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Abstract: Both network security and quality of service (QoS) consume computational resource of IT system and thus may evidently affect the application services. Database systems are widely used in today's computer system, which are adopted for storing and accessing data in various application services. It is important to model the mutual influence between network security and QoS, which can be concurrently optimized in order to provide a better performance under the available computational resource. This paper represents the particle swarm optimization which enhances is designed to get the optimization performance. These obtained security policies not only meet the security requirement of the user, but also provide the global optimum solution easily and has good convergence speed.
Keywords: Database System, QOS, Network Security, Particle Swarm Optimization.
[1]. Zhao, Xuancai, et al. "Optimizing security and quality of service in a Real-time database system using Multi-objective genetic algorithm." Expert Systems with Applications 64 (2016): 11-23.
[2]. Alomari, Firas, and Daniel A. Menasce. "Efficient response time approximations for multiclass fork and join queues in open and closed queuing networks." IEEE Transactions on Parallel and Distributed Systems 25.6 (2014): 1437-1446.
[3]. Alomari, Firas B., and Daniel A. Menascé. "Self-protecting and self-optimizing database systems: Implementation and experimental evaluation." Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference. ACM, 2013.
[4]. Yauri, Aliyu Rufai, et al. "2013 5th International Conference on Computer Science and Information Technology, CSIT 2013-Proceedings." 2013 5th International Conference on Computer Science and Information Technology, CSIT 2013. 2013.
[5]. Gago-Alonso, AndréS, et al. "Indexing and retrieving in fingerprint databases under structural distortions." Expert Systems with Applications 40.8 (2013): 2858-2871.
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Paper Type | : | Research Paper |
Title | : | Secure Distributed Big Data Storage Using Cloud Computing |
Country | : | India |
Authors | : | Sana Khan || Ekta Ukey |
: | 10.9790/0661-1904050812 |
Abstract: The cloud is increasingly being used to store and process the big data. Many researchers have been trying to protect big data in cloud computing environment. Traditional security mechanisms using encryption are neither efficient nor suited to the task of protecting big data in the Cloud. first discuss about challenges and potential solutions for protecting big data in cloud computing. Second, 'Secure distributed big data storage in cloud computing' Architecture for protecting Big Data in Cloud Computing Environment. Model ensures efficient processing of big data in cloud computing..............
[1]. Gunasekaran Manogaran, Chandu Thota. MetaCloudDataStorage Architecture for Big Data Security in Cloud Computing. Procedia Computer Science 87 (2016).
[2]. Awodele, Izang A.A. Big Data and Cloud Computing Issues. International Journal of Computer Applications (0975 – 8887) Volume 133 – No.12, January 2016.
[3]. Yibin Li , Keke Gai. Intelligent cryptography approach for secure distributed big data storage in cloud computing. Software School, Henan University, Kaifeng, Henan, 475000, China.
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Paper Type | : | Research Paper |
Title | : | A Framework for Machine Learning based Multi Agent System |
Country | : | India |
Authors | : | Archana Mangal || Iti Mathur |
: | 10.9790/0661-1904051318 |
Abstract: Applications which can facilitate decision making are gaining importance. They can be found in domains like process control, air traffic control, inventory management, airline reservation etc. Developing applications for these domains have several flaws. First, they do not achieve the desired goal. Second, even if they do, their implementation and maintenance is very costly. Agent oriented system offers a qualitative change in this position. An agent learns from its environment, manipulates itself and coordinates with other agents in the system. Researchers have designed many agents but they were unable to solve complex dynamic applications of this kind. It is still an open problem for the researchers to let the agent learn and adapt as per requirements. In this paper, we addressed this issue. We have focused on design and development of multi agent system (MAS) which can reason and learn for environment. We have chosen Airline reservation as our case study.
Keywords: Multi agent System, Machine learning, Decision tree, Support vector machine, Multi layer perceptron, Radial basis function.
[1]. Ketel, Mohammed. "A mobile agent based framework for web services." Proceedings of the 47th Annual Southeast Regional Conference. ACM, 2009.
[2]. Panait, Liviu, and Sean Luke. "Cooperative multi-agent learning: The state of the art." Autonomous agents and multi-agent systems 11.3 (2005): 387-434.
[3]. Claus, Caroline, and Craig Boutilier. "The dynamics of reinforcement learning in cooperative multiagent systems." AAAI/IAAI 1998 (1998): 746-752.
[4]. Sebastia, Laura, Adriana Giret, and Inma Garcia. "A multi agent architecture for tourism recommendation." Trends in Practical Applications of Agents and Multiagent Systems. Springer Berlin Heidelberg, 2010. 547-554.
[5]. Lenz, Mario. "IMTAS: Intelligent multimedia travel agent system." Information and Communication Technologies in Tourism. Springer Vienna, 1996. 11-17.
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Abstract: present days people of urban and rural are using smart phones and mobile devices intensively. In Particular urban population depends on the applications and gadgets which are provided by the mobile devices and Smart phones to plan their daily life. The applications which are built on these devices mainly depend on the current or preferred locations of the user to provide the services they wish, which may cause damage to the privacy of mobile device users. In general no user wish to reveal their present location or the location they wish to go. We proposed pp algorithms which will provide an optimal location for group of users.
Keywords: Mobile devices, applications, privacy preserving.
[1] (2011, Nov.). Facebook Statistics [Online]. Available: http://www.facebook.com/ /press / info.php? statistics
[2] (2011, Nov.). Facebook Deals [Online]. Available: http://www.facebook.com/deals/
[3] E. Valavanis, C. Ververidis, M. Vazirgianis, G. C. Polyzos, and K. Norvag, "MobiShare: Sharing context-dependent data & services from mobile sources," in Proc. IEEE/WIC Int. Conf. WI, Oct. 2003, pp. 263–270.
[4] (2011). Microsoft Survey on LBS [Online]. Available: http://go.microsoft.com/?linkid=9758039
[5] (2011, Nov.). Orange Taxi Sharing App [Online]. Available: http://event.orange.com/default/EN/all/mondial
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Paper Type | : | Research Paper |
Title | : | Convergent Encryption Using Deduplication on Hybrid Cloud |
Country | : | India |
Authors | : | Pusukuri Lakshmi Prasanna || Lella Kranthi Kumar |
: | 10.9790/0661-1904052529 |
Abstract: This paper represents that, an important data compression technique is data deduplication. In this technique repeating data of duplicate copies are eliminated. To support deduplication we protect confidentiality of sensitive data. For encrypt the data we have been proposed convergent encryption technique. For better protection of data security, identify the problem of authorized data deduplication in first attempt. There have different traditional deduplication systems, from those systems different privileges of users are further considered in duplicate check besides the data itself.............
Keywords: Deduplication, confidentiality, hybrid cloud, authorized duplicate check.
[1] OpenSSL project, http://www.openssl.org/
[2] P. Anderson and L. Zhang. Fast and secure laptop backup with encrypted deduplication. In proc. Of USENIX LISA, 2010.
[3] M. Bellare, S. Keelveedhi, and T. Ristenpart. Dupless: Server-aided encryption for deduplicated storage. In USENIX Security Symposium, 2013.
[4] M. Bellare, S. Keelveedhi, and T. Ristenpart. Message-locked encryption and secure deduplication. In EUROCRYPT, pages 296– 312, 2013.
[5] M. Bellare, C. Namprempre, and G. Neven. Security proofs for identity-based identification and signature schemes. J. Cryptology, 22(1):1–61, 2009.
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Paper Type | : | Research Paper |
Title | : | Emotion Detection from Text: Survey |
Country | : | Egypt |
Authors | : | Salma Elgayar || Abdel ElAziz A.Abdelhamid || Zaki T.A. Fayed |
: | 10.9790/0661-1904053037 |
Abstract: This paper discusses some relevant work of emotion detection from text which is a main field in affecting computing and artificial intelligence field. Artificial intelligence is not only the ability for a machine to think or interact with end user smartly but also to act humanly or rationally so emotion detection from text plays a key role in human-computer interaction. It has attracted the attention of many researchers due to the great revolution of emotional data available on social and web applications of computers and much more in mobile devices. This survey mainly collects history of unsupervised emotion detection from text.
Keywords: Emotion detection, Affecting computing, Machine learning, Human-computer interaction, text classification, Natural language
[1] C. Strapparava and R. Mihalcea, Learning to identify emotions in text in Proceedings of the ACM symposium on Applied computing, pp. 1556 -1560, 2008.
[2] R. Picard, Affective ComputingCambridge, MA: TheMIT Press, 1997.
[3] O. Bruna1, H. Avetisyan, J. Holub Emotion models for textual emotion classification,2016.
[4] Virginia Francisco and Pablo Gervas EmoTag: An Approach to Automated Mark-Up of Emotions in Texts Computational Intelligence, 29(4):680 -721, 2013.
[5] Rafael A Calvo and Sunghwan Mac Kim Emotions in text: dimensional and categorical modelsComputational Intelligence, 29(3), 2013.
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Paper Type | : | Research Paper |
Title | : | Markov Model and Data Mining Approach for PCB Component Defect |
Country | : | India |
Authors | : | Vaddin Prathiba || Dr. M Nagendra |
: | 10.9790/0661-1904053847 |
Abstract: Currently the various electronic components which are manufactured use surface mount technology (SMT). This technology is a higher end assembly technique which produces printed circuit boards with very tiny electronic components. Due to the device area issues, the demand of PCBs is increasing. In order to cater this issue, high-volume production is demanded. Key challenge with PCB manufacturers is to maintain the quality of PCB with zero defects and assured quality. But due to the changing technologies in PCB fabrication, component placements and soldering of surface mount technology, defects are increasing in terms of the number and the type of defect. Various approaches have been proposed................
Keywords: PCB Defect Detection, Image Processing, Feature Distribution, data mining
[1] M A. P. S. Chauhan and S. C. Bhardwaj, "Detection of bare pcb defects by image subtraction method using machine vision," in Proceedings of the World Congress on Engineering, vol. 2, (2011).
[2] J. W. Foster III, P. Griffin, and J. Korry, "Automated visual inspection of bare printed circuitboards using parallel processor hardware," The International Journal of Advanced ManufacturingTechnology, vol. 2, no. 2, (1987), pp. 69–74.
[3] C. Ma, J. Mao and J. Mao, "Research and Develop on PCB Defect Intelligent Visual Inspection Robot," Photonics and Optoelectronics (SOPO), 2012 Symposium on, Shanghai, (2012), 1-4.
[4] Chen-Fu Chien, Huan-Chung Li and Angus Jeang. Data mining for improving the solder bumping process in the semiconductor packaging industry, Intelligent systems in accounting, finance and management, vol. 14, (2006), 43-57
[5] Chien, C. F., Lin, T. H., Liu, Q. W., Peng, C. Y., Xu, S. Z. and Huang, C. C., "Developing a data mining method for wafer binmap classification", Journal of the Chinese Institute of Industrial Engineers, Vol. 19, No. 2, (2002),23-38
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Abstract: Query optimization is a stimulating task of any database system. The results of Entropy Based Restricted Stochastic Query Optimizer (ERSQO) are compared with the results of Exhaustive Enumeration Query Optimizer (EAQO), Simple Genetic Query Optimizer (SGQO), Novel Genetic Query Optimizer (NGQO) and Restricted Stochastic Query Optimizer (RSQO). In terms of Total Costs, EAQO outperforms SGQO, NGQO, RSQO and ERSQO. However, stochastic approaches dominate in terms of runtime.To overcome the issues associated with the existing techniques, a new multi-objective ant colony based query optimization technique is proposed. The effect of query cost and communication overheads will also be considered.The use of ant colony optimization can find optimistic query in order to reduce the query cost.
Keywords: Query optimization,Ant colony based otimization,DSS query optimizer.
[1] Manik Sharma, Gurvinder Singh, Rajinder Singh, Design and analysis of stochastic DSS query optimizers in a distributed database system, Egyptian Informatics Journal, Volume 17, Issue 2, July 2016, Pages 161-173
[2] Zhan Li, Qi Feng, Wei Chen, Tengjiao Wang, RPK-table based efficient algorithm for join-aggregate query on MapReduce, CAAI Transactions on Intelligence Technology, Volume 1, Issue 1, January 2016, Pages 79-89.
[3] Varghese S. Chooralil, E. Gopinathan, A Semantic Web query Optimization Using Resource Description Framework, Procedia Computer Science, Volume 70, 2015, Pages 723-732. [4] Fuqi Song, Olivier Corby, Extended Query Pattern Graph and Heuristics - based SPARQL Query Planning, Procedia Computer Science, Volume 60, 2015, Pages 302-311.
[5] Chen Yan, Zhou Lin, Li Taoying, Yu Yinging. The semi-join query optimization in distributed database system. In: National Conference on Information Technology and Computer Science. Atlantis Press; 2012. p. 606–9.
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Abstract: The accurate prediction of breast cancer has been an area of interest due to the complexities associated with experimental data for breast cancer. This paper has decided to explore the Adaptive Neuro Fuzzy Inference system (ANFIS) using Matrix Laboratory (MATLAB) for the simulation of Winconsin breast cancer experimental data. Twenty (20) Winconsin breast cancer dataset was used for the simulation, comprising of nine (09) attributes with diagnosing values identifying Malignant or Benign. MATLAB ANFIS simulation captured the fundamental editors inclusive of training, testing and ANFIS structure............
Keywords: ANFIS, Breast Cancer, Simulation, Experimental data (dataset)
[1]. CancerQuest, (2016), Cancer Detection and Diagnosis, retrieved online from https://www.cancerquest.org/patients/detection-and diagnosis?gclid=
[2]. MedicineNet, (2016), Breast Cancer, retrieved online from http://www.medicinenet.com/breast_cancer_facts_stages/article.htm
[3]. Oncolex (2016), Breast Cancer symptoms, retrieved from http://oncolex.org/Breast-cancer/Background/Symptoms?gclid=CIC82KqtzNQCFZup7QodILEKlg
[4]. WrongDiagnosis (2011), "Breast cancer: Introduction/Causes and Symptoms", retrieved from http://www.wrongdiagnosis.com
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Paper Type | : | Research Paper |
Title | : | Image feature Extraction System of CBIR using neural network |
Country | : | India |
Authors | : | Ankita Doiphode || Sunil Yadav |
: | 10.9790/0661-1904056169 |
Abstract: In Past, for our ancestors 'Imaging' is a medium through which they can expressed us some information about their life. But, as the beginning of twenty century imaging has grown rapidly in all our walks of life. Henceforth Due to digitization Volume of digital data has increases tremendously. CBIR (Content based image retrieval) is a significantly popular approach which help in retrieval of image data from large collection of storage. In this paper, we represent general-purpose CBIR system that establishes efficient combination of color, texture and shape features. In CBIR, First, The color feature of an image considered here are RGB to HSV as color descriptor, Second we used GLCM.............
Keywords: Feature extraction, neural network, training, testing, CBIR.
[1]. Kumar, SV Bharath, Rakesh Mullick, and Uday Patil. "Textural content in 3 T MR: an image-based marker for Alzheimer's disease." Proc. SPIE. Vol. 5747. 2005.
[2]. Agarwal, Swati, A. K. Verma, and Preetvanti Singh. "Content based image retrieval using discrete wavelet transform and edge histogram descriptor." Information Systems and Computer Networks (ISCON), 2013 International Conference on. IEEE, 2013.
[3]. Bhad, Ashwini Vinayak, and Komal Ramteke. "Content based image retrieval a comparative based analysis for feature extraction approach." Computer Engineering and Applications (ICACEA), 2015 International Conference on Advances in. IEEE, 2015.
[4]. J.Z. Wang, "Wang Database," [Online], Available at: http://wang.ist.psu.edu/, last visited August 12th 2016.
[5]. Suman Khokhar , Satya Verma , "Content Based Image Retrieval with Multi-Feature Classification by Back-propagation Neural Network", International Journal of Computer Applications Technology and Research Volume 6–Issue 7, 278-284, 2017, ISSN:-2319–8656.
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Paper Type | : | Research Paper |
Title | : | Implementation of Reusability Metrics in Object Oriented Applications |
Country | : | India |
Authors | : | Dr T.N.Sharma || Mannam Chandrasekhar |
: | 10.9790/0661-1904057073 |
Abstract: Reusability is one of the major advantages of using object oriented paradigm in software development. The goals of software metrics are to identify and control essential parameters that affect the parameters related to software development. A lots of metrics are defined which are related to reusability of object oriented applications. These metrics are related to various aspects like inheritance, number of members of class etc. Calculation of these metrices are equally important. This paper has taken example of a few projects for calculation of some of the metrices which we use for reusability of object oriented applications
Keywords: Reusability, metrices, Object-Oriented applications, Coupling, Cohesion, Inheritance.
[1]. K.K.Aggarwal, Yogesh Singh(2006), Empirical Study of Object-Oriented Metrics, Journal Of Object Technology, Vol. 5, No. 8
[2]. Brij Mohan Goel(2012), Analysis of Reusability of Object-Oriented System using CK Metrics, International Journal of Computer Applications (0975 – 8887) Volume 60– No.10,
[3]. Avinash Dhole(2013), An Approach For Calculation Of Reusability Metrics Of Object Oriented Program, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 6,
[4]. Aditya Pratap Singh(2014), Estimation of Component Reusability through Reusability Metrics, World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:8, No:11
[5]. Neha Goyal et.al.(2014) , Reusability Calculation of Object Oriented Software Model by Analyzing CK Metric, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3 Issue 7
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Abstract: Opinion mining involves building a system to collect reviews from online social sites and categorize opinions about a product, service. In opinion mining, finding opinion targets and opinion words from online reviews are important tasks. Key component of opinion mining involves detecting opinion relations among words. The primary purpose of this project is to make these extraction processes more effective. Additional types of relation between words, such as "Topical Relations" are considered. Topical relation denotes how well a retrieved document or set of documents meets the information given by the user............
Keywords: Modified naïve bayes, Opinion mining, Opinion Target, Opinion word, Topical relation.
[1] A. M. Alkabani, A. M. Ghamry, F.K. Hussain and O.K. Hussain, Sentiment Analysis and Classification for Software
as a Service Reviews, IEEE 30thInternational Conference on Advanced Information Networking and
Applications(AINA), Crans-Montana, 2016, pp.53-58, doi: 10.1109/AINA.2016.148.
[2] V. Dhanalakshmi, D. Bino and A. M. Saravanan, "Opinion mining from student feedback data using supervised
learning algorithms," 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), Muscat,
2016,pp.1-5. doi: 10.1109/ICBDSC.2016.7460390.
[3] K. Liu, L. Xu and J. Zhao, "Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the
Word Alignment Model," in IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 3, pp.636-
650,March 1 2015. doi: 10.1109/TKDE.2014.2339850.
[4] H. P. Patil and M. Atique, Sentiment Analysis for Social Media: A Survey, 2nd International Conference on
Information Science and Security (ICISS), Seoul, 2015, pp. 1-4. doi: 10.1109/ICISSEC.2015.7371033.
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Paper Type | : | Research Paper |
Title | : | Digital Divide in brief |
Country | : | India |
Authors | : | Sova Pal Bera |
: | 10.9790/0661-1904058487 |
Abstract: The information and communications technology (ICT) has brought vast benefits to mankind to the rich as well as the poor. The world has been changing rapidly with the help of ICT. On the other hand ICT has faced serious problem of digital divide between developed and developing countries. There is a growing discrepancy between those who have access to information and those who do not. The second groups are the majority and most of them live in rural areas of developing countries. There is a gap into those who are able to take advantage of new ICT opportunities and those who are not. So, digital divide affects many nations of the developing world. This paper discusses digital divide in Indian scenario and different aspects of it. Here it is also discussed about the causes and efforts to bridge the digital divide and also the role of libraries and information centers.
Keywords: Digital Divide, Information Communication Technology (ICT)
[1]. A Brief View to Digital Divide in Indian Scenario; Ms. I. Panda, Mr. D. Charan Chhatar, Dr. B. Mharana; International Journal of Scientific and Research Publications, Volume 3, Issue 12, December 2013 1 ISSN 2250-3153
[2]. Bridging Digital Divide in India: Some Initiatives; Dr. S.Y. Bansode, Dr. S.K. Patil; Asia Pacific Journal of Library and Information Science. Vol.1, No.1, January-June 2011
[3]. ICT and Digital Divide in Indian School System; G. K. Thakur; International Journal of Interdisciplinary and Multidisciplinary Studies (IJIMS), 2014, Vol 2, No.2, 34-38; ISSN: 2348 – 0343
[4]. Digital Divide in India ;November 1, 2016; https://www.iaspoint.gktoday.in/article/digital-divide-in-india/
[5]. The Digital Divide, ICT,and Broadband Internet; http://www.internetworldstats.com/links10.htm