SlideShare a Scribd company logo

Fractal Compression of an AVI Video File using DWT and Particle Swarm Optimization

•
•
I
IJCSIS Research Publications

In the current scenario compression of video files is in high demand. Color video compression has become a significant technology to lessen the memory space and to decrease transmission time. Video compression using fractal technique is based on self similarity concept by comparing the range block and domain block. However, its computational complexity is very high. In this paper we presented hybrid video compression technique to compress Audio/Video Interleaved file and overcome the problem of Computational complexity. We implemented Discrete Wavelet Transform and hybrid fractal HV partition technique using Particle Swarm Optimization (called mapping of PSO) for compression of videos. The analysis demonstrate that hybrid technique gives a very good speed up to compress video and achieve Peak Signal to Noise Ratio.

1 of 4
Download to read offline
Fractal Compression of an AVI Video File using DWT and Particle Swarm Optimization Shraddha Pandit,Research Scholor Department of Computer Science & Engineering, University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal, India svpandit_pict@yahoo.co.in Piyush Kumar Shukla,Assistant Professor Department of Computer Science & Engineering University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal, India pphdwss@gmail.com Akhilesh Tiwari,Associate Professor Department of Computer Science & Engineering and Information Technology, Madhav Institute of Technology & Science Gwalior, India atiwari.mits@gmail.com Abstract— In the current scenario compression of video files is in high demand. Color video compression has become a significant technology to lessen the memory space and to decrease transmission time. Video compression using fractal technique is based on self similarity concept by comparing the range block and domain block. However, its computational complexity is very high. In this paper we presented hybrid video compression technique to compress Audio/Video Interleaved file and overcome the problem of Computational complexity. We implemented Discrete Wavelet Transform and hybrid fractal HV partition technique using Particle Swarm Optimization (called mapping of PSO) for compression of videos. The analysis demonstrate that hybrid technique gives a very good speed up to compress video and achieve Peak Signal to Noise Ratio. Keywords- Video Compression, DWT, Fractal, HV partitioning, MATLAB, MSE, PSO,PSNR,ET,CR. I. INTRODUCTION In current scenario digital video plays very, important role in information technology, including [1] teleconferencing, broadcasting, military applications, entertainment and many more [1,2 ]. People need to access video very quickly and within a limited period of time through various digital devices [3, 4]. To deal with this situation compression of video file [5, 6] is the necessity. The DWT transform [1, 3, 9] form the layers of frames in terms of group of frames [2, 4]. The processing of frames [1, 2, 10] in layer is very slow for the compression [6]. Due to slow compression encoding of video [5, 6, 7] is major problem in DWT based video compression [7, 8]. For the encoding and fast processing [3, 5] transform function [7] used partition process in terms of horizontal and vertical [6, 8] for the local processing of layers frames in different groups of frames. The reduction of search space [5, 9] in terms of layers of block for coding used PSO (particle swarm optimization) [1, 3]. The PSO [8] reduces the layers space and decrease the encoding time [6] and reduces encoding time bust [5, 10] the performance of video compression [5]. In this proposed work we represent background of discrete wavelet transform [3, 5] and Fractal HV partition technique [5, 7] with PSO [7, 8] in segment II and segment III represent experimental results and lastly segment IV represent conclusion. II. BACKGROUND DISCRETE WAVELET TRANSFORM Wavelet transforms [1, 2, 10] is broadly used in computer vision [6, 9] as an image compression [4, 6, 8]. The phenomenon of wavelet is closely allied to multi-scale [2] and multi-resolution [4] application and it has been used into image fusion technique [5, 7]. Implementation of Discrete Wavelet Transform as an image processing method generates the transformation values called wavelet coefficient [5, 7]. The fundamental concept behind wavelets is to examine signal according to scale. During recent years, it has gained a lot of interest in the field of signal processing [5, 7], numerical analysis and mathematics [8]. In general, the wavelet transform is an advanced method of signal and image analysis [4, 5]. International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 1, January 2018 128 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
FRACTAL HV PARTITIONING TECHNIQUE AND MAPPING OF PSO In a HV partition [2, 6, 8] a rectangular range square [8] can be part either on a level plane or vertically into two littler rectangles [4]. A choice about the split area must be made. While embraces a model in view of edge area [4], we take after and propose to part a rectangle with the end goal that an estimation by its DC segment (DC segment of a piece is characterized here as the square whose pixel esteems are equivalent [6] to the normal power of the square.) in each part gives an insignificant aggregate square mistake [5]. We anticipate that fractal coding will deliver moderately little collection mistakes with this decision since approximation by the DC segment [3] alone will as of now give little wholes of squared blunders by plan of the part conspire, and for the guess of the dynamic piece [6] of the range squares we have more areas accessible, if the range piece fluctuations are low [2]. The HV partition technique [3, 7] proceed the video data for the process of encoding in terms of domain and Range block in terms of column for the encoding [4] in terms of horizontal and vertical column of video data[1]. Here in this proposed work to reduce the searching time [6] between range and domain block we used PSO technique with HV partition technique [7] which reduces the searching time of block symmetry and increase the block symmetry. III. EXPERIMENTAL RESULTS In this paper the proposed algorithms of DWT, Fractal transform and PSO algorithm [5] has been implemented using MATLAB 8.0 code. For testing varied audio/video interleaved videos [7], we used a configuration of desktop Intel processor with 1.86GZ with 2 GB of RAM [3] running on Windows 2007.For the evaluation of the performance used some standard parameters such as PSNR, MSE, CR and ET of video [5, 7]. The measured parameter gives better result, instead of DWT based video compression [8]. For testing videos are obtained from CV vision library [6, 8]. All process we describe here. Description of Dataset Table 1. Shows description of dataset used for compression of varied videos S.No. Video Name Format of video 1 Battle video Avi 2 Duck video Avi 3 Cartoonduck video Avi 4 Lab video Avi 5 Sumrf video Avi 6 Gunner video Avi 7 Airplane video Avi Figure 1. Shows that the original and compressed video view of battle.avi video using DWT method. Figure 2. Shows that the original and compressed video view of battle.avi video using mapping of PSO method. Also get the result of compression of PSNR, Compression Ratio, Mean Square Error and Encoding time for all the tested videos. The following table shows the comparison of DWT and Mapping of PSO of varied AVI videos with respect to:  CR-Compression Ratio[8]  MSE- Mean Square Error Rate[8]  PSNR- Peak Signal to Noise Ratio[8]  ET- Estimated Time[8] Table 2. Analysis for DWT and Mapping of PSO Method for battle.avi video DWT Mapping of PSO Compression Ratio 0.77 0.81 MSE 11.31 11.09 PSNR 23.14 25.14 Encoding Time 1.80 1.92 International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 1, January 2018 129 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
Table 3. Analysis for DWT and Mapping of PSO Method for duck.avi video DWT Mapping of PSO Compression Ratio 0.44 0.59 MSE 11.55 10.21 PSNR 22.34 24.94 Encoding Time 0.52 0.69 Table 4. Analysis for DWT and Mapping of PSO Method for cartoonduck.avi video DWT Mapping of PSO Compression Ratio 0.89 0.96 MSE 18.74 19.12 PSNR 18.48 21.02 Encoding Time 0.76 0.88 Figure3: Shows the comparative performance of compression ratio using DWT and mapping of PSO method for battle.avi, duck.avi and cartoonduck.avi video Figure4. Shows the comparative performance of MSE using DWT and mapping of PSO method for battle.avi, duck.avi and cartoonduck.avi video Figure5. Shows the comparative performance of PSNR using DWT and mapping of PSO method for battle.avi, duck.avi and cartoonduck.avi video Figure6. Shows the comparative performance of Encoding Time using DWT and mapping of PSO method for battle.avi, duck.avi and cartoonduck.avi video. IV. CONCLUSION It is essential to reduce the storage space and encoding time of video. From our experimentation and results it is conclude that the DWT transform function faced problem of distortion of layers, due to this reason the value of PSNR is decrease. The particle swarm optimization provides the dual searching mode and reduces the multi-scales H-V partition relation of blocks and references blocks. This reduces space speedup the compression technique and also remains the quality of video. The mapping of PSO also reduces the redundant frames of video and reduces the value of MSE and increase the value of PSNR. ACKNOWLEDGMENT We express our gratitude towards all the authors which are mentioned in this paper as we are referring their contribution our research. REFERENCES [1] Swalpa Kumar Roy,Siddharth Kumar, Bhabatosh Chanda,Bidyut B.Chaudhuri,Soumitra Banerjee, “Fractal image compression using upper bound on scaling parameter”, Chaos,Solitons and Fractals, Elsevier, vol.106,Pp.16-22,2018. [2] Milind V. Kulkarni, D.B. Kulkarni, “Analysis of fractal inter frame video coding using parallel approach”, Journal of Signal, Image and Video Processing, Springer, vol.11, no.4, Pp.629-634, 2017. [3] Zhehuang Huang, “Frame-groups based fractal video compression and its parallel implementation in Hadoop cloud computing environment”, International Journal of Multidimensional Systems and Signal Processing, Springer, vol.29, no.100, Pp.1-18, 2017. [4] Sonali V. Kolekar, Prof.Prachi Sorte, “An Efficient and Secure Fractal Image and Video Compression”, International Journal of Innovative Research in Computer and Communication Engineering, vol.4, no.12, Pp.20643-20648, 2016. [5] V. Yaswanth Varma,, T. Nalini Prasad, N. V. Phani Sai Kumar, “IMAGE COMPRESSION METHODS BASED ON TRANSFORM CODING AND FRACTAL CODING”, International Journal of Engineering Sciences and Research Technology,vol.6,no.10,Pp.481-487,2017. International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 1, January 2018 130 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
[6] Ali Ibrahim Khaleel, N.Anupama, “Haar Wavelet Based Joint Compression Method Using Adaptive Fractal Image Compression”, IOSR Journal of Computer Engineering, vol.18, no.3, Pp.69-73, 2016. [7] Umesh B Kodgule,B.A.Sonkamble, “Discrete Wavelet Transform based Fractal Image Compression using Parallel Approach”, International Journal of Computer Applications,vol.122,no.16,Pp.18-22,2015. [8] S.keerthika,S.Vidhya , “Fractal Image Compression with Advanced Particle Swarm Optimization and Sorting”,International Journal of Computer Science Trends and Technology,vol.4,no.5, Pp.309-312,2016. [9] Gauri R.Desai, Mahesh S.Chavan, “Fractal Image Compression by Range Block Classification”, International Research Journal of Engineering and Technology, vol.4, no.1, Pp.525-528, 2017. [10] Veena K.K., Bhuvaneswari P, “Various Techniques of Fractal Image Compression – A Review, International Journal of Engineering And Computer Science, vol.4, no.3, Pp.10984-10987, 2015. AUTHORS PROFILE Shraddha Pandit is presently pursuing Ph.D. in Computer Science & Engineering from University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal. She received B.E. in Computer Science & Engineering from Amravati University and M.E. in Computer Science & Engineering from Pune University, respectively. Her research interest include Image processing, Image Compression, Data Mining and Data Warehousing. Dr. Piyush Kumar Shukla received his Bachelor’s degree in Electronics & Communication Engineering, LNCT, Bhopal in 2001, M.Tech (Computer Science & Engineering) in 2005 from SATI, Vidisha and Ph.D. (Computer Science & Engineering) in 2013 from RGPV, Bhopal. M.P. India. He is a Member of ISTE (Life Member), IEEE, IACSIT, IAENG. Currently he is working as an Assistant Prof. in Department of Computer Science & Engineering, UIT-RGPV Bhopal. He is also I/C of PG Program (Dual Degree Integrated PG-Programs) in DoCSE, UIT, RGPV, Bhopal, Madhya Pradesh, Bhopal. He has published more than 40 Research Papers in various International & National Journals & Conferences, including 04 Papers in SCIE Journals & More than 10 papers in Scopus Journals. Dr. Akhilesh Tiwari has received Ph.D. degree in Information Technology from Rajiv Gandhi Technological University, Bhopal, India. He is currently working as Associate Professor in the department of CSE & IT, Madhav Institute of Technology & Science (MITS), Gwalior, M.P. (India). His area of current research includes knowledge discovery in databases & data mining, Terrorist Network Mining and Business Intelligence. At present he is acting as a member of Board of Studies at MITS Gwalior, India. He has published more than 50 research papers in the Journals & Conferences of International repute. He is the Editor of Technia: International Journal of Computing Science & Communication Technologies. He is also acting as a reviewer & member in the editorial board of various international journals and program committee member/ member in the advisory board of various international conferences. He is in the panel of experts in various government bodies like UPSC, NIELIT etc. He has guided more than 20 dissertations at Master’s level and presently supervising 04 research scholars at doctoral level. He is having the memberships of various Academic/ Scientific societies including IETE, GAMS, IACSIT, and IAENG. International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 1, January 2018 131 https://sites.google.com/site/ijcsis/ ISSN 1947-5500

More Related Content

Fractal Compression of an AVI Video File using DWT and Particle Swarm Optimization

  • 1. Fractal Compression of an AVI Video File using DWT and Particle Swarm Optimization Shraddha Pandit,Research Scholor Department of Computer Science & Engineering, University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal, India svpandit_pict@yahoo.co.in Piyush Kumar Shukla,Assistant Professor Department of Computer Science & Engineering University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal, India pphdwss@gmail.com Akhilesh Tiwari,Associate Professor Department of Computer Science & Engineering and Information Technology, Madhav Institute of Technology & Science Gwalior, India atiwari.mits@gmail.com Abstract— In the current scenario compression of video files is in high demand. Color video compression has become a significant technology to lessen the memory space and to decrease transmission time. Video compression using fractal technique is based on self similarity concept by comparing the range block and domain block. However, its computational complexity is very high. In this paper we presented hybrid video compression technique to compress Audio/Video Interleaved file and overcome the problem of Computational complexity. We implemented Discrete Wavelet Transform and hybrid fractal HV partition technique using Particle Swarm Optimization (called mapping of PSO) for compression of videos. The analysis demonstrate that hybrid technique gives a very good speed up to compress video and achieve Peak Signal to Noise Ratio. Keywords- Video Compression, DWT, Fractal, HV partitioning, MATLAB, MSE, PSO,PSNR,ET,CR. I. INTRODUCTION In current scenario digital video plays very, important role in information technology, including [1] teleconferencing, broadcasting, military applications, entertainment and many more [1,2 ]. People need to access video very quickly and within a limited period of time through various digital devices [3, 4]. To deal with this situation compression of video file [5, 6] is the necessity. The DWT transform [1, 3, 9] form the layers of frames in terms of group of frames [2, 4]. The processing of frames [1, 2, 10] in layer is very slow for the compression [6]. Due to slow compression encoding of video [5, 6, 7] is major problem in DWT based video compression [7, 8]. For the encoding and fast processing [3, 5] transform function [7] used partition process in terms of horizontal and vertical [6, 8] for the local processing of layers frames in different groups of frames. The reduction of search space [5, 9] in terms of layers of block for coding used PSO (particle swarm optimization) [1, 3]. The PSO [8] reduces the layers space and decrease the encoding time [6] and reduces encoding time bust [5, 10] the performance of video compression [5]. In this proposed work we represent background of discrete wavelet transform [3, 5] and Fractal HV partition technique [5, 7] with PSO [7, 8] in segment II and segment III represent experimental results and lastly segment IV represent conclusion. II. BACKGROUND DISCRETE WAVELET TRANSFORM Wavelet transforms [1, 2, 10] is broadly used in computer vision [6, 9] as an image compression [4, 6, 8]. The phenomenon of wavelet is closely allied to multi-scale [2] and multi-resolution [4] application and it has been used into image fusion technique [5, 7]. Implementation of Discrete Wavelet Transform as an image processing method generates the transformation values called wavelet coefficient [5, 7]. The fundamental concept behind wavelets is to examine signal according to scale. During recent years, it has gained a lot of interest in the field of signal processing [5, 7], numerical analysis and mathematics [8]. In general, the wavelet transform is an advanced method of signal and image analysis [4, 5]. International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 1, January 2018 128 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
  • 2. FRACTAL HV PARTITIONING TECHNIQUE AND MAPPING OF PSO In a HV partition [2, 6, 8] a rectangular range square [8] can be part either on a level plane or vertically into two littler rectangles [4]. A choice about the split area must be made. While embraces a model in view of edge area [4], we take after and propose to part a rectangle with the end goal that an estimation by its DC segment (DC segment of a piece is characterized here as the square whose pixel esteems are equivalent [6] to the normal power of the square.) in each part gives an insignificant aggregate square mistake [5]. We anticipate that fractal coding will deliver moderately little collection mistakes with this decision since approximation by the DC segment [3] alone will as of now give little wholes of squared blunders by plan of the part conspire, and for the guess of the dynamic piece [6] of the range squares we have more areas accessible, if the range piece fluctuations are low [2]. The HV partition technique [3, 7] proceed the video data for the process of encoding in terms of domain and Range block in terms of column for the encoding [4] in terms of horizontal and vertical column of video data[1]. Here in this proposed work to reduce the searching time [6] between range and domain block we used PSO technique with HV partition technique [7] which reduces the searching time of block symmetry and increase the block symmetry. III. EXPERIMENTAL RESULTS In this paper the proposed algorithms of DWT, Fractal transform and PSO algorithm [5] has been implemented using MATLAB 8.0 code. For testing varied audio/video interleaved videos [7], we used a configuration of desktop Intel processor with 1.86GZ with 2 GB of RAM [3] running on Windows 2007.For the evaluation of the performance used some standard parameters such as PSNR, MSE, CR and ET of video [5, 7]. The measured parameter gives better result, instead of DWT based video compression [8]. For testing videos are obtained from CV vision library [6, 8]. All process we describe here. Description of Dataset Table 1. Shows description of dataset used for compression of varied videos S.No. Video Name Format of video 1 Battle video Avi 2 Duck video Avi 3 Cartoonduck video Avi 4 Lab video Avi 5 Sumrf video Avi 6 Gunner video Avi 7 Airplane video Avi Figure 1. Shows that the original and compressed video view of battle.avi video using DWT method. Figure 2. Shows that the original and compressed video view of battle.avi video using mapping of PSO method. Also get the result of compression of PSNR, Compression Ratio, Mean Square Error and Encoding time for all the tested videos. The following table shows the comparison of DWT and Mapping of PSO of varied AVI videos with respect to:  CR-Compression Ratio[8]  MSE- Mean Square Error Rate[8]  PSNR- Peak Signal to Noise Ratio[8]  ET- Estimated Time[8] Table 2. Analysis for DWT and Mapping of PSO Method for battle.avi video DWT Mapping of PSO Compression Ratio 0.77 0.81 MSE 11.31 11.09 PSNR 23.14 25.14 Encoding Time 1.80 1.92 International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 1, January 2018 129 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
  • 3. Table 3. Analysis for DWT and Mapping of PSO Method for duck.avi video DWT Mapping of PSO Compression Ratio 0.44 0.59 MSE 11.55 10.21 PSNR 22.34 24.94 Encoding Time 0.52 0.69 Table 4. Analysis for DWT and Mapping of PSO Method for cartoonduck.avi video DWT Mapping of PSO Compression Ratio 0.89 0.96 MSE 18.74 19.12 PSNR 18.48 21.02 Encoding Time 0.76 0.88 Figure3: Shows the comparative performance of compression ratio using DWT and mapping of PSO method for battle.avi, duck.avi and cartoonduck.avi video Figure4. Shows the comparative performance of MSE using DWT and mapping of PSO method for battle.avi, duck.avi and cartoonduck.avi video Figure5. Shows the comparative performance of PSNR using DWT and mapping of PSO method for battle.avi, duck.avi and cartoonduck.avi video Figure6. Shows the comparative performance of Encoding Time using DWT and mapping of PSO method for battle.avi, duck.avi and cartoonduck.avi video. IV. CONCLUSION It is essential to reduce the storage space and encoding time of video. From our experimentation and results it is conclude that the DWT transform function faced problem of distortion of layers, due to this reason the value of PSNR is decrease. The particle swarm optimization provides the dual searching mode and reduces the multi-scales H-V partition relation of blocks and references blocks. This reduces space speedup the compression technique and also remains the quality of video. The mapping of PSO also reduces the redundant frames of video and reduces the value of MSE and increase the value of PSNR. ACKNOWLEDGMENT We express our gratitude towards all the authors which are mentioned in this paper as we are referring their contribution our research. REFERENCES [1] Swalpa Kumar Roy,Siddharth Kumar, Bhabatosh Chanda,Bidyut B.Chaudhuri,Soumitra Banerjee, “Fractal image compression using upper bound on scaling parameter”, Chaos,Solitons and Fractals, Elsevier, vol.106,Pp.16-22,2018. [2] Milind V. Kulkarni, D.B. Kulkarni, “Analysis of fractal inter frame video coding using parallel approach”, Journal of Signal, Image and Video Processing, Springer, vol.11, no.4, Pp.629-634, 2017. [3] Zhehuang Huang, “Frame-groups based fractal video compression and its parallel implementation in Hadoop cloud computing environment”, International Journal of Multidimensional Systems and Signal Processing, Springer, vol.29, no.100, Pp.1-18, 2017. [4] Sonali V. Kolekar, Prof.Prachi Sorte, “An Efficient and Secure Fractal Image and Video Compression”, International Journal of Innovative Research in Computer and Communication Engineering, vol.4, no.12, Pp.20643-20648, 2016. [5] V. Yaswanth Varma,, T. Nalini Prasad, N. V. Phani Sai Kumar, “IMAGE COMPRESSION METHODS BASED ON TRANSFORM CODING AND FRACTAL CODING”, International Journal of Engineering Sciences and Research Technology,vol.6,no.10,Pp.481-487,2017. International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 1, January 2018 130 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
  • 4. [6] Ali Ibrahim Khaleel, N.Anupama, “Haar Wavelet Based Joint Compression Method Using Adaptive Fractal Image Compression”, IOSR Journal of Computer Engineering, vol.18, no.3, Pp.69-73, 2016. [7] Umesh B Kodgule,B.A.Sonkamble, “Discrete Wavelet Transform based Fractal Image Compression using Parallel Approach”, International Journal of Computer Applications,vol.122,no.16,Pp.18-22,2015. [8] S.keerthika,S.Vidhya , “Fractal Image Compression with Advanced Particle Swarm Optimization and Sorting”,International Journal of Computer Science Trends and Technology,vol.4,no.5, Pp.309-312,2016. [9] Gauri R.Desai, Mahesh S.Chavan, “Fractal Image Compression by Range Block Classification”, International Research Journal of Engineering and Technology, vol.4, no.1, Pp.525-528, 2017. [10] Veena K.K., Bhuvaneswari P, “Various Techniques of Fractal Image Compression – A Review, International Journal of Engineering And Computer Science, vol.4, no.3, Pp.10984-10987, 2015. AUTHORS PROFILE Shraddha Pandit is presently pursuing Ph.D. in Computer Science & Engineering from University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal. She received B.E. in Computer Science & Engineering from Amravati University and M.E. in Computer Science & Engineering from Pune University, respectively. Her research interest include Image processing, Image Compression, Data Mining and Data Warehousing. Dr. Piyush Kumar Shukla received his Bachelor’s degree in Electronics & Communication Engineering, LNCT, Bhopal in 2001, M.Tech (Computer Science & Engineering) in 2005 from SATI, Vidisha and Ph.D. (Computer Science & Engineering) in 2013 from RGPV, Bhopal. M.P. India. He is a Member of ISTE (Life Member), IEEE, IACSIT, IAENG. Currently he is working as an Assistant Prof. in Department of Computer Science & Engineering, UIT-RGPV Bhopal. He is also I/C of PG Program (Dual Degree Integrated PG-Programs) in DoCSE, UIT, RGPV, Bhopal, Madhya Pradesh, Bhopal. He has published more than 40 Research Papers in various International & National Journals & Conferences, including 04 Papers in SCIE Journals & More than 10 papers in Scopus Journals. Dr. Akhilesh Tiwari has received Ph.D. degree in Information Technology from Rajiv Gandhi Technological University, Bhopal, India. He is currently working as Associate Professor in the department of CSE & IT, Madhav Institute of Technology & Science (MITS), Gwalior, M.P. (India). His area of current research includes knowledge discovery in databases & data mining, Terrorist Network Mining and Business Intelligence. At present he is acting as a member of Board of Studies at MITS Gwalior, India. He has published more than 50 research papers in the Journals & Conferences of International repute. He is the Editor of Technia: International Journal of Computing Science & Communication Technologies. He is also acting as a reviewer & member in the editorial board of various international journals and program committee member/ member in the advisory board of various international conferences. He is in the panel of experts in various government bodies like UPSC, NIELIT etc. He has guided more than 20 dissertations at Master’s level and presently supervising 04 research scholars at doctoral level. He is having the memberships of various Academic/ Scientific societies including IETE, GAMS, IACSIT, and IAENG. International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 1, January 2018 131 https://sites.google.com/site/ijcsis/ ISSN 1947-5500