iosr-JCE   Volume-21 ~ Issue-6 ~ Nov - Dec 2019

Abstract: In information era, enormous amount of data is produced every day, every minute and every second. This data is further analyzed to make valuable insights out of it by the analysts. Data growth is so rapid, which in turn gives birth to another concerns like managing the data properly, storing the data and maintaining the privacy and confidentiality of data. This data is further analyzed to make valuable insights out of it. So this huge amount of data is to be managed and secured. To secure our data, security challenges need to be studied. Hence this paper discusses the structured and unstructured types of data along with different stages in data management.

[1]. Agarwal, R., Gao, G., DesRoches, C. and Jha, A. (2010). Research Commentary—The Digital Transformation of Healthcare: Current Status and the Road Ahead. Information Systems Research, [online] 21(4), pp.796-809. Available at: http://www.krannert.purdue.edu/academics/mis/workshop/papers/ra_120310_1.pdf [Accessed 2 May 2018].
[2]. Babu, M. et al. (2019). Future Trends of Business Intelligence and Big Data Analytics in Ubiquitous Environment. International Journal Of Engineering And Advanced Technology (IJEAT), 8(3s), 773-778.
[3]. Bhadani, A., Jothimani, D. (2016), Big data: Challenges, opportunities and realities, In Singh, M.K., & Kumar, D.G. (Eds.), Effective Big Data Management and Opportunities for Implementation (pp. 1-24), Pennsylvania, USA, IGI Global
[4]. Bian, J., Topaloglu, U. and Yu, F. (2012). Towards Large-scale Twitter Mining for Drug-related Adverse Events. SHB'12 : Proceedings of the 2012 ACM International Workshop on Smart Health and Wellbeing, [online] pp.25–32. Available at: http://doi.org/10.1145/2389707.2389713 [Accessed 21 May 2018].
[5]. Cdn2.hubspot.net. (2018). Data Analytics for Healthcare: Creating understanding from big data. [online] Available at: https://cdn2.hubspot.net/hub/163225/file-18212299-pdf/docs/data-analytics-for-healthcare.pdf [Accessed 21 May 2018].

Abstract: Today, our world is frequently facing a challenging environment everywhere. The main problem is energy crisis. New technologies like sensor networks have been incorporated in the management of buildings for organizations and cities. In recent years, Sensor networks have led to an exponential increase in the volume of data available , and monetary savings. For this purpose, new approaches and techniques are required to investigate information in big data environments .For this problem, having a relevant system to monitor the power usage is the only solution. This paper proposes an analytical model using energy profiles, which gives power consumption of a consumer over a period of time, to perform quantitative analysis using smart meters that automatically acquire context information..........

Keyword: Power consumption, Smart meter, Data analysis

[1]. Alexander L, Diego Klabjan "Clustering Time-Series and Energy Data from Smart Meters" Energy Efficiency, July 2015, Volume 8, Issue 4, pp 681-689
[2]. Damminda Alahakoon & Xinghuo YuSmart "Electricity Meter Data Intelligence for Future Energy Systems: A Survey on ". IEEE Transactions on Industrial Informatics ( Volume: 12 Feb. 2016 )
[3]. Aylin Jarrah Nezhad, Tri Kurniawan Wijaya, Matteo Vasirani, and Karl Aberer "SmartD: Smart Meter Data Analytics Dashboard".
[4]. Ning Lu, Pengwei Du, Xinxin Guo, and Frank L. Greitzer "Smart Meter Data Analysis". Transmission and Distribution Conference(T&D), 2012 IEEE PES
[5]. Monitoring and control of electricity consumption using rasberry-pi through Iot- Carlo C. Medina, Joe Mel U.

Abstract: Initial studies have shown that automatic inference of high-level video quality or aesthetics is very difficult. In this era of internet that facilitates rapid transmission of images and videos, there is a notable influence on the overall population. This makes the ability to assess the aesthetic value of a video very beneficial to various applications. The image processing and analysis community has, for long, attempted to quantify and rectify video quality at a lower level. At a higher level, the perception often affects our emotion and mood, but there has been little headway made in automatic inferencing of the quality in images that affect mood or emotion. Also, low-level image properties are insufficient to characterize high-level perception of aesthetics. Furthermore, there is a lack........

Keyword: 3D-CNN, Deep Neural Network, Optical Flow, Video Aesthetics, Motion Features, Motion Metrics

[1]. Helmut Leder, Benno Belke, Andries Oeberst and Dorothee Augustin, "A model of aesthetic appreciation and aesthetic judgments" British Journal of Psychology (2004), 95, 489–508.
[2]. Yanran Wang, Qi Dai, Rui Feng, Yu-Gang Jiang, "Beauty is Here: Evaluating Aesthetics in Videos Using Multimodal Features and Free Training Data"School of Computer Science, Fudan University, Shanghai, China.
[3]. Ritendra Datta, Jia Li, and James Z. Wang, "Algorithmic Inferencing Of Aesthetics & Emotion in NaturalImages:AnExpostion" The Pennsylvania State University, University Park, PA16802,USA.
[4]. Sagnik Dhar,Vicente Ordonez, Tamara L Berg, "High Level Describable Attributes for Predicting Aesthetics and Interestingness" Stony Brook University,Stony Brook, NY 11794, USA.
[5]. Yan Ke, Xiaoou Tang, Feng Jing, "The Design of High-Level Features for Photo Quality Assessment" School of Computer Science, Carnegie Mellon; Microsoft Research Asia.

Abstract: This paper present improvement of power factor in a long transmissions lines. In a transmission system to transfer the maximum power power factor is important role. Here improving the power factor means reducing the losses and increasing the efficiency.in order to achieving good power quality in this paper using UPQC The advance power electronic devices reduces harmonics in the supply system which creates a problem in the quality of power deliver .Good Power Quality is very much important for our day to day use of appliances in both industrial and domestic sectors. Researchers have tried and implemented many useful technology for removing all the voltage and current related harmonic occurrence problems which in turn improves the quality of power deliver to the power system. In this paper introduces different configurations of UPQC system for single phase and three phases. at the same time and helps in reduction of Total Harmonic Distortion.

Keyword:Power quality, Harmonic elimination, Unified power quality conditioner, D-Statcom, UPQC Configurations.

[1]. Roger Dugan, Mark McGranaghan, Beaty H.W, Electrical Power system Quality, New York: McGraw-Hill, 1996.
[2]. Chettur Shankaran, Power Quality, Boca Raton, FL: CRC Press, 2002.
[3]. El Habrouk M, Darwish K and P Mehta, Active power Filter: A review, IEEE Electrical power Applied, Vol. 147, no. 5, pp.403-413, sep. 2000.
[4]. Babaei E, M.F. Kangarlu, Mehran Sabahi, Dynamic Voltage restorer based on multilevel Inverter with adjustable DC link; IET power Electron, Vol. 7, no. 3, pp. 576-590, 2014.
[5]. Vigna K. Ramchandaramurthy, K. Chandrasekaran, "An Improved Dynamic Voltage Restorer for Power Quality Improvement", International Journal of Electrical Power and Energy Systems, vol. 82, pp. 354-362, Nov.2016.

Abstract: Personality is the integration of all individual characteristics into a unique entity that is decisive, and modified by effort- his effort in adjusting to a constantly changing environment. Graphlogy is the science or art of reading one's handwriting. Similar to DNA or fingerprints of a person, which has its own uniqueness to characterize the uniqueness of a human being. Graphology has two techniques, namely German and French. German method by looking at a person's entire writing. Analysis in the German method is carried out using the image of someone's handwriting. In the handwritten image, it is pre-processed first, then segmentation which includes line, word, and character / letter segmentation. Whereas the French Method tends to analyze by letter and then combined. The French method is done in the form of a line of reasoning that uses a line of reasoning or question and answer. In the analysis........

Keyword: Expert System, Personality, Graphology, Certainty Factor, Image Processing

[1]. Kretch, David dan Crutchfield, Ricahrd S. (1969). Elements of Psychology. New York : Alfred A. Knopf.
[2]. Champa, H N. and K R Anand Kumar, (2010), ―Artificial Neural Network for Human Behavior Prediction through Handwriting Analysis‖, International Journal of Computer Applications (IJCA) May, 2010 , Volume 2.
[3]. Pratt, WK. (1991), ―Digital Image Processing‖ Wiley & Sons, Incorporated, John.
[4]. Bahrami Sharif A., Kabir E. (2005), Computer Aided Graphology for Farsi Handwriting‖, Department of Information and Communication Systems Engineering, 3(2):73-79.
[5]. Kusumadewi, Sri. (2003), Artificial Intelligence Teknik dan Aplikasinya. Yogyakarta: Graha Ilmu.

Abstract: Textile designing involves producing patterns for cloth used in clothing, household textiles (such as towels) and decorative textiles such as carpets. In the past, numerous printing techniques have been employed in patterning textiles and they give an accurate representation of designs when utilized but designs produced by the conventional method are static and rigid. Hence, MATLAB was used in this work to enable dynamic designs be created for a particular textile material. This work develops patterns for printing different textiles and creating innovative designs using mathematics to serve as instructions for artistic.

Keyword: Pattern, textile design, MATLAB

[1]. Akinwumi T.M., 2008: "The "African Print" Hoax: Machine Produced Textiles Jeopardize African Print Authenticity". The Journal of Pan African Studies, Vol. 2, No.5, July 2008.
[2]. Anshul S., Saroj S., Jeet S and Neelam M. R., 2016: "Development of Motifs: Traditional to Contemporary for Saris". IndiaResearch Journal of Recent Sciences, E-ISSN 2277-2502, Vol. 5(7), 44-46, July 2016.
[3]. Ashitey S., 2013. "Innovative Methods of Developing Patterns for Textile Screen Printing". Department of Industrial Art, Kwame Nkrumah University of Science and Technology, Ghana.
[4]. Bajardi, M. 2010. "Textile". morganbajardi.blogspot.com/2010_05_01_archive.html.
[5]. Marfo J. and Martey E., 2015: "Creating Designs through Mathematical Functions". International Journal of Computer Applications (0975–8887) Vol. 123, No.3, August 2015

Abstract: The emergence of IoT – the networked connection of people, process, data and things – is expected to significantly grow the number of connected devices worldwide, from billions of units we have today, to tens of billions of units expected to be deployed in the coming years as stated by several analysts: • IDC1 states that "By 2018, cloud, mobile, and IoT services providers will own/operate 30% of IT assets in edge locations and micro data centers". • Prediction is that "By 2020, IoT technology will be in 95% of electronics for new product designs,whiledevelopment of listener needs to be sorted". • Forrester recently ranked the Internet of Things the number 1 tech trend from 2018 to 2020.

.....

Abstract: Unemployment is one amongst the foremost issues of India. It is a sign of poverty to the individual who's hired, a shame to the society, and a loss of human resource for the country. Economic increase with out social justice and therefore meaningless. A nation is developed only when it achieves a high rate of participation of its workforce in the process of economic growth. But in India can not well make use of a huge labour force so unemployment goes to be a undertaking for the modern financial system in India. There are more than six crores properly educated in India who are unemployed. In this paper has concentrated on reasons of unemployment, different kinds of unemployment and a few possible measures had been recommended.

Key Words: Unemployment, Jobless, Labour force, redundancy

[1]. Kirandeep Kaur. (2014), "An Empirical Study of Inflation, Unemployment, Exchange Rate and Growth in India‟, AJMS-Asian Journal of Multidisciplinary Studies, Vol.2, No.10, pp.19-21.
[2]. Bhavika Joshi, Serene Shekhar and SaritaSanwal (2015), "Entrepreneurship: A Way out for Unemployed Youth in India‟, IJMSS- International Journal in Management and Social Science, Vol.03, No.02, pp.841-850.
[3]. Chandra SekharPatro and Lohit B. (2014), "Impact of Unemployment on Engineering Graduates in Recession‟, (IOSR-JBM) - IOSR Journal of Business and Management, Vol.16, No.2, pp.01-06.
[4]. Elena Philips and Anthonima K. Robin (2015), "Globalization, Employment and Unemployment in India‟, International Journal of Advance Research in Computer Science and Management Studies, Vol.3, No.5, pp.113- 122
[5]. Sikander Kumar and Rakesh Singh (2011), "Nature and Extent of Rural Unemployment in the Low Hill Zone Agrarian Economy of Himachal Pradesh: A Multi- dimensional.

Abstract: This paper describes overview of emerging applications of IoT and different Protocols used in IoT. IoT:"Internet of Things" is emerging technology in this era. Different things are connected on internet i.e. non living or living things we can club together, and establish a protocol, so that they can communicate effectively. The IoT includes four components like sensors, cloud, analytics & Mobile. The data which to be generated by sensors is collected and processed via different microcomputer like Arduino-Uno, Raspberry-Pi ,beagle board's etc.Then this processed data is send to cloud. Next phase is analytics .Analytics is used to discover meaningful data & apply it to take effective decision. The Data is available on cloud can be accessed through a mobile application. Mobile application provides data in graphical format as well as different beneficial services to keep monitoring & controlling the things connected to internet.

Key Word: IoT; sensors; microcomputer

[1]. Pallavi Sethi and Smruti R. Sarangi," Internet of Things: Architectures, Protocols, and Applications", Journal of Electrical and Computer Engineering, Volume 2017, Article ID 9324035
[2]. Rahul Krishnan Pathinarupothi, Member, IEEE, P Durga, Member, IEEE, Ekanath Srihari Rangan, Member, IEEE, "IoT Based Smart Edge for Global Health: Remote Monitoring with Severity Detection and Alerts Transmission" IEEE Internet of Things Journal, 2018
[3]. Nurzaman Ahmed, Debashis De , Senior Member, IEEE, and Md. Iftekhar Hussain, Member, IEEE," Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas" IEEE IEEE Internet of Things Journal, vol. 5, no. 6, December 2018
[4]. Gaurav Panwar1, Rajat Maurya2, Rajesh Rawat3, Rohit Kanswal4 and Praful Ranjan5,,,, "Home automation using IOT application"" International Journal of Smart Home Vol. 11, No. 9 (2017), pp. 1-8
[5]. Olakunle Elijah,Student Member, IEEE, Tharek Abdul Rahman,Member, IEEE,Igbafe Orikumhi,Member, IEEE,CheeYenLeow,Member, IEEE,and MHD Nour Hindia,Member, IEEE," An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges", IEEE internet of things journal, vol. 5, no. 5, October 2018

Abstract: Rapidly expanding mobile application capabilities and features in smart phones demands increasing attention on a number of security concerns. Since operating systems are complex and vulnerable to many forms of attack, hardware backed security recently gained prominence due to the need for end-to-end security right from hardware. Advanced RISC Machines (ARM) TrustZone, an emerging hardware security technology that splits a system into a secure and normal world, defines security extensions to ARMv6 processors and higher, such as ARM11, CortexA8, A9, and A15. Security applications using TrustZone include protected applications such as Digital Rights Management, mobile payment applications, and etc. that run on the secure kernel. In this paper, first the mobile security.........

Key Word: TrustZone, Trusted Platform Module, Trusted Execution Environment, ARM, Mobile Security, Hardware Backed, Mobile Payment, Enterprise Services.

[1]. W. H. W. Hussin, P. Coulton, and R. Edwards, "Mobile ticketing system employing trustzone technology," in International Confer- ence on Mobile Business (ICMB'05). IEEE, 2005, pp. 651–654.
[2]. B. Ngabonziza, D. Martin, A. Bailey, H. Cho, and S. Martin, "Trustzone explained: Architectural features and use cases," in 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC). IEEE, 2016, pp. 445–451.
[3]. J. Mick, "Arm to bake on-die security into next gen smartphone, tablet, pc cores, april 2012," 2012.
[4]. O. H. Alliance, "Industry leaders announce open platform for mobile devices. press release," 2007.
[5]. T. Cooijmans, J. de Ruiter, and E. Poll, "Analysis of secure key storage solutions on android," in Proceedings of the 4th ACM Workshop on Security and Privacy in Smartphones & Mobile Devices. ACM, 2014, pp. 11–20.

Abstract: This paper focused on the study of various feature extraction techniques applied for fingerprint identification,
verification and classification as it is the most important step for image processing. Feature extraction
techniques are classified into local (low level) and global (high level) features. Global features such as arch,
loop, delta and whorl where as local features such as ridge end and bifurcation called minutiae are majors of
automatic fingerprint recognition system. In this study, it has been observed that most of the fingerprint
recognition systems are based on minutiae features. In this paper we analyze the various feature extraction
methods used so far with their mathematical background to the readers..

Key Word: Feature extraction, Fingerprint..

[1]. Moayer, B. and K.S. Fu, "A tree system approach for fingerprint pattern recognition", IEEE Trans. Pattern Analysis and Machine
Intelligence, 8(3):376–388, 1986.
[2]. Verma, M.R., A.K. Majumdar, and B. Chatterjee, "Edge detection in fingerprints, PatternRecognition", 20(5):513 –523, 1987.
[3]. O'Gorman, L. and J. Nickerson, "An approach to fingerprint filter design, Pattern Recognition", 22:29–38, 1989.
[4]. Sherlock, B.G., D.M. Monro, and K. Millard, "Fingerprint enhancement by directional Fourier filtering", IEE Proc. Vis. Image
Signal Processing, 141:87–94, 1994.
[5]. Szekely, E.N. and V. Szekely, "Image recognition problems of fingerprint identification", Microprocessor and Microsystems,
17(4):215, 1993.

Abstract:As Wireless Sensor Network has gained more focus in the recent years, coverage, redundancy, connectivity,
network lifetime are considered as very important issues to be addressed. Connectivity issue is considered to be
addressed in this paper as it is one of the important issues of wireless sensor network. Connectivity is required
to provide the communication among all the sensor nodes deployed in given region of interest. Without
connectivity among sensor nodes and between base station and sensor nodes, the information collected by
sensors cannot be sent to base station. An efficient connectivity algorithm is proposed in this paper to provide
full connectivity among all the sensors nodes without any duplicate communication links. It also provides
connectivity between base station and sensor node closest to it in wireless sensor network. The simulated results
show that the proposed approach provides full connectivity and full coverage with very less number of sensor
nodes..

Key Word: Coverage, Connectivity, Random Deployment, Redundancy, Wireless Sensor Network.

[1]. J. Al-Karaki and A. Gawanmeh, "The Optimal Deployment, Coverage, and Connectivity Problems in Wireless Sensor Networks:
Revisited", IEEE Access, Vol 5, pp. 18051-18065, 2017.
[2]. M. S. Aliyu, A. H. Abdullah, H. Chizari, T. Sabbah, and A. Altameem, "Coverage Enhancement Algorithms for Distributed Mobile
Sensors Deployment in Wireless Sensor Networks", Hindawi Publishing Corporation, International Journal of Distributed Sensor
Networks, Vol 2016, pp. 1-9, 2016.
[3]. Shobha Biradar and Mallikarjuna Shastry P M, "Enhancement of Coverage in Wireless Sensor Network", IEEE International
Conference on Smart technology for Smart Nation, pp. 982-987,2017.
[4]. B. Carbunar, A. Grama, J. Vitek and O. Carbunar, "Coverage Preserving Redundancy Elimination in Sensor Networks". ACM
Transactions on Sensor Networks, Vol. 2, No. 1, 2006.
[5]. Y. Wang, Y. Zhang, J. Liu and R. Bhandari, "Coverage, Connectivity and Deployment in Wireless Sensor Networks", Recent
Development in Wireless Sensor and Ad-hoc Networks, Springer, pp. 25-44, 2015.

Abstract:In this paper, we compare and investigate facial expression recognition system (FERS) based on well-known
features using Hidden Markov Model (HMM) and Support Vector Machine (SVM) and also with combination of
HMM and SVM. In recent years, there has been increasing usage of deep learning techniques in FERS however
which also suffers from the problem of generalization. In this aspect this paper systematically investigates the
performance of FERS using conventional AI techniques. We exploit well-known features landmark and texture
based LBP. We also propose logical partitioning of face and obtained encouraging results. We have tested on
dataset CK+ and spontaneous own created dataset named 'OAK'. Experiments are conducted with different
combinations of parameters to verify the efficiency of FER. This paper aims to compare the FERS performance
using SVM and HMM. Results clearly reflect the efficiency of SVM with landmark feature.

Key Word: Facial Expression Recognition, HMM, LBP, Landmarks, SVM.

[1]. Neoh, S.C., Zhang, L., Mistry, K., Hossain, M.A., Lim, C.P., Aslam, N., & Kinghorn, P., Intelligent facial emotion recognition
using a layered encoding cascade optimization model, Appl. Soft Comput., 34, 2015, 72-93.
[2]. D. Huang, C. Shan, M. Ardabilian, Y. Wang and L. Chen, "Local Binary Patterns and Its Application to Facial Image Analysis: A
Survey," IEEE Transactions on Systems, Man, and Cybernetics, 2011,765-781, 41(6)
[3]. Neoh, Siew Chin, Zhang, Li, Mistry, Kamlesh, Hossain, Mohammed Alamgir, Lim, Chee Peng, Aslam, Nauman and Kinghorn,
Philip 2015, Intelligent facial emotion recognition using a layered encoding cascade optimization model , Applied soft computing,
vol. 34, pp. 72-93
[4]. S. C. Bakchy, M. J. Ferdous, A. H. Sathi, K. C. Ray, F. Imran and M. M. Ali, Facial Expression Recognition based on Support
Vector Machine using Gabor Wavelet Filter, 2nd International Conference on Electrical & Electronic Engineering (ICEEE),
Rajshahi, 2017, 1-4.
[5]. Zhang, Wei & Zhang, Youmei & Ma, Lin & Guan, Jingwei & Gong, Shijie. (2015). Multimodal learning for facial expression
recognition. Pattern Recognition.elsevier,48, 2015, 3191-3202

Abstract: Agriculture is the backbone of Indian economy. About 50% of Indian population depending on agriculture
sector to meet their day-to-day expenses. The current irrigation system practiced in India is sprinkler water
irrigation, tank water irrigation, inundation irrigation, etc. these methods are less efficient as they do not
scatter to the requirement of the moisture level of the soil and do not make efficient use of water resources. In
the future, as population increases, demand for food crops increases, which eventually results in depending on
water resource on a large scale. The electricity distribution in India is poorly organized and very unsettled.
Currently, India requires technological.......

Key Word: IOT, Moisture sensor, DC Adapter, Bluetooth Module, Arduino UNO.

[1]. Multiple Authors, Leverege, "An Introduction To The Internet Of Things", 1st Edition, Page-1-93.
[2]. Rajkumar Buyya & Amir Vahid Dastjerdi, "Internet of Things: Principles and Paradigms", 1st Edition, Page-3-21.
[3]. Peter Waher, "Learning Internet Of Things, 1st Edition, December 2015, Page 04-48
[4]. Microsoft Virtual Academy
[5]. "Research Paper On Water Irrigation By Using Wireless Sensor Network"- International Journal of Scientific Research Engineering
& Technology , Gaurav Kumar.