Pub Date : 2023-06-13DOI: 10.3991/ijoe.v19i07.38871
Ali Abid Hussan Altalbi, Shaimaa Hameed Shaker, Akbas Ezaldeen Ali
Depending on the context of interest, an anomaly is defined differently. In the case when a video event isn't expected to take place in the video, it is seen as anomaly. It can be difficult to describe uncommon events in complicated scenes, but this problem is frequently resolved by using high-dimensional features as well as descriptors. There is a difficulty in creating reliable model to be trained with these descriptors because it needs a huge number of training samples and is computationally complex. Spatiotemporal changes or trajectories are typically represented by features that are extracted. The presented work presents numerous investigations to address the issue of abnormal video detection from crowded video and its methodology. Through the use of low-level features, like global features, local features, and feature features. For the most accurate detection and identification of anomalous behavior in videos, and attempting to compare the various techniques, this work uses a more crowded and difficult dataset and require light weight for diagnosing anomalies in objects through recording and tracking movements as well as extracting features; thus, these features should be strong and differentiate objects. After reviewing previous works, this work noticed that there is more need for accuracy in video modeling and decreased time, and since attempted to work on real-time and outdoor scenes.
{"title":"Anomaly Detection from Crowded Video by Convolutional Neural Network and Descriptors Algorithm: Survey","authors":"Ali Abid Hussan Altalbi, Shaimaa Hameed Shaker, Akbas Ezaldeen Ali","doi":"10.3991/ijoe.v19i07.38871","DOIUrl":"https://doi.org/10.3991/ijoe.v19i07.38871","url":null,"abstract":"Depending on the context of interest, an anomaly is defined differently. In the case when a video event isn't expected to take place in the video, it is seen as anomaly. It can be difficult to describe uncommon events in complicated scenes, but this problem is frequently resolved by using high-dimensional features as well as descriptors. There is a difficulty in creating reliable model to be trained with these descriptors because it needs a huge number of training samples and is computationally complex. Spatiotemporal changes or trajectories are typically represented by features that are extracted. The presented work presents numerous investigations to address the issue of abnormal video detection from crowded video and its methodology. Through the use of low-level features, like global features, local features, and feature features. For the most accurate detection and identification of anomalous behavior in videos, and attempting to compare the various techniques, this work uses a more crowded and difficult dataset and require light weight for diagnosing anomalies in objects through recording and tracking movements as well as extracting features; thus, these features should be strong and differentiate objects. After reviewing previous works, this work noticed that there is more need for accuracy in video modeling and decreased time, and since attempted to work on real-time and outdoor scenes.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70113256","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 : 2023-06-13DOI: 10.3991/ijoe.v19i07.37431
Kawitsara Eambunnapong, P. Nilsook, P. Wannapiroon
Digital storytelling is a new concept in education that involves creating meaning. It is a tool with great potential, but in Thailand, it is currently not very popular in terms of sharing stories about disease. This review analyzes the components and processes of intelligent digital storytelling to aid the development of an intelligent digital storytelling platform for disseminating health information. Based on the synthesis of relevant documents, the research process involves 9 main steps: 1) identifying the review objectives, 2) reviewing research questions, 3) determining inclusion criteria, 4) finding relevant studies, 5) selecting documents, 6) data extraction, 7) arriving at a conclusion, 8) document synthesis and 9) discussion of the results. A study of articles from the PRISMA Checklist published between 2017 and 2022 revealed that ultimately only 47 articles met the inclusion criteria. From the analysis of the data, it was found that there are four main elements and 16 sub-components of intelligent digital storytelling. There are 12 steps in the process of intelligent digital storytelling with regard to health information dissemination. The optimal length of an intelligent digital narrative video clip relating to health information dissemination is approximately 2-5 minutes when it comes to achieving the best knowledge of health information.
{"title":"A Systematic Review of the Intelligent Digital Storytelling Process in Disseminating Health Information","authors":"Kawitsara Eambunnapong, P. Nilsook, P. Wannapiroon","doi":"10.3991/ijoe.v19i07.37431","DOIUrl":"https://doi.org/10.3991/ijoe.v19i07.37431","url":null,"abstract":"Digital storytelling is a new concept in education that involves creating meaning. It is a tool with great potential, but in Thailand, it is currently not very popular in terms of sharing stories about disease. This review analyzes the components and processes of intelligent digital storytelling to aid the development of an intelligent digital storytelling platform for disseminating health information. Based on the synthesis of relevant documents, the research process involves 9 main steps: 1) identifying the review objectives, 2) reviewing research questions, 3) determining inclusion criteria, 4) finding relevant studies, 5) selecting documents, 6) data extraction, 7) arriving at a conclusion, 8) document synthesis and 9) discussion of the results. A study of articles from the PRISMA Checklist published between 2017 and 2022 revealed that ultimately only 47 articles met the inclusion criteria. From the analysis of the data, it was found that there are four main elements and 16 sub-components of intelligent digital storytelling. There are 12 steps in the process of intelligent digital storytelling with regard to health information dissemination. The optimal length of an intelligent digital narrative video clip relating to health information dissemination is approximately 2-5 minutes when it comes to achieving the best knowledge of health information.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47247427","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 : 2023-06-13DOI: 10.3991/ijoe.v19i07.39285
Luan Gashi, A. Luma, M. Apostolova, Ylber Januzaj
Most cybersecurity frameworks are based on three major components such as confidentiality, integrity, and availability. All these components have their parameters that are used to secure network nodes. But finding the most cyber secure node in a network needs a measurement method. The aim of the paper is to offer a model that can be used to find the most secure network nodes considering these cybersecurity components and their parameters. This is achieved by modelling numeric values of respective weights for parameters of confidentiality, integrity, and availability. The model is applied to a simulated environment where random values standing for cybersecurity parameters are given to 30 wireless network nodes that are used as an example. Then the weighted values are processed with Python programming language by giving the most secure nodes according to needed cybersecurity components. This model can be used to recommend the right network node that can be used to deploy services securely while avoiding potential vulnerabilities and cyber-attacks.
{"title":"A Weighting Model of Cybersecurity Parameters Used for Service Placement","authors":"Luan Gashi, A. Luma, M. Apostolova, Ylber Januzaj","doi":"10.3991/ijoe.v19i07.39285","DOIUrl":"https://doi.org/10.3991/ijoe.v19i07.39285","url":null,"abstract":"Most cybersecurity frameworks are based on three major components such as confidentiality, integrity, and availability. All these components have their parameters that are used to secure network nodes. But finding the most cyber secure node in a network needs a measurement method. The aim of the paper is to offer a model that can be used to find the most secure network nodes considering these cybersecurity components and their parameters. This is achieved by modelling numeric values of respective weights for parameters of confidentiality, integrity, and availability. The model is applied to a simulated environment where random values standing for cybersecurity parameters are given to 30 wireless network nodes that are used as an example. Then the weighted values are processed with Python programming language by giving the most secure nodes according to needed cybersecurity components. This model can be used to recommend the right network node that can be used to deploy services securely while avoiding potential vulnerabilities and cyber-attacks. \u0000 ","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44123284","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 : 2023-06-13DOI: 10.3991/ijoe.v19i07.38903
Chaimae Zaoui, F. Benabbou, Abdelaziz Ettaoufik
Thanks to advancements in artificial intelligence and the Internet of Things (IoT), eHealth is becoming an increasingly attractive area for researchers. However, different challenges arise when sensor-generated information is stored and analyzed using cloud computing. Latency, response time, and security are critical concerns that require attention. Fog and Edge Computing technologies have emerged in response to the requirement for resources near the network edge where data is collected, to minimize cloud challenges. This paper aims to assess the effectiveness of Machine Learning (ML) and Deep Learning (DL) techniques when executed in Edge or Fog nodes within the eHealth data. We compared the most efficient baseline techniques from the state-of-the-art on three eHealth datasets: Human Activity Recognition (HAR), University of Milano Bicocca Smartphone-based Human Activity Recognition (UniMiB SHAR), and MIT-BIH Arrhythmia. The experiment showed that for the HAR dataset, the Support Vector Machines (SVM) model was the best performer among the ML techniques, with low processing time and an accuracy of 96%. In comparison, the K-Nearest Neighbors (KNN) performed 94.43, and 96%, respectively, for SHAR and MIT-BIH datasets. Among the DL techniques, the Convolutional Neural Network with Fourier (CNNF) model performed the best, with accuracies of 94.49% and 98.72% for HAR and MIT-BIH. In comparison, CNN achieved 96.90% for the SHAR dataset.
{"title":"Edge-Fog-Cloud Data Analysis for eHealth-IoT","authors":"Chaimae Zaoui, F. Benabbou, Abdelaziz Ettaoufik","doi":"10.3991/ijoe.v19i07.38903","DOIUrl":"https://doi.org/10.3991/ijoe.v19i07.38903","url":null,"abstract":"Thanks to advancements in artificial intelligence and the Internet of Things (IoT), eHealth is becoming an increasingly attractive area for researchers. However, different challenges arise when sensor-generated information is stored and analyzed using cloud computing. Latency, response time, and security are critical concerns that require attention. Fog and Edge Computing technologies have emerged in response to the requirement for resources near the network edge where data is collected, to minimize cloud challenges. This paper aims to assess the effectiveness of Machine Learning (ML) and Deep Learning (DL) techniques when executed in Edge or Fog nodes within the eHealth data. We compared the most efficient baseline techniques from the state-of-the-art on three eHealth datasets: Human Activity Recognition (HAR), University of Milano Bicocca Smartphone-based Human Activity Recognition (UniMiB SHAR), and MIT-BIH Arrhythmia. The experiment showed that for the HAR dataset, the Support Vector Machines (SVM) model was the best performer among the ML techniques, with low processing time and an accuracy of 96%. In comparison, the K-Nearest Neighbors (KNN) performed 94.43, and 96%, respectively, for SHAR and MIT-BIH datasets. Among the DL techniques, the Convolutional Neural Network with Fourier (CNNF) model performed the best, with accuracies of 94.49% and 98.72% for HAR and MIT-BIH. In comparison, CNN achieved 96.90% for the SHAR dataset. \u0000 ","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44607097","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 : 2023-06-13DOI: 10.3991/ijoe.v19i07.37881
Gowrishankar K., M. V, S. R, D. S., C. Ang
An EEG signal is used for capturing the signals from the brain, which helps in localization of epileptogenic region, thereby which plays a vital role for a successful surgery. The focal and non-focal signals are obtained from the epileptogenic region and normal region respectively. The localization of epileptic seizure with the help of focal signal is necessary while detecting seizures. Hence, the present article provides detailed analysis of EEG signals. The Focal and Non-focal signals are decomposed using EMD-DWT. A combination of EMD-DWT decomposition method in accordance with log-energy entropy gives an efficient accuracy in comparison to other entropy in differentiating the Focal from Non-focal signals. The extracted features are subjected to SVM and KNN classifiers whose performance will be calculated and verified with respect to accuracy, sensitivity and specificity. At the end, it will be shown that KNN produces the highest accuracy when compared to SVM classifier.
{"title":"Novel SVM and K-NN Classifier Based Machine Learning Technique for Epileptic Seizure Detection","authors":"Gowrishankar K., M. V, S. R, D. S., C. Ang","doi":"10.3991/ijoe.v19i07.37881","DOIUrl":"https://doi.org/10.3991/ijoe.v19i07.37881","url":null,"abstract":"An EEG signal is used for capturing the signals from the brain, which helps in localization of epileptogenic region, thereby which plays a vital role for a successful surgery. The focal and non-focal signals are obtained from the epileptogenic region and normal region respectively. The localization of epileptic seizure with the help of focal signal is necessary while detecting seizures. Hence, the present article provides detailed analysis of EEG signals. The Focal and Non-focal signals are decomposed using EMD-DWT. A combination of EMD-DWT decomposition method in accordance with log-energy entropy gives an efficient accuracy in comparison to other entropy in differentiating the Focal from Non-focal signals. The extracted features are subjected to SVM and KNN classifiers whose performance will be calculated and verified with respect to accuracy, sensitivity and specificity. At the end, it will be shown that KNN produces the highest accuracy when compared to SVM classifier.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42786107","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 : 2023-06-13DOI: 10.3991/ijoe.v19i07.39045
Yaser Saleh, Muhanna A. Muhanna
In this paper, a novel approach for classifying archeological sites using publicly available images through the use of Convolutional Neural Networks (CNNs) is presented. To surmount the problem of having a limited amount of data to use in training and testing the CNNs, our approach employs the technique of fine tuning. We conducted an experiment with four popular CNN architectures: VGG-16, VGG-19, ResNet50, and InceptionV3. The results show that our models achieved an impressive accuracy of up to 98% using the VGG-16 and InceptionV3 models and up to 97% using the ResNet50 model, while the VGG-19 model produced results with an accuracy of 95%. The results of this study demonstrate the effectiveness of our proposed approach in classifying archeological sites using publicly available images and highlight the potential of deep learning techniques for archeological site classification.
{"title":"Archeological Sites Classification Through Partial Imaging and Convolutional Neural Networks","authors":"Yaser Saleh, Muhanna A. Muhanna","doi":"10.3991/ijoe.v19i07.39045","DOIUrl":"https://doi.org/10.3991/ijoe.v19i07.39045","url":null,"abstract":"In this paper, a novel approach for classifying archeological sites using publicly available images through the use of Convolutional Neural Networks (CNNs) is presented. To surmount the problem of having a limited amount of data to use in training and testing the CNNs, our approach employs the technique of fine tuning. We conducted an experiment with four popular CNN architectures: VGG-16, VGG-19, ResNet50, and InceptionV3. The results show that our models achieved an impressive accuracy of up to 98% using the VGG-16 and InceptionV3 models and up to 97% using the ResNet50 model, while the VGG-19 model produced results with an accuracy of 95%. The results of this study demonstrate the effectiveness of our proposed approach in classifying archeological sites using publicly available images and highlight the potential of deep learning techniques for archeological site classification.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43743130","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 : 2023-06-13DOI: 10.3991/ijoe.v19i07.39145
Sanjay Grewal, O. Shah
In this paper, a 3-bit frequency divider (FD) using a novel sense amplifier based flip-flop (SAFF) is presented and demonstrated. The delay in this design was meticulously improved resulting in better values of power delay product (PDP).The latching stage of the proposed design makes use of a novel single ended structure. Comparative analysis in 32 nm CMOS technology using T-SPICE revealed significant and quantitative differences between the proposed design and the existing designs. The PDP results were obtained for ±10% voltage variation, wide temperature range of -40 ℃ to 125 ℃ and at extreme corner cases. Results indicated that the PDP of the new design at nominal operating conditions decreased by minimum of 27.28% and maximum of 57.49%. The proposed design was also at par with available design in terms of area and power. The analysis on the FD proved the assertions that the proposed design is a feasible alternative for high performance applications.
{"title":"Design and Analysis of High Performance Frequency Divider in 32 nm CMOS Technology for Biomedical Applications","authors":"Sanjay Grewal, O. Shah","doi":"10.3991/ijoe.v19i07.39145","DOIUrl":"https://doi.org/10.3991/ijoe.v19i07.39145","url":null,"abstract":"In this paper, a 3-bit frequency divider (FD) using a novel sense amplifier based flip-flop (SAFF) is presented and demonstrated. The delay in this design was meticulously improved resulting in better values of power delay product (PDP).The latching stage of the proposed design makes use of a novel single ended structure. Comparative analysis in 32 nm CMOS technology using T-SPICE revealed significant and quantitative differences between the proposed design and the existing designs. The PDP results were obtained for ±10% voltage variation, wide temperature range of -40 ℃ to 125 ℃ and at extreme corner cases. Results indicated that the PDP of the new design at nominal operating conditions decreased by minimum of 27.28% and maximum of 57.49%. The proposed design was also at par with available design in terms of area and power. The analysis on the FD proved the assertions that the proposed design is a feasible alternative for high performance applications.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48459365","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 purpose of this study was to examine the burnout dimensions of professionals working with students with special educational needs and the role played by their personal traits in the prevalence of the syndrome. To examine this objective a sample of Greek teachers was selected. The data was collected using the online form of Maslach Burnout Inventory. The results of this research showed that the main prognostic factors of the syndrome in each dimension are the total previous service with students with special educational needs, the specialty, as well as, the age of the sample.
{"title":"Working with Students with Special Educational Needs and Predictors of Burnout. The Role of ICTs.","authors":"Agathi Stathopoulou, Despina Spinou, Anna-Maria Driga","doi":"10.3991/ijoe.v19i07.37897","DOIUrl":"https://doi.org/10.3991/ijoe.v19i07.37897","url":null,"abstract":"The purpose of this study was to examine the burnout dimensions of professionals working with students with special educational needs and the role played by their personal traits in the prevalence of the syndrome. To examine this objective a sample of Greek teachers was selected. The data was collected using the online form of Maslach Burnout Inventory. The results of this research showed that the main prognostic factors of the syndrome in each dimension are the total previous service with students with special educational needs, the specialty, as well as, the age of the sample.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43055718","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 : 2023-05-16DOI: 10.3991/ijoe.v19i06.34905
Haslinah Mohd Nasir, Noor Mohd Ariff Brahin, S. Zainuddin, Mohd Syafiq Mispan, Ida Syafiza Binti Md Isa, M. N. A. Sha'abani
Breast cancer is one of the life threatening cancer that leads to the most death due to cancer among the women. Early diagnosis might help to reduce mortality. Thus, this research aims to study on different approaches of the deep learning neural network model for breast cancer early detection for better prognosis. The performance of deep learning approaches such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) are evaluated using the dataset from the University of Wisconsin. The findings show ANN achieved high accuracy of 99.9 % compared to others in detecting breast cancer. ANN is able to deliver better results with the provided dataset. However, more improvement needed for better performance to ensure that the approach used is reliable enough for breast cancer early diagnosis.
{"title":"The Comparative Study of Deep Learning Neural Network Approaches for Breast Cancer Diagnosis","authors":"Haslinah Mohd Nasir, Noor Mohd Ariff Brahin, S. Zainuddin, Mohd Syafiq Mispan, Ida Syafiza Binti Md Isa, M. N. A. Sha'abani","doi":"10.3991/ijoe.v19i06.34905","DOIUrl":"https://doi.org/10.3991/ijoe.v19i06.34905","url":null,"abstract":"Breast cancer is one of the life threatening cancer that leads to the most death due to cancer among the women. Early diagnosis might help to reduce mortality. Thus, this research aims to study on different approaches of the deep learning neural network model for breast cancer early detection for better prognosis. The performance of deep learning approaches such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) are evaluated using the dataset from the University of Wisconsin. The findings show ANN achieved high accuracy of 99.9 % compared to others in detecting breast cancer. ANN is able to deliver better results with the provided dataset. However, more improvement needed for better performance to ensure that the approach used is reliable enough for breast cancer early diagnosis.","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45198421","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 : 2023-05-16DOI: 10.3991/ijoe.v19i06.37587
Mohammad K. Abdul-Hussein, H. Alrikabi
In cloud computing, resources are used to communicate instead of local servers or individual devices. However, sharing resources among several users is a difficult issue in cloud communication. Cryptography and steganography techniques are used for cloud storage to address data security challenges. This paper presents a novel method for securely encrypting image data for transmission and link exchange with a cloud storage service. There are two phases to accomplish the encryption process, the first phase encrypts the image file by XORing it with a random key that is generated by a new hybrid of the chaotic map. The second phase converts the encrypted image format to audio format to add another layer of security and improve secure image data transfer. The random key is generated using a hybrid chaotic map and has the benefit of having more than 10256 key spaces and the necessary level of security. Based on a statistical analysis of the encryption, the quality of the image is evaluated using several criteria, and the results demonstrate the algorithm's ability to accomplish resist encryption
{"title":"Secured Transfer and Storage Image Data for Cloud Communications","authors":"Mohammad K. Abdul-Hussein, H. Alrikabi","doi":"10.3991/ijoe.v19i06.37587","DOIUrl":"https://doi.org/10.3991/ijoe.v19i06.37587","url":null,"abstract":"In cloud computing, resources are used to communicate instead of local servers or individual devices. However, sharing resources among several users is a difficult issue in cloud communication. Cryptography and steganography techniques are used for cloud storage to address data security challenges. This paper presents a novel method for securely encrypting image data for transmission and link exchange with a cloud storage service. There are two phases to accomplish the encryption process, the first phase encrypts the image file by XORing it with a random key that is generated by a new hybrid of the chaotic map. The second phase converts the encrypted image format to audio format to add another layer of security and improve secure image data transfer. The random key is generated using a hybrid chaotic map and has the benefit of having more than 10256 key spaces and the necessary level of security. Based on a statistical analysis of the encryption, the quality of the image is evaluated using several criteria, and the results demonstrate the algorithm's ability to accomplish resist encryption","PeriodicalId":36900,"journal":{"name":"International Journal of Online and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43086654","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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