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The availability of doctors is a major problem that people are facing especially in the rural areas. On the other hand, there are some areas which have large number of doctors than actually required. Because of this uneven distribution of doctors, people in the remote areas remain deprived of medical facilities and are living an unhealthy lifestyle. This paper describes the optimization of schedule of doctors using graph coloring and backtracking approach so that they can be evenly distributed across various locations and the people who are currently not getting proper medical facilities will be able to get treatments on time. This technique serves two purposes, namely, resource optimization and cost optimization. An optimized schedule of doctors will be provided. Only the required number of doctors will be present at a particular location. The remaining doctors will be sent to some other locations. If implemented on a large scale such as in government schemes, this can be a great contribution to the society.

Graph coloring, resource optimization, cost optimization

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[3] A. Mohamed, M. Yusoff, I. A. Mohtar, S. Mutalib, S. A. Rahman “Constraint Satisfaction Problem Using Modified Branch and Bound Algorithm”, WSEAS Transactions, Vol. 7, Issue 1, 2008.
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G. Shrivastava, H. Patidar, "Design and Implementation of Doctor Scheduling System Using Graph Coloring and Backtracking Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.438-442, 2019.

This paper presents the essentials of the background, available literature and technologies presently available in e-leaning specifically recommender systems and its range of applications, different techniques used for the general recommender systems, e-learning recommender systems and the specific neighborhood-based recommender methods used. A comprehensive survey has been carried out to elucidate the types of neighborhood-based recommendation methods used in e-learning recommender systems. The paper highlights these methods with an comparative analysis of the recommendation methods.

E-learning, personalized learning, learning styles, recommender systems, neighborhood-based methods

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[16] S. Rafaeli, Y. Dan-Gur and M. Bara, “Social Recommender Systems: Recommendations in Support of E-Learning”, International Journal of Distance Education Technologies, ISSN: 153-3100, Volume: 3, Issue: 3, pp.29–45, 2005.
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[23] T. Tang and G. McCalla, “Smart Recommendation for an Evolving E-Learning System”, Proceedings of the Workshop on Technologies for Electronic Documents for Supporting Learning, Tokyo, Japan, 2003.
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[26] R.J. Nadolski, B.Van den Berg, A. Berlanga, H. Drachsler, H. Hummel, R. Koper and P. Sloep, “Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies”, Journal of Artificial Societies and Social Simulation, ISSN: 1460-7425, Volume: 12, Issue: 14, 2009.
[27] H.G.K. Hummel, B. Van den Berg, A.J. Berlanga, H. Drachsler, J. Janssen, R.J. Nadolski, and E.J.R. Koper, “Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities”, International Journal of Learning Technology ISSN: 1741-8119, Volume: 3, Issue:2, pp.152–168 ,2007.
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[30] H. Drachsler, D. Pecceu, T. Arts, E. Hutten, L. Rutledge, P. Van Rosmalen, H.G.K. Hummel and R. Koper, “ReMashed Recommendations for Mash-Up Personal Learning Environments”, Proceedings of the 4th European Conference on Technology Enhanced Learning, Germany, Berlin, 2009.
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J. Saul Nicholas, F. Sagayaraj Francis, "A Comprehensive Survey of Neighborhood-Based Recommendation Methods used in E-Learning Recommender Systems," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.443-450, 2019.

Data-centers play an important role in today`s time for storing and maintaining huge amounts of data being generated daily and operated in real-time, to keep this data secure becomes as much important as keeping it stored. To address the current flaws in security policy of data-centers and to eradicate those flaws this paper suggest a new security framework for data-centers. This would provide data-centers with all kind of precautions and instructions to handle most of situations that may arise during management and other data related tasks and even after breaches by defining all data storage rules by classifying data, and defining data access rights with security policies for authentication and data verification. These all things make sure that data is secured and more reliable at both aspect storage as well as data authenticity in this framework for Data-Centers.

Data-center, Encryption, Data security, Immutable data, Policy, Real-time backup

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[8] “A Matrix of Security Risks and Solutions"Oracle9i Security Overview, Release 2 (9.2)Oracle Docs.",2002.
[9] The University of Kansas Policy Library."Data Classification and Handling Policy", 2014.
[10] "Data Center Access Policy" Information Technology Services, West Virginia University, 2018.
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Narender Kumar, Avinash Karhana, "Data Security Framework for Data-Centers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.451-456, 2019.

With the rising computational capacity requirements and ubiquitous computing, newer and powerful hardware and software platforms are being created. These newer powerful architectures and platforms are significantly different from their conventional predecessor architectures. These new platforms need to be evaluated, tested and validated for performance measurement before the product release. In the area of mobile ad hoc networks the use of multiple communication channels has emerged as a powerful new hardware platform which can increase the capacity manifold. But one major problem faced by researchers is that the traditional simulation tools don’t support these improved hardware platforms. So to carry out performance analysis of these platforms new test-beds and simulators are required to be developed. In the proposed work a software simulation test-bed for multi-channel Medium Access Control framework for mobile ad hoc Networks (MANET) has been developed and is named MCMSim.

Mobile ad hoc networks, test-beds, NS-3, Multi-channels, Network, Simulation Tools

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[14] Kumar, Rohit, Kavita Taneja, and Harmunish Taneja. "Performance Evaluation of MANET Using Multi-Channel MAC Framework." Procedia computer science 133 (2018): 755-762.
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[22] V. Kumar and R. Kumar, “An Optimized Multichannel MAC Scheme with Dynamic Control Channel Interval in Dense VANET” International Journal of Information Technology, 2019.
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Rohit Kumar, Kavita Taneja, Harmunish Taneja, "MCMSim Test-Bed for Multi-Channel MAC Framework For MANET," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.457-462, 2019.

Cloud computing, with an ever-growing interest, with the promise of revolving computing as a utility after water, electricity, gas and telephony is currently at a stage, where many enterprises are considering adapting to this technology. Resource provisioning policies allow efficient sharing of resources available in a data center and these policies help to evaluate and enhance the cloud performance. Resource provisioning that maintains quality of service with optimum resource utilization is a challenge. It is a multidimensional problem that can have issue based solution in the form of a set of services that help allocation and negotiation of service level agreements. A cloud simulator environment is used and experiments are performed by varying different parameters of Virtual Machines (VM) and the tasks running on VM, to get optimal values for designing templates. The proposed template based resource provisioning (TBRP) method overcomes under-provisioning and over-provisioning of resources for agreed parameters specified by SLA.

Service Level Agreement, Quality of Service, Virtual Machines, Resource Provisioning

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[16] Gillam, L., Li, B., &O’Loughlin, J. (2014). Benchmarking cloud performance for service level agreement parameters. International Journal of Cloud Computing 2, 3(1), 3-23
[17] Emeakaroha, V. C., Brandic, I., Maurer, M., &Dustdar, S. (2010, June). Low level metrics to high level SLAs-LoM2HiS framework: Bridging the gap between monitored metrics and SLA parameters in cloud environments. In High Performance Computing and Simulation (HPCS), 2010 International Conference on (pp. 48-54). IEEE.
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Seema Chowhan, Ajay Kumar, Shailiaja Shirwaikar, "Template-Based Efficient Resource Provisioning and Utilization in Cloud Data-Center," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.463-477, 2019.

Automatic image tagging is an active topic of research in computer vision and pattern recognition. There is a huge urge in the Computer Vision community today to find ways to automatically annotate images. Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) have been used extensively due to their generalization properties.

Component, Formatting, Style, Styling, Insert (key words)

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[8] V. Lavrenko, R. Manmatha, and J. Jeon. “A model for learning the semantics of pictures”. In in NIPS. MIT Press, 2003.
[9] Ying Liua,, Dengsheng Zhanga, Guojun Lua, Wei-Ying Ma, “A survey of content-based image retrieval with high-level semantics”.
[10] Tanveer J. Siddiqui , “Bridging the Semantic Gap”.
[11] Aanchan K Mohan and Marwan A.Torki, “Automatic Image Annotation using Neural Networks”.
[12]Alpesh Dabhi, Bhavesh Prajapati , “A Neural Network Model for Automatic Image Annotation and Annotation Refinement”: A survey 2014 IJEDR | Volume 2, Issue 1 43
[13]Suman Tatiraju, Avi Mehta , “Image Segmentation using k-means clustering, EM and Normalized Cuts”
[14]Sarthak panda, ”Color Image Segmentation Using K-means Clustering and Thresholding Technique” (march 2015)
[15] Dhatri Pandya1, Prof. Bhumika Shah, “Comparative Study on Automatic Image Annotation” (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 3, March 2014)
[16] P. Duygulu, K. Barnard, N. de Freitas, D. Forsyth,2002. "Object recognition as machine translation: learning a lexicon for a fixed image vocabulary", In Seventh European Conference on Computer Vision (ECCV), Vol. 4, pp. 97-112.
[17] Reena Pagare and Anita Shinde , “A study on Image Annotation Techniques”, International Journal of Computer Applications Volume 37-No6. January 2012.
[18] Lei Wu Member,IEEE Rong Jin, Anil K. Jain, Fellow, IEEE , “Tag Completion for Image Retrieval”.
[19] Dongping Tian , “Support vector machine for Automatic Image Annotation” International Journal of Hybrid Information Technology Vol.8 No.11(2015).
[20]Dataset : http://www.ci.gxnu.edu.cn/cbir/Dataset.aspx
[21]Attributes: http://sci2s.ugr.es/keel/dataset/data/multilabel/corel5k-names.txt

S.Khoria, "Meta Analysis and Verification on Automated Image Tagging Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.478-488, 2019.

The growing global demand for power supply is likely to exhaust available resources soon. It is advisable to avoid wastage of electricity as it may overburden consumer adversely. In the present study, we propose an IoT based solution to reduce electric power wastage in organizations. As the organizations are generally divided into sub sections or departments, a frame work can be proposed which allows the managers and supervisors to keep an online track of the ON/OFF status of appliances in their respective departments/sectors. The access to appliances can be provided with a Secure Shell connection through a dedicated server which keeps monitoring all the appliances in the whole organization continuously. Each manager and in-charge along with other officials can be provided with a user ID and password to login with. Each of them is likely to entertain with different level of rights to control various gadgets of the department. This frame work can prove itself to be useful in reducing the problem of various appliances ON in an organization. The frame work has provision for further improvements such that with slight modification it can be implemented controlling and monitoring a weather station situated in the remote forest.

RPi (Raspberry Pi), Arduino UNO, SSH, GSM

[1] Faisal Baig, Saira Baig, and Fahad Khan, Muhammad. Controlling home appliances remotely through voice command. International Journal of Computer Applications, 48(17):0975-888, June 2012.
[2] Shivanka, Ashu Grover, and Nikhil Arora. Controlling electrical appliances through pc and gsm technology. International Journal of Computer Applications, 76(2):09758887, August 2013.

Swaleha Zubair, Uzair Aalam, "An Advanced IoT Based Frame Work to Save Electrical power in an Organization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.489-492, 2019.

In this research work, a soft computing or machine learning approach is used to design an algorithm which is a basic hybridized framework of the feature reduced adaptive resonance theory (ART) and support vector regression (SVR) to effectively predict stock market price as well as behaviour from the historical dataset.Ten different technical indicators are extracted and reduced using particle swarm optimization (PSO). Simulation results on different well-known stock market price like Adani Powers, BHEL, Reliance Industries, SBI and Infosys, stock exchange price is finally presented to test the performance of the established model. With the proposed model, it can achieve a better prediction capability to stocks. The proposed algorithm is compared with ART algorithm and analyzed that proposed model predicts better stock position behavior.

Machine learning, ART, SVR, PSO, Stock Market Indices, Technical indicators, Stock Prediction

[1] Xi, L., Muzhou, H., Lee, M. H., Li, J., Wei, D., Hai, H., & Wu, Y., “A new constructive neural network method for noise processing and its application on stock market prediction”, Applied Soft Computing, vol. 15, pp. 57–66, 2014.
[2] Yu, H., Chen, R., & Zhang, G., “A svm stock selection model within pca”, Procedia computer science, vol. 31, pp. 406–412, 2014.
[3] Chen MY. “A high-order fuzzy time series forecasting model for internet stock trading”, Future GenerComput System, vol. 37, pp. 461-467, 2014.
[4] Cervell´o-Royo, R., Guijarro, F., Michniuk, K., “Stock market trading rule based on pattern recognition and technical analysis: Forecasting the djia index with intraday data”, Expert systems with Applications, vol. 042, pp. 5963–5975, 2015.
[5] Hu, Y., Feng, B., Zhang, X., Ngai, E., & Liu, M., “Stock trading rule discovery with an evolutionary trend following model”, Expert Systems with Applications, vol. 42, pp. 212–222, 2015.
[6] Rahman, H. F., Sarker, R., &Essam, D., “A genetic algorithm for permutation flow shop scheduling under make to stock production system”, Computers & Industrial Engineering, vol. 90, pp. 12–24, 2015.
[7] Nayak, R. K., Mishra, D., &Rath, A. K., “A na¨ıvesvm-knn based stock market trend reversal analysis for indian benchmark price”, Applied Soft Computing, vol 35, pp. 670–680, 2015.
[8] Chiang, W.-C., Enke, D., Wu, T., & Wang, R., “An adaptive stock index trading decision support system”, Expert Systems with Applications, vol 59, pp. 195–207, 2016.
[9] Kim, Y., &Enke, D., “Developing a rule change trading system for the futures market using rough set analysis”, Expert Systems with Applications, vol 59, pp. 165–173, 2016.
[10] Podsiadlo, M., &Rybinski, H, “Financial time series forecasting using rough sets with time-weighted rule voting”, Expert Systems with Applications, vol 66, pp. 219–233, 2016.
[11] Zhong, X., &Enke, D., “Forecasting daily stock market return using dimensionality reduction”, Expert Systems with Applications, vol 67, pp. 126–139, 2017.
[12] Pankaj K. Bharne, Sameer S. Prabhune, “Survey on combined swarm intelligence and ANN for optimized daily stock market price”, International Conference on Soft Computing and its Engineering Applications,IEEE, 2017.
[13] ZhixiLi, Vincent Tam, “A comparative study of a recurrent neural network and support vector machine for predicting price movements of stocks of different volatilites”, IEEE Symposium Series on Computational Intelligence, 2017.
[14] Hasan S.S., Rahman R., Mannan N., Khan H., Moni J.N., Rahman R.M, “Improved Stock Price Prediction by Integrating Data Mining Algorithms and Technical Indicators: A Case Study on Dhaka Stock Exchange”, International Conference on Computational Collective Intelligence, pp 288-297, 2017.
[15] Lei Lei, “Wavelet Neural Network Prediction Method of Stock Price Trend Based on Rough Set Attribute Reduction”, Applied Soft Computing, Vol. 62, pp. 923-932, 2018.
[16] Manas Ranjan Senapati, Sumanjit Das, Sarojananda Mishra, “A Novel Model for Stock Price Prediction Using Hybrid Neural Network”, Journal of The Institution of Engineers, Vol. 99, Issue 6, pp 555–563, 2018.
[17] SotiriosP.ChatzisaVassilisSiakoulis, “Forecasting stock market crisis events using deep and statistical machine learning techniques”, Expert Systems with Applications, Volume 112, pp. 353-371, 2018.
[18] Tae Kyun, Leeab Joon, Hyung ChobDeuk, Sin Kwonb, “Global stock market investment strategies based on financial network indicators using machine learning techniques”, Expert Systems with Applications, vol. 117, pp. 228-242, 2018.
[19] Bruno Mirand, HenriqueVinicius, Amorim Sobreiro, Herbert Kimur, “Stock price prediction using support vector regression on daily and up to the minute prices”, The Journal of Finance and Data Science, Volume 4, Issue 3, pp. 183-201, 2018.
[20] Feng Zhou, Hao-min Zhou, Zhihua Yang, Lihua Yang, “EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction”, Expert Systems with Applications, Vol. 115, pp. 136-151,2018.
[21] M.Janik, P.Bossew, O.Kurihara, “Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data”, Science of The Total Environment Vol. 630, pp. 1155-1167, 2018.
[22] Salim Lahmiri, “Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression”, Applied Mathematics and Computation, Vol. 320, pp. 444-451, 2018.
[23] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, "A Review: Design and Development of Novel Techniques for Clustering and Classification of Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.19-22, 2018.
[24] A. JenitaJebamalar, "Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.6, pp.14-18, 2018.

Manoj Lipton, Sarvottam Dixit, Asif Ullah Khan, "Stock Market Analysis using ART-SVR based on Technical Parameters," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.493-504, 2019.

Moisture meters are used to measure the percentage of water in a given substance. This information can be used to determine if the material is ready for use, unexpectedly wet or dry, or in need of further inspection. Food plays a very vital role in our life so it is very important to check the food quality using moisture meter .Moisture plays a very vital role in the daily operations of Food Corporation of India (FCI). With a change in moisture value, computation of storage loss/ gain in food grains gets affected. Hence capturing the moisture value is very essential. Since many food products are bought and sold by weight, moisture content is a key component in ensuring accuracy for purchasing raw materials. Water in food products also affects various characteristics of the product that are important to its taste, texture, color, density, particle size, etc. Generally, moisture is calculated and reading are noted manually and then it is fed in the system then deploy it online. So, the proposed system gives a solution where the readings of the moisture meter are captured and directly fed into Depot Online System (DOS) using wireless transmission. It will reduce the human labor as well reading will be precise as compared traditional system.

IOT, Moisture Meter, FCI, DOS

[1] W.C.Wang, L.Wang, “Design of Moisture Content Detection System”, In the Proceedings of 2012 International Conference on Medical Physics and Biomedical Engineering, China, pp.1408 – 1411, 2012.
[2] M. Zhiwei, L. Changyou , W. Biying, “Design and Test of Grain Moisture Online Measuring System Based on Floating Ground Capacitance”, In the Proceedings of 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016), China, pp.821-830, 2016.
[3] M. A. Lewis, S. Trabelsi , “Integrating an embedded System In a microwave moisture Meter”, In the Proceedings of 2012 American Society of Agricultural and Biological Engineers, USA, Vol. 28(6): pp.923-931, 2012.
[4] S. Trabelsi, S. O. Nelson,” A Low-Cost Microwave Sensor for Simultaneous and Independent determination of Bulk Density and Moisture Content in Grain and Seed”, 2007 ASABE Annual International Meeting, USA, Paper Number: 076240, 2007.
[5] Z. W. Mai, Chang Y. Li, Y. Zhang, F. Ying Xu, J. M. Li, “Application of Wireless Data Transmission Technique in Drying Equipment”, Advanced Materials Research, USA, Vol. 422, pp.262-267, 2012.
[6] Z. Liu, W. Zidan, Z. Zhang, W. Wung, L. Hexin, “Research on Online Moisture Detector in Grain Drying Process Based on V/F Conversion”, Hindawi Publishing Corporation Mathematical Problems in Engineering,
Volume 2015, Article ID 565764, pp.1-10.
[7] Y. Yueqian, W. Jianping, W. Chengzhi. “Study on line measurement of grain moisture content by neutron gauge”, Transactions of the CSAE, China, pp.99-110, 2000.
[8] L. Changyou, “Design and experiment of online moisture metering device for paddy drying process”, Transactions of the Chinese Society for Agricultural Machinery, China, pp.56-59, 2008.

Nafisa Mapari, Salman Siddiqui, Rumi Shaikh, Rishabh Srivastav, "Upgradation of Food Moisture Analyzer using IOT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.505-508, 2019.

Genetic Algorithm (GA) is a well-known heuristic algorithm inspired by theory of adaptation. GA is applicable to solve many problems of science and engineering. GA performs its operations such as selection, reproduction and mutation to solve a problem. The genetic parameters such as population size, cross over rate and mutation rate control the performance and effectiveness of GA to solve a problem. In this paper, GA is applied to solve Traveling Salesman Problem (TSP). TSP problem is an optimization problem and it is a member of the set NP-Hard problems. In this paper the performance of GA on its parameters is analyzed to solve TSP problem.

NP-Complete, Genetic Algorithm, Genetic Parameters

[1] S. Sharma and K. Gupta, "Solving the traveling salesmen problem through genetic algorithm with new variation order crossover," 2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), Udaipur, 2011, pp. 274-276.
[2] B. H. Hasan and M. S. Mustafa, "Comparative Study of Mutation Operators on the Behavior of Genetic Algorithms Applied to Non-deterministic Polynomial (NP) Problems," 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, Kuala Lumpur, 2011, pp. 7-12.
[3] M. A. H. Akhand, S. Akter, S. Sazzadur Rahman and M. M. Hafizur Rahman, "Particle Swarm Optimization with partial search to solve Traveling Salesman Problem," 2012 International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, 2012, pp. 118-121.
[4] H. ElAarag and S. Romano, "Animation of the Traveling Salesman Problem," 2013 Proceedings of IEEE Southeastcon, Jacksonville, FL, 2013, pp. 1-6.
[5] S. Cui and S. Han, "Ant Colony Algorithm and Its Application in Solving the Traveling Salesman Problem," 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control, Shenyang, 2013, pp. 1200-1203.
[6] Q. Bai, G. Li and Q. Sun, "An improved hybrid algorithm for traveling salesman problem," 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI), Shenyang, 2015, pp. 806-809.
[7] M. Munlin and M. Anantathanavit, "Hybrid K-means and Particle Swarm Optimization for symmetric Traveling Salesman Problem," 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), Auckland, 2015, pp. 671-676.
[8] Muhao Chen, Chen Gong, Xiaolong Li and Zongxin Yu, "Research on solving Traveling Salesman Problem based on virtual instrument technology and genetic-annealing algorithms," 2015 Chinese Automation Congress (CAC), Wuhan, 2015, pp. 1825-1827.
[9] J. Stastný, V. Skorpil and L. Cizek, "Traveling Salesman Problem optimization by means of graph-based algorithm," 2016 39th International Conference on Telecommunications and Signal Processing (TSP), Vienna, 2016, pp. 207-210.
[10] M. Khalil, J. Li, Y. Wang and A. Khan, "Algorithm to solve travel salesman problem efficently," 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, 2016, pp. 123-126.
[11] Q. Hao, L. Fang and S. Tao, "A Discrete Fruit Fly Optimization Algorithm for Traveling Salesman Problem," 2017 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), Wuhan, 2017, pp. 254-257.
[12] I. B. K. Widiartha, S. E. Anjarwani and F. Bimantoro, "Traveling salesman problem using multi-element genetic algorithm," 2017 11th International Conference on Telecommunication Systems Services and Applications (TSSA), Lombok, 2017, pp. 1-4.
[13] A. H. M. Alaidi and A. Mahmood, "Distributed hybrid method to solve multiple traveling salesman problems," 2018 International Conference on Advance of Sustainable Engineering and its Application (ICASEA), Wasit, 2018, pp. 74-78.
[14] Z. Pan, Y. Chen, W. Cheng and D. Guo, "Improved fruit fly optimization algorithm for traveling salesman problem," 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), Nanjing, 2018, pp. 466-470.
[15] M. Bandyopadhyay, S. Chattopadhyay , A. Das, “Emphasis on Genetic Algorithm (GA) Over Different PID Tuning Methods of Controlling Servo System Using MATLAB”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.8-13, 2013.
[16] Rajeev Ranjan , P.J. Pawar, “Assembly Line Balancing Using Real Coded Genetic Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol.2, Issue.4, pp.1-5, 2014.

R.K. Singh, V.K. Panchal, B.K. Singh, "Analysis of GA Performance on Its Various Parameters for Solving Travelling Salesman NP-Hard Problem," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.509-512, 2019.