Pub Date : 2018-07-31DOI: 10.19101/IJACR.2018.837010
Ruth Ramya Kalangi, M. Rao
Data confidentiality and integrity are two major aspects that the cloud users need to consider while deploying data in the cloud. Traditional integrity techniques use cryptographic hash algorithms, but most of these hash algorithms are vulnerable to third party attacks. Traditional encryption algorithms such as advanced encryption standard (AES), fully homomorphic attribute based encryption (FHABE) and key policy attribute based encryption (KP-ABE) are failed to generate biometric based attributes and policies due to limited computing resources and memory. So, novel multi-user fingerprint minutiae with ciphertext-policy attribute based encryption for integrity verification and encryption (MFM-CP-ABE) model is proposed. MFM-CP-ABE model considers fingerprints of multiple users as attributes for encryption and also calculates integrity value. This model is the combination of multi-user fingerprint minutiae (MFM) extraction policy integrity method and improved ciphertext policy attribute based encryption (ICP-ABE) algorithm. This model is efficient in comparison to the traditional models in terms of encryption and decryption time and data size.
数据机密性和完整性是云用户在云中部署数据时需要考虑的两个主要方面。传统的完整性技术使用加密散列算法，但大多数散列算法容易受到第三方攻击。传统的加密算法，如高级加密标准AES (advanced encryption standard)、基于全同态属性的加密(fully homomorphic attribute based encryption, FHABE)和基于密钥策略属性的加密(key policy attribute based encryption, KP-ABE)，由于计算资源和内存的限制，无法生成基于生物特征的属性和策略。为此，提出了一种基于密文策略属性的多用户指纹细节完整性验证与加密(MFM-CP-ABE)模型。MFM-CP-ABE模型将多个用户的指纹作为加密属性，并计算完整性值。该模型结合了多用户指纹细节(MFM)提取策略完整性方法和改进的基于密文策略属性的加密(ICP-ABE)算法。与传统模型相比，该模型在加密和解密时间和数据大小方面是有效的。
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Pub Date : 2018-05-31DOI: 10.19101/IJACR.2018.836018
Rachid Hba, A. E. Manouar
Talking about sustainable development (SD) in the context of information and communication technology (ICT) management invites us to move forward in a new research area that offers a theoretical framework to integrate the new concepts of social responsibility and environmental companies in the development and implementation of the management strategy. The current context of ICT alignment is characterized by strong environmental and societal incentives and constraints to reduce carbon footprint. Companies must therefore reorient their ICT alignment strategies towards a new sustainable mode to maintain the performance of innovation, transformation and differentiation flows. This perspective opens up a new field of research, which takes into account the interactions with the stakeholders, as well as a better of the technology, the economic and the social adjustment. In this article, we present our new “ICT Green Alignment” model as a next generation framework for ICT management. Our model has been conceptualized using a green IT and corporate social responsibility (CSR) approach, with the aim of helping managers in the process of ICT alignment with business strategy and sustainability. This framework provides a theoretical tool for the design of renewed managerial strategies for SD. In order to approve the validity and applicability of our model, the framework has been tested on the basis of a real case study of a telecom operator.
{"title":"ICT green alignment: towards a new generation managerial model based on green IT and corporate social responsibility","authors":"Rachid Hba, A. E. Manouar","doi":"10.19101/IJACR.2018.836018","DOIUrl":"https://doi.org/10.19101/IJACR.2018.836018","url":null,"abstract":"Talking about sustainable development (SD) in the context of information and communication technology (ICT) management invites us to move forward in a new research area that offers a theoretical framework to integrate the new concepts of social responsibility and environmental companies in the development and implementation of the management strategy. The current context of ICT alignment is characterized by strong environmental and societal incentives and constraints to reduce carbon footprint. Companies must therefore reorient their ICT alignment strategies towards a new sustainable mode to maintain the performance of innovation, transformation and differentiation flows. This perspective opens up a new field of research, which takes into account the interactions with the stakeholders, as well as a better of the technology, the economic and the social adjustment. In this article, we present our new “ICT Green Alignment” model as a next generation framework for ICT management. Our model has been conceptualized using a green IT and corporate social responsibility (CSR) approach, with the aim of helping managers in the process of ICT alignment with business strategy and sustainability. This framework provides a theoretical tool for the design of renewed managerial strategies for SD. In order to approve the validity and applicability of our model, the framework has been tested on the basis of a real case study of a telecom operator.","PeriodicalId":273530,"journal":{"name":"International Journal of Advanced Computer Research","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125479108","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 : 2018-05-31DOI: 10.19101/IJACR.2018.836007
A. Mansour
Early detection of unknown adverse drug reactions (ADRs) could save patient lives and prevent unnecessary hospitalizations. Current surveillance systems are not ideal for rapidly identifying rare unknown ADRs. Current methods largely rely on passive spontaneous reports, which suffer from serious underreporting, latency, and inconsistent reporting. A more effective system is needed as the electronic patient records become more and more easily accessible in various health organizations such as hospitals, medical centers and insurance companies. These data provide a new source of information that has great potentials to detect ADR signals much earlier. In this paper, we have developed a methodology that uses both decision tree and fuzzy logic to generate a decision model. The developed model is equipped with a fuzzy inference engine, which enables it to find the causal relationship between a drug and a potential ADR. This could assist healthcare professionals to early detect previously unknown ADRs. Optimizing fuzzy rule weights and fuzzy sets parameters using genetic algorithm has been embedded in the proposed system to achieve excellent performance and improve the accuracy of the developed model. To evaluate the performance of the system, we have implemented the system using Weka and FuzzyJess software packages, and generated simulation results. To conduct the experiments, clinical information on 280 patients treated at the Detroit Veterans Affairs Medical Center was used. Two physicians on the team independently reviewed the experiment results. Kappa statistics show excellent agreement between the physicians and the developed model.
{"title":"Decision tree-based expert system for adverse drug reaction detection using fuzzy logic and genetic algorithm","authors":"A. Mansour","doi":"10.19101/IJACR.2018.836007","DOIUrl":"https://doi.org/10.19101/IJACR.2018.836007","url":null,"abstract":"Early detection of unknown adverse drug reactions (ADRs) could save patient lives and prevent unnecessary hospitalizations. Current surveillance systems are not ideal for rapidly identifying rare unknown ADRs. Current methods largely rely on passive spontaneous reports, which suffer from serious underreporting, latency, and inconsistent reporting. A more effective system is needed as the electronic patient records become more and more easily accessible in various health organizations such as hospitals, medical centers and insurance companies. These data provide a new source of information that has great potentials to detect ADR signals much earlier. In this paper, we have developed a methodology that uses both decision tree and fuzzy logic to generate a decision model. The developed model is equipped with a fuzzy inference engine, which enables it to find the causal relationship between a drug and a potential ADR. This could assist healthcare professionals to early detect previously unknown ADRs. Optimizing fuzzy rule weights and fuzzy sets parameters using genetic algorithm has been embedded in the proposed system to achieve excellent performance and improve the accuracy of the developed model. To evaluate the performance of the system, we have implemented the system using Weka and FuzzyJess software packages, and generated simulation results. To conduct the experiments, clinical information on 280 patients treated at the Detroit Veterans Affairs Medical Center was used. Two physicians on the team independently reviewed the experiment results. Kappa statistics show excellent agreement between the physicians and the developed model.","PeriodicalId":273530,"journal":{"name":"International Journal of Advanced Computer Research","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122051558","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 : 2018-05-31DOI: 10.19101/ijacr.2018.836003
M. H. Mohamed, Hoda Waguih
University and higher institute admission are an intricate decision process and it is an important responsibility of the students to select the correct study track. The increase of the student's major dropout rate in higher education systems is one of the important problems in most institutions. One approach to solve such problem and succeed in academic life is to help the students in selecting a suitable major and assign them to the right track. The objective of our research is to build academic advisor model to students for their higher education which utilize classification data mining for recommending the suitable academic major. The method applied in the research is data mining classification techniques through decision tree method for advising students to select suitable major and help assign them to the right track. The proposed model classifies students and matches them to the proper study tracks according to their features. The three decision tree classification algorithms, namely J48, random tree and reduces error pruning (REP) tree was first applied to real data in a managerial higher institute in Giza Egypt and results are compared between them. Finally, the results showed that J48 algorithm gives 16 rules and we eliminate the rules that give low CGPA and we will use the 5 better rules that have the highest CGPA based on CGPA grade that equal (A) and J48 algorithm gives the highest accuracy 87.64% and classification error was 12.36% and was thus selected as the main classifier for building the proposed model based on the rules that we obtained from J48 algorithm than the two other classification algorithms and thus suggest using the generated J48 decision tree in our proposed student advising model to enhance students’ academic performance and decrease dropout.
{"title":"A proposed academic advisor model based on data mining classification techniques","authors":"M. H. Mohamed, Hoda Waguih","doi":"10.19101/ijacr.2018.836003","DOIUrl":"https://doi.org/10.19101/ijacr.2018.836003","url":null,"abstract":"University and higher institute admission are an intricate decision process and it is an important responsibility of the students to select the correct study track. The increase of the student's major dropout rate in higher education systems is one of the important problems in most institutions. One approach to solve such problem and succeed in academic life is to help the students in selecting a suitable major and assign them to the right track. The objective of our research is to build academic advisor model to students for their higher education which utilize classification data mining for recommending the suitable academic major. The method applied in the research is data mining classification techniques through decision tree method for advising students to select suitable major and help assign them to the right track. The proposed model classifies students and matches them to the proper study tracks according to their features. The three decision tree classification algorithms, namely J48, random tree and reduces error pruning (REP) tree was first applied to real data in a managerial higher institute in Giza Egypt and results are compared between them. Finally, the results showed that J48 algorithm gives 16 rules and we eliminate the rules that give low CGPA and we will use the 5 better rules that have the highest CGPA based on CGPA grade that equal (A) and J48 algorithm gives the highest accuracy 87.64% and classification error was 12.36% and was thus selected as the main classifier for building the proposed model based on the rules that we obtained from J48 algorithm than the two other classification algorithms and thus suggest using the generated J48 decision tree in our proposed student advising model to enhance students’ academic performance and decrease dropout.","PeriodicalId":273530,"journal":{"name":"International Journal of Advanced Computer Research","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126338669","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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