Volume-4 , Issue-4 , Apr - 2016 Go Back

The number of new students who register annually less stable, increasing and decreasing. This has caused difficulties in the adjustment including adjustment of the number of classrooms and lecturers that will impact on the ratio of lecturers. Thus the need to do forecasting or prediction of the number of new students each year. To get the most precise predictions in this study used four methods on Trend Analysis namely methods of semi on average, the least squares method, the method of quadratic trend, exponential trend method, which will be compared to determine the method with the smallest error rate.

Prediction; Forecast; Comparison; Trend Analysis

[1] Santoso, B., “Comparative Analysis of Algorithms Naïve Bayes and C4.5 for Prediction Student Registration at the Dian Nuswantoro University”, Department of Computer Science, Journal of Dian Nuswantoro University, Page No. (1-4), 2015.
[2] Rahanimi, “Forecasting Number of Students Apply search was interest and ability of the Department of Mathematics Automatic Clustering Method Using Fuzzy Logic And Relationships (Case Study at the Institute of Technology Surabaya)”, ITS Undergraduate Paper 13455, Page No. (1-3), Dec 2013.
[3] Abdullah, M. F., “Methods of Use Automatic Clustering and Fuzzy Logical Relationship To Predict Number of New Students Bogor Agricultural Institute”, Departement of Mathematics dan Natural Science Bogor Agricultural Institute, Bogor, 2015.
[4] Arpit Baheti and Durga Toshniwal, “Trend Analysis of Time Series Data Using Data Mining Techniques”, IEEE International Congress on Big Data, 2014, pp.430-437.
[5] Suharyadi, and Purwanto, “Statistics: For Economics & Finance Modern Book 1”, Four Salemba Publisher, First Edition-2003, ISBN: 979-691-162-0.

Yulia Yudihartanti, "Trend Analysis Comparison of Forecasts For New Student," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.145-148, 2016.

With the rapid development of network technology, distributed denial of service (DDoS) attacks become one of the most important issues. Distributed Denial of Service (DDoS) attacks generates enormous packets by a large number of agents and can easily exhaust the computing and communication resources of a victim within a short period of time. So congestion control mechanism is one of the key that keeps any network efficient and reliable for the users. Many mechanisms were projected in the literature over theses years for the efficient control of congestion that occur in the network. Active Queue Management (AQM) is one such mechanism which provides better control in the recent years. The focus of this work is to study the behaviors of various queuing algorithms such as Drop Tail, Fair Queuing (FQ), Stochastic Fair Queuing (SFQ), Deficit Round Robin (DRR) and Random Early Detection (RED) using ns-2 as a simulation environment.

DDoS, AQM, FQ, SFQ, DDR and RED

[1] K. Lee, J. Kim, K. H. Kwon, Y. Han, and S. Kim , "DDoS attack detection method using cluster analysis", Elsevier, Expert System with Applications, Vol.34, 2008, pp.1659-1665.
[2] M. Nabeshima, and K. Yata , “Performance improvement of active queue management with per-flow scheduling" ,IEEE Proc.-Commun., Vol. 152, No. 6, 2005.
[3] B. Braden, D. Clark, J. Crowcroft, B. Davie, S. Deering, D. Estrin, S. Floyd, V. Jacobson, G. Minshall, C. Partridge, L., Peterson, K. Ramakrishnan, S. Shenker, J. Wroclawski, and L. Zhang, "Recommendation on queue management and congestion avoidance in the Internet" . RFC2309, 1998.
[4] V. Santhi, and A. M. Natarajan, “A New Approach to Active Queue Management for TCP with ECN”, IEEE 2009 First International Conference on Advanced Computing, ISSN. 2377-6927, 2009, pp. 76-81.
[5] G. Pibiri, C. M. Goldrick, and M. Huggard , "Using Active Queue Management to Enhance Performance in IEEE802.11", 2009.
[6] Basant Kuamr Verma and Binod Kumar2, "An Improved Weighted Clustering for Ad-hoc Network Security New", International Journal of Computer Sciences and Engineering, Volume-03, Issue-03, Page No (51-55), Mar -2015, E-ISSN: 2347-2693
[7] Singh, Umesh Kumar, et al. "An Overview and Study of Security Issues & Challenges in Mobile Ad-hoc Networks (MANET)." International Journal of Computer Science and Information Security, Volume-9, Noi-4 (2011): 106-110.
[8] S. Floyd and V. Jacobson, “Random early detection gateways for congestion avoidance,” ACM/IEEE Transaction on Networking, Vol. 1, 1993, pp. 397-413.
[9] G. A. Ramachandra, R. Banu and G. F. A. Ahammed, “Analyzing Marking Mod RED Active Queue Management Scheme on TCP Applications”, International Conference on Information and Network Technology, Vol. 37, 2012, pp. 251-257.
[10] F. Lau, R. H. Stuart, and S. H. Michael,., "Distributed Denial of Service Attacks," in Proceedings of 2000 IEEE International Conference on Systems, Man, and Cybernetics, Nashville, TN, Vol.3, 2000, pp.2275-2280.
[11] http://nms.csail.mit.edu/6.829-f06/lectures/bruce-queue.pdf
[12] M. Shreedhar, G. Varghese, E_cient Fair Queuing Using De_cit Round Robin, IEEE/ACM Transactions on Networking, Vol.4, No.3, June 1996.
[13] C. Semiria, “Supporting Differentiated Service Classes: Queue Scheduling Disciplines”, Juniper Networks, Inc.
[14] S. Kumar, A. Bhandari, A. L. Sangal and K. K. Saluja, “Queuing Algorithms Performance against Buffer Size and Attack Intensities”, Global Journal of Business Management and Information Technology. Volume 1, No. 2, 2011, pp. 141-157

Arshdeep Singh, Lakhvinder Kaur and Kulwinder Singh, "Impact of DDoS Attacks on Different Services Using Various AQM Techniques," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.149-155, 2016.

The most wanted wireless networks is Mobile Ad hoc Network (MANET). MANET can configure easily without any infrastructure which one is used by at present as an infrastructure oriented wired or wireless network. It is one of the infrastructures less network consist of mobile devices. Individually each and every device in the Mobile ad hoc network will act as a router as well as node which provide the flexibility in the physical topology, optimal routing and data communication. In this paper the proposed HiLeSec-OpTiB AODV is the combination of En-SIm and OpTiB AODV. This combined algorithm is tested with intruders(active and passive attackers). This proposed HiLeSec-OpTiB AODV is implemented and tested by the use of OmNet++.

SIm AODV; En-SIM AODV; MANET; Self-Configuring; HiLeSec-OpTiB AODV; OmNet++

[1]. Singh, Umesh Kumar, et al. "An Overview and Study of Security Issues & Challenges in Mobile Ad-hoc Networks (MANET)." International Journal of Computer Science and Information Security, Volume-9, No-4 (2011), pp 106-110.
[2]. Nagendra, M., and B. Kondaiah. "A Comparison and Performance Evaluation of On-Demand Routing Protocols for Mobile Ad-hoc Networks." International Journal of Computer Sciences and Engineering, Volume-2, Issue-5 (2014) pp 15-19.
[3]. B.Karthikeyan, N.Kanimozhi and Dr.S.Hari Ganesh, “Complexity in Security Issues of MANET Pertaining to AODV Protocol”, International Conference on Contemporary Trends in Computer Science (CTCS - 2014). Feb 2014.
[4]. B..Karthikeyan, N.Kanimozhi and Dr.S.Hari Ganesh- “Security and Time Complexity in AODV Routing Protocol”, IJAER, pp15542- 155546, Vol 20,June 2015.
[5]. B..Karthikeyan, N.Kanimozhi and Dr.S.Hari Ganesh- “Security Improved Ad-Hoc On-demand Distance Vector Routing Protocol”, IJARE, pp, Vol ,On Print.
[6]. B..Karthikeyan, N.Kanimozhi and Dr.S.Hari Ganesh, “Encrypt - Security Improved Ad Hoc On Demand Distance Vector Routing Protocol (En-SIm AODV)”, ARPN Journal of Engineering and Applied Sciences, VOL. 11, NO. 2, JANUARY 2016
[7]. B.Karthikeyan,Dr.S.Hari Ganesh and Dr. JG.R. Sathiaseelan, “ Optimal Time Bound Ad-Hoc On-demand Distance Vector Routing Protocol (OpTiB-AODV)”, International Journal of Computer Applications (0975 – 8887) Volume 140 – No.6, April 2016
[8].Naincy Juneja, Abhishek Mishra ,An implementation of security policy by using ID in Adhoc routing for mobile network, IJIACS,April 2014.
[9]. Neeraj Saini, Lalit Garg,Enhanced, “AODV Routing Protocol against Black hole Attack”, IJARCSSE, June 2014.
[10]. Rajdeep S. Shaktawat,Dharm Singh, Naveen Choudhary, An Efficient Secure Routing Protocol in MANET Security - Enhanced AODV (SE-AODV), IJCA, July 2014.
[11]. Shabnam, Jitendra Arora, “Detection of Cosmic Dust Attack in MANET under AODV Routing Protocol”, IJRASET, May 2014.
[12]. Radha Krishna Bar, Jyotsna Kumar Mandal, and Moirangthem Marjit Singh, “QoS of MANet Through Trust Based AODV Routing Protocol by Exclusion of Black Hole Attack”, 2013.
[13]. Dr.Mahmood K. Ibrahem , Ameer M. Aboud, “A Secure Routing Protocol for MANET”, IJCSET, July 2014.
[14]. Sunil Taneja, Sima Singh, Ashwani Kush, “Encryption Scheme for Secure Routing in Ad Hoc Networks”,IJICT, Vol 1, No 1, ISSN 0976- 4860,2011,PP 22-29.
[15]. Rajdeep S. Shaktawat, Dharm Singh, Naveen Choudhary, “An Efficient Secure Routing Protocol in MANET Security - Enhanced AODV (SE- AODV)”,IJCA (0975 – 8887), PP 34-41. Volume 97– No.8, July 2014,
[16]. A. Jegatheesan, D. Manimegalai, “Secure Key Sharing in Mobile Ad hoc Network using Content Invisibility Scheme”, WSEAS TRANSACTIONS on COMPUTERS, E-ISSN: 2224-2872, Volume 14, 2015,PP 124-133.

[17]. Vishakha Singhal and Shrutika Suri, "Comparative Study of Hierarchical Routing Protocols in Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Volume-02, Issue-05, Page No (142-147), May -2014
[18]. K.S.Abitha, Anjalipandey, DR.K.P.Kaliyamurthie, “Secured Data Transmission Using Elliptic Curve Cryptography”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 3, March 2015,PP 1419-1425
[19]. S. Abila Judith Suganthi, P. Rajesh, “ENCRYPTION BASED INTRUSION DETECTION IN MANET USING AODV ROUTING PROTOCOL”, Elysium Journal, P-ISSN: 2347-4408, Volume - 2, Issue – 2, April 2015.
[20].Xiaoxia Qi ,Qijin Wang and Fan Jiang, “ Multi-path Routing Improved Protocol in AODV Based on Nodes Energy”, International Journal of Future Generation Communication and Networking Vol. 8, No. 1 (2015), pp. 207-214
[21].Manoj Tolani, Rajan Mishra,l “ Effect of Packet Size on Various MANET Routing Protocols”, International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Volume 4– No.9, December 2012,PP:10-13.
[22].Kewal Vora, Jugal Shah, Shreyas Parmar and Shivani Bhattacharjee, "MANETs: Overview of Vulnerabilities, Security Threats and Prevention and Detection Techniques", International Journal of Computer Sciences and Engineering, Volume-03, Issue-10, Page No (26-31), Oct -2015, E-ISSN: 2347-2693
[23]. Nand Kishore, SukhvirSingh and Renu Dhir, "Energy Related Issues for MANETs: A Study", International Journal of Computer Sciences and Engineering, Volume-02, Issue-03, Page No (98-100), Mar -2014
[24]. Neha Agarwal and Neeraj Manglani, “A New Approach for Energy Efficient Routing in MANETs Using Multi Objective Genetic Algorithm”, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 4, Issue 6, June 2015pp 1780-1784.

B.Karthikeyan, S. Hari Ganesh, J.G.R. Sathiaseelan,and N.Kanimozhi, "High Level Security with Optimal Time Bound Ad-Hoc On-demand Distance Vector Routing Protocol (HiLeSec-OptiB AODV)," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.156-164, 2016.

Data that exists in fixed field in a record is called as structured data and putting away such data into database is broadly expanding to strengthen keyword query yet result lists do not give successful responses to keyword query and subsequently it is hard from user’s point of view. It is useful to grasp such kind of queries which gives results with low positioning. Here we determine identification of such queries to discover power of search performed in reply of query and characteristics of such hard query is identified by considering building blocks of the database and result list. One applicable issue of database is the existence of missing data and it can be resolved by imputation. Here an inTeractive Retrieving-Inferring data imPutation method (TRIP) is utilized which accomplishes retrieving and inferring in successive manner to fill the missing attribute values in the database. TRIP can also analyze optimal scheduling scheme in Deterministic Data Imputation (DDI). Filling missing values in such successive manner, we can improve the precision of imputation. So by considering imputation along with identification of power of query performance over the database, we can achieve successful improvements in the query results.

Keyword Query; Database; Query Performance; Deterministic Data Imputation

[1] N. Sarkas, S. Paparizos, and P. Tsaparas, “Structured
annotations of web queries,” in Proc. ACM SIGMOD
Int. Conf. Manage. Data, Indianapolis, IN, USA, pp. 771–
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[2] Ganti, Y. He, and D. Xin, “Keyword++: A framework to improve keyword search over entity databases,” in Proc. VLDB Endowment, Singapore, vol. 3, no. 1–2, pp. 711–722, Sept. 2010,
[3] V. Hristidis, L. Gravano, and Y. Papakonstantinou, “Efficient IRstyle keyword search over relational databases,” in Proc. 29th VLDB Conf., Berlin, Germany, pp. 850–861,2003.
[4] G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan, “Keyword searching and browsing in databases
using BANKS,” in Proc. 18th ICDE, San Jose, CA, USA,
pp. 431–440,2002.
[5] Nandi and H. V. Jagadish, “Assisted querying using instant response interfaces,” in Proc. SIGMOD 07, Beijing, China, pp. 1156–1158.
[6] E. Demidova, P. Fankhauser, X. Zhou, and W. Nejdl, “DivQ: Diversification for keyword search over structured databases,” in Proc. SIGIR’ 10, Geneva, Switzerland, pp. 331–338.
[7] J. A. Aslam and V. Pavlu, “Query hardness estimation
using Jensen-Shannon divergence among multiple scoring
functions,” in Proc. 29th ECIR, Rome, Italy, pp. 198– 209,2007.
[8] Shiwen Cheng, Arash Termehchy, and Vagelis Hristidis, “Efficient Prediction of Difficult Keyword Queries over Databases”, vol. 26, no. 6, June 2014.
[9] J. D. Gibbons and S. Chakraborty, Nonparametric Statistical
Inference. New York, NY: Marcel Dekker, 1992.
[10] G. Batista and M. Monard. An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5-6):519–533, 2003
[11] Zhixu Li, Lu Qin, Hong Cheng, Xiangliang Zhang, and Xiaofang Zhou, “TRIP: An Interactive Retrieving-Inferring Data Imputation Approach,” IEEE Transaction 2015.
[12] E. Yom-Tov, S. Fine, D. Carmel, and A. Darlow,“Learning to estimate query difficulty: Including applications to missing content detection and distributed information retrieval,” in Proc. 28th Annu. Int. ACM SIGIR Conf. Research Development Information Retrieval, Salvador, Brazil, pp. 512–519,2005.
[13] Y. Zhou and B. Croft, “Ranking robustness: A novel framework to predict query performance,” in Proc. 15th ACM Int. CIKM, Geneva, Switzerland, pp. 567–574,2006.
[14] J. Kim, X. Xue, and B. Croft, A probabilistic retrieval model for semistructured data, in Proc. ECIR, Tolouse, France, pp. 228239, 2009.
[15] J.-J. Shen, C.-C. Chang, and Y.-C. Li. Combined association rules for dealing with missing values. Journal of Information Science, 33(4):468–480, 2007.
[16] Z. Li, M. A. Sharaf, L. Sitbon, S. Sadiq, M. Indulska, and X. Zhou. Webput: Efficient web-based data imputation. In WISE, pages 243–256, 2012.
[17] S. Brin. Extracting patterns and relations from the world wide web. The World Wide Web and Databases, pages 172–183, 1999.
[18] Z. Li, M. A. Sharaf, L. Sitbon, X. Du, and X. Zhou. Core: A context-aware relation extraction method for relation completion. IEEE Transactions on Knowledge and Data Engineering, page 1, 2013.

Priya Pujari and Arti Waghmare, "A Novel Framework For Enhancing Keyword Query Search Over Database," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.165-168, 2016.

Speech emotion detection refers to discovering the speech category based on the training and testing to the database provided. This research work has been classified in four sections namely SAD, HAPPY, FEAR and AGGRESSIVE. There are two major sections in this research work namely Training and Testing. The training has been done on the basis of wave files provided for every group. Features have been extracted for all groups and have been saved into the database. The testing section classifies the training set of data with the help of BACK PROPAGATION NEURAL NETWORK (BPN) classifier and SEQUENTIAL MINIMAL OPTIMIZATION (SMO) classifier. The results of the BACK PROPAGATION NEURAL NETWORK CLASSIFIER have been found superior in terms of classification accuracy.

Speech; Features; SMO; BPNN; Accuracy

[1] D. A. Sauter; F. Eisner, A. J. Calder and S. K. Scott, "Perceptual cues in nonverbal vocal expressions of emotion," The Quarterly Journal of Experimental Psychology, Vol. 63 (11), pp. 2251–2272.
[2] A. Utane, S.L Nalbalwar, “Emotion Recognition Through Speech Using Gaussian Mixture Model And Hidden Markov Model”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 4, April 2013.
[3] J. Bachorowski, "Vocal Expression and Perception of Emotion," Current Directions in Psychological Science, Vol. 8 (2), pp. 53–57, 1999.
[4] Ververidis and C. Kotropoulos “Emotional speech recognition: Resources, features,and methods,” Speech Communication , Vol 48, pp. 1162-1181.
[5] J. C. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Advances in Neural Information Processing Systems 11, M. S. Kearns, S. A. Solla, D. A. Cohn, eds., MIT Press, (1999).
[6] W. Gevaert, G. Tsenov and V. Mladenov, “Neural Networks used for Speech Recognition,” Journal of Automatic Control, Vol. 20, pp. 1-7, 2010.
[7] M. Cilimkovic, “Neural Networks and Back Propagation Algorithm”, Institute of Technology Blanchardstown, Blanchardstown Road North Dublin 15, Ireland.
[8] P. Peng, Q. L. Ma and L. Hong, “The Research Of The Parallel Smo Algorithm For Solving Svm,” Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 2009.
[9] R. Fan, P. Chen and C. Lin, “Working Set Selection Using Second Order Information for Training Support Vector Machines,” Journal of Machine Learning Research Vol. 6 , pp. 1889–1918, 2005.
[10] X. Shao, KunWu, and B. Liao, “Single Directional SMO Algorithm for Least Squares Support Vector Machines,” Computational Intelligence and Neuroscience Vol., 2013, Article ID 968438.
[11] F. R. Bach, G. R. G. Lanckriet and M. I. Jordan, “Multiple Kernel Learning, Conic Duality, and the SMO Algorithm”, Proceedings of the 21st International Conference on Machine Learning, 2004.
[12] S. K. Shevade, S. S. Keerthi, C. Bhattacharyya, and K. R. K. Murthy, “Improvements to the SMO Algorithm for SVM Regression,” IEEE Transactions on Neural Networks, Vol. 11, pp. 1188-1193 , 2000.
[13] Y. S. Rao and i. Patel, “Speech Recognition Using Hmm With Mfcc- An Analysis Using Frequency Specral Decomposion Technique,” Signal & Image Processing : An International Journal(SIPIJ) Vol.1(2), 2010.
[14] W. HAN, C. CHAN, C. CHOY and Kong-Pang PUN, “An Efficient MFCC Extraction Method in Speech Recognition,” International Symposium on Circuits and Systems Proceedings, 2006.
[15] H. Gupta and D. S. Wadhwa, “Speech Feature Extraction and Recognition Using Genetic Algorithm,” International Journal of Emerging Technology and Advanced Engineering, Vol. 4 (1), 2014.
[16] J. C. Platt, “Fast Training of Support Vector Machine using Sequential Minimal Optimization,” In Advances in Kernel Methods: Support Vector Learning,, pp. 185-208, 1999.
[17] T. Glasmachers and C. Igel, “Second Order SMO Improves SVM Online and Active Learning,” Journal Neural Computation, Vol. 20 (2) pp. 374-382, 2008.
[18] Y. Pan, P. Shen , and Liping Shen,” Speech Emotion Recognition Using Support Vector Machine” , International Journal of Smart Home Vol. 6, No. 2, April, 2012.
[19] R. Rojas,” Neural Networks: A Systematic Introduction,” Springer Berlin Heidelberg New York, 2005.
[20] N.Pushpa, R.Revathi, C.Ramya and S.Shahul Hameed, “Speech Processing Of Tamil Language with Back Propagation Neural Network and Semi-Supervised Training,” International Journal of Innovative Research in Computer and Communication Engineering, Vol.2 (1), 2014..
[21] Divesh N. Agrawal and Deepak Kapgate, "Face Recognition Using PCA Technique", International Journal of Computer Sciences and Engineering, Volume-02, Issue-10, Page No (59-61), Oct -2014

Rohit katyal, "Analysis of SMO and BPNN Model for Speech Emotion Recognition System," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.169-174, 2016.

Melanoma is a leading cause of skin cancer. It spreads through metastasis in the body and can affect the whole skin surface. Most of the skin cancers initiate with the formation of skin lesions on the skin surface. If the type of skin lesion is diagnosed properly at an early stage, then the chances of survival can be increased. This project aims at classifying the skin lesion into benign and melanoma.

Melanoma; Best-fit ellipse; Active contour

[1] Qaisar Abbas, M. E. Celebi and Irene Fordon Garcia, “Hair removal methods: A comparative study for dermoscopy images”, Biomedical Signal Processing and Control Elsevier, Page No (395-404), February 2011.
[2] Catarina Barata, Jorge S. Marques and Jorge Rozeira, “A System for the Detection of Pigment Network in Dermoscopy Images Using Directional Filters”, IEEE Transactions on Biomedical Engineering, Volume-59, Issue-10, October 2012.
[3] Nandini. M. N and M. S. Mallikarjunaswamy, “Detection of Melanoma Skin Diseases using Dermoscopy Images”, International Journal of Electronics Communication and Computer Technology, Volume-4, Issue-3, May 2014.
[4] Sumithra R, Mahamad Suhil and D.S. Guru, “Segmentation and Classification of Skin Lesions for Disease Diagnosis”, International Conference on Advanced Computing Technologies and Applications, Page No (76-85), 2015.
[5] Shivangi Jain, Vandana Jagtap, Nitin Pise, “Computer aided Melanoma skin cancer detection using Image Processing”, International Conference on Intelligent Computing, Communication & Convergence, Page No (735-740), 2015.
[6] Md. Amran Hossen Bhuiyan, Ibrahim Azad and Md. KamalUddin, “Image Processing for Skin Cancer Features Extraction”, International Journal of Scientific & Engineering Research, Volume-4, Issue-2, February 2013.
[7] Margarida Silveira, Jacinto C. Nascimento, Jorge S. Marques, André R. S. Marçal, Teresa Mendonça, Syogo Yamauchi, Junji Maeda and Jorge Rozeira, “Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images”, IEEE Journal of Selected Topics in Signal Processing, Volume-3, Issue-1, February 2009.
[8] Omar Abuzaghleh, Buket D. Barkana and Miad Faezipour, “Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention”, IEEE Journal of Translational Engineering in Health and Medicine, Volume-3, 2015.
[9] Dr. H. B. Kekre and Ms. Saylee M. Gharge, “Image Segmentation using Extended Edge Operator for Mammographic Images”, International Journal on Computer Science and Engineering, Volume-02, Issue No-04, Page No (1086-1091), 2010.

Prathamesh A Somnathe, Pratima P Gumaste, "A System for Classification of Skin Lesions in Dermoscopic Images," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.175-179, 2016.

The main purpose of implementing Chemical industry automation is to provide required temperature, air humidity and light levels independent from outer environmental conditions. By this way maximum control from unit area will be obtained so that industry will have been handled in most efficient way. Controller type and its peripherals must satisfy required customer expectations. Traditional systems owners look forward to user friendly interface, scalable input and output ports that are compatible with any type of industrial equipment’s, faultless control system from such automation system. In addition to customer expectation, suitable software algorithm is to developed to save different mechanical conditions in single microprocessor. For related control system we should also clarify right sensors to measure present factory conditions.

EMS, Remote Control, 2- Tier Architecture

[1] Alfredo Gardel Vicente, Ignacio Bravo Munoz Jose Luis Lazaro Galilea and Pedro A. Revenga del Toro, “Remote Automation Laboratory Using a Cluster of Virtual Machines,” IEEE Transactions on Industrial Electronics, vol. 57, no. 10, pp. 3276–3283, 2010.
[2] Amiya Ranjan Panda, Utpal Mandal and Hare Krishna Ratha, “Integrated Monitoring of Encoder Status Parameters and GUI based Remote Control Panel Using Lab view,” IJCA., vol. 43, no. 3, pp. 21–26, 2012.
[3] Arkadiusz Jestratjew and Andrzej Kwiecien, “Performance of HTTP Protocol in Networked Control Systems,” IEEE Transaction on Industrial Informatics, vol. 9, no. 1, pp. 271–276, 2013.
[4] Baosheng, Yanga, Jianxin Lia, and Qian Zhangb, “G Language Based Design of Virtual Experiment Platform for Communication with Measurement and Control,” Elsevier-International Journal of Procedia Engineering, vol. 29, pp. 1549-1553, 2012.
[5] Eva Besada-Portas, Jose A. Lopez-Orozco, Luis de la Torre, and Jesus M. de la Cruz, “Remote Control Laboratory Using EJS Applets and TwinCAT Programmable Logic Controllers,” IEEE Transaction on Education, vol. 56, no. 2, pp. 156–164, 2013.
[6] Md. Nasimuzzaman Chowdhury, Md. Shiblee Nooman and Srijon Sarker, “Access Control of Door and Home Security by Raspberry Pi through Internet,” IJSER, vol. 4, issue. 11, pp. 550–558, 2013.
[7] Mukesh Kumar, Sanjeev Sharma, and Mansav Joshi, “Design of Real Time Data Acquisition with Multi Node Embedded Systems,” IJCA., vol. 42, no. 11, pp. 6–12, 2012.
[8] Ahlam Ansari, Tahir Ansari, Faizan Hingora and Mudassir Ansari,“A Secure Cloud Server Using Raspberry Pi and Kerberos Authentication Protocol” IJCSE, Volume-3, Issue-3, E-ISSN: 2347-2693

Giethu Kavanal, Rakhi ganesh, Shahana Sakkeer and Sreelekshmi R, "Raspberry pi based Energy Monitoring System in Chemical Industry," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.180-183, 2016.

To take care of the directing void issue in geographic steering, high control overhead and transmission postponement are as a rule taken in remote sensor systems. Roused by the structure made out of edge hubs around which there is no steering void, a proficient bypassing void steering convention in light of virtual directions is proposed in this paper. The fundamental thought of the convention is to change an irregular structure made out of void edges into a general one by mapping edge hubs directions to a virtual circle. By using the virtual circle, the covetous sending can be kept from falling flat, so that there is no directing void in sending process from source to destination and control overhead can be lessened. Besides, the virtual circle is helpful to lessen normal length of steering ways and abatement transmission delay. Reproductions demonstrate the proposed convention has higher conveyance proportion, shorter way length, less control parcel overhead, and vitality utilization.

Void Node, Geographic steering

[1] F. Cadger, K. Curran, J. Santos, and S. Moffett, “A survey of geographical routing in wireless ad-hoc networks,” IEEE Commun. Surveys Tuts., vol. 15, no. 2, pp. 621–653, May 2013.
[2] S. Lee, E. Kim, C. Kim and K. Kim, "Localization with a mobile beacon based on geometric constraints in wireless sensor networks", IEEE Trans. Wireless Commun., vol. 8, no. 12, pp. 5801-5805, 2009
[3] W. Liu, E. Dong and Y. Song, "Robustness analysis for node multilateration localization in wireless sensor networks", Wireless Netw., 2014, [online] Available: online
[4] N. Ahmed, S. S. Kanhere and S. Jha, "The holes problem in wireless sensor networks: A survey",Proc. ACM SIGMOBILE MC2R, pp. 4-18
[5] B. Karp and H. T. Kung, "GPSR: Greedy perimeter stateless routing for wireless networks", Proc. ACM 6th Annu. Int. Conf. MobiCom, pp. 243-254

Thasin Taj and Raghu Kumar. K.S, "A Novel Multi Ring Forwarding Protocol for Avoiding the Void Nodes for Balanced Energy Consumption," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.184-186, 2016.

Middleware is a software that resides on top of the operating system. It provides a development environment to the applications on top of it. It is used in diverse areas like distributed systems; networks embedded systems and so on. In telecommunication industries, middleware is also used to provide platform management functions such as context management, fault management, process and node management and so on. Availability and scalability play an important role in these industries that are customer oriented. This paper is a survey on how middleware based on symmetric multiprocessing and cluster systems provide better availability and scalability.

Availability, Middleware Platform, Scalability, Symmetric Multiprocessing

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Varsha V and Anooja Ali, "A Survey on Availability and Scalability Requirements in Middleware Service Platform," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.187-190, 2016.

In recent years, the home environment has seen a rapid introduction of network enabled digital technology. This technology offers new and exciting opportunities to increase the connectivity of devices within the home for the purpose of home automation. Mobile devices are ideal in providing a user interface in a home automation system, due to their portability and their wide range of capabilities. They can communicate with a home automation network through an Internet gateway, but cannot directly communicate with devices in the network, as these devices usually implement low power communication protocols, such as ZigBee, WiFi etc. In this project we aims at controlling Home appliances via Android device using Wifi as communication protocol and 8051 via DTMF and also provide security against intrusion when the home host is not at home. We create a user friendly interface for the android device that allows the user to communicate with the Micro. The server will be interfaced with relay circuit board that controls the appliances such as lights and fans running in Home. The communication with server allows the user to select the appropriate device. The server communicates with the corresponding relay. By this we offers a scalable and cost effective Home automation system.

Wi-Fi, Android, Microcontroller, Relays, ucFlash+.

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Mayur Khatpe, Brij Patel, Tushar Jain and Varun Shah, "Remote Controlled Home Automation Using Android application via Wi-Fi Connectivity," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.191-194, 2016.