Volume-7 , Issue-1 , Jan - 2019 Go Back

The incredible development of shocking PCs, web broadband property and made navigation content has created a world development among that information and communication technology (ICT) is obtain used to rework prepare. There’s a desire to repair up the tutorial system to satisfy the needs higher. The closeness of PCs with refined code has created it come-at-able to return to a call a number of irritated issues currently and at a lower regard. This paper exhibits the characteristics of this E-Learning thus examinations circulated downside resolution and depicts the structure of distributed method stage by process the alternatives of E-Learning. The makers have endeavored to acquaint circulated method with e-learning, amass relate e-learning cloud, degree build associate eager examination and examination for it from the next edges: structure, improvement reasoning and external interface with the model.

Architecture, Cloud Computing, E-learning, Information Technology

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[2]. Nishant Katiyar & Dr. Rakesh Bhujade on “Framework to Cloud Computing Based M-Learning for Higher Education: An Adopt”, International Journal of Engineering Trends and Technology ( IJETT ) – Volume 61 Number 1 – July 2018, Page 40-48
[3]. Nishant Katiyar & Dr. Rakesh Bhujade on “A Survey : Adoption of Cloud Computing in Education Sector”, International Journal of Computer Trends and Technology (IJCTT) – Volume 60 Issue 1- June 2018
[4]. Nishant Katiyar & Sandeep Choudhary on “Current Trends in Cloud Computing and Service Providing Model” International Journal of Engineering Associates, Vol. 2 Issue 4 ISSN: 2320- 0804
[5]. Nishant Katiyar on “Cloud Computing Security Issues and Challenges”, International Journal of Recent Research in Mathematics Computer Science and Information Technology Vol. 2, Issue 1, pp: (253-258), Month: April2015 – September 2015
[6]. “Cloud Computing – Issues, Research and Implementations” Mladen A. Vouk,Department of Computer Science, North Carolina State University, Raleigh, North Carolina, USAJournal of Computing and Information Technology - CIT 16, 2008, 4, 235–246 doi:10.2498/cit.1001391
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[8]. W. Jianmin, “Campus Network`s E-learning Mode”, New Curriculum Research, 2007.08, pp.84-86.
[9]. Y. Wei, Y. Rong, “Research of an E-learning System Model Based on Agent”, Computer Engineering and Applications, Nov. 2004, pp.156- 158.
[10]. A. Gladun, J. Rogushina, F. Garci`a-Sanchez, R. Marti`nez-Be`jar, J. Toma`sFerna`ndez-Breis, “An application of intelligent techniques and semantic webtechnologies in e-learning environments”, Expert Systems with Applications 36, 2009, 922-1931.
[11]. Y. Li, S. Yang, J. Jiang, M. Shi, “Build grid-enabled large-scale collaboration environment in e-learning grid”, Expert Systems with Applications 31,2006, 742-754.
[12]. Z. Chengyun, “Cloud Security: The security risks of cloud computing, models and strategies”, Programmer, May.2010, pp.71-73.
[13]. B. Hayes, "Cloud computing," Comm. Acm, vol. 51, no. 7, pp. 9– 11, 2008.
[14]. E. Tuncay, "Effective use of Cloud computing in educational institutions," Procedia Social Behavioral Sciences, p. 938–942, 2010.
[15]. R. Buyya, C.S. Yeo &S.Venugopal, "Market-oriented Cloud computing: Vision, hype, and reality of delivering IT services as computing utilities," 10th
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[17]. M. Lijun, W.K. Chan & T.H. Tse, "A tale of Clouds: Paradigm comparisons and some thoughts on research issues," Ieee Asia-pasific Services Compute. Conf., Apscca08, pp. 464–469, 2008.
[18]. K. Praveena& T. Betsy, "Application of Cloud Computing in Academia," Iup J. Syst. Management, vol. 7, no. 3, pp. 50–54, 2009.
[19]. K.A. Delic& J.A. Riley, "Enterprise Knowledge Clouds," Next Generation Km Syst. Int. Conf. Inform., Process, Knowledge Management, Cancun, Mexico, pp. 49–53, 2009.
[20]. J. A. Méndez and E. J. González, “Implementing Motivational Features in Reactive Blended Learning: Application to an Introductory Control Engineering.

Nishant Katiyar, Rakesh Bhujade, "E-Learning System based on Cloud Computing: A Review Paper," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.837-842, 2019.

Data mining plays an important role for framing association rules between the huge sets of gathered data. Association between sets of items or entities in transaction, relational database and other data warehouse as well as their frequent patterns can be discovered by using association rules. However, the limitations in using association rules are that it takes too much time to figure out all the frequent itemsets. It is therefore, weblog mining techniques with Genetic Algorithms (GA) are used to find information patterns from the web data with much lesser time in comparison to association rules. By the using GA the outcome of association rule mining and behaviour analysis is improved.GA are powerful and extensively related to stochastic search and optimization technique created on the model of universal selection and evaluation. The aim of this paper is to observe all the frequent itemsets, patterns and produce the association rules from the huge datasets by use of proposed evolutionary algorithm.

Frequent Patterns, Databases, Behaviour Analysis, Patterns, Genetic

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[3]. Dharmendra Singh Rajput, Praveen Kumar Reddy M, D. P. Shrivastavain: IJPT, Mining Frequent Termset for Web Document Data Using Genetic Algorithm, inIJPT, June-2016, Vol. 8, IssueNo.2, 4038-4054.
[4]. Umesh Kumar Patel in IJIACS,Optimization of Association Rule Mining Using Genetic Algorithm , in IJIACS , June 2016, Volume 5, Issue 6, ISSN 2347 – 8616.
[5]. Yang Xu,Mingming Zeng,Quanhui Liu, Xiaofeng Wang, “A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets”,Hindawi Publishing Corporation Mathematical Problems in Engineering, Volume 2014, Article ID 867149, 9 pages
[6]. S.Prince May and E.Baburaj,”Sequential Web Page Access Analysis Using Weblog”, Contemporary Engineering Sciences, Vol 8, 2015, no 13,573-580 Hikari
[7]. Soumadip Ghosh, Sushanta Biswas, Debasree Sarkar, Partha Pratim Sarkar, “Mining Frequent Item sets Using Genetic Algorithm”, IJAIA, October 2010, Vol.1, No.4.
[8]. Wenxiang Dou, Jinglu Hu, Kotaro Hirasawa and Gengfeng Wu, “Quick Response Data Mining Model Using Genetic Algorithm”, SICE Annual Conference 2008, August 20-22, 2008, The University Electro-Communications, Japan
[9]. Wakabi-Waiswa P.P., Baryamureeba V and SarukesiK, “Generalized Association Rule Mining Using Genetic Algorithms”, International Journal of Computing and ICT Research, 2008, Vol. 2 No. 1, pp. 59-69.
[10]. Emine Tug, Merve S¸ Akirog lu ,Ahmet Arslan, “Automatic discovery of the sequential accesses from weblog data files via a genetic algorithm”, Knowledge-Based Systems (Elsevier) 19 (2006) 180–186.
[11]. KarunaNidhi Pandagre, Dr S. Veenadhari, “Data Mining Techniques with Weblog: A Review”, IJARCS, Volume 8, No. 7, July – August 2017.
[12]. Karuna Nidhi Pandagre, Dr S.Veenadhari: IJESI on “Efficient Approach for Finding Strong Patterns from Weblog using Web Usage Mining”, volume 7 Issue 1, January 2018.
[13]. J. Srivasta, R. Cooley, M. Deshpande, P. Tan, Web usage mining: discovery and applications of usage patterns from web data, SIGKDD Explorations 1 (2) (2000) 1–12.
[14]. Chatterjee, Web mining for pattern discovery in E-Commerce applications, Master Thesis, Department of Computer Science, University of Louisville, 2001
[15]. Ashok Kumar D, T. A. Usha, From International Journal of Applied Engineering Research : Improved Apriori Algorithm Using Genetic Algorithm For Itemset Mining, Vol. 10 No.82 (2015)
[16]. Anandhavalli M.*, Suraj Kumar Sudhanshu, Ayush Kumar and Ghose M.K. from Advances in Information Mining,”Optimized association rule mining using genetic algorithm”, 2009, pp-01-04.
[17]. Kolli Prabhakara Rao, G.Kalyana Chakravarthy : International Journal of Engineering Trends and Technology (IJETT) “Intelligence Service Of Web Mining With Genetic Algorithm”, Volume 4 Issue 10 –Oct 2013.
[18]. Ahmad M. Taleb, Anwar A. Yahya, Nasser M. Taleb: DBKDA 2013: The Fifth International Conference on Advances in Databases, Knowledge, and Data Applications, “Parallel Genetic Algorithm Model to Extract Association Rules”, IARIA, 2013.
[19]. Anwar Ali Yahya, Addin Osman, Abd Rahman Ramli and Adlan Balola : Journal of Computer Science on Feature Selection for High Dimensional Data: An Evolutionary Filter Approach , 7 (5): 800-820, 2011.

Karuna Nidhi Pandagre, S.Veenadhari, "Finding Optimized Frequent Pattern using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.843-850, 2019.

Wildcat testing contains a mess of strategy related to it. It is a decent combination of structured thinking and race exploration that may be terribly powerful for locating bugs and substantiate correctness. This paper shows however the wildcat testing mentality is often combined with additional ancient scenario-based and scripted testing. This hybrid technique relaxes a lot of the rigidity unremarkably related to scripting and makes smart use of the wildcat testing steering bestowed. It additionally permits groups that square measure heavily unconditional in existing scripts to feature wildcat testing to their arsenal. Ancient state of affairs testing is incredibly seemingly to be a well-known idea for the reader. Several testers write or follow some type of script or end-to-end state of affairs once they perform manual testing. State of affairs testing is well-liked as a result of it lends confidence that the merchandise can faithfully perform the state of affairs for actual users. The additional the state of affairs reflects expected usage, the additional such confidence is gained. The additional part that wildcat testing lends to the current method is to inject variation into the state of affairs in order that a wider swath of the merchandise gets tested. Users can`t be unnatural to merely execute the software package the manner we have a tendency to intend, therefore our testing ought to expand to hide these extra state of affairs variants.

Hybrid, exploratory, scenarios, testing, tour

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Manas kumar Yogi, Y. Jnapika, Bhanuprakash Ped, "A Review of Hybrid Exploratory Testing Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.851-857, 2019.

Data has become an integral part of almost every organization. This data contains interesting and vital information that is often hidden to naked eye but is in the greater interest to an organization, this reason has led researchers for finding a special interest in extracting the hidden knowledge that is accumulated within it, with some researchers terming it as goldmine of data. In this scenario data mining has found a special place in the healthcare sector. Data mining has been found to be quite successful in healthcare sector in finding out the hidden patterns that are useful for disease prognosis. These data mining techniques have been successfully applied for prognosis of diabetes. Diabetes mellitus commonly known as diabetes is a metabolic disorder condition which is characterized by high level of sugar in blood. Numerous data mining techniques have been used for designing of the model that could aid physicians in predicting diabetes. In this paper the main focus is to make present detailed survey of various data mining techniques and approaches that have been put to use for prognosis of diabetes. The research presented here is a survey focused mainly on evaluation of various computer based tools designed for prognosis of diabetes.

Diabetes, Data mining, Decision tree, Dataset, Prognosis, SVM

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Mirza Shuja, Sonu Mittal, Majid Zaman, "Diabetes Mellitus and Data Mining Techniques: A survey," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.858-861, 2019.

Big Data could be a new term wont to determine the datasets that because of their large size and complexity. Big Data are currently speedily increasing altogether science and engineering domains, as well as physical, biological and medical specialty sciences. Big Data processing is that the capability of extracting helpful data from these large datasets or streams of data, that because of its volume, variability, and velocity, it absolutely was unfeasible before to try to to it. The Big data challenge is turning into one in every of the foremost exciting opportunities for the following years. This paper includes the knowledge concerning what is Big Data, Data Mining, Data Mining with Big Data, Challenging issues and its related work.

Big Data, Data mining, Datasets, Data Mining Algorithms

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Swati Namdev, "Data Mining with Big Data: It’s Issues and Challenges," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.862-864, 2019.

The purpose of this paper is to propose a set of test metrics required to quantify the quality of the software. A detailed research was done to analyse the testing process including functional, performance, security and usability testing around latest technologies covering cloud computing, big data, machine learning, artificial intelligence and internet of things. Effort, schedule, productivity, defects, quality and cost are fundamental parameters of any project. There are several metrics around these parameters covering all phases of project including project initiation, planning, executing, monitoring and controlling and closing. There was a time when weekly, monthly, quarterly or yearly metrics reports were published based on collected data. Confirming authenticity of that collected data was also a challenge. In current scenario looking at the adaption of continuous software engineering we proposed a new term called Automated Continuous Quality (ACQ) Metrics Dashboard which will act as product stability index or project health indicator. This could be used by organizations to track and generate all the required reports at real time. Any individual could select any of the project parameters for any period of time to generate a report. It would use continuous data collected by continuous monitoring of the tools.

Metrics, Continuous Testing, Continuous Delivery, Continuous Integration, 4S Metrics, ACT (Automated Continuous Testing), T Model

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Dheeraj, Kalpana Sharma, "Proposed 4S Quality Metrics and Automated Continuous Quality (ACQ) Metrics Dashboard to Quantify Software Product Quality," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.865-869, 2019.

For the most part, research and advancement in apparatuses trail advancement in UI structure, since it just bodes well to create devices when you know for what sorts of interfaces you are building devices. Given the solidification of the UI on the work area similitude over the most recent 15 years, it isn`t astonishing that devices have developed to the point where business devices have decently effectively secured the essential parts of UI development. Obviously the examination on UI programming apparatuses has had huge effect on the procedure of programming improvement. In any case, we trust that UI configuration is balanced for an extreme change in the close future, primarily expedited by the ascent of omnipresent processing, acknowledgment based UIs, 3D and different advances. Along these lines, we hope to see a resurgence of intrigue and research on UI programming devices with the end goal to help the new UI styles.

Graphical User Interface,Voice User Interface ,TUI,AR

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Manas kumar Yogi, P. Likitha, K. Vishwasree, "Novel Insights into Testing for the Next Generation Interfaces," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.870-875, 2019.

Most of the recent work in sentiment analysis is carried out on textual data. The text based sentiment analysis mainly relies on construction of word dictionaries, using machine learning techniques that learn and extract opinion from large text corpora. Text based sentiment analysis has numerous applications such as customer satisfaction analysis about a brand or product perception, to gauge voting intentions etc. With the rapid growth of social media, users post humongous volume of data in various modalities such as text, image, audio, and video. These multimodal data streams bring new opportunities for going beyond text based sentiment analysis and improving possible results. Since sentiment can be extracted from facial and vocal expressions, prosody and body posture, multimodal sentiment analysis offers new avenues in sentiment analysis. In multimodal sentiment analysis, sentiment is extracted from transcribed content, visual and vocal features. This survey defines sentiment, sentiment analysis, states problems and challenges in multimodal sentiment analysis and finally reviews some of the recent computational approaches used multimodal sentiment analysis.

Sentiment Analysis, Computational Approaches, Multimodal Sentiment Analysis, Challenges, Machine Learning

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Mahesh G. Huddar, Sanjeev S. Sannakki, Vijay S. Rajpurohit, "A Survey of Computational Approaches and Challenges in Multimodal Sentiment Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.876-883, 2019.

IT infrastructure and its maintenance processes are changing in different organizations by the advent of cloud computing and may be able to eliminate their existing hardware. In traditional way of configuring a switch or routers may error-prone and cannot fully utilize the capability of existing network infrastructure. SDN is a way of providing programmability for network application development by its distinguished features decoupling the control plane from the data plane. In this paper we focused on the new concept in computer networking field, software defined network (SDN) and its challenges, issues, solutions. First, we provided and cover its basic model and software used to build a computer network with the help of software defined network mechanism then software tools used are listed, challenges and issues are described.

SDN, OPENFLOW, Performance, Security, CDDA

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Deepak Singh Rana, Shiv Ashish Dhondiyal, Sushil Kumar Chamoli, "Software Defined Networking (SDN) Challenges, issues and Solution," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.884-889, 2019.

The point of our undertaking is to defend the collected grains from being ruined by creepy crawlies, microbial assault, air and water which decline the quality just as amount of sifted (gathered) grains. Capacity is one of the most concerning issues in a farming nation like india where million tones` of gathered grains is lost by various elements headed previously. This distribution center comprises of a programmed rooftop which is comprised of board of sun oriented cells which has sensors for rain, daylight and wind (for keeping it from residue and diverse undesirable particles which causes modification in the nature of grains). This task is structured with flame sensor and gas sensor which actuates on the event of flame which prompts a lot of misfortune.

Grains, Nature of grains, Agricultural Process

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S. Lakshiya Sree, S.Devi, "Systematic Warehouse to Protect The Basic Needs For Common People," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.890-893, 2019.