iosr-JCE   Next Generation Computing Technologies

Abstract: Data mining has pushed the realm of information technology beyond predictable limits. Missing value is one of the major factor, which can render the obtain result beyond use attained from specific data set by applying data mining technique. There could be numerous reasons for missing values in a data set such as human error, hardware malfunction etc. It is imperative to tackle the labyrinth of missing values before applying any technique of data mining; otherwise, the information extracted from data set containing missing values will lead to the path of wrong decision making. Due to improper handling, the result obtained by the researcher will differ from ones where the missing values are present. Several methods have been, and continue to be, developed to draw inferences from data sets with missing values. In this work, we experimented and results are compared for three methods of imputation..........

Keywords - Data mining, distance measure, Imputation, missing values

[1] Horton NJ, Kleinman KP (2007) Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. The American Statistician 61: 79-90.
[2] Saunders JA, Morrow-Howell N, Spitznagel E,Dor P, Proctor EK, et al. (2006) Imputing missing data: A comparison of methods for social work researchers. Social Work Research 30: 19-31.
[3] Luengo J, GarcaS, Herrera F (2012) On the choice of the best imputation methods for missing values considering three groups of classification methods. Knowledge and Information Systems 32:77-108

[4] Brock G, Shaffer J,Blakesley R,Lotz M, Tseng G (2008) Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes. BMC Bioinformatics 9: 1-12.
[5] Celton M, MalpertuyA, Lelandais G, Brevern A (2010) Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments. BMC Genomics 11: 1-16..

Abstract: Opinion mining refers to computational techniques for analyzing the opinions that are extracted from various data sources. Opinion mining involves computational treatment of opinion and subjectivity in text. Before making any decision it is necessary to analyze what other people think. Customers or other people post their opinion, review, experience and feedback about various products, services and government schemes. The amount of data in twitter is massive so, other people and customer cannot read all reviews or opinions. Opinion mining is the appropriate technique to analyze different opinions of the customers or people. The various opinion mining tools and techniques are discussed in this paper.

Keywords – classification, feature extraction, micro blogging, sentiment analysis, tokenization

[1] G. Vinodhini and RM. Chandrashekharan, ―Sentiment Analysis and Opinion Mining : A Survey‖ – International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 6, June 2012.
[2] Sajin. S. Chandran, Murugappan S., ―A Review on Opinion Mining from Social Media Networks‖, European Journal of Scientific Research, pp.430-440, 3rd October, 2012.
[3] Ayesha Rashid1, Naveed Anwer2, Dr. Muddaser Iqbal3, Dr. Muhammad Sher4 ―A Survey Paper: Areas, Techniques and Challenges of Opinion Mining‖, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 6, No 2, November 2013.
[4] Kirti Huda, Md Tabrez Nafis, Neshat Karim Shaukat, ―Classification Technique for Sentiment Analysis of Twitter Data‖ , International Journal of Advanced Research in Computer Science, Volume 8, No. 5, May-June 2017 ISSN No. 0976-5697
[5] M. Govindarajan, Romina M, ― A Survey of Classification Methods and Applications for Sentiment Analysis‖ – International Journal of Engineering and Science (IJES), Volume 2, Issue 12, 2013.

Abstract: This paper discusses the new activities, methods and technology used in digitization and formation of digital libraries. It set out some key points involved and the detailed plans required in the process, offers pieces of advice and guidance for the practicing Librarians and Information scientists. Digital Libraries are being created today for diverse communities and in different fields e.g. education, science, culture, development, health, governance and so on. With the availability of several free digital Library software packages at the recent time, the creation and sharing of information through the digital library collections has become an attractive and feasible proposition for library and information professionals around the world. The paper ends with a call to integrate digitization into the plans and policies of any institution to maximize its effectiveness..

[1] Digital Library Federation. (2001), Registry of Digitized Books and Serial Publication,
[2] Ding, Choo Ming. (2000), Access to Digital Information: Some Breakthrough and Obstacles, Journal of Librarianship and Information Science, Vol.32 No.1
[3] Greenstone Training Workshop Material.(2002).
[4] Ian, H. Witten & David, Brainbridge. (2003), How to Build a Digital Library, London: Morgan Kaufman Publishers
[5] Sitts, Maxine K. (2000), Handbook for Digital Projects: A Management Tool for Preservation and Access. Northeas Document Conservation Center, Andover, Massachusetts USA.http://www.nedcc.org/digital/dman.pdf

Abstract: Mobile ad hoc Network (MANET) is a collection of mobile nodes equipped with every wireless-transmitter and receiver that communicate with one another via bi-directional wireless links either directly or indirectly. A replacement intrusion detection system named Enhanced Adaptive Acknowledgement (EAACK) specially designed for MANETs. Throughout this thesis, a replacement intrusion-detection system named Enhanced Adaptive ACKnowledgment (EAACK) is specially designed for MANETs and ID based mostly digital Signature scheme is projected. The results will demonstrate positive performances against Watchdog, TWOACK and AACK within the cases of receiver collision, restricted transmission power and false misbehavior report, packet delivery Ratio. This EAACK IDentity-based Digital Signature Algorithm (IDDSA) scheme and Packet Loss Ratio is calculated in the proposed work and compared with existing methods for better performance.

Keywords: rSerPool, Watchdog, TWOACK, AACK, EAACK, IDDSA, Packet Loss Ratio (PLR)

[1]. Architecture for reliable server pooling [Online]. Available: www.ietf.org/ids.by.wg/rserpool.html
[2]. Burmester, M., de Medeiros, B., ―On the Security of Route Discovery in MANETs‖, Mobile Computing, IEEE Transactions on (Volume:8 , Issue: 9 ) Page(s): 1180 – 1188.
[3]. Dhurandher, S.K., Obaidat, M.S., Verma, K., Gupta, P., Dhurandher, P., ―FACES: Friend-Based Ad Hoc Routing Using Challenges to Establish Security in MANETs Systems‖, Systems Journal, IEEE 2011 (Volume:5 , Issue: 2 ), Page(s): 176 – 188.
[4]. T. ElGamal, ―A public key cryptosystem and a signature scheme based on discrete logarithms‖, IEEE Transactions on Information Theory 31 (4) (1985) 469–472.
[5]. Mohammed, N., Otrok, H., Lingyu Wang, Debbabi, M., Bhattacharya, P., ―Mechanism Design-Based Secure Leader Election Model for Intrusion Detection in MANET‖, Dependable and Secure Computing, IEEE Transactions on 2011, Page(s): 89 – 103.

Abstract: Thyroid diseases are widespread worldwide. India there is a generic problems caused due to thyroid diseases. research studies emphasis that about 43 million people in India suffer from thyroid diseases. This work focused on developed of a predictive system for thyroid detection such as hypothyroidism, hyperthyroidism, sick people and normal people.Classification is one of the most significant data mining techniques. The supervised learning used to classify predefined data sets. Data mining technique is mainly used in healthcare organizations for decision making, diseases diagonosis and for better treatment to the patients. .It is a process to extract the data information from the huge amount of the data set classification is the analyze, processing time, sensitivity,.............

Keywords: Classification, Thyroid, Hyper Thyroid, Hypo Thyroid, Data Mining (DM), Data Pre-processing (DP)..

[1] Jiawei Han, KamberMicheline (2009). Datamining: Concepts and Techniques, Morgan Kaufmann Publisher.
[2] EBRU TURANOGLU-BEKAR, GOZDE ULUTAGAY, SUZAN KANTARC-SAVAS, "Classification of Thyroid Disease by Using Data Mining Models: A Comparison of Decision Tree Algorithms", Oxford Journal of Intelligent Decision and Data Science, PP: 13-28, 2016.
[3] N MOHANA SURDARAM, VRENUPRIYA, "Artificial Neural Network Classifiers for Diagnosis of Thyroid Abnormalities", International Conference on Systems, Science, Control, Communication, Engineering and Technology, PP: 206-2011, 2016.
[4] VIKRAM V HEGDE, DEEPAMALA N, "Automated Prediction of Thyroid Disease using ANN", International Journal of Innovative Research in Science, Engineering and Technology ,Volume 5, Special Issue, PP: 268, 2016.
[5] SHAIK RAZIA and M. R. NARASINGA RAO, "Machine Learning Techniques for Thyroid Disease Diagnosis - A Review", Indian Journal of Science and Technology, Volume 9, PP: 1- 9, 2016.

Abstract: The face is our primary focus of attention in social life playing an important role in conveying identity and emotions. Emotion is a mental state which involves a lot of behaviors, actions, thoughts and feelings. Emotions play fundamental role during communication. Emotion recognition is the process of identifying human emotion, most typically from facial expressions. Different types of facial expressions are Joy, Sadness, Fear, Disgust, Surprise, and Anger. In this thesis, various existing facial expression recognition techniques are studied and reviewed. In this thesis, mainly focuses face detection for facial emotion recognition process. This thesis discusses Viola –Jones and Image Cropping techniques to extract and identify the mouth regions. The proposed segmentation techniques are applied and compared to found which method is suitable for mouth region splitting, and then mouth region can..........

Keywords: Facial Feature (mouth), Image Enhancement, Edge detect, Morphology Algorithm, Mouth area calculation, Face Emotions

[1]. Manasa B, Dr. Shrinivasa Naika C. L. "Segmentation of Human Facial Features" International Journal of Advanced Research in Computer Science and Software Engineering Volume 6, Issue 4, April 2016.
[2]. Yapa Ashok and Dr.Dasari Subba Rao, "Face Recognition and Facial Expression Identification Using PCA" International Journal & Magazine Of Engineering Technology, Management And Research Oct 2016.
[3]. Deepika Ishwar, Dr. Bhupesh Kumar Singh, "Emotion Detection Using Facial Expression", International Journal of Emerging Research in Management &Technology ISSN: 2278-9359 (Volume-4, Issue-6), June 2015.
[4]. Prasad M , Ajit Danti "Classification of Human Facial Expression based on Mouth Feature using SUSAN Edge Operator" International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 12, December 2014.
[5]. Monika Dubey, Prof. Lokesh Singh, "Automatic Emotion Recognition Using Facial Expression" International Research Journal of Engineering and Technology (IRJET) Volume: 03 Issue: 02 | Feb-2016.

Abstract: The achievement of social developments goal is doubtful without sufficient opportunity not only to propagate information to various parts of the society but also to communicate effectively and reliably. This survey studies position its main objective to investigate the major challenges and importance in the use of radio technology especially cognitive radio and software defined radio for wireless communications. With this in view, the study will proceed to explore the major challenges in the use of cognitive radio, especially in digital communications and the unique opportunities to employ radio technology as one of a means of development in wireless communication, by considering the following specific objective to find out possible solutions for the major obstacle that delay the effective.........

Keywords— CR, SDR, Wireless communication.

[1] Ke-Lin Du & M. N. S. Swamy (2010). Wireless Communication Systems: From RF Subsystems to 4G Enabiling Technologies. Cambridge University Press. p. 188. ISBN : 978-0-521-11403-5.
[2] "Radio – Electronics, "Radio Receiver Technology". Radio-electronics.com. Retrieved 2014-08-02.
[3] The Electromagnetic Spectrum, University of Tennessee, Dept. of Physics and Astronomy
[4] Clint Smith, Curt Gervelis (2003).Wireless Network Performance Handbook. McGraw-Hill Professional.ISBN: 0-07-140655-7.
[5] R. K. Puri (2004). Solid state Physics and Electronics. S. Chand.ISBN: 81-219-1475-2.

Abstract: With the recent development in wireless communication technologies, mobile ad hoc networks (MANETs) have become applicable for several critical applications, mainly, rescue emergency, relief measures for disaster, network coverage for cellular networks, and so on. This paper focuses on End-to-End delay methoding with Multi Hop Routing using Optimal Route Allocation. In this work we propose an integrated approach that addresses both storage buffer and security for MANET with improved end to end delay analysis. Optimal route allocation is first made by applying dynamic routing protocol. The cross-section profile for each route path's buffer constraint is handled by introducing storage of buffer of relay mobile node (i.e. router) using Markovian Queuing method. This is...........

Keywords: Mobile ad hoc networks, Quality of service, End-to-End delay, Integer Programming method, Markovian Queuing, Hash Chain method.

[1]. Jia Liu, Min Sheng, Yang Xu, Jiandong Li, and Xiaohong Jiang, "End-to-End Delay Methoding in Buffer-Limited MANETs: A General Theoretical Framework", IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 15, NO. 1, JANUARY 2016
[2]. Fraser Cadger, Kevin Curran, Jose Santos and Sandra Moffett, "Mobility and Delay in Greedy Geographic Routing", International Journal of Service and Computing Oriented Manufacturing
[3]. Prabha R. and Ramaraj N., "An improved multipath MANET routing using link estimation and swarm intelligence", EURASIP Journal on Wireless Communications and Networking, May 2015.
[4]. Ch. Niranjan Kumara, N.Satyanarayanab, "Multipath QoS Routing for Traffic Splitting in MANETs", International Conference on Intelligent Computing, Communication & Convergence, Elsevier, May 2015.
[5]. Jyoti Prakash Singh, Paramartha Dutta, Arindrajit Pal, "Delay Prediction in Mobile Ad Hoc Network using Artificial Neural Network, Computer, Communication, Control and Information Technology, Elsevier, May 2012

Abstract: Big Data explores in Visual analytics to seek and provide more effective ways to understand and analyze large datasets, and also enable them to act immediately. Visual analytics integrates the analytic capabilities of the computer and the abilities of the human analyst, allowing novel discoveries and empowering individuals to take control of the analytical process. Visual analytics enables unexpected and hidden insights, which may lead to beneficial and profitable innovation. In the visual analysis process, It can be done by automated analysis and interactive visual methods. It deals with massive data, the use of automated methods is mandatory -and for some problems it may be sufficient to only use fully automated analysis methods. The challenges of visual analytics various techniques and limitations.

Keywords: Big Data, Data sets, Interactive Visual Methods, Sense making, Visual Analytics

[1]. Agrawala, M., Heer J., Design Considerations for Collaborative Visual Analytics, IEEE, November 2007
[2]. Arias-Hernández, R., Green, T. M., Wakkary, R., Expanding the Scope: Interaction Design Perspectives for Visual Analytics, IEEE, 2011
[3]. Armour, F., Espinosa, J. A., Kaisler, S., Money, W., Big Data: Issues and Challenges Moving Forward, IEEE, 2012
[4]. Aurelio, D., Visualizing Information Associated With Architectural Design Variations and Simulations, HCI International, July 2013
[5]. Banks, D., Bryson, S., Haimes, R., Liere, R., Uselton., S, Automation or Interaction: What‟s Best for Big Data, NASA Ames Research Center, Panelist debate, no date given Barth, P., Bean, R., Davenport, T.H., How "Big Data‟ is Different, MIT Sloan Management Review, 2012

Abstract: The emergence of wireless communication gives birth to cognitive radio mobile ad hoc networks (CRAHNs). Routing is the major component in both wired and wireless communication. The main objective of this paper is to provide an overview of wireless communication, Cognitive Radio Networks and the recent protocols proposed in this thrust research area namely CRAHN.

Keywords – Wireless, Cognitive, Radio, Spectrum, Protocols

[1] Banaei, A.; Georghiades, C.N.; Shuguang Cui, "Large Overlaid Cognitive Radio Networks: From Throughput Scaling to Asymptotic Multiplexing Gain," Wireless Communications, IEEE Transactions on , vol.13, no.6, pp.3042,3055, June 2014
[2] Pu Wang; Akyildiz, I.F., "Improving Network Connectivity in the Presence of Heavy-Tailed Interference," Wireless Communications, IEEE Transactions on , vol.13, no.10, pp.5427,5439, Oct. 2014
[3] Spachos, P.; Hantzinakos, D., "Scalable Dynamic Routing Protocol for Cognitive Radio Sensor Networks," Sensors Journal, IEEE , vol.14, no.7, pp.2257,2266, July 2014
[4] Jae-Joon Lee; Jaesung Lim, "Cognitive routing for multi-hop mobile cognitive radio ad hoc networks," Communications and Networks, Journal of , vol.16, no.2, pp.155,161, April 2014
[5] Miao Pan; Pan Li; Yang Song; Yuguang Fang; Lin, P.; Glisic, S., "When Spectrum Meets Clouds: Optimal Session Based Spectrum Trading under Spectrum Uncertainty," Selected Areas in Communications, IEEE Journal on , vol.32, no.3, pp.615,627, March 2014

Abstract: Cloud computing is the trendy topic all over the world. As there are so much service providers of the cloud are available in the competitive world. A decision has to be taken that which service provider's services are more advantageous to the organization. The conceptual cost for buying the services of different services providers may lead to increase in budget or wastage of money and time. So the solution to this problem is trying out the simulation tools. these tools may include the different algorithms used by different service providers..

Keywords:- Cloud Computing, Simulation Tools, Comparison between simulation tools, Services of Cloud Computing, Components of Cloud Computing

[1] Buyya Raj kumar, "CloudComputing: Principles and Paradigms", international lconference journal in computer science wiley,2011
[2] Calheiros Rodrigo N, Ranjan Rajiv,"CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms", Wiley Press, New York, USA, January, 2011,pp. 23-50.
[3] B.Wickremasinghe (2009), "Cloud Analyst: A CloudSim based Tool for Modeling and Analysis of Large Scale CloudComputing Environments", MEDC Project Report, 2009.
[4] 4. KaurGaganjot, Kumar Pawan,"Study of Comparison of Various Cloud Computing Simulators", 2nd National Conference in Intelligent Computing & Communication..

Abstract: Childhood leukemia, the most common type of cancer in children and teens, is a cancer of the white blood cells. Abnormal white blood cells form in the bone marrow. They quickly travel through the bloodstream and crowd out healthy cells. This raises the body's chances of infection and other problems. One of the Machine Learning Algorithm Artificial Neural Network is used to find the leukemia, An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.. For data regression and prediction, Visual Gene Developer's NeuralNet class is used

Keywords: Childhood leukemia, Machine Learning , artificial neural network, NeuralNet White blood cells..

[1] Gwaiz L A: BONE MARROW NECROSIS. Annals of Saudi Medicine 1997; 17, No 3,374-376.
[2] Naguib R N G, Sherbet G V: artificial neural networks in cancer diagnosis: prognosis and patient management. CRC press 2001.
[3] SordoM : introduction to neural networks in health care. Open clinical knowledge management for medical care 2002; 1-17.
[4] Khonglah Y, Basu D and Dutta T K: Bone marrow trephine biopsy findings in chronic myeloid leukemia. Malaysian J Pathol 2002; 24(1), 37 – 43.
[5] Richard K: Use of an artificial neural network to quantitate risk of malignancy for abnormal mammograms. Surgery 2001; 459-466.

Abstract: Data Mining is used comprehensively in many sectors today. The successful application of data mining algorithms can be seen in marketing, retail, and other sectors of the industry. The aim of this paper is to present the readers with the various data mining algorithms which have wide applications. This paper focuses on six data mining algorithms K-NN, Naïve Bayes Classifier, Decision tree, C4.5, ANN and ID3. An attempt has been made to do a comparative study on these six algorithms on the basis of theory, its advantages and disadvantages, and its applications. After studying all these algorithms in detail, we came to a conclusion that the accuracy of these techniques depend on various characteristics such as: type of problem, dataset and performance matrix.

Keywords: Data mining, k-NN, Naïve Bayes classifier, Decision Tree, C4.5, ANN, ID3

[1]. Zaki .S. Towfik, Sabiha Fathil Jawad (2012), Application Of Decision Tree As A Data Mining Tool In A Health Care, Kufa For Mathematics And Computer.http://En.Wikipedia.Org/Wiki/K-109.K-Nearest Neighbors Javierbejarhttp://Www.Lsi.Upc.Edu/~Bejar/Apren/Docum/Trans/03d-Algind-Knn-Eng.Pdf
[2]. Zhu Xiaoliang, Wang Jian, Yan Hongcan, Wu Shangzhuo (2009), Research And Application Of The Improved Algorithm C4.5 On Decision Tree, IEEE, Vol. 2 184-187 Peng Liu, Lei Lei, Junjie Yin, Zheng Yao (2005), R-C4.5 Decision Tree Model And Its Applications To Health Care Dataset,IEEE,Vol. 2 1099-1103.
[3]. Decision Tree
[4]. http://Octaviansima.Wordpress.Com/2011/03/25/Decision-Trees-C4-5.
[5]. K-Nearest Neighbors 2005 http://En.Wikipedia.Org/Wiki/K-Nearest_Neighbors_Algorithm Decision Tree 2003.