Conference Proceedings

The idea that the purely phenomenological knowledge that we can extract by analyzing large amounts of data can be useful in healthcare seems to contradict the desire of VPH researchers to build detailed mechanistic models for the need of patients. But in practice no model is ever entirely phenomenological or entirely mechanistic. In Today's world many different people are in need of healthcare at the finest and quickest way possible, to make this possible our application does a critical analysis of the people's profile and comes up with the nearest hospital that could treat the patient the best way possible in terms of Finance, Disease that the patient is diagnosed with. Using this application, the patient enters his basic Information with the added details of the symptoms, allergies or specifically stating to which disease he needs treatment in order to do this, big data technologies must be further developed to cope with some specific requirements that emerge from this application. In turn, the Application does a big data analysis using Hadoop and reduces the result to the best possible solution consisting of hospitals situated the nearest to the patient that meets all his requirements inclusive of his financial status.

Keywords : Big Data, VPH, Healthcare

Recent

[1] A Survey on Big Data Market: Pricing, Trading and Protection Fan Liang; Wei Yu; Dou An; Qingyu Yang; Xinwen Fu; Wei Zhao [2] Medical big data existence flavors; A review Fadia Shah; Jianping Li; Fazal Rehman Shamil;Mubashir Iqbal 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE) [3] Big Data Analytics in Industrial IoT Using a Concentric Computing Model Muhammad Habib ur Rehman; Ejaz Ahmed; Ibrar Yaqoob; Ibrahim Abaker Targio Hashem; Muhammad Imran; Shafiq Ahmad [4] Analysis of Big-Data Based Data Mining EngineXinxin Huang; Shu Gong 2017 13th International Conference on Computational Intelligence and Security (CIS) [5] Is big data for everyone? The challenges of big data adoption in SMEsS. Shah; C. Bardon Soriano; A. D. Coutroubis 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). [6] A Systematic Review of Type-2 Diabetes by Hadoop/Map-Reduce. Munaza Ramzan, Farha Ramzan and Sanjeev Thakur; Indian Journal of Science and Technology, Vol 9(32), DOI: 10.17485/ijst/2016/v9i32/100184, August 2016

The electricity has become a part of daily life, which plays an important role in our homes and industries. The system is now focused on the growing demand of power and the need of finding the alternative energy source. The idea of a ‘smart city’ is the key solution to these power related problems, giving us a futuristic scope. Better understanding of domestic and commercial energy usage brings with it a problem of managing and classifying the sheer amount of data that comes along with it. The work proposal is basically to overcome the demand of power using smart meter in electric power consumption benefiting customer to monitor and manage the electric power usage. This idea is made easier by applying Machine Learning’s. Decision Support System an application of Artificial Intelligence (AI) to classify and distribute energy while managing and enhancing the other supporting features of an Electric S mart Meter (ES M) using Internet of Things (IOT). We plan on introducing smart meters as a ‘live’ communication tool connecting the provider with its customers, which will cause the electrical network industry to face a 360 degree turn around towards a customer-centric business. The system employs the Bayesian Network (BN) prediction model with the three machine learning model that is Naïve Bayes (NB), Decision Tree (DT) and Random Forest (RT). The ES M systems network model is based on the four cornerstones fundamental to IOT: sensing, computing, communication, and actuation.

Keywords : ESM, BN, NB, DT, Gini Index, RF, IOT

Recent

[1] Joseph Siryani, Ph.D. Candidate, Bereket Tanju, Ph.D., and Timothy Eveleigh, D.Sc [2] (2017) -A Machine Learning Decision Support System Improves the Internet of Things’ Smart Meter Operations. [3] http://www.bu.edu/sph/files/2014/05/bayesian-networks-final.pdf [4] http://chemeng.utoronto.ca/~datamining/dm c/decision_tree.htm [5] http://dni-institute.in/blogs/cart-decision-tree-gini-index-exp lained/ [6] http://www.tutorialspoint.com/r/r_random_f orest.htm [7] http://www.cloudbus.org/papers/Internet-of-Things -Vision-Future2012.pdf

Communications between deaf-mute and a standard person have invariably been a difficult task. About nine thousand million people in the world are deaf and dumb. They are introverted closed society. They do not have normal opportunities for learning and Face serious problem in communication with normal people. Existing application focus only on recognition of sign language. The project’s aim is to actually help them to communicate with normal people. The project aims to facilitate individuals by means of a glove based mostly deaf-mute communication interpreter system. The glove is internally equipped with four flex sensors. For every specific gesture, the flex detector produces a proportional amendment in resistance and measures the orientation of hand. Four flex sensors are used to produce 16 combinations which produces 16 Speech outputs. The 16 combinations are binary combinations using four flex sensors. The analog output is converted into digital binary output and depending on the binary output speech output is generated.

Keywords : Flex Sensor, PIC Controller, LUMISENSE Technologies

Recent

[1] Devi, S. And Deb, S., 2017, February. Low cost tangible glove for translating sign gestures to speech and text in Hindi language. In Computational Intelligence & Communication Technology (CICT), 2017 3rd International Conference on (pp. 1-5). IEEE. [2] Padmanabhan, V. and Sornalatha, M., 2014. Hand gesture recognition and voice conversion system for dumb people. International Journal of Scientific & Engineering Research,5(5), p.427. [3] Caporusso, N., Biasi, L., Cinquepalmi, G., Trotta, G.F., Brunetti, A. and Bevilacqua, V., 2017, July. Enabling touch-based communication in wearable devices for people with sensory and multisensory impairments. In International Conference on Applied Human Factors and Ergonomics (pp. 149-159). Springer, Cham. [4] Bandodkar, M. and Chourasia, V., 2014. Low cost real-time communication braille hand-glove for visually impaired using slot sensors and vibration motors. Int. J. Electr. Comput. Energ. Electron. Commun. Eng. [5] N. Kalra, T. Lauwers, D. Dewey, T. Stepleton, M. B. Dias, 2007, - Iterative Design of a Braille Writing Tutor to Combat Illiteracy,‖ funded by Tech Bridge World’s V-Unit program, the IFYRE program, and the National Science Foundation’s IGERT fellowship in assistive technology (DGE-0333420). [6] Ramiro Velázquez, Enrique Preza, Hermes Hernández,2008, ―Making eBooks Accessible to Blind Braille Readers,‖ HAVE 2008 – IEEE International Workshop on Haptic Audio Visual Environments and their Applications Ottawa – Canada. [7] M. Young, the Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989. [8] M. Rajasenathipathi, M. Arthanari, M.Sivakumar,2010, ―An Electronic design of a low cost Braille Hand Glove,‖ International Journal of Advanced Computer Science and Applications, Vol. 1, No. 3. [9] VarunTiwari, Vijay Anand, A. G. Keskar and V. R. Satpute,‖ Sign Language Recognition through Kinect Based Depth Images And Neural Network‖. Advances in Computing, Communications and Informatics International Conference, (2015).

Nowadays for women and children safety is a prime issue in our society. The counts of the victim are increasing day by day. In this paper, we are proposing a model which will help to ensure the safety of women and children all over the globe. Women will be provided with an equipment consisting of GPS (Global Positioning System) module by which we can get the geographical location and in the case of any emergency conditions she can press a button once then the location information will be tracked and sent to police and family members so that she will be protected in proper time. The Smart band integrated with Smart phone has an added advantage so as to reduce the cost of the device and also in reduced size. The GPS and IOT can be used of a smart phone. This also enables in reduced power use and that the watch can be installed with which comes in handy for several days on a single shot of charge.

Keywords : IOT, Microcontroller, GSM, GPS, BluetoothLE

Recent

[1] https://www.irjet.net/archives/V4/i5/IRJET-V4I5604.pdf [2] https://www.theverge.com/circuitbreaker/20 17/7/18/15988362/bluetooth-mesh-networking-standard-released-smart-home [3] http://ijesc.org/upload/bb250456b2eb3228cbd57c12320dfc00.IoT%20Based%20Women%20Safety%20Device%20using%20ARM7. pdf

Abnormal event detection is now a challenging task, especially for crowded scenes. Many existing methods learn a normal event model in the training phase, and events which cannot be well represented are treated as abnormalities. It fails to make use of abnormal event patterns, which are elements to comprise abnormal events. Moreover, normal patterns in testing videos may be divergent from training ones, due to the existence of abnormalities. Anomaly detection finds extensive use in a wide variety of applications such as fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, fault detection in safety critical systems, and military surveillance for enemy activities. The proposed detector treats each sample as a combination of a set of event patterns. Due to the unavailability of labeled abnormalities for training, abnormal patterns are adaptively extracted from incoming unlabeled testing samples. It detects the moving object using Gaussian Mixture Model based on foreground detection and the abnormalities in the videos are detected.

Keywords : Cyber Security, SVM, GSM, Buzzer

Recent

[1] O. Popoola and K. Wang, “Video -based abnormal human behavior recognition -a review,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 42, no. 6, pp. 865–878, Nov 2012. [2] T. Li, H. Chang, M. Wang, B. Ni, R. Hong, and S. Yan, “Crowded scene analysis: A survey,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 3, pp. 367–386, March 2015. [3] G. J. Burghouts, V. P. Slingerland, H. ten R.J.M, H. den R.J.M, and K. Schutte, “Complex threat detection: Learning vs.rules, using a hierarchy of features,” in 11th IEEE International Conference on Advanced Video and Signal Based Surveillance. IEEE, 2014, pp. 375–380. [4] H. Nallaivarothayan, C. Fookes, S. Denman, and S. Sridharan, “An mrf based abnormal event detection approach using motion and appearance features,” in 11th IEEE International Conference on Advanced Video and Signal Based Surveillance. IEEE, 2014, pp. 343–348. [5] C. Piciarelli and G. L. Foresti, “On-line trajectory clustering for anomalous events detection,” Pattern Recognition Letters, vol. 27, no. 15, pp. 1835–1842, 2006. [6] C. Piciarelli, C. Micheloni, and G. L. Foresti, “Trajectory-based anomalous event detection,” IEEE Trans. Circuits Syst. Video Techn., vol. 18, no. 11, pp. 1544–1554, 2008. [7] B. Antic and B. Ommer, “Video parsing for abnormality detection,” in IEEE International Conference on Computer Vision, ICCV 2011, 2011, pp. 2415–2422. [8] A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz,“Robust realtime unusual event detection using multiple fixed-location monitors,” IEEE Transactions on Pattern [9] Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, June 2013, pp. 2611–2618 [10] Y. Benezeth, P.-M. Jodoin, and V. Saligrama, “Abnormality detection using low-level co-occurring events,” Pattern Recognition Letters, vol. 32, no. 3, pp. 423 – 431, 2011. [11] M. Roshtkhari and M. Levine, “Online dominant and anomalousbehavior detection in videos,” in Computer [12] V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos, “Anomaly detection in crowded scenes,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2010, pp. 1975–1981 Analysis and Machine Intelligence, vol. 30, no. 3, pp. 555–560, March 2008. [13] B. Zhao, L. Fei-Fei, and E. Xing, “Online detection of unusual events in videos via dynamic sparse coding,” in Computer Visio and Pattern Recognition (CVPR), 2011 IEEE Conference on, June 2011, pp. 3313–3320. [14] S. Han, R. Fu, S.Wang, and X.Wu, “Online adaptive dictionary learning and weighted sparse coding for abnormality detection, “in 20th IEEE International Conference on Image Processing(ICIP), Sept 2013, pp. 151–155.

Water is a limited resource used in our day to day life for agricultural, recreational, domestic and for a healthy living. So it has become a necessity for adequate and integrated water management such as water level and quality monitoring and to check its efficient usage. Wireless Sensor Network technology helps to monitor the quality of water with the help of sensors immersed in water so as to keep the water resource within the standard that is described for domestic usage and allow to take necessary actions to restore the health of the degraded water. IOT has been used for sensing the parameters and to connect with the GSM. The sensor nodes consist of PIC microcontroller, GSM and water quality sensors to measure the parameters such as pH, water level and gases. Data collected from different nodes will be displayed in the PC. The data collected from the sensors are being sent to the cloud center using GSM modem which is enabled using SIM card. These data values can be viewed in the website or the android application on a recurring basis. The motor can be controlled from the website or the android application that has been created. The asset of the developed system is that it’s compact, optimal usage of power and can be installed easily.

Keywords : GSM- Global System for Mobile, PIC- Peripheral Interface Controller, SIM- Subscriber Identity Module, pH-Potential of Hydrogen, IOT- Internet of Things

Recent

[1] Steven Silva, Hoang N ghia Nguyen, Valentina Tiporlini and Kamal Alameh, “Web Based Water Quality Monitoring with Sensor Network: Employing ZigBee and WiMax Technologies”, IEEE Conference on Local Computer Networks, IEEE 2011. [2] M N Barabde, S R Danve, “A Review on Water Quality Monitoring System”, International Journal of VLSI and Embedded Systems -IJVES, Vol 06, Article 03543; March 2015, pp. 1475-1479. [3] TurkaneSatish,KulkarniAmruta,“Solar Powered Water Quality Monitoring system using wireless Sensor Network”, IEEE Conference on Automation, Computing, Communication, Control and Compressed sensing, IEEE, 2013, pp. 281-285. [4] Li Zhenan, Wang Kai, Liu Bo, “Sensor-Network based Intelligent Water Quality Monitoring and Control”, International Journal of Advanced Research in Computer Engineering Technology, Volume 2, Issue 4, April 2013 [5] Allen, M., Preis, A., Iqbal, M., Srirangarajan, S., Lim, H. B., Girod, L., Whittle, A.J. (2011) “Real-time in- network distribution system monitoring to improve operational efficiency,” Journal American Water Works Association (JAWWA), 103(7), 63–75. [6] Perelman L., Arad J, Housh, M., and Ostfeld A. (2012). "Event detection in water distribution systems from multivariate water quality time series," Environmental Science and Technology, ACS

In this paper, a multi - band patch antenna has been designed for mobile communication. It consis ts of 6 rectangular slots. The antenna has been designed using FR-4 substrate (Effective Permittivity 4.3 lossy tangent 0.025). The software that is used for design simulation and analysis is CST. Defective ground structure (DGS) has been used to improve the return loss characteristics. The antenna radiates at 7 desirable frequencies which are: 2G (1.8 GHz), 3G (2.1 GHz), 4G (2.3 GHz), 5G (3.3 GHz), Wi - Fi (2.4 GHz and 5.9 GHz), and Wi-Max (4.2 GHz).

Keywords : Multi-Band, DGS, CST, Mobile Communication

Recent

[1] Kalika Mehra and Anuj Jain, "Design and analysis of L-slots with rectangular slot multiband microstrip rectangular patch antenna”, July 2017 [International Conference on computer, communications and electronics]. [2] Karn Sharma, Sunny Singh, Shuchismita Pani, Devesh Kumar and M.R.Tripathy, “Multi-band antenna with enhanced gain for wireless applications”, May 2016 [IEEE International Conference on Recent Trends In Electronics Information Communication Technology]. [3] A.Pal, S.Behera and K.J.Vinoy, “Design of multi-frequency microstrip antennas using multiple rings”, April 2008 [IET Microwaves, Antenna and Propagation]. [4] Mahmoud M.A.El-Negm Yousef and Adel B.Abdul Rehman, “Realization of multi-band antenna for WiMax and RFID by etching two interlaced triangles from delta resonator”, March 2017 [34TH National Radio Science Conference]. [5] Sunil Singh, Neelesh Agarwal, Navendu Nitin and Prof.A.K.Jaiswal, “Design considerations of microstrip patch antenna”, [International Journal of Electronics and Computer Science Engineering]. [6] Mukesh Kumar Khandelwal, Binod Kumar Kanaujia and Sachin Kumar, “Defective Ground Structure: Fundamentals, Analysis, and Applications in Modern Wireless Trends”, 2017 [International Journal of antennas and propagation]. [7] Gary Breed, “An Introduction to Defective Ground Structures in Microstrip Circuits”, November 2008 [High frequency electronics, Summit Technical Media].

Distributed Denial-of-Service (DDoS) attacks are usually launched through the botnet, an “army” of compromised nodes hidden in the network. Inferential tools for DDoS mitigation should accordingly enable an early and reliable discrimination of the normal users from the compromised ones. Unfortunately, the recent emergence of attacks performed at the application layer has multiplied the number of possibilities that a botnet can exploit to conceal its malicious activities. New challenges arise, which cannot be addressed by simply borrowing the tools that have been successfully applied so far to earlier DDoS paradigms. In this work, we offer basically three contributions: i) we introduce an abstract model for the aforementioned class of attacks, where the botnet emulates normal traffic by continually learning admissible patterns from the environment; ii) we devise an inference algorithm that is shown to provide a consistent (i.e., converging to the true solution as time elapses) estimate of the botnet possibly hidden in the network; and iii) we verify the validity of the proposed inferential strategy on a testbed environment. Our tests show that, for several scenarios of implementation, the proposed botnet identification algorithm needs an observation time in the order of (or even less than) one minute to i dentify correctly almost all bots, without affecting the normal users’ activity.

Keywords : Distributed Denial-of-Service, DDoS, Inference Algorithm, Botnet, Botmaster

Recent

[1] W. Stallings, Cryptography and Network Security: Principles and Prac-tice, 6th ed., Pearson, 2013. [2] N. Hoque, D. Bhattacharyya, and J. Kalita, “Botnet in DDoS attacks:trends and challenges,” IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2242–2270, fourth quarter 2015. [3] L. Feinstein, D. Schnackenberg, R. Balupari, and D. Kindred, “Statistical approaches to DDoS attack detection and response,” in Proc. DARPA Information Survivability Conference and Exposition, Washington, DC, USA, Apr. 2003, pp. 303–314. [4] J. Yuan and K. Mills, “Monitoring the macroscopic effect of DDoS flooding attacks,” IEEE Trans. Depend. Secure Comput., vol. 2, no. 4, pp. 324–335, Oct. 2005. [5] L. Li, J. Zhou, and N. Xiao, “DDoS attack detection algorithms based on entropy computing,” in Proc. ICICS 2007, Zhengzhou, China, Dec. 2007, pp. 452–466. [6] Y. Xiang, K. Li, and W. Zhou, “Low-rate DDoS attacks detection and traceback by using new information metrics,” IEEE Trans. Inf. Forensics and Security, vol. 6, no. 2, pp. 426–437, Jun. 2011. [7] J. Luo, X. Yang, J. Wang, J. Xu, J. Sun, and K. Long, “On a mathematical model for low-rate shrew DDoS,” IEEE Trans. Inf. Forensics and Security, vol. 9, no. 7, pp. 1069–1083, Jul. 2014. [8] “Layer 7 DDoS.” http://blog.sucuri.net/2014/02/layer-7-ddos-blockinghttp- flood-attacks.html. [9] “Taxonomy of DDoS attacks.” http://www.riorey.com/types-of-ddosattacks/# attack-15. [10] “Global DDoS threat landscape.” https://www.incapsula.com/blog/ddosglobal-threat-landscape-report-q2-2015.html. [11] S. Ferretti and V. Ghini, “Mitigation of random query string DoS via gossip,” Commun. in Comput. and Inf. Sci., vol. 285, pp. 124–134, 2012. [12] S. Marano, V. Matta, and L. Tong, “Distributed detection in the presence of Byzantine attacks,” IEEE Trans. Signal Process., vol. 57, no. 1, pp. 16–29, Jan. 2009. [13] S. Marano, V. Matta, and P. Willett, “Distributed detection with censoring sensors under physical layer secrecy,” IEEE Trans. Signal Process., vol. 57, no. 5, pp. 1976–1986, May 2009. [14] M. Barni and B. Tondi, “The source identification game: an information theoretic perspective,” IEEE Trans. Inf. Forensics and Security, vol. 8, no. 3, pp. 450–463, Mar. 2013. [15] B. Kailkhura, S. Brahma, B. Dulek, Y. S Han, and P. Varshney, “Distributed detection in tree networks: Byzantines and mitigation techniques,” IEEE Trans. Inf. Forensics and Security, vol. 10, no. 7, pp. 1499–1512, Jul. 2015 [16] Yan Ou,and.Chanan Singh, (2002), “Assessment of Available Transfer Capability and Margins”, IEEE transaction on Power systems, Vol.,17, No., 2. pp.463-68 [17] Zimmerman R, MATPOWER, A MATLAB Power system simulation package (version 3.0) Cornell University, New York. [18] University of Washington Electrical Engineering, Power Systems Test Case Archive, 1993, Available from: http://www.ee.washington.edu/research/pstca

In this paper we have proposed an algorithm based on image processing that can efficiently recognize different currencies all over the world with edge detection and artificial neural networks which helps in decision making.

Keywords : Image Processing, Currency Recognition, Neural Networks, MATLAB Software

Recent

[1] Mriganka Gogoi, Syed Ejaz, Ali,Suhra Mukherjee, "Automatic Indian Currency Denomination Recognition System based on artificial neural network”, [2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)]. [2] Ch.RatnaJyothi,Dr.Y.K.SundaraKrishna, Dr.V.Srinivasa Rao,, “Paper currency recognition for color images based on artificial neural Network”, 2016 [International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016].