Energy Efficient Routing Protocol for Secure Wireless Sensor Network
Research Paper | Journal Paper
Vol.5 , Issue.4 , pp.1-4, Apr-2017
Abstract
WSN (Wireless Sensor Network) has proved to be a boon for data collection from hard to reach places which were otherwise beyond the physical constraint of humans. WSN has found its way into key fields such as Environment monitoring, transportation , security, military, catastrophic area and medical industry. WSN strives on limited power consumption, as charging the battery in some circumstances is not a practical solution and so energy consumption of WSN has to be looked after . And for energy efficient communication in WSN, a series of routing techniques has been adopted, the most effective being LEACHES. Simulation results shows a decrease in energy consumption when distance between the cluster heads is reduced.
Key-Words / Index Term
Wireless Sensor Network, LEACH, Hierarchical Routing, Cluster Heads, Energy Efficient Communication, Energy Conservation.
References
[1] N. Neelam, D. Khosla, “The Energy Efficient Techniques for Wireless Sensor Network: A Review”, International Journal for Computer Science and Engineering, Vol.4, Issue.11, pp. 36-41, 2016.
[2] AS. Mandloi, V. Choudhary, “Study of Various Techniques for Data Gathering in WSN”, International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.3, pp.12-15,2013.
[3] P. K. Batra, K. Kant, “A Clustering Algorithm with reduced cluster head variations in LEACH protocol”, International journal of Systems Control and Communications, Vol.7, Issue.4, pp. 321-336, 2016.
[4] R. Bandaru, RS. Basavala , “Information Leakage through Social Networking Websites leads to Lack of Privacy and Identity Theft Security Issues”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.1-7, 2013.
[5] R. Nathiya, S.G. Santhi, “Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.36-43, 2014.
[6] P. Ilango, G.H. Dinesh, S.A. Kumar, “A Modified LEACH Protocol for Increasing Lifetime of the Wireless Sensor Network”, in Cybernetics and Information Technologies (BAS-Institute of Information and Communication Technologies), India, pp.154-164, 2016.
[7] S. Saini, K. Dayal, “A Survey on Various Secure Routing Protocolsin Wireless Sensor Networks”, International Journal of Advanced Research in Engineering and Technology, Vol.3, Issue.9, pp. 105-108, 2015.
[8] R. A. Shaikh, Y. Arfat, “A Survey on Secure Routing Protocols in Wireless Sensor Network”, International Journal For Wireless and Microwave Technologies, Vol.3, No.6, pp. 9-19, 2016.
Citation
R. Kumar, S. Tripathi, R. Agrawal, "Energy Efficient Routing Protocol for Secure Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.1-4, 2017.
Heart Disease Analysis Using Support Vector Machine and Sobel Edge Detection
Research Paper | Journal Paper
Vol.5 , Issue.4 , pp.5-13, Apr-2017
Abstract
Diagnosis of heart disease is a challenging task which requires much knowledge and experience. The most traditional way for predicting heart disease are doctor’s examinations or taking number of medical tests like as Heart MRI, ECG, Stress Test etc. Now a days, health care industry includes large amount of health care data, which is having hidden medical information. For providing a better and efficient result, novel techniques like Support Vector Machine (SVM) and Sobel Edge Detection has been proposed. This proposed technique provides better output for heart disease detection. The pre-processing step improves the image quality of heart disease MRI image. Increasing of image quality makes the process ease to find affected region. The region of interest techniques sharps the edges in scanned image. Region classification is being applied for isolating the abnormal and normal regions in the heart cells with SVM for identification of various types of abnormalities. The training process classifies the features and recognizes the affected region. The Eclipse IDE tool being used for analyzing the heart disease and several type of heart disease image dataset is being collected from various online sources and stored in a database.
Key-Words / Index Term
Heart Disease, Support Vector Machine (SVM), Water Shed Segmentation (WSS), Sobel Edge Detection (SED), ROI segmentation, Eclipse IDE, Heart MRI
References
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Citation
K.Rajalakshmi, K.Nirmala, "Heart Disease Analysis Using Support Vector Machine and Sobel Edge Detection," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.5-13, 2017.
Optimizing Virtual Machine Placement Using Intelligent Water Drop and Simulating Algorithm
Research Paper | Journal Paper
Vol.5 , Issue.4 , pp.14-21, Apr-2017
Abstract
Developing approaches intended to produce top notch answers for tackle troublesome computational enhancement issues by playing out a pursuit over the space of heuristics as opposed to looking the arrangement space specifically. Significant progress in developing search methodologies for a huge variety of application areas still require specialists to integrate their expertise in every problem domain. Researchers have need for developing automated systems to replace the role of a human expert. A hyper-heuristic for the most part goes for diminishing the measure of area information in the inquiry system. Coming about approach ought to be shabby and quick to execute, requiring less mastery in either the issue area or heuristic techniques and it would be vigorous. Resulting approach is cheap and fast to implement, requiring less expertise in either the problem domain as well as hyper heuristic methods and it would be robust.
Key-Words / Index Term
Hyper-Heuristic Algorithms, Virtual Machine Placement problem, Genetic Algorithms, Energy efficient
References
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Citation
V.O. Ramakant, A. Victor, "Optimizing Virtual Machine Placement Using Intelligent Water Drop and Simulating Algorithm," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.14-21, 2017.
A Systematic Literature Review of Sentiment Analysis Techniques
Review Paper | Journal Paper
Vol.5 , Issue.4 , pp.22-28, Apr-2017
Abstract
Development of Web 2.0 has resulted in enormous increase in the vast source of opinionated user generated data. Sentiment Analysis includes extracting, grasping, arranging and presenting the feelings or suppositions communicated in the information gathered from the clients. This paper exhibits an efficient writing survey of different strategies of sentiment analysis. A model for sentiment analysis of twitter data using existing techniques is constructed for comparative analysis of various approaches. Dataset is pre-processed for noise removal and unigrams as well as bigrams are used for feature extraction with term frequency as weighting criteria. Maximum accuracy is achieved by using a combination of SVM and Naïve Bayes at 78.60% employing unigrams and 81.40% employing bigrams as features.
Key-Words / Index Term
Sentiment Analysis, Crowdsourced data, Twitter, Machine Learning Techniques
References
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[7] A Bermingham, AF Smeaton, “Classifying sentiment in microblogs: is brevity an advantage?” In the Proceedings of the 19th ACM international conference on Information and knowledge management, NY, pp.1833-1836,2010.
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[10] C. Nanda, M. Dua, "A Survey on Sentiment Analysis", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.67-70, 2017.
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[12] M. Fernandes , "Data Mining: A Comparative Study of its Various Techniques and its Process", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.19-23, 2017.
[13] J Akaichi, Z Dhouioui, MJ Lopez, “Text mining facebook status updates for sentiment classification”, In 17th International Conference on System Theory Control and Computing (ICSTCC), USA, pp 640-645,2013.
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[15] A Abbasi, S France, Z Zhang, H Chen, “Selecting Attributes for Sentiment Classification Using Feature Relation Networks”, IEEE Transactions on Knowledge and Data Engineering , Vol.23, Issue.3, pp. 447-462, 2011.
[16] KV Ghag, K Shah, “ARTFSC-Average Relative Term Frequency Sentiment Classification”, INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, Vol.12, Issue.6, pp.3591-3601, 2014.
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[23] J Bollen, H Mao, A Pepe, “Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena”, ICWSM, Vol.11,, Issue.2, pp. 450-453,2011.
[24] A Bagheri, M Saraee, F de Jong, “Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews”, Knowledge-Based Systems. Vol.52, Issue.2, pp. 201-213, 2013.
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[26] S Huang, Z Niu, C Shi, “Automatic construction of domain-specific sentiment lexicon based on constrained label propagation”, Knowledge-Based Systems, Vol.56, Issue.9, pp.191-200, 2014.
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[28] CL Huang, JF Dun, “A distributed pso-svm hybrid system with feature selection and parameter optimization”, Applied Soft Computing, Vol.8, Issue.4, pp.1381-1391,2008.
[29] ASH Basari, B Hussin, IGP Ananta, J Zeniarja, “Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization”, Procedia Engineering Vol.53, Issue.2, pp.453-462, 2013.
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[34] LW Ku, YT Liang, HH Chen, “Opinion Extraction, Summarization and Tracking in News and Blog Corpora”, In: AAAI spring symposium on Computational approaches to analyzing weblogs, China, pp.100-107, 2006.
[35] K Cai, SSpangle, Y Chen, L Zhang, “Leveraging sentiment analysis for topic detection”, International Journal of Web Intelligence and Intelligent Agent Technology, Vol.8, Issue.3, pp.265-271, 2008.
[36] P Jambhulkar, S Nirkhi, “A survey paper on cross-domain sentiment analysis”,, Int J Adv Res Comput Commun Eng, Vol.3, Issue.1, pp.5241-5245, 2014.
[37] F Bisio, P Gastaldo, C Peretti, R Zunino, E Cambria, “Data intensive review mining for sentiment classification across heterogeneous domains”, In: International Conference on Advances in Social Networks Analysis and Mining, Canada, pp 1061-1067, 2013.
[38] N O’Hare, M Davy, A Bermingham, P Ferguson, P Sheridan, C Gurrin, AF Smeaton, “Topic-dependent sentiment analysis of financial blogs”, In: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion, China, pp 9-16, 2009.
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MM Mostafa, “More than words: Social networks’ text mining for consumer brand sentiments”,, Expert Systems with Applications, Vol:40, Issue.10, pp. 4241-4251, 2013.
Citation
J. Kaur, S.S. Sehra, S.K. Sehra, "A Systematic Literature Review of Sentiment Analysis Techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.22-28, 2017.
Enhanced the Performance of Digital Image Compression Using Wavelet Transform Function and BP Neural Network Model
Research Paper | Journal Paper
Vol.5 , Issue.4 , pp.29-33, Apr-2017
Abstract
The compression technique play vital role in digital multimedia data. The size of digital multi-media is very high due to this reason used more memory space for storage and need more bandwidth for transmission of data. the data compression techniques used various approaches like pixel based methods and some are transform based method. In the research work introduced better approach for picture pixel size reduction. The approach is addition of WT and BPN model. The BPNN model is very efficient model in terms of processing of data of WT function. The proposed algorithm implemented in MATLAB and used reputed image for compression. Our empirical result shows better PSNR and C.R instead of Wavelet transform method.
Key-Words / Index Term
Digital Image, Wavelet, BP Neural Network
References
[1] J.B. Dibya , “A Novel Approach for Color Image Edge Detection Using Multidirectional Sobel Filter on HSV Color Space”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.154-159, 2017.
[2] PC. Gopi, R. Sharmila, T. Indhumathi, S. Savitha, “An Intelligent New Age Method of Image Compression and Enhancement with Denoising for Bio-Medical Application”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.4, pp.12-16, 2013.
[3] KH. Talukder, K. Harada, “Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image”, IAENG International Journal of Applied Mathematics, Vol.36, Issue.1, pp.1-8, 2017.
[4] E. Ghasemi, J. Shanbehzadeh, N. Fassih, “High Capacity Image Steganography Based on Genetic Algorithm and Wavelet Transform”, Intelligent Control and Innovative Computing, Lecture Notes in Electrical Engineering, US, pp.395-404, 2012.
[5] G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, C. Roux, “Wavelet optimization for content-based image retrieval in medical databases”, Med Image Anal, vol.14, Issue.3,pp.227-241, 2010.
[6] G. Quellec, M. Lamard, P. M. Josselin, G. Cazuguel, B. Cochener, C. Roux, “Optimal wavelet transform for the detection of microaneurysms in retina photographs”, IEEE Trans Med Imaging, vol-27, Issue.2, pp.1230-1241, 2008.
[7] V. Lowanshi, SShrivastava, “Two Tier Architecture for Content Based Image Retrieval Using Modified SVM and knn-GA”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.41-45, 2014.
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[9] AM. Alshoaibi, AK. Ariffin, MN. ALmaghribi, “Development of efficient finite element software of crack propagation simulation using adaptive mesh strategy”, American Journal of Applied Sciences, Vol.6, Issue.4, pp.661-666, 2009.
[10] T. Xianghong, L. Yang, “An Image Compressing Algorithm Based on Classified Blocks with BP Neural Networks”, 2008 International Conference on Computer Science and Software Engineering, Hubei, pp. 819-822, 2008.
[11] M.A. Anwer, D.A. Anwar and S.A. Anwer , “Image Compression: Combination of Discrete Transformation and Matrix Reduction”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.1-6, 2017.
[12] NA. Omaima, “Cascade-Forward vs. Function Fitting Neural Network for Improving Image Quality and Learning Time in Image Compression System”, Proceedings of the World Congress on Engineering, UK, pp.1-6, 2012.
Citation
JS. Yadav, S. Dhariwal, "Enhanced the Performance of Digital Image Compression Using Wavelet Transform Function and BP Neural Network Model," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.29-33, 2017.
Implementing Different Types and Variants for Software Testing Process and Techniques
Review Paper | Journal Paper
Vol.5 , Issue.4 , pp.34-39, Apr-2017
Abstract
Software testing is the most critical phase of the Software Development Life Cycle. Software under test goes through various phases, which as per the study are test analysis; test planning, test case/data/environment preparation, test execution, bug logging and tracking and closure. There is lot of research which has been done in past to optimize overall testing process with intent of improving quality of software in a minimum amount of time. After evaluating all available testing processes it has been found that different development models are used for different types of applications and different testing techniques are performed to test the same. Based on the research during the study of this paper, it has been analyzed that each company modifies their testing process as per the needs and performs testing based on the criticality of the applications. The most critical components of each application have to be tested thoroughly to ensure their functional, performance and security features are behaving as expected. This paper talks about ensuring the quality of all types of software applications by performing certain types of testing techniques and optimized software testing processes. As per the study and research done testing types can be categorized under three major testing techniques which are Functional, Performance and Security Testing and major software testing process called as Analysis, Preparation and Execution and closure.
Key-Words / Index Term
Functional, Performance and Security Testing (FPS), Analysis, Planning and Preparation, Execution and Closure (APEC), ), Software Testing Techniques, Software Testing Life Cycle (STLC), Software Development Life Cycle(SDLC)
References
[1]. SK. Swain, DP. Mohapatra, R. Mall, “Test Case Generation Based on Use Case and Sequence Diagram”, International Journal of Software Engineering, Vol.7, Issue.5, pp.289-321, 2010.
[2]. A. Babu, K. Kolluri, L. Tameezuddin, D. Guddika, “Effective Bug Tracking Systems.Theories andImplementation”, IOSR Journal of Computer Engineering, Vol. 4, Issue.6, pp.31-36, 2012.
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[5] PN. Boghdady, L. Badr, M. Hashem, Tolba, “A Proposed Test Case Generation Technique based on Activity Diagrams”, IJET-IJENS, Vol.3, Issue.7, pp.37-57, 2011.
[6] B. Nirpal, P. Kale, “Using Genetic Algorithm for Automated Efficient Software Test Case Generation for Path Testing”, Int. J. Advanced Networking and Applications, Vol.2, Issue.6, pp.911-915, 2011.
Citation
N. Sudheer, V. Sarma, N. Ahmad, "Implementing Different Types and Variants for Software Testing Process and Techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.34-39, 2017.
A Hybrid Forecasting Model for Stock Value Prediction using Soft Computing skill
Research Paper | Journal Paper
Vol.5 , Issue.4 , pp.40-45, Apr-2017
Abstract
This paper aims to present a hybrid model to forecast stock price by analyzing different trends of stock market. As the stock price are time series but they are not static and highly noise due to the fact that stock market is not stable as it depends on various factors. In this paper we have propose a new approach to forecast stock price using ANFIS model optimized by particle swam optimization (PSO) this model is consisting of an effective algorithm for predicting next day high price of Yahoo stock value and Microsoft stock value. To present this algorithm we have taken real dataset of Yahoo Company and Microsoft Company. This new approach is compared with existing models with real data set and gives more accurate results which give more accuracy result with MAPE of 1%.
Key-Words / Index Term
data Mining, Prediction, Soft computing, Stock market
References
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[7] R. Majhi ,G. Panda ,G. Sahoo , “Efficient prediction of exchange rates with low complexity artificial neural network models”, Expert Systems with Applications, Vol.36, Issue.1, pp.181-189, 2009.
[8] JC. Hung, “Adaptive Fuzzy-GARCH model applied to forecasting the volatility of stock markets using particle swarm optimization”, Information Sciences, Vol.181, Issue.20, pp.4673-4683, 2011.
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[10] P. Chang, D. Wang, C. Zhou, “A novel model by evolving partially connected neural network for stock price trend forecasting”, Expert Systems with Applications, Vol.39, Issue.1, pp.611-620, 2012.
[11] HH. Chu, TL. Chen, CH. Cheng, CC. Huang, “Fuzzy dual-factor time series for stock index forecasting”, Expert Systems with Applications, Vol.36, Issue.1, pp.165-171, 2009.
[12] A. Victor, DT. Antony, A. Ligori “stock prediction using artificial neural network” Master thesis of IIT-Kanpur, India, pp-283-291,2013.
[13] MB Patel, SR Yalamalle, “stock price prediction using artificial neural network”, International Journal of Innovative Research in Science, Engineering and Technology, Vol.3, Issue.6, pp.13755-13762, 2014.
[14] AH. Moein, H. Moghaddam, M. sfandyari, “stock market index prediction using artificial neural network”, Journal of Economics, Finance and Administrative Science, Vol.21, Issue.41, pp.89-93, 2016.
[15] H.R. Pawar, P.G. Gaikwad, U.G. Bombale, D.D Jagtap, S. Durugkar, “Intelligence Stock Forecasting Using Neural Network”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.103-106, 2014.
Citation
S. Barik, S. Das, SK. Sahoo, "A Hybrid Forecasting Model for Stock Value Prediction using Soft Computing skill," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.40-45, 2017.
A Survey On Load Balancing Methods and Algorithms in Cloud Computing
Survey Paper | Journal Paper
Vol.5 , Issue.4 , pp.46-51, Apr-2017
Abstract
the IT trade has played a noteworthy role in computing. Due to this increasing of demands day by day the need of computing and storage is increasing quickly. Client-Users are demanding for services and resources at any time are provided. For this cloud computing requires load balancing techniques to control and handle overloaded demand and requires. Load balancing is one of techniques to control lots of requires at a time and help to utilization of resource and services. In load balancing various algorithm are provide to the user for satisfaction. In this paper, we have done the review of some load balancing algorithms based on different parameters in cloud computing.
Key-Words / Index Term
Cloud Computing, Load Balancing, Virtualization, Load balancingalgorithm , Ant-colonyalgorithm, Genetic algorithm
References
[1]. B. Patel, S. Patel, “Various Load Balancing Algorithms in cloud computing”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol¬.1, Issue.¬2 pp.23-29, 2015.
[2]. R. Nallakumar, N. Sengottaiyan, S. Nithya, “A Survey of Task Scheduling Methods in Cloud Computing”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.9-13, 2014.
[3]. Z. Chaczko, V. Mahadevan, S. Aslanzadeh, C. Mcdermid, “Availability and load balancing in cloud computing”, International Conference on Computer and Software Modeling, Singapore pp.138¬-140, 2011.
[4]. AS. Togadiya , PC. Togadiya, “A Comparative Analysis of Load Balancing Algorithm in Cloud Computing Environment”, International Journal of Advanced Research in Computer Engineering & Technology, Vol.1, Issue.3, pp.120-124, 2012.
[5]. D. Saranya, LS. Maheswar, “Load Balancing Algorithms in Cloud Computing: A Review”, International Journal of Advanced Research in Computer Science and software Engineering, Vol.5, Issue.7, pp.1107-1111, 2015.
[6]. S. Sakshi, NS. Ghumman, “Cloud computing model and its load balancing algortihms,” 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp.2940-2943, 2016.
[7]. Reena Panwar, Bhawna Mallick, “Comparative Study of Load Balancing Algorithms in Cloud Computing” International Journal of Computer Applications, Vol.117, No.24, pp.1-17, 2015.
[8]. Shang¬Liang Chen, Yun¬Yao Chen, Suang¬Hong Kuo, “CLB: A novel load balancing architecture and algorithm for cloud services”, Computers and Electrical Engineering, Issue.56, Issue.2, pp.154-160, 2016.
[9]. R. Gupta, R. Bhatia, “An Enhanced and Secure Approach of Load Balancing in Cloud Computing”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.112-116, 2014.
[10]. Dharmesh Kashyap, Jaydeep Viradiya, “A Survey Of Various Load Balancing Algorithms In Cloud Computing”, International Journal Of Scientific & Technology Research, Vol.3, Issue.11, pp.115-119, 2014.
[11]. RR Patel, SJ Patel, “Improved GA using population reduction for load balancing in cloud computing”, 2016 International Conference on Advances in Computing Communications and Informatics (ICACCI), Jaipur, pp. 2372-2374, 2016.
[12]. M. Rahman, S. Iqbal and J. Gao, “Load Balancer as a Service in Cloud Computing”, 2014 IEEE 8th International Symposium on Service Oriented System Engineering, Oxford, pp. 204-211, 2014.
[13]. P. Kaur, S. Majithia, “Implementation and Analysis of Replicated Agent Based Load Balancing In Cloud Computing”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.21-27, 2014.
Citation
M. Lagwal, N. Bhardwaj, "A Survey On Load Balancing Methods and Algorithms in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.46-51, 2017.
A Study Analysis and Survey of Various Contact Centre Solutions
Review Paper | Journal Paper
Vol.5 , Issue.4 , pp.52-56, Apr-2017
Abstract
Any telecommunication contact centre manage several million calls per day. Calls mostly part arrive on passages and are then dealt with by voice entryways. The non-self-served calls are then sent to a call administration framework based on an ICR, which helps with directing the calls empowering call sharing. This mechanism helps the routing externally and lets the call to arrive in required part of the network. Previously, only voice access was provided by contact centre, but now, they merged various channels such as video, voice, fax, SMS/MMS, WEB and email, etc. It seamlessly integrates various service strategies inside enterprises, so that consumers can communicate with organizations conveniently. Various enterprises such as Genesys, CISCO, Avaya, Huawei and many more have their own contact center solution to establish a customer center service architecture that integrates various departments. In this paper, we will give a detailed explanation of different enterprise contact center architectures related to various factors. A sum of 25 working professionals had demonstrated enthusiasm for our overview that incorporates professionals belonging to different Information Technology sector. The outcomes depict the architectural analysis of the contact centre, which are generally used by the professionals.
Key-Words / Index Term
Contact centre; Call centre; PBX; Switch; Agents; IVR; Benchmarks
References
[1] Vittorio Noce, David Curley, “Genesys Core Applications Positioning and Architecture”, NIPA, India, pp. 1-40, 2016.
[2] Neil Davey, “What does the contact centre industry look”, Infographic, London, pp. 23-56, 2016.
[3] P. Reynolds, “The Math of Contact Center Staffing”, Society of Workforce Professionals, Nashville, pp. 495-650, 2015.
[4] Andrew Pritchard, Raj Mirchandani, “The Contact Centre of the Future”, Smarter Service, NY, pp. 330-456, 2017.
[5] KD. Schwartz, “Predicting the call centre of the future”, crmsearch, pp. 477-699, 2015.
[6] Mike Murphy, Nicola Millard, “The Contact Center of 2020”, Call centre help, USA, pp. 210-250, 2016.
[7] S. Agrawal, K.D. Kulat, M. B.Daigavane, "Evaluation of Routing Algorithm for Ad-hoc and Wireless Sensor Network Protocol", International Journal of Computer Sciences and Engineering, Vol.1, Issue.2, pp.11-18, 2013.
[8] JW. Sarah, CD. Gastine, K. Kerai, “The Digital Evolution Journey of the Contact Centre”, Bearing Point, Amsterdam, pp.104-176, 2017.
[9] Avinash Bhat, Priya Badri, “The Future of Contact Centers”, Cognizant, India, pp.50-170, 2016.
[10] Jeremy Payne, “The Evolution of Contact Centre”, Enghouse Interactive, Phoneix, pp. 233-250, 2015.
[11] Intelenet, “Intelenet’s Solution for Contact Centre”, Intelenet global, Indiana, pp. 200-211, 2014.
[12] Umesh Kumar Singh, Jalaj Patidar and Kailash Chandra Phuleriya, "On Mechanism to Prevent Cooperative Black Hole Attack in Mobile Ad Hoc Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.11-15, 2015.
[13] Martin Prunty, Andrew Pritchard, “The Customer Focused Contact Centre”, Smarter Service, Somers, pp. 280-330, 2017.
Citation
A. Pandit, Manjula R, "A Study Analysis and Survey of Various Contact Centre Solutions," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.52-56, 2017.
GPS and GSM Based Engine Locking System Using Smart Password
Research Paper | Journal Paper
Vol.5 , Issue.4 , pp.57-61, Apr-2017
Abstract
The smart engine locking system is an embedded system based intrusion detection system designed and implemented to prevent unauthorized access of vehicles while parking in insecure places. The proposed system incorporates a micro controller along with GSM and GPS modules. This instrument is installed in the engine of the vehicle whose current position is to be recorded and locked the engine in real time. Main objective of this instrument is to protect the vehicle from any unauthorized access by providing two locking status, theft mode and user mode. These two modes are controlled by Arduino UNO and GPS technology is used for finding current location of the vehicle. A GSM modem is also connected to the micro controller for sending message to the owner’s mobile if the vehicle is in theft mode. This system puts into the user mode if vehicle is handled by the owner or authorized persons, otherwise goes to theft mode. The most important concept in this design is introducing the mobile communications into the embedded system using GSM module. The entire design is on a single board.
Key-Words / Index Term
GSM, GPS, ATmega328
References
[1] Chen Peijiang, Jiang Xuehua, “Design and Implementation of Remote monitoring system based on GSM”, IEEE Computational Intelligence and Industrial Application, Vol.42, Issue.2 , pp.167-175, 2008.
[2] MJ. Asaad, Ibraheem Talib, “Experimentally Evaluation of GPS/GSM Based System Design”, Journal of Electronic Systems, Vol. 2, Issue 2, pp.1-8 , 2012.
[3] Kunal Maurya , Mandeep Singh, Neelu Jain, “Real Time Vehicle Tracking System using GSM and GPS Technology- An Anti-theft Tracking System”, International Journal of Electronics and Computer Science Engineering, Vol.1,Issue 3, pp.1103-1107, 2012.
[4] Vikram Kulkarni, Viswaprakash Babu, “Embedded Smart Car Security System on Face Detection”, IJCCT, Vol.3, Issue.1, pp..86-93, 2013 .
[5] Kai-Tai Song, Chih-Chieh Yang, “Front Vehicle Tracking Using Scene Analysis”, Proceedings of the IEEE International Conference on Mechatronics & Automation Niagara Falls, Canada, pp.1323-1328, 2005.
[6] Albert Alexe, R. Ezhilarasie, “Cloud Computing Based Vehicle Tracking Information Systems”, IJCST, Vol. 2, Issue 1, pp.49-52, 2011.
[7] V. Deepika, M. Suneel, M. Chiranjeevi, T. Satya Vijay Swamy, “Vehicle Engine Locking System Using, Embedded Based GSM Technology”, International Journal of Advances In Computer Science and Cloud Computing, Vol.1, Issue 1, pp.53-56, 2013.
[8] Hammad Afzal , VD. Maheta “Low Cost Smart Phone Controlled Car Security System” IEEE International Conference on Industrial technology, Korea, pp.670-675,2014.
[9] R. Ramani, S. Valarmathy, N. SuthanthiraVanitha, S. Selvaraju, M. Thiruppathi, R. Thangam, “Vehicle Tracking and Locking System Based on GSM and GPS”, IJISA, vol.5, Issue.9, pp.86-93,2013.
[10] NV. Alex, Filma Mathew, Sini Jacob,Vaneza Benny, M. Bineesh, “Car Security - Vehicle Theft Identity and Control System” International Journal of Computer Sciences and Engineering ,Vol.3, Issue.3 , pp.121-124, 2015.
[11] JK. Pany, RND Choudhury, “Embedded Automobile Engine Locking System, Using GSM Technology”, International Journal of Instrumentation, Control and Automation, Vol.1, Issue-2, pp.49-53, 2011.
Citation
A. Omanakuttan, D. Sreedhar, A. Manoj, A. Achankunju , CM. Cherian, "GPS and GSM Based Engine Locking System Using Smart Password," International Journal of Computer Sciences and Engineering, Vol.5, Issue.4, pp.57-61, 2017.