Volume-8 , Issue-4 , Apr - 2020 Go Back

In this paper segmentation procedure of degraded script have been proposed of Odia script. A dataset of 50 documents including 170 words in each document making of 5000 character have been taken after scanning. After that segmentation procedure have been applied to get the accuracy rate of degraded printed Odia script. Also, different level of degradation in a script have been mentioned. Character segmentation on degraded odia printed script have been a tough task due to its Curvy with round format. Due to this style of writing it becomes difficult to segment its Characters. Character Segmentation is an essential part of Optical Character Recognition. Optical Character Recognition is an emerging area of research which helps in converting scanned image or handwritten notes into digital format.

Character segmentation , Connected Components, Degraded Script, Optical Character Segmentation, Odia Script

[1] O. D. Trier, A. K. Jain and T. Taxt, “Feature extraction methods for character recognition: – A survey”, Pattern Recognition, Vol. 29(4), pp. 641-662, 1996.
[2] C. Y. Suen, “Character Recognition by Computer and Applications”, in Handbook of Pattern Recognition and Image Processing, New York: Academic pp. 569-586, 1986.
[3] S. Impedovo, L. Ottaviano and S. Occhinegro, “Optical Character Recognition - A Survey”, International Journal of Pattern Recognition & Artificial Intelligence, Vol. 5, pp. 1-24, 1991.
[4] P.Dussawar at. Al.Text Extraction from Complex Color Image Using Optical Character Recognition”, Vol.4, pp.730-735, 2015.
[5] U. Pal1, at al., A System for Off-line Oriya Handwritten Character Recognition using Curvature Feature ,IEEE,2007.
[6] Isha Sehgal and K.S. Venkatesh ,”Connected Component Labeling For Binary Images.”, International Journal Of Advanced Research, Int.J. of Adv. Res. 7 (8). 916-927.
[7] A. Amin, “Recognition of printed Arabic text based on global features and decision tree learning techniques”, Pattern Recognition, Vol. 33, pp. 1309-1323, 2000.
[8] S. Kahan, T. Pavlidis and H. S. Baird, “On the Recognition of Printed Characters of Any Font and Size”, IEEE Transactions on PAMI, Vol. 9(2), pp. 274- 288, 1987.
[9] M. K. Jindal, G. S. Lehal and R. K. Sharma, “Segmentation Problems and Solutions in Printed Degraded Gurmukhi Script”, International Journal of Signal Processing, Vol. 2(4), pp. 258-267, 2005.
[10] P. D. Gadar, M. Mohamed, and J. H. Chiang, “Handwritten Word Recognition with Character and Inter-Character Neural Networks”, IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, Vol. 27(1), pp. 158-164, 1997.

Ipsita Pattnaik, Tushar Patnaik, "Character Segmentation on Degraded Printed ODIA Script," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.43-45, 2020.

Data security is one of the main issues to be considered when the transmission is through wireless communication. The problems that necessitated this research are: eavesdropping, impersonation attack and the security of the back end database. The aim of this study is to develop an Encryption Standard for RFID database. The objective is to develop a system that can: Provide a Hybrid encryption using Advanced Encryption Standard and Elgamal Encryption Algorithm to secure and validate the integrity of patients’ database. The methodology adopted for this paper is the Object Oriented Analysis and Design methodology (OOADM). In this paper, five text files of different sizes were used to conduct four experiments, where a comparison of three algorithms Advanced Encryption Standard (AES), Elgamal Encryption algorithm and the new Chamberlin Hybrid Encryption Standard (CHES)was performed. Performance of encryption algorithm was evaluated considering the following parameters: encryption time, decryption time and size of encrypted file. Based on our analysis, the new hybrid encryption algorithm has a better performance with respect to the security of patients’ records and the confidentiality of their records is high. The new algorithm will ensure integrity of medical records of patients against potential hackers. This thesis proposes a hybrid encryption algorithm for security of database and protection in radio frequency identification system using an advanced encryption standard and elgamal encryption as a cryptographic primitive. This algorithm protects high-valued sensitive health records against malicious users. With the developed system, one can provide a proof for each record stored in the database of the RFID system because it is sufficiently robust to withstand replay attack, eavesdropping attack and backward traceability. All records are randomized and each tag has its own unique identification data. One recommend this work to Nigeria Police Force and higher institutions to enable them leverage on the digital technology to enhance security.

RFID, AES, CHES, OOADM

[1]. Aissi, S., Al-Hamami, Alaa Hussein, Arabnia, Hamid, and Abuosba Khalil (2006). Proceedings of the 2006 International Conference on Security and Management, SAM’06: Foreword.
[2]. Alomair, B. &Poovendran, R. (2010). Privacy versus Scalability in Radio Frequency Identification Systems, Computer Communication, Elsevier, vol. 33, no. 18, pp. 2155– 2163.
[3]. Atkins, A.S., Zhang, L., Yu, H., & Miao, W. (2009).Application of Intelligent Systems Using Knowledge Hub and RFID Technology in Healthcare Waste Management in UK and China International Conference in e-Business.
[4]. Banks, J., Hanny D., Pachano M.A. & Thompson L.G. (2007). RFID Applied, John Wily & Sons, Inc., Hoboken, New Jersey.
[5]. Burmester, M. & B. de Medeiros, (2007). RFID Security: Attacks, Countermeasures and Challenges, Proceedings of 5th RFID Academic Convocation, the RFID Journal Conference.
[6]. Deshpande, S. G.&Dahikar P.D., (2012). Strengthening of Data Security against its Attack, International Journal of Advanced Networking and Applications3 (5) 29–35.
[7]. Dimitriou, T., (2005). A Lightweight RFID Protocol to Protect Against Traceability and Cloning Attacks, Proceedings of Conference on Security and Privacy for Emerging Areas in Communication Networks.
[8]. Elminaam, D. S.A., Kader, H.M. A. & Hadhoud, M.M. (2010). Evaluating the performance of symmetric encryption algorithms, International Journal of Network Security, 10 (3), 213- 219.
[9]. EPCglobal Inc. EPCTM generation 1 tag data standards version 1.1 rev.1.27, 10 May 2005.
[10]. Garfinkel, S., Jules, A. &Pappu, R. (2005). RFID privacy: an overview of problems and
[11]. Proposed solutions. IEEE Security and Privacy Magazine, 3(3): 34 – 43.
[12]. Garfinkel, S. & Rosenberg, B. (2005). RFID Applications, Security, and Privacy. Addison-Wesley.
[13]. Glover, B. & Bhatt, H. (2006). RFID Essentials. O`Reilly, Gravenstein Highway North, Sebastopol, CA, USA.
[14]. Jhanwar, M.P. &Barua R. (2009)., A Hybrid Public Key Encryption in Standard Model and A New Intractability Assumption, Stat-Math Unit Indian Statistical Institute Kolkata, India
[15]. Jignesh, R.P., Rajesh S. & Vikas K. (2012) Hybrid Security Algorithms for Data Transmission using AES-DES”, International Journal of Applied Information Systems (IJAIS), 2(2)
[16]. John, Justin M., Manimurugan, S., A survey on various Encryption Techniques. International Journal of Soft Computing and Engineering, Volume 2, Issue 1, March 2012.
[17]. Juels, A., Rivest, R.L. & Szudlo, M. (2010).The Blocker Tag: Selective Blocking of RFID
[18]. tags for Consumer Privacy. In the 8th ACM Conference on Computer and Communications Security.
[19]. Kuppuswamy, P.& Al-khalidi, S. Q. Y. (2014). Hybrid Encryption / Decryption Technique Using New Public Key and Symmetric Key Algorithm, Department of Management Information Systems, College of Commerce National Chengchi University & Airiti Press Inc.19(2), 1–13.
[20]. Landau, S. (2004). Polynomials in the Nation’s Service: Using Algebra to Design the Advanced Encryption Standard, MathematicalAssociation of America Monthly (February), 89–117.
[21]. Landt, J. (2001). Shrouds of time: The history of RFID, An AIM Publication, Pittsburg.
[22]. Mateescu, G., &Vladescu, M. (2013). A Hybrid Approach of System Security for Small and Medium Enterprises, Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, 656-662
[23]. Muhammad Iqbal, Andysah Putera Utama Siahaan, Riska Putri Sundari “Combination of MD5 and ElGamal in Verifying File Authenticity and Improving Data security” International Journal for Innovative Research in Multidisciplinary Field Volume 4, Issue 10, October 2018.
[24]. Nover, H. (2012). Algebraic cryptanalysis of AES: an overview, International Conference & Workshop on Recent Trends in Technology, 1–16.
[25]. O` Brien, D. (2006). RFID - Introduction and security considerations, Presentation at the ISS World, Washington, DC.
[26]. Okeke, S. (2014). The Study of the Application of Data Encryption Techniques in Cloud Storage to Ensure Stored Data Integrity and Availability, International Journal of Scientific and Research Publications, 4(10), 1-7.
[27]. Onyesolu, M.O. & Ogwara N.O., (2016). Information Security using a Hybrid Cryptographic Model, International Research Journal ofComputer Science, 11 (4), 15-22
[28]. Page, L. (2007). Hospital tune in the RFID. Materials Management in Health Care, 16 (15), 18-20.
[29]. Rabah, K. (2004). Data Security and Cryptographic Techniques-A Review, Asian Network for Scientific Information Technology3(1) 106-132.
[30]. Reyes, P.L. (2012), Accessing antecedents and outcomes of RFID Implementation in health care. International Journal of Production Economics, 136(1) 137-150.
[31]. Roussos, G. & V. Kostakos, (2009). RFID in Pervasive Computing: State-Of-The-Art and Outlook,Pervasive and Mobile Computing, vol. 5, pp. 110–131.
[32]. Saad, M.K. & Ahmed, S.V. (2007). Vulnerabilities of RFID Systems in Infant Abduction
[33]. Protection and Patient Wander Prevention.
[34]. Singh, N., &Kaur, P. D. (2015). A Hybrid Approach for Encrypting Data on Cloud to prevent DoS Attacks. International Journal of Database Theory & Application, 8(3), 145–153.
[35]. Song, B. & C. J. Mitchell, (2011). Scalable RFID Security Protocols Supporting Tag Ownership Transfer,Computer Communication, Elsevier, vol. 34, no. 4, pp. 556–566.
[36]. Sonia Rani, Harpreet Kaur “Implementation and Comparison of hybrid encryption model for network using AES and Elgamal “International Journal of Advanced Research in computer science Volume 8, No.3, March – April 2017.
[37]. Stallings, W. (2008). Cryptography and Network Security-Principles and Practices, Prentice Hall, Inc., 4th Ed.
[38]. Swedberg, C. (2009). Virtual health expects improved bed management from RFID. RFID Journal http://www.rfidjournal.com/article/view/7220.
[39]. Want, R. (2005). An Introduction to RFID Technology,” IEEE Pervasive Computing, vol. 5.
[40]. Weis, S., S. Sarma, R. Rivest, & D. Engels, (2003). Security and Privacy Aspects of Low-Cost Radio Frequency Identification Systems, (2003). Proceedings of International Conference on Security in Pervasive Computing, Lecture Notes in Computer Science, vol. 2802, pp. 454–469.
[41]. Wi, C. (2010). Implementation of hybrid Encryption Method using Caesar cipher algorithm, Unpublished master thesis, University Malaysia Pahang (UMP), Pahang, Malaysia.
[42]. Wiks, A.V. (2006). Radio frequency identification applications in hospital environments. Hospital Topics 84 (3), 3-8.
[43]. Yang, Z., Sesay, S., Chen Jingwen, and Xu Du (2005). A secure database encryption scheme. 49-53, 10.1109/ccnc.2005. 1405142.

Amanze, B.C., Ononiwu, C. C ., Eleberi, E.L and Chilaka , U.L, "Hybrid Encryption for Radio Frequency Identification in Healthcare System: Object-Oriented Analysis and Design Approach," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.46-63, 2020.

Though Sign language using gestures and facial expressions helps deaf and dumb people to express their thoughts, normal people do not learn sign language and therefore not able to comprehend it thus causing a barrier between the two and a big cause of frustration. With the objective to lower the barrier in communication by opening up conversations between signers and speakers, we have converted sign language to text and speech using flex sensors, Arduino Uno, HC-05: Bluetooth and a mobile device as the hardware and Arduino IDE and MIT App Inventor as the software. Normal driving gloves have been converted to wireless data smart gloves by fitting them with flex sensors along the length of each finger and the thumb and accelero sensor. The signals are passed on Arduino Uno which converts analog signal from the sensors to digital signals. The signal is then passed on to a Bluetooth device HC-05.Then signal is transmitted to a mobile device on a Bluetooth app. Signal is then converted into a voice message. This low cost solution is affordable, portable and easily customizable with additional Feature for GPS location for safety purposes.

Sign language, Flex sensor, Arduino Uno, Gestures, MIT App Inventor

[1]. J. Thilagavathy, A.J. Murugan, S. Darwin, “ Embedded Based Hand Talk Assisting System for Deaf and Dumb”, International Journal of Engineering Research & Technology, Vol. 3, Issue.,3, pp. 318-321,2014
[2]. R.R Itkarkar, A.V.Nandi, “Hand gesture to Speech Conversion Using MATLAB”, 4th ICCCNT,Tiruchengode, India,2013
[3]. O.Khandeshe, V.Salvi, V.Gawade, S.Varia, R.Molawade, “A Comparative study On Gesture control voice command Over Android Device”, International Journal of Modern Engineering Research, Vol.9, Issue 1, pp.33-37, 2019
[4]. M.A Ahmed, B.B ZAidan, A.A ZAidan, M.M Salih, M.M.B Lakulu, “A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017, Sensors(Basel), Vol.18, Issue 7,2018
[5]. M.Tawde, H.Singh, S.Shaikh, “Glove for Gesture Recognition using Flex Sensor”, International Journal of Recent Trends in Engineering & Research”, Vol 03, Issue 03, pp. 35-39, 2017
[6]. P.Y.Ingle, R.Kumar, “Let’s Talk model for converting Gesture to voice using hand glove and IOT”, International Journal of Computer Sciences and Engineering, Vol. 7, Issue., 5,pp.806-809,2019
[7]. T. Jaya, Rajendran .V, "Hand-Talk Assistive Technology for the Dumb," International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.5, pp.27-31, 2018

Seher Taneja and Mandeep Sukhija, "Gift of Voice to Mute: Hand Gestures Converted to Text and Voice," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.64-69, 2020.

Wireless Sensor Network (WSN) is consisting of independent and distributed sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. Routing protocols represent an essential aspect of the performance of mobile wireless networks. This paper presents a comparative analysis between several routing algorithms and their impact on the performance of WSN. In this paper, we used NS-2 to simulate and implemented routing protocols like Destination-Sequenced Distance-Vector Routing (DSDV) protocol, Ad hoc On-Demand Distance Vector (AODV) protocol, Optimized Link State Routing Protocol (OLSR) protocol and Dynamic Source Routing (DSR) protocol for many numbers of nodes. We compared network parameters, analyzed and evaluated the performance with comparing the end to- end delay, packet delivery fraction (PDF), throughput and packet loss rate. As the number of nodes increases and the network expands, the performance of the AODV protocol obtains better results than the other protocols.

[1] Thangaraj, Jaisingh, and Shilpee Kumari. "Evaluating feasibility of using Wireless Sensor Network in agricultural land through simulation of DSR, AOMDV, AODV, DSDV protocol." In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 301-305. IEEE, 2016.
[2] Perkins, Charles E., and Elizabeth M. Royer. "Ad-hoc on-demand distance vector routing." In Proceedings WMCSA`99. Second IEEE Workshop on Mobile Computing Systems and Applications, pp. 90-100. IEEE, 1999.
[3] Amirthalingam, Krishnakumar. "Improved leach: A modified leach for wireless sensor network." In 2016 IEEE International Conference on Advances in Computer Applications (ICACA), pp. 255-258. IEEE, 2016.
[4] Johnson, David B., and David A. Maltz. "Dynamic source routing in ad hoc wireless networks." In Mobile computing, pp. 153-181. Springer, Boston, MA, 1996.
[5] Li, Xiaohui, and Junfeng Wang. "A Generous Cooperative Routing Protocol for Vehicle-to-Vehicle Networks." TIIS 10, no. 11, pp. 5322-5342, 2016.
[6] Imran, Mohd, and Mohammad Abdul Qadeer. "Evaluation study of performance comparison of topology based routing protocol, AODV and DSDV in MANET." In 2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), pp. 207-211. IEEE, 2016.
[7] Sureshkumar, A., V. Ellappan, and K. Manivel. "A comparison analysis of DSDV and AODV routing protocols in mobile AD HOC networks." In 2017 Conference on Emerging Devices and Smart Systems (ICEDSS), pp. 234-237. IEEE, 2017.
[8] Ali, Tareq Emad, Layth A. Khalil al Dulaimi, and Yamaan E. Majeed. "Review and performance comparison of VANET protocols: AODV, DSR, OLSR, DYMO, DSDV & ZRP." In 2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA), pp. 1-6. IEEE, 2016.
[9] Tseng, Fan-Hsun, Hua-Pei Chiang, and Han-Chieh Chao. "Black Hole along with Other Attacks in MANETs: A Survey." Journal of Information Processing Systems 14, no. 1, pp:55-62, 2018.
[10] Aggarwal, Neha, Teglovy Singh Chohan, Karamveer Singh, Rajan Vohra, and Shalini Bahel. "Relative Analysis of AODV & DSDV Routing Protocols for MANET based on NS2." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 3500-3503. IEEE, 2016.

Saranya. N and Manimekalai. K, "Performance Evaluation of Routing for Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.70-74, 2020.

Cloud computing enables on demand network access to a share pool of configurable computing resources such as servers, storage and application. Cloud storage used to deliver the storage resources to the user over the Internet. Private cloud storage is restricted to a specific organization and data security risks are high in cloud storage. Hence, private cloud storage is built by utilizing the commodity machines within the organization and the important data is stored in it. When the utilization of such private cloud storage increases there will be an increase in the connection problem, performance, storage demand, privacy, security and data integrity. For connection problem we will implement offline data store and sync mechanism. Increase in storage demand leads to the enlargement of the cloud storage with additional storage nodes. During such enlargement, storage nodes in the cloud storage need to be balanced in terms of load. The key idea behind this is to develop a dynamic load balancing algorithm based on de-duplication to balance the load across the storage nodes during the expansion of private cloud storage. For maintaining privacy, security and data integrity we will use AES algorithm and SHA algorithm.

Cloud computing, Reliablity, Load balancing, Encryption, Secure De-duplication, Data integrity

[1] Abdelfatah A Tamimi, Raneem Dawood, Lana Sadaqa, “Disaster Recovery Techniques in Cloud Computing”, IEEE Jordan International Joint Conference on Electrical and Information Technology (JEEIT), pp.845-850, 2019.
[2] Savani Nirav M, Prof. Amar Buchade, “Priority Based Dynamic Resource Allocation in Cloud Computing”, International Journal of Engineering Research and Technology (IJERT), Vol.3, Issue.5, 2014.
[3] Pooja Y. Bansode, Payal A. Lokhande, Siddhant Sawant, Prof.R.B.Nangare, “A Survey on: Load Balancing and De-Duplication in Cloud Computing”, International Journal of Advance Research and Innovative ideas in Education (IJARIIE), Vol.3, Issue.2, 2017.
[4] J.M. Nandhini, T.Gnanasekaran, “Fault Tolerance using Adaptive Checkpoint in Cloud- An Approch”, International Journal of Computer Application, Vol.175, No.6, 2017.
[5] Rajkumar Buyya, Chee Shin yeo, Srikumar Venugopal, “Market-Oriented Cloud Computing: Vision, Hype, and Reality for delivering IT services as Computing Utilities”, IEEE International Conference on High Performance Computing and Communication, 2008.
[6] Sandip Sharma, Sarabjit Singh, Meenakshi Sharma, “Performance analysis of Load Balancing Algorithms”, World Academy of Sciences, Engineering and technology, pp.269-272, 2008.
[7] Poonam R Maskare, Sarika R Sulke, “Review paper on E-learning using Cloud Computing”, International Journal of Computer Science and Mobile Computing, Vol.3, Issue.5, pp.1281-1287, 2014.
[8] Manahel Omar Hussen, Nirmla Sharma, Hosam F. EL-Sofany, “A Novel Model for Securing access of Cloud Based E-Learning Systems”, International Journal of Engineering and applied Sciences and technology, Vol.4, Issue.7, pp.1-9, 2019.
[9] Nidhi Jain Kansal, Inderveer Chana, “Existing Load Balancing techniques in Cloud Computing: A Systematic”, Journal of Information Systems and Communication, Vol.3, Issue.1, pp.87-91, 2012.
[10] Aameek Singh, Madhukar Korupolu, Dushmanta Mohapatra, “Server-storage virtualization: Integration and load balancing in data centers”, ACM/IEEE conference on Supercomputing, pp.15-21, 2008.
[11] Rade Stanojevic, Robert Shorten, “Load Balancing vs. Distributed Rate Limiting: An Unifying Framework for Cloud Control”, IEEE ICC, pp.1-6, 2009.
[12] Yi Zhao, Wenlong Huang, “Adaptive Distributed Load Balancing Algorithm Based on Live Migration of Virtual Machines in Cloud”, IEEE 5th International Joint Conference on INC, IMS and IDC, pp.170-175 , 2009.
[13] Nae V., Prodan R. and Fahringer T, “Load Balancing Techniques in Cloud Computing: A Study”, 11th IEEE/ACM International Conference on Grid Computing, pp.9-17, 2010 .
[14] Jinhua Hu, Jianhua Gu, Guofei sun, Tianhai Zhao, “A Scheduling strategy on load balancing of virtual machine resource in cloud computing environment”, 3rd International Symposium on Parallel Architectures, Algorithms and Programming, pp.89-96, 2010.
[15] Abhay Bhadani, Sanjay Chaudhary, “Performance evaluation of web Servers using Central load balancing policy over virtual machines on cloud”, 3rd Annual ACM Bangalore Conference, 2010.
[16] Jiyi WU , Lingdi PING, Xiaoping GE, Ya Wang, Jianqing FU, “Cloud storage as the infrastructure of cloud computing”, International Conference on Intelligent Computing and Cognitive Informatics, KualaLumpur, pp.380- 383, 2010.
[17] Chandrashekhar S. Pawar, Rajnikant B. Wagh, “Priority Based Dynamic Resource Allocation in Cloud Computing with Modified Waiting Queue”, International Conference on Intelligent Systems and Signal Processing (ISSP), pp.311-316, 2013.
[18] Rui Hu, Yong Li, Yan Zhang, “Adaptive Resource Management in PaaS Platform Using Feedback Control LRU Algorithm”, International Conference on Cloud and Service Computing, pp.11-18, 2011.
[19] Ghalem Belalem and Said Limam, “Fault Tolerant Architecture to Cloud Computing using Adaptive Checkpoint”, International Journal of Cloud Applications and Computing, pp.60-69, 2011.

Shital Bedse, Pratiksha Ahire, Prerna Dhondge, Harshali Kunde, Sarika Bhagwat, "A Model for Cloud Computing for Emergency Operation," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.75-79, 2020.

Daily participation of the student in the current academic program plays a significant role in measuring success and tracking efficiency. Calling people names or writing on papers are the traditional approaches used in most organizations that are very time consuming and vulnerable. This article describes the automated attendance management method, for convenience or data reliability. The project`s key working theory is that, identifying and remembering a particular student. This project is built for attendance at school / college. This framework is based on the technique for camera known as facial recognition system. In the centre of the classroom above the black board facing the students, the camera should be attached. As per the timer set for class timing system. The camera takes the shot from the classroom. And this system plays a key role in the present situation of covid19 as it avoid from person to person contact. Which only captures students face by maintaining certain distance.

Facetrack, Smart Attendance, AWS Rekognition, School Attendance

[1]. N.Sudhakar Reddy, M.V.Sumanth, S.Suresh Babu, "A Counterpart Approach to Attendance and Feedback System using Machine Learning Techniques",Journal of Emerging Technologies and Innovative Research (JETIR), Volume 5, Issue 12, Dec 2018.
[2]. Dan Wang, Rong Fu, Zuying Luo, "Classroom Attendance Auto-management Based on Deep Learning",Advances in Social Science, Education and Humanities Research, volume 123,ICESAME 2017.
[3]. Akshara Jadhav, Akshay Jadhav, Tushar Ladhe, Krishna Yeolekar, "Automated Attendance System Using Face Recognition", International Research Journal of Engineering and Technology (IRJET), Volume 4, Issue 1, Jan 2017.
[4]. B Prabhavathi, V Tanuja, V Madhu Viswanatham and M Rajashekhara Babu, "A smart technique for attendance system to recognize faces through parallelism", IOP Conf. Series: Materials Science and Engineering 263, 2017.
[5]. Prajakta Lad, Sonali More, Simran Parkhe, Priyanka Nikam, Dipalee Chaudhari, " Student Attendance System Using Iris Detection", IJARIIE-ISSN(O)-2395-4396, Vol-3 Issue-2 2017.
[6]. Samuel Lukas, Aditya Rama Mitra, Ririn Ikana Desanti, Dion Krisnadi, "Student Attendance System in Classroom Using Face Recognition Technique", Conference Paper DOI: 10.1109/ICTC.2016.7763360, Oct 2016

Pravin Panditrao Chilme and Pathan Naserkhan Jaffarkhan, "Face Recognition Based Smart Attendance System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.80-84, 2020.

The Viterbi algorithm is commonly applied in a number of sensitive usage models including decoding convolutional codes used in communications such as satellite communication, cellular relay, and wireless local area networks. The Viterbi algorithm process is similar to finding the most-likely sequence of states, resulting in sequence of observed events and, thus, boasts of high efficiency as it consists of finite number of possible states. Viterbi decoder is very important part at the receiver side in order to decode the convolutional codes. This paper briefly reviews the power reduction techniques along with their comparative analysis for designing Veterbi decoder at the receiver side of convolutional codes.

Viterbi Decoder, BMU, PMU, SMU, ACS

[1] A. J. Viterbi, “Convolutional codes and their performance in communication systems,” IEEE Transactions on Communication Technology, vol. 19, no. 5, pp. 751–772, 1971.
[2] B. Ozbay, S. Cekli. "Power-Efficient Viterbi Decoder Architecture and Field Programmeble Gate Arrays Fpga Implementation".Electrica, vol. 18, no. 1, pp. 52-59, 2018.
[3] Waqar Ahmad et. al “Accelerating Viterbi Algorithm using Custom Instruction Approach” arXiv:1809.02887v1 [cs.AR] 8 Sep 2018
[4] S.Nanthini Devi et. al “ An Efficient Low Power Convolutional Coding with Viterbi Decoding using FSM” Asian Journal of Applied Science and Technology (AJAST) Volume 1, Issue 2, Pages 111-114, March 2017
[5] Dinesh Kumar et. al “ VLSI implementation of high speed Viterbi decoder” International Journal For Technological Research In Engineering Volume 4, Issue 7, March-2017
[6] Mohd Azlan Abu Harlisya Harun Mohammad Yazdi Harmin Noor Izzri Abdul Wahab Muhd Khairulzaman Abdul Kadir ,(2016),"The design of Viterbi decoder for low power consumption space time trellis code without adder architecture using RTLmodel ", World Journal of Engineering, Vol. 13 Iss 6 pp. 540 – 546
[7] T. Kalavathi Devi et. al “An Asynchronous Low Power and High Performance VLSI Architecture for Viterbi Decoder Implemented with Quasi Delay Insensitive Templates” Hindawi Publishing Corporation Scientific World Journal Volume 2015, Article ID 621012, 13 pages http://dx.doi.org/10.1155/2015/621012
[8] T. Kalavathi Devi and C. Venkatesh, “Design of low power Viterbi decoder using asynchronous techniques,” International Journal on Advances in Engineering and Technology, vol. 4, pp. 561–570, 2012.
[9] Cholan, K. Design and Implementation of Low Power High Speed Viterbi Decoder. Procedia Engineering. 30. 61-68. 10.1016/j.proeng.2012.01.834 .. (2012).
[10] M. Benaissa, and Y. Zhu, “A Novel High-Speed Configurable Viterbi Decoder for Broadband Access,”URASIP Journal on Applied SignalProcessing, pp. 1317-1327, 2003.
[11] Y. Zhu and M. Benaissa, “Reconfigurable Viterbi decoding using a new ACS pipelining technique,” Proceedings of the 2003 IEEE international conference on Application-Specific Systems, Architectures, and Processors (ASAP2003), The Hague, The Netherlands, pp. 360-368., 24-26 June 2003.
[12] K. Page, P. M. Chau, “Improved Architectures for the Add-Compare- Select Operation in Long Constraint Length Viterbi Decoding,IEEE J. Solid-State Circuits, vol. 33, pp. 151-155, no. 1, January 1998.
[13] I. Kang and A. Willson, “Low-Power Viterbi Decoder for CDMA Mobile Terminals,” IEEE J. of Solid-State Circ., vol. 33, no. 3, pp. 473-482, Mar. 1998.
[14] Jinjin He, Huaping Liu, “High-speed Low-power Viterbi Decoder design for TCM Decoders”, IEEE Trans. VLSI, Vol. 20, Apr 2012.
[15] Anubhui Khare, Manish Saxena, Jagdish Patel, “FPGA Based Efficient Implementation of Viterbi Decoder”, International Journal of Engineering and Advanced Technology (IJEAT), Volume-1, Issue-1, October 2011.
[16] J. He, Z.Wang, and H.Liu, “An efficient 4-D 8PSK TCM decoder architecture,” IEEE Trans. Very Large Scale Integer. (VLSI) Syst., vol.18, no.5, May 2010.
[17] J. He, H. Liu, and Z. Wang, “A fast ACSU architecture for viterbi decoder using T-algorithm,” in Proc. 43rd IEEE Asilomar Conf. Signals, Syst. Comput., Nov. 2009.
[18] Chen, Y.H., Su, M.L. and Ni, Y.F. (2014), “FPGA implementation of trellis coded modulation decode on SDR communication system”, International Conference on Information Science, Electronics and Electrical Engineering (ISEEE), Sapporo, 26-28 April, pp. 89-93.
[19] Devi, P.U. and Rao, P.S. (2012), “Viterbi decoder with low power and low complexity for space time trellis codes”, International Journal of Engineering Research and Applications, Vol. 2 No. 3, pp. 1359-1365.
[20] Kavinilavu, V., Salivahanan, S., Bhaaskaran, V.K., akthikumaran, S., Brindha, B. and Vinoth, C. (2011), “Implementation of convolutional encoder and Viterbi decoder using Verilog HDL”, 3rd International Conference on Electronics Computer Technology (ICECT), Kanyakumari, 8-10 April, pp. 297-300.
[21] Mathana, J.M., Rangarajan, P. and Perinbam, J.R.P. (2013), “Low complexity reconfigurable turbo decoder for wireless communication systems”, Arabian Journal for Science and Engineering, Vol. 38 No. 10, pp. 2649-2662.
[22] Mostafa, K., Hussein, A., Youness, H. and Moness, M. (2016), “High performance reconfigurable Viterbi Decoder design for multi-standard receiver”, 33rd National Radio Science Conference (NRSC), Aswan, 22-25
[23] Arun, C. & Vayanaperumal, Rajamani. (2009). A Low Power and High Speed Viterbi Decoder Based on Deep Pipelined, Clock Blocking and Hazards Filtering. IJCNS. 2. 575-582. 10.4236/ijcns.2009.26064.

Jyoti J. Zunzunwala and A. S. Joshi, "Various Design Architectural level Power Reduction Techniques for Viterbi Decoder: A Review," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.85-89, 2020.

The spreading of abnormal cells in the human body with much potential is a basic cause of disease cancer. The growth of abnormal cells may be affected by age group, being disease-oriented, or type of location in which people live and many factors. Because of the circumstances, there is no possibility of avoiding the growth of abnormal cells, but by taking corrective measures the growth can be slowed down to some extent. In addition to that, it will envisage the cancer causes which in turn can be used to create awareness among the people. In this fact, it is important to determine if someone has a high cancer risk by using biological test results which have been recorded. By working on these sample data, we can focus on finding the most influential factors that affect cancer. In this research, by applying a suitable Machine Learning algorithm on the data which have been collected using surveys, we are able to find the most important factors and mainly classification type of Machine Learning algorithms to be used for performance analysis.

Data Analysis, Classification, Machine Learning, Cancer, XGBoost Algorithm

[1] J. Subramanian, R. Govindan, “Lung Cancer in Never Smokers”, Journal of Clinical Oncology, Vol.25(5), pp.561–570, 2007.
[2] A. Sreedevi, R. Javed, A. Dinesh, “Epidemiology of cervical cancer with special focus on India”, International Journal of Women’s Health, Vol.7, pp.405-414, 2015.
[3] K. Fernandes, D. Chicco, J.S. Cardoso, J Fernandes, “Supervised deep learning embeddings for the prediction of a cervical cancer diagnosis”, PeerJ Computer Science, Vol.4(8), p.e154, 2018.
[4] E. Roura, X. Castellsague, M. Pawlita, N. Travier, T. Waterboer, N. Margall et al., “Smoking as a major risk factor for cervical cancer and pre‐cancer: Results from the EPIC cohort”, International Journal of Cancer, Vol.135(2), pp. 453–466, 2014.
[5] V. Menon, D. Parikh, “Machine learning applied to Cervical Cancer Data”, International Journal of Scientific & Engineering Research, Vol. 9, Issue.7, pp.46-50, July-2018.
[6] S. Jayaprakash, E. Balamurugan, “A Comprehensive Survey on Data Preprocessing Methods in Web Usage Mining”, International Journal of Computer Science and Information Technologies, Vol.6 (3), pp. 3170-3174, 2015.
[7] T Chen, C Guestrin, “ XGBoost: A Scalable Tree Boosting System”, Proceedings of the 22nd ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, August 13-17, 2016, San Francisco, CA, USAc©2016 ACM, ISBN 978-1-4503-4232-2/16/08.
[8] S. Zhao, Y Guo, Q. Sheng, Y. Shyr, “Advanced Heat Map and Clustering Analysis Using Heatmap3”, BioMed Research International, Vol. 2014, pp.1-6, 2014.
[9] M. Fernandes, “Data Mining: A Comparative Study of its Various Techniques and its Process”, International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.19-23, February(2017).
[10] S Mahajan, "Convergence of IT and Data Mining with other technologies ", International Journal of Scientific Research in Computer Science and Engineering, Vol.01, Issue.4, pp.31-37, 2013.

V. Naveen Babu, T. Murali, Sk. Meer Hussian, S. Nava Chaitanya and Madda.Varalakshmi, "Data Analysis: Finding the Most Effective Factors Causing Cancer Deaths," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.90-96, 2020.

The online Staff Leave Management System is used to automate the process of leave applications and their approvals. It mainly deals with details of all institutional staff both teaching and non-teaching of various departments and also their leave maintenance. A staff can submit a leave application after verifying his/her current leave balances which will be shown by based on status module. The HOD and Principal verifies the submitted leave and checks the available leave balances for accepting or rejecting the leave. Admin will generate the report for leave management of staff. This system is being developed to convert a traditional process of leave management into automated system that can be process by android devices, where multi-user access is allowed. The AI based system will work with AI to find the available staff for the possible load adjustment from time schedule.

leave management, Notification management, Timetable Management, Adjustment of work Load

[1] Pooja k Dhule, “Web Based Staff Leave Management”, International journal Of Science Technology & Engineering, Volume 3 , Issue 09, March 2017.
[2] Mishal Raj, Prity Satbhaya, Prachir Chauhan , Arhant Chatterjee, ”Employee Leave Management”, IJARIIE, Vol-3 Issue-5 2017.
[3] A.S.Syed Navaz, A. F. (2013). Human Resource Management System. OSR Journal of Computer Engineering (IOSR,8(4),62-71.
[4] Zehra Alakoç Burma, P. E. (2014, September). Human Resource Management and Its Importance for Today’s Organizations. International Journal of Education and Social Science, 1(2), 85.
[5] Ajahar Ismailkha Pathan. "Proposed:Tech Learning Community Management." International Journal for Scientific Research and Development 5.7 (2017): 155-157.
[6] Sayali Pramod Dalke, Shruti Anil Deshmukh, Janabai Govind Dalave, Vaishnavi Nitin Sasane, Pooja K Dhule, “Web Based Staff Management System”, IJSTE - International Journal of Science Technology & Engineering, Volume 3, Issue 09, March 2017.
[7] Azhar Baig M, Sai Sudha K, “An android application on Faculty Leave Automation System”, IJRASET, 2019
[8] A.S.Syed Nawaz, A .S. Syed Fiaz, C.Prabhadevi, V. Sangeetha, S .Gopalakrishnan, “Human Resource Management System” ,IOSR journal of computer Engineering (IOSR-JCE), Volume 8 , Issue 4 (Jan-Feb 2013).
[9] Sahajik Ahmed, Xing Ke, “Human Resource Management, Employees carrier development impact on organizational performance”, ICSSSM, 2016
[10] Ajahar Ismailkha Pathan, Shezad Habeeb Shaikh, “A Survey on ETS Using Android Phone”, International Journal Of Innovative Research In Technology (IJIRT), Volume 5 Issue 3 | ISSN: 2349-6002, Page-99-104, August 2018.
[11] “Employee Development system emerging e-learning technology and application (ICETA)”, IEEE 12th Conference, 14 May, 2015.
[12] S. Selvi, Manas Rath, N.K.Sinha, S.P.Singh, “HR e-Leave Tour Management System”, Published IEEE, Published in ICIT, 2014 International Conference, Dec-2015.

A.I.Pathan, Bhavnashri Nayak, Bhagyashri Nayak, V.B.Dhatrak, A.K.Daivat, "An Design of AI based leave scheduling and managing Application," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.97-99, 2020.

The extensive growth of Online Social Networks (OSNs) over the last decade has changed the way people interact with their friends, family and especially making new friends. These social networks have been an important integral part of our life. Since the count of the users of social networks is increasing drastically, so is the security threat and also private information threat. For making new friends, some OSNs work on the principle of similar profile attributes to let people become friends. However, this principle involves a privacy threat of exposing private and personal profile information to strangers over the internet. The existing solutions to secure users’ privacy are by finding the intersection of the private set of profile attributes of both the users. These schemes have few flaws and cannot hide users’ privacy. Also, in today’s online social networks any random stranger can send a friend request, check your information and misuse it too. In this paper, the community structures are used to represent the online social networks and asymmetric social proximity measure is used as people consider friendships differently. Because of social closeness measure, friend suggestions and requests originate only from relevant communities. Then, based on the asymmetric social proximity measure, a privacy algorithm is used, which provides a high level of privacy and can protect users’ privacy better than the existing works. This concept is designed using Advanced Encryption Standard Algorithm with the exchange of keys. When there is an exchange of Public and Private keys then only information can be accessed. In this paper, a secured online social network system is designed and developed as a Web application to protect the sensitive data and private information of the users and increase the security and privacy issue for OSNs. This OSN is effective and has high-level privacy protection.

Asymmetric Social Proximity, Community Structure, Online Social Network

[1] Arun Thapa, Ming Li, Sergio Salinas and Pan Li, “Asymmetric Social Proximity Based Private Matching Protocols for Online Social Networks”, IEEE Transactions on Parallel and Distributed Systems.
[2] David Easley and Jon Kleinberg, “Networks, Crowds, and Markets: Reasoning About a Highly Connected World”.
[3] H. Zhu, S. Du, M. Li, and Z. Gao, “Fairness-aware and privacy-preserving friend matching protocol in mobile social networks”, IEEE Transactions on emerging topics in Computing.
[4] X. Liang, M. Barua, R. Lu, X. Lin, and X. Shen, “Health Share: Achieving Secure and Privacy-preserving Health Information Sharing through Health Social Networks”, Computer Communications, Elsevier.
[5] W. Dong, V. Dave, L. Qiu, and Y. Zhang, “Secure Friend Discovery in Mobile Social Networks”, IEEE International Conference on Computer Communications.
[6] J. Camenisch, M. Kohlweiss, A. Rial, and C. Sheedy, “Blind and anonymous identity-based encryption and authorised private Searches on public-key encrypted data”, International Workshop on Public Key Cryptography.
[7] C. Hazay and Y. Lindell, “Efficient Protocols for Set Intersection and Pattern Matching with Security against Malicious and Covert Adversaries”, International Association for Cryptographic Research.

P. Gowrishetty, A. Gopi, "Asymmetric Social Proximity Based Community Structured Online Social Network," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.100-105, 2020.