Journal Articles
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Item Maximal Connectivity Test with Channel-Open Faults in On-Chip Communication Networks(Springer, 2020) Bhowmik, B.The networks-on-chip (NoCs) as the prevalent interconnection infrastructure have been continuously replacing the contemporary chip microprocessors (CMPs) while high performance computing is the dominant consideration. Aggressive technology scaling progressively reduces the feature size of the chips resulting in increasing susceptibility to failures and breakdowns due to open faults on communication channels. The reliability and performance issues are then becoming more critical requirement in both current and future NoC-based CMPs. This paper first presents an on-line, distributed built-in-self-test (BIST) oriented test mechanism that particularly detects open faults on communication channels and identifies faulty wires from the channels in NoCs. Next, a suitable test scheduling scheme is presented in order to reduce the overall test time and related performance overhead due the fault. Such scheduling scheme makes the present test solution scalable with large scale NoC architectures in general. Implementation of the test mechanism takes little hardware area and few clocks to detect the fault in channels. The on-line evaluation of the proposed test solution demonstrates the effect of the channel-open faults on the NoC performance characteristics at large real like synthetic traffic. In comparison to wide range of prior works on 16-bit networks, the present scheme provides many advantages, e.g., it improves hardware area overhead by 35.36–67.73% and saves the test time by 96.43%. packet latency and energy consumption by 5.83–42.79% and 6.24–46.38%, respectively on the networks, the proposed scheme becomes competitive with the existing works. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Item 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.Item 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.Item 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.Item 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.Item 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.Item EffiCOVID-net: A highly efficient convolutional neural network for COVID-19 diagnosis using chest X-ray imaging(Academic Press Inc., 2025) Kumar, S.; Bhowmik, B.The global COVID-19 pandemic has drastically affected daily life, emphasizing the urgent need for early and accurate detection to provide adequate medical treatment, especially with limited antiviral options. Chest X-ray imaging has proven crucial for distinguishing COVID-19 from other respiratory conditions, providing an essential diagnostic tool. Deep learning (DL)-based models have proven highly effective in image diagnostics in recent years. Many of these models are computationally intensive and prone to overfitting, especially when trained on limited datasets. Additionally, conventional models often fail to capture multi-scale features, reducing diagnostic accuracy. This paper proposed a highly efficient convolutional neural network (CNN) called EffiCOVID-Net, incorporating diverse feature learning units. The proposed model consists of a bunch of EffiCOVID blocks that incorporate several layers of convolution containing (3×3) filters and recurrent connections to extract complex features while preserving spatial integrity. The performance of EffiCOVID-Net is rigorously evaluated using standard performance metrics on two publicly available COVID-19 chest X-ray datasets. Experimental results demonstrate that EffiCOVID-Net outperforms existing models, achieving 98.68% accuracy on the COVID-19 radiography dataset (D1), 98.55% on the curated chest X-ray dataset (D2), and 98.87% on the mixed dataset (DMix) in multi-class classification (COVID-19 vs. Normal vs. Pneumonia). For binary classification (COVID-19 vs. Normal), the model attains 99.06%, 99.78%, and 99.07% accuracy, respectively. Integrating Grad-CAM-based visualizations further enhances interpretability by highlighting critical regions influencing model predictions. EffiCOVID-Net's lightweight architecture ensures low computational overhead, making it suitable for deployment in resource-constrained clinical settings. A comparative analysis with existing methods highlights its superior accuracy, efficiency, and robustness performance. However, while the model enhances diagnostic workflows, it is best utilized as an assistive tool rather than a standalone diagnostic method. © 2025 Elsevier Inc.Item Louvain community-based label assignment for reject inference in peer-to-peer lending(Springer Science and Business Media Deutschland GmbH, 2025) Hegde, A.; Bhowmik, B.; Bennehalli, S.; Vakkund, S.The digital transformation in the Financial Technology (FinTech) sector has significantly altered traditional banking and lending practices, giving rise to innovative models like peer-to-peer (P2P) lending. P2P lending platforms directly connect lenders and borrowers online, bypassing conventional financial intermediaries and democratizing access to finance. However, this innovation introduces new complexities in the risk assessment process, necessitating advanced analytical methods. This research presents Accept-Reject-Net framework, a three-step modeling approach designed to capture and evaluate the complex relationships of loans within the accept and reject dataset, a crucial aspect of P2P lending. Initially, the datasets are separated using two outlier detection methods that efficiently manage extensive datasets by distinguishing inliers (data points adhering to a specific pattern) from outliers (data points deviating from the anticipated pattern). We then generate four distinct merged datasets by applying two different ratios of accept and reject data. In the second stage, borrowers are systematically represented as nodes, with their Euclidean distances as edges, allowing us to extract graph features that effectively capture the structural attributes and similarities of the loans. These graph features are used to classify entries in the Reject dataset as either default or non-default. Two distinct approaches are introduced Louvain mode and Louvain threshold to facilitate label assignment within detected communities. The threshold is validated across multiple levels to assess its effectiveness in refining label assignment. In the third phase, these features are inputs for training five machine learning models, further enhanced with additional labeled data. To ensure the reliability and robustness of our findings, confidence intervals and permutation tests are used to assess the performance differences between different partitions. The 7:1 ratio of accept:reject with the threshold method of Louvain community detection for assigning labels to the rejected dataset improves the metrics, making the model much more effective for reject inference. This comprehensive approach addresses the biases inherent in traditional credit scoring models and enhances the predictive accuracy and fairness of loan evaluations. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.Item Enhancing Money Laundering Detection in Bank Transactions using GAGAN: A Graph-Adapted Generative Adversarial Network Approach(Springer Science and Business Media Deutschland GmbH, 2025) Kadamathikuttiyil Karthikeyan, G.; Bhowmik, B.The past decade has witnessed profound transformations in the financial sector, driven by the integration of cutting-edge technologies into its core operations. Consequently, banks are increasingly utilizing technologies such as artificial intelligence (AI), blockchain, and big data analytics to offer personalized services, streamline transactions, and improve risk management, enabling the development of new financial products and services that cater to the diverse and evolving needs of customers. Despite these benefits, the banking landscape has also brought about complex challenges, particularly in the fight against money laundering. Money laundering remains a significant threat to the integrity of financial systems, as criminals exploit digital advancements to conceal illicit activities. The growing complexity of digital transactions and the increasing volume of financial data have made detecting and preventing money laundering more challenging than ever. Existing AI-based solutions, while effective to some extent, often grapple with class imbalance issues. This paper addresses the challenge by introducing a novel model named GAGAN (Graph Attention Generative Adversarial Network) and enhances the detection of money laundering activities in bank transactions. The proposed model further addresses the issue of class imbalance, by incorporating Conditional Generative Adversarial Network (cGAN) and Graph Attention Networks (GAT). The GAT classifier is then employed to accurately classify transactions, leveraging attention mechanisms to focus on the most relevant parts of the graph. Empirical results reveal that the proposed model achieves impressive performance metrics, with an accuracy of 98.62%, precision of 98.10%, recall of 98.92%, F1 score of 98.49%, AUC-ROC of 0.99, and a MCC score of 0.991. These results underscore the efficacy of the model in accurately identifying money laundering transactions, showcasing its potential as a robust tool for financial crime detection. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.Item Intelligent money laundering detection approaches in banking and E-wallets: a comprehensive survey(Springer, 2025) Kadamathikuttiyil Karthikeyan, G.; Bhowmik, B.The rapid evolution of financial technologies (FinTech) has propelled the world into a more dynamic and sophisticated digital financial landscape. This transformation has significantly expanded financial inclusion, offering new opportunities to individuals who were previously excluded from or had limited access to traditional banking services. Financial inclusion is crucial as it provides access to a broad spectrum of financial services, including bank accounts, credit and debit facilities, and e-wallets. While the rise in digital transactions has been driven by cost efficiency, convenience, and enhanced security measures, it has also led to an increase in economic crimes, particularly money laundering, resulting in substantial global economic losses. Consequently, the need for effective strategies to combat money laundering has never been more pressing. This study thoroughly investigates the state-of-the-art techniques in money laundering detection harnessing the capabilities of artificial intelligence (AI) technologies. First, we provide an overview of economic crimes and classify their various types, setting the stage for a focused discussion on money laundering. The paper then explores the money laundering landscape, including its impact and recent trends, followed by a discussion on different prevention and detection strategies. The paper also delves into AI-driven detection strategies, particularly those targeting money laundering, including the detection of laundering activities through e-wallets. Additionally, we address the research challenges associated with money laundering detection, such as the issue of class imbalance in financial datasets, and propose solutions to overcome it. Finally, the paper provides insights into future directions for research, aiming to equip the research community with the tools necessary to formulate proactive strategies for preventing and mitigating money laundering and related economic crimes. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.