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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary
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Paper Title | : | Copy Create Video Forgery Detection Techniques Using Frame Correlation Difference by Referring SVM Classifier |
Authors | : | Govindraj Chittapur, S. Murali, Basavaraj S. Anami, , |
Affiliations | : | 1* Department of Computer Applications, Basaveshwar Engineering College, Bagalkot India, 2. Department of Computer Science and Engineering, Maharaja Institute of Technology, Mysore, India ,3. Department of Computer Science and Engineering, KLE Institue of Technology, Hubli, India |
Abstract | : | Video Forensic is a new research avenue in computer forensics. Usually, passive forgery detection techniques have much more import then active forgery techniques to resolve the cost and efficiency of computational video. Forgery detection methods available in copy-move and copy-paste type of forgery. here we propose an algorithm for copy create, which is a combination of copy-move and copy-paste region of video forgery by using frame correlation differences between sets of I-frame in the forged video by using SVM Classifier. We are successful in authenticating the tested video is original or forgery at the same time it returns good result identifying the different I-frame sequence in given forgery videos. Forgery video inputs are customized by referring standard available data set like SULPA, REWIND, VTD, and CVIP. |
: | 0.22362/ijcert/2019/v6/i12/v6i12 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i12/v6i12 |
1. O. I. Al-Sanjary, A. A. Ahmed, A. A. B. Jaharadak, M. A, M. Ali, and H. M. Zangana, "Detection clone an object movement using an optical flow approach," 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, 2018, pp. 388-394. doi: 10.1109/ISCAIE.2018.8405504 2. S. Jia, Z. Xu, H. Wang, C. Fan, and T. Wang, "Coarse-to-Fine Copy-Move Forgery Detection for Video Forensics," in IEEE Access, vol. 6, pp. 25323-25335, 2018. doi: 0.1109/ACCESS.2018.2819624 3. B. Üstüb?o?lu, G. Uluta?, V. V. Nab?yev, M. Ulutas, and A. Üstüb?o?lu, "Using correlation matrix to detect frame duplication forgery in videos," 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, 2018, pp. 1-4.doi: 10.1109/SIU.2018.840436 4 4. L. Su, C. Li, Y. Lai and J. Yang, "A Fast Forgery Detection Algorithm Based on Exponential-Fourier Moments for Video Region Duplication," in IEEE Transactions on Multimedia, vol. 20, no. 4, pp. 825-840, April 2018. doi: 10.1109/TMM.2017.2760098 5. . S. Verde, L. Bondi, P. Bestagini, S. Milani, G. Calcagno, and S. Tubaro, "Video Codec Forensics Based on Convolutional Neural Networks," 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018, pp. 530-534.doi: 10.1109/ICIP.2018.8451143 6. C. Feng, Z. Xu, S. Jia, W. Zhan, and Y. Xu, "Motion-Adaptive Frame Deletion Detection for Digital Video Forensics," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 12, pp. 2543-2554, Dec. 2017. doi: 10.1109/TCSVT.2016.2593612 . 7. C. C. Huang, Y. Zhang and V. L. L. Thing, "Inter-frame video forgery detection based on multi-level subtraction approach for realistic video forensic applications," 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), Singapore, 2017, pp. 20-24. doi: 10.1109/SIPROCESS.2017.8124498. 8. K. Sitara and B. M. Mehtre, "A comprehensive approach for exposing inter-frame video forgeries," 2017 IEEE 13th International Colloquium on Signal Processing and Its Applications (CSPA), Batu Ferringhi, 2017, pp. 73-78. doi:1109/CSPA.2017.8064927. 9. S. Andy and A. Haikal, "Simple duplicate frame detection of MJPEG codec for video forensic," 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, 2017, pp. 321-324. doi: 10.1109/ICITISEE.2017.8285520 10. J. Xu, Y. Liang, X. Tian, and A. Xie, "A novel video inter-frame forgery detection method based on histogram intersection," 2016 IEEE/CIC International Conference on Communications in China (ICCC), Chengdu, 2016, pp. 1-6 doi: 10.1109/ICCChina.2016.7636851 11. Chittapur G.B., Murali S., Prabhakara H.S., Anami B.S. (2014) Exposing Digital Forgery in Video by Mean Frame Comparison Techniques. In: Sridhar V., Sheshadri H., Padma M. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 248. Springer, New Delhi 12. M. Mathai, D. Rajan, and S. Emmanuel, "Video forgery detection and localization using normalized cross-correlation of moment features," 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, 2016, pp. 149-152.doi: 10.1109/SSIAI.2016.7459197. 13. Wang, Q. , Li, Z. , Zhang, Z. and Ma, Q. (2014) Video Inter-Frame Forgery Identification Based on Consistency of Correlation Coefficients of Gray Values. Journal of Computer and Communications, 2, 51-57. doi: 10.4236/jcc.2014.24008.
Paper Title | : | Sentiment Analysis on Social media |
Authors | : | Anumula Manjula, Dr. A. Rama Mohan Reddy, , , |
Affiliations | : | Computer Science and Engineering, Sri Venkatewsara University College of Engineering, Sri Venkateswara University, Tirupati, India |
Abstract | : | The detailed work done in developing a system used for opinion analysis of a product or a service. The system readily processes the tweets by pulling data from twitter posts, pre-processing it and connecting it to Twitter API by REST call method and showing it graphically. We have given the analysis for the public tweets by API and filters them for various products , persons and services. For written product reviews, the best solution is video review. Collecting comments from YouTube videos and extracting the exact tone or behavior behind it. The most widely used approaches in opinion mining focus only on tweets or written product reviews available on websites like Amazon. Various emotions that can deal here namely Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, Curiosity, Excitement, Gratitude, Serenity, Hope, Pride, Amusement, Jealousy, Guilt, Discouragement, Frustration, Rejection, Disappointment, Loneliness, Interest, lack of interest, Concern, Sympathy and Calm. Online news is also now trending and extracting the proper tone behind the news. The analysis which is used to classify the sentiment as positive, negative, neutral, strong positive, weak positive, strong negative, and weak negative. The results shown textually and graphically. |
: | 10.22362/ijcert/2019/v6/i12/v6i12 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i12/v6i12 |
[1] Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Pado, and Roman Klinger,” “Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus ” by” , Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 13-23, Copenhagen, Denmark, September 7-11, 2017. [2] Sunidhi Sharma, D.K.Sharma, Supriti Sharma, “Text Analysis and Sentiment Analysis using Facebook in R Language: Case studies” , International Journal of Computer and Mathematical Sciences, ISSN 2347-8527, vol 6 Issue 12, December 2017. [3] Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo, “Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN” Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social media Analysis, pages 102-111, Copenhagen, Denmark, September 7-11, 2017. [4] Kishaloy Halder, Lahari Poddar, Min-Yen Kan, “Modeling Temporal Progression of Emotional Status in Mental Health Forum : a Recurrent Neural Net Approach” Proceedings of Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 127-135, Copenhagen, Denmark, September 7-11, 2017. [5] Jeremy Barnes, Roman Klinger, and Sabine Schulte im Walde, “Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets” Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 2-12, Copenhagen, Denmark, September 7-11, 2017. [6] Keenen Cates, Pengcheng Xiao, *, Zeyhang, Calvin Dailey, “Can Emoticons Be Used To Predict Sentiment?” Journal of Data Science 355-376, April 04, 2018. [7] Prabaharan Poornachandran,, “deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets” Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 102-111, Copenhagen, Denmark, September 7-11, 2017.
Paper Title | : | A Pattern recognition model of C++ programming language using artificial neural network via Simbrain toolkit |
Authors | : | Shallaw Mohammed Ali, , , , |
Affiliations | : | Assistant Lecturer |
Abstract | : | Background/Objectives: In the field of software development, the diversity of programming languages increase dramatically with the increase in their complexity. This leads both programmers and researchers to develop and investigate automated tools to distinguish these programming languages. Different efforts were conducted to achieve this task using keywords of source codes of these programming languages. Therefore, instead of using keywords classification for recognition, this work is conducted to investigate the ability of detecting the pattern of a programming language characteristic by using simbrain toolkit of neural network and testing the ability of this toolkit to provide detailed analysable results. Methods/Statistical analysis: the method of achieving these objectives is by using backpropagation neural network via Simbrain toolkit based on pattern recognition methodology. Findings: The results show that Simbrain neural network of pattern recognition is able to identify and recognize the pattern of C++ programming language with high accuracy. It also shows the ability of Simbrain toolkit to represent the analysable results through percentage of certainty. Improvements/Applications: it can be noticed from the results the ability of Simbrain toolkit to provide useful platform for studying and analysing the complexity of backpropagation neural network model. |
: | 10.22362/ijcert/2019/v6/i10/v6i1002 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i10/v6i1002 |
[14] J.-h. C. J.-y. S. F. H. Jing Li, “Brief Introduction of Back Propagation (BP) Neural,†springer, vol. 2, pp. 553-558, 2012. [15] K. T. RashmiAmardeep, “Training Feed forward Neural Network With Backpropogation Algorithm,†International Journal Of Engineering And Computer Science, vol. 6, no. 1, pp. 19860-19866, 2017. [16] K. O. ,. S. A. M. N. Mutasem Alsmadi, “Back Propagation Algorithm : The Best Algorithm Among the Multi-layer Perceptron Algorithm,†International Journal of Computer Science and Network Security, vol. 9, no. 4, pp. 378-383, 2009. [17] Y. Z. Alaeldin Suliman, “A Review on Back-Propagation Neural Networks in the Application of Remote Sensing Image Classification,†Journal of Earth Science and Engineering, vol. 5, pp. 52-65, 2015. [18] j. y. Zachary Tosi, “Simbrain 3.0: A flexible, visually-oriented neural network simulator,†Elsevier journal neural network, vol. 83, pp. 1-10, 2016. [19] W.-L. L. ,. L. L. Tung-Hsu Hou, “Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets,†Journal of Intelligent Manufacturing , vol. 14, no. 2, pp. 239-253, 2003.
Paper Title | : | Digital Technologies and Its Scope in Shoplifting Prevention |
Authors | : | Ashesh Kumar Chaudhuri, Mani Sankar Sen, , , |
Affiliations | : | Retail Industry Consultant, IBM India Pvt. Ltd., Kolkata, West Bengal, India |
Abstract | : | Today’s world is based on emerging digital technologies. This paper discussed cutting edge digital technologies and how these can be used in Retail industry to prevent shoplifting – inventory theft. |
: | 10.22362/ijcert/2019/v6/i10/v6i1001 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i10/v6i1001 |
[1] Survey by the National Retail Federation (NRF). [2] K. Wiggers, “AI Guardsman uses computer vision to spot shoplifters†[3] Bloomberg reports published on 4-March-2019. [4] M. Nadimpalli, "Artificial Intelligence – Consumers and Industry Impact", Int J Econ Manag Sci, Volume 6 , Issue 4 , 1000429
Paper Title | : | Accurate Analytics Assurance Using an Apache Spark on Hadoop Yarn Model for Emerging Big Data Systems |
Authors | : | Mallikarjuna Reddy Beram, , , , |
Affiliations | : | Lead I, UST Global, Bengaluru, Karnataka 560066, India, |
Abstract | : | Time and Tendency have made Information Technology to be the market trend, we call Automation, a need each and everywhere, and trending to Data as the important raw material for today’s world we call Big Data. Hence, In this white paper, the energy and the enthusiasm for the time being given stress on the data used for the energetics decision making, where the entire world moves on. Taking the opportunistic advantage of the Big Data environment, where testing is the biggest challenge for the entire Hadoop or spark or any other framework used to analyze the data to give a realistic picture to the end user, where the decision plays into existence. In this, I have given the functional and non-functional deterministic goal-driven approach to make the Data scientist and data engineer model data. Based on the Modelling, the test condition should be written in the map-reduce to know whether the node and function working as expected. The next test has a driven approach to get the optimization and performance like steaming data where spark plays the important role would get the good recommendation. Hence, Big data testing involves the next journey for the optimization, performance, and load balance along with the functional aspect of the data-driven by the data scientist needs to be a parallel process as the end functional is always deterministic to the extent of the end user. |
: | 10.22362/ijcert/2019/v6/i09/v6i0901 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i09/v6i0901 |
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Paper Title | : | A Review: Molecular Imaging Techniques to Monitor NK Cells in Vivo in a Preclinical and Clinical Scenario |
Authors | : | Yi-Chia Lee, Ming-Yii Huang, , , |
Affiliations | : | Kaohsiung Medical University, Canter for Cancer Research |
Abstract | : | In recent years, the use of natural killer (NK) -based immunotherapy has shown promising measures against various cancers. The therapeutic efficacy of NK cell immunotherapy depends to some extent on the migration of NK cells and subsequent infiltration into tumours in animal models or in humans. The continuous improvement of the healing and therapeutic properties of NK cells stimulates the performance and use of immunotherapy based on NK cells. In this review, we summarize the molecular imaging techniques used to monitor NK cell migration and infiltration in vivo, both preclinical and clinically. The advantages and disadvantages of each molecular imaging modality are considered. Finally, we present our understanding of the use of molecular imaging techniques to monitor NK cells in vivo in a preclinical and clinical scenario. |
: | 10.22362/ijcert/2019/v6/i08/v6i0802 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i08/v6i0802 |
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Paper Title | : | BRAIN TUMER CLASSIFICATION USING CNN FRAMEWORK |
Authors | : | Hitesh Sharma, Bapuramji, , , |
Affiliations | : | *1&2Department of Medical Research Lokmanya Tilak Municipal Medical College, Sion Mumbai |
Abstract | : | Brain tumour segmentation is considered a complex procedure in magnetic resonance imaging (MRI), given the diversity of tumour forms and the complexity of determining tumour location, size, and shape. Manual segmentation of tumours is a time-consuming task that is very sensitive to human error. Therefore, this study offers an automated approach that can detect tumour fragments and divide the tumour into all image slices in volume MRI brain scanners. First, a set of algorithms is used in the pre-processing phase to clean up and validate the collected data. Grey-Level Similarity Matrix Analysis and Analysis Of Variance (ANOVA) are used to obtain and select entities, respectively. Multilayer perceptron is accepted as a neural network classification, and a limited 3D window genetic algorithm is used to determine the location of abnormal tissue in MRI slices. Finally, active borderless 3D imaging is used to segment brain tumours using volume MRI exams. The experimental data set contains 165 patient images collected from the MRT unit of Al-Qadimiyah University Hospital in Iraq. Tumour resection results achieved 89% _4.7% accuracy compared to manual procedures. |
: | 10.22362/ijcert/2020/v6/i08/v6i0801 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2020/v6/i08/v6i0801 |
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Paper Title | : | Prediction of dengue with the use of AI and Data mining: An Expert system |
Authors | : | DINESH CHANDRA, SHAZIYA ISLAM, DEEPAK PANDEY, , |
Affiliations | : | M.TECH SCHOLER |
Abstract | : | Background/Objectives: Dengue fever is a mosquito-borne tropical disease caused by the dengue virus. It is a life-threatening disease lots of people died due to dengue because its symptoms are not detected at early stages many parsons thought that it was a normal fever or headache so that they ignore it which cause there are in dangerous situations and worst case they lose their life. Methods/Statistical analysis: We applied data mining techniques along with artificial intelligence technique to create an expert system which can diagnose dengue with the help of symptoms provided by the users. In data mining portion, we use data filtering, data cleaning and clustering, and some other technique to enhance our dataset. Moreover, in AI portion we create an expert system where we create a knowledge base, fact base and GUI portion through user enter their symptoms and our system work is to predict dengue based on symptoms that user feed in GUI as input. Findings: With the implementation of this project, we expect that our expert system is capable of predicting dengue based on person symptom's that we take as a Dataset and saves lots of life of various persons. The main aim of this project is accuracy measure and efficiency also because there is lots of work is pending in this area, and some researchers are searching for new methods. Improvements/Applications: our proposed work will apply in the field of the medical area where a person is capable of checking their dengue symptoms and analyzing their disease. |
: | 10.22362/ijcert/2019/v6/i07/v6i0703 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i07/v6i0703 |
[1] Md Younis Md Alzarrous and Mr. surya prakash mishra, "A novel data mining techniuque to discover pattern from huge text corpous," international jounral of Modern Engineering Research, vol. 4, no. 5, p. 6, may 2014. [2] kashish, Anis, Shadma, Alam, Mansaf Ara Shakil, "Dengue Disease prediction Using Weka Data Mining tool," Elsevier, p. 26, Feb 2015. [3] Dr. Dinesh singh, "An Empirical study of Techniques and Various Domains in Data Mining for Efficient approach in Various Fields," International Jounral of New Inventions in Engineering and Technology , vol. 8, no. 1, p. 7, April 2018. [4] S. Chadsuthi2, K. Jampachaisri3, K. Kesorn* P. Siriyasatien1, "Dengue Epidemics Prediction: A Survey of the state of the art based on data science process," ieee, vol. 20, p. 36, october 2018. [5] Nopember Surabaya, Mohammad Isa Irawan n Abdul Mahatir Najar, "Extreme Learning Machine Method for Dengue Hemorrhagic Fever Outbreak Risk Level Predictio," ieee, vol. 4, p. 5, sep 2018. [6] Marco A. Ferreira Bircky and Ricardo Matsumura Araujoz Virginia Ortiz Andersson, "Towards Predicting Dengue Fever Rates Using Convolutional Neural Networks and Street-Level Images ," ieee, p. 8, June 2018. [7] Ria Arafiyah1 and Fariani Hermin1, "Data mining for dengue hemorrhagic fever (DHF) prediction with naive Bayes method," Jounral of Physics, p. 5, July 2018. [8] Mrs. A.Sumathi [2] P. Sathya [1], "Predicting Dengue Fever Using Data Mining Techniques," IJCST, vol. 6, no. 2, p. 3, March-April3 2018. [9] F. Ibrahim proposed H. Abdul Rahiml, "A NOVEL PREDICTION SYSTEM IN DENGUE FEVER USING NARMAX MODEL ," Ieee, p. 5, october 2017. [10] Dr.A.Anitha proposed Ms.S.Freeda Jebamalar, "A Survey on Prediction of Dengue Fever Using Data Mining Techniques," IJSEM, vol. 2, no. 12, p. 3, Dec 2017. [11] Dr. P. Isakki Devi P. Manivannan, "Dengue Fever Prediction Using K-Means Clustering Algorithm," Ieee, p. 5, Sep. 2017.
Paper Title | : | Energy aware clustering approach for routing mechanism in WSN using Cuckoo Search |
Authors | : | Navdeep Kumar Chopra, Rajesh Kumar Singh, , , |
Affiliations | : | 1*: Ph.D (Research Scholar),CSE, IKGPTU, Jalandhar, Punjab, India <br> 2: Professor and Principal, SUS Institute of Computer Science, Mohali, Punjab, India |
Abstract | : | Background/Objectives: In WSN, numerous obstacles are there in providing Quality of Service (QoS) routing at some preferred level. The main concern of the routing protocols in WSN is to offer energy-efficient framework. To propose an energy-efficient routing protocol, stability and overall lifetime has a major role. Therefore, this research article has an objective to propose an energy-efficient clustering method in WSN using cuckoo search for routing protocol. Methods/Statistical analysis: This research has proposed a route discovery process with the cluster heads (CH). The CH distortion of CH has been identified later on and for the optimization of battery life, Cuckoo Search (CH) optimization algorithm has been used. Findings: It has been discovered that with the utilization of proposed CS mechanism, the CH method become speedy and the selected CH helps in the prevention of packet loss as of maximal energy. Improvements/Applications: With the usage of CS method, an enhancement has been noticed in proposed work by means of remaining battery power, throughput and Packet Delivery ratio (PDR). To be precise, 3.9% of improvement has been drawn than the conventional methods. |
: | 10.22362/ijcert/2019/v6/i07/v6i0702 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i07/v6i0702 |
[1] H. Kim and S. Han, "An Efficient Sensor Deployment Scheme for Large-Scale Wireless Sensor Networks," in IEEE Communications Letters, vol. 19, no. 1, pp. 98-101, Jan. 2015. [2] S. Gowrishankar, T. G. Basavaraju, D. H. Manjaiah, Subir Kumar Sarkar, "Issues in wireless sensor networks", Proceedings of the World Congress on Engineering, vol. 1, pp. 978-988, 2008 [3] Agrawal Palak, P. R. Pardhi, "Routing Protocols For WSN", International Journal Of Computer Science And Applications, vol. 8, no. 1, 2015. [4] Y. Choi, I. Syed, H. Kim, "Event information based optimal sensor deployment for large-scale wireless sensor networks", IEICE Trans. Commun., vol. E95-B, no. 9, pp. 2944-2947, Sep. 2012 [5] V. Karthikeyan, A. Vinod, P. Jeyakumar, "An Energy Efficient Neighbour Node Discovery Method for Wireless Sensor Networks", arXiv preprint arXiv:1402.3655, 2014 [6] S. K. Singh, M. P. Singh, D. K. Singh, "A survey of energy-efficient hierarchical cluster-based routing in wireless sensor networks", Int. J. Adv. Netw. Appl., vol. 2, no. 2, pp. 570-580, Sep. 2010. [7] Farooq Muhamnmad, Omer Abdul, Basit Dogar, Ghalib Asadullah Shah, "MR-LEACH: multi-hop routing with low energy adaptive clustering hierarchy", Sensor Technologies and Applications (SENSORCOMM) 2010 Fourth International Conference, pp. 262-268, 2010. [8] A. Karthikeyan, V. Jagadeep and A. Rakesh, "Energy-efficient multihop selection with PEGASIS routing protocol for wireless sensor networks," 2014 IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, 2014, pp. 1-5 [9] J. Kulshrestha and M. K. Mishra, "DPEGASIS: Distributed PEGASIS for chain construction by the nodes in the network or in a zone without having global network topology information," 2017 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), Aligarh, 2017, pp. 13-17. [10] R. Dutta and S. Gupta, "Energy-aware modified PEGASIS through packet transmission in wireless sensor network," 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, 2016, pp. 443-446 [11] D. Baghyalakshmi, Ebenezer Jemimah, S. A. V. Satyamurty, "Low latency and energy-efficient routing protocols for wireless sensor networks", Wireless Communication and Sensor Computing 2010. ICWCSC 2010. International Conference on, pp. 1-6, 2010. [12] A Razaque, KM. Elleithy, "Energy-efficient border node medium access control protocol for wireless sensor networks", Sensors, vol. 14, no. 3, pp. 5074-117, Mar. 2014. [13] A. Razaque, M. Abdulgader, C. Joshi, F. Amsaad and M. Chauhan, "P-LEACH: Energy-efficient routing protocol for Wireless Sensor Networks," 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), Farmingdale, NY, 2016, pp. 1-5. [14] R. Meelu, R. Anand, "Performance evaluation of cluster-based routing protocols used in heterogeneous wireless sensor networks", International Journal of In-formation Technology and Decision Making, vol. 4, no. 1, pp. 227-231, 2011.
Paper Title | : | Object Identification Using Weakly Supervised Semantic Segmentation |
Authors | : | P.S.Gunde, Dr. S.K.Shirgave, , , |
Affiliations | : | 1 PG Scholar, Dept of Computer Science and Engineering, DKTE Society's Textile & Engineering Institute, Ichalkaranji (An Autonomous Institute), India. 2 Associate Professor, Dept of Computer Science and Engineering, DKTE Society's Textile & Engineering Institute, Ichalkaranji (An Autonomous Institute), India. |
Abstract | : | Image segmentation is referred to as one of the most important processes of image processing. Image segmentation is the technique of dividing or partitioning an image into parts, called segments. It is mostly useful for applications like image compression or object recognition because for these types of applications, it is inefficient to process the whole image. So, image segmentation is used to segment the parts from the image for further processing. Semantic image segmentation is a vast area for computer vision and machine learning researchers. Many vision applications need accurate and efficient image segmentation and segment classification mechanisms for assessing the visual contents and perform real-time decision making. In this paper, we recommend conditional random ï¬eld (CRF) based framework for weakly supervised semantic segmentation. First merging super pixels into large pieces and use these pieces for further use to identify objects. The pieces from all the training images are gathered and associated with appropriate semantic labels by CRF. In the case of testing, by using the potential energy of each piece merged from super pixels are compare with piece library. For results, we use commonly used the dataset for image segmentation is MSRC-21 and VOC 2012 with state-of-art. |
: | 10.22362/ijcert/2019/v6/i07/v6i0701 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i07/v6i0701 |
[1] L. Ladicky, C. Russell, P. Kohli, and P. H. S. Torr, “Graph cut based inference with co-occurrence statistics,†in Proc. Eur. Conf. Comput.Vis., Heraklion, Greece, 2010, pp. 239–253. [2] H. Lu, G. Fang, X. Shao, and X. Li, “Segmenting human from photo images based on a coarse-to-fine scheme,†IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 3, pp. 889–899, Jun. 2012. [3] J. Carreira and C. Sminchisescu, “CPMC: Automatic object segmentation using constrained parametric min-cuts,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 7, pp. 1312–1328, Jul. 2012. [4] Y.-L. Hou and G. K. H. Pang, “Multicue-based crowd segmentation using appearance and motion,†IEEE Trans. Syst., Man, Cybern., Syst., vol. 43, no. 2, pp. 356–369, Mar. 2013. [5] X. Yuan, J. Guo, X. Hao, and H. Chen, “Traffic sign detection via graphbased ranking and segmentation algorithms,†IEEE Trans. Syst., Man, Cybern., Syst., vol. 45, no. 12, pp. 1509–1521, Dec. 2015. [6] Y.-T. Chen, X. Liu, and M.-H. Yang, “Multi-instance object segmentation with occlusion handling,†in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Boston, MA, USA, 2015, pp. 3470–3478. [7] J. Wang and A. L. Yuille, “Semantic part segmentation using compositional model combining shape and appearance,†in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Boston, MA, USA, 2015, pp. 1788–1797. [8] H. Zhang, J. E. Fritts, and S. A. Goldman, “Image segmentation evaluation: A survey of unsupervised methods,†Comput. Vis. Image Understand., vol. 110, no. 2, pp. 260–280, 2008. [9] G. Csurka and F. Perronnin, “A simple high performance approach to semantic segmentation,†in Proc. Brit. Mach. Vis. Conf., Leeds, U.K., 2008, pp. 1–10. [10] F. Wang, Q. Huang, M. Ovsjanikov, and L. J. Guibas, “Unsupervised multi-class joint image segmentation,†in Proc. IEEE Conf. Comput. Vis.Pattern Recognit., Columbus, OH, USA, 2014, pp. 3142–3149. [11] A. Vezhnevets, V. Ferrari, and J. M. Buhmann, “Weakly supervised semantic segmentation with a multi-image model,†in Proc. IEEE Int.Conf. Comput. Vis., Barcelona, Spain, 2011, pp. 643–650. [12] Y. Liu, J. Liu, Z. Li, J. Tang, and H. Lu, “Weakly-supervised dual clustering for image semantic segmentation,†in Proc. IEEE Conf. Comput.Vis. Pattern Recognit., Portland, OR, USA, 2013, pp. 2075–2082. [13] L. Zhang et al., “Probabilistic graphlet cut: Exploiting spatial structure cue for weakly supervised image segmentation,†in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Portland, OR, USA, 2013, pp. 1908–1915. [14] K. Zhang, W. Zhang, Y. Zheng, and X. Xue, “Sparse reconstruction for weakly supervised semantic segmentation,†in Proc. Int. Joint Conf.Artif. Intell., Beijing, China, 2013, pp. 1889–1895. [15] L. Zhang et al., “A probabilistic associative model for segmenting weakly supervised images,†IEEE Trans. Image Process., vol. 23, no. 9,pp. 4150–4159, Sep. 2014. [16] P. O. Pinheiro and R. Collobert, “From image-level to pixel-level labeling with convolutional networks,†in Proc. IEEE Conf. Comput. Vis.Pattern Recognit., Boston, MA, USA, 2015, pp. 1713–1721. [17] J. Dai, K. He, and J. Sun, “Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation,†in Proc. IEEE Int. Conf. Comput. Vis., Santiago, Chile, 2015,pp. 1635–1643 [18] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk, SLIC Superpixels, EPFL Technical Report 149300, June 2010. [19] Yi Li, Yanqing Guo, Member, IEEE, Yueying Kao, and Ran He “Image Piece Learning for Weakly Supervised Semantic Segmentation†IEEE transaction on systems, man, and cybernetics: systems, vol. 47, april 2017.
Paper Title | : | Phishing Urls Detection Using Machine Learning Techniques |
Authors | : | Sushma Joshi, Dr S.M Joshi, , , |
Affiliations | : | Computer Science and Engineering, SDM College of Engineering and Technology, Visveswaraya Technological University, Dharwad, India |
Abstract | : | Phishing is an attempt to get any sensitive information like user identity information, banking details and passwords from target or targets which is considered as fraudulent attack. Phishing causes huge loss to the internet users every year. It is a captivating technique used obtain all the personal and financial information from the pool users of internet. This project deals with the methodologies of identifying the phishing websites with the help of machine leaning algorithms. We have considered the lexical properties, host based and page-based properties of the URLs which are used for identifying the phishing URLs. Various Machine learning algorithms are implemented for feature evaluation of the URLs which have widespread phishing properties. These website properties are refined so that a best suitable classifier tis identified which can distinguish between benign and phishing site. |
: | 10.22362/ijcert/2019/v6/i06/v6i0602 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i06/v6i0602 |
[1] Joby James, Sandhya L, Ciza Thomas: Detection of Phishing URLs Using Machine Learning Techniques. In Proc. Of 2013 International Conference on Control Communication and Computing (ICCC). [2] J. Ma, L. K. Saul, S. Savage and G. M. Voelker,†Beyond Blacklists: Learning to Detect Phishing Web Sites from Suspicious URLsâ€, Proc.of SIGKDD '09 [3] J. Ma, L. K. Saul, S. Savage, and G. M. Voelker,†Learning to Detect Phishing URLsâ€, ACM Transactions on intelligent Systems and Technology, Vol. 2, No.3, Article 30, Publication date: April 2011. [4] Garera S., Provos N., Chew M., Rubin A. D., “A Framework for Detection and measurement of phishing attacksâ€, In Proceedings of the ACM Workshop on Rapid Malloced (WORM), Alexandria, VA. [5] D. K. McGrath, M. Gupta, “Behind Phishing: An Examination of Phisher Modi Operandiâ€, In Proceedings of the USENIX Workshop on Large-Scale Exploits and Emergent Threats (LEET). [6] C. Whittaker, B. Ryner, and M. Nazif. Large-scale automatic classification of phishing pages. In Proc. Of the 17th Annual Network and Distributed System Security Symposium (NDSS’10), California, USA, February 2010. [7] Phistank. https://www.phishtank.com [8] Curlie. https://curlie.org [9] I. Rogers “Google Page Rank – Whitepaper†[10] Shraddha Parekh, Dhwanil Parikh, Srusti Kotak, Prof Smita Sankhe, A New Method for Detection of Phishing Websites: URL Detection “. In Proc Of 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) [11] Ram B. Basnet and Andrew H. Sung, “Learning to Detect Phishing Webpagesâ€, In proceedings of Journal of Internet Services and Information Security. [12] Mohammad Fazli Baharuddin, Tengku Adil Tengku Izhar, Mohd Shamsul Mohd Shoid “Malicious Url Classification System Using Multi-Layer Perceptron Techniqueâ€, In proceedings of the Journal of Theoretical and Applied Information Technology.
Paper Title | : | Strategies to Enhance Physical Activity Participation Level among Pupils |
Authors | : | Angelo R. Ganaden , , , , |
Affiliations | : | College of Teacher Education, President Ramon Magsaysay State University (Formerly Ramon Magsaysay Technological University) Iba, Zambales, Philippines |
Abstract | : | This study explored and determined the participation level in physical activities of Grade VI pupils of Palauig Central Elementary School, Palauig, Zambales, Philippines enrolled during the school year 2017. This study proposed an interventions and/or strategies to increased participation in physical activities which were categorized as role model, instant activity, distribution of equipment and transition. This present study employed a descriptive research method and used a survey questionnaire as main instrument for data gathering. Descriptive such as percentage, frequency counts and weighted mean were employed for the statistical analysis. Findings of the study revealed that the pupil-respondents level of participation in physical activities was rated as Minimal Participation in rolling, dribbling, striking, kicking, bouncing, hopping, leaping, tag games, ball games, skipping, fitness circuit, simple mixer dance and galloping. The pupils strongly agreed to the strategies presented in the present study such as role model, instant activity, distribution of equipment and transition to increase participation level of pupils in physical activities at school. It was highly suggested that the school may provide a concrete plan of activities and/or guideline aimed to increase physical activity opportunities for the pupils as well as all members of the educational community. School administrator and educators should intensify the awareness campaigns for pupils, parents and other educators on the importance of physical activity participation and benefits of being physically fit. |
: | 10.22362/ijcert/2019/v6/i06/v6i0601 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i06/v6i0601 |
[1] The UNESCO, “International Charter of Physical Education, Physical Activity and Sport of 1978.†[2] Physical Activity Guidelines for Americans, Department of Health and Human Services, http://www.health.gov/paguidelines/default.aspx. (2008). [3] Physical Activity Guidelines for Americans, Office of Disease Prevention & Health Promotion, US Department of Health and Human Services (11 January 2010). [4] J. Withall, R. Jago, & K.R. Fox, 2011, “Why some do but most don't. Barriers and enablers to engaging low-income groups in physical activity programmes: a mixed methods study,†BMC Public Health. 2011 [5] C3 Collaborating for Health, “The benefits of physical activity for health and wellâ€being,†2011. [6] C.K. Fox, D. Barr-Anderson, D. Sztainer and M. Wall, “Physical activity and sports team participation: Associations with academic outcomes in middle school and high school students.†Journal of School Health. 2010; 80(1):31–37. 2010. [7] C.R. Fredericks, S.J. Kokot and S. Krog, “Using a developmental movement programme to enhance academic skills in grade 1 learners.†South African Journal for Research in Sport, Physical Education and Recreation. 2006; 28 (1): 29–42. 2006. [8] J.M. Monti, C.H. Hillman and N.J. Cohen, “Aerobic fitness enhances relational memory in preadolescent children: The FITKids randomized control trial.†Hippocampus. 2012; 22(9):1876–1882. 2012. [9] A. Charlton and M. Potter, “Barriers to Participation. Department for Culture, Media and Sports,†2010. Retrieved from www.gov.uk/governtment/uploads/TP_Barri ersrrepot.pdf [10] C.N. Rasberry, S.M.S. Lee, L. Robin, B.A. Laris, L.A. Russell, K.K. Coyle, & A.J. Nihiser, “The association between school-based physical activity, including physical education, and academic performance: A systematic review of the literature,†Preventive Medicine, Elsevier, 2011. [11] A.L., Fedewa and S. Ahn, “The effects of physical activity and physical fitness on children's achievement and cognitive outcomes: A meta-analysis,†Research Quarterly for Exercise and Sport, 82(3):521–535, 2011. [12] C. Cruz, D. Villena, E. Navarro, R. Belecina, and M. Garvida, “Towards Enhancing the Managerial Performance of School Heads,†www.irmbrjournal.com June 2016. International Review of Management and Business Research Vol. 5 Issue.2, 2016. [13] S.M. Lee, C.R. Burgeson, J.E. Fulton, & C.G. Spain, “Physical Education and Physical Activity: Results from the School Health Policies and Programs Study 2006,†Published 2007. [14] National Association for Sport and Physical Education, “Physical education in the Context of Schooling,†2012 http://www.ncbi.nlm.nih.gov/books/NBK201493/ [15] American Heart Association, “Recommendations for Physical Activity in Adults and Kids. 2006. https://www.heart.org/en/healthy-living/fitness/fitness-basics/aha-recs-for-physical-activity-in-adults [16] UNICEF, “World report on child injury prevention†World Health Organization, 2008. [17] SHAPE America, “National PE Standards. National Standards & Grade-Level Outcomes for K-12 Physical Education,†1900 Association Drive, Reston, VA 20191 http://www.shapeamerica.org/standards/pe/ 2014. [18] A.R. Ganaden, G. Barrientos and D.M. Anaud, “Constraints in Sports Participation among Pupils of Cabangan Central Elementary School, Cabangan District, Zambales, Philippines,†TEXTROAD Journals -Journal of Social Sciences and Humanity Studies (JSSHs). Vol. 3, No. 6 November 2017 ISSN 2356-8852, November, 2017. [19] J. W. Kang, “Perceived Constraints on Sport Participation among Young Koreans in Australia,†International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering Vol: 7, No: 5, 2013. Retrieved fromhttp://waset.org/publications/9997661/perceived-constraints-on-sportparticipation-among-young-koreans-in-australia, 2013. [20] S. Dolan, “Nautilus Fitness Center†• Godfrey, IL, United States, https://web.facebook.com/NautilusAlton/posts/1049681801720488?_rdc=1&_rdrJanuary 28, 2016 [21] J.E. Donnelly and K. Lambourne, “Classroom-based physical activity, cognition, and academic achievement,†Preventive Medicine. 2011;52. J.B. Bartholomew and E.M. Jowers, “Physically active academic lessons in elementary children. Preventive Medicine.†2011; 52. 2011. [22] D.L. Kibbe J. Hackett, M. Hurley, A. McFarland, K.G. Schubert, A. Schultz and S. Harris, “Ten years of TAKE 10!®: Integrating physical activity with academic concepts in elementary school classrooms,†Preventive Medicine. 2011; 52. [23] J. M. Durban and R. D. Catalan, “Issues and Concerns of Philippine Education through the Years,†Asian Journal of Social Sciences & Humanities. Vol. 1. No. 2. May 2012. http://www.ajssh.leenaluna.co.jp/AJSSHPDFs/Vol.1(2)/AJSSH2012(1.2-08).pdf\ [24] Coaching Association of Canada, “Coaching Association of Canada partners with Active for Life in promoting physical literacy,†Active for Life. [25] C.H. Hillman, D.M. Castelli and S.M. Buck, “Aerobic fitness and neurocognitive function in healthy preadolescent children,†Medicine and Science in Sports and Exercise, 37(11):1967. 2005.
Paper Title | : | A Review on Typical and Modern Brain MRI Image Segmentation Methods and Challenges |
Authors | : | D.Sreedevi, Prof.K.Samatha, Prof.M.P.Rao, , |
Affiliations | : | 1:Research Scholar, Department of Physics, Andhra University, India, 2:Professor, Department of Physics, Andhra University, India, 3:Professor, Department of Systems Design, Andhra University, India |
Abstract | : | Background: Brain image segmentation is one of the essential tasks in medical image analysis. Digital Brain MR Images usually contain Noise, inhomogeneity, and sometimes deviation due to the capturing device's configuration. Therefore, accurate segmentation of brain MRI images is deployed to measure and visualize the brain's anatomical structures, analyze brain changes, delineate pathological regions, and for surgical planning and image-guided interventions. During the past few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper, several popular methods are used for brain MRI segmentation and focus on their capabilities, advantages, and pitfalls. Likewise, we also discuss modern image segmentation techniques by Deep Learning Technology and deliberate the metrics to evaluate the brain tumor segmentation and dataset availability performance. Eventually, we suggest future research challenges among brain tumor multimodal imaging techniques. |
: | 10.22362/ijcert/2019/v6/i05/v6i0503 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i05/v6i0503 |
[1] Source retrieved from https://www.envrad.com/difference-between-x-ray-ct-scan-and-mri/ on 20 march 2019. [2] Hasan, A., Meziane, F., Aspin, R., & Jalab, H. (2016). Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge. Symmetry, 8(11), 132.doi:10.3390/sym8110132 [3] N. Gordillo, E. Montseny, and P. Sobrevilla, "State of the art survey on MRI brain tumor," In IEEE of Segmentation, 2013. [4] N. Otsu, "A Threshold Selection Method from Graylevel Histogram, IEEE Transaction on System", Man. and Cybernetics, vol.9. no.1 .pp. 62-66, 1979. [5] A. Aslam, E. Khan, and M.M.S. Beg, Improved edge detection algorithm for brain tumour segmentation, Elsevier, 58 (2015), pp. 430–437. [6] Gonzalez, R. C.,Woods, R. E., 2008. Digital image processing. Upper Saddle River, New Jersey. [7] Easha Noureen, Dr. Md. Kamrul Hassan, "Brain Tumor Detection Using Histogram Thresholding to Get the Threshold point", IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), Volume 9, Issue 5, PP 14-19, Sep – Oct. 2014. [8] Radha, R., Lakshman, B., 2013. Retinal image analysis using morphological process and clustering technique. International Journal of Signal and Image Processing, 4(6), 55-32. [9] K.S.A. Viji and J. Jayakumari, "Modified texture based region growing segmentation of M.R. brain images," In Proceedings of the IEEE conference on information and communication technologies, 2013. [10] Bilmes, J.A., 1998. A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden Markov models. International Computer Science Institute. [11] R. Chandra and K.R.H. Rao, "Tumor detection in the brain using genetic algorithm G," In 7th international conference on communication, computing and virtualization, 2016. [12] M. Shasidhar, V. S. Raja and B. V. Kumar, "MRI Brain Image Segmentation Using Modified Fuzzy C-Means Clustering Algorithm," 2011 International Conference on Communication Systems and Network Technologies, Katra, Jammu, 2011, pp. 473-478, doi: 10.1109/CSNT.2011.102. [13] Shweta A. Ingle, Snehal M. Gajbhiye, "Review on Automatic Brain Tumor Detection Technique", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20171047, Volume 6 Issue 2, February 2017, 1553 – 1557 [14] Yousefi S, Azmi R, Zahedi M. Brain tissue segmentation in M.R. images based on a hybrid of MRF and social algorithms. Medical Image Analysis. 2012;16:840–848
Paper Title | : | Big data in healthcare: Challenges and approaches |
Authors | : | P.Murthuja, , , , |
Affiliations | : | Associate professor, Prabhath Institute of Computer Science, Parnapalli village , Bandi atmakur, Kurnool District, Andhra Pradesh, India |
Abstract | : | Now a day’s huge volume of data is generated due to wide usage of social media, online shopping or transactions gives delivery to big data. Visual representation and analysis of this large volume of data is one of the major research topics today. Healthcare is one of the most promising areas for using big data for change. Big data healthcare has enormous potential to improve patient outcomes, obtain valuable information, prevent disease, reduce healthcare delivery costs and improve quality of life. In this paper i focus on challenges associated with healthcare big data and also explore the common approaches for analysing big data in health Care system. |
: | 10.22362/ijcert/2019/v6/i05/v6i0502 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i05/v6i0502 |
[1] Hendrik Schuff, Jeremy Barnes, Julian Mohme, Sebastian Pado, and Roman Klinger,” “Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus ” by” , Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 13-23, Copenhagen, Denmark, September 7-11, 2017. [2] Sunidhi Sharma, D.K.Sharma, Supriti Sharma, “Text Analysis and Sentiment Analysis using Facebook in R Language: Case studies” , International Journal of Computer and Mathematical Sciences, ISSN 2347-8527, vol 6 Issue 12, December 2017. [3] Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo, “Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN” Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social media Analysis, pages 102-111, Copenhagen, Denmark, September 7-11, 2017. [4] Kishaloy Halder, Lahari Poddar, Min-Yen Kan, “Modeling Temporal Progression of Emotional Status in Mental Health Forum : a Recurrent Neural Net Approach” Proceedings of Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 127-135, Copenhagen, Denmark, September 7-11, 2017. [5] Jeremy Barnes, Roman Klinger, and Sabine Schulte im Walde, “Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets” Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 2-12, Copenhagen, Denmark, September 7-11, 2017. [6] Keenen Cates, Pengcheng Xiao, *, Zeyhang, Calvin Dailey, “Can Emoticons Be Used To Predict Sentiment?” Journal of Data Science 355-376, April 04, 2018. [7] Prabaharan Poornachandran,, “deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets” Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 102-111, Copenhagen, Denmark, September 7-11, 2017. [8] Murphy G, Hanken MA, Waters K. Electronic health records: changing the vision. Philadelphia: Saunders W B Co;1999. p. 627. [9] Shameer K, et al. Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams. Brief Bioinform. 2017;18(1):105–24. [10] Service, R.F. The race for the $1000 genome. Science. 2006;311(5767):1544–6.
Paper Title | : | HYBRIDIZATION OF WEB PAGE RECOMMENDER SYSTEMS BASED ON ML TECHNIQUES |
Authors | : | Shrenik R Patil, , , , |
Affiliations | : | Information Technology Department, D.K.T.E.’s Textile and Engineering Institute Ichalkaranji India |
Abstract | : | : World Wide Web is the biggest source of information. Though the World Wide Web contains a tremendous amount of data, most of the data is irrelevant and inaccurate from users’ point of view. Consequently, it has become increasingly necessary for users to utilize automated tools such as recommender systems in order to discover, extract, filter, and evaluate the desired information and resources. recommender systems (RS) are widely used in e-commerce, social networks and several other domains. Web page recommender systems predict the information needs of users and provide them with recommendations to facilitate their navigation. Web content and Web usage mining techniques are employed as conventional methods for recommendation. Machine Learning techniques used for recommender system are Clustering, Association rules and Markov models. These techniques have strengths and weaknesses. Combining different systems to overcome disadvantages and limitations of a single system may improve the performance of recommenders. Hybrid recommender systems can be used to avoid the drawbacks or limitations of previous recommendation method. They combine two or more methods to improve recommender performance. In this paper, the four recommender systems are combined by using different hybridization methods. The effects of the hybrid recommenders are examined by comparing the results of hybrid system against the results of single recommendation method. Result shows that the hybrid recommender provides successful recommendation when the recommended page is generated by all the systems of the hybrid. |
: | 10.22362/ijcert/2019/v6/i05/v6i0501 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i05/v6i0501 |
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Paper Title | : | RPA Based Digital Marketing Robot |
Authors | : | Mr. Shashank Karn, Mr. Sumit Chaurasia, Mr. Kedar Davate, Dr. Milind U. Nemade, Dr. Namrata F. Ansari |
Affiliations | : | Student, Electronics Engineering, K.J.S.I.E.I.T, University of Mumbai, Mumbai, India |
Abstract | : | In the modern era of advertising, the role of marketing has increased exponentially. The marketing strategies of big brands nowadays highly rely on digital media. A new method of marketing known as digital marketing has evolved more than rest of the marketing strategies. Digital marketing has now become the need of the hour for various brands and e-commerce platform. The proposed system will help such brands to reach out the end user more efficiently and frequently. The proposed system is based on implementation of Robotic Process Automation (RPA) which is a method used for deployment of software-based robots that mimic the functionalities of human operating a machine. |
: | 10.22362/ijcert/2019/v6/i04/v6i0402 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i04/v6i0402 |
[1] Cyrille Bataller, Mougins (FR); Adrien Jacquot, Antibes Juan-les- Pins (FR); Sergio Raul Torres, Den Haag (NL), “Robotic Process Automa-tion,†Accenture Global Solutions Limited, Dublin (IE), 8th April 2016.A. Criminisi, P. Perez, and K. Toyama, “Region filling and object removal by exemplar-based image inpainting.â€, IEEE Transactions on Image Processing, vol. 13, no.9, pp. 1200–1212, 2004. [2] Professor Mary Lacity, Curators Professor, University of Missouri-St. Louis Visiting Professor, The London School of Economics and Political Science; Professor Leslie Willcocks The Outsourcing Unit Department of Management the London School of Economics and Political Science; Andrew Craig The Outsourcing Unit Senior Visiting Research Fellow the London School of Economics and Political Science, “Robotic Process Automation at Telefnica O2,â€April 2015. [3] Mauro Bampo, Michael T. Ewing, Dineli R. Mather, David Stewart, Mark Wallace, “The Effects of the Social Structure of Digital Networks on Viral Marketing Performance,†5 Jun 2008. [4] Petra Peura, "Robotic Process Automation Concept for Service Management," Metropolia University of Applied Sciences Bachelor of En-gineering Industrial Management Thesis. 6 May 2018. [5] Sorin Anagnoste, “Robotic Automation Process - The next major rev-olution in terms of back office operations improvement,â€The Bucharest University of Economic Studies, Bucharest, Romania sorin. anag-noste@fabiz.ase.ro. [6] Mickey Williams,Microsoft Visual C# (Core Reference) Microsoft Press Redmond, WA, USA 2002. [7] Blase Ur , Melwyn Pak Yong Ho, Stephen Brawner, Jiyun Lee, Sarah Mennicken , Noah Picard, Diane Schulze, Michael L. Littman Brown University, Carnegie Mellon University, University of Zurich ,â€Trigger-Action Programming in the Wild: An Analysis of 200,000 IFTTT Recipes†[8] STEVEN OVADIA LaGuardia Community College, Long Island City, New York,â€Automate the Internet With If This Then That (IFTTT)â€, Behavioral & Social Sciences Librarian, 33:208211, 2014
Paper Title | : | A Survey of Blockchain Technology Security |
Authors | : | Ayushi Singh, Gulafsha Shujaat, Isha Singh, Abhishek Tripathi, Divya Thakur |
Affiliations | : | Dept. Computer Science Engineering, Buddha Institute of Technology,Gorakhpur (U.P.), India |
Abstract | : | Bitcoin is a popular cryptocurrency that records all transactions in an allotted append-handiest public ledger referred to as a blockchain. The security of Bitcoin heavily relies on the motivation-suitable proof-of-work (PoW) founded dispensed consensus protocol, which is run with the aid of the community nodes known as miners. Because of its inception, blockchain technological know-how has proven promising application possibilities. The spectrum of blockchain functions stages from financial, healthcare, automobile, hazard administration, internet of matters (IoT) to public and social offerings. Several reports focal point on utilizing the blockchain information structure in various applications. These vulnerabilities result in the execution of different security threats to the ordinary functionality of Bitcoin. We then examine the feasibility and robustness of the brand new safety solutions. Moreover, we discuss the current anonymity concerns in Bitcoin and the privateness-related threats to Bitcoin customers together with the evaluation of the comprehensive privacy-keeping solutions. |
: | 10.22362/ijcert/2019/v6/i04/v6i0401 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i04/v6i0401 |
[1] A. Ferrag, . M. Derdour and . M. Mukherjee, “Blockchain technologies for the internet of things: research,†IEEE, 2018. [2] . B. Carminati, . L. Bahri and . E. Ferrari, “Decentralized privacy preserving services for online social networks,†Online Soc Netw media, 2018. [3] Z. Zheng , . S. Xie and H.-N. Dai, “Blockchain challenges and opportunities: a survey,†Int. J. Web and Grid Services, vol. 14, 2018. [4] Unocoin, “What Factors Are Influencing Blockchain Technology,†2018. [5] X. Li, P. Jiang, Q. Wen and X. Luo, “A survey on the security of blockchain systems,†Future Generation Computer Systems, 2017. [6] Z. Zheng, S. Xie and H. Wang, “Blockchain challenges and opportunities: A Survey,†Internat. J. Web Grid Serv., 2016. [7] M. Ghosh, M. Richardson and B. Ford, “A TORPATH TO TORCOIN, PROOF-OF-BANDWIDTH ALTCOINS FOR COMPENSATING RELAYSâ€. [8] Intel, “Proof of elapsed time (poet),†2017. [9] P. Technologies, “Proof of Authority Chains,†2017. [10] A. Gervais, G. Karame and V. Glykantzis, “On the security and performance of proof of work blockchains,,†SIGSAC Conference on Computer and Communications Security, 2016. [11] W. J. Gordo and C. Catalini, “Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability,†Computational and Structural Biotechnology Journal, 2018. [12] M. Hölbl, A. KamiÅ¡ali´ and L. N. Zlatolas, “A Systematic Review of the Use of Blockchain,†Symmetry 2018, 2018. [13] P. Novotny, D. N. Dillenberger and R. Vaculin, “Permissioned Blockchain Technologies forâ€. [14] K. Yilmaz, “Comparison of Permissioned Blockchains,†Coinmonks. [15] H. Mayer, “ECDSA Security in Bitcoin and Ethereum: a Research,†2016. [16] S. Alliance, “Know your ransomware: Ctb-locker,†2017. [17] N. Christin, “Traveling the silk road: A measurement analysis of a large anonymous,†The 22nd International Conference on World wide, 2017. [18] A. Miller, M. Möser, K. Lee and A. Narayanan, “An empirical analysis of linkability in the monero blockchain,†ArXiv preprint, 2017. [19] E. community, “Official Go implementation of the Ethereum protocol,†2017. [20] B. Bodó, J. P. Quintais and D. Gervais, “Blockchain and smart contracts: the missing link in copyright licensing,†International Journal of Law and Information Technology, vol. 26, no. 4, 2018. [21] Z. Zheng, S. Xie and H.-N. Dai, “An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends,†IEEE International Congress on Big Data, 2017. [22] M. Bartoletti, T. Cimoli and L. Pompianu, “Blockchain for social good: a quantitative analysis,†Goodtechs, 2018. [23] A. Kosba, C. Papamanthou and . A. Miller, “Hawk: The Blockchain Model of Cryptography and Privacy-Preserving Smart Contracts,†IEEE Symposium on Security and Privacy, 2016.
Paper Title | : | QUAD-BAND CIRCULARLY POLARIZED PATCH ANTENNA FOR UWB/5G APPLICATIONS |
Authors | : | A.Sivabalan, G.Bharathi, K.Deepeka Rani, , |
Affiliations | : | ECE, Chennai Institute of Technology, Anna University, Chennai, India |
Abstract | : | A quad-band circularly polarised (CP) patch antenna for 7.1/7.6/7.9/8.6 GHz for UWB/5G applications are proposed in this paper. By designing the patch antenna with an inverted U-shaped radiator, I-shaped and L-shaped strips which are all rotated by 45áµ’ at the horizontal axis. The measurement of -37.51 dB of return loss for 8.6 GHz frequency was obtained. A microstrip line feed technique was used for feeding which is one of the contacting schemes used in the feeding methods. A conducting strip is connected directly to the edge of the microstrip patch. It provides a simple planar structure since the conducting strip is smaller in size when compared to the patch. The substrate FR_4 epoxy has high electrical insulation, good mechanical strength, better wear, and corrosion resistance. The measured 3 dB AR information is 5.63% (6.9-7.3 GHz), 5.26% (7.4-7.8 GHz), 5.0% (7.8-8.2 GHz) and 3.50% (8.4-8.7 GHz) severally. This antenna can be used in UWB/5G applications which offer high-speed data transmission. |
: | 10.22362/ijcert/2019/v6/i03/v6i0302 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i03/v6i0302 |
[1]S. Gao, Q. Luo, and F. Zhu, “Circularly Polarized antennas,†Wiley-IEEE Press, New York, November 2013. [2]T. T. Le and H. C. Park, “Very simple circularly polarized printed patch antenna with enhanced bandwidth,†Electron. Lett. vol. 50, no. 25, pp. 1896–1898, 2014. [3]T. V. Hoang and H. C. Park, “Very simple 2.45/3.5/5.8 GHz triple-band circularly polarized printed mono pole antenna with bandwidth enhancement,†Electron. Lett, vol. 50, no. 24, pp. 1792–1793, 2014. [4]S. Verma and P. Kumar, “Compact triple-band antenna for WiMAX and WLAN applications,†Electron. Lett., vol. 50, no. 7, pp. 484–486, 2014. [5] J. G. Baek and K. C. Hwang, “Triple-band unidirectional circularly polarized hexagonal slot antenna with multiple L-shaped slits,†IEEE Trans. Antennas Propag., vol. 61, no. 9, pp. 4831–4835, 2013. [6] K. Agarwal, Nasimuddin, and A. Alphones, “RIS-Based compact circularly polarized mircostrip antennas,â€IEEE Trans. Antennas Propag., vol. 61, no. 2, pp. 547–554, 2013.
Paper Title | : | Wireless Ultrasonic Auto Navigation Robot for Agriculture |
Authors | : | Dr.J.VijiPriya, Dr.S.Suppiah, Ms. Rana Mohammad, , |
Affiliations | : | 1 Assistant Professor, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia 2 Dean, VelTech University, TamilNadu, India 3 College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia |
Abstract | : | At present the machinery and their effortlessness are incredibly essential things that compose our lives full of expediency. They can also be enhanced for employ in sensitive sites that cause a threat to human being. In general a group of people who are involved in agricultural. There are various difficulties they face throughout their works. Naturally the agriculture land is very complex to reach some of the places we are in. This study makes it easy to use a Wireless Ultrasonic Auto Navigation Robot (Wireless UAN Robot) to diminish the risk of injuries that might occur while walking or by car. The modern robot consists of a custom-made structure with a circular shape that is competent to navigate easily in most hazardous places and rotate in rigid spaces. |
: | 10.22362/ijcert/2019/v6/i03/v6i0301 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i03/v6i0301 |
[1] Leo Louis, “Working Principle Of Arduino And Using It As A Tool For Study And Researchâ€, International Journal of Control, Automation, Communication and Systems (IJCACS), Vol.1, Issue.2, 2016. [2] Shubham Dhage , Pradip Patil , Data Kande , Dr. Prakash Pati,“Wireless Controlled Multipurpose Agricultural Robotâ€, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering,2016 [3] Patrick Piper and Jacob Vogelpublished a paper on “Designing an Autonomous Soil Monitoring Robotâ€, IEEE, 2015. [4] Fale and Bhure amit published a paper on “Autonomous farming robot with plant health indicationâ€,IJATES, 2015. [5] Bakker T, van Asselt K, Bontsema J, Müller J, van Straten G,“Autonomous navigation using a robot platform in a sugar beet fieldâ€. Biosyst Eng, 2011 [6] Blasco J, Aleixos N, Roger JM, Rabatel G, Moltó E, “Robotics weed control using machine visionâ€. Biosyst Eng, 2002. [7] Emmi L, Gonzalez-de-Soto M, Pajares G, Gonzalez-de-Santos P, 2014. “New trends in robotics for agriculture: integration and assessment of a real fleet of robots†Sci World J, 2014. [8] Galadima, A.A., "Arduino as a learning tool," in Electronics, Computer and Computation (ICECCO), 2014.
Paper Title | : | Enhanced MBFD Algorithm to Minimize Energy Consumption in Cloud |
Authors | : | Varun Jasuja, Dr. Rajesh Kumar Singh, , , |
Affiliations | : | 1*Research Scholar, PTU, Jalandhar 2 Professor, SUS Institute of Computer, Tangori, Punjab |
Abstract | : | Background/Objectives: Cloud computing is a shared pool of configurable computer system resources and higher-level services. These services quickly configured over the Internet to achieve consistency and economies of scale. Methods/Statistical analysis: In this research, the DVFS (Dynamic Voltage and Frequency Scaling) mechanism is used to save energy in the cloud environment. In the existing work, MBFD has been used to check the resources in the physical machine. In case, if the resources are available, then the VM is placed over the PM. However, the problem is that the MBFD algorithm does not check the PM and hence result in higher energy consumption. Findings: In this paper, the MBFD algorithm is enhanced by using the concept of DVFS along with the concept of location-aware algorithm. Due to this algorithm, VM which is near to the server is executed first by measuring the distance. To measure the performance the parameters such as energy consumption and TCJ are measured. Improvements/Applications: The proposed framework reduced energy consumption and increased the total completed jobs. |
: | 10.22362/ijcert/2019/v6/i02/v6i0202 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i02/v6i0202 |
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Paper Title | : | The Social Studies Curriculum Standards in Junior Secondary Schools; Input to Quality Instruction and Students’ Civic Competence |
Authors | : | Marie Fe D. De Guzman , Roosevelt Ecle, , , |
Affiliations | : | President Ramon Magsaysay State University (PRMSU), Iba, Zambales, Philippines |
Abstract | : | Appraisal of the adequacy of Social Studies Curriculum Standards in ten themes was the main purpose of the research study. This endeavour was intended to provide input to quality Social Studies instruction in the Kto12 Basic Education Program and an enhance students’ civic competence. The study utilized the quantitative descriptive research design with questionnaire as the main instrument in gathering data from one hundred teachers in Department of Education Zone 1 Division of Zambale, Philippines during the school year 2017-2018. Findings established a high adequacy of Social Studies Curriculum Standards on Themes: Culture; Time, Continuity and Change; People, Places and Environments; Power, Authority and Governance; Production, Distribution and Consumption; Science, Technology and Society; Global Connections; and Civic Ideals and Practices. The themes which should include experiences for the study of Individual Development and Identity; and interactions among Individuals, Groups and Institutions were assessed adequately. The Analysis of Variance result revealed a no significant difference on the perception towards dimensions on adequacy of Social Studies Curriculum Standards in the Junior Secondary Schools. It was recommended that the teachers with the support of the School Heads should attend conferences focused on themes - Individual Development and Identity and Individual, Groups and Institution in order to gain more insights how these themes be meaningfully presented to students, thereby contributes to the attainment of the intended goals. |
: | 10.22362/ijcert/2019/v6/i02/v6i0201 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i02/v6i0201 |
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D. de Guzman, “The Project- Based Learning (PBL) Approach in Secondary Social Studies Instruction at Zone 2, Division of Zambales, Philippines,†International Journal of Scientific & Engineering Research Volume 8, Issue 11, November-2017 [20] B. Corpuz and G. Salandanan, “Principles of Teaching." Lorimar Publishing Inc. Quezon City, 2007. [21] B. M. Dalyop, “Evaluation of Social Studies Curriculum on Students’ Appreciation of Cultural Diversity,†Journal of Modern Education Review, ISSN 2155-7993, USA, July 2014, Volume 4, No. 7, pp. 536–540, 2014. [22] E. Neville, “A Case Study of Fifth Grade Social Studies Curriculum for Inclusion of Multicultural Education,†2006. [23] T. Dynneson and R. Gross, “Designing Effective Instruction for Secondary Social Studies,†Prentice Hall. USA, 1999. [24] Samoa, “Teaching History a Guide for Teachers Teaching History for the First Time,†Council of Presidents of Pacific Island History Associations, 2003. [25] M.F.D. de Guzman, J. Ababan and A. 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Paper Title | : | Product Review Summarization for E-Commerce Site Using Gibbs Sampling Based LDA |
Authors | : | Minakshi Ghorpade, Mrs. Megharani Patil, , , |
Affiliations | : | Department of Computer Engineering, TCET Mumbai, India |
Abstract | : | In E-commerce, the Reputation based trust models are extremely important for business growth. E-commerce website becomes more important in our day to days life because of varieties of information provided by it. 75 percent people are utilizing it for buying on the web items. The number of customer reviews on various products are increasing day-by-day. These vast numbers of reviews are beneficial to manufacturers and customers alike. It is a challenging task for a potential customer to read all reviews to make a better purchase decision. This system is a web-based application where user will view and purchase various products online, user can provide review about the products and online shopping services. The System takes review of various users and based on the review, system will specify whether the products and services provided by the E-commerce enterprise is good, bad or worst. The proposed work includes a multidimensional trust model for computing reputation scores from user`s reviews. To implement this a Modified LDA algorithm for mining dimensions of ecommerce feedback comments is used. In this proposed work natural language processing and opinion mining techniques are used. This paper also includes the comparison based on accuracy, time complexity, a brief introduction information world and touch topic likes trust score, reputation trust and their ratings using Gibbs-sampling that creates various categories for feedback and assigns trust score. |
: | 10.22362/ijcert/2019/v6/i01/v6i0102 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i01/v6i0102 |
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Paper Title | : | Detection of Breast Cancer using MRI: A Pictorial Essay of the Image Processing Techniques |
Authors | : | Poonam Jaglan, Dr. Rajeshwar Dass, Dr. Manoj Duhan, , |
Affiliations | : | Research Scholar,Deenbandhu Chhottu Ram University of Science & Technology, Murthal. |
Abstract | : | Medical imaging generates the visual representation of the interior body parts for the clinical analysis/ medical intervention. Now a days, an advanced medical imaging technique i.e. MRI provides acute dissection anatomical information about the human soft tissues. MRI generally suffers from poor contrast, low quality due to improper brightness & blurriness. So contrast manipulation is compulsively needed. Image enhancement is taken as the initial step which defines the accuracy of result. The prime objective is to improve the visual appearance or to provide a better transform representation for future automated image processing like analysis, detection, segmentation & recognition. Among all the existing techniques of image enhancement, the appropriate choice must be influenced by the facts i.e. visual perspective, modality and climatic conditions. A trade-off between noise reduction and feature preservation of the original image depends upon the filter reconstruction ability and noise model. In this paper, four different filtering algorithms such as Median filter (MF), Gaussian filter (GF), Average filter (AF) and Wiener filter (WF) are used to compare the effects of most dominant noises in MR images by calculating the statistical parameters i.e. Mean Square Error, Peak Signal to Noise Ratio, Root Mean Square Error & Mean Absolute Error. Also the noise density was gradually added to MRI image for effective comparative analysis of the filters. Further, the proposed algorithm detected the tumor region appropriately. |
: | 10.22362/ijcert/2019/v6/i01/v6i0101 | |
DOI Link | : | https://doi.org/10.22362/ijcert/2019/v6/i01/v6i0101 |
[1] Rafael C. Gonzalez, Richard E. Woods “ Digital Image Processing†Third Edition [2] M. K. S. Sivasundari, R. Siva Kumar, “Performance Analysis of Image Filtering Algorithms for MRI Imagesâ€, Int. J. Res. Eng. Technol., vol. 3, no. 5, pp. 438–440, 2014. [3] http://cancerworld.net/wpcontent/uploads/2017/01/Growing-cancer-burden-in-Indai.jpg, June, 2018. [4] J Edge et. al., “Magnetic resonance imaging of the breast: A clinical perspectiveâ€, South African Journal of Radiology, Vol. 16, No. 2, pp: 61-64, 2012. [5] https://myleftbreast.net/wpcontent/uploads/2011/04/breast_mri.jpg, March, 2018. [6] https://www.verywellhealth.com/what-does malignant-and-benign-mean-514240, Sept., 2018. [7] https://www.gleneagles.com.sg/facilities-services/centre-excellence/cancer-care/breast-cancer, August, 2018. [8] P. Janani*, J. Premaladha and K. S. Ravichandran , “Image Enhancement Techniques: A Study†Indian Journal of Science and Technology, vol 8(22), September 2015. [9] Yogesh S. Bahendwar, G.R.Sinha “ Efficient Algorithm For Denoising Of Medical Images Using Discrete Wavelet Transforms†Mathematical Methods and Systems in Science and Engineering, ISBN: 978-1-61804-281-1 [10] Shailendra singh Negi, Yatendra Singh Bhandari “ A Hybrid approach to Image Enhancement using contrast stretching on image sharpening and the analysis of various cases arising using Histogram†ICRAIE-2014, 978-1-4799-4040-0/14/2014.
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Citations Indices | All |
Citations | 1026 |
h-index | 14 |
i10-index | 20 |
Source: Google Scholar |
Acceptance Rate (By Year) | |
Year | Rate |
2021 | 10.8% |
2020 | 13.6% |
2019 | 15.9% |
2018 | 14.5% |
2017 | 16.6% |
2016 | 15.8% |
2015 | 18.2% |
2014 | 20.6% |