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Handling imbalanced image sets is a challenging issue being faced by the conventional categorizer. Imbalance problem occur with real world data due to various reasons, to which the ordinary classifiers gets influenced towards major class data. In this paper, we aim to balance bi-class absolute image set by creating synthetic samples of minority class images. Tests on three image sets using five synthetic image generation methods, four image features and three evaluation measures is carried out. KNN classification is performed on all three image set which are pretty imbalanced and the results indicate that synthetic creation of minor class images progresses the performance measures.

Imbalanced image set, k-nn categorization, Synthetic image generation, performance measures improvement

[1] Dr. Durga Prasad, Dr. K Nageswar Rao, “An Imprved approach on class imbalance data using within class minority oversampling technique” International Journal of latest trends in engineering and technology, VOL.7, 2017l.
[2] Yingying Qin, Wenjie Chen, Jie Chen, “Generating images for imbalanced dataset problem” Institute of Electrical and Electronics Engineer (IEEE), 2016.
[3] Bartosz Krawezyk, “Learning from Imbalanced data: open challenges and future data”, 2016, Springerlink.com.
[4] Jiaojiao Li, Qian Du, Wei Li, Yunsng Li “Representation based yper spectral Image Classification with Imbalanced data”, Institute of Electrical and Electronics Engineers (IEEE), 2016.
[5] Gregory Luppescu, Raj Shah,“Personalized Image Enhancement” Institute of Electrical and Electronics Engineers(IEEE) , 2016.
[6] Anu Namdeo, Sandeep Singh Bhadriya, “A Review on Image enhancement techniques with its advantages and disadvantages” IJSART, 2016.
[7] Hebatallah Mostafa Anwer, Mohamed Farouk, Ayman Abdel-HamAida Ali, Siti Mariyam Shamssuddin, Anca L Ralescu, “Classification with class imbalance problem: A review” Intertiol Jourl Of Advances In Soft Computing And Its Applications, 7 (3). pp. 176-204, 2015.
[8] Vaishnavi L Kaundanya, Anita Patil, Ashish Panat “Classfication of Emotions from EGG using KNN classifier” 2015, IJSET.
[9] Apurva Sonak, R A Patankar “A survey on methods to handle Imbalance dataset” 2015 International Conference on Computing, Networking an d Communications(IJCSMC).
[10] Tdd Perry, Mhommed Bader-El-Den, Steven Cooper, “Imbalanced classification using Genetically Optimized Cost sensitive classifiers” Institute of Electrical and Electronics Engineers (IEEE),2015.
[11] M Srinivas, R Bharath, P Rajalakshmi, C Krishna Mohan, “Multi-level categorization method for medical methods”, 2015, 17th International conference n E-Health Networking, Application & Services (HealthCom).
[12] Dr. D Ramyachitra, P Manikandan,“ Imbalanced dataset classification and solutions: A Review” July 4 2014, International Journal of Computing and Business Research (IJCBR).
[13] M Akkil Jabbar, B L Deekshatulu, Priti Chandra “Classification of Heart disease using KNN and Genetic algorithm” International Conference on Cmputational Intelligence: Modelling Techniques and Applications(CIMTA,)2013.
[14] Mr. Rushi Lngadge, Ms. Snehalata S Dngre, Dr. Latesh Malik, “Class Imbalance problem in data mining: A Review”, International Journal of Computer Science and Network (IJCSN), Feb 2013 .
[15] Ruchika Mishra, Utkarsh Sharma, “Review of Image Enhancement Technique”, 7th International Journal of Engineering Research and Technology (IJERT), August 8 2013.
[16] Nour Moustafa, Jill Slay “Improving classification performance for the Minority class in highly imbalanced dataset using Boosting”, 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT`12).
[17] Arun Kumar M N, H S Sheshadri, “On the class of Imbalanced datasets”, International Journal of Computer Applications (IJCA), April, 2012.
[18] Tarek M Bittibssi, Gouda I Salama, Yehia Z Mehaseb and Adel E Henawy, “Image Enhancement Algorithms using FPGA”, 2012 8th International Computer Engineering Conference (ICENCO).
[19] Pradeep N, Girisha H, Sreepathi B and Karibasappa K, “Feature extraction of Mammograms”, International journal of -l of Bioinformatics research, Vol 4, 2012.
[20] G N Srinivasa, Shoba G, “Statistical Texture analysis”, Proceedings of world academy of Science and Technology-vol 36, dec 2008.
[21] Wacharasak Siriseriwan and Krung Sinapiromsaran, “ The Effective Redistribution of Imbalance Dataset: Relocating safe-level SMOTEwith Minority outcast handling”, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10300, Thailand, 12 Nov 2014.

Shivani Guldas, "Efficient Learning on Imbalanced Image Set," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.121-126, 2018.

In this paper, we quantitatively (mathematically) reason the energy savings achieved in a wireless sensor grid network, by using circular leveling and sectoring routing algorithm. Due to the energy constraints on the sensor nodes (in terms of transmission and reception of energy) energy awareness has become crucial in sensor network. We provide analytical expression for the energy wastage that occurs when traditional Data Centric routing algorithm such as Direct Diffusion is utilized. Analytical results are validated through simulation, in NS2 simulator which shows the promising potentials of our leveling and sectoring technique.

Wireless Sensor Networks, Routing, Energy Efficiency, Gauss`s Lattice Point

[1] George E Andrews. 1994. Number theory. Courier Corporation.
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[13] Priyanka Sharma and Garimella Rama Murthy. 2014. “Mathematical Modelling of Energy Wastage in Absence of Levelling and Sectoring in Wireless Sensor Networks”. arXiv preprint arXiv:1402.5845 (2014).

Mohammed Nazeer, Garimella Rama Murthy, "Energy Efficient, Data Centric Routing Algorithm In Mobile Wireless Sensor Nodes (Energy Savings Quantification)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.127-135, 2018.

Today many distinct products exists along with the configuration. Technology is advancing as well, proposed system deals with recommender system based on KNN clustering techniques. KNN along with filtering mechanism is introduced as a base mechanism to predict most likely products to be promoted through the recommender system. Simulation results indicates that the C-KNN (Content based K nearest neighbour technique is better than individual approaches of KNN and content based filtering.

Configuration, Recommender system, KNN, C-KNN

[1] Jabbar MA, Deekshatulu BL, Chandra P. Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm. Procedia Technol [Internet]. Elsevier B.V.; 2013;10:85–94. Available from: http://dx.doi.org/10.1016/j.protcy.2013.12.340
http://www.sciencedirect.com/science/article/pii/S2212017313004945
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Jatinder Kaur, Rajeev Kumar Bedi, S.K. Gupta, "A Hybrid Approach for Performing Accurate prediction of Green Products using Recommender System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.136-139, 2018.

E-healthcare system have been increasingly facilitating health condition monitoring, early intervention and evidence based medical treatment by accepting the Personal Health Information (PHI) of the patients. Myocardial Infarction is one of the most leading cause variations in the health condition and that can be lead to the death of the human being. The early prediction of such disease can reduce or prevent development of it and helps to take the necessary treatment. The proposed system is one efficient tool to predicting such diseases. This system can learn from the past data of those patients to be capable of predicting the survival of death of the patient with myocardial infarction. The health information data of the patients who are suffering from such disease is collected and stored. It consists survival period and some clinical data of patients who suffered from myocardial infarction can be used to train an intelligent system to predict the survival or death of current myocardial infarction patients. The Gaussian Naïve Bayes algorithms are used to train the collected data of patients and generalize the survival or death of current patients suffering from myocardial infarction. Experimentally, the instances are stored in the cloud and used as the trained instance. The test data will be provided by the physician to predict the survival or death of the current patient suffering from myocardial infarction.

E-healthcare system, Authentication, Cloud, Myocardial infarction, Gaussian naïve Bayes classifier algorithm, survival prediction

[1]. Jun Zhou, Zhenfu Cao, Senior Member, IEEE, Xiaolei Dong, and Xiaodong Lin, Senior Member, IEEE, “PPDM: A Privacy-Preserving Protocol for Cloud-Assisted e-Healthcare Systems”, IEEE journal of selected topics in signal processing vol 9, no 7, October 2015.
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[3]. Abdulkader Helwan, Dilber Uzun Ozsahin, Rahib Abiyev, John Bush, “One-Year Survival Prediction of Myocardial Infarction, (IJACSA) International Journal of Advanced Computer Science and Applications, vol 8, no 6, 2017.
[4]. J. Zhou, Z. Cao, X. Dong, and X. Lin, “TR-MABE: White-box traceable and revocable multi-authority attribute-based encryption and its applications to multi-level privacy-preserving e-healthcare cloud computing systems,” in Proc. IEEE INFOCOM, 2015.
[5]. J. Zhou, X. Lin, X. Dong, and Z. Cao, “PSMPA: Patient self-controllable and multi-level privacy-preserving cooperative authentication in distributed m-healthcare cloud computing system,” IEEE Trans. Parallel Distrib. Syst., to be published.
[6]. L. Gatzoulis and L.lakovidis, “ wearable and portable E-health systems”, IEEE Eng. Med. Biol., MF., vol 26, no. 5, pp. 51-56, sep-oct. 2007.
[7]. AS. Mullasari, P. Balaji, and T. Khando, “Managing complications in acute myocardial infarction”, Journal of the Association of Physicians of India 59, pp. 43-8, 2011.
[8]. BW. Karlson, J. Herlitz, O. Wiklund, A. Richter, and A. Hjalmarson, “Early prediction of acute myocardial infarction from clinical history, examination and electrocardiogram in the emergency room”. The American journal of cardiology 68 (2), pp. 171-175, 1991.

Patil C.N., Sumana.M., "Survival Prediction of Myocardial Infarction Disease using Cloud Assistance," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.140-143, 2018.

A cryptocurrency is a digital or virtual currency that uses cryptography for security. A cryptocurrency is difficult to counterfeit because of this security feature. A defining feature of a cryptocurrency, and arguably its most endearing allure, is its organic nature; it is not issued by any central authority, rendering it theoretically immune to government interference or manipulation. The anonymous nature of cryptocurrency transactions makes them well-suited for a host of nefarious activities, such as money laundering and tax evasion. The first cryptocurrency to capture the public imagination was Bitcoin, which was launched in 2009 by an individual or group known under the pseudonym Satoshi Nakamoto. As of September 2015, there were over 14.6 million bitcoins in circulation with a total market value of $3.4 billion. Bitcoin`s success has spawned a number of competing cryptocurrencies, such as Litecoin Namecoin and PPCoin.

cryptocurrency,bitcoin,litecoin

[1]. Sonali singh , Arfiha Khatoon , Sarvesh Kumar , Harshita Chawala “An Analysis of cryptocurrency”,IEEE Confrence,2018
[2]. Prasanta Kumar Dey “Cryptocurrency”:-few words on digital money”, IJTSRD, May-June 2018.
[3]. Mahendra Kumar Shrivas, Thomas Yeboah, “A Crictal Review of Cryptocurrency Systems”, Texila International Journal of Academic Research
[4]. Volume 4, Issue 2, Dec 2017.
[5]. Lewis Tseng, “Bitcoin’s Consistency Property”, 2017 IEEE 22nd Pacific Rim International Symposium on Dependable Computing.
[6]. Florian Tschorsch and Bj¨orn Scheuermann , “Bitcoin and Beyond: A Technical Survey onDecentralized Digital Currencies”, IEEE COMMUNICATION SURVEYS & TUTORIALS,2015.
[7]. Christopher J. Pavlovski, “Reference Architecture for Cryptocurrency in Banking”, IT in Industry, vol. 3, no. 3, 2015.

R. Sujeetha A.P., Sarthak Haldar, Bijoy Krishna Saha, Pranjal Katyayan, "CRITICAL ANALYSIS ON CRYPTOCURRENCY," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.144-148, 2018.

Since long, organizations have been looking for information sources that can store data which can provide structured organization and focuses on meaning access of data. Sematic web helps a lot in this context to retrieve meaningful data. This paper emphasizes on mapping developed ontology terms and their retrieval with the help of Graph Stream. We developed an ontology for Pulmonary diseases which consists of classes , objects, relations, and their properties and division of diseases is given as sub classes and Levenshtein’s Edit system algorithm has been used for similarity calculations. The generated ontology has been sent for preprocessing and is fed to Graph Stream for graph generation. The results produced are comparable to the results of human annotators.

Ontology mapping, pulmonary diseases, similarity calculations, graph generation, human evaluation

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[16] C. Dharma Devi, S. Thaddeus “Information Retrieval Mechanism for Dynamic Health Care” International Journal of Computer Sciences and Engineering Vol.-6, Issue-9, Sept. 2018

P. Radhika, P. Suresh Verma, N. Lakshmi Kalyani, P. Rama Krishna, "Mapping Of Pulmonary Disease Ontology Terms Using Graph Stream," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.149-152, 2018.

Visual analytics employs interactive visualizations to integrate users’ knowledge and inference capability into numerical/algorithmic data analysis processes. It is an active research field that has applications in many sectors, such as security, finance, and business.. This paper aims at providing an overview of visual analytics, need, scope, tools and techniques and the most important technical research challenges in the field.

visual analytics, information visualization, data analysis, user interaction

[1] K. Parimala, G. Rajkumar, A. Ruba, S.Vijayalakshmi,” Challenges and Opportunities with Big Data” International Journal of Scientific Research in Computer Sciences and Engineering, Vol.5, Issue.5, pp.16-20, October (2017)
[2] Shilpa M Patil, Dr. H S Guruprasad,” A Study on Visual Analytics” International Journal of Computer Science and Information Technology Research, Vol. 3, Issue 1, pp: (303-308), Month: January - March 2015.
[3] Yuzuru Tanaka, Jonas Sjobergh, Keisuke Takahashi,” A Need for Exploratory Visual Analytics in Big Data Research and for Open Science, 2016.20th international Conference information Visualization (IV), ISSN: 2375-0138, DOI: 10.1109/IV.2016.42
[4] Keim D A, Kohlhammer J, Ellis G, Mansmann F. Mastering the Information Age: Solving Problems with Visual Analytics. Florian Mansmann, 2010.
[5] https://www.proschoolonline.com/blog/visual-analytics-need-model-tools-applications-2/[4].
[6] http://www.vismaster.eu/faq/the-visual-analytics-process/[5].
[7] Daniel A. Keim, Florian Mansmann, J ̈orn Schneidewind, Jim Thomas, and Hartmut Ziegler” Visual Analytics: Scope and Challenges”,First Publ in: Lecture notes in Computer Science No: 4404(2008), pp 76-79[6].
[8] https://searchbusinessanalytics.techtarget.com/definition/big-data-analytics.
[9] http://infinitylimited.co.uk/visual-analytics-key-attributes-scope-and-advantages/
[10] https://www.softwareadvice.com/bi/visual-analytics-tools-comparison/
[11] https://x.smu.edu.sg/node/68.
[12] http://www.visual-analytics.eu/faq/
[13] Boyd, D. and Crawford, K.,Critical Questions for Big Data, Information, Communication & Society, May 2012.
[14] Keim, D.A., Kohlhammer, J., Pohl, M., Santucci, G., Solving Problems with Visual Analytics, Procedia Computer Science, 2011.
[15] Czerwinski,M., DeLine, R., Drucker, S., Fisher, D.,Interactions with Big Data Analytics, 2012.

R. Shankar, S. Duraisamy, "Visual Analytics: Need, Process, Scope, Tools & Techniques and Challenges," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.153-158, 2018.

Pre-processing is the first and important phase of automatic text summarization. Pre-processing helps in normalizing a text document and generating a structured representation of the text. Major pre-processing tasks include segmentation, tokenization, stop-word removal, stemming and lemmatization. In this paper, we discuss these pre-processing tasks required for automatically summarizing Assamese text documents. Both Stemming and lemmatization play an important role in the pre-processing phase of morphologically rich highly inflected language like Assamese. We present a corpus based approach for stemming the Assamese words using n-gram similarity matching technique. We also propose a hybrid method for lemmatization of the Assamese verbs to obtain the grammatically correct root of a verb. Assamese verbs are the most inflectional compared to other word categories. Stemming alone is not sufficient to find the original roots in case of Assamese verbs. So, after segmentation, tokenization and stop-word removal we first apply stemming to all the words in the text document irrespective of their grammatical categories and then apply lemmatization to only the Assamese verbs. For identifying the Assamese verbs we use a look-up dictionary which contains a list of possible stems along with the corresponding lemma of the verbs.

Pre-processing, Summarization, Stemming, Lemmatization, n-gram

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Gunadeep Chetia, Gopal Chandra Hazarika, "Pre-processing Phase of Automatic Text Summarization for the Assamese Language," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.159-163, 2018.

The Infrastructure of Onion routing has major feature which is its flexibility and that is common to all kind of distributed systems. The Onion routing approach offers anonymous data transfer scheme worldwide. The traffic analysis method can be used to break anonymity of the anonymous network, for example TORs (The Onion Routing). Traffic confirmation attacks in low latency networks, mixing networks and in other similar networks are active fields of research. The main idea behind this research is traffic confirmation and analysis of attacks in anonymous communication. Traffic confirmation attacks are used in this research to make successful analysis of traffic of communicating parties over anonymous communication on Internet. It is described in detail that the nature of dropping the packets of Tor Protocol (the onion router) can put anonymity in danger and can harm it. In this paper advantage has been taken of forward compatibility feature by TOR to perform a new drop mark attack and also explained about different traffic confirmation attacks.

TOR, Services, Security, Privacy, Attacks, Anonymity, Traffic Analysis

[1] “Traffic Analysis.” Wikipedia, Wikimedia Foundation, Aug. 2018, en.wikipedia.org/wiki/ Traffic_analysis.
[2] Leuven, K.U. Introducing Traffic Analysis Attacks, Defences and Public Policy Issues. Dec. 2005, www0.cs.ucl.ac.uk/staff/G.Danezis/talks/TAIntro-prez.pdf.
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Parul, Narinder Sharma, "Traffic Analysis Attacks Over Networks of Anonymous Communication," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.164-171, 2018.

Data mining focuses on collection information from knowledge bases or data warehouses and therefore the info collected that had never been famous before, it`s valid and operational. today instructional data processing is associate rising discipline, involved with varied Approaches like Predicting student performance, Analysis and visual image of information, Providing feedback for supporting instructors, Recommendations for college students, Social network analysis and then thereon mechanically extracts that means from giant repositories of information generated by or associated with people`s learning activities in instructional setting. One of the most important challenges is to enhance the standard of the academic processes therefore on enhance student’s performance. Thus, it`s crucial to line new ways and plans for an improved management of the present processes. This model helps to predict student’s future learning outcomes mistreatment knowledge sets of senior students.

Data Mining, Educational Data Mining

[1] Crist´obal Romero, Member, IEEE, and Sebasti´an Ventura, Senior Member, IEEE, “Educational Data Mining: A Review of the State of the Art ” VOL. 40, NO. 6, NOVEMBER 2010.
[2] Parneet Kaura, Manpreet Singhb ,Gurpreet Singh Josanc “Classification and Prediction based DataMining Algorithms to Predict Slow Learners in Education Sector” Science Direct Procedia Computer Science 57 ( 2015 ) 500 – 508 2015 (ICRTC- 2015).
[3] Renza Campagni, Donatella Merlini, Renzo Sprugnoli, Maria Cecilia Verri, “DataMining Models for Student Careers”, Science Direct - Expert Systems with Applications 42 (2015) 5508–5521.
[4] Harwatia, Ardita Permata Alfiania, Febriana Ayu Wulandari,” Mapping Student’s Performance Based on Data Mining Approach”, Science Direct Agriculture and Agricultural Science Procedia3 (2015) 173 – 177.
[5] Manolis Chalaris, Stefanos Gritzalis, Manolis Maragoudakis, Cleo Sgouropoulou and Anastasios Tsolakidis,“ Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques”, Science Direct - Procedia - Social and Behavioral Sciences 147 ( 2014 ) 390 – 397.
[6] V.Ramesh Assistant Professor Department of CSA, SCSVMV University Kanchipuram India “Predicting Student Performance: A Statistical and Data Mining Approach”, International Journal of Computer Applications (0975 – 8887) Volume 63– No.8, February 2013 35.
[7] Krpan, Slavomir Stankov, “Educational Data Mining for Grouping Students in E-learning System”, Proceedings of the ITI 2012 34th Int. Conf. on Information Technology Interfaces, June 25 – 28, 2012, Cavtat, Croatia.
[8] Dursun Delen, “A Comparative Analysis of Machine Learning Techniques for Student Retention Management”, Science Direct - Decision Support Systems 49 (2010) 498–506.
[9] Ali Buldua, Kerem Üçgüna, “DataMining Application on Students’ Data”, Science Direct - Procedia Social and Behavioral Sciences 2 (2010) 5251–5259.
[10] Jiawei Han , Micheline Kamber , Jian Pei ,”Data Mining: Concepts and Techniques”, Third Edition (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition.
[11] http://www.intechopen.com/books/theory-and-applications-for-advanced-text-mining
[12] Nurbiha A Shukora , Zaidatun Tasira, Henny Vander Meijden, “An Examination of Online Learning Effectiveness using DataMining”,Science Direct - Procedia - Social and Behavioral Sciences 172(2015) 555 –562.

Mabel Christina, "Predicting Student Performance using Data Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.172-177, 2018.