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In this paper, we study the problem of binary sentiment classification on a set of polar movie reviews. There are many models which have achieved state of the art performance, but one has to deal with the problem of tuning a large number of hyper-parameters. With the addition of the deep forest model as proposed by Zhi-Hua and Ji Feng, the number of hyper-parameters to be tuned is less and the architecture is still able to perform well. The goal of this paper is to use Word2Vec, FastText and Doc2Vec for creating word vector representation of the reviews which are then trained on a deep forest model. In order to further enhance the performance, the trained model is further trained on a different set of classifiers and as a result, a significant improvement in performance was noticed.

DeepForest, WordEmbeddings, Word2Vec, FastText, Doc2Vec, SVM

[1] Tulsi Jain, Kushagra Agarwal, Ronil Pancholia, "Sentiment Analysis Based on a Deep Stochastic Network and Active Learning", International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.1-6, 2017.
[2] M. M. Sutar, T. I. Bagban, "Applying Sentiment Analysis to Predict Rating and Classification of Text Review", International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.173-178, 2018.
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[5] Akhtar, M. S., Kumar, A., Ghosal, D., Ekbal, A., & Bhattacharyya, P. (2017). A multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis. In Proceedings of the Conference on Empirical Methods on Natural Language Processing (EMNLP 2017).
[6] Akhtar, M. S., Kumar, A., Ekbal, A., & Bhattacharyya, P. (2016). A hybrid deep learning architecture for sentiment analysis. In Proceedings of the International Conference on Computational Linguistics (COLING 2016).
[7] Abdul-Mageed, M., & Ungar, L. (2017). EmoNet: Fine-grained emotion detection with gated recurrent neural networks. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL 2017)
[8] Cho, K., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2014).
[9] Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. Journal of Machine Learning Research
[10] Guan, Z., Chen, L., Zhao, W., Zheng, Y., Tan, S., & Cai, D. (2016). Weakly-supervised deep learning for customer review sentiment classification. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2016)
[11] Dou, Z. Y. (2017). Capturing user and product Information for document level sentiment analysis with deep memory network. In Proceedings of the Conference on Empirical Methods on Natural Language Processing (EMNLP 2017).
[12] A. Hassan and A. Mahmood, "Deep Learning approach for sentiment analysis of short texts", 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), 2017.
[13] N. Zainuddin and A. Selamat, "Sentiment analysis using Support Vector Machine", 2014 International Conference on Computer, Communications, and Control Technology (I4CT), 2014.
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[15] Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K. and Kuksa, P. (2018). Natural Language Processing (almost) from Scratch. [online] Arxiv.org. Available at: https://arxiv.org/abs/1103.0398 [Accessed 18 Aug. 2018].
[16] Mikolov, T., Chen, K., Corrado, G. and Dean, J. (2018). Efficient Estimation of Word Representations in Vector Space. [online] Arxiv.org. Available at: https://arxiv.org/abs/1301.3781 [Accessed 14 Jun. 2018].
[17] Mikolov, T., Chen, K., Corrado, G. and Dean, J. (2018). Efficient Estimation of Word Representations in Vector Space. [online] Arxiv.org. Available at: https://arxiv.org/abs/1301.3781 [Accessed 18 Aug. 2018].
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Krishna Priya S, Shaksham Kapoor, Kavita S Oza, R.K. Kamat, "A Comprehensive Study on Sentiment Analysis Using Deep Forest," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.115-123, 2018.

In recent years, the use of biometrics has increased exponentially in the areas where security is a playing vital role. The factors that increasing the use of biometric in many applications include: the cost of the devices which are used for capturing and storing the images are greatly reducing, the ease-of-use of available devices, availability of fast hardware devices which are used for computing, the increasing use of networking and internet technology, different algorithms are available for storing the images in compressed format, etc. The role of biometric application is to authenticate the person by their voice, face or fingerprint. In biometric system, finger print is an extensively accepted one because of its advantage like no two person’s finger prints are same in the universe. In this paper we are presenting the detailed survey on fingerprint matching algorithms.

Fingerprint matching, Fingerprint verification, Fingerprint identification, Fingerprint classification, Bag-of-Visual-Words

[1] M. Sadeghi, K. Maghooli, and M. Moein, “Using Artificial Immunity Network for Face Verification,” The International Arab Journal of information Technology, vol. 11, no. 4, pp. 354- 361, 2014.
[2] W. Yang, J. Hu and S. Wnag, “A Delaunay Triangle-Based Fuzzy Extractor for Fingerprint Authentication,” in Proceeding of IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, Liverpool, pp. 66-70, 2012.
[3] M. Khalil, D. Mohamad, M. Khan, and Q. AlNuzaili, “Fingerprint Verification using Statistical Descriptors”, Journal of Digital Signal Processing, vol. 20, no. 4, pp. 1264-1273, 2009.
[4] D. Maltoni, D. Maio, A. Jain, and S. Prabhakar, “Handbook of Fingerprint Recognition”, Springer-Velag, 2009.
[5] H. Deng, and Q. Huo, “Minutiae Matching Based Fingerprint Verification Using Delaunay Triangulation and Aligned-Edge-Guided Triangle Matching,” in Proceeding of Audio and Video Based Biometric Person Authentication, New York, pp. 270-278, 2005.
[6] N.Liu., Y.Yin., and H. Zhang, “A Fingerprint Matching Algorithm Based on Delaunay Triangulation Net,” in Proceedings of the 5th International Conference on Computer and Information Technology, Shanghai, pp. 591-595, 2005.
[7] Parziale G. and Niel A., A Fingerprint Matching Using Minutiae Triangulation, Springer-Verlag, 2004.
[8] Y. Imamverdiyev, A. Teoh, and J. Kim, “Biometric Cryptosystem based on Discretized Fingerprint Texture Descriptors,” Journal of Expert Systems with Applications, vol. 40, no. 5, pp. 1888-1901, 2013.
[9] A. Jin, D. Ling,, and O. Song, “An Efficient Fingerprint Verification System using Integrated Wavelet and Fourier-Melling Invariant Transform,” Journal of Image and Vision Computing, vol. 22, no. 6, pp. 503-513, 2004.
[10] Abraham J., Kwan P., and Gao J., State of the Art in Biometrics, InTech, 2011.
[11] Ross A., Jain A., and Reisman J., “A Hybrid Fingerprint Matcher,” Journal of Pattern Recognition, vol. 36, no. 7, pp. 1661-1673, 2003.
[12] Gago-Alonso A., Hernández-Palancar J., Rodríguez-Reina E., and Muñoz-Briseño A., “Indexing and Retrieving in Fingerprint Databases Under Structural Distortions,” Expert Systems with Applications, vol. 40, no. 8, pp. 2858-2871, 2013.
[13] Girgis M., Sewisy A., and Mansour R., “Robust Method for Partial Deformed Fingerprints Verification Using Genetic Algorithm,” Expert Systems with Applications, vol. 36, no. 2, pp. 2008-2016, 2009.
[14] Nanni L. and Lumini A., “Descriptors for ImageBased Fingerprint Matchers,” Expert Systems with Applications, vol. 36, no. 10, pp. 12414 - 12422, 2009.
[15]Manual F, Gualberto T and Miguel L, “Fingerprint Verification Methods Using Delaunay Triangulations”, The International Journal of Information Technology, Vol. 14, No. 3, May 2017.
[16] Maltoni D., Miao D., Jain A., and Prabhakar S., “Handbook of Fingerprint Recognition”, NewYork: Springer, 2003.
[17] Ahmad F. and Mohamad D., “A Review on Fingerprint Classification Techniques,” International Conference on Computer Technology and Development, Kota Kinabalu, pp. 411-415, 2009.
[18] K Karu and A.K. JAint, “Pergamon FINGERPRINT CLASSIFICATION”, 1996.
[19] L.C. Ern and G. Sulong, “Fingerprint classificationapproach.”, pp.347-350, 2001.
[20] D. M. Z, D. Maltoni, C LAurea, and U Bologna, “ A structural Approach to Fingerprint Classification”, pp. 578-585, 1996.
[21] Ding G., Wang J., and Qin K., “A Visual Word Weighting Scheme Based on Emerging Itemsets for Video Annotation,” Information Processing Letters, vol. 110, no. 16, pp. 692-696, 2010.
[22] Li Z. and Feng X., “Near Duplicate Image Detecting Algorithm Based on Bag of Visual Word Model,” Journal of Multimedia, vol. 8, no.5, pp. 557-564, 2013.
[23] Yang J., Jiang Y., Hauptmann A., and Ngo C., “EvaluatingBag-of-Visual-Words Representations in Scene Classification,” in Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval, Texas, pp. 197-206, 2007.
[24] Zhang J., Sui L., Zhuo L., Li Z., and Yang, Y., “An Approach of Bag-of-Words Based on Visual Attention Model for Pornographic Images Recognition in Compressed Domain,” Neurocomputing, vol. 110, pp. 145-152, 2013.
[25] Pulung A, and Catur S, “Bag-of-Visual-Words for Fingerprint Classification”, International Arab Journal of Information Technology, 2015.
[26] Reza A., “Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement,” Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, vol. 38, no. 1, pp. 35-44, 2004.
[27] H. V. Neto, “Fingerprint Classification with Neural Networks” Hugo Vieira Neto Díbio Leandro Borges Universidade Federal de Goiânia,”,1997.

Y. Suresh, S.V. N. Srinivasu, "Fingerprint Matching Algorithms and BOVW: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.124-127, 2018.

The rapid increase in student population has resulted in expansion of educational facilities at all level. Nowadays, the responsibilities of teachers are many. It is the duty of teachers to guide the students to choose their carrier field according to their abilities and aptitudes. The Data Mining field mines the educational data from large volumes of data to improve the quality of educational processes. Today’s need of educational system is to develop the individual to enhance problem solving and decision making skills in addition to build their social skills. Educational Data Mining is one of the applications of Data Mining to find out the hidden patterns and knowledge in Educational Institutions. Generally, the three important groups of students have been identified: Fast Learners, Average Learners, and Slow Learners. In fact, students are probably struggles in many factors. This work focuses on finding the high potential factors that affects the performance of college students. This finding will improve the students’ academic performance positively.

Educational Data Mining; Feature Selection; Ensemble methods; ExtraTree Classifier

[1] Jayashree M Kudari. 2016. “Survey on the Factors Influences the Students’ Academic Performance”. International Journal of Emerging Research in Management &Technology. ISSN: 2278-9359 (Volume-5, Issue-6)
[2] Jason Brownlee. 2014. “Feature Selection in Python with Scikit-Learn”. (July 2014). Retrieved March 21, 2018 from https://machinelearningmastery.com/feature-selection-in-python-with-scikit-learn/
[3] Jasmina NOVAKOVIĆ, Perica STRBAC, Dusan BULATOVIĆ . 2011. “Toward Optimal Feature Selection Using Ranking
[4] Methods And Classification Algorithms”. Yugoslav Journal of Operations Research.21 (2011), Number 1, 119-135.DOI: 0.2298/YJOR1101119N
[5] Giovanni Seni, John F. Elder. 2010. “Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions”. Morgan & Claypool publishers
[6] Jason Brownlee. 2016. “Ensemble Machine Learning Algorithms in Python with scikit-learn”. (June 2016). Retrieved March 27. 2018 from https:// machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/
[7] Sergio Ledesma, Gustavo Cerda, Gabriel Avina, Donato Hernandez, and Miguel Torre. “Feature Selection Using Artificial Neural Networks”. Mexican International Conference on Artificial Intelligence. MICAI 2008: Advances in Artificial Intelligence pp 351-359 Retrieved from https://link.springer.com/chapter/10.1007/978-3-540-88636-5_34
[8] Zhi-Hua Zhou. 2012. CRC Press Taylor & Francis Group. “Ensemble Methods Foundations and Algorithms”.
[9] Raheela Asif, Agathe Merceron, Syed Abbas Ali and Najmi Ghani Haider. “Analyzing undergraduate students’ performance using educational data mining”. Computers & Education. 113 (2017) 177-194.
[10] Cristobal Romero, Manuel – Ignacio Lopez, Jose- Maria Luna and Sebastian Ventura. “Predicting students’ final Perfromance from participation in on-line discussion forums”. Computers & Education 68(2013) 458-472
[11] Anne – Sophie Hoffait, Michael Schyns. “Early detection of university students with potential difficulties”. Decision Support Systems 101(2017) 1-11.
[12] Ashwin Satyanarayana, Mariusz Nuckowski. “Data Mining using Ensemble Classifiers for Improved Prediction of student Academic Performance”. Spring 2016 Mid- Atlantic ASEE Conference, April 8-9, 2016 GWU.
[13] Mona Zamani. “Cooperative learning: Homogeneous and heterogeneous grouping of Iranian EFL learners in a writing context”. Cognet Education. Volume 3, 2016- Issue 1.Retrived from https://doi.org/10.1080/2331186X.2016.1149959
[14] Irfan Mushtaq, Shabana Nawaz Khan. “Factors Affecting Students’ Academic Performance”. Global Journal of Management and Business Research. Volume 12 Issue 9 Version 1.0 June 2012. Online ISSN: 2249-4588 & Print ISSN: 0975-5853
[15] Daniel T. Larose and Chantal D. Larose. 2016. “Data Mining and Predictive Analytics”. Wiley India Pvt. Ltd. New Delhi.
[16] K.Nagarajan, and S. Natarajan. 2012. “Guidance and Counseling”. Chennai. Ram publishers.
[17] Shoukat Ali, Zubair Haider, Fahad munir, Hamid Khan, Awais Ahmed. “Factors Contributing to the Students’ Academic Performance: A case study of Islamia University Sub- Campus”. American Journal of Education Research. 2013; 1(8): 283-289.
[18] Mrinal Pandey, S. Taruna. “Towards the integration of multiple classifier pertaining to the Student’s performance prediction”. Perspectives in Science (2016) 8, 364—366.
[19] Abimbola R. Iyanda, Olufemi D. Ninan, Anuoluwapo O. Ajayi, Ogochukwu G. Anyabolu, “Predicting Student Academic Performance in Computer Science Courses: A Comparison of Neural Network Models", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.6, pp. 1-9, 2018.DOI: 10.5815/ijmecs.2018.06.01
[20] R. Asif, A. Merceron and M. K. Pathan, “Predicting Student Academic Performance at Degree Level: A Case Study”. International Journal of Intelligent Systems and Applications, 7(1):49. 2014.
[21] Aparna U.R. and Shaiju Paul, “Feature selection and extraction in data mining”. 2016 Online International Conference on Green Engineering and Technologies (IC-GET). DOI: 10.1109/GET.2016.7916845
[22] M. Dash and H.liu, “Feature Selection for Classification”. An International Journal of Intelligent Data Analysis, vol. 1, no. 3, pp. 131-156, 1997
[23] Zhou Zhi-Hua (2012). “Ensemble Methods: Foundations and Algorithms”. Chapman and Hall/CRC. p. 23. ISBN 978-1439830031.
[24] Geurts P, Ernst D, Wehenkel L (2006). "Extremely randomized trees" (PDF). Machine Learning. 63: 3–42. doi:10.1007/s10994-006-6226-1

R. Kaviyarasi, T. Balasubramanian, "Exploring The High Potential Factors That Affects Students’ Academic Performance," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.128-134, 2018.

Breast cancer is a disease where there is excessive growth or uncontrolled growth of cells of the breast tissue. Breast cancer is a type of cancer that is often found as lump, bloody nipple, pain or sore and change in size in the most of the cases. Breast cancer occurs when the cell tissues of the breast become abnormal and uncontrollably divided. These abnormal cells form a large lump of tissues, which consequently becomes a tumor. Digital imaging techniques like Scintimammography are used to analyse metabolic activities and vascular circulation for pre-cancerous analysis based on breast tissue. Finally biopsy result is used to ascertain cancer when other physical exam and mammograms show breast change. Soft computing techniques are interestingly gaining popularity in medical disease diagnosis and decision making. There are different soft computing techniques for medical data processing. These techniques can be used individually or hybridizing more to process medical data yields near accurate results in decision making by medical practioners. This paper reviews different breast cancer diagnosis methodologies which use information obtained from imaging techniques such as Magnetic Resonance Imaging (MRI), Mammogram, Ultrasound and Biopsy. Aim of this paper is to propose or identify methodologies that process cancerous images obtained from different imaging techniques and predict breast cancer with relatively better accuracy. In this paper, we reviewed research articles published in the recent years on breast cancer prediction using soft computing techniques. Comparative analysis of different methods in terms of accuracy, sensitivity, specificity and computational time is presented.

Soft Computing, Machine Learning, Breast Cancer, Disease Diagnosis, Mammograms, MRI, Classification

[1] FeiHan, Guilian Shi, Chengwen Liang, Lin Wang, Kaiyang Li, “A simple and efficient method for breast cancer diagnosis based on infrared thermal imaging”, Cell biochemistry and biophysics, Vol. 71, pp. 491-498, 2015.
[2] Luciano Boquete, Sergio Ortego, Juan Manuel Miguel-Jimenez, Jose Manuel, Roman Blanco, “Automated detection of breast cancer in thermal infrared images, based on independent component analysis”’ Journal of medical systems, Vol. 36, pp. 103-111, 2012.
[3] Ali, Mona AS, Gehad Ismail Sayed, Tarek Gaber, Aboul Ella Hassanien, Vclav Snasel, Lincoln F. Silva, “Detection of breast abnormalities of thermograms based on a new segmentation method”, In Proceeding of Computer Science and Information Systems (FedCSIS), 2015 Federated Conference on IEEE, Vol. 5, pp. 255-261, 2015.
[4] W. Peng, R.V. Mayorga, E.M.A. Hussein, “An Automated Confirmatory System for Analysis of Mammograms”, Computer Methods and Programs in Biomedicine, Vol. , pp. 1-22, 2016.
[5] Hossein Ghayoumi Zadeh, Javad Haddadnia, Maryam Hashemian, Kazem Hassanpour, “Diagnosis of breast cancer using a combination of genetic algorithm and artificial neural network in medical infrared thermal imaging”, Iranian Journal of Medical Physics, Vol. 9, No. 4, pp. 265-274, 2012.
[6] Neha Tuteja, Parvinder Singh, MohitBansal, “Comparative analysis of different techniques for breast cancer detection in Mammograms”, International Journal of Advnce Research, Ideas and Innovtions in Technology, Vol. 4, No. 2, pp. 1361-1365, 2018.
[7] Anuj Kumar Singh, Bhupendra Gupta, “A novel approach for breast cancer detection and segmentation in a mammogram”, Procedia Computer Science, Vol. 54, pp. 676-682, 2015.
[8] Essam A. Rashed, Ismail A. Ismail, Sherif I. Zaki, “Multiresolution mammogram analysis in multilevel decomposition”, Pattern Recognition Letters, Vol. 28, pp. 286-292, 2007.
[9] Prathibha, B. N., and V. Sadasivam, “Mammogram analysis using the SVM classifier in combined transforms domain”, ICTACT Journal on Image and Video Processing, Vol. 1, No. 03, pp. 172-177, 2011.
[10] Nebi Gedik, AytenAtasoy, “A computer-aided diagnosis system for breast cancer detection by using a curvelet transform”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 21, pp. 1002-1014, 2013.
[11] M. Duraraj and R. Deepika, “Comparative Analysis of Classification Algorithms for the Prediction of Leukemia Cancer”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, No. 6, pp. 787–791, Aug. 2015.
[12] Ahmet Mert, NiyaziKilic, ErdemBilgili, Aydin Akan, “Breast Cancer Detection with Reduced Feature Set”, Computational and Mathematical Methods in Medicine, Vol. 2015, article Id 265138, pp. 1-12, 2015.
[13] Zhiming Li, MD, Lan Yu, MD, Xin Wang, MD, Haiyang Yu, MD, Yuanxiang Gao, MD,Yande Ren, MD, Gang Wang, MD, Xiaoming Zhou, MD, “Diagnostic performance of mammographic texture analysis in the differentialdiagnosis of benign and malignant breast tumors”, Clinical Breast Cancer, doi: 10.1016/j.clbc.2017.11.004, 2017.
[14] M. J. Lado, C. Cadarso-Suarez, J. Roca-Pardinas and P. G. Tahoces, “Using generalized additive models for construction of nonlinear classifiers in computer-aided diagnosis systems”, In proceedings of the IEEE Transactions on Information Technology in Biomedicine, Vol. 10, No. 2, pp. 246-253, April 2006.
[15] Petroudi S., Constantinou I., Pattichis M., Tziakouri C., Marias K., Pattichis C, “Evaluation of Spatial Dependence Matrices on Multiscale Instantaneous Amplitude for Mammogram Classification”, In: Lacković I., Vasic D. (eds) 6th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, Vol. 45, 2015.
[16] Selvan, S, Cecil Xavier, C,Karssemeijer, Nico,Sequeira, Jean, A. Cherian, Rekha, Y. Dhala, Bharathi, “Parameter Estimation in Stochastic Mammogram Model by Heuristic Optimization Techniques”, Information Technology in Biomedicine,” IEEE Transactions on. Vol. 10, pp. 685 – 695, 2006.
[17] Li Zhang, Wei-Da Zhou, “Fisher-regularized support vector machine”, Information Sciences, Elsevier, Vol. 343, pp. 79-93, 2016.
[18] Cong Y.C., Brady M., Petroudi S, “Texture Based Mammogram Classification and Segmentation”, In: Astley S.M., Brady M., Rose C., Zwiggelaar R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, Vol. 4046, Springer, Berlin, Heidelberg, 2006.
[19] M. Durairaj, R. Nandhakumar, “Feature Diminution by Hybrid Algorithm for improving the Success Rate for IVF Treatment,” Pakistan Journal of Biotechnology, Vol. 14, No. Sp-II, pp. 1100–1104, 2017.
[20] Woo Kyung Moona, I-Ling Chenb, Ann Yi, Min Sun Baea, Sung Ui Shina, Ruey-Feng Chang, “Computer-aided prediction model for axillary lymph node metastasisin breast cancer using tumor morphological and textural features on Ultrasound”, Computer Methods and Programs in Biomedicine, Elsevier, Vol. 162, pp. 129-137, 2018.

M. Durairaj, K. Priya, "Breast Cancer Prediction Using Soft Computing Techniques – A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.135-145, 2018.

As Search in testing is a most time consuming task which takes approximately 60% work load of the total software development time. If the testing is performed using automated testing then it will lead to reduce in software development cost by a significant margin. Metaheuristic search based testing techniques have been extensively used to automate the process of generating test cases and thus providing solutions for a more cost-effective testing process. Mobile Testing as a Service (known as Mobile TaaS a.k.a MTAAS) provides on-demand testing services for mobile applications and/or SaaS to support software validation and quality engineering processes by leveraging a cloud-based scalable mobile testing environment to assure pre-defined given QoS requirements and service-level-agreements (SLAs)”. Most“MTAAS are managed in an ad-hoc way with very limited mobile test automation tools. This approach offers the benefits of in-the-wild testing without the need to invest in a lab or purchase or rent devices, but at the risk of low testing quality and an uncertain validation schedule. In this paper a methodology is discussed based on Clonal Selection Based Optimization Approach that utilizes Crowed Sourcing.

Software testing , Search based testing ,Crowed Sourcing, Swarm Optimization

[1] Malden A Vouch. Cloud computing: Issues, research and implementations. ITI 2008 30th International Conference on Information Technology Interfaces, 16(4):31–40, 2008.
[2] A.I. Avetisyan, R. Campbell, I. Gupta, M.T. Heath, S.Y. Koi, G.R. Ganger, M.A. Couch, D. O’Halloran, M. Kenzie, T.T. Kwan, K. Lai, M. Lyons, D.S. Milojicic, Hang Yan Lee, Yang Chai SoHo, Ng Kwan Ming, J-Y. Luke, and Han Neigong. Open cirrus: A global cloud computing testbed. Computer, 43(4):35 –43, April 2010.
[3] Wei-Tec Tsai, Qigong Shao, Yu Huang, and Xiaoping Bai. Towards a scalable and robust multi-tenancy SaaS. In Proc. of the Second Asia Pacific Symposium on Internet ware, pages 8:1–8:15, New York, NY, USA, 2010.
[4] K. Mao, L. Capra, M. Harman, and Y. Jian, “A survey of the use of crowdsourcing in software engineering,” Journal of Systems and Software, vol. 126, pp. 57 – 84, 2017.
[5] K. Mao, Y. Yang, Q. Wang, Y. Jian, and M. Harman, “Developer recommendation for crowdsourced software development tasks,” pinprick. Of SOSE’15, 2015, pp. 347–356.
[6] Dolata, R. Vliegendhart, and J. Powles, “Crowdsourcing GUI tests,” in Proc. of ISSTA’13, March 2013, pp. 332–341.
[7] R. Vliegendhart, E. Dolata, and J. Powles, “Crowdsourced user interface testing for multimedia applications,” in Proc. of CrowdMM’12, 2012, pp. 21–22.
[8] Wei-Tec Tsai, Pride Zheng, J. Balasooriya, Minong Chen, Xiaoping Bai, and J. Elton. An approach for service composition and testing for cloud computing. In Autonomous Decentralized Systems (ISADS), 2011 10th International Symposium on, pages 631 –636, March 2011.
[9] Stefan Baucus, Vlad Urschel, Cristian Zafar, and George Candia. Parallel symbolic execution for automated real-world software testing. InProc. Of The Sixth Conference on Computer Systems, pages 183–198, New York, NY, USA, 2011.
[10] Xiaofei Zhang, Hui Liu, Bin Li, Xing Wang, Haitian Chen, and Shushing Wu. Application-oriented remote verification trust model in cloud computing.2010 IEEE Second International Conference on Cloud Computing Technology and Science, pages 405–408, 2010.
[11] Sarang, R. P., & Bunkar, R. K. (2013). Study of Services and Privacy Usage in Cloud Computing. International Journal of Scientific Research in Computer Science and Engineering, 1(6), 7-12.
[12] Palve, A., Sonawane, R. D., & Potgantwar, A. D. (2017). Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark. International Journal of Scientific Research in Network Security and Communication, 5(3), 99-103.

R. Sharma, S. Singh, "Swarm Intelligence Based Automated Testing for MTAAS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.146-150, 2018.

The availability of digital information over the internet can be analyzed for knowledge discovery and intelligent decision making. Text categorization is an important and extensively studied problem in machine learning. Text classification or grouping of text into appropriate categories requires pre-processing techniques and machine learning algorithms. Pre-processing or data cleaning involves removal of html characters, tokenization, stop words removal, stemming, lemmatization and advanced processes such as parts of speech tagging followed by representation in appropriate form for machine learning. This paper experimentally evaluates the impact of stemming and tokenization techniques on text classification on five text datasets.

Tokenisation, stemming, parts of speech tagging, document representation, vector space model

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Susan Koshy, R. Padmajavalli, "Evaluating Techniques for Pre-Processing of Unstructured Text For Text Classification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.151-160, 2018.

The multi-hop ad hoc networking is not a new concept having been around for over twenty years, mainly exploited to design tactical networks. In the past decade wireless multi-hop ad-hoc networks have got a tremendous amount of research focus. Mobile devices enabled with wireless short range communication technologies make possible new applications for continuous communication, interaction and collaboration. The collaboration is used to facilitate communication when mobile devices are not able to establish direct communication paths. So the communication is multi-hop with intermediate nodes acting as routers that forward the messages addressed to other nodes. The multi-hop ad-hoc networking concept was successfully applied in several classes of networks. In this paper, it reviews the categories of wireless multi-hop ad-hoc networks and discusses main evolutions of wireless multi-hop ad-hoc networks specially the opportunistic networks. This networking paradigm is well suited for a world of pervasive devices equipped with various wireless networking technologies which are frequently out of range from a global network but are in the range of other networked devices, and sometime cross areas where some type of connectivity is available. Among multi-hop ad hoc networks, wireless sensor networks have a special role.

Delay tolerant Network, MANET, Mobile Devices, Opportunistic Network, WLAN

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Pradeep G Pillai, Saravanan. P, "Significance of Wireless Multi-Hop AD-HOC Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.161-167, 2018.

Cloud computing provides a platform where services are facilitating to the cloud user through the web, either free of cost or lease base. The cloud user and demands are increasing, due to this large number of service request are submitting to the cloud service provider. To manage those service requests, scheduling plays a key role for the service provider to manage their resource and operational cost. In this paper, the state of art survey has been carried out on recent developments in resource scheduling algorithms for cloud computing. This paper provides the comparative analysis of all the surveyed algorithms in terms of different performance metrics. The observation of the survey provides some research gaps to improve the efficiency of the existing resource management system.

Cloud Computing, Resource Scheduling, Performance, Efficiency

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Bhupesh Kumar Dewangan, Amit Agarwal, Venkatadri M, Ashutosh Pasricha, "Resource Scheduling in Cloud: A Comparative Study," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.168-173, 2018.

Data mining is a relatively new field of research whose major objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. Data mining in the medical domain works on the past experiences (data collected) and analyse them to identify the general trends and probable solutions to the present situations. This paper is concerned with the application of data mining techniques in the domain of the medical field of heart diseases/attack. We carried out extensive experiments applying different data mining techniques including Relevance analysis, Association Rules Mining and Clustering. We report the findings which are very promising.

Diagnosis, prognosis, clustering

[1] Mrs.a.vanitha, Dr.n.nagadeepa, “Analysis of current applications and issues Of data mining in healthcare”, International Journal of Advanced research in Computer science engineering and information technology, 25-oct-2014
[2] Khalid raza, “Application of data mining in bioinformatics”, Indian Journal of computer science and engineering, may 2014.
[3]Akanksha, vinod maan, “Data mining with big data in health informatics”, international journal of computer science trends and technology (ijcst) – volume 5 issue 2, mar – apr 2017
[4] Snehal Chaflekar, “Intermediate Graphical Language using SDT”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, Issue 5, May 2017

Snehal H. Chaflekar, R. Shinganjude, "Review Paper on Application of Data Mining on Healthinformatics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.174-175, 2018.

Mobile Ad hoc Network (MANET) provides on the fly solution for those areas where wired network implementation is difficult. MANETs are network without infrastructure. They do not have central control. All nodes help each other in data communication. The life span of MANET is very small. The main features of MANET are: nodes are dynamic in nature resources availability in scarce and open channel. Due to resource scarcity some nodes may not provide their services to other nodes and some nodes may not participate in data transmission, to save their resources and they become selfish node. MANETs are susceptible for attacks. The open channel, resource scarcity and deployment type of network attract intruders for malicious activities. This survey paper has reviewed various articles from 1999 to 2018, in the view to look at how malicious nodes in a network make attacks on other nodes and what impact goes on network. Earlier work have been discussed about one or two types of attacks. This paper has studied and tried to bring all attacks under the one umbrella. The paper discussed these attacks in following categories: External and internal, active and passive, at protocol stack layer, security goals, attacks affecting routing. This paper also come along with defense line, have been proposed by various researchers for different attacks. Paper also tries to put attacks and their effect on one place. It includes type of attack, status of attacker, interaction, layer and security goals. It is suggested that what type of, defense, line of action should be taken, so that a network must sustain in adverse situation and available for its users.

Wireless, Ad hoc, attack, security, defense, countermeasures

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P. Gupta, P. Bansal, "Different Attacks and their Defense Line in Mobile Ad hoc Networks: A Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.176-190, 2018.