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Co-saliency is utilized for exploring common saliency present within numerous pictures or images and is an area of research that is still under explored. This not only deals with the visual cues present within the images but also covers the cues that are outside the image and hence deals with the shortcoming present within saliency detection of a single-image. It depends upon the visual cues that are already discovered or explored and varies from place to place. In order to address this concern, this paper aims to propose a technique that can be helpful in detecting the co-salient objects on map fusion and are region-wise saliency. This technique takes into account the intra image appearance, its correspondence with the features outside the image, the spatial features or factors and aims at the detection of salience with the help of a saliency that is locally adaptive map fusion through dealing with the problem within the map in relation to energy optimization. This technique or method will be accessed on the basis of a standard dataset that is taken as a benchmark and is compared with other techniques and methods that are available.

Co-saliency detection, graph-based optimization, energy minimization, locally adaptive fusion

[1]. Chung-Chi Tsai1, Xiaoning Qian and Yen-Yu Lin,” Image co-saliency detection via locally adaptive saliency map fusion”, ICASSP 2017.
[2]. Kai-Yueh Chang, Tyng-Luh Liu, and Shang-Hong Lai,"From co-saliency to co-segmentation: An efficient and fully unsupervised energy minimization model," in proc.conf.Computer Vision and Pattern Recognization, 2011.
[3]. Zhicheng Li, Shiyin Qin, and Laurent Itti, "Visual attention guided bit allocation in video compression," j.Image and Vision Computing, 2011.
[4]. Laurent Itti, Christof Koch, and Ernst Niebur, "A model of saliency-based visual attention for rapid scene analysis,"IEEE Trans.on Pattern Analysis and Machine Intelligence, 1998.
[5]. Xiaodi Hou and Liqing Zhang, "Saliency detection: A spectral residual approach," in Proc.Conf. Computer Vision and Pattern Recognization, 2007.
[6]. Ravi Achanta, Sheila Hemami, Francisco Estrada, and Sabine Susstrunk, "Frequency-tuned salient region detection,"in Proc.Conf. Vision and Pattern Recognization, 2009.
[7]. Xiaohui Shen and Ying Wu, "A unified approach to salient object detection via low rank matrix recovery,"in Proc. Conf. Vision and Pattern Recognization, 2012.
[8]. Federico Perazzi, Philipp KrahenbUhl, Yael Pritch, and alexander Hornung, "Saliency filters: Contrast based filtering for salient region detection," in Proc.Conf. Vision and Pattern Recognization, 2012.
[9]. Chuan Yang, Lihe Zhang, Huchuan Lu, Xiang Ruan, and Ming-Hsuan Yang, "Saliency detection via graph based manifold ranking," in Proc.Conf. Vision and Pattern Recognization, 2013.
[10]. Wangjiang Zhu, Shuang Liang, Yichen Wei, and Jian Sun, "Saliency optimization from robust background detection," in Proc.Conf. Vision and Pattern Recognization, 2014.
[11]. Hongliang Li and King Ngi Ngan, "A co-saliency model of image pairs,"IEEE Trans. On Image Processing 2011.
[12]. Fanman Meng, Hongliang Li, and Guanghui Liu, "A new co-saliency model via pairwise constraint graph matching," in IEEE Int’l Symposium, Intelligent Signal Processing and Communications System, 2012.
[13]. Huazhu Fu, Xiaochun Cao, and Zhuowen Tu, "Cluster based co-saliency detection," IEEE Trans. On Image Processing, 2013.
[14]. Xiaochun Cao, Zhiqiang Tao, Bao Zhang, Huazhu Fu, and Wei Feng, "Self-adaptively weighted co-saliency detection via rank constraint," IEEE Trans. On Image Processing, 2014.
[15]. Xiaochun Cao, Zhiqiang Tao, Bao Zhang, Huazhu Fu, and Xuewei Li, "Saliency map fusion based on rank-one constraint," in Proc. Int’l Conf Multimedia and Expo, 2013.
[16]. Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk, "SLIC superpixels," Tech. Rep., 2010.

M.Sreenavya, Chandra Mohan Reddy Sivappagari, "Fusion of Saliency Based Co-Saliency Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.578-583, 2018.

Strategizing is an important aspect which requires critical analysis to determine performance. The key to solve this issue is by tapping the available student talent within the university. In this paper, we have done research in the domain of education. Strategy considered in the research is assessing the skill set of the students. For this approach, we have constructed our Vision Based Page Segmentation algorithm to extract the data from the university. In Unsupervised Machine Learning and Supervised Machine Learning, We have taken Classification and Regression supervised learning to classify the student’s marks. Machine learning models like Neural Network, Random Forest and Logistic Regression have been used to predict the best student team.

Strategizing, Neural Network, Random Forest and Logistic Regression

[1] Rosenblatt, X.Frank, “Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms”, Spartan Books, Washington DC, 1961.
[2] Rumelhart, E. David ,E Geoffrey. Hinton, and R. J. Williams. "Learning Internal Representations by Error Propagation", International Journal of Computer Engineering ,Vol. 1 , 1986.
[3] Ho, Tin Kam ,” Random Decision Forests “,In the Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, pp. 278–282..
[4] Ho TK , "The Random Subspace Method for Constructing Decision Forests" , IEEE Transactions on Pattern Analysis and Machine Intelligence,vol.8,Issue.20.
[5] T. Landgrebe, P. Paclk, R. Duin, and A. Bradley, “Precision-recall operating characteristic (P-ROC) curves in imprecise environments”, In Proceedings of ICPR, 2006.
[6] Y. Baeza and B. R. Neto, “Modern Information Retrieval”, Boston, 1999
[7] J. Davis and M. Goadrich, “The relationship between precision recall and ROC curves”, In Proceedings of the 23rd International Conference on Machine Learning, ser. ICML 06. New York, NY, USA: ACM, 2006, pp. 233240

H S Divyashree, Avinash N, M.Sasi Kumar, S. Dinesh, "Student Strategizing in Education system using a Machine Learning Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.584-588, 2018.

With the advent of internet, the world today is very closely connected. One can easily obtain information about the activities taking place in an overseas continent only in a matter of seconds. However, despite the world being so intimately connected, one doesn’t find such connections in the news anyone comes across. It is seldom that the news websites provide readers with the news about events that happen in other parts of the world, different from the one the reader is currently in. Thus, there is a need to explore the idea of linking news semantically using different Semantic Web Technologies and concepts like Natural Language Processing (NLP) which may play a significant role in efficient and meaningful information extraction. In this paper, first, various concepts and technologies in regard to the above need have been explored. Second, an architecture is proposed, along with its implementation (in Python),for the media outlets (independents and aggregators) to explore the idea of linking their content semantically using the concepts of Linked Data and NLP. The proposed architecture is intended to be applied at the backend in order to render structured and linked data, with the intention of providing the readers with linked news.

Linked Data, Natural Language Processing, Semantic Web Technologies, Ontology, Triplestore, Named Graph, Graph Database, Online News, Structured Data

[1] C. Bizer, T. Heath, and T. Berners-Lee, “Linked Data - The Story So Far”, International Journal on Semantic Web and Information Systems, Vol. 5, Issue. 3, pp. 1–22, 2009.

[2] B. DuCharme, “Learning SPARQL: Querying and Updating with SPARQL 1.1”, O`Reilly Media, USA, pp. 19-45, 2013.

[3] I. Jacobs, N. Walsh, “Architecture of the World Wide Web”, W3C Recommendation, Vol. 1, 2004.

[4] J. Pokorný, “Graph Databases: Their Power and Limitations” In Proceedings of 14th International Conference on Computer Information Systems and Industrial Management Applications, Poland, pp. 58-69, 2015.

[5] S. Patil, G. Vaswani, A. Bhatia, “Graph Databases: An Overview”, International Journal of Computer Science and Information Technologies, Vol. 5, Issue. 1, pp. 657-660, 2014.

[6] D. Balasuriya, N. Ringland, J. Nothman, T. Murphy, and J. R.Curran. “Named entity recognition in wikipedia”, In Proceedings of the Workshop on The People’s Web Meets NLP, Singapore, pp. 10–18, 2009.

[7] R. Alghamdi, and K. Alfalqi. "A Survey of Topic Modeling in Text Mining" International Journal of Advanced Computer Science and Applications Vol. 6, pp. 147-153, 2015.

[8] I. Mohan, K. Janani, M. Karthiga, “A Survey on Sentiment Analysis on Social Network Data”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol. 2, Issue. 2, pp. 1-7, 2017.


[9] R.Schumaker, Y.Zhang, C. Huang, and H. Chen, “Sentiment analysis of financial news articles”, Decision Support Systems, Vol.53, pp. 458-464, 2012.

[10] C. Nanda, M. Dua, “A Survey on Sentiment Analysis”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 5, Issue. 2, pp. 67-70, 2017.

[11] AllegroGraph 6.3.0 [2017-10-24], computer program, Franz Inc., CA 94612, USA.

Pratulya Bubna, Shivam Sharma, Sanjay Kumar Malik, "Linking Online News Semantically Using NLP and Semantic Web Technologies," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.589-598, 2018.

The problem of classification is one of the major problems associated with data mining. Numerous classification algorithms have been implemented, although there was hardly an algorithm that surpasses all individual algorithms with respect to the standard. A decision tree is a type of classification method in which the end result is the class to which the data belongs. There are many problems faced by decision tree and this paper considers two out of them. First problem faced by decision tree is finding the optimal solution, which is resolved by heuristic techniques more quickly and further efficiently than conventional techniques. Another problem facing a decision tree is scalability issue, which is solved by RainForest framework. RainForest framework considers the scalability problem and has different types of algorithms that work in different types of cases. This article provides a brief overview of the framework of RainForest and Heuristics and steepest ascent hill climbing which are utilized to overcome the scalability issues and the limitation of finding the optimal solution respectively.

Decision tree, Classification, RainForest, Heuristics and Steepest ascent hill climbing

[1] Anuradha, G. Gupta, “ A self explanatory review of decision tree classifiers”, In the Proceedings of the 2014 IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014), Jaipur, India, pp.1-7, 2014.
[2] R. Garg, V. Mongia, “Dimensionality Reduction and Comparison of Classification Models for Breast Cancer Prognosis”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.308-312, 2018.
[3] M. Fernandes, “Data Mining: A Comparative Study of its Various Techniques and its Process”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol.5, Issue.1, pp.19-23, 2017.
[4] N. Ghuse, P. Pawar, A. Potgantwar, “An Improved Approach For Fraud Detection in Heath Insurance Using Data Mining Techniques”, International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.5, pp.13, 2017.
[5] D. Singh, N. Choudhary, J. Samota, ”Analysis of Data Mining Classification with Decision tree Technique”, Global Journal of Computer Science and Technology, Software & Data Engineering, Vol.13, Issue.13, pp.1-6, 2013.
[6] J. Gehrke, R. Ramakrishnan, V. Ganti, “RainForest- A framework for Fast Decision Tree Construction of Large Datasets”, In the Proceedings of 24th VLDB conference, New York, USA, Vol.98, pp.416-427, 1998.
[7] Y. Yang, W. Chan, “Taiga: Performance Optimization of the C4.5 Decision Tree Construction Algorithm”, Tsinghua Science and Technology, Vol.21, Issue.4, pp.415-425, 2016.
[8] H. Wang, C. Zaniolo, “CMP: A Fast Decision Tree Classifier Using Multivariate Predictions”, In the Proceedings of the 16th International Conference on Data Engineering (Cat. No. 00CB37073), San Diego, CA, USA, pp.449, 2000.
[9] D. Thakur, N. Markandaiah, D. S. Raj, “Re optimization of ID3 and C4.5 decision tree”, In the Proceedings of the 2010 IEEE International Conference on Computer and Communication Technology (ICCCT), Allahabad, Uttar Pradesh, India, pp.448-450, 2010.
[10] L. Fu, “Novel Efficient Classifiers Based on Data Cube”, International Journal of Data Warehousing and Mining (IJDWM), Vol.1, Issue.3, pp.15-27, 2005.
[11] S. B. Roy, H. Wang, G. Das, U. Nambiar, M. Mohania, “Minimum-effort driven dynamic faceted search in structured databases”, In the Proceedings of the 17th ACM conference on International and knowledge management, Napa Valley, California, USA, pp.13-22, 2008.
[12] A. Newell, H. A. Simon, “Computer science as empirical inquiry: symbols and search”, ACM Turing Award Lecture, New York, USA,2007.
[13] X.-Z. Wang, D. S. Yung, E. C. C. Tsang, “ A Comparative study on heuristic algorithms for generating fuzzy decision trees”, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol.31, Issue.2, pp.215-226, 2001.
[14] P. D. Turney, “Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm”, Journal of artificial intelligence research, Vol.2, pp.369-409, 1994.
[15] J. J. Grefenstette, “Optimization of Control Parameters for Genetic Algorithms”, IEEE Transactions on System, Man, and Cybernetics, Vol.16, Issue.1, pp.122-128, 1986.
[16] J. R. Quinlan, “C4.5: Programs for machine learning”, Elsevier, California, USA, 2014.
[17] Y. Zhang, S. Wang, P. Phillips, G. Ji, “Binary PSO with mutation operator for feature selection using decision tree applied to spam detection”, Knowledge-Based Systems, Vol.64, pp.22-31, 2014.
[18] S. R. Safavian, D. Landgrebe, “A survey of decision tree classifier methodology”, IEEE Transactions on Systems, Man, and Cybernetics, Vol.21, Issue.3, pp.660-674, 1991.
[19] P. H. Swain, H. Hauska, “The decision tree classifier: Design and potential”, IEEE Transactions on Geoscience Electronics, Vol.15, Issue.3, pp.142-147, 1977.
[20] R. Kohavi, G. H. John, “Wrappers for feature subset selection”, Artificial Intelligence, Vol.97, Issue.1-2, pp.273-324, 1997.
[21] R. Kohavi, “ A study of cross-validation and bootstrap for accuracy estimation and model selection”, International Joint Conference on Artificial Intelligence (IJCAI), Vol.14, Issue.2, pp.1137-1145, 1995.
[22] J. Basak, “Online Adaptive decision trees”, Neural Computation, Vol.16, Issue.9, pp.1959-1981, 2004.
[23] M. I. Jordan, R. A. Jacobs, ‘Hierarchical mixtures of experts and the EM algorithm”, In the Proceedings of 1993 International Conference on Neural Networks (IJCNN-93), Nagoya, Japan, pp.1339-1344, 1993.
[24] M. I. Jordan, R. A. Jacobs, ‘Hierarchical mixtures of experts and the EM algorithm”, Neural Computation, Vol.6, Issue.2, pp.181-214, 1994.
[25] Y.-M. Huang, C.-M. Hung, H. C. Jiau, “Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem”, Vol.7, Issue.4, pp.720-747, 2006.
[26] P. Gopalan, A. T. Kalai, A. R. Klivans, “Agnostically learning decision trees”, In the Proceedings of the fortieth annual ACM symposium on Theory of computing, Victoria, British Columbia, Canada, pp.527-536, 2008.
[27] A. T. Kalai, A. R. Klivans, Y. Mansour, R. A. Servedio, “Agnostically learning halfspaces”, In the Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), Pittsburgh, PA, USA, 2005.

N. Sandhu, S. Kumar, "Decision Tree Problem Solving Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.599-604, 2018.

Object tracking in video processing is a significance task because of its applications in surveillance, activity monitoring and recognition, traffic management etc. In outdoor and indoor environment multiple objects tracking is a challenging task because of poor lighting conditions, variation in poses, orientations, changes in location, shape and size etc. This paper proposes a method for tracking multiple objects in a video stream. Haar- like features are used to train the classifier from the training image set. Haar-like rectangular features are extracted and these features are used to train the method to track moving objects from video sequences using particle filter. Proposed method is tested on standard data sets: KTH, Caviar data set. The experimental results show that the proposed method can track multiple objects in a video adequately fast in the presence of poor lighting conditions, variation in poses of objects, shape, size etc. and the technique can handle varying number of objects in a video at various points of time.

Object tracking, video, surveillance, human detection

[1] Ashish Khare, Nguyen Thanh Binh and Nguyen Chi Thanh, “Human tracking based on context awareness in outdoor environment”, KSII Transactions on Internet and Information Systems, vol.1 ,no 6, pp 3104-3120, jun. 2017.
[2] Kabir Hossain, Chi-Woo Lee, “Visual tracking using particle filter”, 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp 98-102, 2012.
[3] Fabian Sigges and Marcus Baum, and Uwe D. Hanebeck, “A likelihood-free particle filter for multi-object tracking”, 20th International Conference on Information Fusion, pp 1 -5, july 10 - 13, 2017.
[4] Aashish Sharma, Ajay Singh, and Rajesh Rohilla, “Color based human detection and tracking algorithm using a non-gaussian adaptive particle filter”, 3rd International Conference on Recent Advances in Information Technology (RAIT), pp 439 - 442, 2016.
[5] Tassaphan Suwannatat and Krisana Chinnasarn, and Nakorn Indra-Payoong, “ Multi-features particle PHD filtering for multiple humans tracking”, International Computer Science and Engineering Conference (ICSEC), pp 1-6, 2015.
[6] Tram Tran Nguyen Quynh, “Improved particle filter for tracking objects in video”,International Journal of Advanced Research in Computer Engineering and Technology (IJARCET), Vol. 4, Issue 11, pp 4254-4261, Nov 2015.
[7] Atsushi Yoshida, Hyoungseop Kim, Joo Kooi Tan, and Seiji Ishikawa, “ Person tracking on kinect images using particle filter”, Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), pp 1486-1489, 2014.
[8] Di Yuan, Guanglei Zhao Donghao Li, Zhenyu He, and Nan Luo, “Visual object tracking based on particle filter re-detection”, International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), pp 1-6, 2017.
[9] Peng Tian, “ A particle filter object tracking based on feature and location fusion”, 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp 762-765, 2015.
[10] Hamd Ait Abdelali, Fedwa Essannouni, Leila Essannouni, and Driss Aboutajdine, “ A new moving object tracking method using particle filter and probability product kernel”, Intelligent Systems and Computer Vision (ISCV), pp 1-6, 2015.
[11] Ding Dongsheng, Jiang Zengru, and Liu Chengyuan, “Object tracking algorithm based on particle filter with color and texture feature”, 35th Chinese Control Conference (CCC), pp 4031-4036, 2016.
[12] Haris Masood, Saad Rehman, Muazzam Khan, Qaiser Javed, M. Abbas, M. Alam, and Rupert Young , “ Tracking of fixed shape moving objects based on modified particle filters”, 19th International Conference on Computer and Information Technology (ICCIT), pp 240-245, 2016.
[13] Mir Abbas Daneshyar, and Manoochehr Nahvi, “Improvement of moving objects tracking via modified particle distribution in particle filter algorithm”, 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA), pp 1-6, 2015.
[14] Tassaphan Suwannatat, Nakron Indra-Payoong, and Krisana Chinnasarn, “ Robust human tracking based on multi-features particle filter”,12th International Joint Conference on Computer Science and Software Engineering (JCSSE) , pp 12-17, 2015.
[15] Abdul-Lateef Yussiff, Suet-Peng Yong, and Baharum B. Bahardin, “ Human tracking in video surveillance using particle filter”, International Symposium on Mathematical Sciences and Computing Research (iSMSC), pp 83-88, 2015.

K. Kaur, A.K.S. Kushwaha, "Improved Particle Filter Approach for Multiple Object Tracking in Crowd Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.605-609, 2018.

Data protection is a crucial security issue for most organizations. Before moving into the cloud, cloud users need to clearly identify data objects to be protected and classify data based on their implication on security, and then define the security policy for data protection as well as the policy enforcement mechanisms. For most applications, data objects would include not only bulky data at rest in cloud servers (e.g., user database and/or file system), but also data in transit between the cloud and the user(s) which could be transmitted over the Internet or via mobile media (In many circumstances, it would be more cost-effective and convenient to move large volumes of data to the cloud by mobile media like archive tapes than transmitting over the Internet.). Data objects may also include user identity information created by the user management model, service audit data produced by the auditing model, service problem information used to describe the service instance(s), temporary runtime data generated by the instance(s), and many other application data. Different types of data would be of different value and hence have different security implication to cloud users. For example, user database at rest in cloud servers may be of the core value for cloud users and thus require strong protection to guarantee data confidentiality, integrity and availability. Sensitive business data is more vulnerable today than ever before, putting reputations and the bottom line at risk. Corporate trade secrets, national security information, personal medical records, Social Security and credit card numbers are all stored, used, and transmitted online and through connected devices.

Cloud Service Provider(CSP), Cloud Service Consumer(CSC), Data at Rest, Data in transit, Data in use

[1] Pesante, L. “Introduction to Information Security”, carnegie Mellon University, 2008.
https://www.us-cert.gov/sites/default/files/publications/infosecuritybasics.pdf
[2] Worlany, E. “A Survey of Cloud Computing Security: ISuues, Challenges and Solutions”, November 2015.
[3] P.Ravikumar, P. Herbert Raj, “Exploring security issues and solutions in cloud computing services”, Cybernatics and information technologies, Vol.17, Issue.4, 2017.
[4] F e r r i l l. T. The Best Identity Management Solutions of 2017. 3 July 2017.
[5] Hari Krishna, B., S. Kiran, “Security issues in cloud computing and associated mitigation techniques”, Procedia Computer Science, Vol.87, pp.246-251, 2016.
[6] Sabahi, F., “Secure Virtualization for Cloud Environment using Hypervisor-Based Technology”, International Journal of Machine Learing and Computing, Vol.2, Issue.1, pp.39-45, 2012.
[7] Gallagher, P.R. “ A Guide to Understanding Data Remanence in Automated Information Systems”, The Rainbow Books, Chapter 3 and 4.1991.
[8] Syed Asad Hussain, Mehwish Fatima, “Multilevel Classification of security concerns in cloud computing” Applied Computing and Informatics, Vol. 13, pp.57-65, 2017.
[9] L. Ponemon, Ponemon 2014 SSH security Vulnerability Report.
[10] G. Pek, L. Buttayan,” A survey of security issues in hardware virtualization”, ACM Comput. Surveys 45(3) pp.1-34, 2013.
[11] J. Idziorek, Exploiting Cloud Utility Models for Profit and Ruin Graduate Theses and Dissertations, Lowa State University, 2012.
[12] R. Bhadauria, S. Sanyal, “Survey on Security issues in cloud computing ans associated mitigation techniques”, International Journal of Computer Applications, Vol.47, Issue.18, pp.47-66, 2012.
[13] Reed, A., C.Rezek, P.Simmonds, “Security Guidance for critical Area of focus in cloud computing”, V3.1. Cloud Security Alliance(CSA).2011, pp.1-176.
[14] OWASP Top 10 Application Security Risks – 2017. Open Web Application Security Project (OWASP).
[15] Rathie, I. An Approach to Application Security. SANS Security Essentials White Paper, SANS Institute.
[16] OWASP Top 10 Backdoors, Open Web Application Security Project (OWASP).
[17] Rouse, M. Data Integrity, September 2005.
[18] Rouse, M. Authentication, February 2015.
Cloud Middleware informatiion avialable URL “https://www.techopedia.com/definition/30630/cloud-middleware-software”.
Data Backup: Cloud Computing VS On-Site Options informatiion avialable URL “https://www.staples.com/content-hub/data-backup-cloud-computing-vs-on-site-options/”
[19] Farahzadi, A., P. Shams, “Middleware Technologies for Cloud of Things – A Survey”, Digital Communications and Networks, Elsevier, 18 April 2017.
[20] Nolle, T. “How to Address Security Risks Posed by Middleware Tools”, December 2014.
[21] Role of Cloud Computing Operating Systems. 8 March 2013.
[22] P. Herbert Raj, P. Jelciana “Exploring security issues and solutions in cloud computing services”, Cybernatics and information technologies, Vol.17, Issue.4, 2017.
[23] Chakravarty, A. Importance of the Network in Cloud Computing. 25 January 2012.
[24] Top 7 Network Attack Types in 2016. Calyptix Blog. 13 June 2016.
[25] Rouse, M. DNS Attack. July 2015.
[26] Jakimoski, K. “Security Techniques for Data Protection in Cloud Computing”, International Journal of Grid and Distributed Computing, Vol. 9, 2016, No 1, pp. 49-56.
[27] J. Rezazadeh, R. Farahbakhsh. Middleware Technologies for Cloud of Things – A Survey. – Digital Communications and Networks, Elsevier, 2017.
[28] Minhaj Ahmad Khan, Khaled Salah, “IoT Security: Review, blockchain solutions, and open challenges”, Future Generation Systems, Elsevier, Vol. 82, pp.395-411, 2018.

R. Denis, P. Madhubala, "Investigation of Security Issues on Data in Triplet Levels of Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.610-621, 2018.

Human activity recognition plays a competent part in human being to evaluate the activities of elder people. However, recently proposed human activity recognition techniques perform poorly whenever object characteristics are similar to background features (i.e., features are merely differing from each other). Therefore, an efficient meta-heuristic technique-based activity recognition technique is required to improve the accuracy of human activity recognition systems. To achieve the objectives of this research work, we have designed a novel hybrid differential evolution based J48 model to efficiently recognize the activities of human beings. Extensive experiments have been carried out to evaluate the effectiveness of the proposed technique. Experimental results reveal that the proposed technique outperforms existing techniques.

Machine learning, Activity recognition, Differential evolution, Neural networks

[1] S. Sasikala, S.A.A. Balamurugana, S.Geetha, “A novel adaptive feature selector for supervised classification”, Information Processing Letters 117 (2017) 25–34.
[2] M.E. Borsuk and C.A. Stow, “Bayesian parameter estimation in a mixed-order model of BOD decay,” Water Research,vol.34(6),pp.1830-1836, 2000.
[3] Z. Zhu, Y.S. Ong, M. Dash, Wrapper–filter feature selection algorithm using a memetic framework, IEEE Trans. Syst. Man Cybern., Part B 10(4) (2006) 392–404.
[4] S. Y. Muhammad, M. Makhtar, A. Rozaimee, A. A. Aziz, A. A. Jamal, “Classification Model for Activity recognition of elderindividual using Machine Learning Techniques”, International Journal of Software Engineering and Its Applications Vol. 9, No. 6 (2015), pp. 45-52.
[5] S.C. Azhar, A.Z. Arisa, M.K.Yusoffa, M.F. Ramli, H. Juahir, “Classification of river activity recognition of elderindividual using multivariate analysis”, International Conference on Environmental Forensics 2015 (iENFORCE2015).
[6] J.R. Barclay, H. Tripp, C.J. Bellucci, G. Warner,A.M. Helton, “Do waterbody classifications predict activity recognition of elderindividual?”, Journal of Environmental Management 183 (2016) 1-12.
[7] Z. Xing, Q. Fu, D. Liu, “Activity recognition of elderindividual Evaluation by the Fuzzy Comprehensive Evaluation based on EW Method”, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).
[8] K.Z. Mao, “Feature subset selection for support vector machines throughdiscriminative function pruning analysis,”IEEE Trans. Syst., Man,Cybern. B, Cybern., vol. 34, no. 1, pp. 60–67, Feb. 2004.
[9] Y. Wang, C. Li and Y. Zuo, “A Selection Model for Optimal Fuzzy Clustering Algorithm and Number of Clusters Based onCompetitive Comprehensive Fuzzy Evaluation”, IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 17, NO. 3, JUNE 2009.
[10] S. Wechmongkhonkon, N.Poomtong, S. Areerachakul, “Application of Artificial Neural Network to Classification Surface Activity recognition of elderindividual,” International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering Vol:6, No:9, 2012.
[11] A. Woznica, P. Nguyen, A. Kalousis, "Model mining for robust feature selection", KDD `12 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and machine learning, ACM New York, NY, USA, PP 913-921, 2012.
[12] L. Khuan, N. Hamzah and R. Jailani, “Recognition of Activity recognition of elderindividual Index(WQI) Based on Artificial Neural Network(ANN)”,Conference onResearch and Development Proceedings, Malasia, 2002, pp. 157-161.
[13] Singh, Dilbag, Deepak Garg, and Husanbir Singh Pannu. "Efficient Landsat image fusion using fuzzy and stationary discrete wavelet transform." The Imaging Science Journal 65, no. 2 (2017): 108-114.
[14] S.W. Athukorala, L.S. Weerasinghe, M. Jayasooria, D. Rajapakshe,L.Fernando, M. Raffeeze, N.P. Miguntanna, “Ananlysis of Activity recognition of elderindividual Variation In Kelani River, Sri Lanka Using Principal ComponetAnalysis,” SAITM Research Symposium on Engineering Advancements2013.
[15] T. Wijesinghe, “Status of Activity recognition of elderindividual of Kelani River, CentralEnvironmental Authority, Sri Lanka,” IIIRR online publication pp.255,2010.
[16] Y. Park, K. H. Cho, J. Park, S. M. Cha, and J. H. Kim, “Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.,” Sci. Total Environ., vol. 502, pp. 31–41, Jan. 2015.
[17] S. Maiti and R. K. Tiwari, “A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level recognition,” Environ. Earth Sci., vol. 71, no.7, pp. 3147–3160, 2013.
[18] Komal D. Khawale, D. R. Dhotre.”
To Recognize Human Emotions Based on Facial Expression Recognition : A Literature Survey”,International Journal of Scientific Research in Computer Sciences and Engineering(ISSN:2320-7639) Vol.2,Issue.2, pp. 35-41,2017
[19] V.K. Jain, N. Tripath.” Speech Features Analysis and Biometric Person Identification in Multilingual Environment”, International Journal of Scientific Research in Network Security and Communication (ISSN:2321-3256) Vol..6,Issue.1, pp.7-11,2018

Nidhi Bhati, "A Novel Framework for Human Activity Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.622-626, 2018.

Combined Automation framework (CAF) is a dynamic automation framework which accumulates the advantages of underlying Keyword driven (KDF) and Data Driven frameworks (DDF) for web applications. This paper presents the comparison of CAF with its underlying frameworks in automation based on various parameters. CAF comprises of the qualities of its underlying frameworks like keyword driven, data driven, module driven (MDF) in terms of keyword reusability, implementation ease, code organization, reporting feature, defect logging and tracking. Users need to be less familiar with code while working with CAF. The paper also compares the CAF and framework-less techniques in terms of data redundancy, script handling and reusability where CAF performs well for a set of automation scripts and outweighs the framework-less technique at any point of time.

CAF, KDF, DDF, MDF, HAF, Framework-Less

[1] K. Metha., S. Chavan, “Towards Developing a Selenium Based GUI Automation Test Pro: a Comparative Study”, Vol. 3 Issue 2, International Journal of Engineering Research & Technology (IJERT), 2014.
[2] R. Angmo , M. Sharma, “Performance Evaluation of Web Based Automation Testing Tools”, The Institute of Electrical and Electronics Engineers (IEEE), 2014.
[3] M. Bolton, ”Rapid Software Testing”, Agile Conference , Berlin ,Germany, 2010.
[4] F. Wang, W. Du, ”A Test Automation Framework Based on WEB “,11th International Conference on Computer and Information Science (IEEE/ACIS), 2012.
[5] R. A. Razak, F. R. Fahrurazi, ” Agile Testing with Selenium”, 5th Malaysian Conference in Software Engineering (MySEC), 2011.
[6] W. D. Yu, G. Patil, “A Workflow-Based Test Automation Framework for Web Based Systems”, Computer Engineering Department, San Jose State University, IEEE, 2007.
[7] A. M. F. V. de Castro, G. A. Macedo , E. F. Collins , A. C. Dias-Neto , ” Extension of Selenium RC Tool to Perform Automated Testing with Databases in Web Applications “, The Institute of Electrical and Electronics Engineers ( IEEE) ; San Francisco, CA, USA,2013.
[8] M. Leotta, D. Clerissi, F. Ricca, C. Spadaro, “Repairing Selenium Test Cases: An Industrial Case Study about Web Page Element Localization” , Proceedings of the Sixth International Conference on Software Testing, Verification and Validation, IEEE Computer Society Press, 2013.
[9] L. Nagowah and K. Doorgah, “Improving Test Data Management in Record and Playback Testing Tools”, Proceedings of the International Conference on Computer & Information Science (ICCIS). IEEE Computer Society, 2012.
[10] N. Uppal, V. Chopra, “Design and Implementation in Selenium IDE with Web Driver”, International Journal of Computer Applications, 2012.
[11] P. Yalla, L. SS Reddy, M. Srinivas, T.S.M. Rao, “ Framework For Testing Web Applications using Selenium Testing tool with respect to Integration Testing”,( IJCST), 2011.
[12] O. Rana, V. Chaudhary, “Comparison between Combined and Data Driven framework for Web Application in Webdriver”, Journal of Emerging Technologies and Innovative Research (JETIR), 2018.
[13] M. Niranjanamurthy, R A. Kumar, S. Srinivas, RK Manoj, “Research Study on Web Application Testing using Selenium Testing Framework”, International Journal of Computer Science and Mobile Computing ( IJCSMC), 2014.

Oshin, V. Chaudhary, "Combined Automation Framework for Web Application Testing in Selenium: Revisited," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.627-633, 2018.

Data mining is the analytical process to explore specific data from large volume of data. It is a process that finds previously unknown patterns and trends in databases. This information can be further used to build predictive models. The main objective of our paper is to learn data mining techniques which can be used in the prediction of heart diseases using any data mining tool. Heart is the most vital part of the human body as human life depends upon efficient working of heart. A Heart disease is caused due to narrowing or blockage of coronary arteries. This is caused by the deposition of fat on the inner walls of the arteries and also due to build up cholesterol. Thus, a beneficial way to predict heart diseases in health care industry is an effective and efficient heart disease prediction system. This system will find human interpretable patterns and will determine trends in patient records to improve health care. In this paper, Random Forest technique is applied to enhance the accuracy of the system.

Data Mining Technique, KNN, Random Forest, Heart Diseases

[1] Bhupendra Kumar Jain , Manish Tiwari, “Prediction Analysis Technique based on Clustering and Classification”, International Journal of Computer Sciences and Engineering, . Vol.6 , Issue.6 , pp.688-692, Jun-2018.
[2] Era Singh Kajal Ms. Nishika “Prediction of Heart Disease using Data Mining Techniques” Volume2, Issue3, pp-1-5 2012.
[3] Indira S. Fal Dessai“ Intelligent Heart Disease Prediction System Using Probabilistic Neural Network” International Journal on Advanced Computer Theory and Engineering, VOL.8 No.8, August 2008.
[4] Bahadur Patel, Ashish Kumar Sen D P, Shamsher Shukla, “A Data Mining Technique for Prediction of Coronary Heart Disease Using Neuro-Fuzzy Integrated Approach Two Level”, International Journal Of Engineering And Computer Science ISSN: 2319-7242, Page No. 2663-2671, Volume 2 Issue 9 Sept., 2013.
[5] Giorgio Barbareschi and Robbert Sanderman et al, “Socioeconomic Status and the Course of Quality of Life in Older Patients with Coronary Heart Disease”, International Journal of behavioral Medicine, Vol.16, PP.197-204, 2009.
[6] Jesmin Nahar and Tasadduq Imam et al,” Association rule mining to detect factors which contribute to heart disease in males and females”, Journal of Expert Systems with Applications Vol.40, PP.1086–1093, 2013
[7] Thomas, J., and R. Theresa Princy. "Human heart disease prediction system using data mining techniques." 2016 International Conference on Circuit, Power and Computing Technologies [ICCPCT]
[8] Daisy JA Janssen and Emiel FM Wouters et al, “Self-perceived symptoms and care needs of patients with severe to very severe chronic obstructive pulmonary disease, congestive heart failure or chronic renal failure and its consequences for their closest relatives: the research protocol”, Journal of BMC palliative care, Vol.7, 2008.

[9] Abhishek Taneja, “Heart Disease Prediction System Using Data Mining Techniques” Oriental Scientific Publishing Co., India, Vol.11, 2013.
[10] Srinivas K, Raghavendra Rao R, Govardhan A, “Analysis of Coronary Heart Disease and Prediction of Heart Attack in Coal Mining Regions Using Data Mining Techniques”, The 5th International Conference on Computer Science & Education Hefei, China. Volume 2, Issue 10, October 2012.
[11] Rajalaxmi R R, “A Data mining Model for predicting the Coronary Heart Disease using Random Forest Classifier”, International Conference on Recent Trends in Computational Methods, Communication and Controls (ICON3C 2012) International Journal of Computer Applications (IJCA), 2012.
[12] Sulabha S. Apte, “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, International Journal of Computer Applications (0975 – 888) Volume 47– No.10, June 2012.
[13] Chitra R and Seenivasagam V, “Review of Heart Disease Prediction System Using Data Mining And Hybrid Intelligent Technique”, ISSN: 2229-6956(online) ICTACT Journal On Soft Computing, JULY 2013, VOLUME: 03, ISSUE: 04, 2013
[14] Amit, Yali; Geman, Donald (1997). "Shape quantization and recognition with randomized trees". Neural Computation. 9 (7): 1545–1588.
[15] N. Aditya Sundar, P. Pushpa Latha and M. Rama Chandra, “Performance analysis of classification data mining techniques over heart disease data base”, International journal of engineering science & advanced technology, Volume-2, Issue-3, May-June 2012, pp. 470 – 478.
[16] T. John Peter, K. Somasundaram, study and development of novel feature selection framwork for Heart disease prediction, International Journal of Scientific and Research Publications, April 2012, Vol. 8 .
[17]Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2008). The Elements of Statistical Learning (2nd ed.). Springer. ISBN 0-387-95284-5.
[18]Shinde, Amit, Anshuman Sahu, Daniel Apley, and Ge.orge Runger. "Preimages for Variation Patterns from Kernel PCA and Bagging." IIE Transactions, Vol.46, Iss.5, 2014
[19]Ho, Tin Kam (1995). Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995.vol.11 pp. 278–282.
[20] Dan Gao1,2, Yan-Xia Zhang1 and Yong-Heng Zhao1
“Random forest algorithm for classification of multiwavelength data”. Research in Astron. Astrophys. 2009 Vol. 9 No. 2, 220–226
[21]Kleinberg, Eugene (1996). "An Overtraining-Resistant Stochastic Modeling Method for Pattern Recognition". Annals of Statistics. 24 (6):2319–2349. MR 1425956.
[22]Kleinberg, Eugene (2000). "On the Algorithmic Implementation of Stochastic Discrimination". IEEE Transactions on PAMI. 22 (5).
[23] Kleinberg, Eugene (2000). "On the Algorithmic Implementation of Stochastic Discrimination" (PDF). IEEE Transactions on PAMI. 22 (5).
[24] Breiman, Leo (2001). "Random Forests". Machine Learning. 45 (1): 5–32.
[25]Liaw, Andy (16 October 2012). "Documentation for R package Random Forest" (PDF). Retrieved 15 March 2013.
[26] ] Y. Lihua, D. Qi, and G. Yanjun, “Study on KNN Text Categorization Algorithm”, Micro Computer Information, Vol.21, pp. 269-271, 2006

H. Kaur, D. Gupta, "Human Heart Disease Prediction System Using Random Forest Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.634-640, 2018.

The data mining is a technique by which the computational algorithms are trained for finding the similar patterns from the huge or raw set of data. The training of the algorithms is performed on the patterns which are required to extract from the data. The training of the algorithms can be supervised or unsupervised. The main advantage of the supervised learning algorithms, these are efficient, accurate and effective as compared to the unsupervised learning approaches. In this presented work the text classification is the key area of study. The text classification techniques are used to classify according to their categories or the domain specific knowledge. Thus the text classification has rich applications. Among a number of applications of the text classification the social media based text classification and the sentiment analysis of the user’s text is comparatively new work in the text mining. In this presented work the social media based text is mined for discovering the user sentiments or moods which are expressed using the twitter based text communication. Therefore big data analytics are used to performing the text classification. First the twitter data is hosted on the HDFS directory and then the features are computed using the Map-reduce technique. The collected features are then labelled using the NLP tool which is used to discover the part of speech composition of the text sentences. After parsing the text using NLP tool the Bayesian classifier is implemented for classification of the social media text. The implementation of the proposed technique is performed using the JAVA technology. After implementation the performance of the proposed system is evaluated in terms of accuracy and the complexity. Both the performance parameters show the proposed sentiment analysis technique is effective and accurate for classifying the social media text for orientation discovery of user text.

classification; sentiment analysis; supervised learning text orientation; text mining

Data Gathering From Live Social Media Streams”, ICMR’14, April 01-04, 2014, Glasgow, United Kingdom. Copyright 2014 ACM 978-1-4503-2782-4/14/04
[2] A Comparison of Several Approaches to Missing Attribute Values in Data Mining, Jerzy W. Grzymala-Busse and Ming Hu, Springer-Verlag Berlin Heidelberg 2001, pp. 378−385,
[3] Ritika, “Research on Data Mining Classification”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 4, April 2014
[4] Abbas Jafari, S.S.Patil, “Use of Data Mining Technique To Design A Driver Assistance System”, Proceedings of 7th IRF International Conference, 27th April-2014, Pune, India, ISBN: 978-93-84209-09-4
[5] A.K. Jain, M.N. Murthy, P. J. Flynn, “Data Clustering: A Review”, © 2000 ACM 0360-0300/99/0900–0001
[6] Khaled Hammouda ,”A Comparative Study of Data Clustering Techniques”, Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1
[7] Hans-Peter Kriegel, Peer Kröger, Jörg Sander, Arthur Zimek, "Density-based Clustering", WIREs Data Mining and Knowledge Discovery 1 (3): 231–240. doi:10.1002/widm.30
[8] B. V. Rama Krishna, B. Sushma, “Novel Approach to Museums Development & Emergence of Text Mining”, ISSN 2249-6343, International Journal of Computer Technology and Electronics Engineering (IJCTEE), Volume 2, Issue 2
[9] H. P. Luhn, “A Business Intelligence System”, Volume 2, Number 4, Page 314 (1958), Nontopical Issue, IBM Research Journals
[10] Andreas Hotho, Andreas Nurnberger, Gerhard Paaß, Fraunhofer AiS, “A Brief Survey of Text Mining”, Knowledge Discovery Group Sankt Augustin, May 13, 2005s
[11] Hien Nguyen, Eugene Santos, and Jacob Russell, “Evaluation of the Impact of User-Cognitive Styles on the Assessment of Text Summarization”, IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, Vol. 41, No. 6, November 2011
[12] Umajancy. S, Dr. Antony Selvadoss Thanamani, “An Analysis on Text Mining –Text Retrieval and Text Extraction”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 8, August 2013
[13] Miloš Radovanović, Mirjana Ivanović, “Text Mining: Approaches And Applications”, Abstract Methods and Applications in Computer Science (no. 144017A), Novi Sad, Serbia, Vol. 38, No. 3, 2008, 227-234
[14] Siva S. Sivatha Sindhu, S. Geetha, A. Kannan, “Decision tree based light weight intrusion detection using a wrapper approach”, Expert Systems with Applications, 2011 Elsevier Ltd. All rights reserved.
[15] M. Jayakameswaraiah and S. Ramakrishna, “Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System”, International Journal of Computer Science and Engineering, Volume-2, Issue-3
[16] Biswajeet Pradhan, “A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS”, Computers & Geosciences, & 2012 Elsevier Ltd. All rights reserved.
[17] Neha Patel, Divakar Singh, “An Algorithm to Construct Decision Tree for Machine Learning based on Similarity Factor”, International Journal of Computer Applications (0975 – 8887) Volume 111 – No 10, February 2015
[18] T. Ramani, M. Ramzan Begam, “Survey: A Techniques implemented on Opinion Mining”, International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 5 No. 10 Oct 2014
[19] Walaa Medhat, Ahmed Hassan, Hoda Korashy, “Sentiment analysis algorithms and applications: A survey”, Ain Shams Engineering Journal, (2014) 5, 1093–1113
[20] Tirivangani BHT Magadza, Addlight Mukwazvure, K.P Supreethi, “Exploring Sentiment Classification Techniques in News Articles”, IJITKM Volume 8 • Number 1 June-Dec 2014 pp. 55-58 (ISSN 0973-4414)
[21] Alya Al Nasseri, Allan Tucker, Sergio de Cesare, “Quantifying StockTwits semantic terms’ trading behavior in financial markets: An effective application of decision tree algorithms”, © 2015 The Authors. Published by Elsevier Ltd.
[22] Rodrigo Moraes, João Francisco Valiati, Wilson P. Gavião Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN”, Expert Systems with Applications, 2012 Elsevier Ltd. All rights reserved.
[23] Suge Wang, Deyu Li, Lidong Zhao, Jiahao Zhang, “Sample cutting method for imbalanced text sentiment classification based on BRC”, Knowledge-Based Systems, 2012 Elsevier B.V. All rights reserved..

Amit Kumar Mittal, Shivangi Mittal, Digendra Singh Rathore, "Bayesian Classification for Social Media Text," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.641-646, 2018.