International Journal of Advanced Computer Research最新文献_第8页

A novel multi-user fingerprint minutiae based encryption and integrity verification for cloud data 一种新的基于多用户指纹细节的云数据加密和完整性验证方法

International Journal of Advanced Computer Research Pub Date : 2018-07-31 DOI: 10.19101/IJACR.2018.837010

Ruth Ramya Kalangi, M. Rao

{"title":"A novel multi-user fingerprint minutiae based encryption and integrity verification for cloud data","authors":"Ruth Ramya Kalangi, M. Rao","doi":"10.19101/IJACR.2018.837010","DOIUrl":"https://doi.org/10.19101/IJACR.2018.837010","url":null,"abstract":"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.","PeriodicalId":273530,"journal":{"name":"International Journal of Advanced Computer Research","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114546869","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}

引用次数: 7

ICT green alignment: towards a new generation managerial model based on green IT and corporate social responsibility 资讯及通讯科技绿色结盟:迈向以绿色资讯及企业社会责任为基础的新一代管理模式

International Journal of Advanced Computer Research Pub Date : 2018-05-31 DOI: 10.19101/IJACR.2018.836018

Rachid Hba, A. E. Manouar

{"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}

引用次数: 1

Decision tree-based expert system for adverse drug reaction detection using fuzzy logic and genetic algorithm 基于模糊逻辑和遗传算法的决策树药物不良反应检测专家系统

International Journal of Advanced Computer Research Pub Date : 2018-05-31 DOI: 10.19101/IJACR.2018.836007

A. Mansour

{"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}

引用次数: 18

A proposed academic advisor model based on data mining classification techniques 提出了一种基于数据挖掘分类技术的学术顾问模型

International Journal of Advanced Computer Research Pub Date : 2018-05-31 DOI: 10.19101/ijacr.2018.836003

M. H. Mohamed, Hoda Waguih

{"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}

引用次数: 10