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A novel meta-heuristic nature-inspired optimization algorithm known as Groundwater Flow Algorithm (GWFA) is proposed in this paper. GWFA is inspired from the movement of groundwater from recharge areas to discharge areas. It follows a position update procedure guided by Darcy's law which provides a mathematical framework of groundwater flow. The proposed optimization algorithm has been evaluated on 23 benchmark functions. The significance of the results is statistically validated using Wilcoxon rank-sum, Friedman and Kruskal Walis tests. To prove the robustness of the algorithm, it has been further applied on several standard engineering problems. From these exhaustive experiments, it has been observed that the proposed GWFA can outperform many state-of-the-art optimization algorithms.
Ritam Guha; Soulib Ghosh; Kushal Kanti Ghosh; Ram Sarkar. Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm. 2021, 1 .
AMA StyleRitam Guha, Soulib Ghosh, Kushal Kanti Ghosh, Ram Sarkar. Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm. . 2021; ():1.
Chicago/Turabian StyleRitam Guha; Soulib Ghosh; Kushal Kanti Ghosh; Ram Sarkar. 2021. "Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm." , no. : 1.
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.
Binarization of document images still attracts the researchers especially when degraded document images are considered. This is evident from the recent Document Image Binarization Competition (DIBCO 2019) where we can see researchers from all over the world participated in this competition. In this paper, we present a novel binarization technique which is found to be capable of handling almost all types of degradations without any parameter tuning. Present method is based on an ensemble of three classical clustering algorithms (Fuzzy C-means, K-medoids and K-means++) to group the pixels as foreground or background, after application of a coherent image normalization method. It has been tested on four publicly available datasets, used in DIBCO series, 2016, 2017, 2018 and 2019. Present method gives promising results for the aforementioned datasets. In addition, this method is the winner of DIBCO 2019 competition.
Suman Kumar Bera; Soulib Ghosh; Showmik Bhowmik; Ram Sarkar; Mita Nasipuri. A non-parametric binarization method based on ensemble of clustering algorithms. Multimedia Tools and Applications 2020, 80, 7653 -7673.
AMA StyleSuman Kumar Bera, Soulib Ghosh, Showmik Bhowmik, Ram Sarkar, Mita Nasipuri. A non-parametric binarization method based on ensemble of clustering algorithms. Multimedia Tools and Applications. 2020; 80 (5):7653-7673.
Chicago/Turabian StyleSuman Kumar Bera; Soulib Ghosh; Showmik Bhowmik; Ram Sarkar; Mita Nasipuri. 2020. "A non-parametric binarization method based on ensemble of clustering algorithms." Multimedia Tools and Applications 80, no. 5: 7653-7673.
With a wide variety of forms being generated in different organizations daily, efficient and quick retrieval of information from these forms becomes a pressing need. The data on these forms are imperative to any commercial or professional purpose and thus, efficient retrieval of this data is important for further processing of the same. An automatic form processing system retrieves the content of a filled-in form image for useful storage of the same. Despite a large population of the world speaking in Bangla, to the best of our knowledge, there is no significant research work found in literature which deals with form data written in Bangla. To bridge this research gap, in the present scope of the work, we have developed a system that addresses four important aspects of processing of form data written using Bangla script. Our work has primarily been divided into four major modules: touching component separation, text non-text separation, handwritten printed text separation and alphabet numeral separation. The vital problem of touching component separation has been addressed using a novel rule-based method. For text non-text separation, handwritten printed text separation and alphabet numeral separation, we have used a machine learning based approach using feature engineering where the model for each case has been finalized after exhaustive experiments. Further, in each of the last three modules, we have applied some new features along with some existing features to appropriately tune the modules to obtain optimum results. Notably, we have also prepared a self-made database of filled-in forms. To create different training models, first the filled-in form images are binarized, and then different types of components are colored uniquely to obtain images which act as the ground truth for our reference. Evaluation of modules on the said database produces reasonably satisfactory results considering the complexity of the research problem. The code along with some filled-in sample form images and their respective ground truth images are provided in the link https://github.com/rajdeep-cse17/Form_Processing.
Rajdeep Bhattacharya; Samir Malakar; Soulib Ghosh; Showmik Bhowmik; Ram Sarkar. Understanding contents of filled-in Bangla form images. Multimedia Tools and Applications 2020, 80, 3529 -3570.
AMA StyleRajdeep Bhattacharya, Samir Malakar, Soulib Ghosh, Showmik Bhowmik, Ram Sarkar. Understanding contents of filled-in Bangla form images. Multimedia Tools and Applications. 2020; 80 (3):3529-3570.
Chicago/Turabian StyleRajdeep Bhattacharya; Samir Malakar; Soulib Ghosh; Showmik Bhowmik; Ram Sarkar. 2020. "Understanding contents of filled-in Bangla form images." Multimedia Tools and Applications 80, no. 3: 3529-3570.
Outlier detection is an important requirement in data mining and machine learning. When data mining and machine learning algorithms are applied on the datasets with outliers, it leads to erroneous conclusion about the data. Therefore, researchers have been working in this field to remove outliers from dataset so that meaningful information from the datasets can be retrieved. In this paper, we take a cluster based ensemble approach for outlier detection, the backbone of which are some conventional clustering algorithms. Keeping in mind the drawbacks of supervised and semi supervised learning, we have relied on unsupervised learning algorithms. For our cluster based ensemble approach, we use three clustering algorithms, namely K-means, K-means++, and Fuzzy C-means. Our model intelligently combines results from individual clustering algorithms, assigning probabilities to each data point in order to decide its belongingness to a certain cluster. We have proposed a technique to assign a membership value to a data point in case of hard clustering algorithms, as we want to keep the flexibility of combining hard and soft clustering algorithms. From the probabilities assigned by the ensemble model, we then identify the outliers from the dataset. After removing these data points from the dataset, we obtain better values of cluster validity indices, thus reaffirming that removal of outliers has resulted in more stringent clusters of data. We have used five different cluster validity indices in our work to measure the goodness of the clusters formed, considering eight widely used datasets for evaluation of the proposed model amongst which three are large datasets. We have noticed a significant improvement in the cluster validity indices after applying our outlier detection algorithm. The experimental results prove that the proposed method is empirically sound.
Akash Saha; Agneet Chatterjee; Soulib Ghosh; Neeraj Kumar; Ram Sarkar. An ensemble approach to outlier detection using some conventional clustering algorithms. Multimedia Tools and Applications 2020, 1 -25.
AMA StyleAkash Saha, Agneet Chatterjee, Soulib Ghosh, Neeraj Kumar, Ram Sarkar. An ensemble approach to outlier detection using some conventional clustering algorithms. Multimedia Tools and Applications. 2020; ():1-25.
Chicago/Turabian StyleAkash Saha; Agneet Chatterjee; Soulib Ghosh; Neeraj Kumar; Ram Sarkar. 2020. "An ensemble approach to outlier detection using some conventional clustering algorithms." Multimedia Tools and Applications , no. : 1-25.
Numeral recognition is treated as a benchmark research problem as this is a basic module for designing a comprehensive optical character recognition system. In this context, unconstrained handwritten numeral recognition is still considered as an open research problem. Most of the feature descriptors found in the literature for the said problem, work well for numeral images written in a particular language. To encounter this shortcoming, in this paper, we have proposed two shape-based feature descriptors, namely Point-Light Source-based Shadow (PLSS) and Histogram of Oriented Pixel Positions (HOPP). We have evaluated the proposed feature descriptors on 10 (9 offline and 1 online) publicly available standard handwritten numeral image datasets written in eight different languages. Besides, to prove the usefulness of the descriptors in real-life scenario, we have considered numeral string images also. We have also shown how the proposed feature descriptors are invariant toward broken, noisy and rotated numeral images. Experimental outcomes soundly prove that the proposed feature descriptors have the ability to estimate the shape of a numeral image almost accurately irrespective of the language in which it is written. Comparison of the proposed feature descriptors with other shape-based as well as texture-based features shows that PLSS and HOPP produce the results which are analogous to state of the art. The code of the proposed feature descriptors can be found at—https://github.com/ghoshsoulib/Numeral-Recognition.
Soulib Ghosh; Agneet Chatterjee; Pawan Kumar Singh; Showmik Bhowmik; Ram Sarkar. Language-invariant novel feature descriptors for handwritten numeral recognition. The Visual Computer 2020, 37, 1781 -1803.
AMA StyleSoulib Ghosh, Agneet Chatterjee, Pawan Kumar Singh, Showmik Bhowmik, Ram Sarkar. Language-invariant novel feature descriptors for handwritten numeral recognition. The Visual Computer. 2020; 37 (7):1781-1803.
Chicago/Turabian StyleSoulib Ghosh; Agneet Chatterjee; Pawan Kumar Singh; Showmik Bhowmik; Ram Sarkar. 2020. "Language-invariant novel feature descriptors for handwritten numeral recognition." The Visual Computer 37, no. 7: 1781-1803.
Due to the enormous application, handwritten digit recognition (HDR) has become an extremely important domain in optical character recognition (OCR)-related research. The predominant challenges faced in this domain include different photometric inconsistencies together with computational complexity. In this paper, the authors proposed a language invariant shape-based feature descriptor using the refraction property of light rays. It is to be noted that the proposed approach is novel as an adaptation of refraction property is completely new in this domain. The proposed method is assessed using five datasets of five different languages. Among the five datasets, four are offline (written Devanagari, Bangla, Arabic, and Telugu) and one is online (written in Assamese) handwritten digit datasets. The approach provides admirable outcomes for online digits whereas; it yields satisfactory results for offline handwritten digits. The method gives good result for both online and offline handwritten digits, which proves its robustness. It is also computationally less expensive compared to other state-of-the-art methods including deep learning-based models.
Roopkatha Samanta; Soulib Ghosh; Agneet Chatterjee; Ram Sarkar. A Novel Approach Towards Handwritten Digit Recognition Using Refraction Property of Light Rays. International Journal of Computer Vision and Image Processing 2020, 10, 1 -17.
AMA StyleRoopkatha Samanta, Soulib Ghosh, Agneet Chatterjee, Ram Sarkar. A Novel Approach Towards Handwritten Digit Recognition Using Refraction Property of Light Rays. International Journal of Computer Vision and Image Processing. 2020; 10 (3):1-17.
Chicago/Turabian StyleRoopkatha Samanta; Soulib Ghosh; Agneet Chatterjee; Ram Sarkar. 2020. "A Novel Approach Towards Handwritten Digit Recognition Using Refraction Property of Light Rays." International Journal of Computer Vision and Image Processing 10, no. 3: 1-17.
Protein secondary structure (PSS) describes the local folded structures which get formed inside a polypeptide due to interactions among atoms of the backbone. Generally, globular proteins are divided into four classes, namely all-α, all-β, α + β, and α/β. As nearly 90% of proteins fall into the said four classes, these are mostly considered for the purpose of computational classification of proteins. Classification of PSS is important for different biological functions that include protein fold recognition, tertiary structure prediction, prediction of DNA-binding sites, and reduction of the conformation search space among others. In this paper, we have proposed a machine learning–based model for secondary structure classification of proteins into four classes: all-α, all-β, α + β, and α/β. In doing so, we have considered both sequence-based and structure-based features. At first, mutual information (MI), a filter-based feature selection method, is used to remove the redundant features, and then these selected features are used to train three different classifiers—random forest, K-nearest neighbor (KNN), and multi-layer perceptron (MLP). After that, some standard classifier combination approaches are applied to integrate the decision made by the said classifiers and it has been found that weighted product rule performs the best among all. The overall accuracies obtained using the proposed model on the four standard datasets, namely 640, 1189, 25pdb, and fc699 are 86.89%, 92.93%, 91.38%, and 94.87% respectively. The proposed model outperforms some state-of-the-art methods considered here for comparison. Significantly high classification accuracy produced by our proposed model on four datasets is attributed to the development of a comprehensive feature set (by eliminating redundant features through feature selection technique) which is then passed through an ensemble consists of three different classifiers. Assigning different weights to the outcome of different classifiers thus proved to be useful in designing the model for predicting the secondary structure of proteins based on its sequence-based and structure-based features. Graphical abstract
Kushal Kanti Ghosh; Soulib Ghosh; Sagnik Sen; Ram Sarkar; Ujjwal Maulik. A two-stage approach towards protein secondary structure classification. Medical & Biological Engineering & Computing 2020, 58, 1723 -1737.
AMA StyleKushal Kanti Ghosh, Soulib Ghosh, Sagnik Sen, Ram Sarkar, Ujjwal Maulik. A two-stage approach towards protein secondary structure classification. Medical & Biological Engineering & Computing. 2020; 58 (8):1723-1737.
Chicago/Turabian StyleKushal Kanti Ghosh; Soulib Ghosh; Sagnik Sen; Ram Sarkar; Ujjwal Maulik. 2020. "A two-stage approach towards protein secondary structure classification." Medical & Biological Engineering & Computing 58, no. 8: 1723-1737.
A novel meta-heuristic nature-inspired optimization algorithm known as Groundwater Flow Algorithm (GWFA) is proposed in this paper. GWFA is inspired from the movement of groundwater from recharge areas to discharge areas. It follows a position update procedure guided by Darcy's law which provides a mathematical framework of groundwater flow. The proposed optimization algorithm has been evaluated on 23 benchmark functions. The significance of the results is statistically validated using Wilcoxon rank-sum, Friedman and Kruskal Walis tests. To prove the robustness of the algorithm, it has been further applied on several standard engineering problems. From these exhaustive experiments, it has been observed that the proposed GWFA can outperform many state-of-the-art optimization algorithms.
Ritam Guha; Soulib Ghosh; Kushal Kanti Ghosh; Ram Sarkar. Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm. 2020, 1 .
AMA StyleRitam Guha, Soulib Ghosh, Kushal Kanti Ghosh, Ram Sarkar. Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm. . 2020; ():1.
Chicago/Turabian StyleRitam Guha; Soulib Ghosh; Kushal Kanti Ghosh; Ram Sarkar. 2020. "Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm." , no. : 1.
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.
Form processing refers to the process of extraction of information from filled-in forms. In this work, we have addressed three very crucial challenges of a form processing system, namely touching component separation, text non-text separation and handwritten-printed text separation. The proposed method is evaluated on a database having 50 filled-in forms written in Bangla, collected during an essay competition in a school. The experimental results are promising.
Soulib Ghosh; Rajdeep Bhattacharya; Sandipan Majhi; Showmik Bhowmik; Samir Malakar; Ram Sarkar. Textual Content Retrieval from Filled-in Form Images. Communications in Computer and Information Science 2019, 27 -37.
AMA StyleSoulib Ghosh, Rajdeep Bhattacharya, Sandipan Majhi, Showmik Bhowmik, Samir Malakar, Ram Sarkar. Textual Content Retrieval from Filled-in Form Images. Communications in Computer and Information Science. 2019; ():27-37.
Chicago/Turabian StyleSoulib Ghosh; Rajdeep Bhattacharya; Sandipan Majhi; Showmik Bhowmik; Samir Malakar; Ram Sarkar. 2019. "Textual Content Retrieval from Filled-in Form Images." Communications in Computer and Information Science , no. : 27-37.