Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Datta, Debabrata
- Fiscal Deficit and Interest Rate in India: A Cointegration Analysis
Abstract Views :388 |
PDF Views:1
Authors
Affiliations
1 Department of Economics, Asutosh College, The University of Calcutta, Kolkata, IN
1 Department of Economics, Asutosh College, The University of Calcutta, Kolkata, IN
Source
Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics, Vol 48, No 1-2 (2006), Pagination: 21-40Abstract
Economists differ in their theoretical conclusions over the relation between fiscal deficit and interest rate. The empirical studies in different countries have also produced varied results. As high fiscal deficit is a characteristic feature of the Indian economy, different econometric studies have been done to search for causal relation between India's fiscal deficit and interest rate but the conclusions derived are divergent. Our paper also enquires into the statistical relation between these two variables. It follows the methodology of unit ischolar_main and reintegration tests using quarterly data up to recent period (2003). The paper finds that the fiscal deficit and interest rate do not appear to be connected in the context of Indian economy during the period of study. Moreover, the non-existence of the relation cannot be attributed to expansion ill either money supply or savings, considered separately. However, macro variables like interest rate, GDP, money supply are found to be linked in an integrated framework combining real and monetary sectors of the economy. Another important finding of the study is non-stationarity of fiscal deficit. This result brings to the fore the issue of sustainability of country's fiscal deficit in the long-run.- Image Classification using Model Ensembling
Abstract Views :141 |
PDF Views:1
Authors
Debabrata Datta
1,
Anweshan Mukherjee
1,
Soumen Mukherjee
2,
Arup Kr. Bhattacharjee
3,
Anal Acharya
1
Affiliations
1 Department of Computer Science, St. Xavier’s College, IN
2 Department of Computer Application, RCC Institute of Information Technology, IN
3 Department of Computer Science and Engineering, RCC Institute of Information Technology, IN
1 Department of Computer Science, St. Xavier’s College, IN
2 Department of Computer Application, RCC Institute of Information Technology, IN
3 Department of Computer Science and Engineering, RCC Institute of Information Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 4 (2022), Pagination: 2679-2692Abstract
Classifying images efficiently using various algorithms is very useful now-a-days given that the field of computer vision is growing rapidly. The research work highlighted in this paper focuses on the independent use of various models to classify images and then combining them together to form a better model in terms of performance than each of the individual models. The dataset used consists of 200 classes with 90,000 training images, 10,000 validation images and 10,000 test images. The data preparation step in this work involves resizing the images (data), shuffling them and transforming them into a data generator to provide input to the models. The images were also augmented using two different sets of image transformation effects to get more data for the models to train on. These data were then used to train five different models (one model trained from scratch and four other models using pre-trained weights and transfer learning) independently. The performance of each model was judged by checking two evaluation metrics – f1-score and categorical accuracy. The models were also tried to be fine-tuned to get a better performance, and finally the models were ensembled together to get a better categorical accuracy and f1-score on unseen (validation and test) data.Keywords
Image Classification, Convolutional Neural Networks, Image Augmentation, Model Ensembling, F1-ScoreReferences
- D.H. Ballard and C.M. Brown, “Computer Vision”, Prentice Hall, 1982.
- Github, “Convolution Neural Networks for Visual Recognition”, Available at: https://cs231n.github.io/classification/, Accessed at 2020.
- Y. Le Cun, L. Bottou, Y. Bengio and P. Haffner, “Gradient-Based Learning Applied to Document Recognition”, Proceedings of the IEEE, Vol. 86, pp. 1-13, 1998.
- Y. Le Cun, Y. Bengio and G. Hinton, “Deep Learning”, Nature, pp. 436-444, 2015.
- A. Krizhevsky, I. Sutskever and G. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Neural Information Processing Systems, Vol. 23, No. 1, pp. 1-14, 2000.
- J. Deng, W. Dong, R. Socher, L. Li, Kai Li and Li Fei Fei, “ImageNet: A Large-Scale Hierarchical Image Database”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009.
- Image Net, “Image Net”, Available at:http://www.image-net.org/. Accessed at 2020.
- F. Sultana, A. Sufian and P. Dutta, “Advancements in Image Classification using Convolutional Neural Network”, Proceedings of International Conference on Research in Computational Intelligence and Communication Networks, pp. 122-129, 2018.
- Image Net, “ImageNet Large Scale Visual Recognition Challenge 2012”. Available at: http://image-net.org/challenges/LSVRC/2012/, Accessed at 2020.
- K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-14, 2014.
- VGG16, Available at: https://neurohive.io/en/popular-networks/vgg16/.Accessed at 2020.
- ImageNet Large Scale Visual Recognition Challenge 2014, Available at: http://image-net.org/challenges/LSVRC/2014/.Accessed at 2020.
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, “Going Deeper with Convolutions”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.
- K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
- ImageNet Large Scale Visual Recognition Challenge 2015, Available at: http://image-net.org/challenges/LSVRC/2015/, Accessed at 2020.
- S. Albawi, T.A. Mohammed and S. Al Zawi, “Understanding of a Convolutional Neural Network”, Proceedings of International Conference on Engineering and Technology, pp. 1-6, 2017.
- A. Mikołajczyk and M. Grochowski, “Data Augmentation for Improving Deep Learning in Image Classification Problem”, Proceedings of International Conference on Engineering and Technology, pp. 117-122, 2018.
- W.H. Beluch, T. Genewein, A. Nurnberger and J.M. Kohler, “The Power of Ensembles for Active Learning in Image Classification”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 9368-9377, 2018.
- V. Thakkar, S. Tewary and C. Chakraborty, “Batch Normalization in Convolutional Neural Networks - A Comparative Study with CIFAR-10 Data”, Proceedings of International Conference on Emerging Applications of Information Technology, pp. 1-5, 2018.
- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, Journal of Machine Learning Research, Vol. 12, No. 1, pp. 1929-1958, 2014.
- E.M. Dogo, O.J. Afolabi, N.I. Nwulu, B. Twala and C.O. Aigbavboa, “A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks”, Proceedings of International Conference on Computational Techniques, Electronics and Mechanical Systems, pp. 92-99, 2018.
- Data Science, “Vanishing Gradient Problem”. Available at: https://towardsdatascience.com/the-vanishing-gradient-problem-69bf08b15484. Accessed at 2020.
- SGD Optimizer, Available at: https://keras.io/api/optimizers/sgd/. Accessed at 2020.
- Reduce LR on Plateau Callback, Available at: https://keras.io/api/callbacks/reduce_lr_on_plateau/, Accessed at 2020.
- Categorical Cross-Entropy Loss, Available at: https://keras.io/api/losses/probabilistic_losses/#categoricalcrossentropy-class, Accessed at 2020.
- Image Data Generator Class, Available at: https://keras.io/api/preprocessing/image/#imagedatagenerator-class, Accessed at 2020.
- Image Augmentation, Available at https://imgaug.readthedocs.io/en/latest/, Accessed at 2020.
- F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1800-1807, 2017.
- Image-Net Data, Available at: http://www.image-net.org/, Accessed at 2020.
- Ada Delta Optimizer, Available at https://keras.io/api/optimizers/adadelta/. Accessed at 2020.
- C. Szegedy,S. Ioffe,V. Vanhoucke andA. Alemi, “Inception-V4, Inception-ResNet and the Impact of Residual Connections on Learning”, Proceedings of International Conference on Artificial Intelligence, pp. 1-7, 2016.
- M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 4510-4520, 2018.
- G. Huang, Z. Liu, L. Van Der Maaten and K.Q. Weinberger, “Densely Connected Convolutional Networks”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 2261-2269, 2017.
- Kaggle Leaderboard, Available at: https://www.kaggle.com/c/image-detect/leaderboard, Accessed at 2020.
- Keras Library, Available at: https://keras.io/, Accessed at 2020.
- Google Colaboratory, Available at: https://colab.research.google.com/, Accessed at 2020.
- Image Dataset, Available at:https://www.kaggle.com/c/image-detect/data, Accessed at 2020.
- Examination Scheduler Using a Linear - Time Graph Coloring Algorithm
Abstract Views :84 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, St. Xavier’s College, IN
1 Department of Computer Science, St. Xavier’s College, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 4 (2022), Pagination: 2678-2684Abstract
The primary aim of the study aims to provide a solution for scheduling examinations for most of the universities and colleges across India which follow the Choice Based Credit System (CBCS) using a graph coloring algorithm. Nowadays, due to the flexibility of opting various subjects, and many students taking up different courses in their colleges and universities, it becomes difficult to schedule these examinations. Creating an examination schedule can be quite challenging and time-consuming for controlling the body of an examination. Our research work focuses on reducing the efforts for scheduling such examinations. With the knowledge of graph theory and graph traversing and coloring algorithms, our algorithm with the help of a few assumptions gives an efficient solution to the examination scheduling problem. A detailed correctness proof along with performance analysis has been done. The efficiency of our proposed algorithm is then compared to that of the coloring algorithm using backtracking.Keywords
Examination Scheduling, Graph Coloring Algorithm, Bipartite Graphs, NP-Complete Problem, Linear Time Complexity, Meta-GraphReferences
- F. Harary, “Graph Theory”, Addison-Wesley Publishing Company, 2001.
- M. Malkawi, M.A.H. Hassan and O.A.H. Hassan, “A New Exam Scheduling Algorithm using Graph Coloring”, International Arab Journal of Information Technology, Vol. 5, No. 1, pp. 1-14, 2008.
- A. Akbulut and G. Yilmaz, “University Exam Scheduling System using Graph Coloring Algorithm and RFID Technology”, International Journal of Innovation, Management and Technology, Vol. 4, pp. 66-78, 2013.
- M. Bharti and R. Kumar, “Better Resource Utilization in Exam Scheduling using Graph Coloring”, Ph.D. Dissertations, Department of Computer Science and Engineering, Thapar University, pp. 1-57, 2012.
- D. Konig, “The Infinite and Infinite Graphs”, Reprinted Chelsea, 1950.
- J.D. Ullman, “NP-Complete Scheduling Problems”, Journal of Computer and System Sciences, Vol. 10, pp. 384-393, 1975.
- N.K. Mehta, “The Application of A Graph Coloring Method to An Examination Scheduling Problem”, Interfaces, Vol. 11, pp. 57-65, 1981.
- R. Ganguli and S. Roy, “A Study on Course Timetable Scheduling using Graph Coloring Approach”, International Journal of Computational and Applied Mathematics, Vol. 12, pp. 469-485, 2017.
- F.T. Ceighton, “A Graph Coloring Algorithm for Large Scheduling Problems”, Journal of Research of The National Bureau of Standards, Vol. 84, pp. 489-506. 1979.
- D. West, “Introduction to Graph Theory”, Prentice Hall, 2001.
- C.L. Liu, “Elements of Discrete Mathematics: A Computer Oriented Approach”, Tata McGraw-Hill, 2008.
- J. Kleinberg, “Algorithm Design”, Pearson India Education Services Pvt Ltd, 2014.
- K. Rosen, “Discrete Mathematics and Its Applications with Combinatorics and Graph Theory”, McGraw-Hill, 2012.
- R. Diestel, “Graph Theory”, Springer, 2017.
- J.A. Bondy, “Graph Theory with Applications”, Elsevier, 1976.