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Water is the most precious and valuable resource. With the increase in population, availability of clean water has become a problem. Today, water-supply department as well as common man is facing problems in real-time operations like water distribution and conservation efficiency. Therefore it is important to find a solution to address water wastage through efficient water monitoring and control system. In this paper, the problem is solved through autonomous water tank filling system using IoT where in embedded sensors are used to monitor the tank status along with some other key attributes like power supply, incoming water supply in real-time. Our intention of this research work was to establish a flexible, economical, easy configurable and most importantly, a portable system which can solve our water wastage problem along with saving the electrical energy. This enhances the efficiency of water distribution and reduces wastage.

Water conservation, Realtime monitoring, Proper utilization of Water, IoT, Sensors, Cloud

[1] Oluigbo Ikenna V., Nwokonkwo Obi C., Ezeh Gloria N., Ndukwe Ngoziobasi G., "Revolutionizing the Healthcare Industry in Nigeria: The Role of Internet of Things and Big Data Analytics", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.1-12, 2017
[2] M. Saraswati, “Design and Construction of Water Level Measurement System Accessible through SMS”, in the Proceedings of the 2012 IEEE Computer Modeling and Simulation (EMS), 2012 Sixth UKSim/AMSS European Symposium, Valetta, Malta.
[3] Prachet Verma, “Towards an IoT based water management system for a campus”, in the Proceedings of Smart Cities Conference (ISC2), 2015 IEEE First International, Guadalajara, Mexico.
[4] Sayali Wadeker, “Smart Water Management using IOT”, in the Proceedings of the 2016 IEEE Wireless Networks and Embedded Systems (WECON), 2016 5th International Conference, Rajpura, India.
[5] Priyen P. Shah, “IoT based Smart Water Tank with Android application”, in the Proceedings of I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2017 International Conference.

S. Nalini Durga, M. Ramakrishna, G. Dayanandam, "Autonomous Water tank Filling System using IoT," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.1-4, 2018.

An individual`s medical record is a vital form that can be used to track patient data accurately, reliably and completely. For all purposes, the exchange of repair information is a basic and test problem. Consequently, in this document, we develop a new structure for human services through the use of cloudlet adaptability. Cloudlet elements include security insurance, information exchange, and breakpoint location. In the information accumulation phase, we initially used the Numerical Theory Research Unit (NTRU) technique to encode client body information collected from a portable device. This information will be send to near cloudlets in a competent form of vitality. In addition, we show another model of trust to allow customers to choose trusted partners who want to share data stored in the cloudlet. The demonstration of trust also makes comparable patients who talk to each other about their illnesses. Third, we isolate the patient`s medical information stored at a distance in three sections and provide them with adequate insurance.

Privacy Protection, Data Sharing, Collaborative Intrusion Detection System (IDS), Healthcare

A. Sajid and H. Abbas, “Data privacy in cloud-assisted healthcare systems: State of the art and future challenges,” Journal of Medical Systems, vol. 40, no. 6, pp. 1–16, 2016.
[2] R. Mitchell and I.-R. Chen, “Behaviour rule specification-based intrusion detection for safety critical medical cyber physical systems,” Dependable and Secure Computing, IEEE Transactions on, vol. 12, no. 1, pp. 16–30, 2015.
[3] Y. Shi, S. Abhilash, and K. Hwang, “Cloudlet mesh for securing mobile clouds from intrusions and network attacks,” in The Third IEEE International Conference on Mobile Cloud Computing, Services, and Engineering,(Mobile Cloud 2015). IEEE, 2015.
[4] M. Quwaider and Y. Jararweh,“ Cloudlet-based efficient data collection in wireless body area networks,” Simulation Modelling Practice and Theory, vol. 50, pp. 57–71, 2015.
[5] M. S. Hossain, “Cloud-supported cyber–physical localization framework for patients monitoring,” 2015.
[6] J. Zhao, L. Wang, J. Tao, J. Chen, W. Sun, R. Ranjan, J. Kołodziej, A. Streit, and D. Georgakopoulos, “A security framework in g-hadoop for big data computing across distributed cloud data centres,” Journal of Computer and System Sciences, vol. 80, no. 5, pp. 994–1007, 2014.
[7] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacy-preserving multi-keyword ranked search over encrypted cloud data,” Parallel and Distributed Systems, IEEE Transactions on, vol. 25, no. 1, pp. 222–233, 2014.
[8] H. Mohamed, L. Adil, T. Saida, and M. Hicham, “A collaborative intrusion detection and prevention system in cloud computing,” in AFRICON, 2013. IEEE, 2013, pp. 1–5.
[9]R. Zhang and L. Liu, “Security models and requirements for healthcare application clouds,” in Cloud Computing (CLOUD), 2010 IEEE 3rdInternational Conference on. IEEE, 2010, pp. 268–275.
[10] K. Hung, Y. Zhang, and B. Tai, “Wearable medical devices for telehome healthcare,” in Engineering in Medicine and Biology Society, 2004. IEMBS’ 04.26th Annual International Conference of the IEEE, vol. 2. IEEE, 2004, pp. 5384–5387.

Kavita V. Dubey, Vina M. Lomte , "Healthcare System with Intrusion Detection and Privacy Protection based Cloudlet," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.5-8, 2018.

In the modern society everybody wants to be familiar with people’s characteristics to predict and be aware of their reaction to diverse situation, though it’s hard to understand psychological nature and characteristics of a person. For this reason, researches have been carried out in this direction to predict the characteristics of a person such as maturity, warmth, intelligence, sociality, dominance, as well as the trustworthiness. Here aim is to identify person’s characteristics based on the facial features by using techniques such as SURF, which is going to be used for the extraction of the facial features and K-nearest neighbor classifier for identification of the characteristics of the human being. With the various features mentioned and by using the appropriate techniques, the characteristics of a person can be predicted. The overall performance of the proposed work has been estimated by well established dataset and results show that the proposed work has performed well.

Speed-Up Robust Features, Interest points,Character recognition

[1] Gonzalo Martinez Munoz and Alberto Suarez, “Switching Class Labels to Generate Classification Ensembles”, Elsevier Science, 2005.
[2] Sheryl Brahnam, “A computational Model of the Trait Impressions of Face for Agent Perception and Face Synthesis”, SSAISB, 2005.
[3] Loris Nanni, Dario Maio, “Weighted Sub-Gabor for Face Recognition”, Elsevier, 2006
[4] Sheryl Brahnam and Loris Nanni, “Predicting Trait Impressions of faces using Classifier Ensembles”, Springer, 2009.
[5] Sheryl Brahnam, Loris Nanni, “Predicting Trait Impressions of Faces using Local Face Recognition Techniques”,Elsevier, 2010.
[6] Mario Rojas, Jordi Vitria, “Predicting Dominance Judgments Automatically: A Machine Learning Approach”, IEEE, 2010.
[7] Alexander Todorov and Jordi Vitria, “Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models”, PLoS ONE, 2011.
[8] Lucas Assirati, Nubia R. da Silva, Lilian Berton, Alneu de A. Lopes, and Odemir M. Bruno, “Performing edge detection by difference of Gaussians using q- Gaussian Kernels”, arXiv, 2013.
[9] Lucas Assirati, Nubia Rosa da Silva, Odemir Martinez Bruno, “Improving texture classification with non extensive statistical mechanics”, X Workshop de Vis ao Computational WVC, 2014.
[10] Oya C¸ eliktutan and Hatice Gunes “Continuous prediction of perceived traits and social dimensions in space and time”, ICIP, 2014.
[11] C. N. Ravi Kumar, P.Girish Chandra, R.Narayana, “Future path way to Biometrics”, IJBB Volume(5), Issue(3), 2011.
[12] Ekaterina Kamenskaya, Georgy Kukharev, “Recognition of Psychological characteristics from Face”, www.researchgate.net, 2010.
[13] Herbert Bay, Andreas Ess,Tinne Tuytelaar, Luc Van Gool,”Speed-Up Robust features(SURF)”,ScienceDirect(Elsevier),15-Decemebr-2007.
[14] P. Simard, L. Bottou, P. Haffner, Y. LeCun, Boxlets”A fast convolution algorithm for signal processing and neural networks, in” NIPS, 1998.
[15] P.A. Viola, M.J. Jones, “Rapid object detection using a boosted cascade of simple feature”, in CVPR, issue 1, pp. 511–518, 2001.
[16] K. Mikolajczyk, C. Schmid, “Indexing based on scale invariant interest points” in ICCV, vol. 1, pp. 525–531, 2001.
[17] T. Lindeberg, “Feature detection with automatic scale selection” IJCV 30 (2) 79–116, 1998.
[18] J.J. Koenderink, “The structure of images” Biological Cybernetics 50 363–370, 1984.
[19] T. Lindeberg, “Scale-space for discrete signal”, PAMI 12 (3) 234–254, 1990.
[20] Hrishikesh Dubey, “Mysteries of Vedic face reading”,2014.
[21] Komal D. Khawale,D.R. Dhotre “To Recognize Human Emotions Based on Facial Expression Recognition : A Literature Survey”, IJSRCSEIT , Vol 2 , Issue 1 , ISSN : 2456-3307, 2017.
[22] Er. Navleen Kour, Dr. Naveen Kumar Gondhi “Facial Expressions Detection and Recognition Using Neural Networks”, IJSRCSEIT , Vol 2 , Issue 7 , ISSN : 2456-3307, 2017.

Mahesh U Nagaral, T. Hanumantha Reddy, "Predicting the Characteristics of a Human from Facial Features by Using SURF," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.9-16, 2018.

In recent years, Internet is growing exponentially and so is the amount of learning resources. Due to overload of information, learners find it difficult to retrieve the appropriate learning resource. In academic domain, recommendation systems are facing problems in providing accurate suggestions to learner due to difference in types of learning resources, learner preferences, knowledge level and quality of the learning resource. In this context, the objective of this paper is four folds: Firstly, the paper discusses various techniques used in creation of recommendation system with a special focus on Academic Domain. Secondly, it compares and contrasts the existing recommender systems in practice today. Thirdly, the paper looks at the possibility of including Sentiment Analysis as an effective technique for recommending learning resources to the learners & it goes at length to give a sequential flow chart for a pilot study of book recommender system. Finally, the paper concludes by drawing the inferences on the introduction of sentiment analysis as a useful technique for recommendation system.

Book Recommendation System, Recommendation System, Sentiment Analysis

[1] M. N. Moreno, S. Segrera, Vivian F. López, M. D. Muñoz ,Á. L. Sánchez “Web mining based framework for solving usual problems in recommender systems. A case study for movies` recommendation,” Neurocomputing, Vol. 176, pp. 72–80, 2015.
[2] J. Bobadilla F. Ortega, A. Hernando, A. Gutiérrez, “Recommender systems survey,” Knowledge-Based System, Vol. 46, pp. 109–132, 2013.
[3] J. Lu, D. Wu, M. Mao, W. Wang, G. Zhang, “Recommender system application developments: A survey,” Decision Support System, Vol. 74, pp. 12–32, 2015.
[4] A. Klašnja-Milićevic, M. Ivanovi, A. Nanopoulos, “Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions,” Artificial Intelligence Review, Vol. 44, no. 4, pp. 571–604, 2015.
[5] S. E. Middleton, N. R. Shadbolt, D. C. De Roure, “Ontological User Profiling in Recommender Systems,” ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 54–88, 2004.
[6] K. I. Bin Ghauth, N. A. Abdullah, “Building an e-learning recommender system using Vector Space Model and good learners average rating,” in Proceedings of 9th IEEE International Conference on Advanced Learning Technologies, pp. 194–196, 2016.
[7] F. Abel, I. I. Bittencourt, E. Costa, N. Henze, D. Krause, J. Vassileva, “Recommendations in online discussion forums for e-learning systems,” IEEE Transactions On Learning Technologies, Vol. 3, no. 2, pp. 165–176, 2010.
[8] A. Klašnja-Milićević, B. Vesin, M. Ivanovi, Z. Budimac, “E-Learning personalization based on hybrid recommendation strategy and learning style identification,” Computers & Education, Vol. 56, no. 3, pp. 885–899, 2011.
[9] C. Rana, S. K. Jain, “Building a book recommender system using time based content filtering,” WSEAS Transactions on Computers, Vol. 11, no. 2, pp. 27–33, 2012.
[10] B. Vesin, A. Klašnja-Milićević, “APPLYING RECOMMENDER SYSTEMS AND ADAPTIVE HYPERMEDIA FOR E-LEARNING PERSONALIZATION,” Computing and Informatics, Vol. 32, pp. 629–659, 2013.
[11] S. Shishehchi, S. Y. Banihashem, N. A. M. Zin, S. A. M. Noah, “Ontological approach in knowledge based recommender system to develop the quality of e-learning system,” Australian Journal of Basic and Applied Sciences, Vol. 6, no. 2, pp. 115–123, 2012.
[12] S. B. Aher, L. M. R. J. Lobo, “Knowledge-Based Systems Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data,” Knowledge-Based Systems, Vol. 51, pp. 1–14, 2013.
[13] W. Chen, Z. Niu,X. Zhao, Y. Li, “A hybrid recommendation algorithm adapted in e-learning environments,” World Wide Web, Vol. 17, no. 2, pp. 271–284, 2014.
[14] S. Fraihat, Q. Shambour, “A Framework of Semantic Recommender System for e- Learning,” Journal of Software, Vol. 10, no. 3, pp. 317–330, 2015.
[15] J. Bernab, E. Herrera-viedma, “A dynamic recommender system as reinforcement for personalized education by a fuzzly linguistic web system,” Procedia Computer Science, Vol. 55, no. Itqm, pp. 1143–1150, 2015.
[16] Y. Zhang, “Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation,” in Proceeding of WSDM `15, Shanghai, China, February 02 - 06, pp. 435–439, 2015
[17] J. K. Tarus, Z. Niu, A. Yousif, “A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining,” Future Generation Computer System, Vol. 72, pp. 37–48, 2017.
[18] A. Koukourikos, G. Stoitsis, P. Karampiperis , “Sentiment Analysis : A tool for Rating Attribution to Content in Recommender Systems,” in Proceedings of RecSysTEL 2012, pp. 61–70, 2012.
[19] R. Sikka, A. Dhankar, C. Rana, “A Survey Paper on E-Learning Recommender System,” International Journal of Computer Applications, Vol. 47, no. 9, pp. 27–30, 2012.
[20] J. Serrano-Guerrero, J. A. Olivas, F. P. Romeroa, E. Herrera-Viedma , “Sentiment analysis: A review and comparative analysis of web services," Information Sciences, Vol. 311, pp. 18–38, 2015.

Anil Kumar, Sonal Chawla, "Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.17-22, 2018.

Forest Fire causes more disasters to the environment. Detecting the fire in the early stage will play a crucial role to prevent the risky effects. The vision-based approaches have gained more impact than the conventional fire detection methods with respect to accuracy and less false alarms. A reliable and efficient computer vision based technique to retrieve fire-colored pixels in still images is proposed in this article. It adopts both RGB and L*a*b* space for segmenting the fire-colored pixels on colour feature. The proposed results are compared with the current methods. The results of proposed method bring satisfactory results than the existing techniques.

Object detection, Color Spaces, Thresholding, Segmentation

[1] Arun and Santhosh, "Lab Color Space Model with Optical Flow Estimation for Fire Detection in Videos ", IOSR Journal of Computer Engineering, 2014, pp 23-28.
[2] B.U. Toreyin, Y. Dedeoglu, and A.E. Cetin, “Computer Vision Based Method for Real-Time Fire and Flame Detection”, Pattern Recognition Letters, vol. 27, no. 1, 2006, pp. 49-58.
[3] B.U. Toreyin, Y. Dedeoglu, and A.E. Cetin, “Flame Detection in Video Using Hidden Markov Models”, Proc. IEEE Int. Conf. Image Process. 2005, pp. 1230-1233, 2005.
[4] Daniel Y. T. Chino, Letricia P. S. Avalhais, Jose F. Rodrigues Jr., Agma J. M. Traina BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis, Proceedings of the 28th SIBGRAPI Conference on Graphics, Patterns and Images, 2015.
[5] Habiboˇglu, Y.H., G¨unay, O., C¸ etin, A.E., “Covariance matrix-based fire and flame detection method in video”, Machine Vision and Applications 23(6), 11031113 (2011).
[6] Hira Lal Gope*1, Machbah Uddin2, Shohag Barman3, Dilshad Islam4, Dr. Mohammad Khairul Islam5, "Fire Detection in Still Image Using Color Model " ,Indonesian Journal of Electrical Engineering and Computer Science Vol. 3, No. 3, September 2016, pp. 618 ~ 625.
[7] J. Zhao, Z. Zhang, S. Han, C. Qu, Z. Yuan, and D. Zhang, “Svm based forest fire detection using static and dynamic features,” Computer Science and Information Systems, vol. 8, no. 3, pp. 821–841, 2011.
[8] J.Liu, W, "Early fire detection in coalmine based on video processing" Advance in Intelligent Systems and Computing, vol.181,, 239-245, 2013.Kumarguru Poobalan1 and Siau-Chuin Liew2, "Fire Detection algorithm using Image Processing Techniques" , Proceeding of the 3rd International Conference on Artificial Intelligence and Computer Science (AICS2015), 12 - 13 October 2015 pp 160-168.
[9] Rossi, L., Akhloufi, M., Tison, Y, " On the use of stereovision to develop a novel instrumentation system to extract geometric fire fronts characteristics”, Fire Safety Journal 46, 920 (2011).
[10] Rudz, S., Chetehouna, K., Hafiane, A., Laurent, H., Sero- Guillaume, O., " Investigation of a novel image segmentation method dedicated to forest fire applications" , In: Measurement Science and Technology 24(7), pp.075403 (2013).
[11] T. Celik et al., “Fire Detection Using Statistical Color Model in Video Sequences,” J. Visual Commun. Image Representation, vol. 18, no. 2, Apr 2007, pp. 176-185.
[12] T. Celik, H. Demirel, and H. Ozkaramanli, “Automatic Fire Detection in Video Sequences,” Proc. European Signal
Process. Conf., Florence, Italy, Sept. 2006.
[13] T.-H. Chen, P.-H. Wu, and Y.-C. Chiou, “An early fire-detection method based on image processing,” in ICIP, vol. 3, 2004, pp. 1707–1710.
[14] Tom Toulouse, Lucile Rossi, Turgay Celik, Moulay Akhlou, "Automatic fire pixel detection using image processing: A comparative analysis of Rule-based and Machine Learning-based methods", Signal Image and Video Processing, 2015, pp.1863-1703.
[15] Turgay Celik, "Fast and Efficient Method for Fire Detection Using Image Processing ", ETRI Journal, Volume 32, Number 6, December 2010.
[16] W. Phillips III, M. Shah, and N. da Vitoria Lobo, “Flame Recognition in Video,” Proc. 5th Workshop Appl. Computer Vision, 2000, pp. 224- 229.
[17] YH Kim, A Kim, HY Jeong, “RGB color model based the fire detection algorithm in video sequences on wireless sensor network ", Int. J. Distrib. Sensor Netw. 2014.
[18] V. Vipin. “Image Processing Based Forest Fire Detection”, IJETAE, 2(2012) 87-95.
[19] C. Emmy Prema*,S. S. Vinsley, S. Suresh,” Efficient Flame Detection Based on Static and Dynamic Texture Analysis in Forest Fire Detection”, Fire Technology Journal , 54, 255–288, 2018
[20] Pasquale Foggia,” Real-time Fire Detection for Video Surveillance Applications using a Combination of Experts based on Color, Shape and Motion”, IEEE Transactions on Circuits and Systems for Video Technology •, pp 1-12, January 2015.

M.Senthil vadivu, Vijayalakshmi M.N, "Efficient Fire Pixel Segmentation Using Color Models in Still Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.23-28, 2018.

Geo-spatial advancements like Remote Sensing (RS) and Geographical Information Systems (GIS) play an essential job in developing thematic maps and integrating analysis for mapping, managing and monitoring the natural resources. RS and GIS technologies have advanced a new era in the field of applied geology and geomorphology. Geomorphology is the science of landforms present on the Earth’s surface and their systematic study is important and unique in order to interpret them as signatures of the past and ongoing geological processes. The present examination plans to delineate geomorphological features in the district of Visakhapatnam in view of visual image translation strategies. The study area mainly comprises Pediment slope(27.39%) followed by Structural hill(25.51%) and Pediplain shallow(18.06%).These maps would be useful in further analysis for natural Earth resources planning, management and decision making. Thematic maps of geomorphology have been generated on satellite data. Standard visual elucidation methods according to the standards given by NRSA have been followed and portrayed on-screen digitations of features.

Remote Sensing, Geographic Information Systems, geomorphology, natural resource planning, decision making

[1] M. Sunandana Reddy, L. Harish Kumar, “GIS based Land Information System for Medchal Mandal of R.R. District”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.43-49, 2018.
[2] Tulli Chandrasekhara Rao, G. Jaisankar, Aditya Allamraju, E. Amminedu, “Geomorphological mapping through Remote Sensing and GIS Techniques for Janjhavathi River basin, Odisha and Andhra Pradesh.”, International Journal of Engineering, Science and Mathematics, Vol.7, Issue.3, pp.164-170, 2018.
[3] Suraj Kumar Singh, Vikash Kumar, Shruti Kanga, “Land Use/Land Cover Change Dynamics and River Water Quality Assessment Using Geospatial Technique: a case study of Harmu River, Ranchi (India)”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.17-24, 2017.
[4] V. Sivakumar, “Geological, Geomorphological and Lineament mapping through Remote Sensing and GIS Techniques, in parts of Madurai, Ramanathapuram and Tiruchirappalli districts of Tamil Nadu”, International Journal of Geomatics and Geosciences, Vol.6, Issue.3, pp, 1669-1675, 2016.
[5] Tripti Jayal, “Study of geomorphology and drainage basin characteristic of Kaphni Glacier, Uttarakhand, India.”, International Journal of Interdisciplinary and Multidisciplinary Studies, Vol.2, Issue.7, pp. 35- 48, 2015.
[6] Tanzeer Hasan, “Geobotanical and geomorphological approach to map the surface lithology using remote sensor data”, International Journal of Geomatics and Geoscience, Vol.4, Issue.3, pp. 558-572, 2014.
[7] S.N. Mohapatra, Padmini Pani, Monika Sharma, “Rapid Urban Expansion and Its Implications on Geomorphology: A Remote Sensing and GIS Based Study.”, Geography Journal, Vol.2014, pp.1-10, 2014.
[8] Aung Lwina, Myint Myint Khaing, “Yangon river Geomorphology identification and its Enviromental Impacts Analysis By Optical And Radar Sensing Techniques”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXIX-B8, pp. 175-179, 2012.
[9] C. Siart, O. Bubenzer, B. Eitel, “Combining digital elevation data (SRTM/ASTER) high resolution satellite imagery (Quickbird) and GIS for geomorphological mapping: A multi-component case study on Mediterranean karst in Central Crete”, Geomorphology, Vol.112 , Issue.1-2 ,pp.106-121 , 2009.
[10] G.Brierley, “Geomorphology and river management”, Kemanusiaan The Asian Journal of Humanities, Vol.15, pp. 13-26, 2008.
[11] S. J. Walsh, D. R. Butler, G. P. Malanson, “An overview of scale, pattern, process relationships in geomorphology: a remote sensing and GIS perspective”, Geomorphology, Vol.21, Issue.3-4, pp. 183-205 , 1998.
[12] J. Krishnamurthy, G. Srinivas, “Role of geological and geomorphological factors in ground water exploration: a study using IRS LISS data”, International Journal of Remote Sensing, Vol.16, Issue.14, pp. 2595-2618, 1995.

B. Sridhar, P. Jagadeeswara Rao, Aditya Allamraju, "Geomorphological Mapping Through Geospatial Technologies In The District of Visakhapatnam, Andhra Pradesh, India," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.29-35, 2018.

Epilepsy is disease which is caused due to neurological disorder of a brain. It may cause recurrent seizures. It can be detected with the EEG signals and records the activity of brain electrically. In this paper K-Nearest Neighbor, Random Forest and Naive Bayes algorithms are used for classification of Electroencephalogram (EEG) signal as epilepsy or normal signal. These Machine learning algorithms learn directly from the data by experience which is not interrupted manually. Supervised learning uses labeled data for training which maps the input to the corresponding output. It classified into two types such as classification and regression. Classification means prediction of output from the input to which class it relies on, such as boy or girl. Whereas Regression means prediction of output from the input but output is predicted as a real value like measurement of rainfall etc. Here Random Forest method performs the best classification other than that of KNN and Naïve Bayes.

Classification, Electroencephalogram, Epilepsy, Machine Learning, Regression

[1] J.V.N. Lakshmi, Ananthi Sheshasaayee, “A Big Data Analytical Approach for Analyzing Temperature Dataset using Machine Learning Techniques”, International Journal of scientific Research in Computer Science and Engineering, vol 5, issue 3, pp 92-97, June (2017), E-ISSN : 2320-7639
[2] Md. Khayrul Bashar, Ishio Chiaki, Hiroaki Yoshida Human Identification from Brain EEG signals Using Advanced Machine Learning Method, 978-1-4673-7791-1/16/$31.00 ©2016 IEEE
[3] Sabrina Ammar, Mohamed Senouci, “Seizure Detection with Single-Channel EEG using Extreme Learning Machine” 978-1-5090-3407-9/16/$31.00 ©2016 IEEE
[4] Vinay K, ”Machine learning approach via an ensemble of classifiers for computer aided lung nodule diagnosis”, Shodhganga :a reservoir of Indian theses @ INFLIBNET, Mar2015
[5] Rishi Das Roy, “Development and application of machine learning tool in deciphering biological information”, Shodhganga :a reservoir of Indian theses @ INFLIBNET, June 2016
[6] Khalid Alkhatib, Hassan Najadat, Ismail Hmeidi, Mohammed K. Ali Shatnawi, ‘Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm’ International Journal of Business, Humanities and Technology Vol. 3 No. 3; March 2013 32
[7] Aman Kataria1, M. D. Singh, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 6, June 2013)
[8] Chin-Min Ma, Wei-Shui Yang and Bor-Wen Cheng, ‘How the parameters of K-Nearest Neighbor Algorithm Impact on the Classification Accuracy: In Case of Parkinson Dataset’ Journal of Applied Sciences 14 (2): 171-176,2014 ISSN 1812-5654 / DOI: 10.3923/ jas 2014.171.176 ©2014 Asian Network for Science Information
[9] Weiting Chen, Yu Wang, Guitao Cao, Guoqiang Chen, Qiufang Gu, December 2013 ‘A random forest model based classification scheme for neonatal amplitude-integrated EEG’ IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2013) Shanghai, China. 18-21
[10] Swati Vaid, Preeti Singh and Chamandeep Kaur, “Classification of Human Emotions using Multiwavelet Transform based Features and Random Forest Technique”’, Indian Journal of Science and Technology, Vol 8(28), DOI: 10.17485/ijst/2015/v8i28/70797, ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 October 2015
[11] Meenakshi Dauwan, Jessica J. van der Zande, Edwin van Dellen, Iris E. C. Sommer, Philip Schelten, Afina W. Lemstra, Cornelis J. Stam, “Random Forest to differentiate dementia with lewy bodies from alzheimers disease”, 2352-8729/Ó2016 Published by Elsevier Inc
[12] Juliano Machado, Alexandre Balbinot, Adalberto schuck “A study of Naïve Bayes Classifier for analyzing imaginary movement EEG signal using the periodogram as spectral estimation”, 2013
[13] Beata szuflitowska, przemyslaw, orlowski, “Comparision of the EEG signal classifier LDA, NBC and GNBC based on time frequency features”, 2017
[14] Ali Akbar Hossinezadeh, Azra Yoghoobi Karimoi, Reza Yaghoobi, Mohammad Ali Khalizadeh, “EEG signal classification using Bayes and Naïve Bayes classifier and extracted features of continues wavelet transform”
[15] Khalid Alkhatib, Hassan Najadat, Ismail Hmeidi, Mohammed K. Ali Shatnawi, “Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm” International Journal of Business, Humanities and Technology Vol. 3 No. 3; March 2013 32
[16] Deepika Mallampati, “An Efficient Spam Filtering using Supervised Machine Learning Techniques”, International Journal of scientific Research in Computer Science and Engineering, vol 6, issue 2, pp 33-37, April (2018), E-ISSN : 2320-7639
[17] Zhongheng Zhang, Submitted, “ Naïve Bayes classification in R”, Accepted for publication Feb 24, 2016. doi: 10.21037/atm.2016.03.38 View this article at: http://dx.doi.org/10.21037/atm.2016.03.38, Jan 25, 2016

T. Perumal Rani, Heren Chellam G., "Epileptic Electroencephalogram Classification Using Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.36-41, 2018.

Moderndayspresent a prevailingmugger which breaks records discretionat some stage in acquiring cryptographic keys by means ofoppression or backdoors in a cryptographic software program. Once the encryption key is uncovered, the most effective possible degree to keep information confidentiality is to limit the attackers can allow accessing the ciphertext. This perhaps executed, for example, by sharing the ciphertext blocks to servers in compound executive domain names subsequently conceited that the attacker cannot reunion all of them. Nevertheless, if records are encrypted with existing schemes, an adversary geared up with the encryption key, can still compromise a single server and decrypt the ciphertext blocks saved therein. In this paper, we look at statistics confidentiality in opposition to an adversary which is aware of the encryption key and has to allow to a huge fraction of the ciphertext blocks. In this case, we endorse Bastion, a unique and efficient scheme that guarantees records confidentiality although the encryption key is leaked and the adversary allow to almost all ciphertext blocks. We examine the security of Bastion, and we evaluate its performance by means of a prototype implementation. We also discuss sensible insights with admire to the combination of Bastion in industrial dispersed storage structures. Our assessment outcomes recommend that Bastion is nicely-appropriate for integration in present systems because it incurs much less than five% overhead as compared to existing semantically at ease encryption modes.

Moderndayspresent, ciphertext

[1] M. Abd-El-Malek, G. R. Ganger, G. R. Goodson, M. K. Reiter, and J. J. Wylie, “Fault-Scalable Byzantine Fault-Tolerant Services,” in ACM Symposium on Operating Systems Principles (SOSP), 2005, pp. 59–74.
[2] M. K. Aguilera, R. Janakiraman, and L. Xu, “Using Erasure Codes Efficiently for Storage in a Distributed System,” in International Conference on Dependable Systems and Networks (DSN), 2005, pp. 336–345.
[3] W. Aiello, M. Bellare, G. D. Crescenzo, and R. Venkatesan, “Security amplification by composition: The case of doublyiterated, ideal ciphers,” in Advances in Cryptology (CRYPTO), 1998, pp. 390–407.
[4] C. Basescu, C. Cachin, I. Eyal, R. Haas, and M. Vukolic, “Robust Data Sharing with Key-value Stores,” in ACM SIGACTSIGOPS Symposium on Principles of Distributed Computing (PODC), 2011, pp. 221–222.
[5] A. Beimel, “Secret-sharing schemes: A survey,” in International Workshop on Coding and Cryptology (IWCC), 2011, pp. 11–46.
[6] A. Bessani, M. Correia, B. Quaresma, F. André, and P. Sousa, “DepSky: Dependable and Secure Storage in a Cloud-ofclouds,” in Sixth Conference on Computer Systems (EuroSys), 2011, pp. 31–46.
[7] G. R. Blakley and C. Meadows, “Security of ramp schemes,” in Advances in Cryptology (CRYPTO), 1984, pp. 242–268.
[8] V. Boyko, “On the Security Properties of OAEP as an Allor-nothing Transform,” in Advances in Cryptology (CRYPTO), 1999, pp. 503–518.
[9] R. Canetti, C. Dwork, M. Naor, and R. Ostrovsky, “Deniable Encryption,” in Proceedings of CRYPTO, 1997.
[10] Cavalry, “Encryption Engine Dongle,” http://www. cavalrystorage.com/en2010.aspx/.
[11] C. Charnes, J. Pieprzyk, and R. Safavi-Naini, “Conditionally secure secret sharing schemes with disenrollment capability,” in ACM Conference on Computer and Communications Security (CCS), 1994, pp. 89–95.
[12] A. Desai, “The security of all-or-nothing encryption: Protecting against exhaustive key search,” in Advances in Cryptology (CRYPTO), 2000, pp. 359–375.

P. Snehasri, T. Aparna, "The Bastion Scheme for Securing Data under Key Revelation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.42-45, 2018.

The higher education sector in India is witnessing a remarkable growth and there are ample opportunities available to the students in terms of choice of courses and institutions for higher education. The institutes are facing a tough competition amongst each other to attract the maximum number of eligible candidates for higher education courses. This paper examines the influence of different factors affecting the college choice decisions of graduate students going for the MCA (Masters of Computer Application) course at the post graduate level. The most relevant factors that were found to influence the decision are faculty, infrastructure, placement, influence of others, extracurricular activities and online advertising. The purpose of study is to identify the factors and their relative influence on the students’ choice for college enrolment. We have used the Pearson’s Chi-square test to conduct the research as we intended to analyse the level of impact of the different factors on the students for the selection of college for higher education. Experimental results have been obtained with the help of different graphs showing the impact of different influential factors on students coming from different graduate level courses. This research can be helpful in providing a guideline to higher education institutes to formulate their strategy to attract the students by considering the various factors that influence the choice of students for selecting a college for higher education.

Chi-squared test, R Programming, Higher Education, College Choice, Data Analysis, Data Science

[1] Water, D., Abrahamson, T. & Lyons, K., High-achieving seniors and the college decision, Lipman Hearne Key Insights, 2009.
[2] Somers, P., Haines, K., & Keene, B.. Toward a theory of choice for community college students. College Journal of Research and Practice, 20, 53-67, (2006).
[3] Niu, S.X., &Tienda, M. , Choosing college: Identifying and modeling choice sets. Social Science Research, 37, 416-433 (2008).
[4] Chapman, R., Toward a theory of college choice: A model of college search and choice behavior. Alberta, Canada: University of Alberta Press (1984).
[5] The analysis of Factors affecting choice of college: A case study of UNLV hotel College students
[6] A study of factors associated with student choice in the university selection process, A Thesis Submitted to the Faculty of Education of The University of Lethbridge in Partial Fulfilment of the Requirements for the Degree MASTER OF EDUCATION, LETHBRIDGE, ALBERTA October, 1989
[7] Factors influencing the college, choice decisions of graduate students, Ruth E. Kallio, Research in Higher Education, Vol. 36, No. 1, 1995
[8] Hayes, Jeremy J., "Increasing Enrollment: Evaluating College-Choice Factors at a Midwest Christian University" (2014). Ed.D. Dissertations. 70.
[9] Temple, Shawn Lea, "Factors that Influence Students` Desires to Attend Higher Education" (2009), Seton Hall University Dissertations and Theses (ETDs). 420.

Deepshikha Aggarwal, Deepti Sharma, "Analysis of the Factors Influencing the Choice of College for Higher Education," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.46-49, 2018.

In this paper, an algorithm about brain tumor detection using the K- means clustering and graphcut technique that uses the color based segmentation method to track tumor objects in magnetic resonance (MR) brain images.Magnetic resonance imaging (MRI) is a advanced medical imaging technique giving rich information about the human soft tissue anatomy.Magnetic Resonance Imaging has become a widely used method of high quality medical imaging..Tumor is an uncontrolled development of tissues in any part of the body. Brain tumor is intrinsically genuine and lifethreatening. Immense quantities of passings have been checked because of the reality of incorrect recognition. Brain tumor detection in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the correct size, shape, boundary extraction and area of tumor. A comparative study on clustering with K-Means algorithm and graphcut algorithm was also done with the MRI image dataset using MATLAB.

Brain Tumor,Clustering,K-means,Magnetic Resonance Imaging (MRI),Thresholding, Histogram-Based method, Graphcut

[1]. B.R. Quazi , Supriya Mali “Survey on Brain Tumor Detection using K-Means Clustering Algorithm” International Journal of Innovative Research in Computer and Communication Engineering,Vol. 5, Issue 1, January 2017.
[2]. Sandhya M. Karande , Jayapal , “A New Approach for Brain Tumor Detection and Area Calculation Using Median Filter, K-Means, SVM and Naïve Bayes Classifier”, International Journal of Advance Research in Computer Science and Management Studies, Volume 2, Issue 11, November 2014.
[3]. Meghana Nagori “Detection of Brain Tumor by Mining fMRI Images” International Journal of Advanced Research in Computer and Communication Engineering, Vol.2,Issue 4, January 2013.
[4]. Riddhi.S.Kapse , S. Salankar , Madhuri.Babar “Literature Survey on Detection of Brain Tumor from MRI Images” Journal of Electronics and Communication Engineering, Volume 10, Issue 1, Jan - Feb. 2015.
[5]. Varun Jain, Sunila Godara, “Analysis of Brain MRI Tumor Detection and Classification using Hybrid Approach”, Volume 8 • Issue 2 March 2017 .
[6]. Zhang, Y. Brady, M.Smith, “Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm”,IEEE trans. on medical imaging 20 (2001) 45.
[7]. Y.Bettinger, K. Shen, L. Reiss,“Automatic segmentation of the caudate nucleus from human brain MR images”. IEEE Transactions on Medical Imaging 26(4) (2007) 509–517.
[8]. Leemput, K. Vandermeulen, “ Automated model-Based tissue classi¯cation of MR images of the brain”,IEEE trans on medical imaging 18 (1999) 897.

R. Dhatchayini, K. Mohamed Amanullah, "Brain Tumor Detection Using Clustering Method," International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.50-57, 2018.