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In the teaching field, whiteboard, duster, and marker are crucial elements. To erase the writings from large size boards manually with a duster is a time-consuming task. It breaks the concentration of both lecturers and listeners. This paper represents the design and construction of automatic whiteboard cleaner. The system consists of a microcontroller, wi-fi module, DC motor, driver module, wooden shaft, electrical switch, and mobile. This application is developed to remotely control the operation. Using the mobile phone, an android application developed which consists of buttons to move the shaft in a forward and backward direction and to stop it. The Automatic whiteboard cleaner reduces the time and efforts required.

Automation, whiteboard, cleaner

[1] Sonia Akhter*, Anindo Saha, Md. Rayhan Parvez Koushik, Md. Asaduzzaman, Razoana, Islam Shorna, Md. Moudud Ahmed,” Automatic Whiteboard Cleaner Using Microcontroller Based Rack and Pinion Mechanism”, International Conference on Mechanical, Industrial and Materials Engineering 2015 (ICMIME2015), pp. 11-13 December 2015.

[2] S. Joshibaamali, K.Geetha Priya, “Automatic duster machine”, International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE). Vol. 12, Issue 1, March 2015.
[3] Sunil R. Kewate, Inzamam T. Mujawar, Akash D. Kewate “Development of new smart design to erase the classroom blackboard of schools/colleges”, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), Vol III, Issue III, March 2016.
[4] S.Nithyananth, A. Jagatheesh, K. Madan, B. Nirmalkumar, “Convertable four wheels steering with three mode operation”, International Journal of Research in Aeronautical and Mechanical Engineering. ISSN 2321-3051.
[5] Dong Yeop Kim, Jae Min Lee, Jongsu Yoon, et al. “Wall shape recognition using limit switch module”, International Journal of Control Theory and Computer Modeling (IJCTCM), April 2014.
[6] Mojtaba Khaliliana, Ali Abedi, Adel Deris Zadeh. Energy Procedia. 2012; 14:1992–1997
[7] Deepjyoti Choudhury, “Real Time and Low-Cost Smart Home Automation System Using Internet of Things Environment”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.225-229, 2019.

Sumit Chavan, Vishal Shinde, Nikhil Murade, Anjali Jagtap, Varsha Degaonkar, "Automatic White Board Cleaner," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.427-430, 2019.

Breast cancer remains the important reason of death among woman in the world. Early detection is essential to improve breast cancer diagnosis. Mammography is the reliable and best existing tool for investigation of breast cancer in its early stage. Understanding the mass region information of cancerous lesions in a mammogram is important for detection of the tumor region and its segmentation. In this paper, Maximum Mean and Least Variance method and Otsu method is implemented and then compared the results to find the suitable technique among them for detection and segmentation of tumor region.

Breast cancer detection, Mammograms, Smoothing, Segmentation, Enhancement, Masses, Microcalcification

[1] J.S.L. Jasmine, S. Baskaran, A.Govardhan, “An Automated Mass Classification System in Digital Mammograms using Contourlet Transform and Support Vector Machine”, International Journal of Computer Applications, vol. 31, pp. 54-61, October 2011.
[2] H. Hahn, “ Wavelet Transforms For Detecting Microcalcification in Mammography”, 1995
[3] R. Kamath, K.S. Mahajan, L. Ashok, T.S. Sanal, “A Study on Risk Factors of Breast Cancer Among Patients Attending the Tertiary Care Hospital, in Udupi District”, Indian Journal of Community Medicine, 2013, pp. 95–99.
[4] M. M. Eltoukhy, I. Faye, B. B. Samir, “A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram”, Computers in Biology and Medicine, 2010, pp. 384–391.
[5] K. Hu, X. Gao, F. Li, “Detection of Suspicious Lesions by Adaptive Thresholding Based on Multiresolution Analysis in Mammograms”, IEEE transactions on instrumentation and measurement, vol. 60, pp. 462-472, February 2011.
[6] H.D.Cheng, X. Cai, X. Chen, L. Hu, X. Lou, “Computer-aided detection and classifcation of microcalcifcations in mammograms: a survey”, Pattern Recognition, vol. 36, 2003, pp. 2967–2991.
[7] M. M. Eltoukhy, I. Faye, B. B. Samir ,“A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation”, Computers in Biology and Medicine , vol. 42, 2012, pp. 123–128
[8] A. K. Singh, B. Gupta, “A Novel Approach for Breast Cancer Detection and Segmentation in a Mammogram”, Procedia Computer Science , vol. 54, 2015, pp. 676 – 682.
[9] N. Al- Najdawi, M. Biltawi, S, Tedmori, “Mammogram Image Visual Enhancement, Mass Segmentation and Classification”, Applied Soft Computing , vol. 35, 2015, pp. 175-185.
[10] http://breast-cancer.ca/mass-chars/
[11] A. A. Rani, G. Rajagopal ,A. Jagadeeswaran , “Bi-Histogram Equalization with Brightness Preservation Using Contras Enhancement”, International Journal of Basics and Applied Sciences.
[12] S. Nimkar, S. Shrivastava and S, Varghese, “Contrast Enhancement and Brightness Preservation using Multi- Decomposition Histogram Equalization”, Signal & Image Processing : An International Journal (SIPIJ), vol.4, June 2013.
[13] J. A. Stark, “Adaptive Image Contrast Enhancement Using Generalization of Histogram Equalization”, IEEE transaction on image processing, vol.9, pp. 889-896, May 2000.
[14] A. Papadopoulos, D.I. Fotiadis, L. Costaridou, “Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques.”, Computers in Biology and Medicine, vol. 38, pp. 1045–1055, 2008.
[15] D. L. Phamy, C. Xu, J. L. Prince, “A Survey of Current Methods in Medical Image Segmentation”, Annual review of biomedical engineering, vol. 2, pp. 315-337, 2000.
[16] A. R. Dominguez, A. K. Nandi, “Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection”, Computerized Medical Imaging and Graphics, vol. 32, 2008, pp. 304–315.
[17] M. Al-Bayati, A. El-Zaart, “Mammogram Images Thresholding for Breast Cancer Detection Using Different Thresholding Methods”, vol. 2, 2013, pp. 72-77.
[18] R. C. Gonzalez, R. E. Woods, “Digital Image Processing”, Third Edition, PEARSON.
[19] P.L. Rosin, E. Ioannidis, “Evaluation of Global Image Thresholding for Change Detection”, Pattern Recognition Letters, vol 14, 2003, pp. 2345-2356.
[20] D.C. Pereira, R. P. Ramos, M.Z. Do.Nascimento, “Segmentation and Detection of Breast Cancer in Mammograms Combining Wavelets Analysis and Genetic Algorithms”, Computer methods and programs in biomedicine, vol. 1, 2014, pp. 88-101.

S. Bhadra, "Analysis of Tumor Detection Methods for Mammogram Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.431-435, 2019.

The Internet has undergone numerous stages of development. The world uses the Internet for maximum of the tasks but it has fundamentally been about connecting computers. Nowadays, internet has entered in its new age called as “Internet of Things” which involves connecting physical objects over the network. Amongst many applications of IoT, Fleet management system is the best-known application in the area of transport. Fleet management system finds its importance in many sectors such as industrial and security ones. Many companies or organizations need to keep track and optimize their fleet. Fleet management systems when empowered with IoT, contributes to efficient and effective management of vehicles. In this survey article, brief overview about the concept of IoT is presented. This review mainly focusses on Fleet Management and Vehicle Tracking. Along with IoT, various technologies contributing in any fleet management system are discussed. Various researches carried out on the concept of fleet management and vehicle tracking are reviewed.

Internet of Thing, Fleet Management System, Vehicle Tracking

[1] D. Miorandi , S. Sicari , F. Pellegrini and I. Chlamtac, "Internet of things: Vision, applications and research challenges", Ad Hoc Networks 10, pp. 1497-1516, 2012.
[2] L. Atzori, A. Iera and G. Morabito, "The Internet of Things: A survey", Computer Networks, Vol. 54, Issue. 15, pp. 2787-2805, 2010.
[3] J. Backman, J. Väre, K. Främling, M. Madhikermi and O. Nykänen, "IoT-based interoperability framework for asset and fleet management", In the proceedings of the 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation, Berlin, pp. 1-4, 2016.
[4] H. S. Dhillon, H. Huang and H. Viswanathan, "Wide-area Wireless Communication Challenges for the Internet of Things", IEEE Communications Magazine, Vol. 55, no. 2, pp. 168-174, February 2017.
[5] P. P. Ray, “A survey of IoT cloud platforms”, Future Computing and Informatics Journal 1, pp. 35-36, 2016.
[6] T. Godavari and J. Umadevi,” Cloud Computing based Real-Time Vehicle Tracking and Speed Monitoring System”, I J C T A, Vol. 09, No. 04, pp. 1823-1830, 2016.
[7] Z. Liu, A. Zhang and S. Li, "Vehicle anti-theft tracking system based on Internet of things", In the proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety, Dongguan, pp. 48-52, 2013.
[8] H.D. Pham, M. Drieberg and C.C. Nguyen, "Development of vehicle tracking system using GPS and GSM modem", In the proceedings of 2013 IEEE Conference on Open Systems, Kuching, pp. 89-94, 2013.
[9] S. K. C. Varma, Poornesh, T. Varma, Harsha, "Automatic Vehicle Accident Detection and Messaging System Using GPS and GSM Modems", International Journal of Scientific & Engineering Research, Vol. 4, Issue 8, 2013.
[10] R. Ramani, S. Valarmathy, Dr. N. SuthanthiraVanitha, S. Selvaraju, M. Thiruppathi and R. Thangam, "Vehicle Tracking and Locking System Based on GSM and GPS", International .Journal Intelligent Systems and Applications, pp 86-93, 2013.
[11] A. Aljaafreh, M. Khalel, I. Al-Fraheed, K. Almarahleh, R. Al-Shwaabkeh, S. Al-Etawi and W. Shaqareen, "Vehicular Data Acquisition System for Fleet Management Automation", In the proceedings of 2011 IEEE International Conference on Vehicular Electronics and Safety, 2011.
[12] D. Istrefi and B. Çiço, “Fleet Management Solution", International Journal of Computer Science and Information Technology & Security, ISSN: 2249-9555, Vol. 3, No.3, June 2013.
[13] B. Chauhan, A. Jain, T. Chaturvedi and S. Saini, “A User Interactive and Assistive Fleet Management and Eco-Driving System", 2015 IEEE Region 10 Symposium, 2015.
[14] R. Malekian, et al. ,”Design and Implementation of a Wireless OBD II Fleet Management System", IEEE Sensors Journal, pp 1154-1164, 2017.
[15] S. S. Aher and R.D. Kokate, “Fuel Monitoring and Vehicle Tracking", International Journal of Engineering and Innovative Technology, Vol. 1, Issue 3, 2012.
[16] M. Mukhtar, “GPS based Advanced Vehicle Tracking and Vehicle Control System", International Journal Intelligent Systems and Applications, pp. 1-12, 2015.
[17] H. Saghaei, “Design and Implementation of a Fleet Management System Using Novel GPS/GLONASS Tracker and Web-Based Software", In the proceedings of 2016 1st International Conference on New Research Achievements in Electrical and Computer Engineering, 2016
[18] C. Gowda V R and K. Gopalakrishna, "Real Time Vehicle Fleet Management and Security System", In the proceedings of 2015 IEEE Recent Advances in Intelligent Computational Systems, 2015.
[19] P. V. Mistary and R. H. Chile, "Real Time Vehicle Tracking System Based on ARM7 GPS and GSM Technology", IEEE INDICON 2015

Prajakta P. Deshpande, "IoT Based Fleet Management Systems : A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.436-443, 2019.

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum. While there is an end number of applications of machine learning in real life one of the most prominent application is the prediction problems. There are various topics on which the prediction can be applied. One such application is what this project is focused upon. Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history – and promote other items you`d be interested in. This ability to capture data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail

Environment Quality, Data Analysis, Business Intelligence, Power BI, SQL Server 2016, Air Quality, Water Quality, Tree Cover, Forest Cover, Predictions, NLP, forecasting, k-means clustering, ARIMA

[1].M. Antonakakis, T. April, M. Bailey, M. Bernhard, E. Bursztein, J. Cochran, Z. Durumeric, J. A. Halderman, L. Invernizzi, M. Kallitsis, D. Kumar, C. Lever, Z. Ma, J. Mason, D. Menscher, C. Seaman, N. Sullivan, K. Thomas, and Y. Zhou, "Understanding the mirai botnet," in Proc. of USENIX Security Symposium, 2017.
[2].Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani,
―Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization‖, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018
[3].Hossein Hadian Jazi, Hugo Gonzalez, Natalia Stakhanova, and Ali A. Ghorbani. "Detecting HTTP-based Application Layer DoS attacks on Web Servers in the presence of sampling." Computer Networks, 2017
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[5].Z. He, T. Zhang, and R. B. Lee, ―Machine Learning Based DDoS Attack Detection from Source Side in Cloud,‖ in Proceedings of the 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), pp. 114–120, New York, NY, USA, June 2017
[6].R. Doshi, N. Apthorpe and N. Feamster, "Machine Learning DDoS Detection for Consumer Internet of Things Devices," 2018 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, 2018, pp. 29-35.
[7].Jerome H. Friedman, (2002), Stochastic gradient boosting, Computational Statistics & Data Analysis, 38, (4), 367-378
[8].Friedman, Jerome H. Greedy function approximation: A gradient boosting machine. Ann. Statist. 29 (2001), no. 5, 1189--1232.

Ashish Chandak, Prajwal Ganorkar, Shyam Sharma, Ayushi Bagmar, Soumya Tiwari, "Car Price Prediction Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.444-450, 2019.

The impact of designing for safety of AI is critical for humanity in the AI era. With humans increasingly becoming dependent of AI, there is a need for neural networks that work reliably, inspite of Adversarial attacks. Attacks can be one of 3 types: I) Similar looking adversarial images that aim to deceive both human and computer intelligence, II) Adversarial attacks such as evasion and exploratory attacks, III) Hacker introduced occlusions/perturbations to misguide AI. The vision for Safe and secure AI for popular use is achievable. To achieve safety of AI, this paper contributes both a strategy and a novel deep learning architecture. To guard AI from adversaries, paper proposes 3 strategies: 1) Introduce randomness at inference time to hide the representation learning from adversaries/attackers, 2) Detect presence of adversaries by analyzing the input sequence to AI, 3) Exploit visual similarity against adversarial perturbations. To realize these strategies, this paper proposes a novel architecture, Dynamic Neural Defense (DND). This defense has 3 deep learning architectural features: I) By hiding the way a neural network learns from exploratory attacks using a random computation graph, DND evades attack. II) By analyzing input sequence to cloud AI inference engine with CNN-LSTM, DND detects fast gradient sign attack sequence. III) By inferring with visual similar inputs generated by VAE, any AI defended by DND approach doesn’t succumb to hackers. Thus, a roadmap to develop reliable, safe & secure AI is presented.

AI, Deep Learning, AI Safety, AI Security, Neural Networks, Adversarial Attacks and Defences, autonomous AI

[1] Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., & Mukhopadhyay, D, “Adversarial Attacks and Defences: A Survey”, CoRR, arXiv:1810.00069, 2018.
[2] Szegedy, C at el., “Intriguing properties of neural networks”, arXiv:1312.6199, 2013.
[3] Papernot, N., Goodfellow, I., Sheatsley, R., Feinman, R. and McDaniel, P., “cleverhans v1. 0.0: an adversarial machine learning library”, arXiv:1610.00768, 2016.
[4] Biggio, B at el, “Evasion attacks against machine learning at test time”, Joint European conference on machine learning and knowledge discovery in databases, Springer, pp. 387-402, 2013.
[5] Sitawarin, C., Bhagoji, A.N., Mosenia, A., Chiang, M., Mittal, P., “Darts: Deceiving autonomous cars with toxic signs”, arXiv:1802.06430, 2018.
[6] Kurakin, Alexey, I. Goodfellow, and S. Bengio. "Adversarial machine learning at scale." arXiv:1611.01236, 2016
[7] Yuan, Xiaoyong, Pan He, Qile Zhu, Xiaolin Li., "Adversarial examples: Attacks and defenses for deep learning." IEEE transactions on neural networks and learning systems, 2019.
[8] Amodei, Dario, Chris O, Jacob S, Paul C, John S, Dan M. "Concrete problems in AI safety", arXiv:1606.06565, 2016.
[9] Liu, G, Issa K, Abdallah K. "GanDef", arXiv:1903.02585, 2019.
[10] Carlini, Nicholas. "Is AmI Robust to Adversarial Examples?.", arXiv:1902.02322, 2019.
[11] Carlini, Nicholas, David W. "Defensive distillation is not robust to adversarial examples.", arXiv:1607.04311, 2016
[12] Mahmood S, Sruti B, Lujo B, and Michael K. “Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition”, Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 1528-1540, 2016.
[13] Tramer, Florian, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel. “Ensemble adversarial training: Attacks and defenses” arXiv:1705.07204 , 2017
[14] U.Kaur, Mahajan, Singh, “Trust Models in Cloud Computing”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.19-23, 2018
[15] Arora, Sharma, ”Synthesis of Cryptography and Security Attacks", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.5, pp.1-5, 2017
[16] Das at el., "Shield: Fast, practical defense & vaccination for deep learning", 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, pp. 196-204, 2018

Rajagopal. A, Nirmala. V, "Strategies to architect AI Safety: Defense to guard AI from Adversaries," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.451-456, 2019.

Biometric Recognition System plays vital role in aspect of security. Among all physiological and behavioral biometric modalities, current study focuses on fingerprint recognition from physiological biometric modality. Fingerprint recognition plays key role in success of user authentication to verify and identify an individual with matching of his/her fingerprint. It is one kind of pattern recognition method which acquires the set of minutiae features from an individual finger image and compare it with the minutiae features available in template image. This research paper focuses on the challenges resides in fingerprint authentication, limitations in methodology used for processing, proposed research model to solve these limitations, Significant of proposed research work show the solution of challenges with implemented work using FVC2000 and FingerDOS databases.

Fingerprint Recognition, Orientation Estimation, Image Enhancement, Thinning, Minutiae Extraction, Core Point Detection

[1] Hong L., Wang Y.F., Jain A.K., Fingerprint Image Enhancement: Algorithm and Performance Evaluation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 777-789.
[2] Maltoni, D., Maio, D., Jain, A.K. and Prabhakar S.: Fingerprint analysis and representations, Handbook of Fingerprint Recognition, 2009, 2nd ed., pp.97–166, Springer, London (2009)
[3] Maltoni, D., Maio, D., Jain, A.K. and Prabhakar, S.: Fingerprint matching, in Handbook of Fingerprint Recognition, 2nd ed., pp.167–233, Springer, London (2009)
[4] S. Gayathri, and V Sridhar, Design and Implementation of Normalization Process of Fingerprint Recognition System, Int. J. on Signal & Image Processing, Vol. 5, 2014, ACEEE
[5] Yi Wang, Jiankun Hu, Fengling Han, “Enhanced gradient-based algorithm for the estimation of fingerprint orientation fields”, 2007, Applied Mathematics and Computation 185, ELSEVIER, PP-823–833
[6] Safaa Saheb Omran, Maryam Abdulmunem Salih, “Comparative Study of Fingerprint Image Enhancement Methods”, 2014 in Journal of Babylon University/Engineering Sciences/ No.(4)/ Vol.(22)
[7] Payel Roy; Saurab Dutta; Nilanjan Dey; Goutami Dey; Sayan Chakraborty; Ruben Ray, “ Adaptive thresholding: A comparative study”, 2014, published in IEEE International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) pp: 1182 – 1186
[8] Waleed Abu-Ain, Siti Norul Huda Sheikh Abdullah, Bilal Bataineh, Tarik Abu-Ain, Khairuddin Omar, “Skeletonization Algorithm for Binary Images”, 2013, published in the 4th International Conference on Electrical Engineering and Informatics (ICEEI), pp:704–709
[9] Ritika Luthra, Gulshan Goyal, “Performance Comparison of ZS and GH Skeletonization Algorithms”, July 2015, International Journal of Computer Applications, (0975 – 8887) Volume 121 – No.24
[10] Danny Thakkar, “Minutiae Based Extraction in Fingerprint Recognition” by available on https://www.bayometric.com/minutiae-based-extraction-fingerprint-recognition/ Accessed on November,2017.
[11] Mouad M.H. Ali, Vivek Hilal Mahale, Pravin Yannawar,Ashok Gaikwad, “ Fingerprint Recognition for Person Identification and Verification Based on Minutiae Matching”, February 2016, IEEE 6th International Conference on Advanced Computing
[12] Gabriel Babatunde Iwasoun and Sunday Olusegun Ojo, “Review and Evaluation of Fingerprint Singular Point Detection Algorithms”, 2014, British Journal of Applied Science & Technology, 4(35): 4918-4938, 2014, ISSN: 2231-0843
[13] P.Gnanasivam, S. Muttan, “An efficient Algorithm for fingerprint preprocessing and feature extraction”, 2010 Science Direct, Procedia Computer Science, Elsevier, 2 (2010) 133–142
[14] Sangeeta Narwal, Daljit Kaur, “Comparison between Minutiae Based and Pattern Based Algorithm of Fingerprint Image”, March 2016, International Journal of Information Engineering and Electronic Business, 2, PP-23-29, in MECS
[15] Daniel Peralta, Mikel Galar, Isaac Triguero, Daniel Paternain, Salvador García, Edurne Barrenechea, José M. Benítez, Humberto Bustince, Francisco Herrera, “A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation”, 2015, Information Sciences, PP-67–87, Published by Elsevier Ltd.
[16] Patel, M. B., Patel, R. B., Parikh, S. M., & Patel, A. R. (2017, August). An improved O`Gorman filter for fingerprint image enhancement. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 200-209). IEEE.
[17] Patel, M. B., Patel, R. B., Parikh, S. M., & Patel, A. R. Performance Improvement in Gradient based Algorithm for the Estimation of Fingerprint Orientation Fields, International Journal of Computer Applications, ISSN:0975 – 8887, Volume 167 – No.2, June 2017.
[18] Patel, M. B., Parikh, S. M., & Patel, A. R. Performance Improvement in Binarization for Fingerprint Recognition. IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN, 2278-0661.
[19] Patel, M. B., Parikh, S. M., & Patel, A. R., An Improved Thinning Algorithm for Fingerprint Recognition, in International Journal of Advanced Research in Computer Science, Volume-8, Issue-7, July-August-2017.
[20] Patel, M. B., Parikh, S. M., & Patel, A. R. (2019). Performance Improvement in Preprocessing Phase of Fingerprint Recognition. In Information and Communication Technology for Intelligent Systems (pp. 521-530). Springer, Singapore.
[21] Patel, M. B., Parikh, S. M., & Patel, A. R. (2017) An Improved Approach in Fingerprint Recognition Algorithm, published in International Conference on Computational Strategies for Next Generation Technologies (NEXTCOM-2017), November 25-26, 2017, Organized by CT Institute of Engineering Management & Technology Shahpur Jalandhar, Proceeding in Springer CCIS Series ISSN No. – 1865-0929
[22] Patel, M., Parikh, S. M., & Patel, A. R. (2018). An Improved Approach in Core Point Detection Algorithm for Fingerprint Recognition in in 3rd International Conference on Internet of Things and Connected Technologies, Elsevier.
[23] D. Maio, D. Maltoni, R. Capelli, J. L. Wayman And A. K. Jain, “Fvc2000: Fingerprint Verification Competition”, Ieee Trans. Pattern Anal. Mach. Intell., Vol. 24, No. 3, Pp. 402-412, 2002.
[24] F. Francis-Lothai And D. B. L. Bong, “Fingerdos: A Fingerprint Database Based On Optical Sensor,” Wseas Transactions On Information Science And Applications, Vol.12, No. 29, Pp. 297-304, 2015.
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M.B. Patel, S. M. Parikh, A.R. Patel, "An Approach for Scaling Up Performance of Fingerprint Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.457-461, 2019.

Balking is a customer behavior in which a customer upon arrival refuses to join the system if large number of customers are already present in the system. But in many businesses such as restaurants, healthcare, investment etc., it can be seen that the reverse of this phenomenon prevails. A large customer base acts as a motivating factor for newly arriving customers in such businesses with notion of getting better quality of service, affordability or both. This phenomenon is termed as Reverse Balking and it results in higher probability of a customer joining the system with respect to increasing customer base. This increasing probability of joining puts service facility under pressure. That in turn results in dissatisfactory and incomplete service at times. A dissatisfied customer may join the queue again for satisfactory service and is termed as a feedback customer in queuing literature. In order to frame an effective operational policy for such a system, it is essential to measure the performance of the system in advance. In this paper we combine above mentioned contemporary challenges of reverse balking and feedback to formulate a new multi-server infinite capacity feedback Markovian queuing system with reverse balking. The system is studied in steady-state. The necessary probability measures and measures of performance are derived. The sensitivity analysis of the model is presented. Later the cost model is developed and cost-profit analysis of the model is also presented. Algorithms are written in MATLAB and MS Excel for sensitivity analysis.

reverse balking, multi-server, queuing theory, feedback queue, infinite capacity

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[5] B. Natvig, “On a Queuing Model Where Potential Customers Are Discouraged by Queue Length”, Scandinavian Journal of Statistics, Vol.2, Issue.1, pp.34-42, 1975.
[6] N. K. Jain, R. Kumar, B. K. Som, “An M/M/1/N Queuing system with reverse balking”, American Journal of Operational Research, Vol.4, Issue.2, pp.17-20, 2014.
[7] L. Takács, “A Single-Server Queue with Feedback”, Bell System Technical Journal, Vol.42, Issue.2, pp.505-519, 1963.
[8] G. D’Avignon, R. Disney, “Single-Server Queues with State-Dependent Feedback. INFOR: Information Systems and Operational Research, Vol.14, Issue.1, pp.71-85, 1976.
[9] A. Santhakumaran, V. Thangaraj, “A single server queue with impatient and feedback customers”, Information and Management Science, Vol.11, Issue.3, pp.57-70, 2000.
[10] Som B. K. and Seth S. “Waiting Time Management at Reverse Balking, Infinite Capacity and Single-server Channel”, GLOGIFT 17, Seventeenth Global Conference, Delhi School of Management, Delhi Technological University, December 11 – 13, 2017.
[11] Som B. K. and Seth S. “A Multi-Server Infinite Capacity Markovian Feedback Queuing System with Reverse Balking”, Fifth International Conference on Business Analytics and Intelligence, IIM Bangalore, 11-13 December, 2017.

Bhupender Kumar Som, Vivek Kumar Sharma, Sunny Seth, "Cost-Profit Analysis of an Infinite Capacity Multi-server Markovian Feedback Queuing System with Reverse Balking," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.462-466, 2019.

Lung cancer is one of the most prominent and deleterious forms of cancer and affects about 2lakh people every year on an average. On a positive note, Lung Cancer death rates have significantly declined over the past decade due to early detection and treatment. Hence, this system uses CT images for detection of lung cancer. It contains several steps like image acquisition, pre-processing, thresholding, segmentation, feature extraction and detection of the presence and the stage of cancer if it is present. Initially, unwanted noise is removed using filters. In the next step, thresholding is used to perform segmentation and highlight the tumour spots. Using flood fill, and masks on the thresholded image, tumour spots which are isolated from the rest of the image are obtained. Features like area, perimeter and number of tumour spots, etc. are extracted by calculating contours using edge detection. Extracted features are given to the classifier model to detect the presence and hence the stage of existing cancer. The system then goes ahead and generates a report which is sent to the doctor for further analysis.

Image Processing, Machine Learning, Preprocessing, Binarization, Segmentation, and Feature extraction

[1] Nachiket Kelkar, Niraj Mate, Abhijit Kulkarni, Atharv Kukade, Pradnya Mehta, “Lung Cancer Detection & Recommendation of Oncologist using Machine Learning”, JETIR 2018.
[2] Karan Sharma, Harshil Soni and Kushika Agarwal, “Lung Cancer Detection in CT Scans of Patients Using Image Processing and Machine Learning Technique”, Springer 2018.
[3] S.Kalaivani, Pramit Chatterjee, Shikhar Juyal, Rishi Gupta, “Lung Cancer Detection Using Digital Image Processing and Artificial Neural Networks.”, ICECA 2017
[4] Sheenam Rattan, Sumandeep Kaur, Nishu Kansal, Jaspreet Kaur, “An optimized Lung Cancer Classification System for Computed Tomography Images.”, IEEE 2017.
[5] G.Niranjana, Dr.M.Ponnavaikko, “A Review on Image Processing Methods in Detecting Lung Cancer using CT Images.”, ICTACC, 2017.
[6] Pooja R. Katre, Dr. Anuradha Thakare, “Detection of Lung Cancer Stages using Image Processing and Data Classification Techniques.” I2CT 2017.
[7] Mansee Kurkure, Anuradha Tharkre, “Lung Cancer Detection using Genetic Approach.” 2017
[8] Md. Badrul Alam Miah, Mohammad Abu Yousuf, “Detection of Lung Cancer from CT Image Using Image Processing and Neural Network,” ICEEICT, 2015.
[9] Sruthi Ignatious, Robin Joseph, “Computer Aided Lung Cancer Detection System.” GCCT 2015.
[10] Anita Chaudhary, Sonit Sukhraj Singh, “Lung Cancer Detection On CT images by using image processing” ICCS, 2012.
[11] Gayatri. D. Patil, Lubdha. M. Bendale, Roshani. L. Jain, "Document Image Noises and Removal Methods", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.48-63, 2018
[12] Roshani. L.Jain, Lubdha M. Bendale, Gayatri D. Patil, "Image Enhancement Using Different Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.73-76, 2018

Nachiket Kelkar, Niraj Mate, Atharv Kukade, Abhijit Kulkarni, Pradnya Mehta, "Implementation of Lung Cancer Detection & Recommendation of Oncologist Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.467-471, 2019.

In order to maintain the air quality, continuous monitoring and analysis of the air pollution data is necessary; especially in areas where industrial and vehicular emissions contribute more to poor air quality. Inhalation of high concentration of fine particulate matter (PM2.5) causes lung, heart and various other diseases, which increase hospital visits and mortalities each day. The focus of this paper is to analyse the historical pollution data and corresponding meteorological data from selected areas, and to forecast PM2.5 over the next 48 hours by using multiple neural networks. In this proposed model, experiment is conducted by including pollutant and meteorology data recorded for every hour from 14 places, which includes northern, southern, western and eastern parts of India. Spatial temporal relations and terrain impact are then extracted. The proposed system applies multiple neural networks including convolutional neural network, artificial neural network, long short-term memory and adaptive neuro-fuzzy inference system to predict the air quality. The proposed model - ANFIS prediction performance - is better than the existing ANN model.

Convolutional neural network; LSTM; adaptive neuro-fuzzy inference system; air quality forecast; dynamic time warping; Euclidean distance

[1] Hao Guo, Sri Harsha Kota, Shovan Kumar Sahu, Jianlin Hu, Qi Ying, Aifang Gao, Hongliang Zhang, “Source apportionment of PM2.5 in North India using source-oriented air quality models”, Environmental Pollution 231 (2017) pp.426-436.
[2] The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: The Global Burden of Disease Study 2017, India State-Level Disease Burden Initiative Air Pollution Collaborators, Lancet Planet Health 2019; 3: e26–39.
[3] Ping-Wei Soh, Jia-Wei Chang, and Jen-Wei Huang, “Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations”,. Ieee Access,Vol-6, 2018, pp. 38186-38199
[4] Chiou-Jye Huang and Ping-Huan Kuo, “A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities”, Sensors 2018, 18, 2220;
[5] Athira V, Geetha P, Vinayakumar R, Soman K P, “Deep AirNet: Applying Recurrent networks for Air Quality Prediction”, Procedia Computer Science 132(2018) pp-1394-1403.
[6] Guyu Zhao, Guoyan Huang, Hongdou He, And Qian Wang, “Innovative Spatial-Temporal Network Modeling and Analysis Method of Air Quality”, Ieee Access, vol.7,2019, pp-26241-26254.
[7] Nazif1 • N. I. Mohammed1 • A. Malakahmad1 • M. S. Abualqumboz1, “Multivariate analysis of monsoon seasonal variation and prediction of particulate matter episode using regression and hybrid models”, International Journal of Environmental Science and Technology, June 2019, vol.16(6), pp-2587-2600.
[8] Li-Yu Hu, Min-Wei Huang, Shih-Wen Ke, and Chih-Fong Tsai, “ The distance function effect on k-nearest neighbor classification for medical datasets”, Springerplus. 2016; 5(1): 1304.
[9] Duarte Folgado, Marília Barandas, Ricardo Matias, Rodrigo Martins, Miguel Carvalho, Hugo Gamboa, “Time Alignment Measurement for Time Series”, Pattern Recognition, Volume 81, September 2018, pp- 268-279.
[10] Sakshi Indolia, Anil Kumar Goswami, S. P. Mishra, Pooja Asopa, “Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach”, International Conference on Computational Intelligence and Data Science (ICCIDS 2018), Procedia Computer Science 132 (2018) pp-679–688
[11] Jianfeng Zhang, Yan Zhu, Xiaoping Zhang, Ming Ye, Jinzhong Yang, “Developing a Long Short-Term Memory (LSTM) based Model for Predicting Water Table Depth in Agricultural Areas”, Journal of Hydrology, Vol. 561, June 2018, pp- 918-929.
[12] Ryan G.Hefron, Brett J.Borghetti, James C.Christensen, Christine M. Schubert Kabban, “Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation”, Pattern Recognition Letters 94 (2017) pp-96–104.

S. Jeya, L. Sankari, "Spatial-temporal, terrain forecasting of air quality model by multiple Deep Neural Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.472-477, 2019.

Fantasy cricket league is a rapidly growing industry in India. It has around 70 million users. Lots of people are really making money from it. Player selection is the most important task in the Fantasy League. The performance of a player depends on various factors such as opposition team, venue, his current form and many more. Fantasy league user has to make own team of 11 players from both the team players. In this paper, we are going to predict the performance of a player in IPL matches by analysing previous year’s ball by ball data (2008-2018) using supervised machine learning techniques. Here we classified the batsman’s runs and bowler’s wickets in a different range to pick or not to pick. We used Decision Tree, Random Forest, Xgboost, Stacking for prediction of the players[6]. Stacking technique found the most accurate classifier for the problem.

Fantasy League, Machine Learning, Decision Tree, Random Forest, Xgboost, Stacking

[1] Fantasy Cricket, https://en.wikipedia.org/wiki/F antasy_cricket
[2] https://www.iplt20.com/teams
[3] Kalpdrum Passi and Niravkumar Pandey, ‘pre- dicting player’s performance in one-day international cricket match using machine learning’, February 2018.
[4] Fantasy cricket league dream11, https://www.dre am11.com/
[5] https://www.analyticsvidhya.com/blog/2018/06/ comprehensive-guide-for-ensemble-models/
[6] https://en.wikipedia.org/wiki/Machine_learning
[7] https://en.wikipedia.org/wiki/Cricket
[8] C. Deep Prakash, C. Patvardhan, C. Vasantha Lakshmi, ‘Data Analytics based Deep Mayo Predictor for IPL-9’, Volume 152 – No.6, October 2016
[9] Debarghya Das, ‘An Integer Optimization Framework for Fantasy Cricket League Selection and Substitution’.
[10] Tim B. Swartz, ‘Research Directions in Cricket’

Nihal Patel, Mrudang Pandya, "IPL Player’s Performance Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.478-481, 2019.