Breast cancer diagnosis using histopathological images is a challenging task due to the scarcity of annotated medical data, particularly for rare cancer stages. Traditional deep learning models struggle to generalize effectively in such low-data scenarios. To address this problem, we propose a few-shot classification framework for breast histopathological images based on metric-based learning. Our approach leverages Vision Transformers (ViTs) for feature extraction, capturing global contextual information better than conventional Convolutional Neural Networks (CNNs). Additionally, we integrate BLIP-2, a Vision Language Model (VLM), to incorporate manual text prompts and contextual textual descriptions, enhancing the model's interpretability and adaptability. The extracted visual and textual features are fused using a novel feature fusion module, and classified the samples based on cosine distance. We evaluated our approach on BreakHis and BACH datasets, showing its effectiveness in few-shot learning (FSL). Our model achieves 57.12% and around 89% in 5-shot setting, respectively, on the BACH and BreakHis datasets. As the number of support samples increases, the performance of the model improves. Our findings suggest that combining transformer-based architectures with VLMs enhances the performance of FSL based medical image classification systems. The code implementation of the methodology is available at MultiModal-FewShot

Visual Question Answering (VQA) is one of the attractive topics in the field of multimedia, affective, and empathic computing to garner user interest. Unlike existing models which aim at addressing challenges of VQA for the scene images, this work aims at developing a new model for Personality Trait Question Answering (PQA). It uses Twitter account information, which includes shared images, profile pictures, banners, text in the images, and descriptions of the images. Motivated by the accomplishments of the transformer, for encoding visual features of the images, a new InfoGain Multi-Axial Wavelet Vision Transformer (IgMaWaViT) is explored here. For encoding textual features in the images and descriptions, a new Information Gain BERT (InfoBert) method is introduced, which can handle the variable length encoding of text by choosing the optimal discriminator. Furthermore, the model fuses encodings of images and text according to the questions on different personality traits for question answering. The model is called InfoGain Multi-Axial Wavelet Vision Transformer for Personality Traits Question Answering (IgMaWaViT-PQA). To validate the efficacy of the proposed model, a dataset has been constructed, and it is used along with standard datasets for experimentation. Comprehensive experiments show that the proposed model is better than the state-of-the-art models. The code is available at the link: https://github.com/biswaskunal29/InfoGain_MultiAxial_PQA .

Personality traits prediction plays a significant role in several real-world applications, such as improving the education system, improving production in manufacturing, monitoring social media content, sentiment analysis of crowds for opinion mining, judging abnormalities in personal behavior, etc. The demand for personality traits prediction has increased drastically after COVID-19. Therefore, numerous methods have emerged to predict personality traits from a variety of sources, including handwriting, interviews, social media, text, images, and audio. This review focuses on methods developed between the years 2020 and 2024, categorizing them into Handwriting (Graphology), Vision (images and videos), Audio (speech and acoustic signals), Textual (status updates, descriptions), and Multimodal (combinations of the aforementioned) approaches. We critically analyze the methods proposed, the datasets, the scope of the work, the results obtained, and noteworthy remarks. Based on our critical analysis, we notice that increasingly methods tend to use deep learning over handcrafted features. Additionally, personality traits prediction methods are trending more toward multimodal methods because they consistently achieve the highest accuracy among the input modalities. Detailed discussions, tabular presentations, and figures facilitate easy comprehension and future reference. Then, we shed light on the challenges in this field. Many key applications are detailed. Additionally, we highlight significant limitations and offer insights into potential future directions.

Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15% and 79.79% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://github.com/AyushRoy2001/FA-Net

Mitosis is a crucial process ensuring the faithful transmission of the genetic information stored in the cell nucleus. Aberrations in this intricate process pose a significant threat to an organism’s health, leading to conditions like cancer and various diseases. Hence, the study of mitosis holds paramount importance. Recent investigations have involved manual and semi-automated analyses of time-lapse microscopy images to understand mitosis better. This paper introduces an approach for predicting mitosis stages, employing a Convolutional Neural Network (CNN) as the initial feature extractor, followed by a Graph Neural Network (GNN) for predicting cell cycle states. A distinctive timestamp is incorporated into the feature vectors, treating this information as a graph to leverage internal interactions for predicting the subsequent cell state. To assess performance, experiments were conducted on three datasets, demonstrating that our method exhibits comparable efficacy to state-of-the-art techniques.

Abstract Influence maximization in a social network focuses on the task of extracting a small set of nodes from a network which can maximize the propagation in a cascade model. Though greedy methods produce good solutions to the aforementioned problem, their high computational complexity is a major drawback. Centrality‐based heuristic methods often fail to overcome local optima, thereby producing sub‐optimal results. To this end, in this article, a framework has been presented which involves community detection in a social network and the utilization of the Shuffled Frog Leaping algorithm, in maximizing the two‐hop spread of influence under the independent cascade model. Local search strategies like the Late acceptance based hill climbing have been employed to improve the solution further. Experiments performed on three real‐world datasets have shown that our method performs markedly well with respect to the comparing algorithms.

In today’s era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the Microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm’s abilities and limitations. We also apply our method on 3 microarray based gene expression datasets used for prediction of cancer to ensure the scalability and robustness as a feature selection method in real-life scenarios. In addition to this, we test our approach using the COVID-19 dataset, and it performs better than several metaheuristic based optimization techniques.

Novel Coronavirus 2019 disease or COVID-19 is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The use of chest X-rays (CXRs) has become an important practice to assist in the diagnosis of COVID-19 as they can be used to detect the abnormalities developed in the infected patients' lungs. With the fast spread of the disease, many researchers across the world are striving to use several deep learning-based systems to identify the COVID-19 from such CXR images. To this end, we propose an inverted bell-curve-based ensemble of deep learning models for the detection of COVID-19 from CXR images. We first use a selection of models pretrained on ImageNet dataset and use the concept of transfer learning to retrain them with CXR datasets. Then the trained models are combined with the proposed inverted bell curve weighted ensemble method, where the output of each classifier is assigned a weight, and the final prediction is done by performing a weighted average of those outputs. We evaluate the proposed method on two publicly available datasets: the COVID-19 Radiography Database and the IEEE COVID Chest X-ray Dataset. The accuracy, F1 score and the AUC ROC achieved by the proposed method are 99.66%, 99.75% and 99.99%, respectively, in the first dataset, and, 99.84%, 99.81% and 99.99%, respectively, in the other dataset. Experimental results ensure that the use of transfer learning-based models and their combination using the proposed ensemble method result in improved predictions of COVID-19 in CXRs.

Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD.

Capturing time and frequency relationships of time series signals offers an inherent barrier for automatic human activity recognition (HAR) from wearable sensor data. Extracting spatiotemporal context from the feature space of the sensor reading sequence is challenging for the current recurrent, convolutional, or hybrid activity recognition models. The overall classification accuracy also gets affected by large size feature maps that these models generate. To this end, in this work, we have put forth a hybrid architecture for wearable sensor data-based HAR. We initially use Continuous Wavelet Transform to encode the time series of sensor data as multi-channel images. Then, we utilize a Spatial Attention-aided Convolutional Neural Network (CNN) to extract higher-dimensional features. To find the most essential features for recognizing human activities, we develop a novel feature selection (FS) method. In order to identify the fitness of the features for the FS, we first employ three filter-based methods: Mutual Information (MI), Relief-F, and minimum redundancy maximum relevance (mRMR). The best set of features is then chosen by removing the lower-ranked features using a modified version of the Genetic Algorithm (GA). The K-Nearest Neighbors (KNN) classifier is then used to categorize human activities. We conduct comprehensive experiments on five well-known, publicly accessible HAR datasets, namely UCI-HAR, WISDM, MHEALTH, PAMAP2, and HHAR. Our model significantly outperforms the state-of-the-art models in terms of classification performance. We also observe an improvement in overall recognition accuracy with the use of GA-based FS technique with a lower number of features. The source code of the paper is publicly available here https://github.com/apusarkar2195/HAR_WaveletTransform_SpatialAttention_FeatureSelection.

This paper studies the high complexity of the calculation of fuzzy measures which can be used in fuzzy integrals to combine the decisions of different learning algorithms. To this end, this paper proposes an alternative low complexity method for the calculation of fuzzy measures that have been applied to Choquet integral for the fusion of deep learning models across different application domains for increasing the accuracy of the overall model. The paper shows that the Dempster-Shafer (DS) belief structure provides partial information about the fuzzy measures associated with a variable, and the paper devises a method to use this partial information for the calculation of fuzzy measures. An infinite number of fuzzy measures is associated with the DS belief structure. This paper proposes a theorem to calculate the general form of a specific set of fuzzy measures associated with the DS belief structure. This specific set of fuzzy measures can be expressed as a weighted summation of the basic assignment function of the DS belief structure. The main advantage of expressing the fuzzy measures in this format is that the monotonic condition which needs to be maintained during the calculation of the fuzzy measure can be avoided and only the basic assignment function needs to be evaluated. The calculation of the basic assignment function is formulated using a method inspired by the Monte Carlo approach used to calculate Value Functions in Markov Decision Process.

Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X-ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X-ray images, a more robust method is therefore required, which can be achieved by using a computer-aided diagnosis (CAD) system. In this study, we develop a two-stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and time-efficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet-201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing-based local search algorithm. The optimized feature subset is utilized for the classification of “Pneumonia” and “Normal” X-ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the highest accuracy of 98.36% and sensitivity of 98.79% with a feature reduction of 85.55% (74 features selected out of 512), using a five-fold cross-validation scheme. Extensive additional experiments on continuous benchmark functions as well as the CEC-2017 test suite further showcase the superiority and suitability of our proposed approach in application to real-valued optimization problems. The relevant codes for the proposed method can be found in https://github.com/soumitri2001/Pneumonia-Detection-Local-Search-aided-SCA.

In the era of the Internet of Things (IoT), the need for human activity recognition (HAR) is growing, especially in smart-healthcare applications using on-body smart sensor devices. These devices amass data and employ various classification models to analyze and discern user activities. However, existing techniques that are susceptible to the data type, user inputs, and ensemble-based models lack the ability to correct a wrong classification made by a base classifier. Addressing the shortcomings, we propose a novel fuzzy ensemble of three deep neural networks, using three nonlinear functions to generate fuzzy scores. The proposed model works on sensor data and can adaptively penalize the activity classes when the classification is assumed to be incorrect. Besides, a novel rewarding technique is proposed that aids the ensemble to extract the correct class in adverse situations. The proposed model reports state-of-the-art accuracy when evaluated on four publicly available wearable sensor data sets. In addition, activities corresponding to real-time sensor data collected using a smartphone are predicted correctly by the proposed model, thereby establishing itself as a reliable smart-HAR model. We also discuss a possible future scope of implementing the model over the cloud for smart activity recognition.

Abstract Compared to other features of the human body, voice is quite complex and dynamic, in a sense that a speech can be spoken in various languages with different accents and in different emotional states. Recognizing the gender, i.e. male or female from the voice of an individual, is by all accounts a minor errand for human beings. Similar goes for speaker identification if we are well accustomed with the speaker for a long time. Our ears function as the front end, accepting the sound signs which our cerebrum processes and settles on our disposition. Although being trivial for us, it becomes a challenging task to mimic for any computing device. Automatic gender, emotion and speaker identification systems have many applications in surveillance, multimedia technology, robotics and social media. In this paper, we propose a Golden Ratio-aided Neural Network (GRaNN) architecture for the said purposes. As deciding the number of units for each layer in deep NN is a challenging issue, we have done this using the concept of Golden Ratio. Prior to that, an optimal subset of features are selected from the feature vector extracted, common for all three tasks, from spectral images obtained from the input voice signals. We have used a wrapper-filter framework where minimum redundancy maximum relevance selected features are fed to Mayfly algorithm combined with adaptive beta hill climbing (A $$\beta$$ β HC) algorithm. Our model achieves accuracies of 99.306% and 95.68% for gender identification in RAVDESS and Voice Gender datasets, 95.27% for emotion identification in RAVDESS dataset and 67.172% for speaker identification in RAVDESS dataset. Performance comparison of this model with existing models on the publicly available datasets confirms its superiority over those models. Results also ensure that we have chosen the common feature set meticulously, which works equally well on three different pattern classification tasks. The proposed wrapper-filter framework reduces the feature dimension significantly, thereby lessening the storage requirement and training time. Finally, strategically selecting the number units in each layer in NN help increases the overall performance of all three pattern classification tasks.

Handwritten script classification is still considered as a challenging research problem in the domain of document image analysis. Although some research attempts have been made by the researchers for solving the challenging issues, a comprehensive solution is yet to be achieved. The case study, undertaken here, analyzes the performances of various state-of-the art handwritten script classification methods for Indian scripts where features, needed for the script classification task, are extracted from the script images at four different granularity levels, i.e., page, block, text line, or word. The results of handwritten script classification at each level have been obtained and compared using eight different feature sets and six different state-of-the-art classifiers. Based on the classification results, an ideal level for performing the handwritten script classification task is suggested among these four classification levels. The results have also been improved by using two feature dimensionality reduction methods. All these experiments are done on two different handwritten Indic script databases, of which one is an in-house developed dataset and the other one is a freely available dataset. Finally, some future research directions that may be undertaken by the researchers as an application of the handwritten Indic script classification problem are also highlighted. The work presented here provides a basic foundation for the construction of a comprehensive handwritten script classification method for official Indian scripts.

Breast cancer is one of the main causes behind cancer deaths in women worldwide. Yet, owing to the complexity of the histopathological images and the arduousness of manual analysis task, the entire diagnosis process becomes time-consuming and the results are often contingent on the pathologist's subjectivity. Thus developing an automated, precise histopathological image classification system is crucial. This paper presents a novel hybrid ensemble framework consisting of multiple fine-tuned convolutional neural network (CNN) architectures as supervised feature extractors and eXtreme gradient boosting trees (XGBoost) as a top-level classifier, for patch wise classification of high-resolution breast histopathology images. Due to the semantic complexity of the patch images, a single CNN architecture may not always extract high quality features, and the traditional Softmax classifier might not provide ideal results for classifying the CNN extracted features. Thus we aim to improve patch wise classification by proposing a hybrid ensemble model that incorporates different discriminating feature representations of the patches, coupled with XGBoost for robust classification. Experimental results show that our proposed method outperforms state-of-the-art methods to the best of our knowledge.

The rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play an important role for an accurate diagnostic. In the present work, a bi-modular hybrid model is proposed to detect COVID-19 from the chest CT images. In the first module, we have used a Convolutional Neural Network (CNN) architecture to extract features from the chest CT images. In the second module, we have used a bi-stage feature selection (FS) approach to find out the most relevant features for the prediction of COVID and non-COVID cases from the chest CT images. At the first stage of FS, we have applied a guided FS methodology by employing two filter methods: Mutual Information (MI) and Relief-F, for the initial screening of the features obtained from the CNN model. In the second stage, Dragonfly algorithm (DA) has been used for the further selection of most relevant features. The final feature set has been used for the classification of the COVID-19 and non-COVID chest CT images using the Support Vector Machine (SVM) classifier. The proposed model has been tested on two open-access datasets: SARS-CoV-2 CT images and COVID-CT datasets and the model shows substantial prediction rates of 98.39% and 90.0% on the said datasets respectively. The proposed model has been compared with a few past works for the prediction of COVID-19 cases. The supporting codes are uploaded in the Github link: https://github.com/Soumyajit-Saha/A-Bi-Stage-Feature-Selection-on-Covid-19-Dataset.

Abstract Handwritten numeral recognition is a challenging research problem because of the enormous varieties of styles in which human beings write the numerals. Several researchers have tried to find solutions to this problem with exceptional recognition accuracies. However, most of these solutions have been dedicated to single script numerals. Such methods are inappropriate for multi‐lingual nations such as India where a large number of scripts are used. Keeping this issue in mind, a new feature descriptor named symbolization of binary images (SBI) is introduced here for the recognition of handwritten numerals of different scripts. Effectiveness of SBI is supported with experiments showing its script‐invariant nature. Classification of numerals using a multiclass support vector machine (SVM) classifier yields the recognition accuracies of 98.18, 96.22, 96.52, and 95.53% on datasets of numerals written in four popular scripts of the world: Arabic, Bangla, Devanagari, and Latin, respectively. This scheme has also been extended to the situation when the script used is not known a priori or the numerals written in a document belong to pairs of mixed scripts of {Arabic, Devanagari, Bangla} with Latin producing recognition rates of 92.97, 91.25, and 91.67%, respectively. When all four scripts are mixed, the recognition rate is still 90.98% overall. Encouraging outcomes suggest that the proposed SBI feature descriptor can recognize numerals invariant of the script class.

The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.

The work reported in this article deals with the ground truth generation scheme for online handwritten Bangla documents at text-line, word, and stroke levels. The aim of the proposed scheme is twofold: firstly, to build a document level database so that future researchers can use the database to do research in this field. Secondly, the ground truth information will help other researchers to evaluate the performance of their algorithms developed for text-line extraction, word extraction, word segmentation, stroke recognition, and word recognition. The reported ground truth generation scheme starts with text-line extraction from the online handwritten Bangla documents, then words extraction from the text-lines, and finally segmentation of those words into basic strokes. After word segmentation, the basic strokes are assigned appropriate class labels by using modified distance-based feature extraction procedure and the MLP ( Multi-layer Perceptron ) classifier. The Unicode for the words are then generated from the sequence of stroke labels. XML files are used to store the stroke, word, and text-line levels ground truth information for the corresponding documents. The proposed system is semi-automatic and each step such as text-line extraction, word extraction, word segmentation, and stroke recognition has been implemented by using different algorithms. Thus, the proposed ground truth generation procedure minimizes huge manual intervention by reducing the number of mouse clicks required to extract text-lines, words from the document, and segment the words into basic strokes. The integrated stroke recognition module also helps to minimize the manual labor needed to assign appropriate stroke labels. The freely available and can be accessed at https://byanjon.herokuapp.com/ .

Action recognition based on skeleton key joints has gained popularity due to its cost effectiveness and low complexity. Existing Convolutional Neural Network (CNN) based models mostly fail to capture various aspects of the skeleton sequence. To this end, four feature representations, which capture complementary characteristics of the sequence of key joints, are extracted with novel contribution of features estimated from angular information, and kinematics of the human actions. Single channel grayscale images are used to encode these features for classification using four CNNs, with the complementary nature verified through Kullback-Leibler (KL) and Jensen-Shannon (JS) divergences. As opposed to straightforward classifier combination generally used in existing literature, fuzzy fusion through the Choquet integral leverages the degree of uncertainty of decision scores obtained from four CNNs. Experimental results support the efficacy of fuzzy combination of CNNs to adaptively generate final decision score based upon confidence of each information source. Impressive results on the challenging UTD-MHAD, HDM05, G3D, and NTU RGB+D 60 and 120 datasets demonstrate the effectiveness of the proposed method. The source code for our method is available at https://github.com/theavicaster/fuzzy-integral-cnn-fusion-3d-har

Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis (CAD) is inevitable. Though, some vaccines have now been authorized worldwide, it will take huge time to reach everyone, especially in developing countries. In this work, we have analyzed Chest X-ray (CXR) images for the detection of the coronavirus. The primary agenda of this proposed research study is to leverage the classification performance of the deep learning models using ensemble learning. Many papers have proposed different ensemble learning techniques in this field, some methods using aggregation functions like Weighted Arithmetic Mean (WAM) among others. However, none of these methods take into consideration the decisions that subsets of the classifiers take. In this paper, we have applied Choquet integral for ensemble and propose a novel method for the evaluation of fuzzy measures using coalition game theory, information theory, and Lambda fuzzy approximation. Three different sets of fuzzy measures are calculated using three different weighting schemes along with information theory and coalition game theory. Using these three sets of fuzzy measures, three Choquet integrals are calculated and their decisions are finally combined. Besides, we have created a database by combining several image repositories developed recently. Impressive results on the newly developed dataset and the challenging COVIDx dataset support the efficacy and robustness of the proposed method. Our experimental results outperform many recently proposed methods.

Recognition of isolated handwritten digits is a well-studied research problem and several models show high recognition accuracy on different standard datasets. But the same is not true while we consider recognition of handwritten digit strings although it has many real-life applications like bank cheque processing, postal code recognition, and numeric field understanding from filled-in form images. The problem becomes more difficult when digits in the string are not neatly written which is commonly seen in freestyle handwriting. The performance of any such model primarily suffers due to the presence of touching digits in the string. To handle these issues, in the present work, we first use a deep autoencoder based segmentation technique for isolating the digits from a handwritten digit string, and then we pass the isolated digits to a Residual Network (ResNet) based recognition model to obtain the machine-encoded digit string. The proposed model has been evaluated on the Computer Vision Lab (CVL) Handwritten Digit Strings (HDS) database, used in HDSRC 2013 competition on handwritten digit string recognition, and a competent result with respect to state-of-the-art techniques has been achieved.

For document image analysis, image binarization is an important preprocessing step. Also, binarization can help in improving the readability of old and historical manuscripts. Such documents are generally degraded due to various reasons such as bleed-through, faded ink, or stains. Achieving good binarization performance on these documents is a challenging task. In this letter, a deep learning based model for document image binarization has been proposed, comprising a Dual Discriminator Generative Adversarial Network (DD-GAN) which uses Focal Loss as generator loss. The DD-GAN consists of two discriminator networks - one looks for the global similarity i.e. on the whole image, and another one explores the image in small patches i.e. local similarity. At the final stage, simple thresholding is performed on the generated images. The method has been tested on five recent DIBCO datasets. It has been found that the method is robust and it provides results comparable with state-of-the-art methods. The code for this letter is available at https://github.com/anuran-Chakraborty/BinarizationDualDiscriminatorGAN.

Abstract In India, which has numerous officially recognized scripts, there is a primary need for categorizing the documents on the basis of the scripts used therein. Identification of script used in a document is essential for its effective handling both manually and digitally. Identification of script in a document image is an important research problem in the pattern recognition field, which, at times, suffers from the issue of growing dimensionality of the feature vector and requires an efficient feature selection technique. Keeping this fact in mind, in this paper, we propose a clustering‐based filter feature selection framework in order to extract an optimal and effective feature subset from the original feature vector. The present feature selection methodology is evaluated on a script classification problem involving handwritten documents in 12 major Indic scripts. Experiments are done at word‐level, text‐line‐level, and block‐level. Experiments demonstrate that a reasonable increment in classification accuracy has been realized using comparatively lesser number of features. The proposed framework for feature selection is computationally inexpensive and can be applied to other pattern recognition problems as well.

Document image binarization classifies each pixel in an input document image as either foreground or background under the assumption that the document is pseudo binary in nature. However, noise introduced during acquisition or due to aging or handling of the document can make binarization a challenging task. This paper presents a novel game theory inspired binarization technique for degraded document images. A two-player, non-zero-sum, non-cooperative game is designed at the pixel level to extract the local information, which is then fed to a K-means algorithm to classify a pixel as foreground or background. We also present a preprocessing step that is performed to eliminate the intensity variation that often appears in the background and a post-processing step to refine the results. The method is tested on seven publicly available datasets, namely, DIBCO 2009-14 and 2016. The experimental results show that GiB (Game theory Inspired Binarization) outperforms competing state-of-the-art methods in most cases.

Abstract Classifier combination methods have proved to be an effective tool to increase the performance of classification techniques that can be used in any pattern recognition applications. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still not matured enough and achieved improvements are inconsistent. In this paper, we propose a novel statistical validation technique known as correlation‐based classifier combination technique for combining classifier in any pattern recognition problem. This validation has significant influence on the performance of combinations, and their utilization is necessary for complete theoretical understanding of combination algorithms. The analysis presented is statistical in nature but promises to lead to a class of algorithms for rank‐based decision combination. The potentials of the theoretical and practical issues in implementation are illustrated by applying it on 2 standard datasets in pattern recognition domain,namely, handwritten digit recognition and letter image recognition datasets taken from UCI Machine Learning Database Repository ( http://www.ics.uci.edu/_mlearn ). An empirical evaluation using 8 well‐known distinct classifiers confirms the validity of our approach compared to some other combinations of multiple classifiers algorithms. Finally, we also suggest a methodology for determining the best mix of individual classifiers.

This article presents a set of novel features for robust online Bangla handwritten character recognition. Two feature extraction methods are presented here. The first describes the transition from background to foreground pixels and vice versa. The second uses a combination of topological features and centre-of-gravity- (CG) based circular features where global information, local information, and Circular Quadrant Mass Distribution information have been extracted. The impact of each along with their combination have also been analyzed. A total of 15,000 isolated online Bangla character samples have been collected and used for the evaluation. A Support Vector Machine classifier records the best recognition rate when the transition count feature, CG-based circular features, and topological features are combined.

Automatic script identification from handwritten document images facilitates many important applications such as indexing, sorting and triage. A given Optical Character Recognition (OCR) system is typically trained on only a single script but for documents or collections containing different scripts, there must be some way to automatically identify the script prior to OCR. For Indic script research, some results have been reported in the literature but the task is far from solved. In this paper, we propose a word-level script identification technique for six handwritten Indic scripts- Bangla, Devanagari, Gurumukhi, Malayalam, Oriya Telugu and the Roman script. A set of 82 features has been designed using a combination of elliptical and polygonal approximation techniques. Our approach has been evaluated on a dataset of 7000 handwritten text words, using multiple classifiers. A Multi-Layer Perceptron (MLP) classifier was found to be the best classifier resulting in 95.35% accuracy. The result is progressive considering the complexities and shape variations of the Indic scripts.