Volume-7 , Issue-6 , Jun - 2019 Go Back

EEG (electroencephalography) is a famous modality to study the appearance of electrical activity over the scalp. This paper includes an experiment which gives 90% accuracy of recorded signals. In this experiment, classification is done in the open eye or closed eye. These signals are decomposed by using DWT into the sub-band frequencies. Then features are extracted from these frequencies. By these features, the classification will carry out by using the ANN classifier. Classification accuracy is a useful content that gives the reliability to perform the imagined movements.

EEG, DWT, NN

[1]. J.Sateesh and P Bhuvaneswari, “Analysis of EEG signals and its categorization” (ICMOC 2012)
[2]. S.A Taywade and Dr. R. D.Raut, “EEG signal analysis with different methodologies”, NCIPET 2014
[3]. Sachin Garg and Rakesh Narvey,” Denoising and feature extraction of EEG signal using wavelet transform. IJES
[4]. Mohd Syakir Fathillah, Rosmina Jaafar , Kalaivani Chellappan ,Rabani Remli, “A Study on EEG signals of eye open and eye closed using Discrete wavelet transform”, IECBES 2016.
[5]. En.wikipedia.org, ‘Daubechies wavelet’
[6]. Z H Khan, Nasir Hussain, Mohsin I. Tiwana, Classification of EEG signals for wrist and grip movements using ESN, BIOMEDICAL
[7]. RESEARCH 2017; 28(3): 1095- 1102, ISSN 0970-938X
[8]. 7)Fatemeh Shahlei, Sajad Banakar, Hadi Salempoor, Mahmood Aflaki,Seyed Mohammad Sadegh, Barghi Keyvan, “ Feature classification of EEG signal using signal energy in MRA and RBF for detecting seizure and epilepsy.2017.
[9]. Sayali M Wani, Sukant Sabut, S.L.Nalbalwar “Detection of epileptic seizure using wavelet transform and neural network classifier”, ICCASP 2018.
[10]. Soumya Sen Gupta and Sumeet Agarwal, “Classification and analysis of EEG signals for imagined motor movements”.

Mrinmai Bhalchandra Goregaonkar, "A Classification of EEG Signals Of Eye-Open and Eye-Closed Using Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.554-558, 2019.

In agriculture, despite large-scale funding and extension of irrigation services it is a serious concern that majority sectors are facing deficits in water management. Irrigation system is one of the major aspects to be enriched meeting the economic and sustainable challenges of the farmers. Recent trends in the area of Wireless Sensor Networks (WSN) have influenced a wide implementation of various applications in the area of precise agriculture. WSNs for environmental condition monitoring with defined knowledge are used for estimating crop growth and yield properties. The proposed system automates the irrigation and fertigation using WSN to make comparatively high yield than the traditional methods. Irrigation scheduling is estimated by use of WSNs real time monitoring of weather and soil properties. The exigent need for solving the constraints, Evapotranspiration (ET) system is integrated with irrigation module which uses Penman-Monteith FAO-56 model for calculating crop water need. The system overcomes limitations of traditional agricultural procedures by utilizing water resource efficiently and also reducing labour cost. As a result, the proposed system helps in water conservation to a great extent and also reduces soil erosion as only the required fertilizers are injected via the drip system. The paper also includes the implementation and results of surface drip irrigation and sub-surface drip irrigation are implemented in maize and sugarcane field respectively

Irrigation System, WSN, Crop Selection

[1] N Seenu Manju, Mohan Jeevanath, “Android Based Intelligent Irrigation System”, International Journal of Pure and Applied Mathematics Volume 119 Issue No. 67-71, 2018.
[2] Akshay Atole, Apurva Asmar, “Iot Based Smart Farming System” Journal of Emerging Technologies and Innovative Research (JETIR), Volume 4, Issue 04, April 2017.
[3] Nikesh Gondchawar1, Prof. Dr. R. S. Kawitkar “IoT based Smart Agriculture” International Journal of Advanced Research in Computer and Communication Engineering IJARCCE Vol. 5, Issue 6, June 2016.
[4] Drishti Kanjilal, Divyata Singh, “Smart Farm: Extending Automation to The Farm Level” International Journal Of Scientific & Technology Research Volume 3, Issue 7, July 2014.
[5] Dr.N.Suma, Sandra Rhea Samson, “IOT Based Smart Agriculture Monitoring System” International Journal on Recent and Innovation Trends in Computing and Communication IJRITCC Volume: 5 Issue: 2 177 – 181, February 2017.
[6] Vaishali S, Suraj S, “Mobile Integrated Smart Irrigation Management and Monitoring System Using IOT” International Conference on Communication and Signal Processing Volume 6- Issue 8, April, 2017.
[7] Prathibha S R, Anupama Hongal, Jyothi M P, “IOT BASED MONITORING SYSTEM IN SMART AGRICULTURE” 2017 International Conference on Recent Advances in Electronics and Communication Technology IEEE Volume 3, Issue 4 April 2017.
[8] Mrs.S.Devi Mahalakshmi, Rajalakshmi.P,“IOT Based Crop-Field Monitoring and Irrigation Automation”. Volume 5, Issue 4 April 2017.
[9] Jason Parmenter, Alex N. Jensen, and Steve Chiu “Smart Irrigation Controller” Volume 3, Issue 4 April 2014.
[10] Ramkumar.R, Kaliappan.S, Vignesh.L, “IoT Based Smart Irrigation System using Image Processing” SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – volume 4 Issue 3 – March 2017.

Vrushali Warkhedkar, M.M. Sardeshmukh, Sagar Shinde, "Smart Drip Irrigation and Fertigation using IOT & WSN," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.559-564, 2019.

Cloud computing has been the buzzword of the ICT industry lately and has been widely accepted because of its pay-as-you-go pricing model and ease of use. Hence the bigger scientific applications which are generally represented as workflow or DAG are also moving on the cloud. Scheduling workflows on the cloud was becoming a problem as the existing traditional workflow scheduling algorithms (for Grid) were not perfectly suitable for cloud environment because of its dynamic nature. This paper tries to explore the existing algorithms for scheduling multiple workflows in cloud computing environment and presents a comparative analysis in tabular form of some existing algorithms along with their parameters, methods and tools used.

Scheduling, Cloud Computing, Multiple Workflow

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[13] T. Thanavanich, A. Siri, K. Boonlom, and A. Chaikaew, “Energy-aware Scheduling of Multiple Workflows Application on Distributed Systems”, 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, 2016
[14] S. Liu, K.Ren, K. Deng, and J. Song, “Time dependence based scheduling strategy for multiple workflows on IaaS cloud platform”, International Symposium on Computer, Consumer and Control, IEEE Computer Society, 2016.
[15] R. Duan, R, Prodan, and X. Li, “Multi-Objective Game Theoretic Scheduling of Bag-of-Tasks Workflows on Hybrid Clouds”, IEEE Transactions on Cloud Computing, vol. 2, no. 1, 2014.
[16] M. A. Rodriguez, and R. Buyya, “Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds”, IEEE Transactions on Cloud Computing, 2014
[17] H. M. Fard, R. Prodan, and T.Fahringer, “A Truthful Dynamic Workflow Scheduling Mechanism for Commercial Multicloud Environments”, IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1203-1212, 2013.
[18] B. Lin, W. Guo, N. Xiong, G, Chen, A. V. Vasilakos, and H.Zhang, “A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multicloud Environments”, IEEE Transactions on Network and Service Management, vol. 13, no. 3, pp. 581-594, 2016.
[19] B.P. Rimal , and M. Maier, “Workflow Scheduling in Multi-Tenant Cloud Computing Environments”, IEEE Transactions on Parallel and Distributed Systems, 2015.
[20] X. Li, L. Quian, and R. Ruiz, “Cloud Workflow Scheduling with Deadlines and Time Slot Availability”, IEEE Transactions on Parallel and Distributed Systems, 2015.
[21] K. Deng, K. Ren, M. Zhu, and J. Song, “A Data and Task Co-scheduling Algorithm for Scientific Cloud Workflows”, IEEE transactions on Cloud Computing, Dec. 2015.
[22] Z. Li, J. Ge, H. Hu, W. Song, and B. Luo, “Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in clouds”, IEEE Transactions on Services Computing, 2015.
[23] M. A. Rodriguez, and R. Buyya, “Deadline Based Resource Provisioning and Scheduling Algorithms for Scientific Workflows on Clouds”,, IEEE transactions on Cloud Computing, Vol. 2. No. 2, April-June 2014.

A. Aggarwal, P. Dimri, A. Agarwal, "Survey on Scheduling Algorithms for Multiple Workflows in Cloud Computing Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.565-570, 2019.

The paper proposes a novel picture portrayal for surface characterization. The ongoing headways in the field of fix based highlights compressive detecting and highlight encoding are joined to plan a hearty picture descriptor. In our methodology, we initially propose the neighbourhood highlights, Dense Micro-square Difference (DMD), which catches the nearby structure from the picture patches at high scales. Rather than the pixel we process the little squares from pictures which catch the miniaturized scale structure from it. DMD can be figured productively utilizing vital pictures. The highlights are then encoded utilizing Fisher Vector strategy to get a picture descriptor which thinks about the higher request measurements. The proposed picture portrayal is joined with straight SVM classifier. The analysis is done on the standard surface datasets (KTH-TIPS-2a, Brodatz and Curet). On KTH-TIPS-2a dataset the proposed strategy beats the best revealed outcomes by 5.5% and has a practically identical exhibition to the best in class techniques on the different datasets.

Texture classification, descriptors, compressive sensing, SURF

[1] Irene Epifanio & Guillermo Ayala, “A Random Set View of Texture Classification,” IEEE Transactions on Image Processing, vol. 11, no. 8, August 2002.
[2] X. Liu & Deliang Wang, “Texture classification using spectral histograms, IEEE Transaction on Image Processing, vol. 12, no.6, June, 2003.
[3] M. Varma & A. Zisserman, “A Statistical Approach to Texture Classification from Single Image,” International Journal of Computer Vision, vol. 62, pp. 61-81, 2005/04/25/2005.
[4] M. Crosier & L. D. Gfiffin, “Texture Classification with a Dictionary of Basic Image Features in Computer Vision & Pattern Recognition, IEEE conference, pages 1-7, June 2008.
[5] T. Ahonen, J. Matas, C. He and M. Pietikainen, “Multiresolution gray scale and Rotation invariant texture classification with local binary patterns”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 61-70.
[6] Y. Xu, H. Ji and C. Fermuller, “Viewpoint invariant texture description using fractal analysis”, Int. J. Comput. Vis., vol. 83, no. 1, pp. 85-100, 2009
[7] L. Liu, P. Fieguth, G. Kuang and H. Zha, “Sorted random projections for robust texture classification”, in Proc. IEEE. Int. Conf. Comput. Vis (ICCV), Nov. 2011, pp. 391-398.
[8] Dang Huu Nghi, Luong Chi Mai “Training Data Selection for Support Vector Machines Model” in International Conference on Information and Electronics Engineering IPCSIT vol.6 (2011) IACSIT Press, Singapore.
[9] Anastasia Dubrovina, Pavel Kisilev, Daniel Freedman, Sagi Schein, Ruth Bergman “Efficient and robust image descriptor for GUI object classification” Copyright 2011 Hewlett-Packard Development Company, L.P.
[10] Li Liu & Pawl W. Fiegnth, “Texture Classification from Random Features,” IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. 3, March, 2012.
[11] Anna Wojnar and Antonio M. G. Pinheiro, “Annotation of Medical Images Using the SURF descriptor”, 2012 IEEEMs.
[12] M. Mushrif and Y. Dubey, “Texture Classification using Cosine- modultaed Wavelet”, International Journal of Computer and Electrical Engineering, vol. 4, no. 3, June 2012.
[13] J. Sanchez, F. Perronnin, T. Mensink, and J. Verbeek, “Image Classification with the Fisher vector: Theory and Practice,” Int. J. Comput. Vis., vol. 105, no.3, pp. 222-245, 2013.
[14] D. Sanghai and Prof. S. Maniar, ‘Performance evolution of texture classification’, International Journal of Emerging Research in Management & Technology, ISSN: 2278-9359, vol. 2, Issue. 10, Oct. 2013
[15] Dr. Y. Venkateswarlu, Dr. U. Babu and Ch Kumar, “An Efficient Texture Clasification Technique Based on Semi Uniform LBP”, IOSR Journal of Computer Engineering (IOSR- JCE), vol. 16, Issue 5, pp. 36-42, 2014.
[16] A. Vupputuri and S. Meher, “Facial Expression Recognition using Local Binary Patterns and Kulluback Leibler Divergence,” IEEE ICCSP conference, 2015.
[17] Parul Prashar and H. Kundra, “Hybrid Approach for Image Classification using SVM Classifier and SURF Descriptor”, International Journal of Computer Science and Information Technology, Vol. 6(1), 2015, 249-251.

Ankita Boni, Sagar Shinde, "Review on Performance Analysis of Dense Micro-block Difference and SURF Method for Texture Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.571-574, 2019.

Traffic congestion during peak hours is a critical issue worldwide. Due to increasing number of private vehicles, traffic jams primarily near cross roads results into significant delay in emergency services. Current surveys suggest that lives of many patients can be saved if emergency medical services could reach in time. With the evolution in sensor and communication technology, it is now possible to provide optimal solution to the problem of delay happening due to traffic related issues. Many researchers have proposed solutions for reducing delays and providing timely emergency services. Suggested solutions range from giving signaling priority to emergency vehicles to establishing a dedicated network to track the position of the emergency vehicle and ensuring an open rout to the destination. In this paper, we explore various mechanisms proposed for effective evacuation of vehicles creating traffic on the rout of emergency vehicle. The objective here is to provide clear view and state of the work done in the said area. This helps a researcher to easily understand and extend the capabilities of mechanisms according to the need.

DSRC, ITS, Traffic Control System

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[2] Neema Davis, Harry Raymond Joseph, Gaurav Raina, Krishna Jagannathan, “Congestion costs incurred on Indian Roads: A case study for New Delhi”, In the Proceedings of the 2015 International Conference on Communication Systems and Networks (COMSNETS), held at Bangaluru, India, 2015.
[3] Anita Acha George, Arun Krishna, Toney Dias, Asheena Sara Vargheese, R S Divya, “Golden aid an emergency ambulance system” In the Proceedings of the 2015 International Conference on Networks & Advances in Computational Technologies (NetACT), held at Thiruvanthapuram, India, 2017.
[4] Kapileswar Nellore, Gerhard P. Hancke, "Traffic Management for Emergency Vehicle Priority Based on Visual Sensing", Sensors, 2016
[5] Veera Venkatesh, Nazneen Syed, "Smart Traffic Control System for Emergency Vehicle Clearance", IIRCCE, 2015
[6] Rahul Pundir, Vikash Kumar Yadav, Sunil Prakash Mandrawal, Deepak Kumar, "Smart Traffic System for Emergency Vehicle",IJSET, 2017
[7] Asmaa Shaalan Abdul Munem , Dr. Muayad Sadik Croock, "Smart Traffic Light Control System for Emergency Ambulance", IJARCET, 2016
[8] Geetha.E, V.Viswanadha, Kavitha.G, "Design of an Intelligent Auto Traffic Signal Controller with Emergency Override", IJESIT, 2014
[9] Papa Rao Nalajala, Rotala Umarani, Naroju Mounika, "Design Of Intelligent Road Traffic Control System For Ambulance Using RF And GSM Technology ", IJATCSE, 2016
[10] Md Asaduzzaman, Krishnamurthy Vidyasankar, "A Priority Algorithm to Control the Traffic Signal for Emergency Vehicles", IEEE, 2017
[11] Mohamed Masoud, Saeid Belkasim, "WSN-EVP: A Novel Special Purpose Protocol for Emergency Vehicle Preemption Systems", IEEE Transaction on Vehicular Technology, IEEE, 2018
[12] Hanene Ben Yedder, Ilham Benyahia, "Reactive Emergency Vehicles Dispatching Based Real-time Information Dissemination", IEEE, 2017
[13] Hairuo Xie, Shanika Karunasekera, Lars Kulik, Egemen Tanin, Rui Zhang, Kotagiri Rama mohana rao, "A Simulation Study of Emergency Vehicle Prioritization in Intelligent Transportation Systems", IEEE, 2017
[14] A. El-Dalil, Maha Sharkas, Mohamed Khedr, "Priority Level Mutualism for Emergency Vehicle using Game Theory", IEEE International Conference on Vehicular Electronics and Safety (ICVES), IEEE, 2017
[15] Abdullahi Chowdhury, "Priority Based and Secured Traffic Management System for Emergency Vehicle using IoT", IEEE, 2016
[16] Dave J R, Bhatia J B, “Issues in Static Periodic Broadcast in VANET”, International Journal of Advances in Engineering & Technology, Vol.6, Issue.4, pp.1712-1717, 2013
[17] Kanwalprit Singh, Harmanpreet kaur, "Evaluation of proposed technique for detection of Sybil attack in VANET", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.10-15, 2018
[18] R. Bhavani, K. S. Suganya, D. Yazhini Priyanka, "Autonomous PHR Sharing: A Patient Centric Scalable and Flexible e-Healthcare Framework", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.11-14, 2018

Jashvant Dave, Shailesh Panchal, "A Survey on Traffic Control System for Emergency Vehicle Clearance," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.575-580, 2019.

In this paper there are modifications in the RSA algorithm by using more than four prime numbers in the combination of public and private key by using this factoring complexity of variables is increased. This is a new technique to provide max security for data over the internet. In this technique we are using more than four prime numbers which is not easily decomposed this technique provides more efficiency and reliability over the network. In this paper we try to increase the level of security by modifying the RSA Encryption Algorithm.

RSA Algorithm, Prime Numbers, Complexisity, Public Key, Private Key

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[16]. Omar G. Abood,Shawkat K.Guirguis, ” A Survey on Cryptography Algorithms ”, ” International Journal of Science and Research Publications ”, Vol:8,Issue:7,2018.
[17]. Swapnil Chaudhari,Mangesh Pahade ,Sahil Bhat,Chetan Jadhav,Tejaswini Sawant, ”A Research Paper on New Hybrid Cryptography Algorithm ”, “International Journal for Research & Development in Technology ”,Vol:9,Issue:5,2018.

Abhishek Guru, Asha Ambhaikar, "Development of “RSA” Encryption Algorithm for Secure Communication," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.581-585, 2019.

Ransomware is a kind of malicious code, or malware, designed to deny access to a computer system or data till a ransom is paid. It generally spreads through phishing emails or by inadvertently visiting associate infected web site. Ransomware is not a brand new threat to the cyber world. Its origins dates back a few years. Over time, this threat has become more and more vicious and harmful. While people were contending with this cyber threat, cybercriminals moved one step further by providing ransomware-as-a-service (RAAS). This paper presents the overview of the RaaS kits. It focuses on the general working of the RaaS, five RaaS kits – Philadelphia, Stampado, Frozr Locker, Satan, Jokeroo. Nowadays it has become increasingly easy to create and launch ransomware, irrespective of skill. Anyone with an ill intent and access to the dark net can advertise the ransomware kits the way an online retailer promotes clothing or toys. This paper provides different defensive measures against ransomwares to individuals and organizations that they can adopt to protect themselves from the threat of ransomwares.

Ransomware, WannaCry, EternalBlue, Affiliates, Philadelphia, Stampado, FrozrLocker, Satan, Jokeroo

[1] D. Palotay, Ransomware as a Service (RaaS): Deconstructing Philadelphia. 2019, p. 3.
[2] B. Brenner, "5 ransomware as a service (RaaS) kits – SophosLabs investigates", Naked Security, 2017.
[3] A. ZAHARIA, "Security Alert: New and Cheap Stampado Ransomware for Sale on the Dark Web", Heimdal Security Blog, 2016.
[4] "Free Ransomware Decryption Tools | Unlock Your Files | Avast", Avast.com.
[5] "Ransomware FILEFROZR", Sinister.ly.
[6] L. Abrams, "New Satan Ransomware available through a Ransomware as a Service.", BleepingComputer, 2017.
[7] L. Abrams, "Jokeroo Ransomware-as-a-Service Offers Multiple Membership Packages", BleepingComputer, 2019.

Harshada Umesh Salvi, "RAAS: Ransomware-as-a-Service," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.586-590, 2019.

Today users prefer blogs and review sites to purchase products online. Thus, user reviews are considered as an important source of information in sentiment analysis applications for decision making. Machine Learning and Lexicon based sentiment analysis are the two popular methods available in the literature. The Machine Learning based classifiers does not work for unlabelled dataset such as tweets. On the other hand existing Lexicon based sentiment analysis approaches are becoming less efficient due to data sparseness, low accuracy and non-consideration n-gram words. N-grams can improve the accuracy of sentiment classification. Following these limitations the proposed work provides a combination of Lexicon and Machine learning based approach to perform sentiment analysis on Twitter datasets.

Sentiment Analysis, Sentiment Classification, Lexicon based Analysis, Sentiment Score

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[6] Dey, Atanu, Mamata Jenamani, and Jitesh J. Thakkar. "Senti-N-Gram: An n-gram lexicon for sentiment analysis." Expert Systems with Applications, Vol. 103, pp.92-105, 2018.
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[10] Hamilton, William L., Kevin Clark, Jure Leskovec, and Dan Jurafsky. "Inducing domain-specific sentiment lexicons from unlabeled corpora." In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, vol. 2016, p. 595. NIH Public Access, 2016.
[11] Khan, Farhan Hassan, Usman Qamar, and Saba Bashir. "A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet." Knowledge and information Systems, Vol. 51, no. 3, pp. 851-872, 2017.
[12] Zhang, Shunxiang, Zhongliang Wei, Yin Wang, and Tao Liao. "Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary." Future Generation Computer Systems, Vol. 8, pp. 395-403, 2018.
[13] Asghar, Muhammad Zubair, Aurangzeb Khan, Shakeel Ahmad, Maria Qasim, and Imran Ali Khan. "Lexicon-enhanced sentiment analysis framework using rule-based classification scheme." PloS one, Vol. 12, no. 2, e0171649, 2017.
[14] C. Nanda, M. Dua, “A Survey on Sentiment Analysis” International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.67-70, 2017.
[15] Amit Palve, Rohini D.Sonawane, Amol D. Potgantwar, “Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark“, International Journal of Scientific Research in Network Security and Communication, Vol.5 , Issue.3 , pp.99-103, Jun-2017.

M. Thenmozhi, R. Indira, R. Dharani, "Using Lexicon and Random Forest Classifier for Twitter Sentiment Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.591-594, 2019.

Cloud computing technology has been emerging and spreading at a great pace due to its service oriented architecture, elasticity, cost-effectiveness, etc. Many organizations are using Infrastructure-as-a-Service (IaaS) public Clouds for data migration away from traditional IT Infrastructure but there are a few fields such as finance, hospitals, military and others that are reluctant to use public Clouds due to perceived security vulnerability. Enterprises in such fields feel more vulnerable to security breaches and feel secure using in-house IT infrastructure. The introduction of private Clouds is a solution for these businesses. This paper focuses on deployment of private Cloud with the help of apache cloud stack, Analysis of data for Building the Business case for Private Cloud. I have proposed a platform and evaluated a service oriented IaaS model of private Cloud which provides a platform to the business suite to sell it as a PaaS service to the customers. Comparative analysis includes cloud services delivery (SaaS, PaaS, IaaS) and deployment models (private, public, and hybrid).

Cloud Computing, Private Cloud, Cloud Orchestration, Infrastructure as a Service, Platform as a Service, CloudStack, VMWARE hypervisor, XenServer, Compute Storage, Multisite Deployment

[1] CloudStack. https://cloudstack.apache.org/. Accessed: 2016-05-10.
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[3] Thomas L., “A Scheme to Eliminate Redundant Rebroadcast and Reduce Transmission Delay Using Binary Exponential Algorithm in Ad-Hoc Wireless Networks”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.1-6, 2017.
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[6] S. Tamilarasan, P.K. Sharma, “A Survey on Dynamic Resource Allocation in MIMO Heterogeneous Cognitive Radio Networks based on Priority Scheduling”, International Journal of Computer Sciences and Engineering, Vol.5, No.1, pp.53-59, 2017.
[7] Phelim Boyle, Mark Broadie, and Paul Glasserman. Monte-carlo methods for security pricing. Journal of economic dynamics and control, 21(8):1267–1321, 1997.
[8] Phelim P Boyle. Options: A Monte-Carlo approach. Journal of financial economics, 4(3):323–338, 1977.
[9] Rajkumar Buyya, Christian Vecchiola, and S Thamarai Selvi. Mastering cloud computing: foundations and applications programming. Newnes, 2013.
[10] S.L. Mewada, “A Proposed New Approach for Cloud Environment using Cryptic Techniques”, In the Proceedings of the 2016 International Conference on Computer Science and Engineering, India, pp.542-545, 2016

Paras Arora, Raj Kumar, "Deployment of Private Cloud and Analysis of data for Business Case," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.595-598, 2019.

The Smart Antenna are the key solution to enhance the performance of Mobile Communication. It allows energy to be transmitted and received in a particular direction rather than dissipating it in all the directions. Smart antenna System (SAS) combines an antenna array with digital signal processor (DSP) to transmit and receive in adaptive manner. The most important and crucial problem in Smart Antenna is DOA (Direction of Arrival) finding. The performance of Smart Antenna depends upon the resolution of Direction of Arrival. The two classic algorithms for DOA finding is MUSIC (Multiple Signal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique). However, these two algorithms are unable to work on coherent signals directly as happens in multiple path propagation. For handling such coherent signals both ESPRIT and MUSIC algorithm requires pre-processing method like spatial smoothing which degrades the performance of the Smart Antenna. However, WSF (Weighted Subspace Fitting) is the most superior algorithm which can handle such coherent signals directly without any pre-processing. But this conventional WSF algorithm requires number of signals to be known in advance. The proposed algorithm is the modified WSF which can automatically detect the number of signals and calculate the DOA with better resolution than conventional algorithms like ESPRIT and WSF.

Smart Antenna (SA), Direction of Arrival (DOA), MUSIC. ESPRIT, WSF

[1] Savitri Katariya, “A Survey on Smart Antenna System”, IJECT Vol. 2, Issue 3, Sept. 2011.
[2] Patidar, S., Kumbhare, K., & Chouhan, S. (2017). “Improvement of Spectral Efficiency and Power Control of Smart Antenna. International Journal of Engineering Sciences & Research Technology”, 6(5), 736-741. doi:10.5281/zenodo.801268.
[3] Mohammed Ali Hussain, P.Suresh Varma, K. Satya Rajesh, Hussain Basha Pathan, Leela Madhav Sarraju, “Use Of Smart Antennas In Ad Hoc Networks”, International Journal of Computer Science & Information Technology (IJCSIT), Vol 2, No 6, December 2010.
[4] Mohammed A. Abdala and Areej K. Al-Zuhairy, “Integration of Smart Antenna System in Mobile Ad Hoc Networks”, International Journal of Machine Learning and Computing, Vol. 3, No. 4, August 2013.
[5] Shivapanchakshari, T. and Aravinda, H. (2017) Review of Research Techniques to Improve System Performance of Smart Antenna. Open Journal of Antennas and Propagation, 5, 83-98.
[6] Bindu Sharma, IndranilSarkar, Tanmoy Maity, P. P. Bhattacharya “An Introduction to Smart Antenna System”, International Journal and Business Engineering Research Vol.8 Nov.2014 ISSN: 0975-0479.
[7] Rutuja Akarte 2018, “Mobile Communication using Smart Antenna System”, International Journal of Innovations & Advancement in Computer Science IJIACS, ISSN 2347 – 8616 Volume 7, Issue 3, March 2018
[8] Dr. A. Jhansi Rani, “Mobile Communication Using SMART Antenna”, International Journal of Research in Advent Technology (E-ISSN: 2321-9637) Special Issue 1st International Conference on Advent Trends in Engineering, Science and Technology “ICATEST 2015”, 08 March 2015.
[9] Singh Neelima, Singh K.M., “A REVIEW OF DIRECTION OF ARRIVAL (DOA) ESTIMATION FOR SMART ANTENNA STRUCTURE”, Journal of Information, Knowledge And Research In Electronics And Communication ISSN: 0975 – 6779| NOV 14 TO OCT 15 | VOLUME – 03, ISSUE – 02.
[10] Marco Di Filippo, Leonardo Lucci, Dania Marabissi, and Stefano Selleri, “Design of a Smart Antenna for Mobile Ad Hoc Network Applications”, Hindawi Publishing Corporation International Journal of Antennas and Propagation Volume 2015.
[11] AKM Arifuzzman, Rumana Islam, and Mohammed Tarique, “Window Based Smart Antenna Design for Mobile Ad Hoc Network Routing Protocol”, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 7, No. 4, August 2015.
[12] Supriya Kulkarni P, Bhavani V, “Implementing Smart Antenna System in Mobile Ad Hoc Networks”, Int. Journal of Engineering Research and Applications, Vol. 4, Issue 6, June 2014, pp.74-79.
[13] Hardeep Singh, Gurwinder Kaur, “A REVIEW ON A CONSTRUCTIVE SMART ANTENNA BEAMFORMING TECHNIQUE WITH SPATIAL DIVERSITY”, International Journal of Advance Engineering and Research Development (IJAERD) Volume 1,Issue 6,June 2014, e-ISSN: 2348 – 4470
[14] Mounissamy Levy, “Rapid Beam Forming in Smart Antennas Using Smart-Fractal Concepts Employing Combinational Approach Algorithms” International Journal of Antennas and Propagation Volume 2012, Article ID 467492.
[15] Anwesha Dhar, Anupama Senapati and Jibendu Sekhar Roy,”Direction of Arrival Estimation for Smart Antenna using a Combined Blind Source Separation and Multiple Signal Classification Algorithm”, Indian Journal of Science and Technology, Vol 9(18), DOI: 10.17485/ijst/2016/v9i18/89342, May 2016.
[16] Gowtham Prasad TVS, T. Ravi Kumar Naidu,” Direction of Arrival (DOA) Estimation Using Smooth Music Algorithm”, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 8, August - 2013 IJERT ISSN: 2278-0181.
[17] Deven Pradhan, Rabindranath Bera, “Direction of Arrival Estimation via ESPRIT Algorithm for Smart Antenna System”, International Journal of Computer Applications (0975 – 8887) Volume 118 – No. 6, May 2015.d
[18] Nermin Makhlouf, “Challenges of Integration of Smart Antennas In Ad Hoc Network”.
[19] Monika Bansal, Laxmi Shricastava, “Performance Analysis of Wireless Mobile Adhoc Network with Different Tpes of Antennas” Asia-pacific Journal of Convergent Research Interchange Vol.3, No.1, March 31 (2017), pp. 29-39 http://dx.doi.org/10.21742/apjcri.2017.03.03.
[20] Haihua Chen, Yiqing Zhou, Lin Tian, Jinglin Shi, Jinlong Hu, Masakiyo Suzuki|, “A Novel Modification of WSF for DOA Estimation”, 2013 IEEE Wireless Communications and Networking Conference (WCNC): PHY.
[21] Susaritha U.S, Ganesh Madhan. M, Ragupathi. R, Ashita Priya Thomas,”A Comparative Study of Different DOA Estimation Schemes and Adaptive Beam Forming Techniques for Target Detection and Tracking” International Journal of Computer Applications (0975 – 8887) International Conference on Communication, Computing and Information Technology (ICCCMIT-2014).
[22] Gupta, Pooja, and S. P. Kar. "MUSIC and improved MUSIC algorithm to estimate direction of arrival." 2015 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2015.
[23] Ritika Sharma, “Beamforming of smart antenna in cellular network using leaky LMS algorithm” ieeexplore.ieee.org/document/8405082.
[24] Rahul Mishra1, Bhushan Salunke, “Performance Analysis Of Smart Antenna System” International Journal For Technological Research In Engineering Volume 4, Issue 1, September-2016 ISSN: 2347 – 4718.
[25] Keerthi A Kumbar, “Adaptive Beamforming Smart Antenna for Wireless Communication System” International Research Journal of Engineering and Technology (IRJET) Volume: 02 Issue: 03 | June-2015.
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[27] Nizar Tayem, “Azimuth/Elevation Directional Finding with Automatic Pair Matching”, International Journal of Antennas and Propagation Volume 2016, Article ID 5063450.
[28] Vijay, Mane Sunita, and U. L. Bombale. "An overview of smart antenna and a survey on direction of arrival estimation algorithms for smart antenna." In Journal of Electronics and Communication Engineering (IOSR-JECE). 2014.
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Gunjan Grover, "Performance Analysis of Smart Antenna for Wireless Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.599-607, 2019.