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The novel SARS-CoV-2 virus, responsible for the dangerous pneumonia-type disease, COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of June 2021 and compromising the health of millions across the globe. Though the vaccination process has started, in developing countries such as India, the process has not been fully developed. Thereby, a diagnosis of COVID-19 can restrict its spreading and level the pestilence curve. As the quickest indicative choice, a computerized identification framework ought to be carried out to hinder COVID-19 from spreading more. Meanwhile, Computed Tomography (CT) imaging reveals that the attributes of these images for COVID-19 infected patients vary from healthy patients with or without other respiratory diseases, such as pneumonia. This study aims to establish an effective COVID-19 prediction model through chest CT images using efficient transfer learning (TL) models. Initially, we used three standard deep learning (DL) models, namely, VGG-16, ResNet50, and Xception, for the prediction of COVID-19. After that, we proposed a mechanism to combine the above-mentioned pre-trained models for the overall improvement of the prediction capability of the system. The proposed model provides 98.79% classification accuracy and a high
Shreya Biswas; Somnath Chatterjee; Arindam Majee; Shibaprasad Sen; Friedhelm Schwenker; Ram Sarkar. Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models. Applied Sciences 2021, 11, 7004 .
AMA StyleShreya Biswas, Somnath Chatterjee, Arindam Majee, Shibaprasad Sen, Friedhelm Schwenker, Ram Sarkar. Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models. Applied Sciences. 2021; 11 (15):7004.
Chicago/Turabian StyleShreya Biswas; Somnath Chatterjee; Arindam Majee; Shibaprasad Sen; Friedhelm Schwenker; Ram Sarkar. 2021. "Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models." Applied Sciences 11, no. 15: 7004.
Shibaprasad Sen; Soumyajit Saha; Somnath Chatterjee; SeyedAli Mirjalili; Ram Sarkar. A bi-stage feature selection approach for COVID-19 prediction using chest CT images. Applied Intelligence 2021, 1 -16.
AMA StyleShibaprasad Sen, Soumyajit Saha, Somnath Chatterjee, SeyedAli Mirjalili, Ram Sarkar. A bi-stage feature selection approach for COVID-19 prediction using chest CT images. Applied Intelligence. 2021; ():1-16.
Chicago/Turabian StyleShibaprasad Sen; Soumyajit Saha; Somnath Chatterjee; SeyedAli Mirjalili; Ram Sarkar. 2021. "A bi-stage feature selection approach for COVID-19 prediction using chest CT images." Applied Intelligence , no. : 1-16.
Online handwriting recognition (OHR) has gained major research interest not just due to the enormous technological advancement in recent years, but also the easy availability of the various electronic devices. This digital revolution is opening up a new dimension in every passing day to the regional and low resource languages with these languages get noticed by the researchers. In this paper, we have targeted a low resource language, Assamese, which is mainly spoken in the eastern region of India. We have proposed a novel and efficient feature vector for recognition of online handwritten Assamese numeral images. Our feature vector has been conceptualized based on the properties of light rays emerging out from a point source. Here we consider that there are multiple hypothetical light emerging sources in a sample numeral image. The amount of light fenced by the image is quantified and considered as a feature. The idea of using point light source to estimate the shape of online handwritten numerals is completely new and efficient. Impressive recognition accuracy is obtained on application of the feature vector on a standard online handwritten Assamese numeral database and it outnumbers some popular and standard feature descriptors, available in the literature. The source code of this work can be found in the following github link: https://github.com/ghoshsoulib/CTRL-Assamese-Digit-Recognition.
Soulib Ghosh; Agneet Chatterjee; Shibaprasad Sen; Neeraj Kumar; Ram Sarkar. CTRL –CapTuRedLight: a novel feature descriptor for online Assamese numeral recognition. Multimedia Tools and Applications 2020, 1 -24.
AMA StyleSoulib Ghosh, Agneet Chatterjee, Shibaprasad Sen, Neeraj Kumar, Ram Sarkar. CTRL –CapTuRedLight: a novel feature descriptor for online Assamese numeral recognition. Multimedia Tools and Applications. 2020; ():1-24.
Chicago/Turabian StyleSoulib Ghosh; Agneet Chatterjee; Shibaprasad Sen; Neeraj Kumar; Ram Sarkar. 2020. "CTRL –CapTuRedLight: a novel feature descriptor for online Assamese numeral recognition." Multimedia Tools and Applications , no. : 1-24.
Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.
Soumyajit Saha; Manosij Ghosh; Soulib Ghosh; Shibaprasad Sen; Pawan Kumar Singh; Zong Woo Geem; Ram Sarkar. Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm. Applied Sciences 2020, 10, 2816 .
AMA StyleSoumyajit Saha, Manosij Ghosh, Soulib Ghosh, Shibaprasad Sen, Pawan Kumar Singh, Zong Woo Geem, Ram Sarkar. Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm. Applied Sciences. 2020; 10 (8):2816.
Chicago/Turabian StyleSoumyajit Saha; Manosij Ghosh; Soulib Ghosh; Shibaprasad Sen; Pawan Kumar Singh; Zong Woo Geem; Ram Sarkar. 2020. "Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm." Applied Sciences 10, no. 8: 2816.
In the present work, a typical Convolutional Neural Network (CNN) architecture has been used for the recognition of online handwritten isolated Bangla characters. A detailed analysis about the effects of using different kernel variations, pooling strategies, and activation functions in the CNN architecture has been performed. In this work, total 10000 character samples have been used and among the samples, 30% have been considered as test set and rest 70% have been used to train the recognition model. On test dataset, the technique has been provided 99.40% recognition accuracy. The outcome is better than some recently proposed handcrafted features used for the recognition of online handwritten Bangla characters.
Shibaprasad Sen; Dwaipayan Shaoo; Sayantan Paul; Ram Sarkar; Kaushik Roy. Online Handwritten Bangla Character Recognition Using CNN: A Deep Learning Approach. Applied Computational Intelligence and Mathematical Methods 2018, 413 -420.
AMA StyleShibaprasad Sen, Dwaipayan Shaoo, Sayantan Paul, Ram Sarkar, Kaushik Roy. Online Handwritten Bangla Character Recognition Using CNN: A Deep Learning Approach. Applied Computational Intelligence and Mathematical Methods. 2018; ():413-420.
Chicago/Turabian StyleShibaprasad Sen; Dwaipayan Shaoo; Sayantan Paul; Ram Sarkar; Kaushik Roy. 2018. "Online Handwritten Bangla Character Recognition Using CNN: A Deep Learning Approach." Applied Computational Intelligence and Mathematical Methods , no. : 413-420.
In the present work, we have proposed a novel Bangla word segmentation technique that is based on stroke-level busy zone formation procedure. In an unconstrained domain, people often write text where strokes may be poorly aligned (due to multi-directional skewness) and varied combination of strokes with various types of joining between them are possible while forming the words. Hence, a segmentation approach for stroke extraction is pertinent for any stroke-based word recognition system. The presence of a large volume of symbols set (58 basic symbols with more than 280 compound characters) in Bangla script makes the task more challenging. In the current experiment, our stroke-level segmentation approach effectively handles such type of Bangla words. A sub-zoning scheme within busy zone followed by a modified Down->Up->Down (DUD) concept within these sub-zones has been used to find valid segmentation points. This scheme avoids over and under-segmentation issues caused by either inherent writing pattern or due to writing style variations up to certain extent. The proposed segmentation approach has been tested on 6500 online handwritten Bangla word samples with 98.45% correct segmentation accuracy (compared with manually generated ground truth of the same database).
Shibaprasad Sen; Shubham Chowdhury; Mridul Mitra; Friedhelm Schwenker; Ram Sarkar; Kaushik Roy. A novel segmentation technique for online handwritten Bangla words. Pattern Recognition Letters 2018, 139, 26 -33.
AMA StyleShibaprasad Sen, Shubham Chowdhury, Mridul Mitra, Friedhelm Schwenker, Ram Sarkar, Kaushik Roy. A novel segmentation technique for online handwritten Bangla words. Pattern Recognition Letters. 2018; 139 ():26-33.
Chicago/Turabian StyleShibaprasad Sen; Shubham Chowdhury; Mridul Mitra; Friedhelm Schwenker; Ram Sarkar; Kaushik Roy. 2018. "A novel segmentation technique for online handwritten Bangla words." Pattern Recognition Letters 139, no. : 26-33.
In this paper, an effort has been made to emphasize the usefulness of Hausdorff Distance (HD) and Directed Hausdorff Distance (DHD) based features for the recognition of online handwritten Bangla basic characters. Every character sample is divided into N number of rectangular zones and then HD- and DHD-based features have been computed from every zone to every other zone. These distance measurements are served as feature values for the present work. Experiment has been done on a set of 10,000 character dataset. Multilayer Perceptron (MLP) produces the best result with an accuracy of 95.57% when sample character is divided into 16 rectangular zones and DHD-based procedure has been considered.
Shibaprasad Sen; Ram Sarkar; Kaushik Roy; Naoto Hori. Recognize Online Handwritten Bangla Characters Using Hausdorff Distance-Based Feature. Advances in Intelligent Systems and Computing 2017, 515, 541 -549.
AMA StyleShibaprasad Sen, Ram Sarkar, Kaushik Roy, Naoto Hori. Recognize Online Handwritten Bangla Characters Using Hausdorff Distance-Based Feature. Advances in Intelligent Systems and Computing. 2017; 515 ():541-549.
Chicago/Turabian StyleShibaprasad Sen; Ram Sarkar; Kaushik Roy; Naoto Hori. 2017. "Recognize Online Handwritten Bangla Characters Using Hausdorff Distance-Based Feature." Advances in Intelligent Systems and Computing 515, no. : 541-549.
In the present work, a new feature vector has been designed towards recognition of handwritten online Bangla basic characters. At first, Center of Gravity (CG) of a particular character sample is determined. After that a circle enclosing the character sample is drawn whose radius is estimated as the distance of farthest data pixel from that CG. From this circular region, a 136-element feature vector is generated considering both the global as well as local information of the character sample. The feature set has been tested with several well-known classifiers on 10,000 isolated Bangla basic characters. Finally, Support Vector Machine (SVM) has produced 98.26 % recognition accuracy.
Shibaprasad Sen; Ankan Bhattacharyya; Avik Das; Ram Sarkar; Kaushik Roy. Design of Novel Feature Vector for Recognition of Online Handwritten Bangla Basic Characters. Advances in Intelligent Systems and Computing 2016, 485 -494.
AMA StyleShibaprasad Sen, Ankan Bhattacharyya, Avik Das, Ram Sarkar, Kaushik Roy. Design of Novel Feature Vector for Recognition of Online Handwritten Bangla Basic Characters. Advances in Intelligent Systems and Computing. 2016; ():485-494.
Chicago/Turabian StyleShibaprasad Sen; Ankan Bhattacharyya; Avik Das; Ram Sarkar; Kaushik Roy. 2016. "Design of Novel Feature Vector for Recognition of Online Handwritten Bangla Basic Characters." Advances in Intelligent Systems and Computing , no. : 485-494.
In this paper, three different feature extraction strategies along with their all possible combinations have been discussed in detail for the recognition of online handwritten Bangla basic characters. Applying a quad-tree based image segmentation approach the target character has been dissected for the extraction of features. Out of these three techniques, one is computing area feature (using composite Simpson’s rule) while other two are extracted local (mass distribution and chord length) features. Authors have also investigated optimal depth of the quad-tree (while segmenting an image), at which classifier reveals its best performance. The current experiment has been tested on 10,000 character dataset. Sequential Minimal Optimization (SMO) produces highest recognition accuracy of 98.5 % when all three feature vectors are combined.
Shibaprasad Sen; Mridul Mitra; Shubham Chowdhury; Ram Sarkar; Kaushik Roy. Quad-Tree Based Image Segmentation and Feature Extraction to Recognize Online Handwritten Bangla Characters. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 246 -256.
AMA StyleShibaprasad Sen, Mridul Mitra, Shubham Chowdhury, Ram Sarkar, Kaushik Roy. Quad-Tree Based Image Segmentation and Feature Extraction to Recognize Online Handwritten Bangla Characters. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():246-256.
Chicago/Turabian StyleShibaprasad Sen; Mridul Mitra; Shubham Chowdhury; Ram Sarkar; Kaushik Roy. 2016. "Quad-Tree Based Image Segmentation and Feature Extraction to Recognize Online Handwritten Bangla Characters." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 246-256.
In this paper, we have proposed a simple but effective feature extraction technique following the distance-based features to recognize online handwritten isolated Bangla basic characters. In this approach, a character is divided into N number of segments and then distances are calculated among each other. These distance values are then used as features for recognition purpose. On evaluation of this feature set on 10,000 Bangla character samples (50-class character set) by various classifiers, the method yields reasonably good result with 98.20 % success rate.
Shibaprasad Sen; Ram Sarkar; Kaushik Roy. A Simple and Effective Technique for Online Handwritten Bangla Character Recognition. Advances in Intelligent Systems and Computing 2015, 201 -209.
AMA StyleShibaprasad Sen, Ram Sarkar, Kaushik Roy. A Simple and Effective Technique for Online Handwritten Bangla Character Recognition. Advances in Intelligent Systems and Computing. 2015; ():201-209.
Chicago/Turabian StyleShibaprasad Sen; Ram Sarkar; Kaushik Roy. 2015. "A Simple and Effective Technique for Online Handwritten Bangla Character Recognition." Advances in Intelligent Systems and Computing , no. : 201-209.