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Image dehazing is a technique to improve the images picked up in poor climate conditions, for instance, cloudiness and obscurity. Existing image dehazing systems are chiefly in perspective on dark channel prior. Since the dark channel isn`t reasonable for sky districts, a sky division and zone wised medium transmission based image dehazing methodology is proposed in this paper. Directly off the bat, sky areas are separated by quad-tree part based segment pixels area. By then, a medium transmission estimation methodology for sky locales is proposed in perspective on shading trademark view of sky areas. The medium transmission is then isolated by an edge sparing guided channel. Finally, in light of the assessed medium transmission, the hazed images are reestablished. Exploratory results demonstrate that the execution of the proposed procedure is better than that of existing methods. The reestablished image is progressively ordinary, particularly in the sky areas

Image dehazing, image segmentation, dark channel prior

[1] Y. K. Wang, and C. T. Fan, “Single image defogging by multiscale depth fusion,” IEEE Trans. Image Process., vol. 23, no. 11, pp. 4826- 4837, Nov. 2014.

[2] I. Yoon, S. Kim, D. Kim, M. H. Hayes, and J. Paik “Adaptive defogging with color correction in the HSV color space for consumer surveillance system,” IEEE Trans. Consum. Electron., vol. 58, no. 1, pp. 111-116, Feb. 2012.
[3] Y. Xu, J. Wen, L. Fei, and Z. Zhang, “Review of video and image defogging algorithms and related studies on image restoration and enhancement,” IEEE Access, vol. 4, pp. 165-188, Mar. 2016
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[5] Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, “Polarizationbased vision through haze,” Appl. Opt., vol. 42, no. 3, pp. 511–525, 2003.
[6] K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp.2341-2353, Dec. 2011.
[7] Y. Zhu, J. Liu, and Y. Hao, “A single image dehazing algorithm using sky detection and segmentation,” in Proc. of IEEE Int. Congr. Image Signal Process. (CISP), pp. 248-252, Oct. 2014. Dalian, China.
[8] K. B. Gibson, D. T. Vo, and T. Q. Nguyen, “An investigation of dehazing effects on image and video coding,” IEEE Trans. Image Process., vol.21, no.2, pp. 662-673, Feb. 2012.
[9] U.S. Department of Transportation Federal Highway Administration. http://ops.fhwa.dot.gov/Weather/
[10] National Highway Traffic Safety Administration. http://www. nhtsa.gov/
[11] Siogkas, G.K., Dermatas, E.S.: Detection, tracking and classification of road signs in adverse conditions. In: IEEE MELECON, pp. 537–540 (2006)
[12] Garg, K., Nayar, S.K.: Vision and rain. Int. J. Comput. Vis. 75(1), 3–27 (2007)
[13] Roser, M., Moosmann, F.: Classification of weather situations on single color images. In: IEEE Intelligent Vehicles Symposium, pp. 798–803. Eindhoven (2008)
[14] Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48(3), 233–254 (2002)
[15] Narasimhan, S.G., Nayar, S.K.: Shedding light on the weather. In: International Conference on Com Computer Vision and, Pattern Recognition, pp. 665–672 (2003)
[16] Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)
[17] Narasimhan, S.G., Nayar, S.K.: Interactive (De) weathering of an image using physical models. In: IEEE Workshop on Color and Photometric Methods in Computer Vision, in conjunction with ICCV (2003)
[18] Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 325–332 (2001)

B. Naveen, P. Bharath Kumar Chowdary, "Fog and Haze Removal Based on Image DeHazing Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.116-120, 2019.

Machine Learning counter in this world beyond the buzzwords to transfigure our living cosmoses. It is made conceivable by the convergence of lively data. Traditionally, Machine learning (ML) is multi-disciplinary inclusive of statistics and computer science in around of computational systems from the collective data prediction than instructions. ML functions to the base fact of predictions of data on the reality of applications. Thence the thrust areas of Machine Learning with its bias are explicated here with certain reality and comprehensive examples like Trusting Scientific Discoveries Made Possible, Facing Volatile Price Trends for Tomato Growers, Gaining Critical Mass for Data Analytics Pros and Finding Hidden Technologies in IIOT. The techno ML is majorly bounded with rule and behavior-based systems, Bayesian and statistical algorithm, Neural Network and Deep Neural Network are also exposed here with their specification and its learning style is deliberated.

Machince Learning, Scenario of ML, Analytics, Techno ML, IIOT, Neural Network

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[2] Francesco Musumeci et al, “An Overview on Application of Machine Learning Techniques in Optical Networks”, IEEE arXiv:1803.07976v4 [cs.NI] 1 Dec 2018.
[3] https://www.thehindubusinessline.com/economy/agri-business/karnataka-tomato-growers-to-get-crop-price-forecasts-from-ibm-using-ai-ml/article26358330.ece
[4] Alvaro F. Fuentes, Sook Yoon, Jaesu Lee, and Dong Sun Park, “ High-Performance Deep Neural Network-Based Tomato Plant Diseases and Pests Diagnosis System With Refinement Filter Bank, 2018 Aug 29. doi: 10.3389/fpls.2018.01162
[5]J. Chen, L. Song, M. J. Wainwright, and M. I. Jordan, “Learn-ing to explain: An information-theoretic perspective on modelinterpretation, ”arXiv preprint arXiv:1802.07814, 2018.
[6] Shu, Z. Xu, and D. Meng, “Small Sample Learning in BigData Era,”ArXiv e-prints, Aug. 2018.
[7] K.Prabavathy and Dr.P.Sumathi , “Information Retrieval Navigation System For Knowledge Discovery From Biomed Articles”, International Journal of Research in Computer and Communication Technology, Volume No: 2,Issue No: 7, July-2013, pp.420-425.

M. Vasumathy, "Thrust Areas of Machine Learning and Its Current Scenario," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.121-123, 2019.

Application and usage of Artificial intelligence is increasing day by day in almost every arena. Therefore, education system has also offered a big platform for execution of intelligence technologies in it. A survey has been conducted on educational environment to understand the importance of different educational methods. In this analysis, 218 faculties of different disciplines from different colleges/universities and from different areas have been included. This research paper is an endeavor to compare and analyze artificial intelligence based educational environment and traditional environment using some common issues of teaching and learning.

Artificial Intelligence, Education, Intelligent Systems, Intelligent Technologies

[1] A. Drigas, R. Ioannidou, "Artificial Intelligence in Special Education: A Decade Review", International Journal of Engineering Education, Vol. 28, No. 6, pp. 1366–1372, 2012.
[2] M. Sasikumar, "A compilation of AI research in India", Computer Society of India, pp. 6-48, 2016.
[3] L.K. Ojha, L.K. Tiwary, R. Sharma, “Information Communication Technology Integration in Education”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.14-15, 2016.
[4] J. Russell, P. Norvig, "Artificial Intelligence: A Modern Approach", Prentice-Hall, Inc. A Simon & Schuster Company Englewood Cliffs, New Jersey, pp. 1-946, 1995.
[5] S. Dubey, S. Prajapat, R. Verma, R. Jhaggar, "Solution of Differential Equations by Parallel Processing and Analysis of Performance Improvement", International Journal of Scientific Research in Computer Science and Engineering, Volume-5, Issue-5, pp.57-62, 2017.

Purushottam Lal Bhari, C.D. Kumawat, "A Study of Artificial Intelligence Education System and Traditional Education System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.124-129, 2019.

This paper proposes a scheme for password management by storing password encryptions on a server. The method involves having the encryption key into a share for the user and one for the server. The user’s share shall be based only on a selected passphrase. The server’s share shall be generated from the user’s allocate and the encryption key. The security and conviction are achieved by performing both encryption and decryption on the client side. We also address the issue of countering dictionary attack by providing a further enhancement of the scheme. Password is the most ordinary method for users to authenticate themselves when entering computer systems or websites. It acts as the first line of guard against unlawful access, and it is therefore critical to maintain the usefulness of this line of guard by strictly committed a good password management policy. This paper aims to grant a set of guiding principle and best practices for handling and managing passwords.

Password Encryption, Password Storage, Identity Management, Secret Sharing

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[4] Zhao, R. and Yue, C. (2013) All Your Browser-Saved Passwords Could Belong to Us: A Security Analysis and Acloud-Based New Design. Proceedings of the 3rd ACM Conference on Data and Application Security and Privacy, San Antonio, February, 2013, 333-340. http://dx.doi.org/10.1145/2435349.2435397
[5] Silver, D., Jana, S., Boneh, D., Chen, E. and Jackson, C. (2014) Password Managers: Attacks and Defenses. 23rd USENIX Security Symposium (USENIX Security 14), San Diago, August 2014, 449-464.
[6] Li, Z., He, W., Akhawe, D. and Song, D. (2014) The Emperor’s New Password Manager: Security Analysis Ofweb- Based Password Managers. 23rd USENIX Security Symposium (USENIX Security 14), San Diago, August 2014, 465- 480.
[7] Haque, T., Wright, M. and Scielzo, S. (2013) A Study of User Password Strategy for Multiple Accounts. Proceedings of the 3rd ACM Conference on Data and Application Security and Privacy, 173-176. http://dx.doi.org/10.1145/2435349.2435373
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[9] Kenneth Giuliani1, V. Kumar Murty1, Guangwu Xu2 Copyright © 2016 by authors and Scientific Research Publishing Inc. . http://www.scirp.org/journal/jis http://dx.doi.org/10.4236/jis.2016.73016
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[13] Ari Juels | Cornell Tech Thomas Ristenpart | University of Wisconsin Honey Encryption Encryption beyond the Brute-Force Barrier,
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Ankita Hinduja, Pradip Sharma, "A Survey Paper on Password Security Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.130-136, 2019.

The embedded cum LabVIEW technology is now its prime and wealth of knowledge. Embedded technology plays a important role in integrating the various functions associated with it. This needs to tie up the various sources of the department in a closed loop system. This proposed system reduces man power, it also save time and operates efficiently without human interference. This project gives forth to first step for achieving the desired target. I have implemented Report generation & IoT unit for the wind turbine based industry for continuously acquiring the data and implementing the cryptography algorithm in it for security reasons.

IoT, Lab VIEW, Report Generation

[1] Adel Nazemi Babadi , Mohammad Niyazi , Ronald A. Coutu, (2018) “Serviceability Optimization of the Next Generation Wind Turbines Using Internet of Things Platform” IEEE 2018 Smart Grid Conference (SGC), doi.org/10.1109/SGC.2018.8777861.
[2] Andrew Kusiak and Anoop Verma, (2012) “A Data-Mining Approach to Monitoring Wind Turbines” IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, Vol. 3, No. 1, pp.150 – 157.
[3] Chandra Bajracharya , Robin Grodi , Danda B. Rawat, (2015) “Performance Analysis of Wireless Sensor Networks for Wind Turbine Monitoring Systems” IEEE SoutheastCon 2015, doi.org/10.1109/SECON.2015.7133053.
[4] D.Kalyanraj, S.Lenin prakash, and S.Sabareswar , (2016) “Wind Turbine Monitoring and Control Systems Using Internet of Things” IEEE 2016 21ST century energy needs- materials, systems and applications (ICTFCEN). doi.org/10.1109/ICTFCEN.2016.8052714.
[5] Evangelos Papatheou, Nikolaos Dervilis, Andrew Eoghan Maguire, Ifigeneia Antoniadou, and Keith Worden, (2015) “A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm” IEEE transactions on industrial electronics, Vol.62, No.10, pp. 6636 – 6644.
[6] Grzegorz Swiszcz, Andrew Cruden, Campbell Booth and William Leithead, (2008) “A Data Acquisition Platform for the Development of a Wind Turbine Condition Monitoring System” IEEE 2008 International Conference on Condition Monitoring and Diagnosis, doi.org/10.1109/CMD.2008.4580521.
[7] Guoqian Jiang, Haibo He, Jun Yan, and Ping Xie, (2018) “Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox” IEEE transactions on industrial electronics, Vol.66, No.4, pp. 3196 – 3207 .
[8] Hae-Jin Sung, Byeong-Soo Go, and Minwon Park, (2019) “A Performance Evaluation System of an HTS Pole for Large-Scale HTS Wind Power Generators” IEEE transactions on applied superconductivity, Vol.29, No.5, doi.org/10.1109/TASC.2019.2908601.
[9] Haolin Yin, Rong Jia, Fuqi Ma, Dameng Wang, (2018) “Wind Turbine Condition Monitoring based on SCADA Data Analysis” IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC 2018). doi.org/10.1109/IAEAC.2018.8577523.
[10] Huan Long, Long Wang, Zijun Zhang, Zhe Song, and Jia Xu, (2015) “ Data-Driven Wind Turbine Power Generation Performance Monitoring” IEEE Transactions on Industrial Electronics, Vol.62, N0.10, pp. 6627 – 6635.
[11] Keun-Young Kang, Mohamed A. Ahmed and Young-Chon Kim, (2014)“Implementation of Condition Monitoring and Control System for Small-scale Wind Turbines” IECON 2014-40th annual conference of the IEEE industrial electronics society, doi.org/10.1109/IECON.2014.7048795.
[12] Long Wang, Zijun Zhang, Huan Long, Jia Xu, and Ruihua Liu, (2016) “Wind Turbine Gearbox Failure Identification with Deep Neural Networks” IEEE Transactions on Industrial Informatics, Vol.13, No.3, pp. 1360 – 1368.
[13] Peng Guo, (2012) “Wind Turbine Generator Bearing Condition Monitoring with NEST Method” IEEE 2012 24th Chinese control and decision conference (CCDC) doi.org/10.1109/CCDC.2012.6244033.
[14] Peng Sun, Jian Li, Yonglong Yan, Xiao Lei, Xiaomeng Zhang,(2014) “Wind Turbine Anomaly Detection Using Normal Behavior Models based on SCADA Data” IEEE 2014 ICHVE International Conference on high voltage engineering and application, doi.org/10.1109/ICHVE.2014.7035504.
[15] R. Morello, C. De Capua, G. Fulco , S.C. Mukhopadhyay, (2017)” A Smart Power Meter to Monitor Energy Flow in Smart Grids: The Role of Advanced Sensing and IoT in the Electric Grid of the Future” IEEE Sensors Journal, Vol. 17, No. 23, pp. 7828 - 7837.
[16] Ravi Kumar Pandit, David Infield, (2019) “SCADA based nonparametric models for condition monitoring of a wind turbine” The Journal of Engineering, Vol.2019, No.18, pp. 4723 – 4727.
[17] S. S. Tian, Z. Qian, L. X. Cao, (2016) “Wind Turbine Power Generation Performance Evaluation under Faults Condition” IEEE 2016 International Conference on Condition Monitoring and Diagnosis (CMD), doi.org/10.1109/CMD.2016.7757880
[18] Wilmar Hernandez, and Jorge L. Maldonado-Correa, (2017) “Power Performance Verification of a Wind Turbine by using the Wilcoxon Signed-Rank Test” IEEE transactions on energy conversion, Vol.32, No.1 , pp. 394 – 396.
[19] WU Xin, SU Liancheng , (2017) “Wind Turbine Modeling Research Based on the Combination of SCADA and Vibration Signals” IEEE 2017 4th International Conference on Information Science and Control Engineering(ICISCE), doi.org/10.1109/ICISCE.2017.277.
[20] Yan Pei, Zheng Qian, Siyu Tao, Hao Yu, (2018) “Wind Turbine Condition Monitoring Using SCADA Data and Data Mining Method” IEEE 2018 international conference on power system technology (POWERCON), doi.org/10.1109/POWERCON.2018.8601803.
[21] Zhixin Fu, Yang Luo, Chenghong Gu, Furong Li, Yue Yuan, (2018) “Reliability Analysis of Condition Monitoring Network of Wind Turbine Blade Based on Wireless Sensor Networks” IEEE Transactions on Sustainable Energy, Vol. 10 No. 2 ,pp. 549 – 557.

D.Visali, K. Muthulakshmi, "Survey of Smart Data Acquisition System in Wind Turbine Machines Using Labview and IoT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.137-143, 2019.

Artificial Neural Network (ANN) is an important part of the artificial brain. This study presents the promise ofartificial neural network in development of artificial brain and controls intelligent educational systems. Some important factors that may influence the performance of artificial neural network are outlined in this research paper. Moreover, applications of a variety of neural network architectures in intelligent educational systems are surveyed. Further an attempt has been made to explore the relations between the fields of natural science and neural networks in a unified presentation

Artificial Neural Network, Intelligent Educational System, Intelligent Technology

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[2] A.K. Gupta, S. Gupta, “Neural Network through Face Recognition” International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.38-40, 2018.
[3] M. Ziaaddini, and A. Tahmasb "Artificial Intelligence Handling through Teaching and Learning Processandit’s Effecton Science-based Economy", International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.3, No. 1, 2014.
[4] S. Athanasios, R. Ioannidou, "Artificial Intelligence in Special Education: A Decade Review", International Journal of Engineering Education Vol. 28, No. 6, pp. 1366–1372, 2012.
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[6] A.Yadav, V.K. Harit, “Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique” International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.6, pp.1-7, 2016.
[7] O. Awodele, O. Jegede, "Neural Networks and Its Application in Engineering", Proceedings of Informing Science & IT Education Conference (InSITE) 2009.
[8] Chahar, R., Agarwal, A., Malhotra, V., Jain, R. and Chandiok, A. (2013), "Intelligent Online Education Transformation System", IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 2, pp. 438 - 444, 2013.
[9] H. Chen, "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms", Journal of the American Society for Information Science, 1995-04, Vol.46, Issue-3, pp. 194-216, 2004.

Purushottam Lal Bhari, C.D. Kumawat, "Role of Artificial Neural Network in an Intelligent Educational System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.144-148, 2019.

In the era of digital communication, digital image play a important role in most of industrial and corporate forensic applications. Digital imaging has experienced unremarkable revolution in recent decades, and digital images have been used in a increasing number of applications. Digital Images are used as authenticated proof for any crime and if these images do not remain veritable then it will create question on the validation process. Detecting these types of faking has become serious issue. To determine whether a digital image is original or fake is a big challenge. The detection of image meddling in a digital image is a challenging task. This paper presents a literature survey on some of the image influence detection techniques such as image pre-processing, image compression, edge detection, segmentation, contrast enhancement detection, splicing and composition detection, image tampering and more. Comparison of all the techniques finds the better approach for its future research

Digital Forensics, Digital Image Processing, Image Manipulation, Contrast Enhancement Edge Detection, Segmentation

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[7] “Manipulation detection on image patches using FusionBoost” by H. Cao and A. C. Kot, IEEE Trans. Inf. Forensics Security, vol. 7, no. 3, pp. 992–1002, Jun. 2012.
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[12] “Contrast Enhancement-Based Forensics in Digital Images” by Gang Cao, Yao Zhao, Rongrong Ni IEEE transactions on information forensics and security, vol. 9, no. 3, march 2014
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Syed Faraz Ahmed Naqvi, Kamal Niwaria, Bharti Chourasia, "A Comprehensive & Investigative Review of Literature on Digital Image Processing Technique for Multidisciplinary Industrial Application," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.149-155, 2019.

The application of science and technology in predicting the weather of a given area is weather forecasting. The whole world is experiencing extreme climatic change which causes side effects .In order to reduce these side effects we use mathematical algorithms and techniques on big data of weather data to analyse the current situation and predict the future weather conditions. In this research we will use be using Mongo DB to analyse the data on weather in Delhi. The outcomes shows us the analysis of the weather data available.

MongoDb, Weather, Analysis, Queries

[1] Garima Jain1 , Bhawna Mallick2 Student, “A Review on Weather Forecasting Techniques”, International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 5, Issue 12, December 2016 Department of Computer Science and Engineering, Galgotias Educational Institutions, Greater Noida, Uttar Pradesh, India1 Head of Department (Computer Science), Galgotias Educational Institutions, Greater Noida, Uttar Pradesh, India.
[2] Sushmitha Kothapalli, S. G. Totad, “A Real-Time Weather Forecasting and Analysis”, IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017), pp 1567- 1570
[3] Tiwari, R. Sam and S. Shaikh, "Analysis and prediction of churn customers for telecommunication industry," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 218-222. doi: 10.1109/I-SMAC.2017.8058343
[4] S. Navadia, P. Yadav, J. Thomas and S. Shaikh, "Weather prediction: A novel approach for measuring and analyzing weather data," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 414-417. doi: 10.1109/I-SMAC.2017.8058382
[5] S. Shaikh, S. Rathi and P. Janrao, "IRuSL: Image Recommendation Using Semantic Link," 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN), Tehri, 2016, pp. 305-308. doi: 10.1109/CICN.2016.66
[6] S. Shaikh, S. Rathi and P. Janrao, "Recommendation System in E-Commerce Websites: A Graph Based Approached," 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 931-934. doi: 10.1109/IACC.2017.0189
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Aliya A. Kazi, Shifa Shaikh, Shahbaj Shaikh, Shakila Shaikh, "Delhi Weather Analysis : A Mongo Db Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.156-158, 2019.

This study reviews the problems of data management in the Library of International Islamic University Malaysia (IIUM), in order to identify, to what extent such problems have affected the smooth running and operations of the library, and to successfully address and solve these problems. inside IIUM library, we observed that, they engage in standard library operations such as lending, borrowing and also operating as a repository to the university community, where documents, books, journals and other reading materials of the IIUM university are being kept as a valid reference to the public & members of IIUM, such as IIUM students, staff, as well as students of other universities in Malaysia and abroad, researchers, academicians and other interested parties. All the related data being used by the library for such activities must be consistent, relevant and reusable by all stakeholders of the IIUM community, which would have to play a significant role in ensuring adequate data management. However, there were several efforts on tools of measurement or techniques of keeping and maintaining a data with the right features, but unfortunately, such efforts still need to be evaluated due to the continuous changes and occurrence of these problems of data management nature

Management, Data, Library, Issues

[1] Harmanpreet Singh, Amritpal Singh Danewalia, Deepak Chopra and Naveen Kumar N, "Randomly Generated Algorithms and Dynamic Connections", International Journal of Scientific Research in Network Security and Communication, Vol.2, Issue.1, pp.1-4, 2014
[2] A. Singh, N. Jain, "Internet Surfing Prediction System using Association Rule Mining based on FP-Growth", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.4, pp.1-6, 2016
[3] Cummins, F. Enterprise Integration: An Architecture for Enterprise Application and Systems Integration. Wiley Computer Publishing: OMG press. 2002
[4] Chandra, S., Juarez, R. A Practical Approach to Enterprise Integration. 2009
[5] Khurshid, Z., Al-Baridi, S. Symphony: SirsiDynix`s flagship integrated library system: a horizon user`s perspective. Computers in Libraries, 29(7): 6-10. 2009
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[8] HM Zangana. "Library Data Quality Maturity (IIUM as a Case Study)", IOSR-JCE, Vol. 19, Issue 2, pp. 38-44, 2017
[9] HM Zangana. "Developing Data Warehouse for Student Information System ( IIUM as a Case Study ) ", IOSR-JCE, Vol. 20, Issue 1, pp. 09-14, 2018
[10] HM Zangana. ITD DATA QUALITY MATURITY (A CASE STUDY). IJECS, Vol. 8, Issue 10, pp. 24851-24854, 2019

Hewa Majeed Zangana, "Issues of Data Management in the Library: A Case Study," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.159-163, 2019.

The Water level indicator is a modification of an alarm system that is used in water tanks (not mandatory, can be used in any liquid container) to measure the water level and to indicate respectively. This article is mainly focussed on how can we save water from wastage as the amount of water is decreasing day by day due to various issues. Here we are using an Arduino UNO and an ultrasonic sensor to sense the water level so that we can aware of the people. Buzzers are used for buzzing purpose and an LCD is used for the display. Here the Arduino and the ultrasonic sensor make this model quite simple and easily can be modulated. This model is full of advanced technologies and its quite a user- friendly

Arduino microcontroller, ultrasonic sensors, LCD display, buzzer

[1] 2017International Conference on Information Communication and Embedded Systems (ICICES),Date Added to IEEE Xplore: 19October 2017 INSPEC Accession Number: 17261648DOI: 10.1109/ICICES.2017.8070773,Publisher: IEEE
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[8] 2014 IEEE Conference on Systems, Process and Control (ICSPC 2014),INSPECAccessionNumber: 15058732DOI: 10.1109/SPC.2014.7086225,Publisher: IEEE
[9] IEEETransactions on Instrumentation and Measurement ( Volume: 56 , Issue:5 ,Oct.2007 ),Page(s): 1532 1536,INSPECAccessionNumber: 9632998,DOI: 10.1109/TIM.2007.895665Publisher: IEEE
[10] 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST),INSPEC Accession Number: 18485785,DOI: 10.1109/ICREST.2019.8644344,Publisher: IEEE

Dwaipayan Saha, Indrani Mukherjee, Jesmin Roy, Sumanta Chatterjee, "Microcontroller Based Water Level Indicator And Controller System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.164-167, 2019.