ORIGINAL_ARTICLE
Learners’ Feedback on the Effectiveness of Replacing an Instructional MOOC Video with Augmented Reality in a Practice-Based Course
< p class="chekide">Recently, Massive Open Online Courses have become sensational in the field of distance learning. There is a plethora of advantages being listed in learning through MOOCs but this pedagogy lacks in few areas when compared with traditional classes. One of those inabilities of MOOC is its support to prepare the students for laboratory-based courses. The authors of this study chose a MOOC course that teaches Digital Photography and created an Augmented Reality (AR) experience for a module that explains the different parts of a digital camera. The 2nd year Multimedia students of Vellore Institute of Technology have been asked to experience the MOOC video followed by the AR experience. Their feedbacks before and after the AR experience has been statistically tested and reported. The results revealed that the students feel more confident and concentrate more when the instructional video was given as an AR experience. This study suggests that AR integrated MOOC modules might help in training students better for practice-based courses.
https://jitm.ut.ac.ir/article_79186_90087626edc3b3912f191ef6397b1de8.pdf
2020-12-01
1
10
10.22059/jitm.2020.79186
MOOC
Augmented reality in education
Distant learning
Practice-based courses
Surendheran
Kaliyaperumal
surensmart@gmail.com
1
Research Scholar, Department of Visual Communication, Karunya Institute of Technology and Sciences, Coimbatore, India.
AUTHOR
Mallika
Vijayakumar
mallikavijayakumar@karunya.edu
2
Assistant Professor, Department of Visual Communication, Karunya Institute of Technology and Sciences, Coimbatore, India.
AUTHOR
Blackburn, R. A. R., Villa-Marcos, B., & Williams, D. P. (2019). Preparing Students for Practical Sessions Using Laboratory Simulation Software. Journal of Chemical Education, 96(1), 153–158. https://doi.org/10.1021/acs.jchemed.8b00549
1
Di Serio, Á., Ibáñez, M. B., & Kloos, C. D. (2013). Impact of an augmented reality system on students’ motivation for a visual art course. Computers and Education. https://doi.org/10.1016/j.compedu.2012.03.002
2
Díaz, G., Loro, F. G., Castro, M., Tawfik, M., Sancristobal, E., & Monteso, S. (2013). Remote electronics lab within a MOOC: Design and preliminary results. Proceedings - 2013 2nd Experiment@ International Conference, Exp.at 2013. https://doi.org/10.1109/ExpAt.2013.6703036
3
Eriksson, T., Adawi, T., & Stöhr, C. (2017). “Time is the bottleneck”: a qualitative study exploring why learners drop out of MOOCs. Journal of Computing in Higher Education, 29(1), 133–146. https://doi.org/10.1007/s12528-016-9127-8
4
Evans, S., & Myrick, J. G. (2015). How MOOC instructors view the pedagogy and purposes of massive open online courses. Distance Education, 36(3), 295–311. https://doi.org/10.1080/01587919.2015.1081736
5
Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers and Education, 98, 157–168. https://doi.org/10.1016/j.compedu.2016.03.016
6
Khan, T., Johnston, K., & Ophoff, J. (2019). The Impact of an Augmented Reality Application on Learning Motivation of Students. Advances in Human-Computer Interaction. https://doi.org/10.1155/2019/7208494
7
Phatak, D. B. (2015). Adopting MOOCs for Quality Engineering Education in India. In R. Natarajan (Ed.), Proceedings of the International Conference on Transformations in Engineering Education (pp. 11–23). Springer India. https://doi.org/10.1007/978-81-322-1931-6_3
8
Sampaio, D., & Almeida, P. (2018). Students’ motivation, concentration and learning skills using Augmented Reality. 1559–1566. https://doi.org/10.4995/head18.2018.8249
9
Thompson, K. (2011). THINGS YOU SHOULD KNOW ABOUT … TM MOOC s. EDUCAUSE Learning Initiative, 1–2. https://library.educause.edu/~/media/files/library/2011/11/eli7078-pdf.pdf
10
Watted, A., & Barak, M. (2018). Motivating factors of MOOC completers: Comparing between university-affiliated students and general participants. Internet and Higher Education, 37(December 2017), 11–20. https://doi.org/10.1016/j.iheduc.2017.12.001
11
Yousef, A. M. F., & Sunar, A. S. (2015). Opportunities and Challenges in Personalized MOOC Experience. ACM WEB Science Conference 2015, Web Science Education Workshop 2015, May, 3–5.
12
ORIGINAL_ARTICLE
Long Short-Term Memory Approach for Coronavirus Disease Predicti
Corona Virus (COVID-19) is a major problem among people, and it causes suffering worldwide. Yet, the traditional prediction models are not yet suitably efficient in catching the fundamental expertise as they cannot visualize the difficulty in the health's representation problem areas. This paper states prediction mechanism that uses a model of deep learning called Long Short-Term Memory (LSTM). We have carried this model out on corona virus dataset that obtained from the records of infections, deaths, and recovery cases across the world. Furthermore, producing a dataset which includes features of geographic regions (temperature and humidity) that have experienced severe virus outbreaks, risk factors, spatio-temporal analysis, and social behavior of people, a predictive model can be developed for areas where the virus is likely to spread. However, the outcomes of this study are justifiable to alert the authorities and the people to take precautions.
https://jitm.ut.ac.ir/article_79187_610e84342be9afc08de825da1a72d188.pdf
2020-12-01
11
21
10.22059/jitm.2020.79187
Deep learning
LSTM
Prediction
Covid-19
Recurrent Neural Network (RNN)
Omar Ibrahim
Obaid
alhamdanyomar23@gmail.com
1
Department of Computer Science, College of Education, AL-Iraqia University, Baghdad, Iraq.
AUTHOR
Mazin
Mohammed
mazinalshujeary@uoanbar.edu.iq
2
Ph.D., College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Iraq.
LEAD_AUTHOR
Salama A.
Mostafa
salama@uthm.edu.my
3
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, 86400, Malaysia.
AUTHOR
Abd Ghani, M. K., Mohammed, M. A., Arunkumar, N., Mostafa, S. A., Ibrahim, D. A., Abdullah, M. K., Jaber, M. M., Abdulhay, E., Ramirez-Gonzalez, G., & Burhanuddin, M. A. (2020). Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Computing and Applications, 32(3), 625–638. https://doi.org/10.1007/s00521-018-3882-6
1
Abed Mohammed, M., Khanapi Abd Ghani, M., Mostafa, S., Taha Al-Dhief, F., Ibrahim Obaid, O., Mostafa, S. A., & Taha AL-Dhief, F. (2018). Evaluating the Performance of Machine Learning Techniques in the Classification of Wisconsin Breast Cancer Performance evaluation of Wisconsin Breast Cancer. Article in International Journal of Engineering and Technology, 7(4), 160–166. https://doi.org/10.14419/ijet.v7i4.36.23737
2
Al-Dhief, F. T., Latiff, N. M. azzah A., Malik, N. N. N. A., Salim, N. S., Baki, M. M., Albadr, M. A. A., & Mohammed, M. A. (2020). A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and Machine Learning Algorithms. IEEE Access, 8, 64514–64533. https://doi.org/10.1109/ACCESS.2020.2984925
3
Carneiro, T., Da Nobrega, R. V. M., Nepomuceno, T., Bian, G. Bin, De Albuquerque, V. H. C., & Filho, P. P. R. (2018). Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications. IEEE Access, 6, 61677–61685. https://doi.org/10.1109/ACCESS.2018.2874767
4
Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I. and Mrzljak, V., 2020. Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron. Computational and Mathematical Methods in Medicine, 2020.
5
CHEN, M., HAO, Y., HWANG, K., WANG, L., & WANG, L. (2019). Disease Prediction by Machine Learning from Healthcare Communities. International Journal of Scientific Research in Science and Technology, 29–35. https://doi.org/10.32628/ijsrst19633
6
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015). Long-term recurrent convolutional networks for visual recognition and description. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015, 2625–2634. https://doi.org/10.1109/CVPR.2015.7298878
7
E.V., S., Konradi, A.O., Arutyunov, G. P., Arutyunov, A. G., Bautin, A. E., Boytsov, S. A., Villevalde, S.V., Grigoryeva, N. Y., Duplyakov, D. V., Zvartau, N. E., & Koziolova, N. A. (2020). Guidelines for the diagnosis and treatment of circulatory diseases in the context of the COVID-19 pandemic. Russian Journal of Cardiology, 25(3), 3801.
8
Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5–6), 602–610. https://doi.org/10.1016/j.neunet.2005.06.042
9
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 1735–1780.
10
Mohammed, M. A., Abdulkareem, K. H., Al-Waisy, A. S., Mostafa, S. A., Al-Fahdawi, S., Dinar, A. M., Alhakami, W., Baz, A., Al-Mhiqani, M. N., Alhakami, H., Arbaiy, N., Maashi, M. S., Mutlag, A. A., Garcia-Zapirain, B., & De La Torre Diez, I. (2020). Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods. IEEE Access, 8, 99115–99131. https://doi.org/10.1109/ACCESS.2020.2995597
11
Mohammed, M. A., Abdulkareem, K. H., Mostafa, S. A., Ghani, M. K. A., Maashi, M. S., Garcia-Zapirain, B., Oleagordia, I., Alhakami, H., & Al-Dhief, F. T. (2020). Voice pathology detection and classification using convolutional neural network model. Applied Sciences (Switzerland), 10(11), 1–13. https://doi.org/10.3390/app10113723
12
Muhammad, L.J., Islam, M.M., Sharif, U.S. and Ayon, S.I., 2020. Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients Recovery.
13
M. Rubaiyat Hossain Mondal, Subrato Bharati, Prajoy Podder, Priya Podder, Data analytics for novel coronavirus disease, Informatics in Medicine Unlocked,Vol 20,100374, 2020.
14
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Mathieu, B., Peter, P., Ron, W., & Vincent, D. (2011). Scikit-learn. GetMobile: Mobile Computing and Communications, 19(1), 29–33. https://doi.org/10.1145/2786984.2786995
15
Rassem, A., El-Beltagy, M., & Saleh, M. (2017). Cross-Country Skiing Gears Classification using Deep Learning. 1–14. http://arxiv.org/abs/1706.08924
16
Raw.githubusercontent.com. 2020. [online] Available at: https://raw.githubusercontent.com/datasets/ covid-19/ master/time-series-19-covid-combined.csv [Accessed 22 March 2020].
17
Shanmugamani, R. (2018). Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras
18
ORIGINAL_ARTICLE
Automatic Chest CT Image Findings of Novel Coronavirus Pneumonia (COVID-19) Using U-Net Based Convolutional Neural Network
The continuing outbreak of COVID-19 pneumonia is globally concerning. Timely detection of infection ensures prompt quarantine of patient which is crucial for preventing the rapid spread of this contagious disease and also supports the patient with necessary medication. Due to the high infection rate of COVID-19, our health management system needs an automatic diagnosis tool that equips the health workers to pay immediate attention to the needy person. Chest CT is an essential imaging technique for diagnosis and staging of 2019 novel coronavirus disease (COVID-19). The identification of COVID-19 CT findings assists health workers on further clinical evaluation, especially when the findings on CT scans are trivial, the person may be recommended for Reverse-transcription polymerase chain reaction (RT-PCR) tests. Literature reported that the ground-glass opacity (GGO) with or without consolidation are dominant CT findings in COVID-19 patients. In this paper, the U-Net based segmentation approach is proposed to automatically segment and analyze the GGO and consolidation findings in the chest CT scan. The performance of this system is evaluated by comparing the auto-segmented infection regions with the manually-outlines ones on 100 axial chests CT scans of around 40 COVID-19 patients from SIRM dataset. The proposed U-Net with pre-process approach yields specificity of 0.91 ± 0.09 and sensitivity of 0.87 ± 0.07 on segmenting GGO region and specificity of 0.81 ± 0.13 and sensitivity of 0.44 ± 0.17 on segmenting consolidation region. Also the experimental results confirmed that the automatic detection method identifies the CT finding with a precise opacification percentage from the chest CT image.
https://jitm.ut.ac.ir/article_79188_b4146996c664f57927d941ea16dda921.pdf
2020-12-01
22
35
10.22059/jitm.2020.79188
Covid-19
CT imaging findings
Segmentation
Deep learning
Ground-glass opacities
U-Net
S.
Akila Agnes
akilaagnes@karunya.edu
1
Assistant Professor, Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, India.
AUTHOR
J.
Anitha
anitha_j@karunya.edu
2
Assistant Professor, Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, India.
LEAD_AUTHOR
A.
Arun Solomon
arunsoloman@karunya.edu
3
Assistant Professor, Department of Civil Engineering, Karunya Institute of Technology and Sciences, India.
AUTHOR
Agnes, S. A., Anitha, J., & Peter, J. D. (2018). Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Computing and Applications, 1–11.
1
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, Wenzhi., Qian., Xia & Liming. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 200642.
2
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495.
3
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z. A., Zhang, N. (2020). Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology, 200463.
4
Commission, C. N. H., (2020). Diagnosis and treatment of pneumonitis caused by new coronavirus (trial version 6). Beijing: China National Health Commission.
5
Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020). Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology, 200432.
6
Hao, W., & Li, M. (2020). Clinical diagnostic value of CT imaging in COVID-19 with multiple negative RT-PCR testing. Travel Medicine and Infectious Disease.
7
Hui, D. S., Azhar, E. I., Madani, T. A., Ntoumi, F., Kock, R., Dar, O. (2020). The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—The latest 2019 novel coronavirus outbreak in Wuhan, China. International Journal of Infectious Diseases, 91, 264.
8
Kanne, J. P. (2020). Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist. Radiological Society of North America.
9
Khagi, B., & Kwon, G.-R. (2018). Pixel-Label-Based Segmentation of Cross-Sectional Brain MRI Using Simplified SegNet Architecture-Based CNN. Journal of Healthcare Engineering, 2018.
10
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.-W., & Heng, P.-A. (2018). H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Transactions on Medical Imaging, 37(12), 2663–2674.
11
Long, C., Xu, H., Shen, Q., Zhang, X., Fan, B., Wang, C., Li, H. (2020). Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT? European Journal of Radiology, 108961.
12
Mahase, E. (2020). China coronavirus: WHO declares international emergency as death toll exceeds 200. Bmj, 368, m408.
13
Meng, H., Xiong, R., He, R., Lin, W., Hao, B., Zhang, L. (2020). CT imaging and clinical course of asymptomatic cases with COVID-19 pneumonia at admission in Wuhan, China. Journal of Infection.
14
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 234–241.
15
Shelhamer, E., Long, J., & Darrell, T. (2017). Fully convolutional networks for semantic segmentation. IEEE Annals of the History of Computing, (04), 640–651.
16
Ye, Zheng et al. 2020. “Chest CT Manifestations of New Coronavirus Disease 2019 (COVID-19): A Pictorial Review.” European radiology: 1–9.
17
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 3–11). Springer.
18
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J. (2020). A novel coronavirus from patients with pneumonia in China, 2019. New England Journal of Medicine.
19
ORIGINAL_ARTICLE
An Intelligent Method for Indian Counterfeit Paper Currency Detection
The production of counterfeit paper currencies has become cheaper because of the advancement in the printing technologies. The circulation of counterfeit currencies down the economy of a country. By leveraging this, there is a mandate to develop an intelligent technique for the detection and classification of counterfeit currencies. The intelligent techniques play a major role in the field of Human Computer Interaction (HCI) too. This paper deals with the detection of counterfeit Indian currencies. The proposed method feature extraction is based on the characteristics of Indian paper currencies. The first order and second order statistical features are extracted initially from the input. The effective feature vectors are given to the SVM classifier unit for classification. The proposed method produced classification accuracy of 95.8%. The experimental results are compared with state-of-the methods and produced reliable results.
https://jitm.ut.ac.ir/article_79189_f94c25f5f78a31be12a51c8bd53fc7ee.pdf
2020-12-01
36
49
10.22059/jitm.2020.79189
Counterfeit currency
Indian paper currency
SVM
Intelligent system
Currency detection
Diana
Andrushia
andrushia@gmail.com
1
Department of ECE, Karunya Institute of Technology and Sciences, India.
LEAD_AUTHOR
Mary
Neebha
neebha08@gmail.com
2
Department of ECE, Karunya Institute of Technology and Sciences, India.
AUTHOR
Thusnavis
Bella Mary
bellamary@karunya.edu
3
Assistant Professor, Department of ECE, Karunya Institute of Technology and Sciences, India.
AUTHOR
Ahmadi & Omatu, S. (2003) A Methodology to Evaluate and Improve Reliabilty in Paper Currency Neuro-Classifiers. IEEE International Symposium on Computational Intelligence in Robotics and Automation 2003, Japan
1
Andrushia, A.D., & Thangarajan, R. (2015) Visual attention-based leukocyte image segmentation using extreme learning machine. Int J Adv Intell Paradig 7(2),172–186
2
Andrushia, A.D., & Thangarajan, R. (2019) RTS-ELM: an approach for saliency-directed image segmentation with ripplet transform. Pattern Anal Appl. https ://doi.org/10.1007/s1004 4-019-00800 -8
3
Bhattacharjee, S.D., Yuan J., Jiaqi, Z., & Tan Y.P (2017) Context-aware graph-based analysis for detecting anomalous activities, ICME 2017, IEEE, pp. 1021–1026
4
Bhavani, R., & Karthikeyan, A. (2014) A novel method for counterfeit banknote detection. Int. J. Comput. Sci. Eng. 2, 165–167.
5
Bhavsar. K., Jani K., & Vanzara R. (2020) Indian Currency Recognition from Live Video Using Deep Learning. In: Chaubey N., Parikh S., Amin K. (eds) Computing Science, Communication and Security. COMS2 2020. Communications in Computer and Information Science, vol 1235. Springer, Singapore. https://doi.org/10.1007/978-981-15-6648-6_6
6
Debnath, K.K., Ahdikary, J.K., & Shahjahan, M. (2009) A currency recognition system using negatively correlated neural network ensemble, 12th International Conference on Computers and Information Technology, pp. 367–372.
7
Euisun Choi., Jongseok Lee., & Joonhym Yoon. (2006) “Feature Extraction for Bank Note Classification Using Wavelet Transform” IEEE International conference on Pattern recognition, China; DOI: 10.1109/ICPR.2006.553
8
Hasanuzzaman, F.M., Yang X., and Tian Y (2011) Robust and effective component-based bank note recognition by SURF features, 20th Annual Wireless and Optical Communications Conference, pp. 1–6.
9
Huber-Mörk R., Heiss-Czedik D., Mayer K., Penz, H., & Vrabl, A., (2007) Print process separation using interest regions. Lect. Notes Comput. Sci. 4673, 514–521.
10
Ji Woo Lee., Hyung Gil Hong., Ki Wan Kim & Kang Ryoung Park. (2017) A Survey on Banknote Recognition Methods by Various Sensors, Sensors, 17, 313; doi:10.3390/s17020313
11
Kang, K., & Lee, C., (2016) Fake banknote detection using multispectral images, in: 7th International Conference on Information, Intelligence, Systems & Applications, IISA, pp. 1–3.
12
Khin Nyein Nyein Hlaing (2015) “First Order Statistics and GLCM Based Feature Extraction for Recognition of Myanmar Paper Currency” Proceedings of 31st The IIER International Conference, Bangkok, Thailand
13
Lim, H., & Murukeshan, V. (2017) Hyperspectral imaging of polymer banknotes for building and analysis of spectral library, Opt. Lasers Eng. 98, 168–175
14
Mahajan, S., & Rane, K.P.(2014) A survey on counterfeit paper currency recognition and detection. In Proceedings of the International Conference on Industrial Automation and Computing, Nagpur, India, pp. 54–61.
15
Mizra, R., & Nanda, V. (2012) Design and Implementation of Indian paper currency authentication system based on feature extraction by edge based segmentation using sobel operator. Int.J Eng. Res., Dev 3(2) 41-46
16
Pourhabibi, T., Kok-Leong Ong., Booi H. Kam., & Yee Ling Boo (2020) Fraud detection: A systematic literature review of graph-based anomaly detection approaches, Decision Support Systems 133, 113303, https://doi.org/10.1016/j.dss.2020.113303
17
Rashid, A., Prati A., & Cucchiara, R. (2013) On the design of Embedded Solutions to banknote recognition. Opt. Eng. 52(9), 093106-1-093106-12
18
Roy, A., Halder, B., Garain, U., & Doermann, D.S. (2015) Machine-assisted authentication of paper currency: An experiment on Indian banknotes. Int. J. Doc. Anal. Recognit. 18, 271–285
19
Rusanov, V., Chakarova, K..Winkler, H., Trautwein, A.X., & Mössbauer (2009) and X-ray fluorescence measurements of authentic and counterfeited banknote pigments. Dyes Pigments 81, 254–258.
20
Russ J.C., The Image Processing Handbook, 5th edition, CRC Press, Boca Raton, FL, USA, 2006.
21
Sangwook Baek, Euisun Choi, Yoonkil Baek, Chulhee Lee (2018) Detection of counterfeit banknotes using multispectral images Digital Signal Processing,78, Pages 294-304
22
Sargano, A., Sarfraz M,. & Haq, N., (2013) Robust features and paper currency recognition system. The 6th International Conference on Information Technology ICIT 2013, Amman, Jordan
23
Sarkar A., Verma R., & Gupta G. (2013) Detecting Counterfeit Currency and Identifying Its Source. In: Peterson G., Shenoi S. (eds) Advances in Digital Forensics IX. DigitalForensics 2013. IFIP Advances in Information and Communication Technology, vol 410. Springer, Berlin, Heidelberg
24
Sharma, B., Kaur, A., & Vipan, (2012) Recognition of Indian Paper Currency based on LBP. Int.J.Comput.Appl. 59(1), 24-27
25
Sun, B., & Li J (2008) Recognition for the banknotes grade based on CPN. In Proceedings of the Computer Science and Software Engineering (CSSE 2008), Hubei, China, pp. 90–93.
26
Takeda, F., and Nishikage, T., (2000) Multiple kinds of paper currency recognition using neural network and application for Euro currency, Joint Conference on Neural Networks 2, 143–147.
27
Takeda, F., Sakoobunthu, L., & Satou, H (2003) Thai bank note recognition using neural network and continues learning by DSP unit, Knowledge based Intelligent Information and Engineering System, (2003), pp. 1169–1177.
28
Vanessada Silva Oliveira, Ricardo Saldanha Honorato, Fernanda AraújoHonorato, Claudete Fernandes Pereir (2018) Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics, Forensic Science International, 286, 121-127
29
West, J., & Bhattacharya, M. (2016) Intelligent financial fraud detection: a comprehensive review, Computers & Security 57, 47–66, https://doi.org/10.1016/j.cose.2015.09.005
30
Zhang, Q., Yan,W.Q., & Kankanhalli,M (2019) Overview of currency recognition using deep learning. J. Banking Financ. Technol. 3(1), 59–69.
31
ORIGINAL_ARTICLE
An Efficient Privacy-preserving Deep Learning Scheme for Medical Image Analysis
In recent privacy has emerged as one of the major concerns of deep learning, since it requires huge amount of personal data. Medical Image Analysis is one of the prominent areas where sensitive data are shared to a third party service provider. In this paper, a secure deep learning scheme called Metamorphosed Learning (MpLe) is proposed to protect the privacy of images in medical image analysis. An augmented convolutional layer and image morphing are two main components of MpLe scheme. Data providers morph the images without privacy information using image morphing component. The human unrecognizable image is then delivered to the service providers who then apply deep learning algorithms on morphed data using augmented convolution layer without any performance penalty. MpLe provides sturdy security and privacy with optimal computational overhead. The proposed scheme is experimented using VGG-16 network on CIFAR dataset. The performance of MpLe is compared with similar works such as GAZELLE and MiniONN and found that the MpLe attracts very less computational and data transmission overhead. MpLe is also analyzed for various adversarial attack and realized that the success rate is as low as . The efficiency of the proposed scheme is proved through experimental and performance analysis.
https://jitm.ut.ac.ir/article_79191_98185cb91d1f02ead0e094533739065a.pdf
2020-12-01
50
67
10.22059/jitm.2020.79191
Deep learning
Data privacy
Image privacy
Medical image analysis
Data morphing
J.
Andrew Onesimu
onesimu@gmail.com
1
Ph.D. Candidate, School of Computer Science and Engineering, Vellore Institute of Technology, India.
LEAD_AUTHOR
J
Karthikeyan
karthikeyan.jk@vit.ac.in
2
Assistant Professor, School of Information Technology and Engineering, Vellore Institute of Technology, India.
AUTHOR
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep Learning with Differential Privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS’16, 308–318. https://doi.org/10.1145/2976749.2978318
1
Al, M., Chanyaswad, T., & Kung, S.-Y. (2018). Multi-Kernel, Deep Neural Network and Hybrid Models for Privacy Preserving Machine Learning. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2891–2895. https://doi.org/10.1109/ICASSP.2018.8462336
2
Alguliyev, R. M., Aliguliyev, R. M., & Abdullayeva, F. J. (2019). Privacy-preserving deep learning algorithm for big personal data analysis. Journal of Industrial Information Integration. https://doi.org/10.1016/j.jii.2019.07.002
3
Andrew, J., & Kathrine, G. J. W. (2018). An intrusion detection system using correlation, prioritization and clustering techniques to mitigate false alerts. In Advances in Intelligent Systems and Computing (Vol. 645). https://doi.org/10.1007/978-981-10-7200-0_23
4
Andrew, J., Mathew, S. S., & Mohit, B. (2019). A Comprehensive Analysis of Privacy-preserving Techniques in Deep learning based Disease Prediction Systems. 0–9. https://doi.org/10.1088/1742-6596/1362/1/012070
5
Andrew J, & Karthikeyan J. (2019). Privacy-Preserving Internet of Things: Techniques and Applications. International Journal of Engineering and Advanced Technology (IJEAT), 8(6), 3229–3234. https://doi.org/10.35940/ijeat.F8830.088619
6
Biryukov, A., De Cannière, C., Winkler, W. E., Aggarwal, C. C., Kuhn, M., Bouganim, L., Guo, Y., Preneel, B., Bleumer, G., Helleseth, T., Canetti, R., Varia, M., Peters, C., Kaliski, B., Desmedt, Y., Kesidis, G., De Soete, M., Bleumer, G., Schoenmakers, B., … Smith, S. W. (2011). Differential Privacy. In Encyclopedia of Cryptography and Security (pp. 338–340). Springer US. https://doi.org/10.1007/978-1-4419-5906-5_752
7
Cui, L., Huang, S., Wei, F., Tan, C., Duan, C., & Zhou, M. (n.d.). SuperAgent: A Customer Service Chatbot for E-commerce Websites. 97–102. https://doi.org/10.18653/v1/P17-4017
8
Ding, X., Yang, W., Raymond Choo, K.-K., Wang, X., & Jin, H. (2019). Privacy preserving similarity joins using MapReduce. Information Sciences, 493, 20–33. https://doi.org/10.1016/J.INS.2019.03.035
9
Dorner, S., Cammerer, S., Hoydis, J., & Brink, S. Ten. (2018). Deep Learning Based Communication over the Air. IEEE Journal on Selected Topics in Signal Processing, 12(1), 132–143. https://doi.org/10.1109/JSTSP.2017.2784180
10
Gentry, C., & Boneh, D. (2009). A fully homomorphic encryption scheme. http://cs.au.dk/~stm/local-cache/gentry-thesis.pdf
11
Goldreich, O. (1998). Secure Multi-Party Computation.
12
Hesamifard, E., Takabi, H., Ghasemi, M., & Jones, C. (2017). Privacy-preserving Machine Learning in Cloud. Proceedings of the 2017 on Cloud Computing Security Workshop - CCSW ’17. https://doi.org/10.1145/3140649.3140655
13
J, A., Karthikeyan, J., & Jebastin, J. (2019). Privacy Preserving Big Data Publication On Cloud Using Mondrian Anonymization Techniques and Deep Neural Networks. 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 722–727. https://doi.org/10.1109/ICACCS.2019.8728384
14
Juvekar, C., Mtl, M., Vaikuntanathan, V., & Chandrakasan, A. (n.d.). GAZELLE: A Low Latency Framework for Secure Neural Network Inference. Retrieved August 9, 2020, from https://www.usenix.org/conference/usenixsecurity18/presentation/juvekar
15
Krizhevsky, A., Hinton, G., & others. (2009). Learning multiple layers of features from tiny images.
16
Li, J., Kuang, X., Lin, S., Ma, X., & Tang, Y. (2020). Privacy preservation for machine learning training and classification based on homomorphic encryption schemes. Information Sciences, 526, 166–179. https://doi.org/10.1016/j.ins.2020.03.041
17
Li, P., Li, J., Huang, Z., Li, T., Gao, C.-Z., Yiu, S.-M., & Chen, K. (2017). Multi-key privacy-preserving deep learning in cloud computing. Future Generation Computer Systems, 74, 76–85. https://doi.org/10.1016/J.FUTURE.2017.02.006
18
Li, P., Li, T., Ye, H., Li, J., Chen, X., & Xiang, Y. (2018). Privacy-preserving machine learning with multiple data providers. Future Generation Computer Systems, 87, 341–350. https://doi.org/10.1016/J.FUTURE.2018.04.076
19
Li, T., Li, X., Zhong, X., Jiang, N., & Gao, C. (2019). Communication-efficient outsourced privacy-preserving classification service using trusted processor. Information Sciences, 505, 473–486. https://doi.org/10.1016/J.INS.2019.07.047
20
Li, Y., Wang, Y., & Li, D. (2019). Privacy-preserving lightweight face recognition. Neurocomputing, 363, 212–222. https://doi.org/10.1016/J.NEUCOM.2019.07.039
21
Liang, J., Mahler, J., Laskey, M., Li, P., & Goldberg, K. (2018). Using dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot. IEEE International Conference on Automation Science and Engineering, 2017-August, 1–8. https://doi.org/10.1109/COASE.2017.8256067
22
Liu, J., Juuti, M., Lu, Y., & Asokan, N. (n.d.). Oblivious Neural Network Predictions via MiniONN transformations. Retrieved August 9, 2020, from http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.
23
Ma, X., Zhang, F., Chen, X., & Shen, J. (2018). Privacy preserving multi-party computation delegation for deep learning in cloud computing. Information Sciences, 459, 103–116. https://doi.org/10.1016/J.INS.2018.05.005
24
Ma, Z., Ma, J., Miao, Y., & Liu, X. (2019). Privacy-preserving and high-accurate outsourced disease predictor on random forest. Information Sciences, 496, 225–241. https://doi.org/10.1016/J.INS.2019.05.025
25
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246.
26
Phan, N. H., Vu, M. N., Liu, Y., Jin, R., Dou, D., Wu, X., & Thai, M. T. (2019). Heterogeneous Gaussian mechanism: Preserving differential privacy in deep learning with provable robustness. IJCAI International Joint Conference on Artificial Intelligence, 2019-Augus(June), 4753–4759. https://doi.org/10.24963/ijcai.2019/660
27
Phong, L. T., & Phuong, T. T. (2019). Privacy-Preserving Deep Learning via Weight Transmission. IEEE Transactions on Information Forensics and Security, 1–1. https://doi.org/10.1109/TIFS.2019.2911169
28
Rahim, N., Ahmad, J., Muhammad, K., Sangaiah, A. K., & Baik, S. W. (2018). Privacy-preserving image retrieval for mobile devices with deep features on the cloud. Computer Communications, 127, 75–85. https://doi.org/10.1016/J.COMCOM.2018.06.001
29
Sagirlar, G., Carminati, B., & Ferrari, E. (2018). Decentralizing privacy enforcement for Internet of Things smart objects. Computer Networks, 143, 112–125. https://doi.org/10.1016/J.COMNET.2018.07.019
30
Shen, D., Wu, G., & Suk, H.-I. (2017). Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering, 19(1), 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
31
Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 909–910. https://doi.org/10.1109/ALLERTON.2015.7447103
32
Wang, J., Zhu, X., Zhang, J., Cao, B., Bao, W., & Yu, P. S. (2018). Not just privacy: Improving performance of private deep learning in mobile cloud. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2407–2416. https://doi.org/10.1145/3219819.3220106
33
Wang, W., Li, S., Dou, J., & Du, R. (2020). Privacy-preserving mixed set operations. Information Sciences, 525, 67–81. https://doi.org/10.1016/j.ins.2020.03.049
34
Wang, Y., Adams, S., Beling, P., Greenspan, S., Rajagopalan, S., Velez-Rojas, M., Mankovski, S., Boker, S., & Brown, D. (2018). Privacy Preserving Distributed Deep Learning and Its Application in Credit Card Fraud Detection. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 1070–1078. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00150
35
Xiong, L., & Dong, D. (2019). Reversible data hiding in encrypted images with somewhat homomorphic encryption based on sorting block-level prediction-error expansion. Journal of Information Security and Applications, 47, 78–85. https://doi.org/10.1016/j.jisa.2019.04.005
36
Yang, G., Cao, J., Chen, Z., Guo, J., & Li, J. (2020). Graph-based neural networks for explainable image privacy inference. Pattern Recognition, 105, 107360. https://doi.org/10.1016/j.patcog.2020.107360
37
Zhang, D., Chen, X., Wang, D., & Shi, J. (2018). A Survey on Collaborative Deep Learning and Privacy-Preserving. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), 652–658. https://doi.org/10.1109/DSC.2018.00104
38
Zhang, Q., Yang, L. T., & Chen, Z. (2016). Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning. IEEE Transactions on Computers, 65(5), 1351–1362. https://doi.org/10.1109/TC.2015.2470255
39
ORIGINAL_ARTICLE
Development of Robot Journalism Application: Tweets of News Content in the Turkish Language Shared by a Bot
Today, news texts can be created automatically and presented to readers without human participation through technologies and methods such as big data, deep learning, and natural language generation. With this research, we have developed an application that can contribute to the literature regarding The Studies on Robot Journalism Applications with a technology-reductionist perspective. Robot journalism application named Robottan Al Haberi (the English equivalent of the application name is “get the news from the robot”) produces news text by placing weather, exchange rates, and earthquake data in certain templates. The news texts, which are produced by placing the data in appropriate spaces on the template and with a maximum length of 280 characters, are automatically shared via the Twitter account @robottanalhaber. The weather information is shared once a day, the exchange rate information is shared three times a day, and the earthquake information is shared instantly. Here, we aim to produce automatic and short news by using the available structured data by placing them in specific news templates suggesting different options or a combination of them for different situations.
https://jitm.ut.ac.ir/article_79335_8b4f464fc848c92bb13b34176a7d7ec3.pdf
2020-12-01
68
88
10.22059/jitm.2020.79335
Natural language generation
Artificial Intelligence
Robot journalism
Data journalism
Newspapers
Hikmet
Tosyalı
hikmettosyali@maltepe.edu.tr
1
Assistant Professor, Faculty of Communication, Maltepe University, Istanbul, Turkey.
LEAD_AUTHOR
Çiğdem
Aytekin
cigdem.aytekin@marmara.edu.tr
2
Associate Professor, Faculty of Communication, Marmara University, Istanbul, Turkey.
AUTHOR
Ali, W., & Hassoun, M. (2019). Artificial intelligence and automated journalism: Contemporary challenges and new opportunities. International Journal of Media, Journalism and Mass Communications, 5(1), 40-49. https://doi.org/10.20431/2454-9479.0501004.
1
Bucher, T. (2017). Machines don’t have instincts: Articulating the computational in journalism. New Media & Society, 19(6), 918-933. https://doi.org/10.1177/1461444815624182.
2
Clerwall, C. (2014). Enter the robot journalist: Users’ perceptions of automated content. Journalism Practice, 8(5), 519–531. https://doi.org/10.1080/17512786.2014.883116.
3
Dalen, V. A. (2012). The algorithms behind the headlines: How machine-written news redefines the core skills of human journalists. Journalism Practice, 6(5-6), 648-658.
4
Day, C. (2018). Robot science writers. Computing in Science & Engineering, 20(3), 101-101. https://doi.org/10.1109/MCSE.2018.03202638.
5
Dörr, K. N. (2016). Mapping the field of algorithmic journalism. Digital Journalism, 4(6), 700–722. https://doi.org/10.1080/21670811.2015.1096748.
6
Graefe, A. (2016). Guide to automated journalism. Columbia Journalism School. https://academiccommons.columbia.edu/doi/10.7916/D8QZ2P7C/download
7
Güz, N., & Yeğen, C. (2018). Bir dijital gazetecilik biçimi: Robot gazetecilik. Proceedings of the International Symposium on Communication in the Digital Age (pp. 328-339). Mersin, Turkey.
8
Jung, J., Song, H., Kim, Y., Im, H., & Oh, S. (2017). Intrusion of software robots into journalism: The public’s and journalists’ perceptions of news written by algorithms and human journalists. Computers in Human Behavior, 71, 291-298. https://doi.org/10.1016/j.chb.2017.02.022.
9
Kaa, H., and Krahmer, E. (2014). Journalist versus news consumer: The perceived credibility of machine written news. Proceedings of the Computation+Journalism Conference (pp. 1-4). New York.
10
Karaduman, M. (2017). Changing journalism and its new types. In E. Doğan & E. Geçğin (Eds.), Current debates in public relations, cultural & media studies (pp. 131-146). Ijopec.
11
Karlsen, J., & Stavelin, E. (2014). Computational journalism in norwegian newsrooms. Journalism Practice, 8(1), 34-48. https://doi.org/10.1080/17512786.2013.813190.
12
Kim, D., & Kim, S. (2017). Newspaper companies’ determinants in adopting robot journalism. Technological Forecasting & Social Change, 117, 184-195. https://doi.org/10.1016/j.techfore.2016.12.002.
13
Latar, N. L. (2018). Robot journalism: Can human journalism survive?. World Scientific.
14
Lee, N., Kim, K., & Taeseon, Y. (2017). Implementation of robot journalism by programming custombot using tokenization and custom tagging. Proceedings of the 19th IEEE International Conference on Advanced Communications Technology, 566-570. Pyeongchang, Korea.
15
Lindén, C. G. (2017). Algorithms for journalism: The future of news work. The Journal of Media Innovations, 4(1), 60-76. https://doi.org/10.5617/jmi.v4i1.2420.
16
Marconi, F., & Siegman, A. (2017). The future of augmented journalism: A guide for newsrooms in the age of smart machines. AP Insights.
17
Mayes, R. (2014). The future of futurists: Can a machine produce this forecast? The Futurist, 48(6), 21-23.
18
McCartney, P. (2015). Robotic journalism and nursing. MCN: The American Journal of Maternal/Child Nursing, 40(5), 330. https://doi.org/10.1097/NMC.0000000000000175.
19
Melin, M., BäCk, A., SöDergåRd, C., Munezero, M. D., LeppäNen, L. J., & Toivonen, H. (2018). No landslide for the human journalist - An empirical study of computer-generated election news in finland. IEEE Access, 6, 43356-43367. https://doi.org/10.1109/ACCESS.2018.2861987.
20
Montal, T., & Reich, Z. (2017). I, robot. You, journalist. Who is the author? Digital Journalism, 5(7), 829-849. https://doi.org/10.1080/21670811.2016.1209083.
21
Monti, M. (2018). Automated journalism and freedom of information: Ethical and juridical problems related to AI in the press field. Opinio Juris in Comparatione, 1(1), 1-20.
22
Narin, B. (2017). Uzman görüşleri bağlamında haber üretiminde otomatikleşme: Robot gazetecilik. Galatasaray Üniversitesi İleti-ş-im Dergisi, 27, 79-108. https://doi.org/10.16878/gsuilet.373242.
23
Ombelet, P. J., Kuczerawy, A., & Valcke, P. (2016). Employing robot journalists: Legal implications, considerations and recommendations. Proceedings of the 25th International Conference Companion on World Wide Web (pp. 731-736). Montréal Québec.
24
Rutkin, A. (2014). Machines write the news. New Scientist, 2962, 22.
25
Sarılar, N. B. E. (2019). Robot journalist or human journalist?: An analysis is over news articles. Proceedings of the Communciation and Technology Congress (pp. 201-208). Istanbul Turkey.
26
Shekhar, S. (2016). Robot journalism: The advent of high tech storytelling. PCQuest, April, 20-22.
27
Sim, D. H., & Shin, S. J. (2016). Implementation of algorithm to write articles by stock robot. International Journal of Advanced Smart Convergence, 5(4), 40-47.
28
Szews, P. (2018). Data journalism, geojournalism, CAR i robot journalism jako nowe odmiany i terminy w dziennikarstwie. Acta Universitatis Lodziensis. Folia Litteraria Polonica, 51(5), 209-222.
29
Tatalovic, M. (2018). AI writing bots are about to revolutionise science. Journal of Science Communication, 17(1), 1-7. https://doi.org/10.22323/2.17010501.
30
Túñez-López, J. M., Toural-Bran, C., & Cacheiro-Requeijo, S. (2018). Uso de bots Y algoritmos para automatizar la redacción de noticias: Percepcıón Y actıtudes de los periodistas en España. El profesional de la información, 27(4), 750-758. https://doi.org/10.3145/epi.2018.jul.04.
31
Twitter. (2020). About Twitter’s APIs. Help Center. https://help.twitter.com/en/rules-and-policies/twitter-api
32
Vállez, M., & Codina, L. (2018). Computational journalism: Evolution, cases and tools. El profesional de la información, 27(4), 759-768. https://doi.org/10.3145/epi.2018.jul.05.
33
Verhulst, J., Rofman, M., Heussen, N., Geer, C., & Vos, K. A. J. (2017). MARVIN: An interactive robot journalism simulation. https://pdfs.semanticscholar.org/702a/240e1dd1d749b580c39e59c523da8b4d7af2.pdf?_ga=2.31931390.1598439443.1589498673-637886212.1589498673
34
Waddell, T. F. (2019). Attribution practices for the man-machine marriage: How perceived human intervention, automation metaphors, and byline location affect the perceived bias and credibility of purportedly automated content. Journalism Practice, 13(10), 1255-1272. https://doi.org/10.1080/17512786.2019.1585197.
35
Wölker, A., & Powell, T. (2018). Algorithms in the newsroom? News readers’ perceived credibility and selection of automated journalism. Journalism, February, 1-18. https://doi.org/10.1177/1464884918757072.
36
Young, A. (2016). All Things Digital in 2016. E&P Editor & Publisher, 149(2), 50-56.
37
Zheng, Y., Zhong, B., & Yang, F. (2018). When algorithms meet journalism: The user perception to automated news in a cross-cultural context. Computers in Human Behavior, 86, 266-275. https://doi.org/10.1016/j.chb.2018.04.046.
38
Tornoe, R. (2014, September 23). Learn to Stop Worrying and Love Robot Journalists. Editor&Publisher. https://www.editorandpublisher.com/columns/digital-publishing-learn-to-stop-worrying-and-love-robot-journalists/
39
Birer, G. C. (2016, May 25). Bir bilgisayarın roman yazdığı gün. Tübitak Bilim Genç. https://bilimgenc.tubitak.gov.tr/makale/bir-bilgisayarin-roman-yazdigi-gun
40
İrvan, S. (2017, August 7). Robot gazeteciler geliyor. Yeni Medya ve Gazetecilik. https://suleymanirvan.blogspot.com/2017/08/robot-gazeteciler-geliyor.html
41
Loosen, W. (2018, March 18). Four forms of datafied journalism. Journalism’s response to the datafication of society. Communicative Figuration Working Paper. https://www.kofi.uni-bremen.de/fileadmin/user_upload/Arbeitspapiere/CoFi_EWP_No-18_Loosen.pdf
42
News Center. (2018, April 20). Knowhere yapay zekası etik haberciliği geri getirecek. xTRlarge. https://www.xtrlarge.com/2018/04/20/knowhere-yapay-zeka-etik-habercilik
43
Caswell, D., & Dörr, K. (2018). Automated journalism 2.0: Event-driven narratives. Journalism Practice, 12(4), 477-496. https://doi.org/10.1080/17512786.2017.1320773
44
ORIGINAL_ARTICLE
Feature Selection and Hyper-parameter Tuning Technique using Neural Network for Stock Market Prediction
The conjecture of stock exchange is the demonstration of attempting to decide the forecast estimation of a particular sector or the market, or the market as a whole. Every stock every investor needs to foresee the future evaluation of stocks, so a predicted forecast of a stock’s future cost could return enormous benefit. To increase the accuracy of the Conjecture of stock Exchange with daily changes in the market value is a bottleneck task. The existing stock market prediction focused on forecasting the regular stock market by using various machine learning algorithms and in-depth methodologies. The proposed work we have implemented describes the new NN model with the help of different learning techniques like hyperparameter tuning which includes batch normalization and fitting it with the help of random-search-cv. The prediction of the Stock exchange is an active area for research and completion in Numerai. The Numerai is the most robust data science competition for stock market prediction. Numerai provides weekly new datasets to mold the most exceptional prediction model. The dataset has 310 features, and the entries are more than 100000 per week. Our proposed new neural network model gives accuracy is closely 86%. The critical point, it isn’t easy with our proposed model with existing models because we are training and testing the proposed model with a new unlabeled dataset every week. Our ultimate aim for participating in Numerai competition is to suggest a neural network methodology to forecast the stock exchange independent of datasets with reasonable accuracy.
https://jitm.ut.ac.ir/article_79368_0b4b23a0320651a6df09c50331d7e7e2.pdf
2020-12-01
89
108
10.22059/jitm.2020.79368
neural network
Stock market prediction
Numerai
NMR
Deep learning
Karanveer
Singh
karan.cps080798@gmail.com
1
School of Computer Science and Engineering, Galgotias University, Greater Noida, India.
AUTHOR
Rahul
Tiwari
rahultiwari.201307@gmail.com
2
School of Computer Science and Engineering, Galgotias University, Greater Noida, India.
AUTHOR
Prashant
Johri
johri.prashant@gmail.com
3
School of Computer Science and Engineering, Galgotias University, Greater Noida, India.
AUTHOR
Ahmed A.
Elngar
elgnar_7@yahoo.co.uk
4
Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, Egypt.
AUTHOR
Al-Hmouz, R., Pedrycz, W., & Balamash, A. (2015). Description and prediction of time series: A general framework of Granular Computing. Expert Systems with Applications, 42(10), 4830-4839. doi:10.1016/j.eswa.2015.01.060
1
Babu, M., N.Geethanjali, & B.Satyanarayana, P. (2012, January 02). Clustering Approach to Stock Market Prediction. Retrieved from http://paper.researchbib.com/view/paper/59204
2
Bagheri, A., Peyhani, H. M., & Akbari, M. (2014). Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization. Expert Systems with Applications, 41(14), 6235-6250. doi:10.1016/j.eswa.2014.04.003
3
Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. Plos One, 12(7). doi:10.1371/journal.pone.0180944
4
Bengio, Y. (2009). Learning Deep Architectures for AI. Foundations and Trends® in Machine Learning, 2(1), 1-127. doi:10.1561/2200000006
5
Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205. doi:10.1016/j.eswa.2017.04.030
6
Ghosh, P., Neufeld, A., & Sahoo, J. K. (2020, April 21). Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. Retrieved from https://arxiv.org/abs/2004.10178
7
Gunduz, H., Cataltepe, Z., & Yaslan, Y. (2017). Stock market direction prediction using deep neural networks. 2017 25th Signal Processing and Communications Applications Conference (SIU). doi:10.1109/siu.2017.7960512
8
Hiransha M, Dr. E. A. Gopalakrishnan, Vijay Krishna Menon, Dr. Soman K. P, (2018). NSE Stock Market Prediction Using Deep-Learning Models. Procedia Computer Science, 132, 1351-1362. doi:10.1016/j.procs.2018.05.050
9
Idrees, S. M., Alam, M. A., & Agarwal, P. (2019). A Prediction Approach for Stock Market Volatility Based on Time Series Data. IEEE Access, 7, 17287-17298. doi:10.1109/access.2019.2895252
10
Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2020). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing. doi:10.1007/s12652-020-01839-w
11
Khan, W., Malik, U., Ghazanfar, M. A., Azam, M. A., Alyoubi, K. H., & Alfakeeh, A. S. (2019). Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis. Soft Computing, 24(15), 11019-11043. doi:10.1007/s00500-019-04347-y
12
Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990). Stock market prediction system with modular neural networks. 1990 IJCNN International Joint Conference on Neural Networks. doi:10.1109/ijcnn.1990.137535
13
Kusuma, R. M., Ho, T., Kao, W., Ou, Y., & Hua, K. (2019, February 26). Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market. Retrieved from https://arxiv.org/abs/1903.12258
14
Lathuiliere, S., Mesejo, P., Alameda-Pineda, X., & Horaud, R. (2020). A Comprehensive Analysis of Deep Regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1-1. doi:10.1109/tpami.2019.2910523
15
Lee, J., Kim, R., Koh, Y., & Kang, J. (2019). Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network. IEEE Access, 7, 167260-167277. doi:10.1109/access.2019.2953542
16
Liu, G., & Wang, X. (2019). A Numerical-Based Attention Method for Stock Market Prediction With Dual Information. IEEE Access, 7, 7357-7367. doi:10.1109/access.2018.2886367
17
Minh, D. L., Sadeghi-Niaraki, A., Huy, H. D., Min, K., & Moon, H. (2018). Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network. IEEE Access, 6, 55392-55404. doi:10.1109/access.2018.2868970
18
Nguyen, T., & Yoon, S. (2019). A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning. Applied Sciences, 9(22), 4745. doi:10.3390/app9224745
19
Parmar, I., Agarwal, N., Saxena, S., Arora, R., Gupta, S., Dhiman, H., & Chouhan, L. (2018). Stock Market Prediction Using Machine Learning. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). doi:10.1109/icsccc.2018.8703332
20
Parmar, I., Agarwal, N., Saxena, S., Arora, R., Gupta, S., Dhiman, H., & Chouhan, L. (2018). Stock Market Prediction Using Machine Learning. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC). doi:10.1109/icsccc.2018.8703332
21
Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268. doi:10.1016/j.eswa.2014.07.040
22
Qiu, J., Wang, B., & Zhou, C. (2020, January 03). Forecasting stock prices with long-short term memory neural network based on attention mechanism. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/31899770
23
Ren, R., Wu, D. D., & Liu, T. (2019). Forecasting Stock Market Movement Direction Using Sentiment Analysis and Support Vector Machine. IEEE Systems Journal, 13(1), 760-770. doi:10.1109/jsyst.2018.2794462
24
S Abdulsalam Sulaiman Olaniyi, Adewole, Kayode S. Jimoh, R. G. Stock Trend Prediction Using Regression Analysis – A Data Mining Approach, ARPN Journal of Systems and Software, Volume 1 No. 4, JULY 2011
25
Selvamuthu, D., Kumar, V., & Mishra, A. (2019). Indian stock market prediction using artificial neural networks on tick data. Financial Innovation, 5(1). doi:10.1186/s40854-019-0131-7
26
Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). doi:10.1109/icacci.2017.8126078
27
Son, Y., Noh, D., & Lee, J. (2012). Forecasting trends of high-frequency KOSPI200 index data using learning classifiers. Expert Systems with Applications, 39(14), 11607-11615. doi:10.1016/j.eswa.2012.04.015
28
Stoean, C., Paja, W., Stoean, R., & Sandita, A. (2019). Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. Plos One, 14(10). doi:10.1371/journal.pone.0223593
29
Wang, Y., Liu, H., Guo, Q., Xie, S., & Zhang, X. (1970). Stock Volatility Prediction by Hybrid Neural Network: Semantic Scholar. Retrieved from https://www.semanticscholar.org/paper/Stock-Volatility-Prediction-by-Hybrid-Neural-Wang-Liu/310b54f1913ac93cb2817e810c62e92e6a65e326
30
Yuan, X., Yuan, J., Jiang, T., & Ain, Q. U. (2020). Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market. IEEE Access, 8, 22672-22685. doi:10.1109/access.2020.2969293
31
ORIGINAL_ARTICLE
Classification of Lung Nodule Using Hybridized Deep Feature Technique
Deep learning techniques have become very popular among Artificial Intelligence (AI) techniques in many areas of life. Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. In this work, a hybridized approach has been followed to classify lung nodule as benign or malignant. This will help in early detection of lung cancer and help in the life expectancy of lung cancer patients thereby reducing the mortality rate by this deadly disease scourging the world. The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. The dimensionality reduction technique, Principle Component Analysis (PCA) has been introduced to improve the performance of hybridized features with SVM. The experiments have been carried out with 14 different methods. It has been found that GLCM + VGG19 + PCA + SVM outperformed all other models with an accuracy of 94.93%, sensitivity of 90.9%, specificity of 97.36% and precision of 95.44%. The F1 score was found to be 0.93 and the AUC was 0.9843. The False Positive Rate was found to be 2.637% and False Negative Rate was 9.09%.
https://jitm.ut.ac.ir/article_79369_c5abf1274531aa0c9c8485bc78aee6ae.pdf
2020-12-01
109
128
10.22059/jitm.2020.79369
CNN
Transfer Learning
GLCM
SVM
PCA
Malin
Bruntha
malin.bruntha@gmail.com
1
Assistant Prof., Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore – 641114, Tamil Nadu, India.
LEAD_AUTHOR
Immanuel
Alex Pandian
immans@karunya.edu
2
Assistant Prof., Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore – 641114, Tamil Nadu, India.
AUTHOR
Siril Sam
Abraham
abrahamcyril77@gmail.com
3
Computer Vision Intern, Vasundharaa Geo Technologies, Pune, Maharashtra, India.
AUTHOR
Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
1
Armato III, S. G., McLennan, G., Bidaut, L., McNitt‐Gray, M. F., Meyer, C. R., Reeves, A. P., ... & Kazerooni, E. A. (2011). The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical physics, 38(2), 915-931.
2
Balaji, K., & Lavanya, K. (2018). Recent Trends in Deep Learning with Applications. In Cognitive Computing for Big Data Systems Over IoT (pp. 201-222). Springer, Cham
3
da Nóbrega, R. V. M., Peixoto, S. A., da Silva, S. P. P., & Rebouças Filho, P. P. (2018, June). Lung nodule classification via deep transfer learning in CT lung images. In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS) (pp. 244-249). IEEE
4
de Carvalho Filho, A. O., Silva, A. C., de Paiva, A. C., Nunes, R. A., & Gattass, M. (2018). Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network. Pattern Recognition, 81, 200-212.
5
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). IEEE.
6
Dhara, A. K., Mukhopadhyay, S., Dutta, A., Garg, M., & Khandelwal, N. (2016). A combination of shape and texture features for classification of pulmonary nodules in lung CT images. Journal of digital imaging, 29(4), 466-475.
7
Fukushima, K. (1980). Biological cybernetics neocognitron: a self‐organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern, 36, 193-202.
8
Han, F., Wang, H., Zhang, G., Han, H., Song, B., Li, L., ... & Liang, Z. (2015). Texture feature analysis for computer-aided diagnosis on pulmonary nodules. Journal of digital imaging, 28(1), 99-115.
9
Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621.
10
Hua, K. L., Hsu, C. H., Hidayati, S. C., Cheng, W. H., & Chen, Y. J. (2015). Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets and therapy, 8, 2015-2022.
11
Hussein, S., Cao, K., Song, Q., & Bagci, U. (2017, June). Risk stratification of lung nodules using 3D CNN-based multi-task learning. In International conference on information processing in medical imaging (pp. 249-260). Springer, Cham.
12
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
13
Kumar, D., Wong, A., & Clausi, D. A. (2015, June). Lung nodule classification using deep features in CT images. In 2015 12th Conference on Computer and Robot Vision (pp. 133-138). IEEE.
14
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324
15
Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (Eds.), (2020) Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems, Springer.
16
Lo, S. C. B., Chan, H. P., Lin, J. S., Li, H., Freedman, M. T., & Mun, S. K. (1995). Artificial convolution neural network for medical image pattern recognition. Neural networks, 8(7-8), 1201-1214.
17
Ma, Y., Xie, Q., Liu, Y., & Xiong, S. (2019). A weighted KNN-based automatic image annotation method. Neural Computing and Applications, 1-12
18
Mastouri, R., Khlifa, N., Neji, H., & Hantous-Zannad, S. (2020). Deep learning-based CAD schemes for the detection and classification of lung nodules from CT images: A survey. Journal of X-Ray Science and Technology, (Preprint), 1-27
19
Olivas, E. S., Guerrero, J. D. M., Martinez-Sober, M., Magdalena-Benedito, J. R., & Serrano, L. (Eds.). (2009). Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques: Algorithms, Methods, and Techniques. IGI Global.
20
Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G., & Wells, W. (Eds.). (2016). Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II (Vol. 9901). Springer.
21
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Berg, A. C. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3), 211-252.
22
Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., Van Riel, S. J., ... & van Ginneken, B. (2016). Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE transactions on medical imaging, 35(5), 1160-1169.
23
Shen, W., Zhou, M., Yang, F., Yang, C., & Tian, J. (2015, June). Multi-scale convolutional neural networks for lung nodule classification. In International Conference on Information Processing in Medical Imaging (pp. 588-599). Springer, Cham.
24
Shen, W., Zhou, M., Yang, F., Yu, D., Dong, D., Yang, C., ... & Tian, J. (2017). Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition, 61, 663-673.
25
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
26
Tajbakhsh, N., & Suzuki, K. (2017). Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs. Pattern recognition, 63, 476-486.
27
Wang, H., Zhao, T., Li, L. C., Pan, H., Liu, W., Gao, H., ... & Liang, Z. (2018). A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation. Journal of X-ray Science and Technology, 26(2), 171-187.
28
Wani, M. A., Bhat, F. A., Afzal, S., & Khan, A. I. (2020). Advances in deep learning (Vol. 57). Berlin: Springer.
29
Wei, G., Cao, H., Ma, H., Qi, S., Qian, W., & Ma, Z. (2018). Content-based image retrieval for lung nodule classification using texture features and learned distance metric. Journal of medical systems, 42(1), 13.
30
Wei, G., Ma, H., Qian, W., Han, F., Jiang, H., Qi, S., & Qiu, M. (2018). Lung nodule classification using local kernel regression models with out-of-sample extension. Biomedical Signal Processing and Control, 40, 1-9.
31
Weng, J. J., Ahuja, N., & Huang, T. S. (1993, May). Learning recognition and segmentation of 3-D objects from 2-D images. In 1993 (4th) International Conference on Computer Vision (pp. 121-128). IEEE.
32
Zhu, W., Liu, C., Fan, W., & Xie, X. (2018, March). Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 673-681). IEEE.
33