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Image Steganography is data hiding technology to transmit securely significant data in an open channel. In this paper, we present performance analysis of Least Significant Bit (LSB) substitution and Pixel-Value Differencing (PVD) methods commonly used in image steganography. The comparison of these methods is performed by using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and payload values. The 512 x 512 size of colored and gray-scale cover images as Lena, Baboon, Peppers, and Airplane are used in the experimental studies. In the LSB method, the PSNR values are about 51.6, while the PVD method is between 37.83 and 41.28 for colored cover image. In gray-scale images, while PVD is between 38.52 and 41.42, the LSB is about 51.14. In our paper results shows that PSNR and SSIM values are higher in LSB substitution than PVD method. However, PVD method embeds more secret data than LSB substitution method into cover image with less visual perceptibility.

LSB, PVD, Image Steganography, Security, Data hiding

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[8] Neha, Mr Mohit, "A Review on “Image Steganography with LSB & DWT Techniques”", International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.813-818, 2018.
[9] K. Arora, G. Gandhi, "A Review of Approaches for Steganography", International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.118-122, 2014.
[10] D.C. Wu, W.H. Tsai, “A steganographic method for images by pixel-value differencing”. Pattern Recognition Letters, Vol 24 Issue 9-10, pp.1613-1626, 2003.
[11] A. Sancheti, “Pixel Value Differencing Image Steganography Using Secret Key”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN, 2278-3075, 2012.
[12] C.M. Wang, N.I. Wu, C.S. Tsai, M.S. Hwang, “A high quality steganographic method with pixel-value differencing and modulus function”, Journal of Systems and Software, Vol 8, Issue 1, pp. 150-158, 2008.
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Serdar SOLAK and Umut ALTINIŞIK, "LSB Substitution and PVD performance analysis for image steganography," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.1-4, 2018.

High-efficiency converters play an important role in the field of renewable energy, switch mode dc regulators, electric vehicles. Compared to linear voltage regulation switching conversion is more power efficient which dissipates unwanted power as heat. Switched mode converter due to its high efficiency reduces the heat sinking needed and increases battery endurance of portable equipment. Basically, DC-DC converters are designed to move unidirectionally, from input to output whereas the switching regulator topologies can be designed to move bidirectionally. A bi-directional conversion is useful in regenerative braking of vehicles where power is supplied from wheels during braking. Selection of an appropriate converter topology is an important part of designing the systems as the converter plays a major role in determining the overall performance of the system. Based on an area of application, a suitable converter can be proposed. This paper presents different topologies of DC-DC converters used in various applications with reduced ripple by MATLAB SIMULINK software.

MOSFET, modular multilevel, renewable energy, ripple, switching elements-inductor, diode, capacitor, soft switching, sepic converter

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[9] T. Jackie, C. Hung,“Neural network Method: Loss Minimization control of a PMSM with core Resistance Assessment”,International Journal of Scientific Research in Network Security and Communication(IJSRNSC), Volume 4, Issue 6,pp.10-18,Dec.2016

M. Devika Rani, V. Sai Geetha Lakshmi, "Comparative Study of Reduced Ripple DC-DC Converters for Various Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.5-10, 2018.

The “speedy progress in multimedia and imaging technology, the numbers of images uploaded and shared on the internet have increased. It leads to develop the highly effective image retrieval system to satisfy the human needs. The content-Based image retrieval (CBIR) system which retrieves the image based on the Low level features such as color, texture and shape which are not sufficient to describe the user’s high level perception for images. Therefore reducing this semantic gap problem of image retrieval is challenging task. Some of the most important notions in image retrieval are keywords, terms or concepts. Terms are used by humans to describe their information need and it also used by system as a way to represent images. Here in this paper different types of features their advantage and disadvantages are described. We have carried out comparative analysis of different techniques used in our system to determine best suitable technique to be used for our proposed system. We have analyze the our proposed system on large image dataset and our approach gives high precision and required less computations which proves efficiency of our system. In our proposed system we have evaluated the performance of our feature extraction techniques i.e. FCH and GWT using precision and recall metric and compared the result with existing feature extraction approaches i.e. color moment and GWT. Implementation results show that the feature extraction techniques for the proposed system are better than the existing techniques. SVM Classifier also gives good accuracy using these feature extraction” techniques.

CBIR, Color Moment, Fuzzy Color Histrogram, Gabor Wavelate, Support Vector Machine

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A.D. Mahajan, S. Chaudhary, "Hybrid Features For Content Based Image Retrieval System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.11-15, 2018.

Part of Speech (POS) tagging is the process of assigning grammatical category to words. POS tagger has wide variety of applications in the field of natural language processing, speech processing, information retrieval, machine translation, sentiment analysis, question answering etc. For Indian languages, the research in the field of POS tagging is still in progress. Marathi is the fourth spoken language in India and morphologically rich language. In this paper, we compared performance of Marathi POS tagger using generative and discriminative models. Using 32 tags, specified by Unified POS standard for Marathi, POS tagged dataset of 1500 news sentences, from different domains like sports, politics, entertainment etc., is generated. The Naive Bayes, Decision Tree, Neural Network, K Nearest Neighbour, Hidden Markov Model and Conditional Random Fields give 81%, 79%, 85%, 78%, 79% and 86% accuracy on test data respectively. Results show that neural network and Conditional Random Fields give better performance.

Part of speech tagging, Generative models, Discriminative models, Naive Bayes, Decision tree, Neural network, Hidden markov model, Conditional Random Fields

[1] Vadivukarassi, M., N. Puviarasan, and P. Aruna. "Identification of Opinion Words and Polarity of Reviews in Tweets using Aspect Based Opinion Mining." International Journal of Scientific Research in Computer Science, Engineering and Information Technology pp.282-289, 2017.
[2] Vidya, S. "Cross Domain Sentiment Classification Using Natural Language Processing." IJSRCSEIT pp.348-353,2018.
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[4] Singh, Jyoti, Nisheeth Joshi, and Iti Mathur. "Part of speech tagging of Marathi text using trigram method." arXiv preprint arXiv:1307.4299,2013.
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Rushali Dhumal Deshmukh, "Comparison of Generative and Discriminative Models of Part of Speech Taggers for Marathi Language," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.16-21, 2018.

Cloud Computing is a computing interpretation which provides convenient way to access resources and in which data can be stored on paid basics. It has become a major component of our life. It provides storage of data at minimal cost. Resource allocation plays an important role in Cloud Computing, as it optimize the response time on cloud. Cloud Computing become popular as it provide access on paid basics. Thus, assets allocation is necessary. Load Balancing is one of the methods in Cloud Computing which helps in balancing loads as it increase the throughput and minimize the response time. It distributes loads uniformly on nodules and increase overall performance in the system. The aim of Load balancing is to allocate resources and guarantees user satisfaction. In this paper I explore two of the Cloud computing Algorithms to overcome load balancing in it.

Cloud Computing, Load Balancing, Static Algorithms, Dynamic Algorithms

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[6] J. Yao, and J. H. He, Load balancing strategy of cloud computing based on artificial beealgorithm. Proc. 8thInternational Conference on Computing Technology and Information Management (ICCM), 2012, 185-189.
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Priyam Tyagi, Amit Kishor, "LOAD BALANCING IN CLOUD COMPUTING," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.22-27, 2018.

Large number of users shares their opinion on social networking sites. So, on the web an enormous quantity of data is generated daily. Usually there is not enough human resource to examine this data. The methods for automatic opinion mining on online data are becoming increasingly. From the past few years, methods have been developed that can successfully analyze the sentiment from digital text. These developments enable research into prediction of sentiment. Sentiment prediction has been used as a tool for movie review prediction. The aim of this work is to explore the use of lexicons to extract the sentiment prediction for a number of movie reviews. In this paper, a comparative analysis of lexicon based models has to predict the sentiments of movie reviews dataset together with evaluation metrics.

Movie reviews, Lexicon based model, Predicting sentiment

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[2] Pushpendra Kumar and Ramjeevan Singh Thakur, “Recommendation system techniques and related issues: a survey”, International Journal of Information Technology, Vol.10 (4), pp. 495–501, 2018.
[3] Vinod Kumar, Pushpendra Kumar and R.S. Thakur, “A brief Investigation on Data Security Tools and Techniques for Big Data”, International Journal of Engineering Science Invention, Vol. 6(9), PP. 20-27, 2017.
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[26] Rafael M., D’Addio , Marcos A., Domingues, Marcelo G., and Manzato, “Exploiting feature extraction techniques on users reviews for movies recommendation”, Journal of the Brazillian Computer Society, Vol.23, Pp-7, 2017.
[27] Kushal Dave, Steve Lawrence and David M. Pennock , “Mining the Peanut gallery: Opinion extraction and Semantic Classification of product reviews” , In Proceedings of WWW 2003, Pp. 519-528, 2003.
[28] G. Vinodhini and R. Chandrasekaran, “Sentiment Analysis and Opinion Mining : A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Issue 6, june 2012.
[29] I.V. Shravan, “Sentiment Analysis in Python using NLTK”, OSFY- Open Source For You, 2016.

Binita Verma, Ramjeevan Singh Thakur, Shailesh Jaloree, "PREDICTING SENTIMENT FROM MOVIE REVIEWS USING LEXICON BASED MODEL," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.28-34, 2018.

Security from intrusions for alone system and in network nodes are always important. The protection and prevention methods available with antivirus and IDS/IPS are works on intrusions and provide security on all known intrusions. They have a database to match the pattern or signature of intrusion and based on that they complete the action of quarantine/repair or cleaning of file. Sometimes installation and configuration of such software occupies large space of memory and heavily slows down the speed of computer as well network. To work in full swing they must be updated time to time and also need the combinations of other security features including online facilities. Operating system and File system provided security features are not all default, they may be applied on interest of user. We are suggesting a model which will be default feature of operating system and will work for all files regardless of different interface of interaction with user.

Operating system, Network security, Intrusion Filtration model, IFS, TSC, token secuirty log, file log

[1]. Zhenfang ZHU, "Study on Computer Trojan Horse Virus and Its Prevention", International Journal of Engineering and Applied Sciences (IJEAS) ISSN: 2394-3661, Volume-2, Issue-8, August 2015
[2]. Hossein Rouhani Zeidanloo, S. Farzaneh Tabatabaei, Payam Vahdani Amoli and Atefeh Tajpour, " All About Malwares (Malicious Codes)" Faculty of Computer Science and Information System, University of Technology Malaysia(UTM) , Kuala Lumpur, Malaysia
[3]. Benjamin A. Kuperman, Carla E. Brodley, Hilmi Ozdoganoglu, T.N. Vijaykumar, and Ankit Jalote, " DETECTION AND PREVENTION OF STACK BUFFER OVERFLOW ATTACKS", COMMUNICATIONS OF THE ACM November 2005/Vol. 48, No. 11

R. Dewanjee, S. Barde, "Implementation of Intrusion Filtration Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.35-40, 2018.

Different types of studies are going on among researchers and different approaches are adopted by the bankers to analyze the behavior of the loan applicants to approve those loans. Bankers collect customer data to analyze their behavior in order to predict the possibility of recovery of the amount. Domain experts can think about a new approach to make this process fast. The data used to analyze customer behavior are actually patterns. And Artificial Neural Networks (ANN) are very good tool to train a system for known patterns and later can be used to identify unknown patterns. In this paper a two dimensional binary pattern matrix is formed on the basis of some questionnaires to identify different customer behavior. The matrix is further segmented row-wise and each row is presented to perceptron for training purpose of the ANN, which is used to complete the process of loan approval.

ANN, Row-wise Segmentation, Perceptron, Behavioral Pattern of customers

[1] Rakesh Kumar Mandal and N R Manna, “Loan Customer Analysis System using Column-wise Segmentation of Behavioural Matrix (CSBM)”, International Journal of Computer Sciences and Engineering, Volume-3, Special Issue-1 E-ISSN: 2347-2693, pp 18-22, February, 2015. Available Online: http://www.ijcseonline.org/pdf_spl_paper_view.php?paper_id=3&PID%203.pdf
[2] G.N. Swamy, G. Vijay Kumar, “Neural Networks”, Scitech, India, 2007.
[3] L. Fausett, “Fundamentals of Neural Networks, Architectures, Algorithms and Applications”, Pearson Education, India, 2009.
[4] Apash Roy and N R Manna,“Character Recognition using Competitive Neural Network with Multi-scale training”, UGC Sponsor National Symposium on Emerging Trends In Computer Science (ETCS 2012) on 20-21 January 2012, pp 17-20.
[5] Apash Roy and N R Manna, “Competitive Neural Network as applied for Character Recognition ” - International Journal of advanced research in Computer science and Software Engineering, Volume 2, Issue 3, 2012, pp 06-10.
[6] V Moonasar, Credit Risk Analysis using Artificial Intelligence: Evidence from a Leading South African Banking Institution. Available: www.academia.edu/502093/credit_risk_analysis_using_artificial_intelligence_evidence_from_a_leading_South_African_banking_institution.
[7] Ifeyinwa Ajah, Chibueze Inyiama, Loan Fraud Detection And IT-Based Combat Strategies, Journal of Internet Banking and Commerce, 2011, Vol. 16 No. 2, pp 1-13. Avialable: www.arraydev.com/commerce/JIBC/2011_08/Ajah.pdf

Rakesh Kumar Mandal, "Loan Customer Analysis System using Row-wise Segmentation of Behavioral Matrix (RSBM)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.41-43, 2018.

The security for the documentations, signature, manually written and mark is challenge errand and essential continuously applications. To address the issues in existing outcomes, the proposed work analyzes the outcomes utilizing three signature forgery detection algorithms, Error Level Analysis, Copy-Paste clone Detection and Fourier based Resembling Detection. Every technique is found to have its own arrangement of points of interest and confinements. An Error Level Analysis gave better outcomes on beforehand compacted, excellent JPEG signatures. The Copy-Paste Clone Detection is exceedingly fruitful on signatures produced utilizing cloning techniques, however the general runtime has considerably higher than alternate strategies, and because of the idea of the calculation false positives is routinely distinguished. Signature Resembling Detection (SRD) worked on a wide assortment of signatures which are taken from genuine signature, transcribed and signature, gives great general outcomes on each dataset, and the rate of false positives is low. The calculation has profoundly proficient in the database absolutely 10 signatures which have brilliant measures are subjected to proposed calculations to segregate amongst innovation and phony archives or transcribed or marks. The proposed work gives a perfect base to a client to decide the most relevant signature fabrication location strategy for their utilization, contingent upon the kinds of signatures that they routinely manage.

Signature Resembling Detection, DWT, SVD,SURF,CMDF,SIFT

[1]. Ali MUMCU & Ibrahim Savran “Copy Move Forgery Detection with Using FAST Key Points and SIFT Description Vectors”, 978-1-5386-1501-0 2018 IEEE.
[2]. Anil Dada Warbhe.et.al, "A Survey on Key point Based Copy-Paste Forgery Detection Techniques", International Conference on Information Security & Privacy (ICISP2015), 11-12 December 2015, Nagpur, INDIA, Procedia Computer Science 78 ( 2016 ), pp. 61 – 67, Published by Elsevier.
[3]. Anil Dada Warbhe.et.al, “A Scaling Robust Copy-Paste Tampering Detection for Digital Image Forensics", 7th International Conference on Communication, Computing and Virtualization 2016, Published by Elsevier.
[4]. Anushree U. Tembe & Supriya S. Thombre “Survey of Copy-Paste Forgery Detection in Digital Image Forensic”, International Conference on Innovative Mechanisms for Industry Applications, pp 248-252, July 2017 IEEE.
[5]. Ashraf Y. A. Maghari & Mohammed N. Nazli “Comparison Between Image Forgery Detection Algorithms” 8th International Conference on Information Technology (ICIT) 978-1-5090-6332-1 2017 IEEE.
[6]. Ashwini V Malviya.et.al, "Pixel based Image Forensic Technique for copy-move forgery detection using Auto Color Correlogram", in proceedings of 7th International Conference on Communication, Computing and Virtualization 2016, Procedia Computer Science 79 ( 2016 ), pp. 383 – 390, Published by Elsevier.
[7]. C.S. Lu, C.Y. Hsu, S.W. Sun, and P.C. Chang, “Robust Mesh-Based Hashing for Copy Detection and Tracing of Images”, Proc. IEEE Int’l Conf. Multimedia and Expo, vol.1, pp. 731-734, 2004.
[8]. Chi-Man Pun, Senior Member, IEEE.et.al, "Image Forgery Detection Using Adaptive Over-Segmentation and Feature Point Matching", This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TIFS.2015.2423261, IEEE Transactions on Information Forensics and Security.
[9]. E. Agnes, S. Devi Mahalakshmi, Dr. K. Vijayalakshmi "A Forensic Method for Detecting Image Forgery Using Codebook”, International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 3, March 2013.
[10]. F. Khelifi and J. Jiang, “Perceptual image hashing based on virtual watermark detection,” IEEE Trans. Image Process., vol. 19, no. 4, pp.981–994, Apr. 2010.
[11]. Gul MUZAFFER, Eda Sena ERDOL ve Guzin ULUTA “A Copy-Move Forgery Detection Approach Based on Local Intensity Order Pattern and PatchMatch”, IEEE Trans. Information Forensics and Security, vol. 8, no. 1,pp. 55-63, Jan. 2018.
[12]. Haodong Li.et.al, "Image Forgery Localization via Integrating Tampering Possibility Maps", IEEE Transactions On Information Forensics And Security", 1556-6013 (c) 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
[13]. Jian Li.et.al, "Segmentation-based Image Copy-move Forgery Detection Scheme", This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TIFS.2014.2381872, IEEE Transactions on Information Forensics and Security.
[14]. Mejren Mohammad Al-Hammadi & Sabu Emmanuel “Improving SURF Based Copy-Move Forgery Detection Using Super Resolution”, IEEE International Symposium on Multimedia, pp 341-344, June 2016.
[15]. Prajwal Pralhad Panzade.et.al, "Copy-Move Forgery Detection by Using HSV Preprocessing and Keypoint Extraction", 978-1-5090-3669-1/16, 2016, IEEE.
[16]. Rahul Dixit, Ruchira Naskar and Aditi Sahoo “Copy–Move Forgery Detection Exploiting Statistical Image Features”, IEEE WiSPNET conference, pp 2277-2281, Sep 2017.
[17]. Rahul Dixit,"Review, analysis and parameterisation of techniques for copy–move forgery detection in digital images", IET Image Process., 2017, Vol. 11 Iss. 9, pp. 746-759.
[18]. Sawinder Singh Mangat.et.al, "Improved Copy-move Forgery Detection in Image by Feature Extraction with KPCA and Adaptive Method", 2016 2nd International Conference on Next Generation Computing Technologies (NGCT-2016) Dehradun, India 14-16 October 2016, 978-1-5090-3257-0/16/$31.00 ©2016 IEEE.
[19]. Sreelakshmy I J.et.al, "An Improved Method For Copy-move Forgery Detection In Digital Forensic", 2016 Online International Conference on Green Engineering and Technologies (IC-GET), 978-1-5090-4556-3/16, 2016 IEEE.
[20]. Swaminathan, Y. Mao, and M. Wu, “Robust and Secure Image Hashing,” IEEE Trans. Information Forensics and Security, vol. 1, no. 2, pp. 215-230, June 2006.
[21]. Tarman & Hardeep saini, “A Review on Various Techniques of Image Forgery Detection”, 4th IEEE International Conference on Signal and Processing Computing and Control, pp 425-430, Sep 2017.
[22]. Toqeer Mahmood.et.al, "Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images", Hindawi Publishing Corporation, Mathematical Problems in Engineering, Volume 2016, Article ID 8713202, pp. 13 pages, http://dx.doi.org/10.1155/2016/8713202.
[23]. Tu Huynh-Kha, Thuong Le-Tien, Synh Ha-Viet-Uyen,Khoa Huynh-Van, Marie Luong “A Robust Algorithm of Forgery Detection in Copy- Move and Spliced Images”, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 3, 016. www.ijacsa.thesai.org
[24]. V. Monga and B.L. Evans, “Perceptual Image Hashing via Feature Points: Performance Evaluation and Tradeoffs,” IEEE Trans. Image Processing, vol. 15, no. 11, pp. 3452-3465, Nov. 2006.
[25]. V. Monga and M. K. Mihcak, “Robust and secure image hashing via non- negative matrix factorizations,” IEEE Trans. Inf. Forensics Security, vol. 2, no. 3, pp. 376–390, Sep. 2007.
[26]. X. Lv and Z. J. Wang, “Perceptual image hashing based on shape contexts and local feature points,” IEEE Trans. Inf. Forensics Security, vol.7, no. 3, pp. 1081–1093, Jun. 2012.
[27]. Y. Zhao, S. Wang, X. Zhang, and H. Yao, “Robust Hashing for Image Authentication Using Zernike Moments and Local Features,” IEEE Trans. Information Forensics and Security, vol. 8, no. 1, pp. 55-63, Jan. 2013.

S. B. Pratapur, S. D. Chikte, "Pixel Based Forensic Image Forgery Detection using Signature Resembling Detection and Signature Detection Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.44-53, 2018.

Tremendous traffic demands and emerging multimedia applications significantly increase the energy consumption of battery-powered mobile devices. In contrast with wired networks, energy consumption is an essential constraint in Mobile Adhoc Networks. Mobile devices have restricted battery lifetime and are most vulnerable to the energy constraints. Therefore, energy concerns have to be properly implemented while defining routing metrics. In this paper a Shortest and Energy Efficient Routing Protocol for MANET (SEERP) is proposed. It defines a strategy for selecting energy efficient route taking into consideration that in MANET, channel and energy capacity are scarce resources. SEERP chooses a routing path from source to destination based on the residual energy and number of hops. The traditional route request packet is altered to calculate the minimum energy of nodes.

MANET, Energy, protocol, shortest path, routing

[1] Shivashankar, Varaprasad.G, Suresh H.N Devaraju G Jayanthi.G “Performance Metrics Evaluation of Routing Protocols in MANET, ” International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 3, March 2013
[2] Nikitha V Suvarna, Chandra Naik, “Energy Efficient Routing Protocol in MANET” , International Journal of Science and Research, Volume 4 Issue 5, May 2015
[3] Handy, M., Haase, M., Timmermann, D., “Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-Head Selection,” 4th IEEE International Conference on Mobile and Wireless Communications Networks, Stockholm, 2002
[4] Intanagonwiwat, C., Govindan, R., Estrin, D., “Directed Diffusion: a scalable and robust communication paradigm for sensor networks”, ACM Press, 2000
[5] Xiang-Yang Li and Peng-Jun Wan. Constructing minimum energy mobile wireless networks. SIGMOBILE Mob. Comput. Commun. Rev., 5(4):55– 67, 2001.
[6] N. Li, J.C. Hou, and L. Sha. Design and analysis of an mst-based topology control algorithm. In INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, pages 1702 – 1712, mar. 2003.
[7] Jang-Ping Sheu, Shin-Chih Tu, and Chi-Hung Hsu. Location-free topology control protocol in wireless ad hoc networks. Comput. Commun., 31(14):3410–3419, 2008.
[8] Taha, Aqeel, et al. "Energy Efficient multIpath routing protocol for Mobile ad-hoc Network using the Fitness Function." IEEE Access 5 (2017): 10369-10381.
[9] R. Kumari1, P. Nand, “Performance Analysis for MANETs using certain realistic mobility models: NS-2”, International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.1, pp.70-77, February (2018) E-ISSN: 2320-7639
[10] Gopinath, et al. “Energy Efficient Routing Protocol for MANET”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012
[11] Narayanan et al., “A Complete Study on Energy Efficient Techniques for Mobile Adhoc Networks”, International Journal of Advanced Research in Computer Science and Software Engineering 2 (9), September- 2012, pp.129-133
[12] Pradeep Chouksey, "Study of Routing in Ad hoc network", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.2, pp.55-57, 2017

E. Edwin Lawrence, R. Latha, "Shortest and Energy Efficient Routing Protocol for MANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.54-57, 2018.