Ram Sarkar

Jadavpur University | JU · Department of Computer Science and Engineering

This invention presents a novel technology designed to enhance the visual clarity of videos affected by haze and rain, significantly improving their quality. The device, equipped with a camera, processing unit, vision processing unit, and display, efficiently removes haze and rain in real-time, making it highly suitable for integration into surve...

Deep learning and computer vision methods are nowadays predominantly used in the field of ophthalmology. In this paper, we present an attention-aided DenseNet-121 for classifying normal and glaucomatous eyes from fundus images. It involves the convolutional block attention module to highlight relevant spatial and channel features extracted by Dense...

Skin cancer is a highly dangerous type of cancer that requires an accurate diagnosis from experienced physicians. To help physicians diagnose skin cancer more efficiently, a computer-aided diagnosis (CAD) system can be very helpful. In this paper, we propose a novel model, which uses a novel attention mechanism to pinpoint the differences in featur...

Accurate nuclei segmentation in histopathological images is crucial for cancer diagnosis. Automating this process offers valuable support to clinical experts, as manual annotation is time-consuming and prone to human errors. However, automating nuclei segmentation presents challenges due to uncertain cell boundaries, intricate staining, and diverse...

Breast cancer is a major global health concern. Pathologists face challenges in analyzing complex features from pathological images, which is a time-consuming and labor-intensive task. Therefore, efficient computer-based diagnostic tools are needed for early detection and treatment planning. This paper presents a modified version of MultiResU-Net f...

Breast cancer remains a critical global concern, underscoring the urgent need for early detection and accurate diagnosis to improve survival rates among women. Recent developments in deep learning have shown promising potential for computer-aided detection (CAD) systems to address this challenge. In this study, a novel segmentation method based on...

Automatic Vehicle Classification (AVC) systems have become a need of the hour to manage the ever-increasing number of vehicles on roads and thus maintain a well-organized traffic system. Researchers around the world have proposed several techniques in the last two decades to address this challenge. However, these techniques should be implemented on...

The class imbalance problem is prevalent in many classification tasks such as disease identification using microarray data, network intrusion detection, and so on. These are tasks in which the class distribution is skewed towards one class, more commonly known as the majority class. In such cases, traditional classifiers may not perform well as the...

Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich func...

The process of modifying digital images has been made significantly easier by the availability of several image editing software. However, in a variety of contexts, including journalism, judicial processes, and historical documentation, among others, the authenticity of images is of utmost importance. In particular, copy-move forgery is a distinct...

Graph-based methods, distinguished by their resilience to noise, have demonstrated efficacy in image segmentation compared to alternative techniques. Despite their notable capabilities, these methods present over- and under segmentation problems. This paper introduces a novel graph-based approach specially designed to solve these issues effectively...

Noise introduced due to weather can reduce the efficiency of computer vision applications as the visibility of the objects in images is greatly affected. Haze and rain are the most common weather conditions seen in nature. However, most of the algorithms found in the literature apply rain and haze removal approaches separately. To this end, in this...

The work presents a newly designed penalty function to be added with an existing Cross Entropy based fitness function [3] for optimal selection of multi-level thresholds for image segmentation. The extended fitness function so designed is tested here for Brain magnetic resonance (MR) image segmentation using nature-inspired meta-heuristics such as...

An automatic vehicle classification (AVC) system designed from either still images or videos has the potential to bring significant benefits to the development of a traffic control system. On AVC, numerous articles have been published in the literature. Over the years, researchers in this domain have created and used a variety of datasets, but most...

Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to dete...

In this paper, we propose a deep learning-based model, called Weight and Attention Network (WANet), for video summarization. The network comprises a simple multi-head attention mechanism, followed by a feed-forward network to obtain the frame importance scores. Summary keyshots are obtained from the scores using a combination of kernel temporal seg...

Vehicle make and model recognition (VMMR) is a crucial task for developing automatic vehicle recognition (AVR) systems, and has gained significant attention in the fields of computer vision and artificial intelligence in recent years. The ability to automatically identify a vehicle's make and model has numerous practical applications, such as traff...

Steels serve as the most widely-used structural metallic materials in industrial practice. Steels have a wide variety of microstructures and mechanical properties. The microstructure of a metal and its physical properties are highly correlated. Manual Microstructure analysis and characterization tasks require deep expertise in this field, and it is...

Development of automatic vehicle detection (AVD) systems using either images or videos from traffic scenarios would be quite beneficial for making an automated traffic management system. There is an abundance of AVD-based research articles that have been published in the literature. This paper focuses on three major object detection algorithms unde...

Lung cancer remains a prevalent and deadly disease, claiming numerous lives annually. Early detection plays a pivotal role in significantly improving survival rates, by up to 50–70%. Therefore, developing a robust lung cancer detection system holds immense potential to positively impact human survival. Computed tomography (CT) scan images offer inv...

Breast ultrasound medical images often have low imaging quality along with unclear target boundaries. These issues make it challenging for physicians to accurately identify and outline tumors when diagnosing patients. Since precise segmentation is crucial for diagnosis, there is a strong need for an automated method to enhance the segmentation accu...

Estimating the degree of multiple personality traits in a single image is challenging due to the presence of multiple people, occlusion, poor quality etc. Unlike existing methods which focus on the classification of a single personality using images, this work focuses on estimating different personality traits using a single image. We believe that...

Particle Swarm Optimization (PSO) is a classic and popularly used meta-heuristic algorithm in many real-life optimization problems due to its less computational complexity and simplicity. The binary version of PSO, known as BPSO, is used to solve binary optimization problems, such as feature selection. Like other meta-heuristic optimization techniq...

A metaheuristic method is an optimization technique that is generally inspired by natural or physical processes. The use of metaphors has created a tendency to reproduce existing algorithms with slight modifications or variations rather than encouraging the development of novel algorithmic techniques and principles. On the other hand, a complex net...

Cervical cancer is one of the most concerning carcinogenic diseases among women worldwide. The condition is especially bad in low- or middle-income countries due to the lack of medical facilities. In such situations, computer-aided diagnosis (CAD) systems can alleviate the need to a large extent. However, sometimes a single learning model may not b...

Existing Optical Mark Recognition (OMR) systems tend to be expensive and rigid in their operation, often resulting in erroneous evaluations due to strict correction protocols. This scenario airs the need for a flexible OMR system. Hence, in this work, we propose a lightweight transfer learning based Convolutional Neural Network (CNN) model, dubbed...

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...

Stress has become one of the major concerns in modern human life, especially after the outbreak of the COVID-19 pandemic, and it has had a great impact on human daily life activities. Detecting stress from physiological signals at an early stage is crucial as it prevents it from outgrowing severe health issues. Most researchers interested in stress...

Automatic human facial expression recognition, a challenging research problem, has many real-life applications in the fields of social media, digital marketing, and healthcare, etc. Facial expression recognition using machine learning is a challenging task due to the variability in facial expressions across different people and situations, as well...

Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of this ailment is vital for preventing the tumours to spread and plan treatment to possibly eradicate the disease. However, population-wide screening is stunted by the requirement of medical professiona...

One of the difficult tasks in the field of computer vision is the classification and detection of vehicles. Researchers from all over the world are working to create autonomous vehicle detection (AVD) systems due to their numerous practical applications, including highway management and surveillance systems. Deep learning techniques, which require...

One of the most common types of cancer in the world is lung cancer, which is a cause of increasing mortality. It is most often discovered in the middle and later stages as it does not have obvious symptoms due to which its treatment is often missed. Studies show that most lung cancers are in the form of lung nodules, which can be categorized as ben...

Breast cancer is one of the most common reasons for the premature death of women worldwide. However, early detection and diagnosis of the same can save many lives. Hence, computer scientists across the world are also striving to develop reliable models to deal with this disease. One of the reliable methods of detecting breast cancer is through ther...

In supervised learning algorithms, the class imbalance problem often leads to generate results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal aspect of separating the overlapping classes. This is the reason why most of these methods are prone to overfit on the training data. To...

One of the most frightening and talked-about diseases in the modern world is cancer. Huge amounts of research are conducted worldwide to make this ailment less fearsome, be it by finding its cure, discovering ways to detect it in much earlier stages to reduce the mortality rate, or identifying precautions for humans to avoid it. The availability of...

A new contagious disease or unidentified COVID-19 variants could provoke a new collapse in the global economy. Under such conditions, companies, factories, and organizations must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate inf...

Stock market prediction is the process of determining the value of a company's shares and other financial assets in the future. This paper proposes a new model where Altruistic Dragonfly Algorithm (ADA) is combined with Least Squares Support Vector Machine (LS-SVM) for stock market prediction. ADA is a meta-heuristic algorithm which optimizes the p...

Metallographic images or often called the microstructures contain important information about metals, such as strength, toughness, ductility, corrosion resistance, which are used to choose the proper materials for various engineering applications. Thus by understanding the microstructures, one can determine the behaviour of a component made of a pa...

We have recently been witnessing that our society is starting to heal from the impacts of COVID-19. The economic, social and cultural impacts of a pandemic cannot be ignored and we should be properly equipped to deal with similar situations in future. Recently, Monkeypox has been concerning the international health community with its lethal impacts...

Graph neural networks (GNN) uphold the essence of irregularly structured information embedded in a graph via message passing among the nodes and aggregating the node features at various levels of the graph. In the past, researchers have extensively used the GNN models for several semi-supervised node classification tasks. Existing GNN models do not...

Video plays a key role in carrying authenticity, especially in the surveillance system, medical field, court evidence, journalism, and social media among others. However, nowadays the trust in videos is decreasing day by day due to the forgery of the videos made by easily accessible video editing tools. Hence, a thrust for finding a robust solution...

Designing an automatic vehicle detection (AVD) system from still images or videos would be a useful tool to cater to the requirements of the traffic management system. Over the past few years, numerous databases have been developed for the use of researchers in this field of AVD. However, most of them are not acceptable in the Indian scenarios due...

This paper proposes a binary adaptation of the recently proposed meta-heuristic, Equilibrium Optimizer (EO), called Discrete EO (DEO), to solve binary optimization problems. A U-shaped transfer function is used to map the continuous values of EO into the binary domain. To further improve the exploitation capability of DEO, Simulated Annealing (SA)...

In recent times, microarray gene expression datasets have gained significant popularity due to their usefulness to identify different types of cancer directly through bio-markers. These datasets possess a high gene-to-sample ratio and high dimensionality, with only a few genes functioning as bio-markers. Consequently, a significant amount of data i...

In the recent past, video forgery has increased rapidly due to the easy availability of the required tools to accomplish that. Temporal copy-move or frame duplication is one of the most common video forgery methods in which a set of consecutive frames is copied somewhere in the same video. This work proposes an ensemble based method to detect dupli...

Digital face manipulation has become a concern in the last few years due to its harmful impacts on society. It is especially concerning for high-profile celebrities because their identities can be easily manipulated using mobile or web applications such as FaceSwap and FaceApp. These manipulated faces are so close to real ones that it becomes reall...

In modern era it has become increasingly easier to manipulate and tamper digital images, one of the primary reasons being the boon of commonplace availability of powerful image editing tools and software. These tools become a bane when used for malicious reasons as users can possibly add or remove important features from an image without leaving an...

Breast cancer is one of the deadliest diseases worldwide among women. Early diagnosis and proper treatment can save many lives. Breast image analysis is a popular method for detecting breast cancer. Computer-aided diagnosis of breast images helps radiologists do the task more efficiently and appropriately. Histopathological image analysis is an imp...

In this paper, a new population initialization method for metaheuristic algorithms is proposed. In our approach, the initial set of candidate solutions is obtained through the sampling of the objective function with the use of the Metropolis–Hastings technique. Under this method, the set of initial solutions adopts a value close to the prominent va...

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 pr...

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...

For easy accessibility of the information from the digitized document images, optical character recognition (OCR)-based software can be used. But in the case of handwritten documents, the performance of the state-of-the-art OCR systems is not satisfactory owing to the complexity of the unconstrained handwriting. Hence, research affinity comes up wi...

Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Computer-aided based diagnosis (CAD) systems can be used in such cou...

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 Moth Flame Optimization algorithm is an emerging meta-heuristic widely used in science and industry. Solving optimization problems using this algorithm requires addressing a number of challenges, including multiple objectives, constraints, binary decision variables, large-scale search space, dynamic objective function, and noisy parameters.

Developing an Optical Character Recognition (OCR) system for handwritten texts is a challenging research problem. Handwritten text can be largely different even for the same piece of text since the writing style differs from person to person. On the other hand, for many regional languages, unavailability of datasets having a large quantity of varie...

Breast cancer is the second deadliest disease amongst women worldwide. Breast histopathology image analysis is one of the most powerful ways used for the detection of tumour malignancies. Manual breast histopathology image analysis is, however, subjective, time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has become a popular...

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 analyse and discern user activities. However, existing techniques are susceptible to the data type,...

The novel coronavirus (COVID-19), has undoubtedly imprinted our lives with its deadly impact. Early testing with isolation of the individual is the best possible way to curb the spread of this deadly virus. Computer aided diagnosis (CAD) provides an alternative and cheap option for screening of the said virus. In this paper, we propose a convolutio...

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 lear...

Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate this shortcoming and identify the...

Histopathological image classification has become one of the most challenging tasks among researchers due to the fine-grained variability of the disease. However, the rapid development of deep learning-based models such as the Convolutional Neural Network (CNN) has propelled much attentiveness to the classification of complex biomedical images. In...

Due to the imbalanced and limited data, semi-supervised medical image segmentation methods often fail to produce superior performance for some specific tailed classes. Inadequate training for those particular classes could introduce more noise to the generated pseudo labels, affecting overall learning. To alleviate this shortcoming and identify the...

Pneumonia is one of the major reasons for child mortality especially in income-deprived regions of the world. Although it can be detected and treated with very less sophisticated instruments and medication, Pneumonia detection still remains a major concern in developing countries. Computer-aided based diagnosis (CAD) systems can be used in such cou...

With the advent of image generative technologies, there is a huge growth in the development of facial manipulation techniques that allow people to easily modify media data like videos and images by changing the identity or facial expression of the target person with another person’s face. Colloquially, these manipulated videos and images are termed...

Contrast enhancement is an important pre-processing task for several image and video processing applications. The objective of a contrast enhancement method is to improve the quality of the visual information contained in the images for further processing. Due to the enormous challenges, it is still considered as an open research problem. Several a...

Still-image based human action recognition is a challenging task in the field of computer vision due to the limited information available in a single image. Hence, it is important to efficiently extract visual cues and structural information from the image in the process of classification. To this end, in this work, we utilize the Convolutional neu...

In ancient times, there was no system to record or document music. A basic notation system to write European music was formulated around 14th century in the Baroque period which slowly evolved into the standard notation system that we have today. Later, the musical pieces from the classical and post-classical period of European music were documente...

Image contrast enhancement (ICE) is an important step in image processing and analysis as the quality of an image plays a pivotal role in human understanding. Moreover, contrast is considered a key aspect for the assessment of picture quality. Incomplete beta function (IBF) is one of the widely used transformations and histogram equalization (HE) i...

Ali, AsfakSarkar, RamDas, Debesh KumarGalois field multiplication has received a lot of attention from researchers due to its use in encryption, channel coding, and digital signal processing. This paper proposes an area-efficient Galois field multiplier. A sequential approach is adopted here to implement the multiplier in field programmable gate ar...

The novel COVID-19 pandemic, has effectively turned out to be one of the deadliest events in modern history, with unprecedented loss of human life, major economic and financial setbacks and has set the entire world back quite a few decades. However, detection of the COVID-19 virus has become increasingly difficult due to the mutating nature of the...

The global pandemic caused by the coronavirus (COVID-19) disease has collapsed the worldwide economy. Elements such as non-obligatory vaccination, new strain variants and lack of discipline to follow social distancing measures suggest the possibility that COVID-19 may continue to exist, exhibiting the behavior of a seasonal disease. As the socio-ec...

The Memetic algorithm (MA) breaks down complex optimization problems into smaller sub-parts called memes and operates on them to find optimal solutions. MA uses local search to increase its exploitation capabilities, which makes it a high-performing universal heuristic. This inspires us to work on MA and we attempt to improve the local search abili...

The prime goal of creating synthetic digital data is to generate something very closer to real ones when the original data are scarce. However, the trustworthiness of such digital content is dipping potentially in society owing to malicious users. Deepfake method that uses computer graphics and computer vision techniques to replace the face of one...

Due to the ever increasing traffic on roads, there has been a pressing need for Automatic Vehicle Detection (AVD) systems, so that the real-time traffic can be observed as well as managed in an efficient way. To address this research problem, several techniques have been put forward in the last decade by researchers across the world. In this articl...

Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a larg...

In predictive modelling it is important to use any feature selection methods as irrelevant features when used with powerful classifiers can lead to over-fitting and thus create models which fail to perform as good as when these features are not used. Particularly it is important in case of disease datasets where various features or attributes are a...

Breast cancer is caused by the uncontrolled growth and division of cells in the breast, whereby a mass of tissue called a tumor is created. Early detection of breast cancer can save many lives. Hence, many researchers worldwide have invested considerable effort in developing robust computer-aided tools for the classification of breast cancer using...

Background and Objective: Cervical cancer is one of the leading causes of womens death. Like any other disease, cervical cancers early detection and treatment with the best possible medical advice are the paramount steps that should be taken to ensure the minimization of after-effects of contracting this disease. PaP smear images are one the most e...

In the recent past, deep learning-based models have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated datasets. An interesting application of deep learning is synthetic data generation, especially in the domain of medical image analysis. The need for such a task arises due to the scarcity of origina...

The rapid outbreak of COVID-19 has affected the lives and livelihoods of a large part of the society. Hence, to confine the rapid spread of this virus, early detection of COVID-19 is extremely important. One of the most common ways of detecting COVID-19 is by using chest X-ray images. In the literature, it is found that most of the research activit...