of getting their remuneration via digital transactions of cryptocurrency even though by law, it is illegal in their country. We also explore the unique characteristics of cryptocurrency that make it distinct from e-cash and other types of currencies.
{"title":"Why do some IT freelancers in certain countries prefer digital payment with cryptocurrency despite it being illegal?","authors":"A. Pathan","doi":"10.1080/1206212X.2023.2193778","DOIUrl":"https://doi.org/10.1080/1206212X.2023.2193778","url":null,"abstract":"The legal status of cryptocurrency varies from country to country. The intent of this article is to investigate the key reasons behind some IT Freelancers’ preference of getting their remuneration via digital transactions of cryptocurrency even though by law, it is illegal in their country. We also explore the unique characteristics of cryptocurrency that make it distinct from e-cash and other types of currencies.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"51 1","pages":"285 - 287"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76242936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/1206212X.2023.2199966
Mani Deep Karumanchi, J. I. Sheeba, S. Devaneyan
Most of the traditional cloud-based applications are insecure and difficult to compute the data integrity with variable hash size on heterogeneous supply chain datasets. Also, cloud storage systems are independent of integrity computational and data security due to structured data and computational memory. As the size of the cloud data files is increasing in the public and private cloud servers, it is difficult to provide strong data security due to file format and high dimensionality. The computational time and storage space of the conventional attribute-based encryption and decryption models are high during the data integrity verification in the traditional blockchain frameworks. This paper implements a hybrid variable size data integrity algorithm on the heterogeneous cloud supply chain data files for the strong data encryption and decryption process. This work implements an optimized blockchain framework using the advanced heterogeneous integrity computation approach and integrity policy-based attribute encryption and decryption approach for better cloud data security. Experimental results proved that the proposed integrity-based encryption model has better efficiency than the traditional integrity-based encryption frameworks on cloud heterogeneous data types.
{"title":"An efficient integrity based multi-user blockchain framework for heterogeneous supply chain management applications","authors":"Mani Deep Karumanchi, J. I. Sheeba, S. Devaneyan","doi":"10.1080/1206212X.2023.2199966","DOIUrl":"https://doi.org/10.1080/1206212X.2023.2199966","url":null,"abstract":"Most of the traditional cloud-based applications are insecure and difficult to compute the data integrity with variable hash size on heterogeneous supply chain datasets. Also, cloud storage systems are independent of integrity computational and data security due to structured data and computational memory. As the size of the cloud data files is increasing in the public and private cloud servers, it is difficult to provide strong data security due to file format and high dimensionality. The computational time and storage space of the conventional attribute-based encryption and decryption models are high during the data integrity verification in the traditional blockchain frameworks. This paper implements a hybrid variable size data integrity algorithm on the heterogeneous cloud supply chain data files for the strong data encryption and decryption process. This work implements an optimized blockchain framework using the advanced heterogeneous integrity computation approach and integrity policy-based attribute encryption and decryption approach for better cloud data security. Experimental results proved that the proposed integrity-based encryption model has better efficiency than the traditional integrity-based encryption frameworks on cloud heterogeneous data types.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"19 1","pages":"337 - 351"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82732058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/1206212X.2023.2198785
R. Djemili, Merouane Zamouche
Human activity recognition (HAR) has recently witnessed outstanding growth in health and entertainment applications. Owing to the availability of smartphones, many new methods and protocols for using the data from smartphones’ embedded sensors are emerging. Nonetheless, the methods carried out and published in the literature leave a wide area for improvement, in terms of accuracy, resource economy, and adaptation to real-world nuisances. On top of that, a novel classification method that is more economical and efficient is proposed in this paper using both 1D convolutional neural network (1D-CNN) parameters and handcrafted temporal and frequency features with the proficiency of a multilayer perceptron neural network (MLP) classifier. The method proposed requires only tri-axial accelerometer data, allowing it to be deployed even into lower equipment devices; it was tested within the two well-known benchmark datasets: UCI-HAR and Uni-MIB SHAR. Experimental results yield a classification accuracy exceeding 99%, outperforming many of the methods recently shown in the literature.
{"title":"An efficient deep learning-based approach for human activity recognition using smartphone inertial sensors","authors":"R. Djemili, Merouane Zamouche","doi":"10.1080/1206212X.2023.2198785","DOIUrl":"https://doi.org/10.1080/1206212X.2023.2198785","url":null,"abstract":"Human activity recognition (HAR) has recently witnessed outstanding growth in health and entertainment applications. Owing to the availability of smartphones, many new methods and protocols for using the data from smartphones’ embedded sensors are emerging. Nonetheless, the methods carried out and published in the literature leave a wide area for improvement, in terms of accuracy, resource economy, and adaptation to real-world nuisances. On top of that, a novel classification method that is more economical and efficient is proposed in this paper using both 1D convolutional neural network (1D-CNN) parameters and handcrafted temporal and frequency features with the proficiency of a multilayer perceptron neural network (MLP) classifier. The method proposed requires only tri-axial accelerometer data, allowing it to be deployed even into lower equipment devices; it was tested within the two well-known benchmark datasets: UCI-HAR and Uni-MIB SHAR. Experimental results yield a classification accuracy exceeding 99%, outperforming many of the methods recently shown in the literature.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"5 1","pages":"323 - 336"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89218452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1080/1206212X.2023.2196902
Ali Akram Abdul-Kareem, W. A. M. Al-Jawher
Image encryption is essential for ensuring data transmission security over open public networks. Using Multi-Wavelet Transform, Arnold transform, and two chaotic systems, a novel color image encryption technology is designed in this paper. In the proposed algorithm, the primary color components of the input image undergo a multi-wave transform before the Arnold Transform confounds the sub-bands of each color component. Each color component is then divided into blocks shuffled in a predetermined order. Finally, the encrypted image is generated using secret keys derived from Nahrain’s and WAM’s chaotic systems. Notably, the initial conditions of the chaotic maps are generated using image data to increase the algorithm’s sensitivity to the input image. Security analyses conducted to validate the practicability of the new algorithm reveal that it possesses excellent encryption efficiency, high key sensitivity, and the ability to withstand a wide variety of attacks.
{"title":"Image Encryption Algorithm Based on Arnold Transform and Chaos Theory in the Multi-wavelet Domain","authors":"Ali Akram Abdul-Kareem, W. A. M. Al-Jawher","doi":"10.1080/1206212X.2023.2196902","DOIUrl":"https://doi.org/10.1080/1206212X.2023.2196902","url":null,"abstract":"Image encryption is essential for ensuring data transmission security over open public networks. Using Multi-Wavelet Transform, Arnold transform, and two chaotic systems, a novel color image encryption technology is designed in this paper. In the proposed algorithm, the primary color components of the input image undergo a multi-wave transform before the Arnold Transform confounds the sub-bands of each color component. Each color component is then divided into blocks shuffled in a predetermined order. Finally, the encrypted image is generated using secret keys derived from Nahrain’s and WAM’s chaotic systems. Notably, the initial conditions of the chaotic maps are generated using image data to increase the algorithm’s sensitivity to the input image. Security analyses conducted to validate the practicability of the new algorithm reveal that it possesses excellent encryption efficiency, high key sensitivity, and the ability to withstand a wide variety of attacks.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"5 5 1","pages":"306 - 322"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81227166","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 : 2023-03-28DOI: 10.1080/1206212X.2023.2191072
T. J. Anande, Sami Al-Saadi, M. Leeson
Generative Adversarial Networks (GANs) have remained an active area of research, particularly due to their increased and advanced evolving application capabilities. In several domains such as images, facial synthesis, character generation, language processing and multimedia, they have been implemented for advanced tasks. However, there has been more limited progress in network traffic data generation due to the complexities associated with data formats and distributions. This research implements two GAN architectures that include data transforms to simultaneously train and generate categorical and continuous network traffic features. These architectures demonstrate superior performance to the original ‘Vanilla’ GAN approach, which is included as a baseline comparator. Close matches are obtained between logarithms of the means and standard deviations of the fake data and the corresponding quantities from the real data. Moreover, similar principal components are exhibited by the fake and real data streams. Furthermore, some 85% of the features from the fake data could replace those in the real data without detection.
{"title":"Generative adversarial networks for network traffic feature generation","authors":"T. J. Anande, Sami Al-Saadi, M. Leeson","doi":"10.1080/1206212X.2023.2191072","DOIUrl":"https://doi.org/10.1080/1206212X.2023.2191072","url":null,"abstract":"Generative Adversarial Networks (GANs) have remained an active area of research, particularly due to their increased and advanced evolving application capabilities. In several domains such as images, facial synthesis, character generation, language processing and multimedia, they have been implemented for advanced tasks. However, there has been more limited progress in network traffic data generation due to the complexities associated with data formats and distributions. This research implements two GAN architectures that include data transforms to simultaneously train and generate categorical and continuous network traffic features. These architectures demonstrate superior performance to the original ‘Vanilla’ GAN approach, which is included as a baseline comparator. Close matches are obtained between logarithms of the means and standard deviations of the fake data and the corresponding quantities from the real data. Moreover, similar principal components are exhibited by the fake and real data streams. Furthermore, some 85% of the features from the fake data could replace those in the real data without detection.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"42 1","pages":"297 - 305"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84338647","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 : 2023-03-05DOI: 10.1080/1206212X.2023.2182966
Lucy Sumi, Shouvik Dey
Closed-Circuit Television (CCTV) cameras in public places have become more prominent with the rising firearm-related criminal activities, such as robberies, open firing, threats at gunpoint, etc. Early detection of firearms in surveillance systems is crucial for security and safety concerns. In this paper, we present a You Only Look Once (YOLOv5)-based weapon detection system that detects different types of weapons such as rifles, pistols, knives, etc. The main objective of this work is to show the impact of data augmentation on different types of datasets and make a detailed comparative examination with the existing baseline study and other similar works in the literature. The results give new insights to consider for weapon detection systems and object detection, in general. A crisp taxonomy of the existing state-of-the-art and object detection trends over the past decades is also presented in the paper.
随着抢劫、公开射击、持枪威胁等与枪支有关的犯罪活动的增加，公共场所的闭路电视(CCTV)摄像机变得越来越突出。在监视系统中及早发现枪支对于安保和安全问题至关重要。本文提出了一种基于YOLOv5 (You Only Look Once)的武器检测系统，该系统可以检测步枪、手枪、刀具等不同类型的武器。本工作的主要目的是展示数据增强对不同类型数据集的影响，并与现有基线研究和文献中其他类似工作进行详细的比较检查。总的来说，这些结果为武器探测系统和目标探测提供了新的见解。在过去的几十年里，现有的先进技术和目标检测趋势的一个清晰的分类也在论文中提出。
{"title":"YOLOv5-based weapon detection systems with data augmentation","authors":"Lucy Sumi, Shouvik Dey","doi":"10.1080/1206212X.2023.2182966","DOIUrl":"https://doi.org/10.1080/1206212X.2023.2182966","url":null,"abstract":"Closed-Circuit Television (CCTV) cameras in public places have become more prominent with the rising firearm-related criminal activities, such as robberies, open firing, threats at gunpoint, etc. Early detection of firearms in surveillance systems is crucial for security and safety concerns. In this paper, we present a You Only Look Once (YOLOv5)-based weapon detection system that detects different types of weapons such as rifles, pistols, knives, etc. The main objective of this work is to show the impact of data augmentation on different types of datasets and make a detailed comparative examination with the existing baseline study and other similar works in the literature. The results give new insights to consider for weapon detection systems and object detection, in general. A crisp taxonomy of the existing state-of-the-art and object detection trends over the past decades is also presented in the paper.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"8 1","pages":"288 - 296"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88327287","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 : 2023-02-10DOI: 10.1080/1206212X.2023.2175435
Febina Ikbal, R. Gopikakumari
Watermarking is a generic strategy for overcoming numerous issues associated with multimedia security. Performance of a watermarking system in terms of imperceptibility, robustness and payload is highly dependent on the positions used for embedding. The choice of sequency packets plays an important role in the performance when Sequency-based Mapped Real Transform (SMRT) is used for embedding. A novel sequency selector for robust, imperceptible and payload-efficient color image watermarking of binary watermark is proposed using SMRT and two-level optimization. SMRT of block partitioned Cb channel of cover image is input to sequency selector where results of first-level optimization, based on imperceptibility and robustness, along with payload is optimized using knapsack optimization in the second level. The binary watermark is LSB embedded in SMRT coefficients present in each packet of the optimal combination of sequencies chosen by the sequency selector. Simulation results show better performance compared to existing optimized and non-optimized embedding techniques.
{"title":"Block SMRT and knapsack optimization-based sequency selector for robust, imperceptible, and payload-efficient color image watermarking for binary watermark","authors":"Febina Ikbal, R. Gopikakumari","doi":"10.1080/1206212X.2023.2175435","DOIUrl":"https://doi.org/10.1080/1206212X.2023.2175435","url":null,"abstract":"Watermarking is a generic strategy for overcoming numerous issues associated with multimedia security. Performance of a watermarking system in terms of imperceptibility, robustness and payload is highly dependent on the positions used for embedding. The choice of sequency packets plays an important role in the performance when Sequency-based Mapped Real Transform (SMRT) is used for embedding. A novel sequency selector for robust, imperceptible and payload-efficient color image watermarking of binary watermark is proposed using SMRT and two-level optimization. SMRT of block partitioned Cb channel of cover image is input to sequency selector where results of first-level optimization, based on imperceptibility and robustness, along with payload is optimized using knapsack optimization in the second level. The binary watermark is LSB embedded in SMRT coefficients present in each packet of the optimal combination of sequencies chosen by the sequency selector. Simulation results show better performance compared to existing optimized and non-optimized embedding techniques.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"49 1","pages":"269 - 283"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87588673","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.2020.1735764
Dan Wang, Kaidi Zhao, Yi Wang
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: Dan Wang, Kai Zhao & Yi Wang (2020) Based on deep learning in traffic remote sensing image processing to recognize target vehicle, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2020.1735764 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: Based on deep learning in traffic remote sensing image processing to recognize target vehicle","authors":"Dan Wang, Kaidi Zhao, Yi Wang","doi":"10.1080/1206212X.2020.1735764","DOIUrl":"https://doi.org/10.1080/1206212X.2020.1735764","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: Dan Wang, Kai Zhao & Yi Wang (2020) Based on deep learning in traffic remote sensing image processing to recognize target vehicle, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2020.1735764 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":"113 1","pages":"180 - 186"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73284098","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.2020.1727659
Ting Fu, Hong Chen, Fei Wu, Yuxin Su, L. Zhuang
The big data clustering process is a random nonlinear process with high uncertainty. Because traditional methods require prior knowledge to learn, they cannot adapt well to the real-time changes of big data, and cannot effectively achieve big data clustering. A good clustering structure can reduce redundancy, optimize network resource configuration, and reduce node overhead and balance the network. The collaborative correlation space is a powerful tool that will simulate the model to form a spatial analysis and process simulation. Therefore, in order to improve the fast processing and recognition ability of big data, a collaborative correlation spatial big data oriented to clustering network is proposed. Simulation experiments show that using this algorithm for big data clustering can effectively improve the data clustering efficiency, reduce energy consumption, has better anti-interference and adaptability, and has higher clustering accuracy. In the flow anomalydetectionexperiment,resultsshowthatthemethodproposedinthispaperhashighertrafficanomaly identificationaccuracythank-meansanddecisiontreealgorithm,andtherecallrateandROCareaarethelargest.
{"title":"Retracted Article: Collaborative correlation space big data clustering algorithm for abnormal flow monitoring","authors":"Ting Fu, Hong Chen, Fei Wu, Yuxin Su, L. Zhuang","doi":"10.1080/1206212X.2020.1727659","DOIUrl":"https://doi.org/10.1080/1206212X.2020.1727659","url":null,"abstract":"The big data clustering process is a random nonlinear process with high uncertainty. Because traditional methods require prior knowledge to learn, they cannot adapt well to the real-time changes of big data, and cannot effectively achieve big data clustering. A good clustering structure can reduce redundancy, optimize network resource configuration, and reduce node overhead and balance the network. The collaborative correlation space is a powerful tool that will simulate the model to form a spatial analysis and process simulation. Therefore, in order to improve the fast processing and recognition ability of big data, a collaborative correlation spatial big data oriented to clustering network is proposed. Simulation experiments show that using this algorithm for big data clustering can effectively improve the data clustering efficiency, reduce energy consumption, has better anti-interference and adaptability, and has higher clustering accuracy. In the flow anomalydetectionexperiment,resultsshowthatthemethodproposedinthispaperhashighertrafficanomaly identificationaccuracythank-meansanddecisiontreealgorithm,andtherecallrateandROCareaarethelargest.","PeriodicalId":39673,"journal":{"name":"International Journal of Computers and Applications","volume":"50 1","pages":"136 - 143"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86345459","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.2019.1710664
Lei Zhao, H. Yu
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: Lei Zhao & Huang Yu (2020) A wireless network remote monitoring method driven by artificial intelligence, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2019.1710664 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: A wireless network remote monitoring method driven by artificial intelligence","authors":"Lei Zhao, H. Yu","doi":"10.1080/1206212X.2019.1710664","DOIUrl":"https://doi.org/10.1080/1206212X.2019.1710664","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: Lei Zhao & Huang Yu (2020) A wireless network remote monitoring method driven by artificial intelligence, International Journal of Computers and Applications, DOI: 10.1080/1206212X.2019.1710664 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":"30 1","pages":"115 - 123"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80738763","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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