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This paper discusses the medical imaging and the noise present in the images. Different denoising and filtering techniques are also discussed. The paper focuses on the NLM filter and further types used to denoise rician noise present in MRI images.NLM filter enhances the textures and edges of an image. NLM filter provides a feasible method or ways to get the least results by reduction of geometrical configuration.

Image Denoising, Noise, Filters, MRI, NLM filter

[1] Buades A.Coll B.Morel JM (2005). A review of image denoising algorithms with a new one, SIAM Multiscale Model Simul: 4(2):490-530 DOI: 10.1137/040616024.
[2]T. Kalaiselvi, N. Kalaichelvi, “Investigation on Image Denoising Techniques of Magnetic Resonance Images”, International Journal of Computer Sciences and Engineering, Vol.06, Issue.04, pp.104-111, 2018.
[3]Zhang, K., Zuo, W., & Zhang, L. (2018). FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising. IEEE Transactions on Image Processing, 27(9), 4608–4622. DOI: 10.1109/tip.2018.2839891.
[4]Diwakar, M., & Kumar, M. (2018). A review on CT image noise and its denoising. Biomedical Signal Processing and Control, 42, 73–88. DOI: 10.1016/j.bspc.2018.01.010.
[5]Rajneesh Mishra, Priyanka Pateriya, D.K. Rajoriya, Anand Vardhan Bhalla, “Comparative Analysis of Various Image Denoising Techniques: A Review Paper”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.35-39, 2015.
[6]Channoufi, I., Bourouis, S., Bouguila, N., & Hamrouni, K. (2018). Image and video denoising by combining unsupervised bounded generalized Gaussian mixture modeling and spatial information. Multimedia Tools and Applications. DOI: 10.1007/s11042-018-5808-9.
[7] Tulsi, Prabhpreet Kaur & Singh, Gurvinder & Kaur, Parminder. (2017). A Review of Denoising Medical Images Using Machine Learning Approaches. Current Medical Imaging Reviews. 13. 10.2174/1573405613666170428154156.
[8]Oulhaj, H., Amine, A., Rziza, M., & Aboutajdine, D. (2012). Noise Reduction in Medical Images - comparison of noise removal algorithms. 2012 International Conference on Multimedia Computing and Systems. DOI: 10.1109/icmcs.2012.6320218.
[9]Cruz, C., Mehta, R., Katkovnik, V., & Egiazarian, K. O. (2018). Single Image Super-Resolution Based on Wiener Filter in Similarity Domain. IEEE Transactions on Image Processing, 27(3), 1376–1389. DOI: 10.1109/tip.2017.2779265.
[10]Riji, R., Rajan, J., Sijbers, J., & Nair, M. S. (2014). Iterative bilateral filter for Rician noise reduction in MR images. Signal, Image and Video Processing, 9(7), 1543–1548. DOI: 10.1007/s11760-013-0611-6.
[11]Sharif, M., Hussain, A., Jaffar, M. A., & Choi, T.-S. (2015). Fuzzy-based hybrid filter for Rician noise removal. Signal, Image and Video Processing, 10(2), 215–224. DOI: 10.1007/s11760-014-0729-1.
[12]Sudeep, P. V., Palanisamy, P., Rajan, J., Baradaran, H., Saba, L., Gupta, A., & Suri, J. S. (2016). Speckle reduction in medical ultrasound images using an unbiased non-local means method. Biomedical Signal Processing and Control, 28, 1–8. DOI: 10.1016/j.bspc.2016.03.001.
[13]Wiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S. P., & Barillot, C. (2008). Rician Noise Removal by Non-Local Means Filtering for Low Signal-to-Noise Ratio MRI: Applications to DT-MRI. Lecture Notes in Computer Science, 171–179. DOI: 10.1007/978-3-540-85990-1_21.
[14]Shreyamsha Kumar, B. K. (2012). Image denoising based on non-local means filter and its method noise thresholding. Signal, Image and Video Processing, 7(6), 1211–1227. DOI: 10.1007/s11760-012-0389-y.
[15]Yang, J., Fan, J., Ai, D., Wang, X., Zheng, Y., Tang, S., & Wang, Y. (2016). Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image. Neurocomputing, 195, 88–95. DOI: 10.1016/j.neucom.2015.05.140.
[16]Singh, C., Ranade, S. K., & Singh, K. (2016). Invariant moments and transform-based unbiased nonlocal means for denoising of MR images. Biomedical Signal Processing and Control, 30, 13–24. DOI: 10.1016/j.bspc.2016.05.007.
[17]Xu, S., Yang, X., & Jiang, S. (2017). A fast nonlocally centralized sparse representation algorithm for image denoising. Signal Processing, 131, 99–112. DOI: 10.1016/j.sigpro.2016.08.006.

Damini, Prabhpreet Kaur, "Denoising MRI images using NLM filter," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1-11, 2019.

The Internet of Things is a network of interconnected devices or objects that are provided with the ability to send and receive data, thereby paving the way for a smarter world. The existing bus tracking system is based only on Global Positioning System and provides the current location of the bus in terms of latitude and longitude. The proposed system uses Global Positioning system and the Radio Frequency Identification Technology to track, locate and monitor the buses. The system uses an android application to list out all the available buses operating between the specified source and destination along with the route and their operating time slots. The estimated arrival time of the bus and the passenger’s reaching time to their destination, is predicted based on the calculated Haversine distance between the source and destination, GPS location of the bus and the average speed of the bus. An automatic passenger counting system is provided to keep in track of the total number of passengers travelling in the bus and the overcrowded buses are intimated to the users through the android application.

Internet of Things, Bus tracking, Passenger count, Arrival Time Prediction, Haversine

[1] Mr. Anilkumar J Kadam, Mr. Virendra Patil, Mr. Kapish Kaith, Ms. Dhanashree Patil, Ms. Sham “Developing a Smart Bus for Smart City using IoT Technology”, International conference on Electronics, Communication and Aerospace Technology ICECA, pp.1138-1143, 2018.
[2] Kai Qin, Jianping Xing, Gang Chen, Linjian Wang,Jie Qin, “The design of Intelligent Bus Movement Monitoring and Station Reporting System”, Proceedings of IEEE International Conference on Automation and Logistics, pp.2822-2827, 2008.
[3] Muhammad Umar Farooq,Aamna Shakoor,Abu Bakar Siddique, “GPS based Public Transport Arrival Time Prediction”, International Conference on Frontiers of Information Technology, pp.76-81, 2017.
[4] Ahmet Sayar,Suleyman Eken,“A Smart Bus Tracking System based on Location aware Services and QR codes”, IEEE International Symposiums on Innovations in Intelligent Systems and Applications Proceedings, 2014.
[5] Pengfei Zhou, Yuanqing Zheng Mo Li,“How long to wait? Predicting bus arrival time with mobile phone based participating system”, IEEE Transactions on mobile computing, Vol.13, No. 6, 2014.
[6] Sujatha k, Sruthi K J, Nageswaro Rao P V, Arjuna Rao A,“Design and Development of Android Mobile based Bus tracking System” , IEEE, First International Conference on Networks and Soft Computing, pp.231-235, 2014.
[7] Polamarasetty Anudeep, N. Krishna Prakash, “Intelligent Passenger Information System Using IoT for Smart Cities”, Smart Innovations in Communication and Computational Sciences, Advances in Intelligent Systems and Computing, 2018
[8] Peilan He, Guiyuan Jiang , Siew-Kei Lam, and Dehua Tang, “Travel-Time Prediction of Bus Journey with Multiple Bus Trips”, IEEE transactions on Intelligent Transportation Systems, 2018.
[9] Komal Satish Agarwal, Kranti Dive, “RFID Based Intelligent Bus Management and Monitoring System”, International Journal of Engineering Research and Technology, vol. 3 no. 7, 2014.
[10] Jay Lohokare, Reshul Dani, Sumedh Sontakke,“Scalable Tracking System for Public Buses Using IoT Technologies”, International Conference on Emerging Trends and Innovation, pp.104-109, 2017.
[11] Manini Kumbhar,Meghana Survase,Pratibha Mastud,Avdhut Salunke, “Real Time Web based Bus Tracking System”, International Research Journal of Engineering and Technology,vol. 03,no. 02, 2016.
[12] Liang H S, “Intelligent bus System Design Based on Internet of Things Technology”, Science and Technology Wind, vol.35, no.3, pp.15, 2014.
[13] R. Maruthaveni, V. Kathiresan, "A Critical Study on RFID", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.2, pp.62-65, 2018.
[14] Mahajan J.R., C.S. Rawat, "Object Detection and Tracking using Congnitive Approach", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.136-140, 2017.

M. Muthuselvi, M.A. Abi, V.A. Arthi, M.S. Deivanayagi, "An Intelligent Bus System Based on Internet of Things for Urban Environments," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.12-18, 2019.

Technological advancements and the usage of internet have been increasing very rapidly in the modern society from day to day. These advancements are leading to many security threats and security issues. There is a need to provide security to confidential information which has been a major concern from the past to the present times. In order to arrest all these security challenges a novel approach has been provided using Steganography and Cryptography through which data security and confidentiality can be ensured. Steganography deals about hiding the data within an image, audio or video file whereas Cryptography is all about transforming the data into unreadable format. In the proposed system the secret message is encrypted using RSA and Column Transposition technique. Later the encrypted message is embedded into the image using the LSB technique. Whenever the combination of Cryptography and Steganography have been used then the level of security has been increased rather than using the Steganography alone.

Steganography, Cryptography, Encryption, Decryption, Public key, Private key

[1] Swati Nimje, Amruta Belkhede, Gaurav Chaudhari, Akanksha Pawar and Kunali Kharbikar,"Hiding Existence of Communication Using Image Steganography " in International Journal of Computer Science and Engine and Engineering(IJCSE), Volume-2, Issue-3 ,E-ISSN: 2347-2693.Mar-2014.
[2] Unik Lokhande, A.K.Gulve “Steganography using Cryptography and Pseudo Random Numbers” in an International Journal of Computer Applications (0975 – 8887) Volume 96– No.19, June 2014 .
[3] M. S. Sutaone,M.V. Khandare,"Image Based Steganography Using LSB Insertion Technique" in 2008 IET International Conference on Wireless, Mobile and Multimedia Networks.
[4] Arati Appaso Pujari,Sunita Sunil Shinde,"Data Security using Cryptography and Steganography" in IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 4, Ver. V (Jul.-Aug. 2016), PP 130-139.
[5] Pooja Rani, Mrs. Preeti Sharma,"Cryptography Using Image Steganography" in an International Journal of Computer Science and Mobile Computing, Vol.5 Issue.7, July- 2016, pg. 451-456.
[6] Miftah Ul Uroos, Sukhvinder Kaur ,Muheet Ahmed Butt , “Steganography: A Comparative Survey Conducted on Digital Images” in IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 08, Issue 10 (October. 2018), ||V (I) || PP 52-61 .
[7] Sneha Arora, Sanyam Anand “A Proposed Method for Image Steganography using Edge Detection” in an International Journal of Engineering Sciences, Issue June 2013, Vol. 8.
[8] Shamim Ahmed Laskar, Kattamanchi Hemachandran ,“High Capacity data hiding using LSB Steganography and Encryption” in an International Journal of Database Management Systems(IJDMS) Vol.4, No.6, December 2012.

G. Divya Sri, A. Ramani, A. Jhansi Priya, B. Santhi, SK. Wasim Akram, "Design of Image Steganography using Asymmetric Key Cryptography," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.19-22, 2019.

Nowadays, Databases are mostly usable in business applications and financial transactions in Banks. Most of the database servers stores confidential and sensitive information of a mobile device. Database forensics is the part of digital forensics especially for the investigation of different databases and the sensitive information stored on a database. Mobile databases are totally different from the major database and are very platform independent as well. Even if they are not attached to the central database, they can still linked with the major database to drag and change the information stored on this. . SQLite Database is mostly needed by Android application development. SQLite is a freely available database management system which is specially used to perform relational functional and it comes inbuilt with android to perform database functions on android appliance. This paper will show how a message can be decrypted by using block cipher modes and which mode is more secured and fast.

Database Forensics,Mobile Device ,Android,SQLite, Modes, Tools

[1] Database encryption using asymmetric keys: a case study, AlexandruBoicea, Florin Radulescu, Ciprian-OctivianTruica, CritinaCostea, 2017 21st International Conference on Control System and computer science.
[2] Common Investigation Process Model for Database Forensic Investigation Discipline, Arafat Aldhaqm, ShukorAbdRazak, SitiHajar Othman, 1st ICRIL-International Conference on Innovation in Science and Technology (IICIST-2015).
[3] Key Technologies for Mobile Phone Forensics and Application, Qingchao Su, Bin Xi, 2017 2nd International Conference on Multimedia and Image Processing.
[4] Research on the Data Recovery Method of Deleted SMS for iPhone, ZHANG Kai-xiang; ZHOU An-min, Modern Computer 2015.
[5] Cryptography and Network security, Tata McGraw-Hill education 2003, Atul Kahate.

Nibedita Chakraborty, Krishna Punwar, "A Decryption Process for Android Database Forensics," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.23-26, 2019.

In today’s digital world, all the web applications are used password for their login process. It is fact that, all the Internet applications still used the text based passwords with encrypted form. In encryption technique, hackers are easily hacking the password with decryption process. It is not a secure method to implement in password process. There are so many possibilities of decrypting a password and gets the password by hackers. It was the existing method to protect the unauthorized person to access or enter into the account. Today’s technology revolution, the hackers are supposed to be hacked the encrypted text based passwords. In order to avoid this, we used salted hashing password technique using SHA-256 algorithm [1]. The main objective of this research paper is to secure the user’s password in order to give protection from hacking. In this paper we had implemented password security in SHA-256 hashing algorithm at server side.

Password Security, Salt, Salted Password, Password Attacks, SHA-256, Hash, Hashing Algorithm, 2018 Password Stolen, encryption vs hashing

[1] R. Roshdy, M. Fouad, M. Aboul-Dahab “Design And Implementation A New Security Hash Algorithm Based On Md5 And Sha-256”, International Journal of Engineering Sciences & Emerging Technologies (IJESET), Vol.6, Issue.1, pp.29-36, 2013
[2] H. B. Pethe, Dr. S. R. Pande “An overview of Cryptographic Hash Functions MD-5 and SHA”, IOSR Journal of Computer Engineering (IOSR-JCE), Vol.4, Issue.3, pp.37-42, 2016
[3] Pritesh N.Patel,Jigisha K.Patel and Paresh V.Virparia “A Cryptography Application using Salt hash Technique”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Vol.2, Issue.6, pp.236-239, 2013
[4] Samir Pakojwar, Dr.N.J.Uke “Security in Online Banking Services- A Comparative Study”, International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), Vol.3, Issue.10, pp.16850-16857, 2014
[5] S.Vaithyasubramanian, A.Christy and D.Saravanan “Two Factor Authentications for Secured Login in Support of Effective Information Preservation and Network Security”, APRN Journal of Engineering and Applied Sciences (APRN), Vol.10, Issue.5, pp.2053-2056, 2015
[6] S.Vaithyasubramanian, A.Christy and D.Saravanan “Access to Network by Three-Factor Authentication for Effective Information Security”, Hindawi Publishing Corporation,The Scientific World Journal, Vol.10, Issue.4, pp.127-132, 2016
[7] Sirapat Boonkrong and Chaowalit Somboonpattanakit “Dynamic Salt Generation and Placement for Secure Password Storing”, IAENG International Journal of Computer Science, Vol.43, Issue.1, pp.18-27, 2016
[8] Dr.Abdelrahman Karrar,Talal Almutiri,Sultan Algrafi,Naif Alalwi,Ammar Alharbi “Enhancing Salted Password Hashing Technique Using Swapping Elements in an Array Algorithm”, International Journal of Computer Science and Technology, Vol.9, Issue.1, pp.21-25, 2018

T. Ebanesar, G. Suganthi, "Improving Login Process by Salted Hashing Password Using SHA-256 Algorithm in Web Applications," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.27-32, 2019.

Fire accidents cause huge amount of death and injuries to people every year. Life of people is the most important thing which requires more protection and care at any situations. In many situations, the number of death and injuries can be reduced by providing proper alerts regarding fire accidents to the occupants of the building. To prevent from the fire related damages in buildings it is needed to provide effective fire detection and fire alert system. The proposed work presents an IoT based fire detection and fire alert system that contains a voice system and alarm facility. When any of the fixed fire sensors on the floor detects the fire, the system will provide an alarm in the building and location of the building will be sent to the fire Station with a notification sound. The notification sound is used to intimate about the occurrence of the fire accident to the fire fighters, then they can locate the fire affected building by using the location information. Using the information from the voice system users can take path for escape from the fire affected building.

Internet of Things, Fire Detection, Voice System, Alert System

[1] RaviKishoreKodali and Suyerroju (2017) ‘IoT Based Smart Emergency Response System for Fire Hazards’ International Conference on Applied and Theoretical Computing and Communication Technology, pp.194-198.
[2] Sheila Abaya, EjayCabico, Rommel Diaz, Hiro Kojima (2016) ‘An Embedded System of Dedicated and Real-time Fire Detector and Locator Technology as an Interactive Response Mechanism in Fire Occurrences’, International Conference on Advances in Electronics Communication and Computer Technology, pp. 407-411.
[3] S.R.Vijayalakshmi, S.Muruganand (2016) ‘Real time monitoring of wireless fire detection node’, International Conference on Emerging trends in Engineering,Science&Technology, pp. 1113-1119.
[4] Huiping Huang, Shide Xiao, XiangyinMeng,YingXiong (2010) ,‘A Remote Home Security System Based on Wireless Sensor Network and GSM Technology’, Second International Conference on Networks Security,Wireless Communication and Trusted Computing, pp.535-538.
[5] Haibing Hu Gang Wang Qixing Zhang Jinjun Wang Jun Fang Yongming Zhang (2009), ’Design of smart fire detection system’, Annual Conference of the IEEE Industry Electronics Society, pp.4249-4254.
[6] Mr.C.Santhana Krishnan ,AkhileshGalla, Naveen Arlapalli (2018), ‘A Survey on implementation of Fire detection System Based on ZigBee Wi-Fi Networks’, International Journal of Pure and Applied Mathematics, Volume 118 No. 20.
[7] KB Deve, GP Hancke and BJ Silva, (2016), ‘Design Wireless Multi-sensor Fire Detection and Alarm System Based on ARM’, International Conference on Electronic measurement & instruments, pp.285-288.
[8] Yeon-sup Lim1 (2007), ’A Fire Detection and Rescue Support Framework with Wireless Sensor Networks’, International Conference on Convergence Information Technology, pp.135-138.
[9] Ahmed Imteaj (2017), ‘An IoT based Fire Alarming and Authentication
System for Workhouse using Raspberry Pi 3’, International Conference on Electrical, Computer and Communication Engineering, pp. 899-904.
[10] SwarnadeepMajumder (2017), ’Smart Apparatus for Fire Evacuation An IoT based Fire Emergency monitoring and Evacuation System’, IEEE MIT undergraduate Research Technology Conference, pp. 1-4.
[11] Min-Yuan Cheng ,Kuan Cheng (2017), ‘BIM integrated smart monitoring technique for building fire prevention and disaster relief’, Advanced Engineering Informatics, Volume 84
[12] Gurpreet Kaur, Manreet Sohal (2018), ‘The phase changer in Healthcare Industry’ ISSN International Journal of Scientific Research in Network Security and Communication, Volume-6, pp.34-39.
[13] Amusha Bharati, Ritika Thakur, Kavita Mhatre,(2017) ‘Protection of Industrial and Residential areas by Wireless GAS Leakage Detector using IoT and WSN’, E-ISSN International Journal of Scientific Research in Computer Science and Engineering, Volume -5, pp.62-67

M. Muthuselvi, O. Fathima Thesneem, D. Grace Angeline Hepzibha, M. Indu, "An Internet of Things Based Fire Detection and Fire Alert System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.33-38, 2019.

This paper aims at assessing the state as well as the progress made in classifying emotions in the music. Music is known as “language of emotions”, hence its logical to consider it as a medium for determining the emotions as well as categorize the music based on the emotions they bring forth [1]. Different segments of a particular music may express different emotions and since emotions are interpreted by humans there may arise some conflicts to come to a well-defined answer. The ability to deduce the emotions exhibited by music is of great significance. For example, the ability to deduce emotions can help understanding the patients suffering from Alexithymia, online music vendors like Spotify, iTunes etc. can provide customized playlists based on moods. The task of emotion determination comes under the task of Music Information Retrieval henceforth referred to as MIR. The paper explores the methods of emotion retrieval that includes methods that use textual information (lyrics, tags etc.), content-based approaches and systems combining multiple methods [2].

Acoustic features, Music Emotion Recognition, MIREX, Social Tagging, MFCC, Centroid, Flux, Rolloff, Chroma, Gaussian Mixture Model, Support Vector Machine, VA Model, PAD Values

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[7] X. Hu, J. Downie, C. Laurier, M. Bay, and A. Ehmann, “The 2007 MIREX audio mood classification task: Lessons learned,” in Proc. of the Intl. Conf. on Music Information Retrieval, Philadelphia, PA, 2008.
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[12] K. Trohidis, G. Tsoumakas, G. Kalliris, and I. Vlahavas, “Multilabel classification of music into emotion,” in Proc. Of the Intl. Conf. on Music Information Retrieval, Philadelphia, PA, 2008.
[13] F. Miller, M. Stiksel, and R. Jones, “Last.fm in numbers,” Last.fm press material, February 2008.
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[15] M. I. Mandel and D. P. W. Ellis, “A web-based game for collecting music metadata,” in Proc. of the Intl. Conf. on Music Information Retrieval, Vienna, Austria, 2007, pp. 365–366.
[16] Y. Kim, E. Schmidt, and L. Emelle, “Moodswings: A collaborative game for music mood label collection,” in Proc. Intl. Conf. on Music Information Retrieval, Philadelphia, PA, September 2008.
[17] O. Celma, P. Cano, and P. Herrera, “Search sounds: An audio crawler focused on weblogs,” in Proc. of the Intl. Conf. on Music Information Retrieval, Victoria, Canada, 2006.
[18] P. Knees, T. Pohle, M. Schedl, D. Schnitzer, and K. Seyerlehner, a Document-Centered Approach to a Natural Language Music Search Engine. Springer Berlin / Heidelberg, 2008, pp. 627–631.
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[21] R. H. Chen, Z. L. Xu, Z. X. Zhang, and F. Z. Luo, “Content based music emotion analysis and recognition,” in Proc. Of the Intl. Workshop on Computer Music and Audio Technology, 2006.
[22] O. C. Meyers, “A mood-based music classification and exploration system,” Master’s thesis, Massachusetts Institute of Technology, June 2007.
[23] Y. Hu, X. Chen, and D. Yang, “Lyric-based song emotion detection with affective lexicon and fuzzy clustering method,” in Proc. of the Intl. Society for Music Information Conf., Kobe, Japan, 2009.
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[25] E. M. Schmidt, D. Turnbull, and Y. E. Kim, “Feature selection for content-based, time-varying musical emotion regression,” in MIR ’10: Proc. of the Intl. Conf. on Multimedia Information Retrieval, Philadelphia, PA, 2010, pp. 267–274.
[26] T. Li and M. Ogihara, “Detecting emotion in music,” in Proc. of the Intl. Conf. on Music Information Retrieval, Baltimore, MD, October 2003.
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Swati Goel, Parichay Agrawal, Sahil Singh, Prashant Sharma, "A Historical View of the Progress in Music Mood Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.39-45, 2019.

The face is one of the distinguishable marks of humans. Face Recognition can be used as a personal identification system that uses the unique characteristics of a person to identify a person’s identity. Some of the existing applications of face recognition systems are Biometric Information Process, Human-Computer Interaction, Deployment and Security Services, Criminal Identification, Health Care, Access and Security and so on. In general finger prints were used for identifying criminals. In this paper, we focus our task to Criminal Identification through face recognition technology. Here we maintain the images of criminals in a database. When an image is given as an input to the system, using a face recognition algorithm, the system needs to identify whether the inputted image exists in the criminal list or not. If exists then displays the name of the identified criminal otherwise displays as unknown.

Face Recognition, Biometric Information, Criminal Identification

[1] M. Merlin Steffi , J. John Raybin Jose, “Comparative Analysis of Facial Recognition involving Feature Extraction Techniques”, International Journal of Computer Sciences and Engineering, Vol. 6, Issue. 2, March 2018.
[2] Deng Cai, Xiaofei He, Jiawei Han, “Training Linear Discriminant Analysis in Linear time”, Dept. of Computer Science, University of Illinois at Urbana Champaign 1334 Siebel Center, 201 N. Goodwin Ave, Urbana, IL 61801, USA.
[3] Sourabh Hanamsheth, Milind Rane, “Face recognition using histogram of oriented gradients”, International Journal of Advance Research in Computer Science and Management Studies.
[4] Sang-Jean Lee, Sang-Bong Jung, Jang-Woo Kwon*, Seung-Hong Hong, “face detection and recognition using PCA”, 1999 IEEE TENCON.
[5] Sanjay Kumar Pal, “A method for face detection based on Wavelet transform and optimized feature selection using Ant Colony optimization in support vector machine”, Department of CSE, University Institute of Technology, RGPV, Bhopal, India.
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[7] Gordon Stein, Yuan Li, Dr. Yin Wang, “One Sample Per Person Facial Recognition With Local Binary Patterns and Image Sub-Grids”, Department of Math and Computer Science Lawrence Technological University Southfield, Michigan, USA.
[8] Yingnan Zhao, Shiwei Jin, “face recognition with single training image per person based on wavelet transform and virtual information”, 2010 First International Conference on Pervasive Computing, Signal Processing and Applications.
[9] Matthew A. Turk and Alex P. Pentland Vision and Modeling Group, “Face Recognition Using Eigenfaces”, The Media Laboratory Massachusetts Institute of Technology.
[10] Ahonen, Timo, Abdenour Hadid, Matti Pietikainen, “Face description with local binary patterns: Application to face recognition.” IEEE transactions on pattern analysis and machine intelligence 28.12 (2006): 2037–2041.
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Y. Lakshmi Prasanna, U. Bhargava Lakshmi, V. Tanuja, V. Divya, A. Prashant, "Criminal Identification through Face Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.46-49, 2019.

Nowadays, Wireless sensor network (WSN) exhibits growth in application such as disaster management, wildlife monitoring and military surveillance. As the environment is unfriendly, human cannot access the place to sensor monitory or to deploy the sensor in this applications. Hence, the sensor must be deployed remotely and to be operated in a robotic mode. The nearby nodes are linked to produce a cluster without disjoint or overlap to aid scalability. From the different published WSN clustering method, clustering attributes classification are projected in this paper. The clustering methods are compared depending on the measures like uniform clustering, position awareness, sensor mobility, efficient energy-based, cluster stability, clusters overlap.

WSN; Energy; Clustering; CH (key words)

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N. Thilagavathi, Christy Wood, V. Hemalakshumi, V. Mathumiithaa, "A State of Art Approaches on Energy Efficient Clustering Techniques in WSN," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.50-54, 2019.

The area of Handwritten Signature Verification has been extensively investigated in the most recent decades yet remains an open research issue in image recognition. The target of signature verification is to segregate if the given signature is genuine (delivered by the guaranteed individual), or a fraud (created by an impostor). These curves describe the general shape of the signature and ignore the slight details that vary from a genuine signature to another. The verification is based on the comparison of characteristic curves by dynamic programming, which is a very powerful method for curves comparison. Much progression has been proposed in the writing in the last 5-10 years, most remarkably the use of Convolutional Neural Networks (CNN) strategies to take in highlight portrayals from signature pictures. Convolutional Neural Networks are utilized to order pictures and perform object recognition in the images. In this paper, we present how the issue has been taken care of in the previous couple of decades, discuss the ongoing progressions in the field, and the potential headings for future research.

Forgery, Handwritten Recognition, Image Processing, Neural Networks

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[3] Larkins, R. Mayo, M., “Adaptive Feature Thresholding for Off-Line Signature Verification”, In: Image and vision computing NewZealand, 2008, pages: 1-6.
[4] Kovari, B. Kertesz, Z. and Major, a., Off-Line Signature Verification Based on Feature Matching: In: Intelligent Engineering Systems,2007, pages 93-97.
[5] B. Fang, C.H Leung, Y.Y. Tang, K.W. Tse, P.C.K. Kwok, and Y.K. Wong, Off-line signature verification by the tracking of feature and stroke positions, Pattern Recognition, vol. 36, pp. 91-101, 2003.

D.V.S. Abhigna, D. Srujana, D. Hema Varshini, G. Niharika, A. Vishnu Vardhan, "Handwritten Signature Verification Using Convolutional Neural Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.55-59, 2019.