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Intrusion Detection Systems are used to monitor computer system for sign of security violations over network or cloud environment. On detection of such sign triggers of IDSs is to report them to generate the alerts. These alerts are presented to a human analyst or user who evaluates the alerts and initiates an adequate response. In Practice, IDSs have been observed to trigger thousands of alerts per day, most of which are mistakenly triggered by begin events such as false positive. This makes it extremely difficult for the analyst to correctly identify alerts related to attack such as a true positive. Recently Data Mining methods have gained importance in addressing network or cloud security issues, including network intrusion detection and cloud Intrusion detection systems, these systems aim to identify attacks with a high detection rate and a low false alarm rate. Consequently, Unsupervised Learning methods have been given a closer look for network and cloud intrusion detection. We present unsupervised based Clustering Technique and compare with traditional centroid-based clustering algorithms for intrusion detection. These techniques are applied to the KDD Cup98 data set .In addition; a Comparative analysis shows the advantage of proposed approach over Traditional clustering-based Methods over in identifying new or unseen attack. Experimental result show that A.I based Hill Climbing aided k-means Clustering algorithm improves the detection rate in IDS than K-Mean algorithm and achieved 92% detection rate in IDS System.

Intrusion Detection, AI, Clustering

[1] Tich Phu oc Tran, “Machine Learning and Data Mining: Introduction to Principles and Algorithms”, Horwood Publishing Limited, 2007.
[2] Ye Yuan, "Mining Audit Data to Build Intrusion Detection Models," Proc. Fourth International Conference Knowledge Discovery and Data Mining pp. 66-72, 1999
[3] Snehal A, "The Research of Intrusion Detection Based on Support vector machine", Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, IEEE.2008
[4] Shun J and Malki H. A., "Network Intrusion Detection System using Neural Networks", IEEE computer society.2008.
[5] Muna Mhammad T. Jawhar and Monica Mehrotra, “A Study On Fuzzy Intrusion Detection”, In Proceedings of the Data Mining, Intrusion Detection, In formation Assurance, And Data Networks Security, SPIE, Vol. 5812, pp. 23-30, Orlando, Florida, USA, 2005
[6] Neal, and Hunt and Dasgupta, Cao, and Yang, “Anomaly Network Intrusion Detection Based on Improved AIS Technique”, Journal of Computers, Vol." “Adaptive Model Generation: An Architecture for the Deployment of Data Mining-Based Intrusion Detection Systems, Applications of Data Mining in Computer Security”, Kluwer Academic Publishers, Boston, MA, pp. 154-191, 2002
[7] Pohsiang Tsai,’A novel intrusion detection system based on hierarchical clustering and support vector machines", Expert Systems with Applications, Vol: 38, No: 1, pp: 306-313, 2011.
[8] Aida Hu Zhengbing, ”Approaches and machine learning Techniques for Intrusion Detection Systems”, Vol. 9, No. 12, pp. 181-186, 2009.
[9] Amit Kumar Choudhary,”An Effective Approach to Network Intrusion Detection System using Neural network technique”, International Journal of Computer Applications, Vol.1, No.3, pp.26–32, February 2010

Susheel Kumar Tiwari, Chandikaditya Kumawat, Manish Shrivastava, "Intrusion Detection and Prevention System to Increase the Detection Rate Using Data Mining Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.617-620, 2018.

In this world many of task is very challenged for researchers. By the way the accurate weather prediction is one of the disputes for the meteorologist. So this paper focuses the weather prediction for an implementing the classification technique of C4.5 Classification technique. This technique can be analyzed for the performance and accuracy of weather condition. Also this decision tree algorithm can be applied in weather prediction parameter of training data under the various regions. Such as, Tamil Nadu, Andra Pradesh, Gujarat and Odhisa states are taken from India for this research work. These states are mainly focus for the purpose of different monsoon seasons and climates vary from actual period of time. Finally, weather condition can be predicted on various monsoons seasonally on the respective class label of climate range.

C4.5; Temperature; Cloudcover; Vapor pressure; Relative humidity; Confusion matrix

[1] Olaiya, Folorunsho, and Adesesan Barnabas Adeyemo. "Application of data mining techniques in weather prediction and climate change studies."International Journal of Information Engineering and Electronic Business (IJIEEB) 4.1 (2012): 51.
[2] Kantardzic, Mehmed. “Data mining: concepts, models, methods, and algorithms.” John Wiley & Sons, 2011.
[3] Berkhin, Pavel. "A survey of clustering data mining techniques." Groupingmultidimensional data.Springer Berlin Heidelberg, 2006.25-71.
[4] Allen, Richard G. "Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56." FAO, Rome 300 (1998): 6541.
[5] Lawrence, Mark G. "The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications." Bulletin of the American Meteorological Society 86.2 (2005): 225-233.
[6] Howarth, Edgar, and Michael S. Hoffman. "A multidimensional approach to the relationship between mood and weather." British Journal of Psychology 75.1 (1984): 15-23.
[7] Pasanen, A-L., et al. "Laboratory studies on the relationship between fungal growth and atmospheric temperature and humidity." Environment International17.4 (1991): 225-228.
[8] Burk, R. L., and M. Stuiver. "Oxygen isotope ratios in trees reflect mean annual temperature and humidity." Science 211.4489 (1981): 1417-1419.
[9] Zhang, Guang Jun, and Michael J. Mcphaden. "The relationship between sea surface temperature and latent heat flux in the equatorial Pacific." Journal of climate 8.3 (1995): 589-605.
[10] Campbell, A., et al. "Temperature requirements of some aphids and their parasites." Journal of applied ecology (1974): 431-438.
[11] Thornton, Peter E., Steven W. Running, and Michael A. White. "Generating surfaces of daily meteorological variables over large regions of complex terrain."Journal of Hydrology 190.3 (1997): 214-251.
[12] Solomon, M. E. "Control of humidity with potassium hydroxide, sulphuric acid, or other solutions." Bulletin of Entomological Research 42.03 (1951): 543-554
[13] Zhu, Xingquan, and Ian Davidson, eds. “Knowledge Discovery and Data Mining”. Challenges and Realities. Igi Global, 2007.
[14] Manish Verma, MaulySrivastava, NehaChack, Atul Kumar Diswar and Nidhi Gupta, “A Comparative Study of Various Clustering Algorithms in Data Mining”, International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 3, May-Jun 2012, pp.1379
[15] Hall, Mark, et al. "The WEKA data mining software: an update." ACM SIGKDD Explorations Newsletter 11.1 (2009): 10-18.
[16] T.F. Gonzales. “Clustering to minimize the maximum inter cluster distance”. Theoretical Computer Science,1985,38(2-3):293-306.
[17] Kannan, M., S. Prabhakaran, and P. Ramachandran. "Rainfall forecasting using data mining technique."(2010)
[18] Arun K Pujari, “Data mining techniques”, University Press (India).2013
[19] Jiawei Han Mischeline Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publisher an imprint of Elsevier. 2006

M. Manikandan, R. Mala, "Optimal Prediction of Weather Condition Based on C4.5 Classification Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.621-627, 2018.

Wild animal detection helps wildlife researchers to analyze and study wild animal habitat and behavior. Discriminative Feature-oriented Dictionary Learning (DFDL) was utilized for learning discriminative features of positive images that have animals present in positive class, in addition of negative images that do not have animals present in that class. But, this approach has low performance for detection of visual wild animals. Hence, in this paper, Multi-Cluster Feature Selection (MCFS) is proposed for unsupervised feature selection and wild animal detection. Those features are chosen, which the multi-cluster structure of the data is well preserved. Based on spectral analysis approaches, the proposed method suggests a principled manner for calculating the correlations among various features without label information. Thus, the proposed technique handles the data with multiple cluster structure. The experimental results show that the proposed approach provides the better results.

Dictionary Learning, Multi-Cluster Feature Selection, Wild animal detection, Spectral analysis

[1] Neff, M. G., Cheng, S. N., & Johnson, T. L. (2011). U.S. Patent No. 7,999,849. Washington, DC: U.S. Patent and Trademark Office.
[2] KaewTraKulPong, P., & Bowden, R. (2002). “An improved adaptive background mixture model for real-time tracking with shadow detection”. In Video-based surveillance systems (pp. 135-144). Springer, Boston, MA.
[3] Bhat, K. S., Saptharishi, M., & Khosla, P. K. (2000). “Motion detection and segmentation using image mosaics”. InMultimedia and Expo, 2000. ICME 2000. 2000 IEEE International Conference on (Vol. 3, pp. 1577-1580). IEEE.
[4] Reilly, V., Idrees, H., & Shah, M. (2010, September). “Detection and tracking of large number of targets in wide area surveillance”. In European conference on computer vision (pp. 186-199). Springer, Berlin, Heidelberg.
[5] Wang, Y., Zhang, Z., & Wang, Y. (2012, December). “Moving object detection in aerial video”. In Machine Learning and Applications (ICMLA), 2012 11th International Conference on(Vol. 2, pp. 446-450). IEEE.
[6] Gupta, P., & Verma, G. K. “Wild Animal Detection using Discriminative Feature-oriented Dictionary Learning”.
[7] Fang, Y., Du, S., Abdoola, R., Djouani, K., & Richards, C. (2016). “Motion based animal detection in aerial videos.”Procedia Computer Science, 92, 13-17.
[8] Nguyen, H., Maclagan, S. J., Nguyen, T. D., Nguyen, T., Flemons, P., Andrews, K., ... & Phung, D. (2017, October). “Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring”. In Data Science and Advanced Analytics (DSAA), 2017 IEEE International Conference on (pp. 40-49). IEEE.
[9] Parham, J., Stewart, C., Crall, J., Rubenstein, D., Holmberg, J., & Berger-Wolf, T. (2018, March). “An Animal Detection Pipeline for Identification”. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1075-1083). IEEE.
[10] Zhu, C., Li, T. H., & Li, G. (2017, October). “Towards automatic wild animal detection in low quality camera-trap images using two-channeled perceiving residual pyramid networks”. InComputer Vision Workshop (ICCVW), 2017 IEEE International Conference on (pp. 2860-2864). IEEE.
[11] Zhang, T., Wiliem, A., Hemsony, G., & Lovell, B. C. (2015, April). “Detecting kangaroos in the wild: the first step towards automated animal surveillance”. In ICASSP (pp. 1961-1965).
[12] Jaskó, G., Giosan, I., & Nedevschi, S. (2017, September). “Animal detection from traffic scenarios based on monocular color vision”. In Intelligent Computer Communication and Processing (ICCP), 2017 13th IEEE International Conference on (pp. 363-368). IEEE.

S. Keerthana, E. Mary shyla, "WILD ANIMAL DETECTION USING MULTI-CLUSTER FEATURE SELECTION," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.628-632, 2018.

Evolution of surplus gadgets accessing web facilities has eventually actuated the resource congestion. Several alluring data grabbing techniques are utilized with their respective pros and cons. The paper administers a concept which is based on the integration of the Li-Fi with the Cognitive Radio technology, diversifying the wireless communication with hiking spectrum utilization and vast capacity. The concept adheres to improve the multi-user communication by relying on the use of both the optical as well as the radio frequency spectrums in a communicating network which is more effective instead of using Li-Fi or Cognitive Radio independently.

Cognitive Radio (CR), LED (Light Emitting Diode), Light Fidelity (Li-Fi), Visible Light Communication (VLC).

[1] J. Mitola and G. Q. Maguire, “Cognitive Radios: Making Software Radios More Personal”, IEEE Pers. Commun., Vol. 6, no. 4, pp. 13–18, 1999.
[2] K. Sharma, A. Rana, and B. Aneja, “A fuzzy-logic based framework for dynamic channel allocation with improved transmission in Cognitive Radio”, International Conference on Signal Processing and Communication (ICSC), Noida, pp. 31-36, 2016.
[3] P. Verma, Dr. J. Shekhar, Preety and Dr. A. Asthana, “Light-Fidelity (Li-Fi): Transmission of Data through Light of Future Technology”, International Journal of Computer Science and Mobile Computing, Vol. 4, Issue. 9, pg. 113-124, 2015.
[4] N.K. Randhawa and A.S. Buttar, “Sensing of Spectrum Holes in Cognitive Radio Networks: A Survey”, International Journal of Computer Sciences and Engineering,Vol. 2(8), pp. 28-34, 2014.
[5] M. Ayyash, “Coexistence of WiFi and LiFi Toward 5G: Concepts, Opportunities, and Challenges”, IEEE Communication Magazine, Vol. 54, pp. 64-71, 2016.
[6] I. Hanif, M. Zeeshan, A. Ahmed, “Traffic Pattern Based Adaptive Spectrum Handoff Strategy for Cognitive Radio Networks”, International Conference on Next Generation Mobile Applications, Security and Technologies, pp. 13-23, 2016.
[7] Y. Wang, S. Liao and J. Chang, “A fuzzy-based dynamic channel allocation scheme in cognitive radio networks”, 2015 8th International Conference on Ubi-Media Computing (UMEDIA), Colombo, pp. 49-54, 2015.
[8] L. Safatly, M. Bkassiny, M. Al-Husseini, and A. El-Hajj, “Cognitive Radio Transceivers: RF, Spectrum Sensing, and Learning Algorithms Review”, International Journal of Antennas and Propagation, Vol. 2014, Article ID 548473, 21 pages, 2014.

Ridhima, "Nurturing Wireless Communication: Coalition of Cognitive Radio with Li-Fi," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.633-636, 2018.

Image Processing is a technique which converts an image into a digital image to obtain some enhancement or to select some effective information from it. Classification of fruit quality or grading is helped by detection of defects present on fruit peel. As there is a great demand for high-quality fruits in the market, the task of defect detection in fruit is very vital in the agricultural industry. However, defect detection by the human is labour-intensive and time-consuming. The proposed methodology is useful in supermarkets for automatic sorting of fruits from a set of different kinds of fruits. This system minimizes error and also speeds up the time of processing. The objective of this work is to present a novel method to detect surface defects of fruit using RGB images. The proposed method uses pre-processing, segmentation, edge-detection and feature extraction to classify the fruit as defected or fresh.

Image Processing, Defect detection, Pre-processing, Filtering, Background subtraction, Binary image

[1] S.V. Phakade, “Automatic Fruit Defect Detection Using HSV and RGB Colour Space Model”, International Journal of Innovative Research in Computer Science & Technology, Vol.2, Issue.3, pp.67-73, May 2014.
[2] A. Davenel, T. Labarre, “Automatic Detection of Surface Defects on Fruit by Suing a Vision System”, Journal of Agricultural Engineering Research, Vol.41, Issue.1, pp. 1-9, September 1988.

[3] Van Huy Pham, Byung Ryong Lee, “An Image Segmentation Approach for Fruit Defect Detection Using K-means Clustering and Graph-Based Algorithm”, Vietnam Journal of Computer Science, Vol.2, Issue.1, pp. 25-33, February 2015.
[4] Ghobad Moradi, “Fruit Defect Detection from Colour Images Using ACM and MFCM Algorithm”, International Conference on Electronic Devices Systems and Applications (ICEDSA), pp.182-186, April 2011.
[5] Dameshwari Sahu, “Identification and Classification of Mango Fruits Using Image Processing”, International Journal of Scientific Research in Computer Science,Engineering and Information Technology, Vol.2, Issue.2, pp.203-210, 2016 .
[6] Khune Sonali B, “Fruit Quality Assessment and Classification Using Image Processing”, International Journal of Innovative Research and Creative Technology, Vol.2, Issue.4, pp.156-159, 2015.
[7] Manali R. Satpute, “Automatic Fruit Quality Inspection System”, International Conference on Inventive Computation Technologies (ICICT), 2016.
[8] Ashwani Kumar Dubey, “Fruit Defect Detection Using Speeded Up Robust Feature Technique”, 5th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), pp.590-594, 2016.
[9] R. Sudha, “AFGDA: Defect Detection and Classification of Apple Fruit Images Using the Modified Watershed Segmentation Algorithm”, International Journal of Science Technology & Engineering , Vol.3, Issue.06, pp.75-85, December 2016.
[10] R.C. Gonzalez, R.E. Woods, “Digital Image Processing”, Prentice Hall, New Jersey 07548, 2007.

Siddhika Arunachalam, Harsh H. Kshatriya, Mamta Meena, "Identification of Defects in Fruits Using Digital Image Processing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.637-640, 2018.

In this paper, we propose a new algorithm to find a minimum Connected Dominating Set(CDS) of Mobile Adhoc Network(MANET) modelled as a unit disk graph. The new algorithm known as New Connected Dominating Set(NCDS) reduces the number of message that needs to be broadcasted and thereby saving time and elongating lifetime of the network and also supports dynamic topology of the network. Further important theorems of Unit disk graph of the form (2n+8, 6n+14) , having degree (1≤deg≤6) for n ≥1 have been proposed and proved.

Mobile Adhoc Network (MANET), Connected Dominating Set (CDS), Unit Disk Graph (UDG).

[1] S. Guha, and. S. Khuller, “Approximation Algorithms for Connected Dominating Sets”, Algorithmica Vol. 20, 4, pp. 374-387, 1998.
[2] X. Yan , Y.Sun,Y.Wang, “A Heuristic Algorithm for Minimum Connected Dominating Set with Maximal Weight in Ad Hoc Networks”, M. Li et al. (Eds.): GCC 2003, LNCS 3033, pp. 719–722, 2004. © Springer-Verlag Berlin Heidelberg 2004
[3] O. Chaturvedi, P Kaur, N Ahuja, T. Prakash, “Improved Algorithms for Construction of Connected Dominating Set in MANETs”, 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), 978-1-4673-8203-8/16/$31.00_c 2016 IEEE
[4]K..G. Preetha, A. Unnikrishnan , “ Enhanced domination set based routing in mobile ad hoc networks with reliable nodes”, @2017 Published by Elsevier Ltd.
[5]P.S..Vinayagam, “A Survey of Connected Dominating Set Algorithms for Virtual Backbone Construction in Ad Hoc Networks”, International Journal of Computer Applications (0975 – 8887) Volume 143 – No.9, June 2016
[6] K..G. Preetha, A. Unnikrishnan, “Improving the Routing Performance of Mobile Ad hoc Networks Using Domination Set”, International Conference on Information and Communication Technologies (ICICT 2014)
[7]K.Islam, S.G, Akl, H..Meijer, “Maximizing the Lifetime of Wireless Sensor Networks through Domatic Partition”, 2009 IEEE 34th Conference on Local Computer Networks (LCN 2009) Zürich, Switzerland; 20-23 October 2009
[8] B.Yin, H. Shi, Yi. Shang, “An efficient algorithm for constructing a connected dominating set in mobile ad hoc networks”, Journal of Parallel and Distributed Computing archive, Volume 71 Issue 1, January, 2011, Pages 27-39
[9] J. Wu, H. L. Li, “On Calculating Connected Dominating Set for Efficient Routing in Ad Hoc Wireless Networks”, Proc. of the 3rd International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, pp. 7-14, Aug. 1999.
[10] B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris, “Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks,” ACM Wireless Networks J., vol. 8, no. 5, pp. 481-494, Sept. 2002.
[11] M.Avula, S.M. Yoo, S Park , “Constructing Minimum Connected dominating Set in Mobile Adhoc Networks, International Journal of applications of graph theory in wireless ad hoc network and sensor networks” (GRAPH-HOC)Vo4, No 23,September 2012.

B. Kalita, A. K. Das, A.U. Islam, "Power Efficient Routing in Mobile Adhoc Network (MANET) Using Connected Dominating Set," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.641-649, 2018.

Cloud Computing plays a obvivious role in the field of web based computing.Multicloud is an advancement of the cloud computing. Multicloud is an environment comprises of more number of cloud service providers available to cater the needs of the heterogenous users. Task scheduling is one of the major issue affects the performance of the multicloud computing systems. This paper is designed to solve task based scheduling problem for the multicloud systems. Priority is one of the key concern to the service consumers. The high prioritized tasks are given due importance and they are executed in the high speed virtual machines. This article proposes a novel priority based independent task based scheduling algorithm for the multicloud environment. Fuzzy rule is used in this algorithm , to select the user prioritized tasks with larger length. Mandomi Inference system to generate a new rule to select and execute tasks. The proposed algorithm is merely emphasized on minimizing the total completion time of the tasks. There are five different categories of user priorities illustrated in this algorithm. The proposed algorithms outperforms the existing max-min algorithm in terms of makespan and cost.

max-min , makespan ,Multicloud , Mandomi

[1]. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, ”Cloud computing and emerging IT platforms: Vision, Hype, and Reality for Delivering computing as the 5th utility”, Future Generation Computer Systems, ACM Volume25,No.6,pp:599-616,2009.
[2]. T Chatterjee, V. K. Ojha, M. Adhikari, S. Banerjee, U. Biswas and V. Snasel (2014), “Design and Implementation of a New Datacenter Broker policy to improve the QoS of a Cloud”, Proceedings of ICBIA 2014, Advances in Intelligent Systems and Computing, vol. 303, pp- 281-290, 2014
[3]. BJ.Hubert Shanthan, L.Arockiam,”Resource Based Load Balanced Min Min Algorithm(RBLMM) for static Meta task Scheduling in Cloud”,International Conference on Advances in computer Science and Technology (IC-ACT’18) – 2018, International Journal of Engineering and Techniques(IJET), Special Issue, pp.1-5, 2018.
[4]. V.A.Jane, B.J.Hubert Shanthan,. A Survey of Algorithms for Scheduling in the Cloud: In a metric Perspective, International Journal of Computer Sciences and Engineering,Vol.6,Special Issue 2, pp.66-70, 2018.
[5]. Botta, A., de Donato, W., Persico, V. and Pescape. A, “Integration of cloud computing and internet of things: a survey. Future Generation Computer Systems, Vol:56, pp.684-700, 2016.
[6]. Sirisha Potluri, Katta Subba Rao, “Quality of Service based Task Scheduling Algorithms in Cloud Computing”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 7, No. 2, pp. 1088-1095, 2017.
[7]. L. Benedict Carvin, BJ. Hubert Shanthan, A. DalvinVinothKumar, Dr.L.Arockiam, “ Role of Scheduling and Load Balancing Algorithms in cloud to improve the Quality of Services”, International Journal of Computer Science, Vol.5,No.1,pp:1454-1462, 2017.
[8]. BJ. Hubert Shanthan, A. DalvinVinothKumar, Er. Karthigai Priya Govindarajan , Dr.L.Arockiam, “ Scheduling for Internet of Things Applications on Cloud: AReview”,Imperial Journal of Inter disciplinaryResearch(IJIR), Vol.1, No.3, pp:1649-1653,2017.
[9]. Jiang, Hui, Jianjun Yi,Shaoli Chen, Xiaomin Zhu,"A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly." Journal of Manufacturing Systems, Vol.41, pp:239-255, 2016.
[10]. Wang, G., Wang, Y., Liu, H., Guo, “HSIP: A Novel Task Scheduling Algorithm for Heterogeneous Computing.”, Scientific Programming, pp:1-11, 2016.
[11]. Kamali Gupta , Vijay Katiyar , “Survey of Resource Provisioning Heuristics in Cloud and their Parameters”, International Journal of Computational Intelligence Research, Vol.5,No.13,pp: 1283-1300, 2017.
[12]. B. Rajasekar, S.K. Manigandan, “An Efficient Resource Allocation Strategies in Cloud Computing”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, pp.1239-1244, 2015.
[13]. Kokilavani T, Amalarethinam DD,” Load balanced min-min algorithm for static meta-task scheduling in grid computing”, International Journal of Computer Applications,Vol:20, No:2, :43-49. 2011.
[14]. Gaurang Patel, Rutuvik Mehta , Upenndra Bhoi , “Enhanced Load Balanced Min Min Algorithm for static meta task scheduling in cloud computing “,Elsveir Procedia Computer Science, ICRTC-2015, Vol:57,2015.
[15]. Maheswaran M, Ali S, Siegal HJ, Hensgen D, Freund RF. “Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems”. In Heterogeneous Computing Workshop,.(HCW`99) Proceedings, IEEE, pp. 30-44, 1999.
[16]. Afaf Abd Elkader, "Enhancing the Minimum Average Scheduling Algorithm (MASA) based on Makespan Minimizing" , Artificial Intelligence and Machine Learning Journal, Vo. 17, No. 1, Delaware, USA, pp. 9-13, 2017.
[17]. Braun TD, Siegel HJ, Beck N, Bölöni LL, Maheswaran M, Reuther AI, Robertson JP, Theys MD, Yao B, Hensgen D, Freund RF. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed computing.Vol:61, No.6, pp: 810-37. 2001.
[18]. Thomas A, Krishnalal G, Raj VJ. Credit based scheduling algorithm in cloud computing environment. Procedia Computer Science, Vol :31, No:46, pp: 913-20, 2015.
[19]. Parsa S, Entezari-Maleki R,”RASA: A new task scheduling algorithm in grid environment.”, World Applied sciences journal, Vol:7, pp:152-60, 2009.
[20]. Sharma G, Banga P. “Task aware switcher scheduling for batch mode mapping in computational grid environment”, International Journal of Advanced Research in Computer ScienceandSoftwareEngineering,Vol:3,No:6,pp:1292-1299, 2013.

BJ. Hubert Shanthan, L. Arockiam, "LAHUBMAX –Priority Based Meta Task Scheduling Algorithm in multicloud," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.650-655, 2018.

Face recognition has acquired abundant attention in market and research communities, but still remained very accosting in real time applications. It is one of the various techniques used for identifying an individual. The major factors affecting the face recognition system are pose, illumination, identity, occlusion and expression. The image variations due to the change in face identity are less than the variations among the images of the same face under different illumination, expression, occlusion and viewing angle. Among the several factors that influence face recognition, illumination and pose are the two major challenges. Next to pose and illumination, the major factors that affect the performance of face recognition are occlusion and expression. So in order to overcome these issues, we proposed an efficient 3d face recognition system based on partial occlusion and expression. The similar blocks in the face image are identified. Then the occlusion can be recovered using the block matching technique. Finally, the face can be recognized by using the PCA. From the implementation result, it is proved that the proposed method recognizes the face images effectively.

Facerecognition,OcclusionDetection,Expression,BlockmatchingAlgorithm,PrincipalComponentAnalysis(PCA)

[1] Hongzhou Zhang, Yongping Li, Lin Wang and Chengbo Wang, "Pose insensitive Face Recognition Using Feature Transformation", IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.2, February 2007.
[2] Anil K. Jain, Arun Ross, and Salil Prabhakar, “An Introduction To Biometric Recognition”, IEEE Transactions On Circuits And Systems For Video Technology, Vol. 14, No. 1, Jan. 2004.
[3] Zhiwei Zhu and Qiang Ji, "Robust Pose Invariant Facial Feature Detection and Tracking in Real-Time", In proc. of the 18th International Conference on Pattern Recognition, pp.1092-1095, 2006.
[4] Shang-Hung Lin, "An Introduction to Face Recognition Technology", Informing Science Special Issue on Multimedia Informing Technologies, Vol.3, No. 1, 2000.
[5] Gregory Shakhnarovich and Baback Moghaddam, "Face Recognition in Subspaces", Springer, Heidelberg, May 2004.
[6] Zhao, W. Chellappa, R., Phillips, P. J. and Rosenfeld, A., “Face Recognition: A Literature Survey”, ACM Computing Survey, pp. 399-458, Dec 2003.
[7] N.V.S.L. Swarupa and D.Supriya, "Face Recognition System" ,International Journal of Computer Applications, Vol. 1,No. 29,pp.36-41,2010.
[8] Hazim Kemal Ekenel and Bulent Sankur, "Multiresolution face recognition", Image and Vision Computing, Vol.23, pp.469-477, 2005.
[9] De Marsico, M. Nappi, M. Riccio, D., “Face Recognition Against Occlusions and Expression Variations”Systems, Man and Cybernetics, IEEE Transactions, Vol.40, Issue 1, pp. 121-132, 2010
[10] F. Tarrés, A. Rama, L. Torres, "A Novel Method for Face Recognition under Partial Occlusion or Facial Expression Variations", In proc. of the 47th International Symposium (ELMAR-2005) on Multimedia Systems and Applications, Zadar, Croatia, June 2005.
[11] Aleix M. MartõÂnez, "Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class", IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.24, No.6, pp.748-763, 2002.
[12] B. Kepenekci, F. B. Tek, and G. B. Akar, "Occluded face recognition by using gabor features", In proc. of the 3rd COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, Budapest, pp. 1-6,Oct. 2002.
[13] Dahua Lin and Xiaoou Tang, "Quality-Driven Face Occlusion Detection and Recovery", In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-7, 2007.
[14] Kazuhiro Hotta, "Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel", Image and Vision Computing, Vol.26, No. 11, pp.1490–1498, 2008.
[15] Hyun Jun Oh, Kyoung Mu Lee and Sang Uk Lee, "Occlusion invariant face recognition using selective local non-negative matrix factorization basis images", Computer Vision, Vol.3851, pp.120-129, 2009.
[16] A. Srivastava, E. Klassen, S.H.S. Joshi, and I.H.I. Jermyn, “Shape Analysis of Elastic Curves in Euclidean Spaces,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 7, pp. 1415-1428, July 2011.
[17] C. Samir, A. Srivastava, and M. Daoudi, “Three-Dimensional Face Recognition Using Shapes of Facial Curves,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 1858-1863, Nov. 2006.
[18] C. Samir, A. Srivastava, M. Daoudi, and E. Klassen, “An Intrinsic Framework for Analysis of Facial Surfaces,” Int’l J. Computer Vision, vol. 82, no. 1, pp. 80-95, 2009.

Kshipra Soni, R.K. Kapoor, "Modified Technique of Three Dimensional Face Recognition in the Presences of Facial Expression," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.656-661, 2018.

This paper outlines the long-term vision to automate a language-less visualization system for communication with minimum language dependency. It proposes an interactive approach for Preksha, as Hindi text visualizer. Preksha is the process of understanding a natural language, transform the knowledge into a language independent abstract form and rendering a 3D Scene for visualization. Considering linguistic nature as morphologically rich and free-word-order for ‘Hindi’ language, the proposed work offers an approach for user interaction with system, in case of requirement.

Human Computer Interface (HCI), text visualization, natural language understanding, natural language processing, 3D scene generation, VRML, computer graphics

[1] Jain, P., Bhavsar, R., Kumar, A., Pawar, B. V., Darbari, H. and Bhavsar, V. C.: “Tree Adjoining Grammar based Parser for a Hindi text-to-scene conversion system” in 4th International Conference for Convergence in Technology (I2CT). 2018
[2] Winograd, T.: “Understanding Natural Language”. pp. 1-191. New York: Academic Press. pp. 191. New York: Academic Press, also published in Cognitive Psychology, 3:1, 1970.
[3] Chang, A. X., Savva, M. and Manning. C. D.: “Learning spatial knowledge for text to 3D scene generation” pp. 1-11. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, 2014.
[4] Clay, S.R., Wilhelms, J.: “Put: Language-based interactive manipulation of objects”. pp. 31-39. In IEEE Computer Graphics and Applications. 1996.
[5] Coyne, B., Sproat, R.: “WordsEye: An automatic text-to-scene conversion system”. pp. 487-496. SIGGRAPH, Computer Graphics Proceedings. 2001.
[6] Dupuy, S., Egges, A., Legendre, V., Nugues, P.: “Generating a 3d simulation of a car accident from a written description in natural language: The carsim system”. pp. 1-8. In Proceedings of ACL Workshop on Temporal and Spatial Information Processing. 2001.
[7] Earley, J.: “An Efficient Context-Free Parsing Algorithm”. pp. 94-102. In Commun. ACM 13(2). 1970.
[8] Hassani, K., and Lee, W. S.: “Visualizing Natural Language Descriptions: A Survey”, In ACM Computing Surveys (CSUR) Surveys Homepage archive, Article No. 17, Volume 49, Issue 1, July 2016.
[9] Jain, P. and Pawar, P.: "From `Pre-Position` to `Post-position`", pp. 66-71. In International Journal of Modern Computer Science (IJMCS) ISSN: 2320-7868 (Online), Volume 4, Issue Oct, 2016.
[10] Jain, P., Bhavsar, R. P., Lele, A. Kumar, A., Pawar, B. P., Darbari, H.: “Knowledge acquisition for automatic text visualization”.In National Conference on Advances in Computing (NCAC-2017). 2017
[11] Jain, P., Darbari, H., and Bhavsar, V, C.: ‘Vishit: A Visualizer for Hindi Text’. pp. 886-890. In Fourth International Conference on Communication Systems and Network Technologies (CSNT), IEEE Xplore. 2014.
[12] Jain, P., Darbari, H., and Bhavsar, V. C.: “Text Visualization as an Aid to Language Learning Disability”, pp. 88. In ELELTECH 2013 National Conference on e-Learning and e-Learning Technologies, India. 2013.
[13] Jain, P., Darbari, H., and Bhavsar, V. C.: “Cognitive support by Language Visualization: A case study with Hindi Language”. pp. 110-115.In 2nd International Conference for Convergence in Technology (I2CT), IEEE Xplore. 2017.
[14] Jain, P., Darbari, H., and Bhavsar, V. C.: “Spatial Intelligence from Hindi Language Text for Scene Generation”. pp. 132-138.In 2nd International Conference for Convergence in Technology (I2CT), IEEE Xplore. 2017.
[15] Jain, P., Pawar, P., Koriya, G., Lele, A., Kumar, A., Darbari, H.: “Knowledge acquisition for Language description from Scene understanding”.In IEEE International Conference on Computer, Communication and Control (IC4-2015) Conference. IEEE explore. 2015.
[16] Jain, P., Bhavsar, R. P., Pawar, B. V. and Darbari, H.: “VRML for automatic generation of 3D Scene”. In International Journal of Computer Application (2250-1797) Issue 8 Volume 2, March-April 2018.
[17] Joshi, A. K., Levy, L. S., and Takahashi, M.: “Tree Adjunct Grammars”. In J. Comput. Syst. Sci. 10(1). 1975.
[18] Jain, P., Bhavsar, R. P., Pawar, B. V. and Darbari, H.: “Empirical Evaluation for Hindi text-to-scene generation system” in International Journal of Creative Research Thought © 2018 IJCRT | ISSN: 2320-2882 | Volume 6, Issue 1 February 2018
[19] Ma, M.: (2006), ‘Automatic conversion of natural language to 3D animation’. Ph.D. thesis, University of Ulster, Derry, Ireland, pp. 1-250.
[20] B. Senthil Kumar, Jaya Prakash D, "A Study on The Relationships Between The Virtual Reality & Learning", International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.966-969, 2018.
[21] Y. Galphat, M. Gangwani, A. Bhave, B.S. Chadha, S. Adnani, "Integrating BCI with Virtual Reality", International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.129-131, 2018.

Shashak Samaiya, Yastika Jain, Priyanka Jain, "User Interactivity for Text Visualization System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.662-667, 2018.

Now a days researchers are using natural images for their research work. Natural images contain text also. Text in natural images typically adds meaning to an object or scene. So extract the text from natural images is very important for applications which are processing with text. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely challenging. Extracting text from an image can be done with image processing which deals with digital images. Extraction of text involves in different stages. They are named as preprocessing, detection, localization, extraction and recognition of the text from a given image. The aim of pre-processing is an improvement of the image data that suppresses unwanted distortions or enhances some image features important for further processing. So pre-processing is important stage to improve the image quality. This paper presents the existing histogram techniques for preprocessing and proposed a new technique named enhanced CLAHE which gives best contrast enhancement for natural scene images with text.

preprocessing, noise removal, image enhancement, histogram techniques, skew correction

[1] Asit kumar et al., “Detection and Recognition of Text from Image using Contrast and Edge Enhanced MSER Segmentation and OCR”, IJO-Science,ISSN: 2455-0108.
[2]Balvant Singh, Ravi Shankar Mistra, Puran Gour, “Analysis of Contrast Enhancement Techniques For Underwater Image,” IJCTEE Volume 1, Issue 2, 2009
[3] Gllavata et al., “A Robust algorithm for Text Detection in Images” , 2017.
[4] Huang et al., “A SWT Verified Method of Natural Scene Text Detection”, Advances in Computational Intelligence ISBN : 978-1-61804-343-6
[5] A.J.Jadhav, Vaibhav Kolhe, Sagar peswe, “Text Extraction from Images : A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, volume 3,Issue 3, March 2013.
[6] Neumann et al., “Realtime Scene Text Localization and Recognition”, IEEE journal ,2012.
[7] Partha sarathi giri “Text Information Extraction And Analysis From Images Using Digital Image Processing Tchniques” , Special Issue of International Journal on Advanced Computer Theory and Engineering (IJACTE) , ISSN : 2319 -2526, Volume 2, Issue 1,2013
[8] Raimondo Schettini and Silvia Corchs, “Review Article - Underwater Image Processing : State of the Art of restoration and Image Enhancement Methods,” EURASIP Journal on Advances in Signal Processing, Volume 2010.
[9] Rajesh Garg, Bhawna Mittal, sheetal garg, “Histogram Equalization Techniques For Image Enhancement,” International Journal of Electronics & Communication Technology, Volume 2, Issue 1, March 2011.
[10] Rajesh Kumar Rai, Puran Gour, Balvant Singh, “Underwater Image Segmentation using CLAHE Enhancement and Thresholding,” International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 1, January 2012
[11] Ramyashree N, Pathra P, Shruthi T V, Dr. JharnaMajumdar, “Enhacement of Aerial and Medical Image using Multi resolution pyramid,” Special Issue of IJCCT Vol. 1 Issue 2,3,4; International Conferecnce - ACCTA-2010
[12] Ramesh Neelamani, Thesis report on “ Inverse problems in image processing ” Electrical and Computer Engineering Rice University, Houston, Texas.
[13] Reginald L. Lagendijk and Jan Biemond, “Basic methods for image restoration and Identification ”, Lagendijk – Biemond , February, 1999.
[14] Seokjun et al., “Text Region Extraction in High Contrasting Image”, International Journal of Future Computer and Communication, Vol.6, No. 3, September 2017.
[15] Stephen M. Pizer, E. Philip Amburn, John D. Austin, Robert Cromartie, “Adaptive Histogram Equalization and Its Variations,” Computer Vision, Graphics, And Image Processing 39, 355-368 (1987) .
[16] Yin et al., “Robust Text Detection in Natural Scene images”, IEEE journal , June 2013.
[17] K. Zuiderveld, “Contrast Limited Adaptive Histogram Equalization”, Academic Press Inc.,

S. Shiyamala, S .Suganya, "Pre-Processing for Text Extraction System using Histogram Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.668-673, 2018.