Volume-6 , Issue-2 , Feb - 2018 Go Back

the existing work of training & placement in Sanjay Ghodawat Institute (SGI) is fully manual process and due to this manual process, management faces many problems for collecting student details. The aim of this project is Automation of Training and Placement unit of SGI, Atigre. The project will include minimum manual work and maximum optimization, abstraction and security. This is an android application which will help students and Training and Placement Officer (TPO) to carry out every placement related activity. The system can be accessed throughout the SGI organization with proper login. It is used by the TPO to manage the student’s information and fast access in placement related activities such as applying for drive. Students logging should be able to upload their information. The key feature of this project is that it has a decision Forum, where queries asked by students can be solved by higher authorities. Students will get notified when TPO’s upload study material or any campus drive information, through this system. Automatic Training and placement is developed to maintain the details of student information, trace the details of student also maintain the information about the company availability.

Automation, Login, Placement, Android, Smartphones

[1] Sanket R. Brahmankar, Prof. A. Vinaykumar, “Anroid application for Trainning and Placement cell”, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN(O)-2394-4396, Vol-1 , Issue-4 ,2015.
[2] Mayur Wadje,Yuvraj Madake, Gireedhar Rodge, Shahaji Yadav, “And Iteractive Andriod Application for Training and Placement system”, International Journal on Recent and Innovation Trends in Computing and Communication”, Vol-5, Issue-4, April 2017.
[3] Prof. Shilpa Hadkar, Prof. Snehal Baing, Prof. Trupti Harer, Prof. Sonam Wankhede, Prof.K.T.V.Reddy, “College Collaboration Portal with Training and Placement”,IOSR Journal of Computer Engineering (IOSR-JCE), Vol.16, Issue 2, 2014.
[4] Mahesh N. Nair, Vinay Hegade, Saumya Omanakuttan, “Placement cell Mobile Training App”, Internation Journal of computer Science and Information Technology Research, Vol-4, Issue-1, pp(261-268), Month: January-March 2016, Available at: www.researchpublish.com.
[5] Prof. Seema Shah Assistant Professor, Mr Nilesh Rathod,“Design Paper on Online Training and Placement System(OTaP)”, 2013.
[6] Akshay Kumar,Sanjay.V. Mahendrakar, Smriti, Vishal.A.V, G.Ramesh” NIE PLACEMENT HUB”, International Research
Journal of Engineering and Technology (IRJET), Vol-4, Issue-4, 2017.
[7] J. Horiton “Android Programming for Beginners”

S.M.Shaikh, A.S.Ganbavale, S.P.Gaikwad, M.R.Bhujbal, K.M.Aldar, "Training and Placement Android Application," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.217-221, 2018.

Cloud environment allows us to share the resources like CPU, Memory, etc to multiple tenants. These tenants put their tasks to the cloud server through the cloudlets. These cloudlets are treated as Process Elements (PEs). There are basically three entities Cloud Information Service [CIS], Data Centre, and Broker. All communications takes place between these three entities for executing the jobs or tasks. In this paper, we have created a cloud simulation environments, two sample sets are designed i.e. Table 1 and Table 2 to analyze the impacts of Submitted Tasks, Number of Virtual Machines variations on the Average Execution Time per task and illustrated through Figure 2 and Figure 3. It is observed that if the number of tasks and other environment constraints remains constant, increase in VMs decreases the Average Execution time per task, but limited number of VM can be increased according to the server architecture. If the number tasks are increased by keeping VMs and other simulation environments constant, the Average Execution time per task increases linearly.

Cloud Computing, Virtual Machines, Data Centre, Process Elements, Broker, Cloud Informatio Centre.

[1] A.Singh, D. Juneja, M. Malhotra, “A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing”, Journal of King Saud University – Computer and Information Sciences (2015), pp. 1-10, 1319-1578.
[2] S.A. Hussain, M. Fatima, A.Saeed, I. Raza, R.K. Shahzad, “Multilevel classification of security concerns in
[3] cloud computing”, Applied Computing and Informatics (2016), pp.2-9, http://dx.doi.org/10.1016/j.aci.2016.03.001
[4] Saraswathi AT, Kalaashri.Y.RA, Dr.S.Padmavathi, “Dynamic Resource Allocation Scheme in Cloud Computing”, Procedia Computer Science 47 ( 2015 ) 30 – 36, doi: 10.1016/j.procs.2015.03.180
[5] M.Verma, GR Gangadharan, NC Narendra, R Vadlamani, V.Inamdar, L. Ramachandran, “Dynamic resource demand prediction and allocation in multi-tenant service clouds”, Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/cpe.3767
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[7] W. Lin, J.Z. Wang, C. Liang, D. Qi, “A Threshold-based Dynamic Resource Allocation Scheme for Cloud Computing”, Procedia Engineering 23(2011), pp. 695-703
[8] P. Pradhan, R.K.Behera, BNB Ray, “Modified Round Robin Algorithm for Resource Allocation in Cloud Computing”, International Conference on Computational Modeling and Security (CMS 2016), Procedia Computer Science 85 ( 2016 ), pp. 878 – 890
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[15] Xiaoyun Zhu, Zhikui Wang, SharadSinghal Utility-driven workloadmanagement using nested control design. In Proc. AmericanControl Conference, 2006.
[16] B. Urgaonkar, M. S. G. Pacifici, P. J. Shenoy, and A. N.Tantawi. An analytical model for multi-tier internet services and its applications. In Proc. SIGMETRICS, 2005.
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Amit Chaturvedi, Aaqib Rashid, "Analyzing Effects on Average Execution time by varying Tasks and VMs on Cloud Data Centre," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.222-225, 2018.

Breast cancer is one of the most leading causes of death among women. The early detection of anomalies in breast enables the doctor’s in diagnosing the breast cancer easily which can save numerous of lives. In this work, Wisconsin Diagnosis Breast Cancer database is used for experiments in order to predict the breast cancer either benign or malignant. Supervised Machine Learning algorithms namely Decision tree and Random Forests are used to classify the breast cancer. R programming language is used to classify the breast cancer. The performances of the algorithms are measured in terms of accuracy, specificity and sensitivity. The functionality of the algorithms are analysed and the results were discussed.

Breast Cancer, Classification, Decision tree, Random Forests, R programming

[1] Jain R, “Introduction to data mining techniques”, hhtp:// www.iasri.res.in/ebook/expertsystem/datamining.pdf
[2] Borges and Lucas Rodrigues, “Analysis of Wisconsin Breast Cancer Dataset and Machine Learning for Breast Cancer Detection”, Proceedings of XI Workshop de Visão Computational, October 05th‐07th, 2015.
[3] Dubey, A.K., Gupta, U. & Jain, S, “Analysis of k-means clustering approach on the breast cancer Wisconsin dataset”, International Journal of Computer Assisted Radiology and Surgery, Vol.11, Issue 11, pp. 2033–2047, November 2016 .
[4] P.Dhivyapriya and Dr.S.Sivakumar, “Classification of Cancer Dataset in Data Mining Algorithms Using R Tool”, International Journal of Computer Science Trends and Technology (IJCST) – Vol.5, Issue 1, Jan – Feb 2017
[5] F.Paulin et al., “Classification of Breast cancer by comparing Back propagation training algorithms”, International Journal of Computer Sciences and Engineering (IJCSE), Vol 3,No 1, pp 327 – 332,Jan 2011.

N.Sridevi, S.Anitha , "Prediction of Breast Cancer using Decision tree and Random Forest Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.226-229, 2018.

A summary condenses a lengthy document by highlighting salient features. It helps reader to understand completely just by reading summary so that the reader can save time and also can decide whether to go through the entire document. Summaries should be shorter than the original article so make sure that to select only pertinent information to include the article. The main goal of newspaper article summary is, the readers to walk away with knowledge on what the newspaper article is all about without the need to read the entire article. This work proposes a news article summarization system which access information from various local on-line newspapers automatically and summarizes information using heterogeneous articles. To make ad-hoc keyword based extraction of news articles, the system uses a tailor-made web crawler which crawls the websites for searching relevant articles. Computational Linguistic techniques mainly Triplet Extraction, Semantic Similarity calculation and OPTICS clustering with DBSCAN is used alongside a sentence selection heuristic to generate coherent and cogent summaries irrespective of the number of articles supplied to the engine. The performance evaluation is done using ROUGE metric.

Text Summarization, Natural Language Processing, News Paper Articles, Intelligence mining, RDF Triplets ,NER

[1]. McKnight, W. “Text data mining in business intelligence”, Information Management, 15(1):80, 2005.
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Laxmi B. Rananavare, P. Venkata Subba Reddy, "Automatic News Article Summarization," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.230-237, 2018.

Sensor networks in underwater environments, that is, forming underwater wireless sensor networks (UWSNs), has been attracted significant attention recently from both academia and industry. The characteristics of underwater sensor networks are fundamentally different from that of terrestrial networks. In this paper, we overviewed the main routing issues and challenges for efficient communications in underwater acoustic sensor networks.

UWSN, propagation delay, bandwidth, routing Introduction

[1] Jain Neha., Jangir Hitesh, “Survey on Underwater Acoustic Wireless Sensor Networks of Routing Protocols”, International Journal of Advance research, Ideas and Innovations in Technology, Volume3, Issue2. pp. 1171-1174, 2017.
[2] Tariq Ali, Low Tang Jung, Ibrahima Faye, “Classification of Routing Algorithms in Volatile Environment of Underwater Wireless Sensor Networks”, International Journal of Communication Networks and Information Security (IJCNIS), Vol. 6, No. 2, pp.129-147, August 2014.
[3] Manju Bhardwaj, "Faulty Link Detection in Cluster based Energy Efficient Wireless Sensor Networks", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.1-8, 2017.
[4] Syed Abdul Basit Andrabi1, Manoj Kumar, “A comparative study of routing protocols in underwater wireless sensor networks”, International Journal of Computer Science & Communication (IJCSC), Volume 8, Issue 1, pp. 27-29, March, 2017.
[5] Sangeeta Vhatkar, Mohammad Atique, “Design Issues, Characteristics and Challenges in Routing Protocols for Wireless Sensor Networks”, in the proceeding of International Conference and Workshop on Emerging Trends in Technology 2013, pp. 42-47, 2013.

[6] Ahmed M, “ Routing protocols based on protocol operations for underwater wireless sensor network: A survey”, Egyptian Informatics Journal, Volume 7, issue 2, pp. 1-6, June,2017.
[7] Goyal, N., “Data aggregation in underwater wireless sensor network: Recent approaches and issues”, Journal of King Saud University – Computer and Information Sciences (2017), Volume 4, issue 7, pp. 1-12, April, 2017.
[8] K. Bansal, P. Singh, “Energy Efficient Hierarchical Clustered Based Routing for Underwater Sensor Networks”, International Journal of Computer Sciences and Engineering, Vol.5(11), pp. 115-119, Nov 2017.
[9] Pushpender Sarao, “A new Strategy for Performance Enhancement of DSR in Vehicular Ad-Hoc Network”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18, pp. 7884-7890, 2017.

Pushpender Sarao, Kannaiah Chattu, Ch. Swapna, "Routing issues and challenges in Underwater Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.238-241, 2018.

Advertising has seen tremendous growth in recent years. Lot of companies, organizations, small and large businesses, from simple app to big ventures wants to advertise and open themselves in front of the world. Today Advertising firms and customers facing many problems such as lack of brand advertiser availability, Sales and operational inefficiency, Ineffective creative formats, Wrong display target for Premium Advertisements etc. To remove all the barriers and limitations we are going to implement a project called YammerAds: Instant Access to Reliable and Affordable Advertising Services. This system is a platform for Businesses and Advertising service providers based on different locations. The system will explore appropriate service providers for businesses and individual customers at different locations along with their details of key-services, cost plans, showcases. YammerAds provides genuine service providers to users who want to publish advertisements for their brands, so they can easily work with remote service providers to explore their business through advertising.

Advertisement, Crowd Advisor, Commercial, Advertising service providers, freelancer, Crowdsourcing

[1] Kumar Abhinav, Alpana Dubey, Sakshi Jain, Gurdeep
Virdi, Alex Kass and Manish Mehta, “CrowdAdvisor: A Framework for Freelancer Assessment in Online Marketplace”, 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Practice Track, DOI 10.1109/ICSE-SEIP.2017.23.
[2] Axelyo Primastomo, L. Eva Utari Cintamurni and Ferdi Areanto, “Analysis of virtual worker website freelancer.com, “2015 International Conference on Information, Communication Technology and System (ICTS).
[3] N. Narwal, “Noise Removal from News Web Sites”, “IJCSE Volume 5, Issue 9, 2017”.

T. Bhaskar, V.K. Patil, M.S. Ghumare, A.V. Aher, "YammerAds: Instant Access to Reliable and Affordable Advertising Services," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.242-244, 2018.

Cloud computing is rising field in computing . it is vast technology enhancing day by day when people are online they can get faster access to their data due to massive storage. It is the way to maximise capacity and capabilities without spending a lot to buy the infrastructure and software cloud computing is also involved in education sector ,because education is highly important in today’s life. In this paper we show hoe cloud computing used in education sector to improve teaching and learning methodology. As education is not just restricted to classroom with student today’s education is heavily depend on information technology. Cloud provide solution for that user use the platform and application on-campus or off-campus or combination of both depending on need. It offer service at least cost. In this paper we proposed architectures for cloud and did comparative analysis to make more secure data over cloud every technology has security threats.

Cloud, E-learning , Threats

[1]. Shakeel Ahemad , Hemand Kumar Mehta, “On Applying Big Data and Cloud Computing For Quality Improvement In Int’l Conf. On Advance In Big Data Analysis”. International journal of recent trends in engineering and research vol 02,issue 03, march 2016
[2]. S. Dhanalakshmi, S. Suganya and K. Kokilavani, "Mobile Learning Using Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.102-108, 2014.
[3]. Prof L.J. Sankpal,Ankush Kawalkar, Suhas Bhattu, Gaurang Parnik Aksh Sager , “Cloud Computing in Education System”., International Journal of Advanced Research in Computer and Communication Engineering vol 3, issue 2, feb 2014.
[4]. Wang,L.Z.G. Von laszewski, P.Chen,etal. Provide virtual machine information for grid computing [j] IEEE Transation On System MAN and Cybernetics Part a System And Human 2010
[5]. xiaojun wang,Daohua Huang, “Architecture of open education based on cloud computing”, 978-1-61284-704-7/11/26.00@2011 IEEE
[6]. Alwi, Najwa Hayaati Mohd, and IP-Shing Fan “Threats analysis for e-learning”. International Journal of Technology Enchanced Learning 2.4(2010): 358-371
[7]. Alwi, Najwa Hayaati Mogd,and Ip-Shing Fan.”International security threats analysis for e-learning ”Technology Enhanced Learning. Quality of Teaching and Educational Reform. Springer Berlin Heidelberg, 2010. 285-291.
[8]. Saju Mathew, “Implementation of cloud computing in education –A Revolution”, International Journal of Computer Theory and Engineering , Vol 4, No 3, June 2012.

Vishal S. Patil, Pooja Kale, Priyanka Sawale, "A Review on Use of Cloud in Education Sector," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.245-248, 2018.

Stock market is playing a vital role as investments option and investors make short-term investments as well as long-term investments. But here the main question arises “Where to invest?” and “when to invest?” even if an investor is aware about where to invest, it is still unpredictable whether or not stocks will have good future returns over time. To eliminate this dilemma predictive algorithms were introduced that will help investors in making investments by predicting which stocks will have positive expected returns. However, predicting stock returns with predictive algorithms alone is not enough. Clustering algorithms are widely used to cluster the stocks that have related returns over time. Using Cluster-Then-Predict approach we are going to prove that it provides more accurate results than the original predictive (Logistic Regression) model.

Predictive Algorithms, Regression, Classification, Clustering, Logistic Regression, Stock Returns, Cluster-Then-Predict

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[8] Swati Joshi, Farhat Ullah Khan and Narina Thakur, “Contrasting and Evaluating Different Clustering Algorithms: A Literature Review”, International Journal of Computer Science and Engineering, Vol. 2, Issue.4, pp. 2347-2693, 2014.

A. Bhattacharjee, J. Kharade, "Cluster-Then-Predict and Predictive Algorithms (Logistic Regression)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.249-252, 2018.

The leading cause of death in recent days due to heart diseases for both men and women. When the heart is too weak to pump blood throughout the body, heart failure occurs. The heart attack happens suddenly when there is a total blockage of an artery which supplies the blood to the heart. The chances of survival after the attack happen to a person is very low and most cases death will be an ultimate result if exact first step measures are not taken immediately. Most of the people who are in heart failure die within five years of diagnosis. Thus, we had proposed AI techniques to analyze the patient information in this paper including natural language processing methods to obtain accuracy in prediction. We used AI and NLP techniques in which decision tree algorithm is used to extract information from unstructured data such as doctor’s notes by analyzing the information and data that includes risk criteria or other types of symptoms. The aim of this paper is to predict whether a patient is likely to affect heart disease or not in early. This intelligent system decides the line of treatment to be followed by suitable databases obtained.

Heart disease; Artificial Intelligence AI; Natural language processing NLP

[1] Kuldip Singh, Singh G, “Alterations in Some Oxidative Stress Markers in Diabetic Nephropathy”, Journal of Cardiovascular Disease Research, Vol. 8, No. 1, pp.24-27, 2017.
[2] Nagre SW, “Mobile Left Atrial Mass – Clot or Left Atrial Myxoma”, Journal of Cardiovascular Disease Research, Vol. 8, No. 1, pp.31-34, 2017
[3] Krishnamurthy VT, Venkatesh SA, “Negative Pressure Pulmonary Oedema after Sedation in a Patient Undergoing Pacemaker Implantation”, Journal of Cardiovascular Disease Research, Vol. 8, No. 1, pp.28-30, 2017.
[4] Paryad E, Balasi LR, Kazemnejad E, Booraki S, “Predictors Of Illness Perception In Patients Undergoing Coronary Artery Bypass Surgery”, Journal of Cardiovascular Disease Research, Vol. 8, No. 1, pp.16-18, 2017.

[5] Jiang F, Jiang Y, Zhi H, et al, “Artificial intelligence in healthcare: past, present and future”, Stroke and Vascular Neurology, 2017.
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[10] Jiber H, Blitti MC, Bouarhroum A, “Acute type B Aortic Dissection Complicated by Acute Limb Ischemia: Case Report”, Journal of Cardiovascular Disease Research, Vol. 7, No.2, pp.97-99, 2016.
[11] Muhammad Subhi Al- Batah, “Testing the probability of Heart Disease using Classification and Regression Tree Model”, Annual Research & Review in Biology, Vol. 4, Issue. 11, pp.1713-1725, 2014.
[12] Setty HS, Hebbal VP, Channabasappa YM, Jadhav S, Ravindranath KS, Patil SS, et al, “Assessment of RV function following Percutaneous Transvenous Mitral Commissurotomy (PTMC) for rheumatic mitral stenosis”, Journal of Cardiovascular Disease Research, Vol. 7, No. 2, pp.58-63, 2016.
[13] Patnaik L, Pattanaik S, Sahu T, Panda BK, “Awareness of symptoms and risk factors of Myocardial Infarction among adults seeking health care from a rural hospital of India”, Journal of Cardiovascular Disease Research, Vol. 7, No. 2, pp.83-85, 2016.

S. Sabeena, V. Sujitha, "Prediction of Heart Disease Using AI and NLP Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.253-257, 2018.

The advent of machine learning has brought about a revolution and has become key to classification and prediction problems encompassing a variety of application domains. There are a number of machine learning techniques with Naïve Bayes, Artificial Neural Networks (ANN) and decision tree being the more popular ones. This paper reviews the aforementioned machine learning algorithms in terms of their suitability for particular problem domains. It presents a comprehensive discussion on the strengths and weaknesses of each of these machine learning algorithms. Uncertainty prevails in data as learning data is usually imprecise, incomplete or noisy. The uncertainities in data mining affect the quality of results which are based on the data.The traditional data mining approaches are not suitable to handle some forms of uncertainty and vagueness. Several forms of vagueness and ambiguities are handled successfully by hybrid machine learning techniques.The paper further studies the efficacy of hybrid machine learning algorithms used in different application domains. It presents a discusssion on how uncertainty in data analyses can be addressed in an effective manner by the usage of hybrid machine learning techniques.

Naïve Bayes, artificial neural networks, decision tree, hybrid machine learning, uncertainty

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S. Maitra, S. Madan, R. Kandwal, "A Review of Hybrid Machine Learning approaches for handling Uncertainty in Data analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.285-263, 2018.