Pub Date : 2020-02-01DOI: 10.1109/AECT47998.2020.9194180
O. Reyad, M. E. Karar, Kadry Hamed
Pseudorandom bit generators (PRBG) can be designed to take the advantage of some hard number theoretic problems such as the discrete logarithm problem (DLP). Such type of generators will have good randomness and unpredictability properties as it is so difficult to find an easy solution to the regarding mathematical dilemma. Hash functions in turn play a remarkable role in many cryptographic tasks to achieve various security strengths. In this paper, a pseudorandom bit generator mechanism that is based mainly on the elliptic curve discrete logarithm problem (ECDLP) and hash derivation function is proposed. The cryptographic hash functions are used in consuming applications that require various security strengths. In a good hash function, finding whatever the input that can be mapped to any pre-specified output is considered computationally infeasible. The obtained pseudorandom bits are tested with NIST statistical tests and it also could fulfill the up-to-date standards. Moreover, a $256 times 256$ grayscale images are encrypted with the obtained pseudorandom bits following by necessary analysis of the cipher images for security prove.
{"title":"Random Bit Generator Mechanism Based on Elliptic Curves and Secure Hash Function","authors":"O. Reyad, M. E. Karar, Kadry Hamed","doi":"10.1109/AECT47998.2020.9194180","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194180","url":null,"abstract":"Pseudorandom bit generators (PRBG) can be designed to take the advantage of some hard number theoretic problems such as the discrete logarithm problem (DLP). Such type of generators will have good randomness and unpredictability properties as it is so difficult to find an easy solution to the regarding mathematical dilemma. Hash functions in turn play a remarkable role in many cryptographic tasks to achieve various security strengths. In this paper, a pseudorandom bit generator mechanism that is based mainly on the elliptic curve discrete logarithm problem (ECDLP) and hash derivation function is proposed. The cryptographic hash functions are used in consuming applications that require various security strengths. In a good hash function, finding whatever the input that can be mapped to any pre-specified output is considered computationally infeasible. The obtained pseudorandom bits are tested with NIST statistical tests and it also could fulfill the up-to-date standards. Moreover, a $256 times 256$ grayscale images are encrypted with the obtained pseudorandom bits following by necessary analysis of the cipher images for security prove.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116154051","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194205
Tasneem Alkarakatly, Shatha Eidhah, Miaad Al-Sarawani, A. Al-Sobhi, M. Bilal
Skin cancer is a serious public health problem due to its increasing incidence and subsequent high mortality rate. Deep learning is one of the most important approaches in image analysis used to detect melanoma skin cancer. In this paper, we propose a 5-layer Convolutional Neural Network (CNN) for classifying skin lesions of three categories, including melanoma belonging to deadly skin cancer. The CNN based classifier trained and tested on the PH2 dataset of Dermoscopic images, which is developed for research and benchmarking purposes. The proposed model was evaluated by four well-known performance measures namely, classification accuracy, sensitivity, specificity and area under the curve (AUC). It achieved almost 95% accuracy, 94% sensitivity, 97% specificity, and 100% AUC on the test set. Moreover, in one case of the experiment, the proposed model achieved 100% accuracy.
{"title":"Skin Lesions Identification Using Deep Convolutional Neural Network","authors":"Tasneem Alkarakatly, Shatha Eidhah, Miaad Al-Sarawani, A. Al-Sobhi, M. Bilal","doi":"10.1109/AECT47998.2020.9194205","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194205","url":null,"abstract":"Skin cancer is a serious public health problem due to its increasing incidence and subsequent high mortality rate. Deep learning is one of the most important approaches in image analysis used to detect melanoma skin cancer. In this paper, we propose a 5-layer Convolutional Neural Network (CNN) for classifying skin lesions of three categories, including melanoma belonging to deadly skin cancer. The CNN based classifier trained and tested on the PH2 dataset of Dermoscopic images, which is developed for research and benchmarking purposes. The proposed model was evaluated by four well-known performance measures namely, classification accuracy, sensitivity, specificity and area under the curve (AUC). It achieved almost 95% accuracy, 94% sensitivity, 97% specificity, and 100% AUC on the test set. Moreover, in one case of the experiment, the proposed model achieved 100% accuracy.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132435498","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194164
Muhammad Nasir Mumtaz Bhutta, H. Cruickshank, A. Nadeem
Key Management in Delay Tolerant Networks (DTN) still remains an unsolved complex problem. Due to peculiar characteristics of DTN, important challenges that make it difficult to design key management architecture are: 1) no systematic requirement analysis is undertaken to define its components, their composition and prescribed functions; and 2) no framework is available for its seamless integration with Bundle Security Protocol (BSP). This paper proposes a Key Management Architecture for DTN (KMAD) to address challenges in DTN key management. The proposed architecture not only provides guidelines for key management in DTN but also caters for seamless integration with BSP. The framework utilizes public key cryptography to provide required security services to enable exchange of keying material, and information about security policy and cipher suites. The framework also supports secure exchange of control and data information in DTNs.
{"title":"A Framework for Key Management Architecture for DTN (KMAD): Requirements and Design","authors":"Muhammad Nasir Mumtaz Bhutta, H. Cruickshank, A. Nadeem","doi":"10.1109/AECT47998.2020.9194164","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194164","url":null,"abstract":"Key Management in Delay Tolerant Networks (DTN) still remains an unsolved complex problem. Due to peculiar characteristics of DTN, important challenges that make it difficult to design key management architecture are: 1) no systematic requirement analysis is undertaken to define its components, their composition and prescribed functions; and 2) no framework is available for its seamless integration with Bundle Security Protocol (BSP). This paper proposes a Key Management Architecture for DTN (KMAD) to address challenges in DTN key management. The proposed architecture not only provides guidelines for key management in DTN but also caters for seamless integration with BSP. The framework utilizes public key cryptography to provide required security services to enable exchange of keying material, and information about security policy and cipher suites. The framework also supports secure exchange of control and data information in DTNs.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131045620","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194170
Suhni Abbasi, Hameedullah Kazi
Students often encounter difficulties when trying to understand the concept of object-orientation (OO). For learners specially for beginners, the harmonization between the concept of the real world and the concept of OO seems to be very serious and requires a modest and attractive approach. It is also challenging for an educator to align the learning objectives with learning outcomes by continuously monitoring the students progression of learning without disrupting the flow of learning. OOg, a Serious Game (SGs) approach, designed to help students to learn OO interactively and engagingly. OOg integrates Stealth Assessment (SA) technology to continuously monitor individual students interaction with the game to use an adaptive selection of activities and provides tailored feedback for the achievement of better learning outcomes. This article provides initial evidence that SA is likely to be included in the SGs to enhance teaching and learning. Future work involves analyzing and comparing the results of SA with external assessments.
{"title":"Stealth assessment in serious games to improve OO learning outcomes","authors":"Suhni Abbasi, Hameedullah Kazi","doi":"10.1109/AECT47998.2020.9194170","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194170","url":null,"abstract":"Students often encounter difficulties when trying to understand the concept of object-orientation (OO). For learners specially for beginners, the harmonization between the concept of the real world and the concept of OO seems to be very serious and requires a modest and attractive approach. It is also challenging for an educator to align the learning objectives with learning outcomes by continuously monitoring the students progression of learning without disrupting the flow of learning. OOg, a Serious Game (SGs) approach, designed to help students to learn OO interactively and engagingly. OOg integrates Stealth Assessment (SA) technology to continuously monitor individual students interaction with the game to use an adaptive selection of activities and provides tailored feedback for the achievement of better learning outcomes. This article provides initial evidence that SA is likely to be included in the SGs to enhance teaching and learning. Future work involves analyzing and comparing the results of SA with external assessments.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122188049","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194196
Z. Khan, S. Alotaibi
This article proposes a novel method combining the Neural Networks (NN) and the features of Gray Level Co-Occurrence Matrix (GLCM) for segmenting Region of Interest (ROI) of multiple medical images. The proposed methodology combines the texture of different pixels of medical images with the Radial Bias Function Neural Networks (RBFNN) in order to increase the performance of ROI segmentation and to obtain an optimal segmented region. The proposed approach works in two steps. Initially, the image borders are detected in order to separate the background skin and the ROI. This starts by extracting the GLCM features by the process of texture analysis which represents the ROI border clearly. GLCM features such as energy, homogeneity, contrast and the correlation are extracted. Secondly, the extracted features are passed towards the RBFNN for generating the ROI as segmented area. 870 scans of multiple medical images stored in a database are used for analysing the accuracy of the proposed methodology. Analysis of accuracy shows that the proposed methodology segments the ROIs of multiple medical images more accurately.
{"title":"Computerised Segmentation of Medical Images using Neural Networks and GLCM","authors":"Z. Khan, S. Alotaibi","doi":"10.1109/AECT47998.2020.9194196","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194196","url":null,"abstract":"This article proposes a novel method combining the Neural Networks (NN) and the features of Gray Level Co-Occurrence Matrix (GLCM) for segmenting Region of Interest (ROI) of multiple medical images. The proposed methodology combines the texture of different pixels of medical images with the Radial Bias Function Neural Networks (RBFNN) in order to increase the performance of ROI segmentation and to obtain an optimal segmented region. The proposed approach works in two steps. Initially, the image borders are detected in order to separate the background skin and the ROI. This starts by extracting the GLCM features by the process of texture analysis which represents the ROI border clearly. GLCM features such as energy, homogeneity, contrast and the correlation are extracted. Secondly, the extracted features are passed towards the RBFNN for generating the ROI as segmented area. 870 scans of multiple medical images stored in a database are used for analysing the accuracy of the proposed methodology. Analysis of accuracy shows that the proposed methodology segments the ROIs of multiple medical images more accurately.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134247556","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194158
Sahar I. Ghanem, A. A. Ghoneim, Nagia M. Ghanem, M. Ismail
The study of the Genome-wide association study (GWAS) and the complex diseases is of high importance nowadays. The epistasis describes the analysis of the single nucleotide polymorphisms (SNPs) interactions and their effects on the complex diseases. However, enormous number of SNPs interactions should be tested against the disease that is highly computational expensive. In this paper, High Performance Computing (HPC) is being applied on a supercomputer to reduce the processing time. Parallel Deep Learning (PDL) is applied and tested using different datasets. Simulated datasets of 12 different models and the real WTCCC Rheumatoid arthritis (RA) dataset are being tested. Results show the high accuracy, specificity and true positive rate values. Moreover, they show low values of the false discovery rate and the robustness of power through the different simulated models. When tested on the real RA dataset, our model shows the ability to detect the 2-way interaction SNPs with their promising related genes with high accuracy due to the parallel deep learning architecture.
{"title":"High Performance Computing for Detecting Complex Diseases using Deep Learning","authors":"Sahar I. Ghanem, A. A. Ghoneim, Nagia M. Ghanem, M. Ismail","doi":"10.1109/AECT47998.2020.9194158","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194158","url":null,"abstract":"The study of the Genome-wide association study (GWAS) and the complex diseases is of high importance nowadays. The epistasis describes the analysis of the single nucleotide polymorphisms (SNPs) interactions and their effects on the complex diseases. However, enormous number of SNPs interactions should be tested against the disease that is highly computational expensive. In this paper, High Performance Computing (HPC) is being applied on a supercomputer to reduce the processing time. Parallel Deep Learning (PDL) is applied and tested using different datasets. Simulated datasets of 12 different models and the real WTCCC Rheumatoid arthritis (RA) dataset are being tested. Results show the high accuracy, specificity and true positive rate values. Moreover, they show low values of the false discovery rate and the robustness of power through the different simulated models. When tested on the real RA dataset, our model shows the ability to detect the 2-way interaction SNPs with their promising related genes with high accuracy due to the parallel deep learning architecture.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116602539","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194222
Toqeer Ali, A. Nadeem, Ali Alzahrani, Salman Jan
Performing various transactions on real estate records in the government organization is a cumbersome task and has many pitfalls. There are a number of problems with the existing land registry systems. First, the process of land registry and transferring of a land is currently manual. Even though, some of the processes and records are digitized, however, it still has many problems, such as, it is developed on a centralized system, it cannot maintain the complete history of the land and it does not have a built-in mechanism for record tempering detection. To overcome these problems there is no viable solution based on a centralized system. The decentralized systems, such as, Blockchain comes built-in with this feature. In this proposal, aforementioned problems are addressed. The lands are registered over Blockchain network by designing a smart contract. The proposed studies can provide several features to the stakeholders including efficiency, transparency, trustworthiness, integrity for various entities and processes involved in buying and selling a property. Essentially the framework offers services that will provide a detailed history and untampered information regarding a property to ensure that the record is not manipulated. There is an external link available via Restful to the traditional property dealing apps so they can extract the real-time record of the land, such as, dimension, location, price of that specific land. The proposed system will ultimately safeguard confidence in doing real estate business over the Internet.
{"title":"A Transparent and Trusted Property Registration System on Permissioned Blockchain","authors":"Toqeer Ali, A. Nadeem, Ali Alzahrani, Salman Jan","doi":"10.1109/AECT47998.2020.9194222","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194222","url":null,"abstract":"Performing various transactions on real estate records in the government organization is a cumbersome task and has many pitfalls. There are a number of problems with the existing land registry systems. First, the process of land registry and transferring of a land is currently manual. Even though, some of the processes and records are digitized, however, it still has many problems, such as, it is developed on a centralized system, it cannot maintain the complete history of the land and it does not have a built-in mechanism for record tempering detection. To overcome these problems there is no viable solution based on a centralized system. The decentralized systems, such as, Blockchain comes built-in with this feature. In this proposal, aforementioned problems are addressed. The lands are registered over Blockchain network by designing a smart contract. The proposed studies can provide several features to the stakeholders including efficiency, transparency, trustworthiness, integrity for various entities and processes involved in buying and selling a property. Essentially the framework offers services that will provide a detailed history and untampered information regarding a property to ensure that the record is not manipulated. There is an external link available via Restful to the traditional property dealing apps so they can extract the real-time record of the land, such as, dimension, location, price of that specific land. The proposed system will ultimately safeguard confidence in doing real estate business over the Internet.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128614065","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194176
Ahmad Nadeem, N. Arshad
As the electricity market is growing, the need for accurate Short Term Load Forecasting (STLF) is increasing. Electrical grid operators require STLF to plan schedules for power generation plants. With the introduction of intermittent renewable resources, the stakes are now even higher. Developed countries have been fortunate in this regard as most of the research on STLF focused on these countries and developed highly accurate models. There is now a need to focus on developing countries as these are substantial energy markets with thriving economies and high population growth rates. With the 43rd largest economy by GDP and 6th largest nation by population, Pakistan is one such country. As the energy demand of Pakistan is increasing, there is a need to understand the energy demand patterns of its citizens better. PRECON is an electricity consumption dataset of residential buildings in Pakistan that can help in this regard. In this paper, we present preliminary results of applying STLF techniques on PRECON. These initial results show that Multiple Linear Regression and Support Vector Regression perform better than Artificial Neural Network ambient temperature and autoregressive attributes as input variables. The results also discuss various performance metrics, such as ME, RMSE, and MPE. The results show a unique phenomenon, load shedding, not experienced in developed countries.
{"title":"Short Term Load Forecasting on PRECON Dataset","authors":"Ahmad Nadeem, N. Arshad","doi":"10.1109/AECT47998.2020.9194176","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194176","url":null,"abstract":"As the electricity market is growing, the need for accurate Short Term Load Forecasting (STLF) is increasing. Electrical grid operators require STLF to plan schedules for power generation plants. With the introduction of intermittent renewable resources, the stakes are now even higher. Developed countries have been fortunate in this regard as most of the research on STLF focused on these countries and developed highly accurate models. There is now a need to focus on developing countries as these are substantial energy markets with thriving economies and high population growth rates. With the 43rd largest economy by GDP and 6th largest nation by population, Pakistan is one such country. As the energy demand of Pakistan is increasing, there is a need to understand the energy demand patterns of its citizens better. PRECON is an electricity consumption dataset of residential buildings in Pakistan that can help in this regard. In this paper, we present preliminary results of applying STLF techniques on PRECON. These initial results show that Multiple Linear Regression and Support Vector Regression perform better than Artificial Neural Network ambient temperature and autoregressive attributes as input variables. The results also discuss various performance metrics, such as ME, RMSE, and MPE. The results show a unique phenomenon, load shedding, not experienced in developed countries.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124003716","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194206
W. Ahmed, S. M. Hizam, Ilham Sentosa, Jawad Ali, Toqeer Ali
As the disruptive innovations are being penetrated to each level of society, the acceptance of modern technology is regulating the education activities. Managing the educational usage of computing through cloud applications is a matter of discussion nowadays. The aim of this study is to instigate the assessment of the adoption of cloud computing from end-users perspective. Technology acceptance model is applied to assess the behavioural intention towards the adoption of cloud computing. An online survey was conducted with close-ended questionnaire. Data was collected from 298 respondents. Structural equation modeling (SEM) was conducted to undertake the model fit and path analysis through AMOS software. The analysis demonstrates that perceived usefulness and personal innovativeness are most significant element towards behaviour shaping while the subjective norm is found insignificant towards cloud computing use. The study will help to focus on individual-level innovativeness in academia.
{"title":"Structural Equation Modeling for Acceptance of Cloud Computing","authors":"W. Ahmed, S. M. Hizam, Ilham Sentosa, Jawad Ali, Toqeer Ali","doi":"10.1109/AECT47998.2020.9194206","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194206","url":null,"abstract":"As the disruptive innovations are being penetrated to each level of society, the acceptance of modern technology is regulating the education activities. Managing the educational usage of computing through cloud applications is a matter of discussion nowadays. The aim of this study is to instigate the assessment of the adoption of cloud computing from end-users perspective. Technology acceptance model is applied to assess the behavioural intention towards the adoption of cloud computing. An online survey was conducted with close-ended questionnaire. Data was collected from 298 respondents. Structural equation modeling (SEM) was conducted to undertake the model fit and path analysis through AMOS software. The analysis demonstrates that perceived usefulness and personal innovativeness are most significant element towards behaviour shaping while the subjective norm is found insignificant towards cloud computing use. The study will help to focus on individual-level innovativeness in academia.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131805800","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 : 2020-02-01DOI: 10.1109/AECT47998.2020.9194212
Abnash Zaman, S. Khattak, Zohaib Hassan
Tuberculosis (TB) is the second largest death disease after Human Immunodeficiency Virus (HIV). In Pakistan as well as other countries radiologists faced a lot of problems in diagnosis the TB. The population of Pakistan is about 180,000,000 in which mostly peoples are poor. This paper presents our findings regarding this issue. In this work there is a complete description and review of the previous work done by the researchers. First Chest X-ray (CXR’s) images are segmented through random walker segmentation method and then a set of features on the base of intensity is computed. Computed features will help chest X-ray images to be classified on the bases of computed features as infected or healthy using SVM classifier. System performance is measured on dataset which is taken from Indiana University Hospital Network and data set collection is composed of 100 images. System accuracy is calculated through SVM classifier which is 73%. This research concludes with suggestions for further effort to more improve the quality of the system.
{"title":"Medical Imaging for the Detection of Tuberculosis Using Chest Radio Graphs *","authors":"Abnash Zaman, S. Khattak, Zohaib Hassan","doi":"10.1109/AECT47998.2020.9194212","DOIUrl":"https://doi.org/10.1109/AECT47998.2020.9194212","url":null,"abstract":"Tuberculosis (TB) is the second largest death disease after Human Immunodeficiency Virus (HIV). In Pakistan as well as other countries radiologists faced a lot of problems in diagnosis the TB. The population of Pakistan is about 180,000,000 in which mostly peoples are poor. This paper presents our findings regarding this issue. In this work there is a complete description and review of the previous work done by the researchers. First Chest X-ray (CXR’s) images are segmented through random walker segmentation method and then a set of features on the base of intensity is computed. Computed features will help chest X-ray images to be classified on the bases of computed features as infected or healthy using SVM classifier. System performance is measured on dataset which is taken from Indiana University Hospital Network and data set collection is composed of 100 images. System accuracy is calculated through SVM classifier which is 73%. This research concludes with suggestions for further effort to more improve the quality of the system.","PeriodicalId":331415,"journal":{"name":"2019 International Conference on Advances in the Emerging Computing Technologies (AECT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124681743","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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