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Data logging are an important aspect in a modern day measurement and instrumentation system. Almost all the industrial process requires data logging. Nowadays, cheap and feasible solution of data logging in industrial and scientific process is a difficult task with proprietary data logger. In this research paper, we proposed the design and development of two channel data logger which provide the cheap and feasible solution for monitoring and recording the voltage, current, power and energy of two PV solar panels. The designed prototype data logger is based on Arduino UNO and facilitated the data logging on SD card or on the memory of Bluetooth enabled android mobile phone. Remote monitoring and recording of data is possible with this data logger. The design of this data logger is completely based on the open source software and hardware devices instead of proprietary hardware devices and commercial software. Measurement and monitoring of voltage, current, power and energy of two PV solar panels and its logging on suitable electronic medium are smart features of this data logger.

Data Logger, Arduino UNO, SD card, Bluetooth Module HC-05, DS-3231 Real Time Clock

[1] N. N. Mahzan, A. M. Omar, L. Rimon, S. Z. Mohammad Noor, M. Z. Rosselan, “Design and Development of an Arduino Based Data Logger for Photovoltaic Monitoring System”, International Journal of Simulation Systems, Science, and Technology, Vol. 17, Issue. 41, pp. 15.1-15.5, 2017.
[2] Shubhankar Mandal, Dilbag Singh “Real Time Data Acquisition of Solar Panel using Arduino and Further Recording Voltage of the Solar Panel”, International Journal of Instrumentation and Control Systems, Vol. 7, Issue. 3, pp. 15-25, 2017.
[3] Aboubakr El Hammoumi, Saad Motahhir, Abdelilah Chalh, Abdelaziz El Ghzizal, Aziz Derouich, “Low-Cost Virtual Instrumentation of PV Panel Characteristic using Excel and Arduino in Comparison with Traditional Instrumentation”, Renewables: Wind, Water and Solar, Vol. 5, Issue. 3, pp. 2-16, 2018.
[4] S. Fanourakis, K. Wang, P. McCarthy, L. Jiao, “Low-Cost Data Acquisition Systems for Photovoltaic System Monitoring and Usage Statistics”, IOP Conference Series: Earth and Environment Science, Vol. 93, pp. 1-10, 2017.
[5] Nyoman Sugiartha, I Made Sugina, I Dewa Gede Agus Tri Putra, Made Alwin Indraswara, Luh Ika Dhivtyasari Suryani, “Developement of an Arduino-based Data Acquisition Devices for Monitoring Solar PV System Parameters”, Proceedings of the International Conference on Science and Technology, Vol. 1, pp. 995-999, 2018.
[6] S. S. Pawar, Akash Yadav, Darshana A. Marathe, Neha D. Vyavhare, “Data Logger for Solar Photovoltaic Power Stations”, International Journal of Electronics, Electrical and Computational System, Vol. 7, Issue. 3, pp. 428-435, 2018.
[7] M. Fuentes, M. Vivar, J. M. Burgos, J. Aguilera, J. A. Vacas, “Design of an Accurate, Low-Cost Autonomous Data Logger for PV System Monitoring using Arduino™ that Complies with IEC Standards”, Solar Energy Materials & Solar Cells, Vol. 130, pp. 529-543, 2014.
[8] Pallavi Soni, Gautam Gupta, Vishal Sarode, Shravil Kapoor, Sushma Parihar, “Data Logger Module for Data Acquisition System”, International Journal of Application or Innovation in Engineering and Management, Vol. 4, Issue. 4, pp. 254-259, 2015.
[9] Wai Mar Myint Aung, Yadanar Win, Nay Win Zaw, “Implementation of Solar Photovoltaic Data Monitoring System”, International Journal of Science, Engineering and Technology Research, Vol. 7, Issue. 8, pp. 591-596, 2018.
[10] Patricia A. Beddows, Edward K. Mallon, “Cave Pearl Data Logger: A Flexible Arduino-Based Logging Platform for Long-Term Monitoring in Harsh Enviornments”, Sensors, Vol. 18, Issue. 2, pp. 2-26, 2018.
[11] Muhammad Abu Bakar Sidik, Mohd Qamarul Arifin Rusli, Zuraimy Adzis, Zolkafle Buntat, Yanuar Zulardiansyah Arief, Hamizah Shahroom, Zainuddin Nawawi, Muhammad Irfan Jambak, “Arduino-Uno Based Mobile Data Logger with GPS Feature”, Telkomnika, Vol. 13, Issue. 1, pp. 250-259, 2015.

Tarun Singh, Ritula Thakur, "Design and Development of PV Solar Panel Data Logger," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.364-369, 2019.

Saarthi is a website/app that provides an integrated system to farmer for providing transportation facility of their yields. The concept is simple and works on the basic necessities of agricultural India. As we know that agriculture in India constitutes approximately 18% of the total GDP. As India is a country with a vast regional diversity it is required for Saarthi to provide with a familiar list of fruits and vegetables for the farmer. It is also made available in multiple languages to make it more understanding for farmer in India. Our approach is to develop a user-friendly interface accessible through smart device available such as smart phone and websites. The details of the yields can be added by the farmer in any language. The aim is to provide the farmers with the best transportation facility at their doorstep. The Saarthi is going to provide a transportation facility in 3 modes. 1) Regional Transportation 2) State Transportation 3) Local Transportation. Saarthi is going to provide 24*7 customer support and chat bots will be available. To achieve 100% transparency in the transactions between farmers and transportation facilities Saarthi aims to cut out the middlemen so that the farmer get the deserved worth of their yields.

Farmer, GDP (Gross Domestic Product), Market, Services, Transport, Weather

[1] Kokane Gauri K1, Kolhe Sushma R2, Labade Dipali M3, Vaidya Geeta B, “Krishi-Mitra:-An ICT enabled Interface for
Farmers Security and Communication”, IJARIE, Vol.4, Issue.3, pp.2395-4396 2018.
[2] Gauravjeet Dagar “study of agriculture marketing information systems models and their implications”, AJMR-AIMA, Vol.9, Issue.2/4, May 2015.
[3] Aditya Gupte, Anuja Gaonkar “Online Cab Booking System”, IJSRD, Vol.4, Issue.10, pp. 679-683, 2016.
[4] Oloyede M.O. Alaya S.M., Adewole K.S. “Development of an Online Bus Ticket Reservation System for a
Transportation Service in Nigeria”, Computer Engineering and Intelligent Systems IISTE, Vol.5, Issue.12, pp.9-17, 2014.

D. Anantha Reddy, Mohd. Jabeed Rihaz, Saroj Shambharkar, "Saarthi an Innovative Platform for Farmers to Get Yield in India," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.370-373, 2019.

The business-to-consumer aspect of electronic commerce (e-commerce) is the most visible business use of the World Wide Web. The primary goal of an e-commerce site is to sell goods and services online with security of customers’ giving optimum consideration. This paper deals with development of fraud detection and alerting system using Hidden Markov Model and Artificial Neural Network. The system is implemented using a 3-tier approach, with a backend database, a middle tier of WAMP Server, and a web browser as the front end client. In order to finalize payment of goods, the customer must authenticate this approach through Code and OTP match. Whereby the authentication process fails, the transaction would not be completed and real-time alert would be sent to both the e-commerce system and payment system. This will enable their (e-commerce and payment system) platform to electronically deactivate the victims’ account.

ANN, HMM, e-Commerce and Payment System

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[4] Tae-Hwan, S., Paula, S. (2008): “Identifying Effectiveness Criteria for Internet Payment Systems”, A Journal of Internet Research: Networking Applications and Policy, v-8 number 3, pp 202-218.
[5] Edwards.com
[6] Creditcard.com
[7] Kovach, S and Ruggiero, W. V. (2011): “Online Banking Fraud Detection Based on Local and Global Behavior” ICDS 2011: The Fifth International Conference on Digital Society.
[8] Kappelin, F. and Rudvall, J. (2015): “Fraud Detection within Mobile Money: A mathematical statistics approach” MSc Thesis submitted to the Dept. Computer Science & Engineering Blekinge Institute of Technology SE–371 79 Karlskrona, Sweden.
[9] Chen, M., Han, J. and Yu, P. S. (2012) “Data mining: An Overview from a Database Perspective,” IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 866-883.
[10] Ngai, E., Hu, Y., Wong, Y., Chen, Y. and Sun, X. (2011): “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature,” Decision Support Systems, pp. 559-569, 2011.
[11] Meyer, D. (2012): “Support Vector Machines,” Technische Universit¨at Wien,, Austria, 2012.

Amanze, B.C., Okoronkwo, M.C., Chilaka, U.L, "Hybrid Model for Online Payment Syetem with Object-Oriented Methodology," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.374-385, 2019.

The performances of six color models and their features are compared for classification of Karjat-6, Karjat-2, Ratnagiri-4 and Ratnagiri-24 the four paddy varieties. Total of 15 color features-mean, standard deviation, variance, skewness and kurtosis for each channel are extracted from the high-resolution images of kernels and used as input features for classification. Different feature models consisting of the combination of the above features (MSVSK and MSV) are tested for their ability to classify these cereal grains. Effect of using different features on the accuracy of classification is studied. The most suitable feature from the feature set for accurate classification is identified. The accuracy percentage for YCbCr-MSVSK1 is 71.2 % and YCbCr –MSV1 is 65.4%. The MSVSK feature set outperformed the MSV feature set in most of the instances of classification. Similarly YCbCr color model performed well as compared to rest of the color models.

Mean, Neural-Network, Standard-deviation, Skewness, Kurtosis and Variance

[1] Archana Chaugule and Suresh N. Mali, “Evaluation of Texture and Shape Features for Classification of Four Paddy Varieties,” Hindawi Publishing Corporation, Journal of Engineering, Vol 2014, Article ID 617263, 8 pages, http://dx.doi.org/10.1155/2014/617263.
[2] Archana Chaugule and Dr. Suresh Mali, “Seed Technological Development – A Survey,” ACEEE, Proc. of International Conference on Information Technology in Signal and Image Processing, doi:03.LSCS.2013.6.528 , pp. 71-78, 2013.
[3] Cao Weishi, Zhang Chunqing, Wang Jinxing, Liu shuangxi, and Xu Kingzhen, “Purity Identification of Maize Seed Based on Discrete Wavelet Transform and BP Neural Network,” Transactions of the chinese society of Agricultural Engineering, vol. 28, pp. 253-258, 2012.
[4] Chandra B. Singh, Digvir S. Jayas, Jitendra Paliwal, Noel D.G. White, “Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital color imaging,” Elsevier, Computers and Electronics in Agriculture, vol. 73, pp. 118–125, 2010.
[5] H.K. Mebatsion, J. Paliwal , D.S. Jayas, “Automatic classification of non-touching cereal grains in digital images using limited morphological and color features,” Elsevier, Computers and Electronics in Agriculture, vol. 90, pp. 99–105, 2013.
[6] J. Paliwal; N.S. Visen; D.S. Jayas; N.D.G. White, “Cereal Grain and Dockage Identification using Machine Vision,” PH - Postharvest Technology, Biosystems Engineering, vol. 85, no. 1, pp. 51–57, 2003a.
[7] J. Paliwal; N.S. Visen; D.S. Jayas; N.D.G. White, “Comparison of a Neural Network and a Non-parametric Classifier for Grain Kernel Identification,” AE- Automation and Emerging Technologies, Biosystems Engineering, vol. 85, no. 4, pp. 405–413, 2003b.
[8] K. Kiratiratanapruk and W. Sinthupinyo, “Color and texture for corn seed classification by machine vision,” in Proceedings of the 19th IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS ’11), Chiang Mai, Thailand, December 2011.
[9] Li Jingbin, Chen Bingqi, ,Shao Luhao,Tian Xushun, Kan Za, “Variety Identification of Delinted Cottonseeds Based on BP Neural Network,” Transactions of the Chinese society of Agricultural Engineering, vol. 28, pp. 265-269, 2012.
[10] Marian Wiwart, Elzbieta Suchowilska, Waldemar Lajszner, ukasz Graban, “Identification of Hybrids of Spelt and Wheat and their Parental Forms Using Shape and Color
[11] Descriptors,” Elsevier, Computers and Electronics in Agriculture, vol. 83, pp. 68-76, 2012.
[12] M. Zhao,W.Wu, Y. Q. Zhang, and X. Li, “Combining genetic algorithm and SVM for corn variety identification,” in Proceedings of the International Conference onMechatronic Science, Electric Engineering and Computer (MEC ’11), pp. 990–993, Jilin, China, August 2011.
[13] N. S. Visen, J. Paliwal, D. S. Jayas, and N.D.G.White, “Specialist neural networks for cereal grain classification,” Biosystems Engineering, vol. 82, no. 2, pp. 151–159, 2001.
[14] Pablo M. Granitto, Hugo D. Navone, Pablo F. Verdes, H.A. Ceccatto, “Weed Seeds Identification by Machine Vision,” Elsevier, Computers and Electronics in Agriculture, vol. 33, pp. 91-103, 2002.
[15] Pablo M. Granitto, Pablo F. Verdes, H. Alejandro Ceccatto, “Scale Investigation of Weed Seed Identification by Machine Vision,” Elsevier, Computers and Electronics in Agriculture, vol. 47, pp. 15-24, 2005.
[16] Xiao Chena, Yi Xunb, Wei Li, Junxiong Zhang, “Combining Discriminant Analysis and Neural Networks for Corn Variety Identification,” Elsevier, Computers and Electronics in Agriculture, vol. 71S, pp. S48-S53, 2010.
[17] B.P. Dubey, S.G. Bhagwat, S.P. Shouche, and J.K. Sainis, “Potential of Artificial Neural Networks in Varietal Identification using Morphometry of Wheat Grains,” Elsevier, Biosystems Engineering, PH—Postharvest Technology, vol. 95, no.1, pp. 61–67, 2006.

Archana Chaugule, "Comparison of Six Color Models for Variety Identification of Four Paddy Grains," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.386-394, 2019.

Variational inequalities are studied in various models for a large number of mathematical, physical, economics, finance, optimization, game theory, engineering and other problems(see[1],[2],[12], [14],[15], [21]). The fixed point formulation of any variational inequality problem is not only useful for existence of solution of the variational inequality problem, but it also provides the facility to develop algorithms for approximation of solution of variational inequality problem. A lot of research has been carried out to develop various iterative algorithms to find solution of a variational inequality problem. In this paper, we have studied various algorithms or methods used for solving Variational inequality problems and studied the developments of such methods and compared their convergence rate . Our result helps in understanding the development in iterative algorithms for VI. AMS Subject Classification: 49H09; 47H10; 47J20; 49J40, 47J05

Proximal Point Algorithm, KKT based method ,Linear Approximation method, strong convergence, variational inequality, Projection based methods

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Poonam Mishra, Shailesh Dhar Diwan, "Algorithms for Variational Inequalities," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.395-399, 2019.

Computing capacity and storage space need of these devices are ever-increasing tremendously, it demands the secure way of storing the data in cost efficient model. There are vast numbers of users who use cloud services through mobile devices such as mobiles, PDA, tablets, laptops outstanding to its portability feature. Cloud Computing has many advantages inherent in it, but yet there are several risks and constraint exists, for e.g. protection, data access control, efficiency, bandwidth, etc a novel remote data integrity checking model: IDP (identity-based proxy) in multi-cloud storage. The formal system model and security model are given. Based on the bilinear pairings, a concrete IDP protocol is designed.To analyze the efficiency of various well known cryptographic algorithms such as Identity-based cryptography,Proxy public key cryptography, these symmetric algorithms were implemented on cloud background and through the results derived from real time implementation of these algorithms on various handheld procedure, it is shown that which cryptographic technique can provide efficient and reliable security mechanism for information access control and security of user’s outsourced information in cloud computing.

Data security, Cryptographic techniques; Identity-based cryptography, Proxy public key cryptography

[1]. Kumar, K., Lu, Y.-H.: Yung-Hsiang Lu: Cloud Computing for Mobile Users: Can Offloading Computation Save Energy? Computer 43(4), 51– 56 (2010)
[2]. Simoens, P., De Turck, F., Dhoedt, B., Demeester, P.: Remote Display Solutions for Mobile Cloud Computing. Computer 44(8), 46–53 (2011)
[3]. Ayesha Malik, Muhammad Mohsin Nazir, "Security Framework for Cloud Computing Environment: A Review," Journal of Emerging
Trends in Computing and Information Sciences, 2012.
[4]. Shashi Mehrotra Seth, Rajan Mishra, “Comparative Analysis Of Encryption Algorithms For Data Communication," IJCST Vol. 2, Iss ue 2,
June 20 I I .
[5]. Shahryar Shafique Qureshi1 , Toufeeq Ahmad1, Khalid Rafique2, Shuja-ul-islam3 “Mobile cloud computing as future for mobile applications – implementation methods and challenging issues”-2011.
[6]. Mell P, Grance T (2011) The NIST definition of Cloud Computing. NIST, Special Publication 800–145, Gaithersburg, MD
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[8]. Pearson, S., Y. Shen, and M. Mowbray, “A Privacy Manager for Cloud Computing”, in Proceedings of the 1st International Conference on Cloud Computing. 2009, Springer-Verlag: Beijing, China. p. 90-106.
[9]. Wang, Q., et al., “Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing”, in Computer Security – ESORICS 2009, M. Backes and P. Ning, Editors. 2009, Springer Berlin / Heidelberg. p. 355-370.
[10]. Hoang T. Dinh, Chonho Lee, Dusit Niyato, and Ping Wang. A Survey of Mobile Cloud Computing: Architecture Applications, and Approaches,In Wireless Communications and Mobile Computing 2011.
[11]. Wei Ren, Linchen Yu, Ren Gao, Feng Xiong.Lightweight and Compromise Resilient Storage Outsourcing with Distributed Secure Accessibility in Mobile Cloud Computing. Tsinghua Science And Technology,ISSNl1007-0214ll06/09llpp520 528.Volume 16, Number 5, October 2011.
[12]. Liu Q, Wang G, Wu J. Efficient sharing of secure cloud storage services. In: 2010 IEEE 10th International Conference on Computer and
Information Technology (CIT10). Bradford, West Yorkshire, UK, 2010: 922-929.
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1-4244- 7674-9/10., IEEE, 2010.
[15]. Rahul Bhatnagar, Suyash Raizada, Pramod Saxena, SECURITY IN CLOUD COMPUTING ,International Journal For Technological
Research In Engineering, ISSN (Online) : 2347 4718, December - 2013.
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vol.43, pp. 51-56, April 2010.
[18]. E. Lagerspetz and S. Tarkoma, “Mobile Search and the Cloud: The Benefits of Offloading,” IEEE International Conference on Workshops (PERCOM Workshops), pp. 117–122, March 2011.
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A. Ashik hussain, R.C. Subashini, "Identity-Based Proxy-Oriented Data Uploading and Remote Data Integrity Checking in Public Cloud," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.400-405, 2019.

Breast cancer is second leading invasive cancer causes death after lung cancer. The accurate diagnosis is a very crucial aspect of breast cancer treatment. For this purpose, data mining techniques guide doctors in correct decision-making for diagnosis. This paper demonstrates various data mining methods for breast cancer diagnosis. The proposed algorithm is distinguished into two sections. First section consists of feature selection methods to reduce the computational complexity, as genetic algorithm is used to eliminate the irrelevant features from the dataset and second section describes different classification algorithms named Multilayer Perceptron, Random Forest, and Naive Bayes classification to determine whether breast cancer is malignant or benign type. The proposed algorithm is applied to four datasets of Wisconsin Breast Cancer Dataset and at last comparison is made between various classification algorithms to achieve highest classification accuracy.

Feature Selection, Genetic Algorithm, Multilayer Perceptron, Random Forest, Naive Bayes

[1] G. Devi , et at. "Breast Cancer Prediction System using Feature Selection and Data Mining Methods", International Journal of Advanced research in Computer Science, Vol. 2, Issue 1, pp. 81-87, 2011.
[2] R. Nithiya, et al. "A data Mining Techniques for Diagnosis of Breast Cancer Disease", World Applied Sciences Journal, 29 (Data Mining and Soft Computing Techniques), pp. 18-23, 2014.
[3] B. Zheng, et al. "Breast Cancer Diagnosis based on Feature Extraction using a Hybrid of K-means and Support Vector Machine Algorithms". Expert Systems with Applications, Elsevier, 2013.
[4] C. Lu, et al. "An Intelligent System for Lung Cancer Diagnosis Using a New Genetic Algorithm Based Feature Selection Method". Journal of Medical Systems, Springer Science, Vol. 38, pp. 88-97, 2014.
[5] S. Aalaei, et al. "Feature Selection using Genetic Algorithm for Breast Cancer Diagnosis: Experiment on three Different Datasets". Iran Journal of Basic Medical Sciences, Vol. 19, Issue 5, pp. 476-482, May 2016.
[6] C.R., K., T, M., "Feature Selection Methods for Classification: A Comparison". International Journal of Research in Engineering and Technology, Vol. 06, Issue 06, pp. 130-137, June 2017.
[7] B. Tamilvanan, V. M. Bhaskaran, "New Feature Selection Techniques Using Genetics Search and Random Search Approaches for Breast Cancer". Biosciences Biotechnology Research Asia, Vol. 14, Issue 1, pp. 409-414, 2017.
[8] E. Alickovic, et al. "Breast Cancer Diagnosis using Genetic Algorithm Feature Selection and Rotation Forest". The Neural Computing and Applications, Springer, 2015.
[9] D. Dumitru, et al. "Prediction of Recurrent Events In Breast Cancer using the Naive Bayesian Classification". Mathematics and Computer Science, Vol. 36, Issue 2, pp. 92-96, 2009.
[10] T. Karthikeyan, et al. "Genetic Algorithm based CFS and Naive Bayes Algorithm to Enhance the Prediction Accuracy". Indian Journal of Science and Technology, Vol. 8, Issue 27, 2015.
[11] S. Vanaja, et al. "Analysis of Feature Selection Algorithms on Classification: A Survey". International Journal of Computer Applications, Vol. 96, Issue 17, pp. 1-8. June 2014.
[12] R. Tiwari, et al. "Correlation Based Attribute Selection Using Genetic Algorithm". International Journal of Computer Applications, Vol. 4, Issue 8, pp. 28-34, August 2010.
[13] R. Suji, S. P. Rajagopalan."Multi-Ranked Feature Selection Algorithm for Effective Breast Cancer Detection". Biomedical Research, pp. S99-S102, 2016.
[14] D. Lavanya et al. "Analysis of Feature Selection with Classification: Breast Cancer Datasets". Indian Journal of Computer Science and Engg. Vol. 2, Issue 5, pp. 576-563, Oct-Nov 2011.
[15] Z. Karanpunar, et al. "Breast Cancer Diagnosis via Data Mining: Performance Analysis of Seven Different Algorithms". Computer Science and Engineering: An International Journal, Vol. 4, Issue 1, pp. 35-46, Feb. 2014.

Shivangi Singla, Pinaki Ghosh, Uma Kumari, "Breast Cancer Detection using Genetic Algorithm with Correlation based Feature Selection: Experiment on Different Datasets," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.406-410, 2019.

Identifying the economic situation at sub-regional level has always been a challenge due to data constraints. In this paper we show the potential of remote sensing technologies to provide a possible solution to this problem. In this paper, we use the approach of using convolutional neural networks and transfer learning to process satellite imagery of the towns of West Bengal which can be used to predict its economic development indicator values.

Deep learning, Convolutional Neural Networks, Transfer Learning, Asset Index, Satellite Imagery, Night Light Intensity, District Census Handbook

[1] Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell. DeCAF: “A Deep Convolutional Activation Feature for Generic Visual Recognition”, 2013
[2] Neal Jean , Marshall Burke, Michael Xie, W. Matthew Davis, David B.Lobell, Stefano Ermon.” Combining satellite imagery and machine learning to predict poverty“
[3] Andrew Head, Mélanie Manguin, Nhat Tran, and Joshua E. Blumenstock. “Can Human Development be Measured with Satellite Imagery?” In Proceedings of ICTD ’17, Lahore, Pakistan 11 pages, November 16–19, 2017.
[4] Abdisalan M Noor, Victor A Alegana, Peter W Gething, Andrew J Tatem and Robert W Snow. “Using remotely sensed night-time light as a proxy for poverty in Africa”, 2008.
[5] Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang “On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima”,2017
[6] Map Box api code source: Jimut Bahan Pal https://github.com/Jimut123/apple_maps_api.
[7] Buddhadeb Ghosh, Prabir De ”Role of Infrastructure in Regional Development-A Study over the Plan Period”,1998.
[8] Major cities of West Bengal https://en.wikipedia.org/wiki/Cities_and_towns_in_West_Bengal#Major_cities
[9] District Census Handbook data,2011 http://censusindia.gov.in/2011census/dchb/DCHB.html
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Tanuj Sur, Asoke Nath, "An Innovative Method to Calculate the Economic Development Index of Important Cities in West Bengal from Satellite Imagery," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.411-417, 2019.

The number of online blood banks are available but none of them offer direct contact between donor and recipient. algorithm. The optimization of donor is also on the basis of most nearest location of requested person i.e. recipient. Based on the constraint satisfaction and most nearest location of donor the fittest donor is found out. Contact information of fittest donor is made available to recipient at any time even in urgent need of blood.

Genetic Algorithm; Constraints, Fitness Function, Donor, Blood Bank, Crossover, Mutation, Genetic Operators

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K.S.Wagh, Shubhangi Mangrulkar, Tejaswini Nagawade, Aishwarya Ingewar, Rohit Pende , "RED DROP: Optimisation of Blood Donor Using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.418-426, 2019.

Traffic congestion is a situation of increased disturbance of the motion of traffic. India, accompanied by much growing vehicles on the road, so that congestion of the traffic is quickly increasing. Traffic is still cannot thoroughly forecast under which case Traffic Jam may abruptly occur. This study proposes self-similarity structure; it plays a crucial role in queuing system in the field of congestion traffic. The proposal summarizes that whether vehicle arrival pattern on Highways is self-similar in nature or not? Also depict the results in terms of Length of the Queue, Waiting Time Distribution, Traffic Intensity etc., using Queuing models. For this we provided the data from V.R Technique Consultant Pvt. Ltd, India, as of Toll Plaza reports from Delhi Gurgaon section of National Highway 8(NH8) in India. Few techniques to test the self-similarity have been used and obtained values of Hurst parameter are reasonably close to each other. Using M/M/1 queuing model and an empirical with Hurst index terms mean queue length has been computed against traffic intensity. Results of the study reveal that mean queue length increases as and increase. This kind of research is to forecasting the performance analysis and chronic improvement of toll plazas.

Queuing Model, System Design, Self-similarity, Hurst Index, Queue Length, Waiting Distribution, Traffic Intensity

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Pushpalatha Sarla, D. Mallikarjuna Reddy, Thandu Vamshi Krishna, Manohar Dingari, "Self- Similar Behaviour Highway Traffic Analysis –Using Queuing Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.427-441, 2019.