Volume-07 , Special Issue-08 , Apr - 2019 Go Back

Ultrasound imaging is widely used in medical diagnosis, because of its non invasive nature, low cost, capability of forming real time imaging and continued improvement in image quality. The main drawback during diagnosis is the distortion of visual signals. These distortions are termed as speckle noise, which makes the image unclear and make diagnostic more difficult. Many denoising techniques are proposed for effective suppression of speckle noise. In this paper three adaptive filters are used and we have compared and evaluated the performance of famous filters for speckle noise reduction in fetal ultrasound image.

ultrasound images, speckle, PSNR, MSE, MAE

[1] S. Gupta, L. Kaur, R. C. Chauhan, and S. C. Saxena, “A Versatile Technique for Visual Enhancement of Medical Ultrasound Images,” IEEE Transactions on Digital Signal Processing, 17, pp. 542–546, 2007.
[2] A. Pizurica, W. Philips, “ A Versatile Wavelet Domain Noise Filtration Technique for Medical Imaging,” IEEE Transactions on Medical Imaging, 22(3), pp. 323–331, 2003.
[3] A. Buades, B. Coll and J. M. Morel, “A Review of Image Denoising Algorithms with a New One,” IEEE Transactions on Image Denoising Algorithms, 4(2), pp. 490–530, 2005.
[4] S. Karabchevsky, D. Kahana, Ortal Ben-Harush, and H. Guterman,” FPGA-Based Adaptive Speckle Suppression Filter for Underwater Imaging Sonar,” IEEE Journal Of Oceanic Engg., 36(4), 2011.
[5] Antonio Fernandez-Caballero and Juan L. Mateo, “Methodological Approach to Reducing Speckle Noise in Ultrasound images,”International Conference on BioMedical Engineering and Informatics, IEEE Computer Society, DOI 10.1109/BMEI.2008.328, 2008.
[6] J. C. Dainty, “Detection of Images Immersed in Speckle Noise,” Optica Acta, Vol. 18, No. 5, pp. 327-339, 1971.
[7] A. K. Jain,”Fundamental of Digital Image Processing,” Englewood Cliffs, NJ: Prentice-Hall, 1989.
[8] T. Joel and R. Sivakumar, “Despeckling of Ultrasound Medical Images: A Survey,” Journal of Image and Graphics, Vol. 1, No. 3, Sept. 2013.
[9] C. I. Christodouloul, C. Loizou, C.S. Pattichis, M. Pantziarisl, E. Kyriakoul, M. S. Pattichis, C. N. Schizas, A. Nicolaides, De-speckle Filtering in Ultrasound Imaging of the Carotid Artery,” Proceedings of the Second Joint EMBSiBMES Conference Houston, TX, USA, Oct. 23-26, 2002 IEEE.
[10] O. Lopera, R. Heremans, A. Pizurica, and Y. Dupont, “Filtering Speckle Noise in SAS Images to Improve Detection and Identification of Seafloor Targets,” International Waterside Security Conference, 2010.
[11] J. Lee, “Digital Image Enhancement and Noise Filtering by Use of Local Statistics,” IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. Pami-2, No 2, March 1980.
[12] S. K. Narayanan and R. S. D. Wahidabanu, “ A View on Despeckling in Ultrasound Imaging,” International Journal of Signal Processing, Image Processing and Pattern Recognition, 2(3), 2009.
[13] R. Czerwinski, D. L. Jones, and W. D. O’Brien, “Detection of Lines and Boundaries Speckle Images Application to Medical Ultrasound,” IEEE Transactions on Medical Imaging, 18(2), 1999.
[14] K. Bala Prakash, R. Venu Babu, B. Venu Gopal, “Image Independent Filter for Removal of Speckle Noise,” International Journal of Computer Science Issues, 8(3), 2011.
[15] C. Li, “Two Adaptive Filters for Speckle Reduction in SAR imagery by Using the Variance Ratio,” International General of Remote Sensing, Vol. 9, No. 4, pp. 641-653, 1988.

S. Fathimuthu Joharah, S. Kother Mohideen, "Speckle Noise Reduction in Fetal Ultrasound Image Using Various Filtering Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.54-57, 2019.

Comparison identical twins using their face images are a challenge in biometrics. This paper presents experiments done in facial recognition using data from a set of images of twins. Identical twins have the closest genetics-based relationship and therefore, the maximum similarity between face is expected to be found among identical twins. Facial comparision techniques should be able to operate even when similar-looking individuals are encountered of identical twins. The capability of biometric techniques to distinguish between the twins features of multiple reasons. Human face matching capability is often considered as a benchmark for assessing and improving automatic face recognition techniques. This study gives us some clues and shows the various aspects of personality are differently subjected.

Computational studies, Face matching system, Biometric, Twins

[1]. D.Boot, An investigation into the degree of similarity in the handwriting of identical and fraternal twins in New Zealand, J. Am. Soc. Quest. Doc. Exam. 1(2):70–81, 1998.
[2]. K. W. Bowyer, What surprises do identical twins have for identity science?, Computer 44(7):100-102, 2011.
[3]. A. Cohen and T. Vaich, On the Identification of Twins By Their Voices, ESCA Workshop on Automatic Speaker Recognition, Identification and Verification, 213-216, 1994.
[4]. B. Budowle, Editors’ pick: Molecular genetic investigative leads to differentiate monozygotic twins, Investigative Genetics 5:11, 2014.
[5]. J. Daugman, How iris recognition works, IEEE Trans. on Circuits and Systems for Video Technology 14 (1), 21-30, 2004.
[6]. Jain Anil K et al. Biometrics: Personal Identification in Networked Society. Springer, 1999
[7]. Bhanu Bir, Tan Xuejun, Computational Algorithms for Fingerprint Recognition. USA: Kluwer Academic Publishers, 2004
[8]. Zhang T. Y, Suen C. Y, "A Fast Parallel Algorithm for Thinning Digital Patterns". Communications of the ACM. March 1984, Pages 236 – 239
[9]. O’Gorman L., “Overview of fingerprint verification technologies,” Elsevier Information Security Technical Report, Vol. 3, No. 1, 1998.

Y. Angel Blessy, "Integrated Analysis of Face Similarities in Twins", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.58-60, 2019.

Laptops finds wide application in different fields by different users. School and College students are provided with Laptops freely distributed by the Government. These laptops are used by the students for various purposes like academic, programming, writing, editing documents, etc. To carry out the task of analysing the Laptop utilization characteristics, data has been collected from college students by supplying questionnaires. This paper examines student’s perceptions related to the usage of laptop by analyzing its utilization characteristics using Simple K-Means clustering algorithm

Simple K-Mean Clustering, Centroids, Clusters, WEKA tool

[1] Amir Ahamad, Lipika Dey, “A K-Mean clustering algorithm for mixed numerical and categorical data”, Data & Knowledge Engineering, pp 503-527, Vol.63, Iss. 2, November 2007.
[2] Saroj, Kavitha, “Study on Simple k Mean and Modified k Mean Clustering Technique”, IJCSET, (279-281) Vol. 6- No.7, July 2016.
[3] Richa Agarwal, Jitendra Agarwal, “Analysis of Clustering Algorithm of WEKA Tool on Air Pollution Dataset”, International Journal of Computer Applications, (0975-8887) Vol. 168- No.13, June 2017.
[4] Bharat Chaudhari, Manan Parikh, “A Comparative Study of Clustering algorithms Using WEKA tools”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), (2319-4847) Vol. 1- No.2, October 2012.
[5] Harjot Kaur, Er. Prince Verma, “Comparative WEKA Analysis of Clustering Algorithm’s”, I.J. Information Technology and Computer Science, (56-67) No.8, August 2017.
[6] Akanksha Mahajan, Er. Neena Madan, “Survey of K means Clustering and Hierarchical Clustering for Road Accident Analysis”, International Reearch Journal of Engineering and Technology, (2395-0056) Vol. 4- No.6, June 2017.
[7] G. Shiyamala Gowri, Ramasamy Thulasiram and Mahindra Amit Baburao, “Academical Data Mining Application for Estimating Students Performance in WEKA Environment”, ICSET, Vol. 14, 2017.
[8] Jiawei Han, Micheline Kamber, Jian Pei, “Data Mining Concepts and Techniques”, 3rd Edition.

V. Lakshmi Praba, M.A. Saira Banu, "Illustration of K Mean Clustering Algorithm for Analysing Laptop Utilization Dataset", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.61-65, 2019.

Spoofing and Hijacking is a major threat in network security. Spoofing involves attacks that which are associated with the impersonation of third party to steal the credential information’s from a network. Major IP spoofing attacks includes ARP spoofing attacks and DNS spoofing attack, which targets the server. This attack is also called as IP address forging which tries to take away the major information’s from the organizations network systems. There are several tools available to prevent this intrusion prevention system. Some of them are snort, suricata, firewall, netfilter and IPfilter.Penetrating into the network can be prevented using be found using some testing tools like Nmap, Netcat and Hping. Certain attacks denial of service attack and man in the middle attack are more prone to these penetrating malicious threats. Therefore, it is mandatory to take necessary actions to prevent network from these attacks. Defensive strategies like filtering the packets, using an upper layer, using access control list and using a router that is encrypted in nature are encouraged to make the network secure. In this paper, various hijacking spoofing attack is analyzed and their preventive methods are mentioned to enable the network to be well secured. Certain specific protocols are encouraged to do this security measure to prevent the network attacks.

hijacking,spoofing,blindspoofing,zombie,cookie,ferret,wireshark

[1] Abdullah H. Alqahtani, Mohsin Iftikhar,”International Journal of Science and Modern Engineering”, Volume-1, Issue-10, September 2013.
[2] Saurabh Jha, Shabir Ali, “Mobile Agent Based Architecture to Prevent Session Hijacking Attacks in IEEE 802.11 WLAN” International Conference on Computer and Communication Technology,2014.
[3] Vanajakshi, Srikanth Prabhu, ”An Effective Method for Preventing SQL Injection Attack and Session Hijacking” IEEE International Conference On Recent Trends in Electronics Information & Communication Technology 2017.
[4] Nitin Anand, Anil Sharma, “Ferret: A Host Vulnerability Checking Tool”, Proceedings of the 10th IEEE Pacific Rim International Symposium on Dependable Computing, 2004.
[5] Matin Tamizi, Matt Weinstein, “Automated Checking for Windows Host Vulnerabilities”, Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering.
[6] Resul Das, Gurkan Tuna,”Packet Tracing and Analysis of Network Cameras with Wireshark”,IEEE,2017.
[7] Shaoqiang Wang, DongSheng Xu,“Analysis and Application of Wireshark in TCP/IP Protocol Teaching”,International Conference on E-Health Networking, Digital Ecosystems and Technologies, IEEE 2010.
[8] Auttapon Pomsathit, “Effective of Unicast And Multicast IP Address Attack Over Intrusion Detection System with Honeypot” U.S. Government work not protected by U.S. copyright.
[9] Pooja Sharma, Sanjeev Kumar, “BotMAD: Botnet Malicious Activity Detector Based on DNS Traffic Analysis”, 2nd International Conference on Next Generation Computing Technologies, 2016.
[10] Zhenhai Duan, Peng Chen, Fernando Sanchez, “Detecting Spam Zombies by Monitoring Outgoing Messages”,IEEE.
[11] Thawatchai Chomsiri, “Sniffing Packets on LAN without ARP Spoofing” Third 2008 International Conference on Convergence and Hybrid Information Technology,472-477.
[12] S .Raguvaran,“Spoofing Attack: Preventing in Wireless Networks” International Conference on Communication and Signal Processing, April 3-5, 2014,117-121.
[13] Nikhil Tripathi, “Neminath Hubballi Exploiting DHCP Server-side IP Address Conflict Detection: A DHCP Starvation Attack”.
[14] Yongle Wang/s, JunZHang CHen/s,” Hijacking spoofing attack and defense strategy based on Internet TCP sessions”, 2013 IEEE 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation,507-509.
[15] Enos LETSOALO, Prof Sunday OJO,“Session Hijacking Attacks in Wireless Networks: A Review of Existing Mitigation Techniques” IIMC International Information Management Corporation, 2017.
[16] Jeffrey Cashion and Mostafa Bassiouni “Protocol for Mitigating the Risk of Hijacking Social Networking Sites” International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), Orlando, Florida, USA, October 15-18,2017.
[17] Veysel Harun “TOPOLOGY DISCOVERY OF PROFINET NETWORKS USING WIRESHARK” 2013 IEEE.
[18] Pedro R. M. In´acio, Paulo P. Monteiro,” Zombie Identification Port” The Third International Conference on Internet Monitoring and Protection,67-73.
[19] Zhenhai Duan, Peng Chen, Fernando Sanchez,”Detecting Spam Zombies by Monitoring Outgoing Messages”, IEEE INFOCOM 2009 proceedings.
[20] Feng Luo, “Method and Implementation of Building ForCES Protocol Dissector Based on Wireshark” 2010 IEEE.

B.Aravind, D Murugan, "Hijacking Spoofing Attack and Defence Strategy Based on Secured Network Protocols", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.66-70, 2019.

Image fusion is the process of combining two or more images to produce one single fused image. The Fusion process was worked on collecting all the relevant information from the input images and returns a single image. This single image has more informative and accurate than the other input images, and it is consist of all the necessary information. The final fused image is formed by combining many input images into one single image. This paper presents PCA and wavelet transform based Image Fusion.

Image fusion, PCA, wavelet transform, Multi focuses Image

[1] Kusum Rani., Reecha Sharma.“ Study of Different Image fusion Algorithm” Int. J. Emerging Technology and Advanced Engineering, Vol. 3, Iss. 5, May 2013
[2] Nalini B. Kolekar1., R. P. Shelkikar. “Decision level based Image Fusion using Wavelet Transform and Support Vector Machine” Int. J. of Scientific Engineering and Research (IJSER) 2015
[3] VinaySahu., Gagan Sharma “A Hybrid Approach of Image Fusion Using Modified DTCWT with High Boost Filter Technique” Int. J. Computer Science and Network Security Vol.16 No.3, March 2016
[4] Apurva Sharma., Anil Saroliya. “A Brief Review of Different Image Fusion Algorithm” Int. J. Science and Research (IJSR) 2013
[5] HarmandeepKaur., “Analytical Comparision of Various Image Fusion Techniques” vol.5, iss. 5,may 2015
[6] Zhu Shu-long., “Image Fusion using Wavelet Transform”
[7] Agarwal Ruchi Sanjay, Rajkumar Soundrapandiyan, Marimuthu Karuppiah, Rajasekaran Ganapathy. “CT and MRI Image Fusion Based on Discrete Wavelet Transform and Type-2 Fuzzy Logic” Int. J. Intelligent Engineering and System, Vol.10, No.3, 2017
[8] R.Johnson Suthakar, J.Monica Esther M.E, D.Annapoorani, F.Richard Singh Samuel “Study of Image Fusion- Techniques, Method and Applications” Int. J. Computer Science and Mobile Computing Vol. 3, Iss. 11, November 2014.

P. Induja, G. Heren Chellam, "Image Fusion using Principal Component Analysis and Discrete Wavelet Transform", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.71-74, 2019.

Nowadays numerous applications based on Radio Frequency Identification (RFID) systems are developed and also applied to different areas such as building system, health, agriculture, hospitals industry and educational institution. RFID technology means Radio Frequency Identification include automatic wireless identification using electronic tags such as passive and active readers. In this paper, we try to solve the attendance problem in educational sector using this technology. The purpose of this function is to monitor the student attendance to eliminate the waste of time instead of manual attendance process. Therefore they capture the face to face recognition and also allocate the suitable attendance scores for further process.

RFID, Attendance, Active tag, Reader, face recognition

[1]Chitresh, S and Amit K (2010),”An efficient Automatic Attendance Using Fingerprint Verification Techniquee” ”,International Journal on ComputerScience and Engineering (IJCSE),Vol 2 No.pp 264-269
[2]Henry.SS. Arivazhagan and Ganesan(2003),Fingerprint verifiationsusing wavlet Transform ,International Conference on computational and Multimedia Applications, 2003.[3]
[3][Maltoni D, D. Maio, A. K. Jain, S. Prabhaker (2003), “Handbook of Fingerprint Recognition”, Springer, New York,Pp 13-20.
[4] Victor S, Jonathan M,Reece J,and Lemire(2003),”StudentWolfpackClub TrackingSystem”, North Carolina State University. USA.
[5] Nambiar A.N. (2009),” A supply chain perspective of RFID Systems”, World Academy of Science, Engineering and Technology Journal, Volume 6,pp1-5.
[6] Mohamed A.B, Abdel-Hamid A and Mohammed K.Y.,(2009), ”Implementation of an Improved secure system detection for E passport by using EPC RFID tags”, World Academy of Science, Engineering and Technology Journal, Volume 6,pp1-5.
[7]Dawes A.T. (2004),”Is RFIDRight for Your Library”, Journal of Access Services, Volume 2(4), pp 7-13.
Longe O.O.(2009),”Implementation of Student Attendance System using RFID Technology”, B. Tech Project Report, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
[8]Liu C.M. and chen L.S.(2009),”Applications of RFID technology for improving production efficiency in an Integrated –circuit packaging house,”International Journal ofProduction Research, vol47,no.8,pp.2203-2216.
[9]RFID SensNet Lab(2005), A white paper on Automatic attendance System Texas A&M University, Texas, USA
[10]Bardaki, C.Kourouthanassis, P. and Pramatari,K,(2012), Deploying RFID-Enabled Services in the Retail Supply Chain:Lessons toward the Internet of Things, Information Systems Management, Vol.29:no.3,pp 233-245.
[11]Stockman, H. (1948). Communication by Means of Reflected Power, Proceedings of the Institute of Radio Engineers, 36(10) 1196-1204.
[12]Strassner, B. & Chang, K. (2003). Integrated antenna system for wireless RFID tag in monitoring oil drill pipe, Proceedings of the International Symposium on Antennas and Propagation Society, IEEE, 1 208-211.
[13]Terez, T.J. (1992), Bar codes move into maintenance management, Hydraulics & Pneumatics, 45(4) 36.
[14]Domdouzis, K., Kumar, B. & Anumba, C. (2007). Radio-Frequency Identification (RFID) applications: A brief introduction, Advanced Engineering Informatics, 21(4) 350-355.
[15]Fontelera, J. (2005), RFID exploration, Converting Magazine, 23(9) 28-32.
[16]Hunt, V.D., Puglia, A.& Puglia, M. (2007), RFID-A Guide to Radio Frequency Identification, Wiley-Interscience.
[18]. Ko, C.H. & Wang, S.F. (2007), Overcoming difficulties in weekly work planning. Research Journal of Applied Sciences, 2(7) 823-827.

K. Amutha, V. Vallinayagi, "E-security Through RFID", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.75-78, 2019.

Image compression is a challenging field in this era of communication. There is a need to learn and examine the literature of image compression, as the demand for images, video sequences and computer animation has increased at very high rate so that the increment is radically over the years. In this paper we illustrate some current developments that have taken place in still image compression. We deal with about special compression methods and also provide a performance comparison. Functionality and morality of compression methods are discussed in a unified manner. This survey reviews more recent articles on image compression and discuss their role in current research directions. There are several image compression algorithms some of them are lossy and some of them are lossless. Thus medical image, pre press industry, art work, remote sensing images for lossless image compression

Compression; image; Lossless; Lossy; GAP; EDP;

[1] w.B.Pennebaker and J.L.Mitchell,JPEG Still Image data Compression standard. New York,NY,USA: Van Nostrand Reinhold,1993.
[2] Howard,P.G .and Vitter,J.s.(1993) Fast and efficient lossless image compression. In storer,J.A. and Cohn, M.(eds),Proc.Data compression conf.,pp.351-360.IEEE computer society press,los alamitos ,CA.
[3] X.Wu and N.Memon,”Context based adaptive lossless image coding”,IEEE Trans,Commun.,Vol.45,no.4,pp.437-444,Apr.1997.
[4] N.Memon and X.Wu,”recent developments in context based predictive techniques for lossless image compression “,comput.j.,vol.40,no2/3,pp.127-136,1997.
[5] Bernd Meyer, Peter Tischer,” TMW-A new method for lossless image compression “ in 1983.
[6] H.Ye,G.Deng,and J.Devlin , “ Least Squares approach for lossless image coding ,”in proc.5th Int.Symp.signal processing Applications, vol.1,1999,pp.63-66.
[7] xinLi, T.Orchard ,” Edge –Directed Prediction for lossless compression of natural images “ in IEEE Transaction on image compressing”,vol.10.no.6,june 2001.
[8]Seyun Kim & Nam Ik Cho,”Hierarchical Prediction and context adaptive coding for lossless color image compression “ in IEEE vol.23 no.1.jan 2014.
[9] Chee Sun Won, "A Block-Based MAP Segmentation for Image Compressions", IEEE Trans.on Circuits and systems for video technology, vol.8,no. 5,pp.592-601, September. 1998.
[10] N. Ranganathan, Steve G. Romaniuk, and Kameswara Rao Namuduri," A Lossless Image Compression Algorithm Using Variable Block Size Segmentation",” IEEE Trans. Image Process., vol.14,no.10, pp.1396-1405, Oct.1995.
[11] Krishna Ratakonda, and Narendra Ahuja," Lossless Image Compression With Multiscale Segmentation", IEEE Trans.Image Processing,vol.11,no.11, pp.1228-1237, Nov.2002.
[12] N. Ahuja, “A transform for the detection of multiscale image structure,” IEEE Trans. Pattern Anal. Machine Intell., vol. 18, pp. 1211–1235, Dec.1996.
[13] G. Ding, F. Yang, Q. Dai and W. Xu , " Distributed source coding theorem based region of interest image compression method" IEEE Electronics Letters, vol.41, no.22, Oct.2005.
[14] A. Bradley and F. Stienford,”JPEG2000 and region of interest coding”,in proc. Int. conf.DICTA2002, Melbourne, Australia, Jan2002 .
[15] Ricardo L. de Queiroz “Processing JPEG-Compressed Images and Documents”, IEEE Transactions on Image Processing, Vol. 7, No. 12, Pp1661-1667december 1998.
[16] A.A. El-Harby and G.M. Behery," Qualitative Image Compression Algorithm Relying on Quadtree" , ICGST-GVIP, ISSN 1687-398X, Volume (8), Issue (III), October 2008.
[17] Firas A.jassim ,” Image compression by embedding five modulus methos into JPEG” in signal & image processing A: An international journal (SIPIJ) vol4,no.2,april 2013.
[18] Mohammad H.Asghari, Bahram jalali,”discrete Anamorphic transform for image compression” in IEEE signal processing letters ,vol.21,no.7,july 2014.
[19] Merav Huber-lerner,ofer hadar,Stanley r.rotman &revital huber-shalem,” Compression of hyperspectral images containing a subpixel target” in IEEE journal of selected topics in applied earth observations and remote sensing ,2014.

V.Lakshmi Praba, R.S. Rajesh, S. Anitha, "An Image Compression: - A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.79-82, 2019.

Fractal dimension is a one of the vital parameters in fractal geometry that find applications in the area of image processing. Image analysis is a high-level image processing technique which includes texture analysis. Texture analysis characterizes the regions in an image, based on their texture content. It categorizes texture qualities as roughness, smoothness, silkiness, bumpiness, etc. The image intensities are transformed to fractal dimension domain to carry out texture analysis. In this paper, determining the texture for two images- smooth image and coarse image is considered. Differential Box Counting algorithm is applied in the considered two images with different textures. The performance metrics considered are, fractal dimension average, fractal dimension standard deviation and lacunarity. The differential box counting algorithm with different pixels from different regions of the same image is implemented and the obtained results are analyzed.

Image processing, Box-Counting, Texture Analysis, Fractal Dimension, Lacunarity

[1]. P. Shanmugavadivu & V. Sivakumar “Fractal Dimension Based Texture Analysis of Digital images”, SciVerse Science Direct (2012).
[2]. Haiyan Zhang Xingke Tao “Leaf image recognition based on Wavelet and fractal Dimension”, journal of Computational information Systems 11:1 (2015) 141-148.
[3]. Omar S. Al kadi & D. Watson, “Texture Analysis of Aggressive and Nonaggressive Lung Tumor CE CT Images” (2008).
[4]. Mehmet Bayirli,salami selvi and ugur cakilcioglu “Determining different plant leaves fractal dimensions a new approach to taxonomical study of plants”, Bangladesh J. Bot. 43(3): 267-275, December 2014.
[5]. S. Mancuso “Fractal geometry based image analysis of grapevine leaves using the box counting algorithm”, Vitis 38(3), 97-100(1999).
[6]. N. S. Nikolaidis and I. N. Nikolaidis,”A variation of box counting algorithm applied to colour images”, Research Gate, july 2011.

[7]. N. Sankar and B. B Chaudhun,”An efficient differential box counting approach to compute fractal dimension of image”, IEEE Trans Svst Man, Cybern, vol 1, pp 115-120, jan 1994.
[8]. N. C. Kenkel and D. J. Walker, “Fractal and ecology”, ELTE, Budapest Abstracta Botanica17 (1-2); 53-70, 1993.
[9]. Elio Conte and Maria pieralice on “Estimation of fractal dimension on inner structure of leaf samples by using the box counting method”, IJRRAS Volume6 Issue4 PP, 48-59 October 2013.
[10]. Michael J. Ostwald. Josephine Vaughan “The Fractal Dimension of Architecture”, Bikhauser.

V. Lakshmi Praba, S. Esakkiammal, "Texture Analysis in Images with Differential Box Counting Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.83-86, 2019.

Finding the Motif in biological sequences of protein synthesis is a basic problem in determining the protein structure. Detection of the Motif is used with many applications in gene regulation, protein family identification and determination of functionally and structurally important identities. Large amount of biological data is used to resolve the problem of discovering patterns in biological sequences computationally. In this research, we have designed an approach using a system of clustering in data mining to detect frequently occurring informative motifs that are high in information content. We have proposed a comparative approach for Skin Melanin associated problems(SMA) detection in preliminary stages using protein sequence. We have used the protein sequence with normal and abnormal data as the trained dataset. Test instances are classified into normal to abnormal by comparing it with the fundamental dataset. In this paper, We compare and evaluate the performance of two clustering algorithms namely K-means and fuzzy c-means clustering for protein sequences.

clustering ; k-means and fuzzy c-means; SMA

[1] Jipkate, BR & Gohokar, VV 2012 ‘A Comparative Analysis of Fuzzy C-Means Clustering and K Means Clustering Algorithms’. Int. J. of Computational Engineering, vol. 2, no. 3, pp. 737-739
[2] Bezdek, JC 1981, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York., doi: 10.1007/978-1-4757-0450-1
[3] Bora, DJ & Gupta, AK 2014 ‘A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm’. Int. J. of Computer Trends and Technology, vol. 10, no. 2, pp. 108-113
[4] R. Durbin, S. Eddy, A. Krogh, and G. Mitchison, Biological Sequence Analysis: Probabilistic Models of Protein and Nucleic Acids. Cambridge,U.K.: Cambridge Univ. Press, 1998.
[5] J. Finer-Moore and R. M. Stroud, “Amphipathic analysis and possible formation of the ion channel in an acetocholine receptor,” Proc. Nat.Acad. Sci. USA, vol. 81, no. 1, pp. 155–159, 1984.
[6] D. Frishman and P. Args, “Knowledge-based protein secondary structure assignment,” Proteins Struct. Funct. Genet., vol. 23, pp. 566–579, 1995.
[7] S. K. Gupta, K. S. Rao, andV. Bhatnagar, “K-means clustering algorithm for categorical attributes,” in Proc. Data Warehousing and Knowledge Discovery (DaWaK-99), pp. 203–208

Geethamani. R, Kalaivani. B, "Detection of Motif in Protein Sequence Using K-Means and Fuzzy C-Means Algorithms", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.87-90, 2019.

This paper focuses an extensive study of the existing computational work related to prediction of gene regulatory sequences and the relevant factors based on different properties. A hybrid approach is studied which combines position correlation score function and increment of diversity to elucidate signal features and composition features of sequences to improve the accuracy of promoter classifiers. It is found that Markov Model of order K is used to extract features from k-mer frequency of the sequence. Also a Support Vector Machine (SVM) is applied with the transcription signals such as Gc box, TATA box, CAAT box, NIT box and CpG islands and modified Mahalanob discriminant to predict Eukaryotic and Prokaryotic promoters. It is studied that a new approach is implemented using Artificial Neural Network (ANN) with the properties namely curvature, stacking energy and Stress Induced duplex Destabilization (SIDD). To analyze sequence characteristics of prokaryotic and eukaryotic promoters, Convolutional Neural Networks (CNN) is found to be contributing a significant role. This paper analyses the use of a tool bTSSfinder for promoter predicting models. It is identified that an algorithm exist for promoter prediction based on evolutionarily conserved sequences by concentrating AT-rich elements and G-quadruplex sequences using various statistical measures such as recall, precision, specificity accuracy and F1- scores . Algorithms using machine learning based approach are studied to discover promoters in nucleotide sequence using entropy based feature. Some of the remarkable DNA structural features such as DNA bending stiffness, duplex free energy, duplex disrupt energy, stacking energy, DNA denaturation, protein deformation, nucleosome position, propeller twist are studied. Multifarious promoter prediction models which are found to be predicting promoters associated with PoI II sequence, sigma factors such as σ70, σ66, σ54 and transcription factor binding sites. This paper studied that bidirectional genes are co expressed and tends to be involved in the same biological functions with stronger expression correlation. Also studied the intergenic regions enriched of regulatory elements are essential for the transcription initiation. Though various models are found to be effective, still they need to uncover various characteristics. Because of the dynamicity of gene regulatory process, promoter prediction models still require improvement .Indeed this field has a wider exposure of detailed research work.

Eukaryotic and Prokaryotic Promoters, Sigma Factor, TSS

[1] Humberto Barrios, Brenda Valderrama & Enrique Morett , “Compilation and analysis of σ54 dependent promoter sequences” , Nucleic Acids Research 1999 , Vol. 27 , No.22 4305-4313
[2]Qian-Zhong Li, Hao Lin , “The recognition and prediction of sigma70 promoters in Escherichia coli K-12 “ , Journal of theoretical Biology 242 (2006) 135 - 141
[3]Ronna R Mallios, David M Ojcius and David H Ardel , “An iterative strategy combining biophysical criteria and duration hidden Markov models for structural predictions of Chlamydia trachomatis σ66 promoters” , BMC Bioinformatics 2009 , 10:27
[4] Wang Q1, Wan L, Li D, Zhu L, Qian M, Deng M , “Searching for bidirectional promoters in Arabidopsis thaliana” , BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S29
[5] Guofeng Meng Axel Mosig and Martin Vingron, “A computational evaluation of over-representation of regulatory motifs in the promoter regions of differentially expressed genes”, BMC Bioinformatics 2010 11:267
[6] Charles Bland, Abigail S Newsome, Aleksandra A Markovets , “Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks” 7th Annual MCBIOS Conference Bioinformatics: Systems, Biology, Informatics and Computation Jonesboro, AR, USA. 19-20 February 2010 https://doi.org/10.1186/1471-2105-11-S6-S17
[7]Ravi Gupta, Priyankara Wikramasinghe, Anirban Bhattacharyya, Francisco A Perez, Sharmistha Pal and Ramana V Davuluri, “Annotation of gene promoters by integrative data-mining of ChIP-seq Pol-II enrichment data” , BMC Bioinformatics 2010, 11(Suppl 1):S65
[8] C. Premalatha, Chandrabose Aravindan, Kamarajan Kannan , “ Promoter prediction in eukaryotes using soft computing techniques” , 2011 IEEE Conference - Recent Advances in Intelligent Computational Systems
[9]Hao Lin Qian-Zhong Li , “Eukaryotic and prokaryotic promoter prediction using hybrid approach”, Theory Biosci. (2011) 130: 91–100
[10] Yanglan Gan, Jihong Guan and Shuigeng Zhou , “A comparison study on feature selection of DNA structural properties for promoter prediction”, BMC Bioinformatics 2012, 13:4
[11] Zaw ZawHtikea , Shoon LeiWin , “ Recognition of Promoters in DNA Sequences Using Weightily Averaged One-dependence Estimators” , Procedia Computer Science, 2013 , Vol 23, Pages 60-67
[12] Smitha C S , Saritha R , “Computational Transcription Factor Binding Prediction Using Random Forests” , International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) 2014
[13] ]Lin H, Deng EZ, Ding H, Chen W, Chou KC , “ iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition” , Nucleic Acids Res. 2014 Dec 1;42(21):12961-72.
[14] Gusmao EG, Dieterich C, Zenke M, Costa IG , “.Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications “, Bioinformatics. 2014 Nov 15;30 (22): 3143-51
[15] Tianyin Zhoua, Ning Shenb,c, Lin Yanga, Namiko Abed, John Hortonc,e, Richard S. Mannd,f, Harmen J. Bussemakerf,g, Raluca Gordânc,e and Remo Rohsa , “ Quantitative modeling of transcription factor binding specificities using DNA shape” , Proceedings of National Academy of Sciences , 2015 , 112(15), 4654–4659.
[16] Ramzan Kh. Umarov and Victor V. Solovyev , “Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks” , PLoS One . 2017; 12(2): e0171410.
[17] Ilham Ayub Shahmuradov , Rozaimi Mohamad Razali, Salim Bougouffa, Aleksandar Radovanovic and Vladimir B. Bajic , “bTSSfinder: a novel tool for the prediction of promoters in cyanobacteria and Escherichia coli “ , Bioinformatics, 33(3), 2017, 334–340 doi: 10.1093/bioinformatics/btw629
[18] Wenying He , Cangzhi Jia, Yucong Duan and Quan Zou , “70ProPred: a predictor for discovering σ70 promoters based on combining multiple features “ , 11th International Conference on Systems Biology (ISB 2017) Shenzhen, China. 18-21 August 2017
[19] Marco Di Salvo, Eva Pinatel, Adelfia Tala, Marco Fondi , Clelia Peano and Pietro Alifano , “G4PromFinder: an algorithm for predicting transcription promoters in GC-rich bacterial genomes based on AT-rich elements and G-quadruplex motifs” BMC Bioinformatics (2018) 19:36

Sasikala S, Ratha Jeyalakshmi T, "Extensive Review on Computational Predictions of Genomic Regulatory Sequences", International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.91-94, 2019.