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Virtualization and automation is used to provide the user with self-service computing experience available at infinite scale at very low cost, with the tremendous growth of communications, connectivity and Internet technology cloud computing have become a principal reference of computing for IT industry. Major challenge for data centers is to streamline the requests, as cloud data centers and the users of the cloud-computing are globally situated, Present research provides environment that equally distributes workload across all the nodes. It also provides a way of achieving the proper utilization of resources and better user satisfaction. Current research is focused on the load-balancing algorithm, which distributes the incoming jobs among virtual machine optimally in cloud data centers. The proposed algorithm in this paper has been executed using Cloud Analyst Simulator and the performance of the suggested algorithm is compared with the three algorithms which are pre-exists based on response time. The experiment result shows that the recommended algorithm performs better than the existing algorithms.

Virtual Machine, Load Balancing, Cloud Analyst, Cloud Data Center, Virtualization, User Base

[1] W. Bhathiya, “CloudAnalyst a CloudSim-based tool for modelling and analysis of large scale cloud computing environments”, MEDC Project, Cloud Computing and Distributed Systems Laboratory, University of Melbourne, Australia, pp. 1-44, June 2009.
[2] Domanal, S. G., & Reddy, G. R. M., “Load Balancing in Cloud Computing using Modified Throttled Algorithm” IEEE international conference on Cloud Computing in Emerging Market, pp. 1-5, October 2013.
[3] Prachi Verma et.al, “Enhancing Load Balancing in Cloud Computing by Ant Colony Optimization Method” International Journal of Computer Engineering In Research Trends, 4(6):pp:277-284 ,June-2017.
[4] Sundaram, C. R. M., & Narahari, Y., “Analysis Of Dynamic Load Balancing Strategies Using A Combination Of Stochastic Petri Nets And Queueing Networks” Springer Conference, Berlin Heidelberg, pp. 397-414, 1993.
[5] Mondal, B., Dasgupta, K., & Dutta, P., “Load Balancing In Cloud Computing Using Stochastic Hill Climbing-A Soft Computing Approach” Elsevier, Procedia
Technology, pp.783-789, 2012.
[6] M. Katyal et.al, “A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment”, International Journal of Distributed and Cloud Computing Volume 1 Issue 2 December 2013
[7] Bhadani, A., & Chaudhary, S., “Performance Evaluation of Web Servers Using Central Load Balancing Policy Over Virtual Machines on Cloud”, ACM, Bangalore Conference, January 2010.
[8] W. Itani, A. Kayssi, and A. Chehab, “Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures,” 2009 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2009, pp. 711-716.
[9] B. Grobauer, T. Walloschek, and E. Stöcker, “Understanding Cloud Computing Vulnerabilities,” 2011 IEEE Security and Privacy, pp. 50-57, DOI= March/April 2011.
[10] W. A. Jansen, “Cloud Hooks: Security and Privacy Issues in Cloud Computing,” Proceedings of the 44th Hawaii International Conference on System Sciences, 2011.
[11] A. Sebastian, S. Sivagurunathan "A Survey on Load
Balancing Schemes in RPL based Internet of Things"
IJSRNSC Volume-6, Issue-3, June 2018.
[12] Vikas Mangotra, Richa Dogra "Cloud reliability
enhancement mechanisms: A Survey", International
Journal of Scientific Research in Computer Science
and Engineering Vol.6, Issue.3, pp.31-34 , June (2018)

Sanjay Khajure, Shubham Yelne, "Encumbrance Collating in Cloud Data Center Using Modified Active Monitoring Load Balancer," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.72-76, 2018.

In This study, AGV System (Automated Guided Vehicle) is considered as the most flexible equipment of MHS (Material Handling System) and one old. In the Indian context, few applications of AGV System have been started in automobile Industry. In this paper, studied the different parts of the AGV, different motions of the AGV driving wheels for carried out various motions, applied reverse engineering technique for developing the 3D CAD model of the existing AGV system, implemented necessary changes in the 3D CAD model for making the AGV suitable for carrying a payload of 50 Kg, analyze the developed model for stress and deflection under the payload, studied the power drive and control system of the existing AGV using Reverse Engineering technique, carried out testing of the different control features of the AGV motions including the sensors, tested the AGV for its basic motions by running test programs in personal computer etc.

AGVS, Automated Guided Vehicle, FMS, MHS, Reverse Engineering.

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[4] M. Sharma, “Control Classification of Automated Guided Vehicle Systems”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-1, October 2012.
[5] G. Klancar, A. Zdesar, S. Blazic and L. Skrjanc, “Wheeled Mobile Robotics”, Butterworth-Heinemann, UK, pp 387-418, 2017, ISBN No 9780128042045.
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[9] K. H. Kim, S. M. Jeon and K. R. Ryu, “Deadlock prevention for automated guided vehicles in automated container terminals”, Container Terminals and Cargo System, Springer, Berlin, pp. 243-263, 2007.
[10] C. Wuwei, K. Mills and S. Wenwu, “A new navigation method for an automatic guided vehicle”, Journal of Robotic System, volume- 21, Issue-3, pp.129-139, 21(3):129–139. doi:10.1002/rob.20004, 2004.
[11] Alves and Junior, “Mobile ultra sonic sensing in mobile robot” In: IECON 02 28th Annual Conference of Industrial Electronics Society, Spain, 2002.
[12] P. S. N. Priyanka, J.S.S. Rositha, K. SriNavya and N. Priyanka, “DTMF based home Automation without using Microcontroller”, International Journal of Scientific Research in Computer Sciences and Engineering, Volume- 6, Issue-2, pp. 1-4, April 2018.

S. Hossain, J. Saha, "A Case Study on The Automated Guided Vehicle System Through Reverse Engineering," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.77-87, 2018.

Image processing is a classical domain of research and development. A number of different applications in medical sciences, education and engineering the techniques of image processing are frequently used. The image processing techniques are used for discovering the objects, contents or images and lightening effects in digital images and video frames. In this presented work an application of image processing technique for recognizing the image objects is proposed for design and implement. More specifically it is tried to track and identify the moving objects. Therefore the video based object recognition technique is proposed to work. In order to design such technique video processing techniques and morphological image processing technique is used. The morphological image processing is a technique of image processing on which the pixel level structures are analysed and tracked. In addition of that for identifying the objects a threshold based technique is implemented. That technique computes the area of covered object and based on the count of pixel coverage and threshold the moving object in camera frame is detected and identified. The implementation of the proposed object detection technique is performed on MATLAB technology. Additionally the experiments on this technique are performed for finding efficiency and accuracy of the proposed system. The experiments based results demonstrate the proposed technique efficient and accurate for object tracking.

Vehicle Detection, Image Processing, Digital Image, Video Frame, Morphological

[1] Tang, Yong, Congzhe Zhang, Renshu Gu, Peng Li, and Bin Yang, "Vehicle detection and recognition for intelligent traffic surveillance system", Multimedia tools and applications 76, no. 4 (2017): 5817-5832.
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[7] A. K. Jain “Fundamentals of digital image processing”. Prentice-Hall, 1989
[8] Shailendra Kumar Dewangan, “Importance & Applications of Digital Image Processing”, International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 7 No. 07 Jul 2016
[9] “Image Processing and Related Fields”, available online at: http://fourier.eng.hmc.edu/e161/lectures/e161ch1.pdf
[10] Alper Yilmaz, Omar Javed, and Mubarak Shah. Object tracking: A survey. Acm Computing Surveys (CSUR), 38(4):13, 2006
[11] Alan J Lipton, Hironobu Fujiyoshi, and Raju S Patil. Moving target classification and tracking from real-time video, In Applications of Computer Vision, 1998, WACV’98, Proceedings, Fourth IEEE Workshop on, pages 8–14. IEEE, 1998
[12] Cucchiara, Rita, Massimo Piccardi, and Paola Mello, "Image analysis and rule-based reasoning for a traffic monitoring system", IEEE Transactions on Intelligent Transportation Systems 1, no. 2 (2000): 119-130
[13] Newlin Rajkumar , Ranjani , and Venkatesa Kumar ,” Toll Gate Vehicle Monitoring System” International Journal of Computer Sciences and Engineering, Vol. 5, Issue 2, feb 2017
[14] Jitendra Oza , Zunnun Narmawala , Sudeep Tanwar, Pradeep Kr Singh ”Public Transport Tracking and its Issues”, International Journal of Computer Sciences and Engineering, Vol. 5, Issue 11, nov 2017

Anshul Vishwakarma, Amit Khare, "Moving Object Tracking System Using Morphological Image Processing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.88-93, 2018.

In the wireless communication especially in cellular network, energy efficiency is a major concern among other issues, specifically in high-speed data network i.e. 3G/4G. A Smartphone is a widely used handheld device to surf data in 3G/4G. Unfortunately, one of the constraints of Smartphone is having limited battery backup, which always makes the user inconvenience. Increasing of battery backup is not merely a solution to increase the user experience, because the growth of mobile technology is 25% per year but at the same time battery capacity increases 10% per year; it’s not a balanced. So that the researchers essentially focus on energy efficient development to extend battery life. This paper analyzes various factors influences of power consumption and their characteristics (like RRC state transitions, inactivity timer setup and screen On/Off) and the paper also reviews the proposals of energy-aware developments through real-time measurements.

RRC State Transition, Inactivity Timers, Keep-Alive Messages, Energy Efficient Strategies, Energy aware 3G/4G

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[3] Li, D., Hao, S., Gui, J., & Halfond, W.G. “An Empirical Study of the Energy Consumption of Android Applications”. 2014 IEEE International Conference on Software Maintenance and Evolution, 121-130, 2014.
[4] Qian, F., Wang, Z., Gao, Y., Huang, J., Gerber, A., Mao, Z.M., Sen, S., & Spatscheck, O. “Periodic transfers in mobile applications: network-wide origin, impact, and optimization”. WWW, 2012.
[5] Kononen, V., & Paakkonen, P. Optimizing power consumption of always-on applications based on timer alignment. 2011 Third International Conference on Communication Systems and Networks (COMSNETS 2011), pp. 1-8, 2011.
[6] Haverinen, H., Siren, J., & Eronen, P. Energy Consumption of Always-On Applications in WCDMA Networks. 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring, pp. 964-968, 2007.
[7] GSMA, “Fast Dormancy Best Practices”, GSMA Official Document TS.18, 2011.
[8] Huang J, F. Qian, A. Gerber, Z. M. Mao, S. Sen, and O. Spatscheck. “A Close Examination of Performance and Power Characteristics of 4G LTE Networks”. In MobiSys’12. pp. 225-238, 2012.
[9] Qualcomm Engineering Services Group, “System Parameter Recommendations to Optimize PS Data User Experience and UE Battery Life”, Technical Document, 2007.
[10] Wang, Z, Qian, F., Gerber, A., Mao, Z.M., Sen, S., & Spatscheck, O. “TOP: Tail Optimization Protocol for Cellular Radio Resource Allocation”. The 18th IEEE International Conference on Network Protocols, pp. 285-294, 2010.
[11] Pandikumar, S, and Sumathi, M. “Analysis of Energy Profilers in Smartphone Environment”, International Journal of Advanced Research in Science and Engineering, Vol.06 Issue 02, pp. 20-29, 2017.
[12] Qian, F., Wang, Z., Gerber, A., Mao, Z.M., Sen, S., & Spatscheck, O. “Characterizing radio resource allocation for 3G networks”. Internet Measurement Conference. pp. 137-150, 2010.

S.Pandikumar, G.Sujatha, M.Sumathi, "Analysis of Radio Resource Energy Consumption Pattern in Cellular Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.94-102, 2018.

This paper describes a new machine vision system and Artificial Neural Networks based system for quality assessment of apple in real time, attending to external quality features of the fruits as size, color symmetry, weight and external defects. Based on external features, apple is correctly classified in this finding. The ANN model is developed using BP-ANN with a single hidden layer and sigmoid activation functions in MATLAB. The output variable is the quality of the apple. The modeling results showed that there was an excellent agreement between the experimental data and predicted values, with very good performance, fewer parameters and shorter calculation time. The model might be an alternative method for quality assessment of apple and provide consumers with a safer food supply.

Machine Vision; Real-Time Fruit Quality; Scaled Conjugate Gradient; Multilayer Perceptron; Back-Propagation Artificial Neural Network; Mean Square Error

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“Fruit classification using computer vision and feedforward neural network”, Journal of Food Engineering, pp 167–177, 2014.
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SOCIETY, Knowledge Culture and Communication, An analysis of the performance of Artificial Neural Network technique for apple classification, ISSN 0951-5666, AI & Soc DOI 10.1007/s00146-012-0425-z Volume 24, Number 1 August 2009.
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[15]A.K. Bhatt & D Pant, AI & SOCIETY, Knowledge Culture and Communication, Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation, ISSN 0951-5666, AI & Soc DOI 10.1007/s00146-012-0425-z Volume 30, Number 1 Feb, pp 45-56, 2015.
[16]Praveen Tripathi et. al., “ Efficiency Comparisons of Various Ann-Based and Svm-Based Techniques for Classification Problems: A Review”, International Journal of Scientific & Engineering Research Volume 8, Issue 10, October 2017, ISSN 2229-5518 pp 90-95, 2017.

Praveen Tripathi, R. Belwal, A.K.Bhatt, "Assessment of Apple Quality based on Scaled Conjugate Gradient Technique, using Artificial Neural Network Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.103-108, 2018.

Software engineering has witnessing rapid changes from time to time. One such change of late is to have Service Oriented Architecture – based applications that can provide a comprehensive service to end users. Due to the emergence of web services technology with distributed, scalable, and interoperable computing capabilities, they are widely used to have service oriented applications. Such applications are known as Service Based Systems (SBSs). It is important to have automatic selection of composition of services at runtime to have dynamic integration of services. The problem with existing approaches is that they expect SOA techniques from software engineers and thus the quality of SBS depends on the expertise of the engineers. To overcome this problem, in this paper, introduced and implemented a framework that guides users to have certain keywords to search and build new SBS that improves Quality of Service (QoS) of generated SBSs. The service discovery and service composition are evaluated with a prototype application built to demonstrate proof of the concept. The experimental results revealed the usefulness of the proposed architecture which supports relevant keyword search for building SBS.

Keyword Search, Service Based System, Web Service, Service Discovery, Service Composition

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[4] A. Brogi, S. Corfini, and R. Popescu, "Semantics-Based Composition-Oriented Discovery of Web Services," ACM Transactions on Internet Technology, vol. 8, no. 4, pp. 19:1-19:39, 2008.
[5] V. Cardellini, E. Casalicchio, V. Grassi, S. Lannucci, F. Lo Presti, and R. Mirandola, "MOSES: A Framework for QoS Driven Runtime Adaptation of Service-Oriented Systems," IEEE Transactions on Software Engineering, vol. 38, no. 5, pp. 1138-1159, 2012.
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[8] A. Klein, F. Ishikawa, and S. Honiden, "Towards Network-Aware Service Composition in the Cloud," Proc of 21st World Wide Web Conference (WWW 2012), Lyon, France, pp. 959-968, 2012.
[9] K. Golenberg, B. Kimelfeld, and Y. Sagiv, "Keyword Proximity Search in Complex Data Graphs," Proc of 28th ACM SIGMOD International Conference on Management of Data (SIGMOD 2008), pp. 927-940, 2008.
[10] Q. He, J. Han, Y. Yang, H. Jin, J.-G. Schneider, and S. Versteeg, "Formulating Cost-Effective Monitoring Strategies for Services-based Systems," IEEE Transactions on Software Engineering, vol. 40, no. 5, pp. 461-482, 2014.
[11] X. Liu, Y. Ma, G. Huang, J. Zhao, H. Mei, and Y. Liu, "Data-Driven Composition for Service-Oriented Situational Web Applications," IEEE Transactions on Services Computing (TSC), vol. 8, no. 1, pp. 2-16, 2015
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[13] V. Hristidis and Y. Papakonstantinou, "DISCOVERY: Keyword Search in Relational Databases," Proc of 28th International Conference on Very Large Data Bases (VLDB 2002), Hong Kong, China, pp. 670-681, 2002.
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G.B. Sai Preethi, P. Radhika Raju, A. Ananda Rao, "Relevant Keyword Search for Building Service-Based System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.109-114, 2018.

In this paper security based and energy efficient system is developed for the intelligent industrial automation. The system works on a solar energy. Raspberry Pi 3 used in this system. Gas leakage and temperature information are sensed by sensors. And this information is sent to the android phone and a laptop via WiFi. The gas leakage and temperature in the industries automatically detect which controls solenoid valve and fan, which is connected to the Raspberry Pi 3. The solenoid valve is use of this system for the purpose of gas leakage valve close and exhaust fan use for cooling purpose. Automatically alert about critical situation in the industry via an alarm. The main two parts of this system; detection of gas and temperature, live video streaming and whole this information is sent to the laptop and mobile. Camera used in this system sends live video to the laptop. The aim of the proposed system is managing the power utilizes and control the critical situation in the industries.

Gas Sensor, Temperature Sensor, Raspberry Pi 3, Solar Panel, Camera

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[11]. V.S. Varnika “Cloud Computing Advantages and Challenges for Developing Nations” International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.3, pp.51-55 , June (2018).

P.A.Wange, S. M. Rajbhoj, "Energy Efficient Industrial Application Using IOT," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.115-118, 2018.

Image based surveillance is increasingly gaining importance both in martial and private applications. Development of advanced image compression algorithms which achieve higher CR than what is available now will greatly help in transmission of video or set of images with less delay in the time required for transmission in sensitive applications. Thus it is proposed to study image compression algorithms with a view to applying them in various applications so that a set or large number of images can be transmitted at the same time consuming lesser file size or storage space required. Hence there is a need to design and develop an efficient algorithm. To transmit the images or videos in large numbers, it takes more time for transmission due to the size of the files, also higher the size, higher the storage space required. Hence there is a need to design and develop an efficient algorithm which can reduce the size of the images to compress set of images for compression ratios higher than the present technologies 3-D, considering 64 frames at a time.

Image coding, Transform coding, data compression, JPEG compression, 3D-Discrete Cosine Transform, Discrete Wavelet Transform, Set Partitioning in Hierarchical Trees

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G. Pandyan, Arthi. H, "Design and Development of A Novel Algorithm For Quality of Jpeg Compressed Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.119-125, 2018.

Thyroid disorders occur due to dysfunction of the thyroid gland or pituitary gland, iodine deficiency, cancer in some parts of the body, or due to side-effects from other medications. Hyperthyroidism, Hypothyroidism, Goitre, and Thyroid cancer are some of the ailments that result due to thyroid disorders. Some other reasons like pregnancy, or medications for other illnesses may also show abnormal levels of thyroid hormones This research study aims to identify conditions based on which we could predict the type of thyroid disorder in patients. This could help in further diagnosis and treatment. We study various attributes commonly found in patients with thyroid disorders to identify those attributes that may specifically describe the type of thyroid disorder in a person. Moreover we analyze six different classes of thyroid disorders, their symptoms and try to classify what kind of disorder a person has based on the symptoms. Totally 1535 records with 29 attributes are taken for the study. Statistical techniques are used to analyze the frequency of occurrence of various factors towards each type of the disease and test for significance of factors is also done. The results are used to build a data model that helps to predict the occurrence of specific type of thyroid disorder in a patient based on significant symptoms. The results emphasize that age, sex, values of hormones like TSH, T3, T4 and FTI of a patient play a predominant role in classifying and determining the type of thyroid disorder in the person. We also classify the given dataset using various decision tree techniques in different ways and compare the results.

Data mining, Thyroid disorder, Classification, Prediction

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[2]. Irina Ionitak Liviu Ionita, “Prediction of Thyroid Disease Using Data Mining Techniques”, BRAIN. Broad Research in Artificial Intelligence and Neuroscience. Vol.7. pp.115-124. Nov 18, 2017
[3]. Prerana, Parveen Sehgal, Khushboo Taneha, “Predictive Data Mining for Diagnosis of Thyroid Disease using Neural Network”, International Journal of Research in Management, Science & Technology, e-ISSN 2321-3264, Vol 3(2), April 2015
[4]. Ebru Turanoglu Bekar, Gozde Ulutagay, Suzan Kantarci Savas, “Classification of Thyroid Disease by Using Data Mining Models: A comparison of Decision Tree algorithms.” The Oxford Journal of Intelligent Decision and Data Science, Vol 2016(2), Pages 13-28, doi: 10.5899/2016/ojids-00002,
[5]. K.Rajam, R. Jemina Priyadarsini, “A Survey on Diagnosis of Thyroid Disease using Data Mining Techniques”, International Journal of Computer Science and Mobile Computing, ISSN 2320-088x , Vol 5(5), pg 354-358, May 2016
[6]. Dr.B.Srinivasan, K.Pavya, “Diagnosis of Thyroid Disease Using Data Mining Techniques: A study”, International Research Journal of Engineering and Technology, e-ISSN 2395-0056, Vol 3(11), Nov 2016.
[7]. Dr. G.Rasitha Banu, M.Baviya, Dr. Murtaza Ali, “A Study on Thyroid Disease using Data Mining Algorithm”, International Journal of Technical Research and Applications, e-ISSN 2320-8163, Vol 3(4), PP 376-379, Aug 2015
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[11]. Limin Wang, FangYuan Cao, ShuangCheng Wang, MingHui Sun, LiYan Dong, “Using k-dependence causal forest to mine the most significant dependency relationships among clinical variables for thyroid disease diagnosis”, PLOS One, Vol 12(8), e0182070, 2017, doi: 10.1371/journal.pone.0182070
[12]. Kevin M.Pantalone et al., “Measurement of Serum Free Thyroxine Index may provide additional case detection compared to free thyroxine in the diagnosis of central hypothyroidism”, Case Reports in Endocrinology, Vol 2015, Dec 8, 2015, doi: 10.1155/2015/965191

N. Vijayalakshmi, P. Nithya, "Information Retrieval From Thyroid Database Through Data Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.126-130, 2018.

This work deals with performance evaluation of integral square error (ISE) objective function in determining the optimal parameters of proportional-integral-derivative (PID) controller for control of ball hoop system using Grey Wolf Optimization (GWO) algorithm. The GWO is recently proposed bio inspired heuristic algorithm inspired by both the social hierarchy and hunting strategy of grey wolves. Comparison of proposed GWO/PID scheme with other existing techniques has also been shown in graphical and tabular forms. It has been observed that proposed GWO/PID approach with ISE as an objective function gives less settling time and overshoot when compared with existing approaches in the literature.

Ball Hoop System, PID Controller, Grey Wolf Optimization, Meta-Heuristic, Integral Square Error (ISE)

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Vijay Kumar, Girish Parmar, Rajesh Bhatt, "Application of GWO in Control of BH System with ISE Objective Function," International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.131-136, 2018.