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Autism Spectrum Disorder (ASD) or simply Autism a progression of deep-rooted neuron developmental issue which is basically a brain development disorder found in the kids when they are around eighteen months. The kids suffer from communication, social interaction and repetitive behavior problems and are very difficult to identify autism on a proper way. If Autism is remained undetected in children within the age of 1 to 3 years from birth there are chances of regeneration of the symptoms manifested by the autism in the adult stage which is generally noted to be after or around the age of 60 years. Through our paper, we have proposed an IoT based detection model to get data for five symptoms using different sensors and can be transferred to the destination machine for testing using the internet from a remote location. The goal of this paper is to review the Autism Spectrum Disorder problem by generating some IF-THEN rules and build a system to identify the level of Autism using Adaptive Neuro-Fuzzy Inference System (ANFIS).

ASD, Adaptive Neuro-Fuzzy Inference System, AOI, eye tracker, IoT

[1] R. Raz, A. L. Roberts, K. Lyall, J.E. Hart, A.C. Just, F. Laden, M.G. Weisskopf, “Autism spectrum disorder and particulate matter air pollution before, during, and after pregnancy: a nested case–control analysis within the Nurses”, Health Study II cohort. Environmental Health Perspectives, 123(3), 264, 2015
[2] D.H. Geschwind, M.W. State, “Gene hunting in autism spectrum disorder: on the path to precision medicine”, The Lancet Neurology, 14(11), 1109-1120, 2015
[3] A. Dey, G. Abowd, D. Salber, “A conceptual framework and toolkit for supporting the rapid prototyping of context-aware applications in special issue on context-aware computing”, Hum. Comput. Interact, 16 (2–4), 97–166, 2001
[4] LoParo, Devon, and I. D. Waldman. "The oxytocin receptor gene (OXTR) is associated with autism spectrum disorder: a meta-analysis", Molecular psychiatry 20.5, 640, 2015
[5] Jang, J.-S. R., "ANFIS: Adaptive-Network-based Fuzzy Inference Systems", IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, May 1993.
[6] Timothy J. Ross, John Wiley & Sons, “Fuzzy Logic with Engineering Applications”, Third Edition ISBN: 978-0-470-74376-8, 2010
[7] Dawson, Geraldine, and Raphael Bernier, "A quarter century of progress on the early detection and treatment of autism spectrum disorder", Development and psychopathology 25.4pt2 1455-1472, 2013
[8] J. Yip, J.J. Soghomonian,G.J. Blatt, “Decreased GAD67 mRNA levels in cerebellar Purkinje cells in autism: pathophysiological implications”, Acta neuropathologica, 113(5), 559-568, 2007
[9] C. Wong, S.L. Odom, K.A. Hume, A. Cox, A. Fettig, S. Kucharczyk, T.R. Schultz, “Evidence-based practices for children, youth, and young adults with autism spectrum disorder”, A comprehensive review. Journal of Autism and Developmental Disorders, 45(7), 1951-1966, 2015
[10] Fletcher, R. Richar, Ming-Zher Poh, and Hoda Eydgahi. "Wearable sensors: opportunities and challenges for low-cost health care", Engineering in Medicine and Biology Society (EMBC), nnual International Conference of the IEEE, IEEE, 2010.
[11] Cabibihan, John-John, et al. "Sensing Technologies for Autism Spectrum Disorder Screening and Intervention.", Sensors, 17.1:46, 2016
[12] Bourgeron, Thomas. "From the genetic architecture to synaptic plasticity in autism spectrum disorder", Nature Reviews. Neuroscience 16, no. 9:551, 2015

Sipali Pradhan, Surajit Das, Samaleswari Prasad Nayak, J.K. Mantri, "An IOT Based Detection Model for the Level of Autism in a Context-Aware Health Care", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.152-156, 2019.

Dynamic data is a common challenge in digital Image processing. It has played an important role in modern and previous era in community. In past, masses were using the process of Images processing in photogenic devices such as cameras, but as time changes technology changes. The camera has been replaced by Mobile Camera which is one of the convent resources. As the technology does not remain same and has been changed so far by new thoughts and ideas as different image processing, techniques, equipment’s, is programming tools. In research various programming language tools are used for manipulation of images processing (advance java, Mat lab, Scilab, Opencv, Artificial Neural Network). Medical imaging plays an important role in clinical study such as (x-ray, Ultrasound, Mammography, Computed tenography (C.T.), Magnetic Resonance imaging (MRI) are getting popular. In this context we have used agent–based coordinate between medical and research institution. In this paper we have studied the approximation of Image processing and proficiency applied for medical Image processing in Madhya Pradesh (M.P), India. In the first segment of our paper; we have given introduction of Satna district in Madhya Pradesh, Percentage of cancer patients in Madhya Pradesh and how it is affecting the people of Satna and about cancer research center and education in Satna. In second segment we have discussed digital image processing, techniques, equipment’s and some related work used in breast cancer diagnosis. In the third segment; we involve recent tools which are used for breast cancer diagnosis. Forth segment we have given list of observation that we have seen in the hospitals during our visit. In fifth segment conclusion of our work has been draw.

Approximation; Masses; Cancer; Breast

[1] Deepak Ganjewala, 2009, “Prevalence of Cancers in Some Parts of Madhya Pradesh and Uttar Pradesh in India”, Volume 2, issue 1, ISSN-1995-8943, pp-12-18.
[2] http://www.satna.nic.in
[3] https://timesofindia.indiatimes.com/city/bhopal/Cancer-among-females-on-the-rise-in-state/articleshow/27581678.cms
[4] https://www.21co.com/radiation-therapy/technologies/siemens-primus
[5] Johan zorab et al.,”Magnetic Resonance Imaging(MRI)”Breast Page 1 of 9 RadiologyInfo.org Reviewed May-7-2013
[6] Narain Bhavana, Zadaonkar A.S. Kumar Sanjay, 2013,”A Multi view Approach of images-Based Transformation” Vol 5, issue 9 September 2016
7.NarainBhavana,Zadgaonkar A.S, Kumar Sanjay Kumar, 2013“Impact of Digital Image Processing on Research and Education”National Seminar and “Computer Application in Science Teaching, Learning & Research”, By Govt. E. Raghvendra Rao P.G. Science College Bilaspur, Chhattisgarh, India, pp74-75.
[7] S. Whitley, C. Sloane, G. Hadley, A. D. Moore & C. W. Alsop., 2005“Clark Position in Radiography, Oxport University”, pp-1-517.
11.Sharma Usha (2017)"Suitability of Neural Network for Disease Prediction: A Comprehensive Literature Review”, vol5, issue 6,ISSN 2320 – 6527 pp-12-20
[8] Usha,Karmakar Sanjeev, Shrivastava Navita,2016”Recent Application of Neural Network in Predication of Non-Linear and Dynamic System” vol5 issue 9 .
[9] Sharma Usha, Karmakar Sanjeev, Srivastava Navita, 2016,"Application of Back-Progation Neuralnetwork in HoroscopePredication” Volume 2 ISSN 2249-0868pp11-15.

U. Sharma, B. Narain, "Impact of Breast Cancer in Women’s of Satna District, Madhya Pradesh", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.157-161, 2019.

Emphysema is a chronic pulmonary disease caused due to devastation of air sacs present inside the lungs. Emphysema symptoms can be slowed with medications but can’t be cured. Accuracy of diagnosis is very limited and there are several factors behind its misdiagnosis. Poor textural quality is one of thesignificant factors behind poor diagnosis of emphysema. In this article, emphysema images textural quality is enhanced using UnsharpMasking and Contrast Limited Adaptive Histogram Equalization. Result alsosupports a significant increase in contrast improvement of emphysema images.This method potentially suppresses noise and enhances the image contrast as well.

Contrast Limited Adaptive Histogram Equalization (CLAHE), Computer Tomography (CT),Contrast Enhancement(CE), Emphysema,UnsharpMasking(UM).

[1] Chin-Chuan Hsu, Ping-Yuan Chen, Chih-Cheng Lai, Thigh emphysema as the initial presentation of colon ischemia, The American Journal of Emergency Medicine, Available online 6 December 2017
[2] ShukoMashimo, ShotaroChubachi, Akihiro Tsutsumi, NaofumiKameyama, Mamoru Sasaki, Masahiro Jinzaki, Hidetoshi Nakamura, Koichiro Asano, John J. Reilly, Tomoko Betsuyaku, Relationship between diminution of small pulmonary vessels and emphysema in chronic obstructive pulmonary disease, In Clinical Imaging, Volume 46, 2017, Pages 85-90.
[3] Abbas Al Ramzi, Ashraf Barghash, MaysounKassem, Valsalva-type maneuver induced cervicofacial subcutaneous emphysema: A case report, In Future Dental Journal, 2017.
[4] Abdul Shameer, NeelamPushker, GautamLokdarshi, ShabeerBasheer, Mandeep S. Bajaj, Emergency Decompression of Orbital Emphysema with Elevated Intraorbital Pressure, In The Journal of Emergency Medicine, Volume 53, Issue 3, 2017, Pages 405-407.
[5] Rajeev Subramanyam, Andrew Costandi, Mohamed Mahmoud, Congenital lobar emphysema and tension emphysema, In Journal of Clinical Anesthesia, Volume 29, 2016, Pages 17-18.
[6] K. Ding, L. Xiao and G.Weng, Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation, Signal Processing, vol. 134, 2017, pp.224-233.
[7] W. Zhang, Y. Zhao, T.P. Breckon and L. Chen, Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels, Pattern Recognition, vol. 63, 2017, pp. 193-205.
[8] J. Joseph, J. Sivaraman, R. Periyasamy and V.R. Simi, An objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR images, Biocybernetics and Biomedical Engineering, vol. 37, issue 3, 2017, pp. 489-497.
[9] J. Joseph and R. Periyasamy, A fully customized enhancement scheme for controlling brightness error and contrast in magnetic resonance images, Biomedical Signal Processing and Control, vol. 39, 2018, pp. 271-283.
[10] H. Lidong, Z. Wei, W. Jun and S. Zebin, Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement, IET Image Processing, vol. 9, no. 10, 2015, pp. 908-915.
[11] Contrast Limited Adaptive Histogram Equalization, Documentation,https://in.mathworks.com/help/images/ref/adapthisteq.html.
[12] A.H.H. Alasadi and A.K.H. Al-Saedi, A Method for Micro-calcifications Detection in Breast Mammograms, Journal of Medical Systems, vol. 41, issue 4, 2017, pp. 61-68.
[13] G. Deng, A Generalized Unsharp Masking Algorithm, IEEE Transactions on Image Processing, vol. 20, no. 5, 2011, pp. 1249-1261.
[14] UnsharpMasking,Documentation,https://in.mathworks.com/help/images/ref/imsharpen.html.
[15] C. Pham and J. W. Jeon, Efficient image sharpening and denoising using adaptive guided image filtering,IET Image Processing, vol. 9, no. 1, 2015, pp. 71-79.
[16] H. Ibrahim and N. S. Pik Kong, Image sharpening using sub-regions histogram equalization,IEEE Transactions on Consumer Electronics, vol. 55, no. 2, 2009, pp. 891-895.
[17] Bhateja, M. Misra and S. Urooj, Human visual system based unsharp masking for enhancement of mammographic images, Journal of Computational Science, vol. 21, 2017, pp. 387-393.
[18] F. Russo, An image enhancement technique combining sharpening and noise reduction, IEEE Transactions on Instrumentation and Measurement, vol. 51, no. 4, 2002, pp. 824-828.
[19] T. C. Aysal and K. E. Barner, Quadratic Weighted Median Filters for Edge Enhancement of Noisy Images, IEEE Transactions on Image Processing, vol. 15, no. 11, 2006, pp. 3294-3310.
[20] M.K. Sharma, J. Joseph and P. Senthilkumaran, Directional edge enhancement using superposed vortex filter, Optics & Laser Technology, vol. 57, 2014, pp. 230-235.
[21] V. Bhateja, M. Misra and S. Urooj, Non-linear polynomial filters for edge enhancement of mammogram lesions, Computer Methods and Programs in Biomedicine, vol. 129, 2016, pp. 125-134.
[22] S. Anand, R. ShanthaSelvaKumari, S. Jeeva and T. Thivya, Directionlet transform based sharpening and enhancement of mammographic X-ray images, Biomedical Signal Processing and Control, vol. 8, issue 4, 2013, pp. 391-399.
[23] K. Wang, Directional and omnidirectional edge enhancement based on radial Hilbert transform of Gabor filter,Electronics Letters, vol. 52, no. 9, 2016, pp. 701-703.
R. Jung and J. Scharcanski, Sharpening Dermatological Color Images in the Wavelet Domain, IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 1, 2009, pp. 4-13.

Sibu Thomas, A. K. Shrivas, "Studyof Unsharp Masking and Contrast Limited Adaptive Histogram Equalization on CT Images of Emphysema", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.162-166, 2019.

10 scientific techniques are used to analyze the academic data in this paper. Hence the new factors are obtained to generalize the objective of academics. The illustration is given in this paper for applying this compact method to the academic institutes. New algorithm is proposed to execute the model. These big data have been unutilized mostly. The present study is proposed several model based on modern scientific and computing technique for utilizing these big data. The applicability, usefulness, significance and importance of these big data are studied in this project.

Academic Data, Big Data

[1] Potgieter, Ingrid, and Melinde Coetzee. "Employability attributes and personality preferences of postgraduate business management students." SA Journal of Industrial Psychology 39.1 (2013): 01-10.
[2] Jantawan, Bangsuk, and Cheng-Fa Tsai. "The Application of Data Mining to Build Classification Model for Predicting Graduate Employment." International Journal Of Computer Science And Information Security (2013).
[3] Bakar, Noor Aieda Abu, Aida Mustapha, and Kamariah Md Nasir. "Clustering Analysis for Empowering Skills in Graduate Employability Model." Australian Journal of Basic and Applied Sciences 7.14 (2013): 21-28.
[4] Singh, Samrat, and Vikesh Kumar. "Performance Analysis of Engineering Students for Recruitment Using Classification Data Mining Techniques." International Journal of Computer Science & Engineering Technology, (2013).
[5] Pandey, Umesh Kumar, and Brijesh Kumar Bhardwaj. "Data Mining as a Torch Bearer in Education Sector." Technical Journal of LBSIMDS (2012).
[6] Srimani, P. K., and Malini M. Patil. "A Classification Model for Edu-Mining." PSRC-ICICS Conference Proceedings. 2012.
[7] Archer, Elizabeth, Yuraisha Bianca Chetty, and Paul Prinsloo. "Benchmarking the habits and behaviors of successful students: A case study of academic-business collaboration." The International Review of Research in Open and Distance Learning 15.1 (2014).
[8] Sakurai, Yoshitaka, Setsuo Tsuruta, and Rainer Knauf. "Success Chances Estimation of University Curricula Based on Educational History, Self-Estimated Intellectual Traits and Vocational Ambitions." Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on. IEEE, 2011.
[9] Pandey, Umesh Kumar, and Saurabh Pal. "A Data mining view on class room teaching language." International Journal Of Computer Science Issues (2011).
[10] Arjariya, Tripti, et al. "Data Mining and It’s Approaches towards Higher Education Solutions." International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307.
[11] Aher, Sunita B., and L. M. R. J. Lobo. "Data mining in educational system using Weka." IJCA Proceedings on International Conference on Emerging Technology Trends (ICETT). Vol. 3. 2011.
[12] Suthan, G. Paul, and S. Baboo. "Hybrid chaid a key for mustas framework in educational data mining." IJCSI International Journal of Computer Science Issues 8 (2011): 356-360.
[13] Kumar, Varun, and Anupama Chadha. "An empirical study of the applications of data mining techniques in higher education." International Journal of Advanced Computer Science and Applications 2.3 (2011).
[14] Sharma, Mamta, and MonaliMavani. "Accuracy Comparison of Predictive Algorithms of Data Mining: Application in Education Sector." Advances in Computing, Communication and Control. Springer Berlin Heidelberg, 2011. 189-194.
[15] Gokuladas, V. K. "Technical and non‐technical education and the employability of engineering graduates: an Indian case study." International Journal of Training and Development 14.2 (2010): 130-143.
[16] Ayesha, Shaeela, Tasleem Mustafa, Ahsan Raza Sattar, and M. Inayat Khan. "Data mining model for higher education system." Europen Journal of Scientific Research 43, no. 1 (2010): 24-29.
[17] Kovacic, Zlatko. "Early prediction of student success: Mining students` enrolment data." (2010).
[18] Al-shargabi, Asma A., and Ali N. Nusari. "Discovering vital patterns from UST students data by applying data mining techniques." Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on. Vol. 2. IEEE, 2010.
[19] Yan, Zhi-min, Qing Shen, and Bin Shao. "The analysis of student`s grade based on Rough Sets." Ubi-media Computing (U-Media), 2010 3rd IEEE International Conference on. IEEE, 2010.
[20] Ningning, Gao. "Proposing Data Warehouse and Data Mining in Teaching Management Research." Information Technology and Applications (IFITA), 2010 International Forum on. Vol. 1. IEEE, 2010.
[21] Knauf, Rainer, Yoshitaka Sakurai, Setsuo Tsuruta, and Kouhei Takada. "Empirical evaluation of a data mining method for success chance estimation of university curricula." In Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on, pp. 1127-1133. IEEE, 2010.
[22] Wu, X., Zhang, H., & Zhang, H. (2010, October). Study of comprehensive evaluation method of undergraduates based on data mining. In Intelligent Computing and Integrated Systems (ICISS), 2010 International Conference on(pp. 541-543). IEEE.
[23] Youping, BianXiangjuan Gong. "The application of data mining technology in analysis of college student`s performance." Information Science (2010).
[24] Liu, Zhiwu, and Xiuzhi Zhang. "Prediction and Analysis for Students` Marks Based on Decision Tree Algorithm." Intelligent Networks and Intelligent Systems (ICINIS), 2010 3rd International Conference on. IEEE, 2010.
[25] Balakrishnan, Julie M. David. "Significance of Classification Techniques In Prediction Of Learning Disabilities." International Journal Of Artificial Intelligence & Applications (2010).
[26] Ranjan, Jayanthi, and Raju Ranjan. "Application of data mining techniques in higher education in India." Journal of Knowledge Management Practice 11.1 (2010): 1-9
[27] Romero, Cristóbal, and Sebastián Ventura. "Educational data mining: a review of the state of the art." Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 40, no. 6 (2010): 601-618.
[28] Baker, Ryan SJD, and KalinaYacef. "The state of educational data mining in 2009: A review and future visions." JEDM-Journal of Educational Data Mining1.1 (2009): 3-17.

Swati Jain, Vikas Kumar Jain, Sunil Kuamr Kashyap, "Skill-Employability Development Models (SEDM) based on Academic Data Mining (ADM)", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.167-169, 2019.

This paper is focused on water resource management(WRM) with particular reference to responsibility and function of water resource department(WRD) Water is becoming a central issue in this new periods through this paper researcher can make aware common people and asses the possibilities of water resource development in water productivity and to save water is more highlighted .Environment is affected due to growth of population and degrading through uncontrolled expansion of urbanization, industrialization, intensified agricultural activities of human being. In India, due to rapid growth of population and expansion of development activities, environment is adversely affected. India is the second most populous country in the world having over 1.271 billion population (17.5%) of world’s total population determining that how much pressure of the population on environment and natural resources . There are many regions where our freshwater resources are inadequate to meet domestic needs, lack of adequate clean water to meet human drinking water. The role of storage of water by traditional and modern method to increase ground water level for irrigation purpose and solution of water crisis is discussed .From last 5 years, a crisis of water has been recorded in the state, in summer. This paper identifies those problems and try to solve them to create a more sustainable and desirable future. Few suggestions are given to common people through this paper. Data is collected with help of secondary method. Books, newspapers, magazines , journals and websites are used for data.

Water resources ,Water resource management ,water resource department

[1] R.S Chowhan , P.Dayya ,U.N Shukla, “Sustainable E-Agriculture Knowledgebase for Information Dissemination to Develop Indian Agriculture Sector and Empower Rural Farmer” , International Journal of Advanced Research in Computer and Communication Engineering ,Vol. 7, Issue 4, PP 105-112, 2018
[2] W.R Depietro , “GDP per capita and its challengers as a measures of happiness” ,International Journal of Social Economics, Vol. 33, Issue: 10, pp.698-709,2006.
[3] Y. Liu, H .Gupta , E. Springer , T. Wagener , “Linking science with environmental decision making: Experiences from an integrated modelling approach to supporting sustainable water resources management”, Environmental Modelling & Software, Vol. 23, pp. 846-858, 2008.
[4] H . Mehanuddin , G. R Nikhitha , K S Prapthishree , L B Praveen., H G Manasa, “Study on Water Requirement of Selected Crops and Irrigation Scheduling Using CROPWAT 8.0”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 7, Issue 4, pp. 3431-3436, 2018
[5] S. Arlosoroff, “Israel – a case study of water-demand management”, International Rescue Committee, Vol. 22, No. 2, pp 4-7. , 2003.
[6] S. D. Silva, “The experiences of Water Management Organizations in Bangladesh”, International Water Management Institute, pp. 1-50, 2102.
[7] S. J. Yelapure 1 , Dr. R. V. Kulkarni, “Literature Review on Expert System in Agriculture”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (5) , 2012,5086-5089

Aashima Franklin, "A Study on Water Resources Management by Water Resources Department in Chhatttisgarh", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.170-172, 2019.

Online reviews provide accessible and plentiful data for relatively easy analysis for a given product.This paper seeks to apply and extend the current work in the field of Natural Language processing and sentiment analysis to retrieve information from Amazon Product reviews classify them using Naïve bayes classifier . This work presents a methodology that shows how text data can provide insight into various features of a product found in the customer reviews and feature selection method.

Feature selection , Sentiment classification, Categorization

[1] Y. Yang and J.O Pedersen, “A Comparative study on Feature Selection in Text Categorization “,In International Conference on Machine Learning (ICML), 1997.
[2] L. Dey, S. Chakraborty, A. Biswas, B. Bose, and S. Tiwari, “Sentiment analysis of Review Datasets using Naïve Bayes’ and k-NN Classifiers.” ,Int. J. of Information Engineering and Electronic Business,vol. 4, pp. 54-62, 2016.
[3] I. Hemalatha , G. P Saradhi V., &A. Govardhan, “Preprocessing the Informal Text for efficient Sentiment Analysis” , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS),Volume 1, Issue 2,2012.
[4] Wei Gao , Fabrizio Sebastiani ,” TweetSentiment: From Classification to Quantification’,Springer-Verlag Wien 2016
[5] M. Bouazizi and T. Ohtsuki, ‘‘Sentiment analysis in Twitter: Fromclassification to quantification of sentiments within tweets,’’ inProc. IEEEGLOBECOM, Dec. 2016, pp. 1–6.
[6] Aashutosh Bhatt, Ankit Patel, Harsh Chheda, Kiran Gawande,” Amazon Review Classification and SentimentAnalysis”, International Journal of Computer Science and Information Technologies, Vol. 6 (6) , 2015, 5107-5110

Smita Suresh Daniel, Ani Thomas, Neelam Sahu, "Feature Selection and Classification for Sentiment Analysisof Amazon Product Reviews", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.173-177, 2019.

Nowadays, time is very important in computational field. Today every field in computer science has a huge amount of data, and we need to process data to get valuable information out of it. To reduce the processing time and using of maximum capacity of processor, we divide a large computation problem in to small chunks that is processed by individual processor. Recent microprocessors, becomes possible to utilize the parallelism using multi-cores which support improved SIMD instructions. In this paper we present the GPU based of Parallel Processing architecture, working and its applications for performing fast execution of a task.

Parallel computing technique. GPU Architecture, Memory architecture

[1] Kai Ma†, Xue Li‡, Wei Chen†, Chi Zhang‡, and Xiaorui Wang†, “GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures,” 2012 41st International Conference on Parallel Processing.
[2] Rafiqul Zaman Khan, Md Firoj Ali, “Current Trends in Parallel Computing, International Journal of Computer Applications” (0975 – 8887) Volume 59– No.2, December 2012.
[3] Bhavna Samel, Shubhrata and Prof. A.M. Ingole, “GPU Computing and Its Applications, International Research Journal of Engineering and Technology (IRJET),” Volume: 03 Issue: 04 | Apr-2016.
[4] Shuichi Asano, Tsutomu Maruyama and Yoshiki Yamaguchi, “Systems and Information Engineering,” University of Tsukuba 1-1-1 Ten-ou-dai Tsukuba Ibaraki 305-8573 JAPAN, 978-1-4244-3892-1/09/$25.00 ©2009 IEEE
[5] http://www.nvidia.com/object/cuda home.html.
[6] T. Brandes1,a, A. Arnold2, T. Soddemann1, and D. Reith1, “The European Physical Journal Special Topics,” Eur. Phys. J. Special Topics 210, 73–88 (2012)c_EDP Sciences, Springer-Verlag 2012
[7] DOI: 10.1140/epjst/e2012-01638-7
[8] Ben Cope,Department of Electrical & Electronic Engineering, Imperial College London benjamin.cope@imperial.ac.uk
[9] Gurindar Singh and Aman Kaura, “Recent Trends in Parallel Computing,” GIAN JYOTI E-JOURNAL, Volume 6, Issue 1 (Jan-Apr 2016).
[10] Bhavna Samel, Shubhrata and Prof. A.M. Ingole, “GPU Computing and Its Applications, International Research Journal of Engineering and Technology (IRJET),” Volume: 03 Issue: 04 | Apr-2016.
[11] Craig A. Lee, Member, IEEE, Samuel D. Gasster, Senior Member, IEEE, Antonio Plaza, Senior Member, IEEE, Chein-I Chang, Fellow, IEEE, and Bormin Huang, “Recent developments in High Performance Computing for Remote Sensing: A Review,” IEEE Journal of selected topics in applied earth observations and remote sensing,” VOL. 4, NO. 3, SEPTEMBER 2011.
[12] Christian Märtin, Augsburg University of Applied Sciences Augsburg, Germany, embedded world 2014, exhibition and conference
[13] Rafiqul Zaman Khan, Md Firoj Ali, “Current Trends in Parallel Computing, International Journal of Computer Applications,” (0975 – 8887) Volume 59– No.2, December 2012.
[14] Amdahl G. M. “Validity of the Single-processor Approach to Achieving Large Scale Computing Capabilities”. In AFIPS Conference Proc., Atlantic City, New Jersey, pp.483-485, 1967.
[15] https://fossbytes.com/top-5-supercomputers-of-the-world/ till july 2018.
[16] George Teodoro, Rafael Sachetto, Olcay Sertel,Metin N. Gurcan, Wagner Meira Jr., Umit Catalyurek, Renato Ferreira1, “Coordinating the Use of GPU and CPU for Improving Performance of Compute Intensive Applications,” 978-1-4244-5012-1/09/$25.00 ©2009 IEEE.
[17] L. Teot, R. Khayat, S.Qualman, G.Reaman, and D.Parham, “The Problems and Promise of Central Pathology Review: Development of a Standardized Procedure for the Children’s Oncology Group,” Pediatric and Developmental Pathology, vol. 10, no. 3, pp. 199–207, 2007.
[18] Craig A. Lee, Member, IEEE, Samuel D. Gasster, Senior Member, IEEE, Antonio Plaza, Senior Member, IEEE, Chein-I Chang, Fellow, IEEE, and Bormin Huang, “Recent Developments in High Performance Computing for Remote Sensing: A Review,” IEEE Journal of selected topics in applied earth observations and remote sensing,” VOL. 4, NO. 3, SEPTEMBER 2011.
[19] J. Nickolls and W. J. Dally, “The GPU computing era,” IEEE Micro, vol. 30, pp. 56–69, 2010.
[20] T. Balz and U. Stilla,“Hybrid GPU-based single- and double-bounce SAR simulation,” IEEE Trans. Geosci. Remote Sens.,” vol. 47,no. 10, pp. 3519–3529, 2009.
[21] http://codesandtutorials.com/hardware/computerfundamentals/cpu-block_diagram-working.php as on July 7, 2018.
[22] https://www.researchgate.net/figure/GPU-Schematic-Architecture_fig1_220489693 as on July 7, 2018
[23] https://computing.llnl.gov/tutorials/parallel_comp/#Flynn as on July 7, 2018.

Durgesh Kumar Keshar, Sanjay Kumar, V.K. Patle, "A Comparative Study of GPU Computing Techniques: A Review", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.178-181, 2019.

The profitability and productivity of organization depends on the performance and commitment of its employees. Every employee has a personal and professional life; both of these are very difficult to separate. If an organization wishes to have better productivity and more commitment from employees then they have to be committed and satisfied. This can be achieved by an individual when have a fulfilled life inside and outside and his work and is accepted and respected for the mutual benefit of the individual and the organization. Organizations are social systems where human resources are the most important factors for effectiveness and efficiency and need effective managers and employees to achieve their objectives. Work life balance is partly employer’s and partly individual responsibility. The present article deals with surveying the past literatures on work life balance and job satisfaction in various sectors of Industries in India and abroad.

Employee, Organisation, Work life balance, Job satisfaction

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A. K. Pathak, P. Dubey, Deepak singh, "Work Life Balance & Job Satisfaction: A Literature Review", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.182-187, 2019.

Intruders are available anywhere. They want to take the benefits of the hidden or confidential information of the user. They are trying access by the different – different techniques. Intruder finding is a big problem at the current time. So that security is important to secure our system or confidential information of any organization. Intrusion Detection System (IDS) is a popular technique for finding intruders that will be available on a network. We will use the KDD CUP 99 dataset for the training purpose of the Back Propagation based IDS model. BPN is an algorithm of the artificial neural network. KDD CUP 99 dataset are authentic dataset for the intruders. This data set will be collected by the UCI Repository.

Intrusion Detection System (IDS), Backpropagation (BPN) algorithm, Cloud Computing (CC), Support Vector Machine (SVM), Network Intrusion Detection System

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[3] Chirag Modi, Dhiren Patel, Bhavesh Borisanya, Hiren Patel, Avi Patel, Muttukrishnan Rajaranjan “A survey of intrusion detection techniques in Cloud”, Elsevier, published the Journal of Network and Computer Applications, Vol.-35, Issue-1, pp 42-57, 2013.
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[12] Mahbod Tavallaee "Detail Analysis of the KDD CUP 99 Data Set", Proceeding of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense application (CISDA 2009), 2009.
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[14] Data collection form Knowledge Discovery Dataset, UCI Repository http://kdd.ics.uci.edu/databases/kddcup99.

D.K. Singh, M. Shrivastava, "Novel Approach for Intrusion Detection Using Back Propagation Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.188-191, 2019.

Biological classification of Papillomaviridae leads to several hundred different genera (classes) of Human Papilloma Viruses (HPV) that are discriminated on the basis of more than hundred different characteristics. Statistical procedures of classification based on genome and gene size are being applied to biologically define different class labels for HPV. In this paper, Fisher’s linear discriminant analysis (LDA) has been used for classification of HPV on the basis of total genome size and gene sizes. Univariate and multivariate modes of classification have been employed to recognize two distinct classes of HPV viz., alpha- papilloma and beta-papilloma that cause cervical cancer in humans. The aim is to build a classification model so as to predict unknown samples. The accuracy of the proposed model has been measured on a sample dataset.

Genome, Genes, HPV, LDA, Papillomaviridae, Multivariate analysis, and Univariate analysis

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S. Swain, M.R. Patra, "Genome Based Classification of Human Papilloma Virus Using Linear Discriminant Analysis", International Journal of Computer Sciences and Engineering, Vol.07, Issue.03, pp.192-196, 2019.