Browsing by Author "Bhowmik, B."

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2025) Reddy, M.R.V.S.R.S.; Kumar, S.; Bhowmik, B.
Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum Machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. Â© 2025 IEEE.
ADConv-Net: Advanced Deep Convolution Neural Network for COVID-19 Diagnostics Using Chest X-Ray and CT Images
(Springer, 2025) Kumar, S.; Bhowmik, B.
The worldwide COVID-19 epidemic has emerged as a significant concern, affecting daily lives and underscoring the importance of early diagnosis for effective treatment in medical and healthcare settings. Current diagnostic testing for COVID-19 is sluggish, typically requiring hours to get results. Detection of COVID-19 from medical imaging presents a challenging task that has gained substantial interest from experts worldwide. Essential imaging modalities for diagnosing COVID-19 include chest X-rays and computed tomography (CT) scans. By contrast, most of the chest radiography can be completed in within fifteen minutes. Thus, employing chest radiography gives a possibility for early and reliable diagnosis of COVID-19, intending to relieve therapeutic obstacles for patients and speed up the diagnostic process. Recently, deep learning (DL) techniques have been shown to be effective in image-based diagnostics. This paper proposed an advanced deep convolution neural network (ADConv-Net) for COVID-19 detection and categorization using chest X-ray and CT images. The proposed technique is not only capable of recognizing critical connections and similarities in image classification, but also leads to improved diagnostic accuracy. The proposed model undergoes thorough evaluation for standard performance metrics. After evaluation, the ADConv-Net model achieves high accuracies of 98.84% and 97.25% in training and testing for X-ray images and 99.41% and 98.87% in training and testing for CT images, respectively. Additionally, the proposed model demonstrates strong performance, with AUC values of 0.993 and 0.996 for X-ray and CT images, respectively. Further, the model also introduces a heatmap approach for displaying COVID-19 disease areas. Subsequently, radiologists can find COVID-19 disorders in chest X-ray and CT images with this approach. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Advancements in Credit Scoring, Profit Scoring, and Portfolio Optimization for P2P Lending
(Institute of Electrical and Electronics Engineers Inc., 2024) Nayaka, P.; Hegde, A.; Bhowmik, B.
The Peer-to-peer (P2P) lending platform allows borrowers to connect directly with lenders outside traditional banking systems. Therefore, for the sustainability of these platforms, they must accurately assess the credit risk and profitability of the loans. Various credit scoring techniques, including Logistic Regression, neural networks, and ensemble methods, can be used to estimate the likelihood of borrower default. It is imperative to analyze the profit the lenders generated and enhance the credit scoring so that the investors face minimum loss. Once the profit analysis is done, then it is crucial to advise the investors about the portfolio of loans. This paper presents recent credit scoring, profit scoring, and portfolio optimization trends for P2P lending. We highlight the significant issues in incorporating machine learning models into credit scoring systems. The analysis emphasizes the need for a data-driven approach to perfecting lending practices, thus benefiting both borrowers and investors in the rapidly changing P2P landscape. Â© 2024 IEEE.
AI Technology for NoC Performance Evaluation
(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, B.; Hazarika, P.; Kale, P.; Jain, S.
An on-chip network has become a powerful platform for solving complex and large-scale computation problems in the present decade. However, the performance of bus-based architectures, including an increasing number of IP cores in systems-on-chip (SoCs), does not meet the requirements of lower latencies and higher bandwidth for many applications. A network-on-chip (NoC) has become a prevalent solution to overcome the limitations. Performance analysis of NoC's is essential for its architectural design. NoC simulators traditionally investigate performance despite they are slow with varying architectural sizes. This work proposes a machine learning-based framework that evaluates NoC performance quickly. The proposed framework uses the linear regression method to predict different performance metrics by learning the trained dataset speedily and accurately. Varying architectural parameters conduct thorough experiments on a set of mesh NoCs. The experiments' highlights include the network latency, hop count, maximum switch, and channel power consumption as 30-80 cycles, 2-11, $25\mu \text{W}$ , and $240\mu \text{W}$ , respectively. Further, the proposed framework achieves accuracy up to 94% and speedup of up to $2228\times $. © 2004-2012 IEEE.
An Integrated MPI and OpenMP Approach for Plasma Dynamics Simulations
(Institute of Electrical and Electronics Engineers Inc., 2024) Prakash, Y.M.; Girish, K.K.; Verma, L.; Kumar, S.; Bhowmik, B.
Plasma dynamics is the behavior exhibited by two or more charged species with respect to electric or magnetic fields. In high-performance computing (HPC) applications, it requires all these factors: the accuracy of parallel implementations, effective inter-process communication, and scalability with respect to workload. This paper points out the limitations in the current approaches to the plasma dynamics problems, and discusses the use of MPI continuation tasks and of its performance enhancement with OpenMP methods. Within the framework of the Vlasov-Poisson system, we develop theory of MPI continuation and describe techniques optimal for its use, which allows to efficiently combine communication with computation, which is quite a difficult task in most of the cases, especially in the case of multidimensional simulations. The results allow better insights on how to increase the level of parallelism and reduce the time to compute, which in turn fosters the formulation of more effective high-performance strategies and also the understanding of the parallelism in plasma simulations using the MPI standard. Â© 2024 IEEE.
Analysis of Selected Binarization Techniques for Brain Tumor Magnetic Resonance Images
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumar, A.; Ajith, A.; Bhowmik, B.
The identification and therapy of brain tumors have greatly improved as a result of recent developments in the field of medical imaging. Among the different imaging techniques, magnetic resonance imaging (MRI) is essential for identifying and describing brain tumors. However, accurately segmenting tumor regions from MRI scans remains a persistent challenge due to tumors' complex and diverse appearances. To address this challenge, extensive evaluation of novel approaches and comparative analysis of existing methods are essential to unlock the potential of binarization techniques. This paper presents the transformative capacity of binarization techniques in elevating overall brain tumor management. We select a set of binarization techniques for MRIs. The methods are implemented to find a better approach that can be employed for better segmentation and detection of brain tumors from the input MRI dataset. Consequently, we propose an alternative binarization technique. Through precise and personalized healthcare interventions, the proposed approach holds promise for enhancing patient outcomes and improving the quality of life for individuals affected by brain tumors. Â© 2023 IEEE.
Analysis of Selected Load Balancing Algorithms in Containerized Cloud Environment for Microservices
(Institute of Electrical and Electronics Engineers Inc., 2024) Saxena, D.; Bhowmik, B.
Microservice architecture has become a widely accepted solution to address the challenges, particularly scala-bility, deployment, and flexibility associated with monolithic architecture. A vital attribute of the microservices architecture is its capability to handle load balancing on a large scale. The load balancer collaborates with a scaler to distribute the workload efficiently across multiple instances. In the literature, different studies employ load-balancing algorithms for efficient microservice load balancing. These works overlook cloud-based microservice applications or focus solely on virtual machines, neglecting containers. This paper addresses these limitations by comparatively assessing selected load-balancing algorithms. The three most used algorithms, random, round-robin, and least connection, are studied on a microservice application. The extensive experiments are conducted using Elastic Container Service (ECS) of Amazon Web Service (AWS) for containerized cloud setup where each service resides in a cluster and traffic is generated through Locust. Experimental results show that throughput and response time range of 6.2-288.7 and 312.2-3375.8 ms, respectively. Â© 2024 IEEE.
AutoCov22: A Customized Deep Learning Framework for COVID-19 Detection
(Springer, 2023) Bhowmik, B.; Varna, S.; Kumar, A.; Kumar, R.
The novel coronavirus disease 2019 (COVID-19) spill has spread rapidly and appeared as a pandemic affecting global public health. Due to the severe challenges faced with the increase of suspected cases, more testing methods are explored. These methods, however, have several disadvantages, such as test complexity and associated problems—sensitivity, reproducibility, and specificity. Hence, many of them need help to achieve satisfactory performance. Motivated by these shortcomings, this work proposes a custom deep neural network framework named “AutoCov22” that detects COVID-19 by exploiting medical images—chest X-ray and CT-scan. First, multiple neural networks extract deep features from the input medical images, including popularly used VGG16, ResNet50, DenseNet121, and InceptionResNetV2. Then, the extracted features are fed to different machine-learning techniques to identify COVID-19 cases. One objective of this work is to quicken COVID-19 detection. Another goal is to reduce the number of falsely detected cases by a significant margin. Comprehensive simulation results achieve a classification accuracy of 99.74%, a precision of 99.69%, and a recall of 98.80% on exercising chest X-ray images. Extended experiment results in accuracy, precision, and recall up to 87.18%, 84.98%, and 85.66%, respectively, in processing CT-scan images. Thus, the AutoCov22 approach demonstrates a promising and plausible best solution over several methods in the state-of-the-art. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Automated Rice Leaf Disease Diagnosis Using CNNs
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumar, A.; Bhowmik, B.
Rice is a staple food in Bharat (India) and many other parts of the world. However, the increasing demand for rice due to population growth forces various challenges, including degraded crop quality and quantity due to rice plant diseases. Diseases such as brown spots, bacterial blight, and hispa can significantly reduce farming output, thereby impacting the productivity of the agriculture sector. To address this challenge, various solutions such as Agricultural cyber-physical systems (ACPS) and precision agriculture have been proposed, along with the application of deep learning techniques. This paper presents a rice leaf disease detection method using deep transfer learning. The proposed approach explores well-known pre-trained deep Convolutional Neural Network (CNN) models - VGG19, DenseNet201, InceptionV3, ResNet50, EfficientNetB3, EfficientNetB7, and XceptionNet, for image-based rice disease classification. Experimental results show that the DenseNet model by the proposed method achieved the highest classification accuracy of 98.75% when fine-tuned properly. The proposed scheme outperforms many existing approaches, delivering a superior disease control solution for rice leaf diseases. Â© 2023 IEEE.
Automated Segmentation of COVID-19 Infected Lungs via Modified U-Net Model
(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.; Bhowmik, B.
The COVID-19 pandemic has led to significant outbreaks in more than 220 countries worldwide, profoundly impacting the public health and lives. As of February 2024, over 774 million cases have been reported, with more than 7,035,337 deaths recorded. Therefore, there is a significant need for automated image segmentation to serve as clinical decision support. This paper presents a novel automated segmentation framework that dynamically generates distinct and randomized image patches for training using preprocessing techniques and extensive data augmentation. The proposed architecture employs a semantic segmentation approach, ensuring accuracy despite limited data availability. Experimental assessment comprises a visual inspection of the predicted segmentation outcomes. Quantitative evaluation of segmentation includes standards performance metrics such as precision, recall, Dice score, and Intersection over Union (IoU). The results exhibit a remarkable Dice coefficient score of 98.3% and an IoU rate surpassing 96.8%, demonstrating the model's robustness in identifying COVID-19-infected lung regions. Â© 2024 IEEE.
Big Data Analytics for Industry 5.0
(wiley, 2025) Hegde, A.; Bhowmik, B.
The steam engine's power, the assembly line's efficiency, and the computer's processing speed: these disruptive new technologies were the driving forces behind the first three industrial revolutions. The fourth industrial revolution, also known as Industry 4.0, is propelled by intelligent technologies. Industry 5.0, the fifth industrial revolution, fosters collaboration between humans and robots, thereby enhancing Industry 4.0 technologies. It is anticipated that this will generate employment that is more valuable, thereby allowing individuals to engage in more creative and design-oriented activities. It is possible for factories to remain competitive and adjust to the changing requirements of their customers by implementing this change. With the implementation of suitable investments, Industry 5.0 has the potential to foster economic growth and establish a more sustainable, collaborative future for both humans and machines. Finance, healthcare, retail, and manufacturing are among the sectors that have already experienced this transformation. Industries 5.0 has been rendered feasible by technologies including blockchain, cloud computing, Big Data Analytics (BDA), Internet of Things (IoT), and 6G networks. The administration of substantial quantities of data is facilitated by BDA, in particular. To optimize the utilization of human resources and minimize waste and inefficiency, sophisticated big data management and analysis systems implement artificial intelligence and machine learning techniques. Furthermore, the enhanced customization, precision, and productivity of Industry 5.0, which is a component of the IoT, are ensured by the increased use of intelligent devices and sensors. This chapter outlines the current trends, design principles, and applications of Industry 5.0. This chapter outlines the fundamentals of Industry 5.0, its emergence, and the significance of BDA as a technology. Furthermore, this chapter outlines the architecture, design principles, and opportunities that are linked to Industry 5.0, including optimization of human efficiency, personalized services, enhanced automation, and higher-value employment. In this chapter, Industry 5.0 faces a variety of obstacles, such as a scarcity of qualified workers, a time-consuming process, a substantial budget requirement, and security and privacy concerns. Furthermore, this chapter provides a comprehensive analysis of the most recent developments in the field, the paradigm shift toward Industry 5.0, and a diverse array of prospective futures. This chapter outlines the primary challenges, interests, and problems of Industry 5.0 in relation to BDA. © 2025 by John Wiley & Sons Inc. All rights reserved.
Big Data Insights: Pioneering Changes in FinTech
(Institute of Electrical and Electronics Engineers Inc., 2024) Anusha Hegde, H.; Bhowmik, B.
The amount of data generated and stored by finance sector companies is rapidly increasing, allowing corporations to conduct data analytics and enhance their businesses. However, data scientists face immense challenges in efficiently handling massive amounts of data and generating insights with real business value. Big Data Analytics (BDA) tools and methods are required to handle vast data. Financial Technology's (FinTech's) growth in mobile Internet, cloud computing, big data, search engines, and blockchain technology has dramatically changed the financial industry. The appropriate application of big data in the management and business innovation of FinTech is therefore a significant concern that confronts the whole finance industry. This paper explores the significance of big data methods in the financial sector and offers insights into the difficulties in applying them as well as future potential for technological advancement. Along with its classifications, the paper examines how FinTech evolved from traditional to modern banking. Corporate banking encompasses several aspects, such as financial markets, corporate credit, and trade, involving substantial transactions and monetary resources. Consequently, this sector has a favorable opportunity to use emerging information technology (IT) advancements. Lastly, the study examines how BDA contributes to FinTech difficulties and projects how FinTech will develop in the future within the context of BDA. Â© 2024 IEEE.
COVID-19 Waves and Their Impacts to Society
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumar, S.; Bhowmik, B.
The COVID-19 pandemic has led to a global medical crisis and significant disruptions to daily life since its emergence in December 2019. Rapidly, it spread to 218 countries affecting more than 754 million people. The virus appears in different variants bringing significant implications at all societal levels. Recently, different variants of the virus have emerged, which have caused significant consequences in society. This paper presents the state-of-the-art on other waves caused by COVID-19 variants, their impacts on society, and challenges. The paper also details recent advancements to combat this disease. Â© 2023 IEEE.
Design of a Fault-Tolerant Pseudo-3D Routing
(Institute of Electrical and Electronics Engineers Inc., 2023) Bhowmik, B.; Gagan, N.
A network on chip (NoC) exposes faults that disturb overall performance. Subsequently, a routing algorithm with fault tolerant facility has become a holistic aspect of reliable NoC communications. We propose a routing mechanism including fault tolerance in channels of a pseudo-3D mesh NoCs. The concept of a detour path is the foundation of the proposed solution. Its goal is to ensure the delivery of almost all packets with a detour of a few without utilizing broken or faulty communication functionalities. On evaluations, the suggested technique produces 11.47% greater throughput, 38.38% lesser latency, and 65.50% improved energy consumption compared to the baseline mesh NoC. Based on the findings, one may conclude that the proposed fault-Tolerant routing performs more effectively even at higher traffic load levels. Â© 2023 IEEE.
Detecting COVID-19 Infection Using Customized Convolutional Neural Network
(Institute of Electrical and Electronics Engineers Inc., 2024) Kumar, S.; Kisku, B.; Vardhan K, S.H.; Kumar, S.; Bhowmik, B.
The COVID-19 pandemic has affected 775 million people globally, with an estimated death toll of 7 million. Detection methods like reverse transcription polymer chain reaction (RT-PCR) face multiple challenges, including false positive cases, time-consuming, and high cost. A rapid, precise, affordable screening alternative is essential to expedite COVID-19 detection. Various efforts have focused on expediting COVID-19 detection due to the high costs and logistical challenges associated with traditional methods. This paper proposes a customized deep-learning framework architecture for automatically identifying COVID-19 infection in chest X-ray (CXR) images. Multiple neural networks extract deep features from the CXR images, including popular models such as VGG19, DenseNet201, EfficientNet, MobileNetV2, and InceptionV3. The proposed model undergoes training and testing using the QaTa-COVID-19 dataset. The proposed model achieves classification accuracy of 97.06%, with precision, recall, and F1 score rates for COVID-19 cases recorded at 97.34%, 96.36%, and 97.01%, respectively, for the 4-class cases (COVID vs. Normal vs. Pediatric Bacterial Pneumonia vs. Pediatric Viral Pneumonia). Â© 2024 IEEE.
Detection and Localization of Channel-Short Faults in Regular On-Chip Interconnection Networks
(Springer, 2023) Bhowmik, B.
With the rapid developments in VLSI technology, the communication channels in networks-on-chip (NoCs) can place many wires for sustaining high-performance requirements over the communication bottleneck in multicore, multiprocessor systems-on-chip (MPSoCs). Consequently, NoC channels, due to increased wire density, are exposed to different logic level faults, e.g., short resulting in reliability and yield issues in NoC-based systems. These faults can appear at any stage of the lifetime of a chip. While existing in an NoC communication architecture, the channel-short faults bring the system into various failures that surprisingly cause a significant deviation from its expected performance. In this work, an online, distributed test solution is presented that detects and diagnoses intra-channel and inter-channel short faults and analyzes the effect of these faults on various performance metrics. Fault simulations ensure up to 100% coverage metrics. Network simulation shows insight into the impact of channel shorts in NoC performances. It is observed that the amount of test time is reduced to 10 × concerning a set of prior works while employed for a group of NoCs. It is also seen on these NoCs that average packet latency is improved by 15.14–46.79% while energy consumption is reduced by 13.68–39.13% by the current solution than the set of existing solutions. Moreover, the proposed solution scales to all NoCs irrespective of size, topology, and channel width at an acceptable test cost. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Development of IoT-Based Smart Home Application with Energy Management
(Institute of Electrical and Electronics Engineers Inc., 2023) Prathyusha, M.R.; Bhowmik, B.
IoT Evolution is a trusted source in the Internet of Things (IoT) that describes an ecosystem of connected devices. Interconnected smart devices have become a ubiquitous part of daily lives. In other words, the rising popularity of the IoT in day-to-day life has led to people incorporating innovative applications, e.g., smart home environments, to improve convenience, comfort, energy efficiency, and safety. However, these intelligent appliances provide additional energy costs. Thus, energy management is essential to minimize this energy cost in an intellectual environment. The cost incurred for improved energy or other innovative services varies with the design of an intelligent system. This paper presents a smart home automation system designed along with energy management. In this work, each room of the proposed smart home is modeled with a set of selected smart things and simulated. Also, their energy consumption is estimated and analyzed for efficient energy management. Â© 2023 IEEE.
Diagnosis of SARS-CoV-2 Via Rapid Antigen Kits
(Institute of Electrical and Electronics Engineers Inc., 2023) Kumar, S.; Bhowmik, B.
The recent emergence of SARS-CoV-2, also known as COVID-19, poses a significant health threat and has rapidly spread to 223 countries, affecting more than 755 million people. Globally, more than seven billion COVID-19 tests have been performed. Rapid antigen kits have emerged as valuable detection techniques in the global fight against COVID-19, providing quick and accessible testing capabilities. However, ensuring the accuracy and reliability of these kits is crucial for effective disease management. This paper presents the emergence of different COVID-19 biomarker techniques to detect these viruses. Next, explore the role of the formal verification technique, addressing the trustworthiness of rapid antigen kits. Furthermore, the paper explores the diverse quick antigen kits approved by the Indian Council of Medical Research (ICMR) available in the literature. Formal verification provides substantial implications in the context of rapid antigen kit verification, and it is increasingly becoming a prominent trend in medical diagnostics. Â© 2023 IEEE.
Dugdugi: An Optimal Fault Addressing Scheme for Octagon-Like On-Chip Communication Networks
(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, B.
Network-on-chip (NoC) has emerged as a scalable on-chip communication platform and, hence, has become more popular. However, as the sole communication medium, a single point of failure raised by any permanent fault can cause the failure of the entire system. Subsequently, the NoC has become a critically exposed unit that must be protected. This article primarily presents a test-time-independent and optimally distributed test scheme named 'Dugdugi' that addresses channel faults, e.g., short in an Octagon and similar NoC architectures to achieve high reliability. The proposed scheme is extended to cover other channel faults, such as stuck-at and transient faults, to give its impression of a comprehensive approach. Experimental results show that the proposed scheme incurs little hardware area and detects all modeled short faults by a few clocks with achieving fault coverage metric up to 100%. Online evaluation reveals the effect of channel-short faults on various network performance metrics. In comparison to prior methodologies, the proposed scheme improves hardware area overhead up to 71.79% and reduces test time over 94.20%. Furthermore, performance overhead, such as packet latency and energy consumption, reduces up to 40.85% and 43.87%, respectively. © 1993-2012 IEEE.
Dynamic Checkpointing: Fault Tolerance in High-Performance Computing
(Institute of Electrical and Electronics Engineers Inc., 2024) Bhowmik, B.; Verma, T.; Dineshbhai, N.D.; Reddy, M.R.V.; Girish, K.K.
Parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical. Â© 2024 IEEE.