International Journal of Advanced Studies in Computer Science and Engineering (IJASCSE)

                                                                                                                                                                                                   ISSN : 2278 7917 

Call For Papers

March 2019

  Submission         March 10

  Acceptance         March 20

  Publication          March 31

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International Journal of advanced studies in Computer Science and Engineering (IJASCSE) is here to provide a timely and broad coverage of research in ever-challenging field of Computer Science and Computer Science Engineering. IJASCSE is an interdisciplinary, peer reviewed, fully refereed, monthly, Open Access journal for research scholars with a mix of regular and theme based issues to share their new and advanced research in Computer Science and Engineering. 


The papers published at IJASCSE are currently Abstracted & Indexed by some world famous databases.

 Call For Papers

April  2019

     Submission                     April 10

      Notification                     April 20

      Publication                     April 30

Bookmark and Share

Open Access Database


IJASCSE is an online peer reviewed quality publication, which publishes research papers from diverse fields in computers, sciences, engineering and technologies that emphasizes new research, development and their applications. It provides an open access database for all who are interested to exchange their research work, technical notes & surveying results among professionals through out the world.

 

 IJASCSE volume 8 issue 2   

 

A Machine Learning Approach to Dropout Early Warning System Modeling

Isiaka R.M., Babatunde R.S., Ajao F.J., Abdulsalam S.O.

 

Abstract-This paper presents the procedure for building the adaptive model, that is the core element for the realization of the prevention component of the framework. The model development is guided by the knowledge of the domain experts. An experimental
approach was used to identify the K-Nearest Neighbors (KNN) as the best of the six algorithms considered for the adaptive models. Other algorithms explored are Logistic
Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB) and Support Vector Machines (SVM). Conversely, the Precision, Recall and F1-Scores on the entry attributes are 0.90, 0.95 and 0.92 respectively. The implication of this is that the academic performance is a very significant factor for dropping out of the educational system. The developed model can provide an early signal on the students with the propensity to dropout, thereby serving as an advisory system for the students, parents and school managementtowards curtailing the menace.


Investigating Mobile Money Usage Patterns in Zambia

Mwiza Norina Phiri, Dani Eliya Banda


Abstract-The purpose of this study is to investigate the usage patterns of MNO mobile money services in Zambia. It adopted cross-sectional survey to collect quantitative data from users of mobile money services. A self-administered questionnaire was prepared and circulated in Lusaka province. From the 200 questionnaires circulated, 112 useable questionnaires were returned (56% response rate) and subsequently analysed using SPSS and excel. The research reviewed that the most used mobile money service was airtime recharges followed by fund transfers. Mobile money accounts were least used for purchasing and savings. Most mobile money transfers were payments to recipients outside town. The transfers were mostly to relatives and friends. The research further reviewed that most mobile money users were occasional users and that users were switching between mobile money products depending on their current need.


 

Approach for Efficient Web Service Selection Based on QoS Parameters

Neerja Negi, Satish Chandra


 

Abstract — The growth of the new technologies such as cloud computing and IOT also plays a significant role in the growth of the web services. But to select appropriate web service from this large pool of web services is a very crucial task. As many of the web services provide the equivalent functionality so it is very difficult for the user to find out the right one according to their requirement. So, nonfunctional parameters (QoS) such as response time, security, reliability, latency play a very significant role in the selection of web services that best meet the user’s requirements. A hybrid approach has been proposed in this paper for the selection of efficient web services. 

 

Ring-LWE Quantum-Secure Key Exchange Protocol 

Simran Choudhary, Gupta Anil

 

Abstract- —The development of quantum computer is on full pace, hopefully it might be available in a decade. Thus, it is necessary to develop efficient quantum secure public key cryptosystems to provide safe key exchange in quantum era. In this paper we presented an efficient implementation of a Ring-Learning with errors public key cryptosystem whose security is based on intractability of hard problem on lattices. And comparative analysis of proposed cryptosystem with RSA, ECDH and LWE based key exchange protocols is presented.

Keywords- dropout, adaptive-model, data mining, machine learning prediction











 




Keywords— Mobile Money, MNO-Mobile Network Operator, Zambia, Usage patterns.

 







 

 

 









Keywords- IOT, Cloud Computing, Resposne Time, QoS

 

 

 


 

 

 

 

 

 

KeywordsLattices, Learning with errors, Ring-Learning with errors, Reconciliation mechanism

machine_learning_early_warning_system_modeling.pdf
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mobile_money.pdf
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