Volume-06 , Special Issue-03 , Apr - 2018 Go Back

Fire detectors are designed to detect one or more of the three characteristics of fire-smoke, heat and flame. The fire detection and alarm system function together to provide timely fire warnings to those in emergency zones. But the drawback is that it may include false alarms. The proposed idea is to monitor industries and to detect fire and smoke using sensors and camera. In order to save energy, space and cost, Raspberry pi 3 is used which acts as both processor and controller. The sensors detect flame and smoke using the spectrum range. If it detects any issues, then it signals the motor to sprinkle water either automatically or manually. The camera acts by detecting the thermal motion of any organisms and if any fluctuation is detected, that moment is captured and sent as a mail to the user via a third party authentication system. The videos captured by the camera are stored in cloud and is used as proof during fire accidents to claim insurance. This system also avoids false alarm.

Raspberry pi, PIR sensor, Intruder detection, Mail Service

[1] Sanoob.A.H. J.Roselin, Dr. P. Latha,”Smartphone enabled intelligent surveillance”, IEEE Sensors Journal – 2016,16(5),pp.1361-1367.
[2] S. Tanwar, P. Pately, K. Patelz, S. Tyagix, N. Kumar, M. S. Obaidat, ”An advanced internet of thing based security alert system for smart home”, IEEE Conference – 2017.
[3] Ahmed Imteaj, Tanveer Rahman, Muhammad Kamrul Hossain, Mohammed Shamsul Alam and Saad Ahmad Rahat, “An iot based fire alarming and authentication system for workhouse using raspberry pi 3”, In Electrical, Computer and Communication Engineering (ECCE), International Conference on (pp. 899-904). IEEE.
[4] Md Iftekharul Mobin, Md Abid-Ar-Rafi, Md Neamul Islam, and Md Rifat Hasan, “An intelligent fire detection and mitigation system safe from fire (sff)”,International Journal of Computer Applications, 133(6).
[5] Jaeseok Yun and Min-Hwan Song, “Detecting direction of movement using pyroelectric infrared sensors”,IEEE Sensors Journal, 14(5), pp.1482-1489.
[6] Karwan Muheden, Ebubekir Erdem and Sercan Vançin, “Design and Implementation of the Mobile Fire Alarm System Using Wireless Sensor Networks”, In Computational Intelligence and Informatics (CINTI), 2016 IEEE 17th International Symposium on (pp. 000243-000246). IEEE.
[7] Khirod Chandra Sahoo and Umesh Chandra Pati, “IoT Based Intrusion Detection System Using PIR Sensor”, In Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2017 2nd IEEE International Conference on (pp. 1641-1645).
[8] Simarjit Singh Saini, Hemant Bhatia, Vatanjeet Singh and Ekambir Sidhu, “Rochelle salt integrated PIR sensor aurdino based intruder detection system(ABIDS)” , In Control, Computing, Communication and Materials (ICCCCM), 2016 International Conference on (pp. 1-5). IEEE.
[9] Aasma Shahid, Alina Tayyab, Musfira Mehmood, Rida Anum, Abdul Jalil, Ahmad Ali, Haider Ali, Javed Ahmed,”Computer Vision Based Intruder Detection Framework (CV-IDF)”, In Computer and Communication Systems (ICCCS), 2017 2nd International Conference on (pp. 41-45). IEEE
[10] Farrukh Shahzad, “Low-cost intruder detection and alert system using mobile phone proximity sensor”, In Innovations in Electrical Engineering and Computational Technologies (ICIEECT), 2017 International Conference on (pp. 1-5).

Akshaya.S Yamini.E Veeshal Sheev.V.B C.Jerin Mahibha, "An Intelligent Fire Detection and Surveillance System", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.158-162, 2018.

Drought is arguably the single biggest threat climate change. Its impacts are global. Therefore, there is a need for technological intervention to monitor basic information about the weather and soil condition, in order to identify and predict drought. The proposed wireless sensor drought monitoring system is capable of remote real-time monitoring for extended periods, identifying drought in the early stages. A combination of wireless sensor networks using TEEN (Threshold sensitive Energy Efficient sensor Network protocol) along with cloud technology would make data on humidity and salinity of the soil, temperature and light intensity on the surface that are accessible to end users. The data collected would then be transmitted to a public or hybrid cloud through satellite transmission protocols. Mobile cloud computing apps can be developed to raise an alarm when the drought conditions approach the critical stage. In this paper, we propose a novel IoT based monitoring system that assimilates data from real time weather and soil condition sensors. Thus providing a mobile client with which the user can monitor the drought conditions throughout the country, thereby indicating promptly when to take corrective measures.

Real-time monitoring, Wireless sensor network, Satellite transmission protocols, Alarm, Drought conditions, Internet of Things

[1] W.-S. Jang, W. M. Healy, and M. J. Skibniewski, “Wireless sensor networks as part of a web-based building environmental monitoring system,” Automation in Construction, vol. 17, no. 6, pp. 729-736, 2008.
[2] R. Mittal and M. P. S. Bhatia, “Wireless Sensor Networks for Monitoring the Environmental Activities,” Analysis, 2010.
[3] López, Juan A., Antonio-Javier Garcia-Sanchez, F. Soto, A. Iborra, Felipe Garcia-Sanchez, and Joan Garcia-Haro. "Design and validation of a wireless sensor network architecture for precision horticulture applications." Precision agriculture Vol. 12, Issue. 2, pp: 280-295, 2011.
[4] S. E. Díaz, J. C. Pérez, A. C. Mateos, M.-C. Marinescu, and B. B. Guerra, “A novel methodology for the monitoring of the agricultural production process based on wireless sensor networks,” Computers and Electronics in Agriculture, vol. 76, no. 2, pp. 252-265, May 2011.
[5] Tait, Andrew, James Sturman, and Martyn Clark. "An assessment of the accuracy of interpolated daily rainfall for New Zealand." Journal of Hydrology, no. 1 (2012), pp: 51.
[6] World-health, “World Health Organization,” 2008, website (http://www.who.int/world-health-day/toolkit/report_web.pdf)
[7] Liu Ping, “Agricultural Drought Data Acquisition and Trans- mission System Based on Internet of Things”, China, 2014.
[8] Mahdi Jalili, Joobin Gharibshah, Seyed Morsal Ghavami, Mohammadreza Beheshtifar, and Reza Farshi, Nationwide “Prediction of Drought Conditions in Iran Based on Remote Sensing Data”, Iran, 2014.

N. Nafeela Banu, G.Deepika, "IoT based Monitoring System for Drought Prediction", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.163-166, 2018.

Software Personal Assistants (PA) have become the trademark of organizations which are involved in developing services that serve the digital needs of the public. Their aim through this trend is to integrate their core technologies into a single point of access in a user customized fashion using prediction mechanisms. Excluding circumstances where data has to be fetched or stored in online sources, most of the PAs existing in the market have a very local reach in its line of services. To enhance the reach of these services, we wish to integrate the functionalities of a Home Assistant (HA) to our PA such that users have the provision of controlling and monitoring devices in their homes amidst carrying out other PA functions in the Android Mobile Platform. The uniqueness in our PA lies in the usage of a Timestamp based Cipher primarily in the Chat Module and in other Modules that comprise of Cloud storage within our PA. Further, we introduce a novel HA Technology, that comprises of an Arduino and an ESP8266-01 Module, which performs the tasks necessary to provide the ambiance of a Smart Home. This Technology has the least investment necessary to set up the same up to date due to the simplicity of the hardware involved. The Smart Home will be powered by Internet of Things (IoT) which allows for the storage, retrieval and monitoring of data through the PA where the data corresponds to the states of the Smart Home at specific points of time.

Personal Assistant, Home Assistant, Android, Timestamp based Cipher, Arduino, ESP8266-01, Smart Home, Internet of Things

[1] J. Santos et al., “An IoT-based mobile gateway for intelligent personal assistants on mobile health environments”, Journal of Network and Computer Applications, Vol.71, pp.194-204 2016.
[2] L. Y. Mano et al., “Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition”, Computer Communications, Vol.89, No.90, pp.178-190, 2016
[3] U. Kumar et al., “Home Automation with Personal Assistant”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol.5, Issue.5, pp.842-845, 2017
[4] J. Santos, J. J. P. C. Rodrigues, J. Casal, K. Saleem, and V. Denisov, “Intelligent Personal Assistants Based on Internet of Things Approaches”, IEEE Systems Journal, Vol.PP, Issue.99, pp.1-10, 2016
[5] A. Shinde, A. Wagh, A. Bhopale, O. Ghone, A. Harsola, “Intelligent Workplace Assistant”, International Journal of Soft Computing and Artificial Intelligence, Vol.3, Issue.2, pp.80-83, Nov, 2015
[6] S.V. Zanjal, G.R. Talmale, “Medicine Reminder and Monitoring System for Secure Health Using IOT”, Procedia Computer Science, Vol.78, pp.471-476, 2016
[7] N. Gokels-Canbek, M.E. Mutlu, “On the track of Artificial Intelligence: Learning with Intelligent Personal Assistants”, International Journal of Human Sciences, Vol.13, Issue.1, pp.592-601, 2016
[8] R. Anerao, U. Mehta, A. Suryawanshi, “Personal Assistant for User Task Automation”, SSRG International Journal of Computer Science and Engineering, Vol.2, Issue.3, pp.176-180 March, 2015
[9] N. Sriskanthan, F. Tan, A. Karande, “Bluetooth based home automation system”, Microprocessors and Microsystems, Vol.26, pp.281-289, 2002

F.M. Thomas, R. Juliana, "Integrating novel state of the art techniques into generic Personal Assistants with Home Assistant functionality", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.167-172, 2018.

The traffic signals in many countries are controlled by traffic police manually. Conventional traffic light system is based on fixed time allotment at each side of the junction which cannot be varied as per varying traffic density. This existing system is not intelligent enough to make perfect decisions in varying the signal timings. The proposed system is to develop a smart density based dynamic traffic signal system. This system has ultrasonic sensors placed on dividers to detect the traffic density on each road and changes the signal timing as per the traffic density classification low, medium and high. It also paves way for emergency vehicles by making use of RF transceiver where the emergency vehicle driver sends RF signal and the signal is changed to green for that particular path. The entire system maintains free flow of traffic without manual operations and also optimizes waiting time for the emergency vehicles. Regulating the traffic through this system is inexpensive and it provides better traffic control.

Traffic density, Emergency Vehicles, Ultrasonic Sensors, RF Signal, Timer, Traffic congestion

[1] A. KirushnaKumar, M. Arun, A. Kirubanand, S. Mukesh, Aravindan Sivakumar. A, “Smart Traffic Control Systems”, International Journal for Research in Applied Science & Engineering Technology, Vol. 4, Issue IV, pp.314-320,2016.
[2] H. Peyrebrune, A.L.C. de Cerreño, “Security Applications of Intelligent Transportation Systems: Reflections on September 11 and Implications for NewYork State”, A Report to the Legislature by the NYU Wagner Rudin Center for Transportation Policy and Management, New York, July 2002.
[3] Y. J. Zheng, W. Ritter, R. Janssen, “An Adaptive System for Traffic Sign Recognition”, In the proceedings of IEEE Intelligent Vehicles Symposium 94, Paris, France, pp.165-170,1994.
[4] L. Studer, M. Ketabdari, G. Marchionni, “Analysis of Adaptive Traffic Control Systems Design of a Decision Support System for Better Choices”, Journal of Civil & Environmental Engineering, Vol. 5, Issue 6, pp. 1-10, 2015.
[5] M. Tubaishat, Y. Shang, H. Shi, “Adaptive Traffic Light Control with Wireless Sensor Networks”, In the proceedings of Consumer Communications and Networking Conference (CCNC 2007) 4th IEEE, Las Vegas, NV, USA, pp.187-191.
[6] G. Lakshminarasimhan, V. Parthipan, Mohammed Irfan Ahmed, S.H.K Nvm, D. Dhanasekaran., “Traffic Density Detection and Signal Automation using IOT”, International Journal of Pure and Applied Mathematics, Vol.116, Issue 21, pp.389-394,2017.
[7] Z. Shuai, Songhwai Oh, M-H. Yang., “Traffic Modeling and Prediction using Camera Sensor Networks”, In the proceedings of 2010 4th ACM/IEEE International Conference on Distributed Smart Cameras(ICDSC), Atlanta, Georgia, pp. 49-56, 2010.
[8] N. Lanke, S. Koul., "Smart Traffic Management System", International Journal of Computer Applications, Vol. 75, Issue 7, pp.19-22, 2013.
[9] M.K. Abbas, M.N. Karsiti, M. Napiah, B.B. Samir, M. Al-Jemeli., “High Accuracy Traffic Light Controller for Increasing the given Green time Utilization”, Journal of Computers and Electrical Engineering, Vol. 41, Issue C, pp. 40-51, 2015.
[10] S. Jeon , E. Kwon, I. Jung, “Traffic Measurement on Multiple Drive Lanes with Wireless Ultrasonic Sensors”, Sensors Journal, Vol. 14, Issue 12, pp. 22891-22906, 2014.
[11] A. Jain, M. Mittal, H. Verma, A. Rai, “Traffic Density Measurement based On-road Traffic Control using Ultrasonic Sensors and GSM Technology”, In the proceedings of the International Conference on Emerging Trends in Engineering and Technology, Nagpur, India, pp.778 – 786, 2013.
[12] M. Tideman, R. Bours, H. Li, T. Schulze, T. Nakano, “Integrated Simulation Toolkit for ADA System Development”, In the Proceedings of the FISITA 2012 World Automotive Congress, Lecture Notes in Electrical Engineering 200, Beijing, China, pp.23-33, 2013.
[13] P.J. Yauch, “Traffic Signal Control Equipment: State of the Art”, Transportation Research Board, National Research Council, Washington, pp. 23, 1990.

A Keerthi Infanta Francy, S Visalachi, R Deepa, "Adaptive Traffic Density Management System", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.173-176, 2018.

Image-based sequence recognition has been a long-standing research topic in the field of computer science and technology. This project focuses on investigating the problem of text recognition, which is among the most important and challenging tasks in image-based sequence recognition. It mainly focuses on effects of uniform and full height map correction methods for dwarfing book spread images in an automated book reader design for individuals with visual impairment and blindness. The accuracy of the book spread images is quantified and measured by introducing the corrected images to an Optical Character Recognition engine. Based on character recongnization algorithm one can achieve the text to speech conversion and accuracy of speech is more. Camera-based assistive text reading framework and audio output along with Raspberry Pi is used in this project. Robotic assistive based page rotation is used to turn the pages of the book. It is working based on deep learning system especially applied to address the challenging process of book digitization.

Image-based sequence recognition, text recognition, Optical Character Recognition, Assistive reading

[1] X. Chen, J. Yang, J. Zhang, and A. Waibel, “Automatic detection and recognition of signs from natural scenes,” IEEE Trans. Image Process., Vol. 13, Issue 1, pp. 87–99, Jan. 2004.
[2] D. Dakopoulos and N. G. Bourbakis, “Wearable obstacle avoidance electronic travel aids for blind: A survey,” IEEE Trans. Syst., Man, Cybern., Vol. 40, Issue. 1, pp. 25–35, Jan. 2010.
[3] B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform,” in Proc. Comput. Vision Pattern Recognit., 2010, pp. 2963–2970.
[4] Y. Freund and R. Schapire, “Experiments with a new boosting algorithm,” in Proc. Int. Conf. Machine Learning, 1996, pp. 148–156.
[5] N. Giudice and G. Legge, “Blind navigation and the role of technology,” in The Engineering Handbook of Smart Technology for Aging, Disability,and Independence, A. A. Helal, M. Mokhtari, and B. Abdulrazak, Eds. Hoboken, NJ, USA: Wiley, 2008.
[6] A. Shahab, F. Shafait, and A. Dengel, “ICDAR 2011 robust reading competition: ICDAR Robust Reading Competition Challenge 2: Reading text in scene images,” in Proc. Int. Conf. Document Anal. Recognit., 2011, pp. 1491–1496.

P. Sabin Prasanna, B. Bernadine Infenta, S. Maria Keerthana, Sherril Sophie Maria Vincent, "Book Reader using Raspberry Pi", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.177-183, 2018.

Image stitching is basically integrating two or more images to form a single panorama. This process of integrating can be performed by overlapping the images contains common scenes. The panoramic stitching of images is widely used for virtual reality photography and 360 degree cameras. Now a day, panoramic video has also gained importance over a period of time. It has many application built in over it such as security, surveillance, computer vision, robotics, virtual reality and much more. Generation panoramic video can be divided into many steps namely extracting frames from the input video from variety of multiple devices, web-cam etc., time synchronizing the extracted frames, stitching frame to create panoramic frames and then combines those panoramic frames into video. An off-line video of such panoramic frame could be made since there is no time constraint. However generating real time panoramic video is more challenging because of computation resources required and optimization required to produce panoramic real time video. This paper describes the concept of generating real time panoramic video. It also uses the image stitching methods to generate the panorama with the help of Open CV. We are proposing to develop such application which will be very useful in security and surveillance.

Panoramic Videos, Real-time Panoramic Video, Open CV, Video Surveillance

[1] You-Jin Ha, Hyun-Deok Kang, “Evaluation of Feature ased Image StitchingAlgorithm using OpenCV”,IEEE, 2017.
[2] Shangchen Liu, Dakun Zhang. “Panoramic Images Automatically Stitching Algorithm Introduction”, Computer and Information Science, Vol. 1, No. 4.
[3] Zhong Min, Zeng Jiguo, Xie Xusheng. “Panorama Stitching Based on SIFT Algorithm and Levenberg-Marquardt Optimization”, 2012 International Conference on Medical Physics and Biomedical Engineering.
[4] Pranoti Kale, K.R.Singh, “A Technical Analysis of Image Stitching Algorithm”, International Journal of Computer Science and Information Technologies, Vol. 6 (1) , 2015, 284-288.
[5] Oh-Seol Kwon and Yeong-Ho Ha, “Panoramic Video using Scale-Invariant Feature Transform with Embedded Color-Invariant Values”, IEEE Transactions on Consumer Electronics, Vol. 56, No.2, May 2010.
[6] Zhen Hua, Yewei Li, Jinjiang Li, “Image Stitch Algorithm Based on SIFT and MVSC”, IEEE 7th International Conference on Fuzzy Systems and Knowledge Discovery, vol. 6, pp. 2628-2632, Aug 2010.
[7] Lei Xiaohua, Jiang Xiuhua, Wang Caihong, “Design and Implementation of a Real-time Video Stream Analysis System Based on FFMPEG”, Fourth World Congress on Software Engineering, 2013.
[8] Pritam Prakash Shete , Dinesh Madhukar Sarode and Surojit Kumar Bose. “Real-time Panorama Composition for Video Surveillance using GPU”, 2016, Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016, Jaipur, India.

K. Rajasri, D. Gayathri, Balasundari Ilanthirayan, A. Sundra, "Real Time Panoramic Video in OpenCV using Image Stitching Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.184-187, 2018.

During the development of the software the developers have a chance to copy the code continuously. Due to copying of the code there is a chance of having the identical or more similar code fragments in the software and it is called as software clones or code clones. These clones can be detected from the existing code that is in c, c++, java etc programming languages. By the Argo UML tool to the existing code to generate the class diagrams by using reverse engineering process. In software development process, coping of existing code fragment and pasting them with or without modification is a frequent process. Code clone means copy of an original form or duplicate. Software clone detection is important to reduce the software maintenance cost and to recognize the software system in a better way. There are many software code clone detection techniques such as text- based, token-based, Abstract Syntax tree based etc. and they are used to spot and finding the existence of clones in software system. Mainly detection of clones is on the type-1, type-2 and type-3 clones. These clones can be detected by using several novel algorithms are ARIMA, Back propagation, Multi objective genetic algorithm, support vector machines and also with several hybrid techniques with respect to recall and precision.

Code Clones, Software maintenance, Type-1, Type-II and Type-III clones, Recall and Precision

[1] Deepali, Ankur Gupta, Chirag Batra "Hybrid approach for Detecting Code Clone by Metric and Token based comparison," Volume 7, No. 6(Special Issue), November 2016, 978-93-85670-72-5 © 2016 (RTCSIT) pp. 297-302,2016.
[2] JAYADEEP PATI, BABLOO KUMAR, DEVESH MANJHI, AND K K SHUKLA "A Comparison Among ARIMA, BP-NN, and MOGA-NN for Software Clone Evolution Prediction," 2169-3536, 2017 IEEE, VOLUME 5, 2017, pp.11841-11851,2017
[3] Stefan Bellon, Rainer Koschke," Comparison and Evaluation of Clone Detection Tools," IEEE Transactions on Software Engineering, Vol. 33, No. 9, SEPTEMBER 2007,pp.577-591,2007
[4] Elizabeth Burd, John Bailey," Evaluating Clone Detection Tools for Use during Preventative Maintenance," Proceedings of the Second IEEE International Workshop on Source Code Analysis and Manipulation (SCAM’02) 0-7695-1793-5/02 $17.00 © 2002 IEEE
[5] Shruti Jadon," Code Clones Detection Using Machine Learning Technique: Support Vector Machine," ISBN: 978-1-5090-1666-2/16/$31.00 ©2016 IEEE, pp.299-303
[6] Ira D. Baxter, Andrew Yahin, Leonardo Moura, Marcelo Sant’Anna, Lorraine Bier," Clone Detection Using Abstract Syntax Trees," Copyright 1998 IEEE. Published in the Proceedings of ICSM’98, November 16-19, 1998, pp.1-10, 1998
[7] Chanchal K. Roy ," Detection and Analysis of Near-Miss Software Clones," 978-1-4244-4828-9/09/$25.00 2009 IEEE Proc. ICSM 2009, Edmonton, Canada ,pp.447-450
[8] Chanchal K. Roy and James R. Cordy," NICAD: Accurate Detection of Near-Miss Intentional Clones Using Flexible Pretty-Printing and Code Normalization," The 16th IEEE International Conference on Program Comprehension, 978-0-7695-3176-2/08 $25.00 © 2008 IEEE DOI 10.1109/ICPC.2008.41
[9] Jeffrey Svajlenko Chanchal K. Roy," Evaluating Clone Detection Tools with Big Clone Bench." 978-1-4673-7532-0/15/$31.00, 2015 IEEE, ICSME 2015, Bremen, Germany, pp.131-140
[10] Jaweria Kanwal, Katsuro Inoue , Onaiza Maqbool," Refactoring Patterns Study in Code Clones during Software Evolution," 978-1-5090-6595-0/17/$31.00 ,2017 IEEE, pp.45,46
[11] Ripon K. Saha, Chanchal K. Roy, Kevin A. Schneider, Dewayne E. Perry," Understanding the Evolution of Type-3 Clones: An Exploratory Study," 978-1-4673-2936-1/13, 2013 IEEE, pp.139-148
[12] Richard Wettel Radu Marinescu," Archeology of Code Duplication: Recovering Duplication Chains From Small Duplication Fragments," 0-7695-2453-2/05 $20.00 © 2005 IEEE
[13] Toshihiro Kamiya," CCFinder: A Multilinguistic Token-Based Code Clone Detection System for Large Scale Source Code," IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 28, NO. 7, JULY 2002, pp.654-670
[14] Yang Yuan, Yao Guo, "CMCD: Count Matrix Based Code Clone Detection," apsec, pp.250-257, 2011 18th Asia-Pacific Software Engineering Conference, 2011.
[15] Gehan M. K. Selim, King Chun Foo, Yung Zou," Enhancing Source-Based Clone Detection Using Intermediate Representation," 2010 17th Working Conference on Reverse Engineering, 1095-1350/10 $26.00 © 2010 IEEE DOI 10.1109/WCRE.2010.33,pp.227-236
[16] Brenda S. Baker," On Finding Duplication and Near-Duplicate ion in Large Software Systems," 0-8186-7111-4/95 $4.00 0 1995 IEEE,pp.86-95
[17] Flavius-Mihai Lazar, Ovidiu Banias," Clone detection algorithm based on the Abstract Syntax Tree approach," 978-1-4799-4694-5/14/$31.00 ©2014 IEEE, pp.73-78

V. Guna, M. Sunil Kumar, "A Survey on Software Code Clone Detection to Improve the Maintenance Effort and Maintenance Cost of the Software", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.188-192, 2018.

Breast cancer and infertility are the universal problem, the recorded data from hospitals can be used to develop a decision tree to analyze the risk of breast cancer. Estrogens, Progesterone, FSH and LH are natural hormones that are important in sexual development and other body functions. Circumstances that raise your lifetime estrogen levels or lengthen the amount of time your body gets exposed to these hormones may increase your breast cancer risk. FSH and LH levels, on the other hand, seem to exert dual actions in premenopausal and postmenopausal breast cancer patients. An electronic sensor can detect low levels of estrogen (E2), the primary estrogen hormones, FSH and LH in liquids (BLOOD). The electronic sensor attached to the device senses the presence of these hormones and further tests these hormone levels in bodily fluids using decision tree concept in machine learning. This system senses the amount of these hormones secreted in the women’s body fluid (BLOOD) using electronic sensor connected to the kit. Using decision tree it tests the range of secretion of hormones based on age. When the level of secretion of these hormones is abnormal (less or higher than normal range) it alerts the individual for early diagnosis by sending SMS to their registered mobile number.

Fuzzy logic, MATLAB, Seriousness, Decision supporting system, Tumor, Node, Metastasis, Estrogen

[1] Rawaa Abdulridha Kadhim, “Classification and prediction of breast cancer risk factors using ID3” , The International Journal of Engineering and Science(IJES), Volume 5, Issue 11, ISSN:2319-1813, 2016.
[2] L.Surya Prashanthi, R.Kiran Kumar, “ID3 and its application in generation of decision trees across various domains-survey”, International Journal of Computer Science and Information Technologies, Volume 6, ISSN: 0975-9646, 2015.
[3] Ronak Sumbaly, N.Vishnusri, S.Jeyalatha, “Diagnosis of breast cancer using decision tree data mining technique”, International Journal of Computer Applications, Volume 90, ISSN: 0975-8887, 2014.
[4] Norbaya Engku Muda, Mohd. Azman Abu Bakar, Burhanudin Yeop Majlis, “The Use of Electronic Sensor in Hormone Analysis”, International Journal of Computer Science and Information Technologies, 2015.
[5] K.Rajesh, Dr.Shiela Anand, “Analysis of SEER dataset for breast cancer diagnosis using C4.5 classification algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Volume 1, Issue 2, ISSN: 2278-1021, 2012.
[6] D.Lavanya, Dr.K.Usha Rani, “Analysis of feature selection with classification of breast cancer datasets”, Indian Journal of Computer Science and Engineering, Volume 2, Issue 5, ISSN: 0976-5166, 2011.
[7] A.Sheeba Christina, “Serum hormonal profile and its clinical utility in breast cancer patients among tamil women”, Journal of advanced Scientific Research, Volume 4, Issue 3, ISSN: 0976-9595, 2013.
[8] Jaimini Majali, Rishikesh Niranjan, Vinamra Phatak, Omkar Tadakhe, “Data Mining Techniques for Diagnosis and Prognosis of Cancer”, International Journal of Advanced Research in Computer and Communication Engineering, Volume 4, Issue 3, ISSN: 2278-1021, 2012.
[9] A.Subasini, Nirase Fathima Abubacker, Dr.Rekha, “Analysis of classifier to improve medical diagnosis of breast cancer detection using data mining techniques”, International Journal of Advanced in Networking and Applications, Volume 5, Issue 6, ISSN: 0975-0290, 2014.
[10] Alaa M. Elsayad, H.A.Elsalamony, “Diagnosis of breast cancer using decision tree models and SVM”, International Journal of Computer Applications, Volume 83, Issue 5, ISSN: 0975-8887, 2013.

Monisha A.S, Keerthana Infanta Francy, Anigo Merjora, "Female Self Hormone Analyzer using Decision Tree and Electronic Sensor", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.189-192, 2018.

Green computing is one of the important aspects in which manufacturing, clearing and recycling of electronic devices are taken into account. The ultimate aim of green computing is to produce possibly less harm to the environment and to promote optimal usage of power in an eco-friendly manner. Equipping electronic devices in a large scale all over the globe leads to the environmental degradation because numerous tons of carbon-di-oxide is produced indirectly due to computation. A unique team is working in testing and applying eco-friendly materials in computation. The main idea is to reduce the environmental impact of industrial process along with innovative technological growth. In this paper we discuss about the average power wastage due to computing and also the ways of green computing effectively with power saving ideologies including natural resources and sensors.

Green computing, electronic devices, eco-friendly, power, sensors

[1] Priya Rana, “Green computing saves saves green”, International journal of advanced computer and mathematical sciences, Vol.1,Issue:1 ,pp.45-51, 2010.
[2] K.Kant, “Data center evolution:A tutorial on state of art,issues,and challenges”, Computer Networks,Vol.53, pp.2939-2965, 2009.
[3] Swasti Saxena, “Green computing : Need of an hour”, International journal of current Engineering and Technology, Vol.5,Issue:1, pp.331-334, 2015.
[4] S. Mittal, "A survry of techiques for improving energy efficiency in embedded computing systems", IJCAET, Vol.6,Issue:4, pp.440–459, 2014.
[5] E. Curry, B. Guyon, C. Sheridan, and B. Donnellan,” Developing a sustainable IT capability:lesson from intel’s journey”, MIS Quarterly Executive, Vol. 11, Issue. 2, pp. 61–74, 2012.
[6] Gholami, Roya; Binti Sulaiman, Ainin; Ramayah, T; Molla, Alemayehu.”Senior managers, perception on green information systems(IS) adoption and environmental performance:Results from a field survey”, Information & Management,Vol. 50, Issue.7, pp.431, 2013.
[7] Navdeep kochhar and Arun Garg,”Eco-friendly computing: green computing”, International journal of computing and buisness research, Vol. 2, Issue.2, 2011.
[8] Wu Chun Feng, “The green computing book: tackling energy efficiency in large scale”, CRC Press, India, pp.251-257, 2014.

Shobana. G, Avinash Wilson. J, Arun. P, "Power Saving Strategies-Green Computing", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.197-201, 2018.

The cyclone Ockhi of the recent past which devastated the fishing community of Tamilnadu and Kerala brought into focus the fact that till date there is no set mechanism to track fishermen stranded in the sea specially during emergencies such as cyclones. Many fishermen were missing and the rescue teams had no information that could be used to track or search for the missing fishermen. Currently the only way of tracking the fishermen in Tamilnadu are the tokens distributed for fuel subsidy to boats registered with the State Government. The precautionary measures the fishermen could take to provide locational information to facilitate tracking during emergencies like cyclone need to be addressed immediately. It is towards this end, the “FISHERS BUDDY” is developed using the Open Data Kit which is an android based multiform data collection tool incorporating form data, GPS tags, photos, videos files etc. The aim of the paper is to present a prototype frame work built using smart phones and available technologies to address fishermen safety and aid rescue efforts during emergencies such as cyclones.

cyclone, Android, Open Data kit, mobile application

[1] https://www.thebetterindia.com/123360/cyclone-ockhi-track-fishermen/
[2] https://opendatakit.org/
[3] C. Reynolds, James and P. Denaro, Robert & M. Kalafus, Rudolph. (1990). GPS-Based Vessel Position Monitoring and Display System. Aerospace and Electronic Systems Magazine, IEEE. 5. 16 - 22. 10.1109/62.134216.
[4] A.Gupta, S.Kumar, M.A. Qadeer,”location based services using android (LBSOID)”, IEEE International conference on multimedia services architecture and application, pp.1-5, 2009.
[5] R. Dinesh kumar, M Shubin Aldo, J Charles Finny Joseph “alert system for fishermen crossing border using android”, International Conference on Electrical, Electronics, and Optimization Techniques, pp. 4791-4795, 2016.
[6] E Krishnamoorthy, S ManiKandan, M Mohammed Shalik, S Muthukumara Samy “Border alert system and emergency contacts for contact for fisherman using RSSI”, 2017.
[7] J Charles Finny Joseph, R Dinesh Kumar, M Shubin Aldo, “Alert system for fishermen crossing border using android”, International conference on electrical electronics and optimization techniques, 2016
[8] Aishwarya Dalvi, Ridhee Borad, Nidhi Dawda, Niraj Bangera, “Fishermen Nautical Border Alert System”, International Journal of Advanced Research in Computer Engineering & Technology, Vol. 5 Issue.3, pp. 656-662, 2016.

Ryan Jebaraj, Rufina Fernandez, Beryl Sarah, "Fishers Buddy - An Android and ODK based Mobile Application for Fishermen Safety", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.202-204, 2018.