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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Nayaka, Premkumar Hegde, Anusha Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Surathkal India National Institute of Technology Karnataka Brics Laboratory Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331528201

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. © 2024 IEEE.

关键词： Decentralized finance

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Enhancing Movie Recommendation Systems with MapReduce Genetic Algorithms: Addressing Scalability and Accuracy Challenges

Enhancing Movie Recommendation Systems with MapReduce Geneti...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Patidar, Priyansh Posa, Sathwik Venkata Girish, K. Rao, Shaifali Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Lab Department of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528126

In the world of big data, the efficacy of movie recommendation systems is crucial for personalizing user experiences in digital entertainment. Traditional methods, including collaborative and content-based filtering, often encounter limitations such as data sparsity, cold start problems, and scalability issues. This paper introduces a novel approach that integrates MapReduce technology with Genetic Algorithms (GAs) to address these chal-lenges. Utilizing the Hadoop framework, our MapReduce Genetic Algorithm (MRGA) efficiently processes extensive datasets by distributing tasks across a cluster of machines. The genetic algorithm component optimizes recommendation accuracy through advanced techniques like selection, crossover, and mutation. Our experimental results, based on the MovieLens 100K dataset, demonstrate that the MRGA approach outperforms traditional collaborative filtering methods in terms of recommendation accuracy and scalability. By leveraging MapReduce's distributed computing power and the GA's optimization capabilities, this research offers a robust solution to improve movie recommendations and handle large-scale data efficiently. © 2024 IEEE.

关键词： MapReduce

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Performance Analysis and Predictive Modeling of MPI Collective Algorithms in Multi-Core Clusters: A Comparative Study 17

Performance Analysis and Predictive Modeling of MPI Collecti...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Reddy, Manideep Raya V.S.R.S. Raju, Sanjay Ramapogu Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Lab Department of Computer Science and Engineering Surathkal Mangalore India

ISBN: (纸本)9798331531195

Efficient communication is the foundation of parallel computing systems, enabling seamless coordination across multiple processors for optimal performance. At the core of this communication lies the Message Passing Interface, a crucial framework designed to facilitate data exchange between processors through collective operations. However, these MPI operations often face challenges, including fluctuating process counts, varying message sizes, and increased communication overhead. These issues can significantly impact execution times and scalability, leading to potential bottlenecks in large-scale systems. To address these concerns, this paper provides an in-depth evaluation of key MPI collective algorithms - Flat Tree, Chain, and Binary Tree - by examining their performance under varying configurations. By analyzing execution times and communication overhead, the study reveals the trade-offs inherent in each algorithm, offering insights into strategies for reducing communication costs. Through this analysis, we aim to provide valuable guidance to improve the efficiency and scalability of parallel computing, particularly in high-performance systems where communication efficiency is critical. © 2025 IEEE.

关键词： Scalability

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ADConv-Net: Advanced Deep Convolution Neural Network for COVID-19 Diagnostics Using Chest X-Ray and CT Images

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SN computer science 2025年第5期6卷 1-22页

作者： Kumar, Sunil Bhowmik, Biswajit Maharshi Patanjali CPS Lab BRICS Laboratory Department of Computer Science Engineering National Institute of Technology Karnataka Surathkal Karnataka Mangalore 575025 India

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.

关键词： Artificial Intelligence Classification COVID-19 CT Images Deep Learning X-Ray Images

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Outlier Detection in Streaming Data Using Deep Learning Models

Outlier Detection in Streaming Data Using Deep Learning Mode...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Dudipala, Samagnya Gangavarapu, Shalini Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Surathkal Ishwarchandra Vidyasagar AIT Lab BRICS Lab Department of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528126

In the realm of the Internet of Things (IoT), devices continuously generate a vast and relentless stream of data, providing a real-time representation of digital landscape. The continuous and high-velocity nature of this streaming data poses significant challenges for real-time analysis. Accurate outlier detection within this data is essential, as such anomalies may indicate critical issues, attacks, or errors. Nevertheless, the dynamic and rapidly evolving characteristics of streaming data render traditional outlier detection methods inadequate. This paper investigates the application of Artificial Neural Networks (ANNs), specifically a Multi-Layer Perceptron (MLP), for outlier detection in streaming IoT data. The selection of the MLP from a range of Deep Neural Networks (DNNs) is based on its optimal balance between computational efficiency and model complexity. The model's efficacy is confirmed through rigorous experimentation, demonstrating strong performance across diverse scenarios and data classes. The MLP achieved an accuracy of 99.4%, underscoring its ability to detect even minor deviations from expected patterns. This high level of accuracy establishes the MLP as a robust tool for outlier detection in dynamic IoT environments. © 2024 IEEE.

关键词： Data streams

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LR-Based Performance Evaluation of MoCs 4

LR-Based Performance Evaluation of MoCs

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4th IEEE International Conference on VLSI Systems, Architecture, Technology and Applications, VLSI SATA 2024

作者： Hazarika, Pallabi Bhowmik, Biswajit National Institute Of Technology Karnataka Swami Vivekananda NoC Lab Brics Lab Department Of Computer Science And Engineering Mangalore Surathkal575025 India

ISBN: (纸本)9798350362268

In the recent decade, on-chip communication net-works have developed into a potent platform for tackling chal-lenging and significant computation issues. However, many applications cannot achieve high-performance communication needs due to the seamless integration of computing cores in systems-on-chip (SoCs). Subsequently, a network-on-chip (NoC) has emerged as a prominent on-chip communication infrastructure in SoCs. Performance analysis of NoC's is essential for its architectural design and is traditionally evaluated employing a simulator. How-ever, simulation-based performance evaluation is relatively slow and may take a long time with varying architectural NoC sizes. This paper presents an AI-based approach for investigating mesh-based NoC (MoC) performance over the traditional simulation-based performance evaluation. The proposed framework targets to reach two objectives- quickly and accurately evaluation of various NoC performance metrics. Simulations are performed at varying architectural setups on a set of mesh NoCs to generate the training dataset for the proposed framework. Consequently, the framework satisfactorily predicts different performance metrics. For example, network and packet latency;hop count;switch, channel, and total power consumption;and total area are in the range of 58.14-88.49 and 58.69-106.97 cycles;6.231-6.257;1.44-13.02, 13.73-129.06, and 25.26- 177.44 μ W;and 1.35874 μ m2, respectively while the proposed framework is applied on the 9 x 9 mesh NoC. The metrics are with 94% accuracy and predicted at very significantly less time. The LR model saves 99.45 % evaluation time resulting in the speedup of 260 x than a simulation - based method. © 2024 IEEE.

关键词： Network-on-chip

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Money Laundering Detection in Banking Transactions using RNNs and Hybrid Ensemble 15

Money Laundering Detection in Banking Transactions using RNN...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Lab Department of Computer Science and Engineering Mangalore Surathkal India

ISBN: (纸本)9798350370249

The financial sector has witnessed significant transformations due to the emergence of financial technology (FinTech), transitioning from traditional paperbased processes to a dynamic digital ecosystem. Despite the industry's advancements driven by FinTech innovations, concerns persist, particularly regarding financial fraud, notably money laundering. Perpetrators exploit modern technologies to launder illicitly obtained funds, posing a global threat to economies. Effective detection mechanisms for money laundering are crucial. This paper introduces a novel approach utilizing a recurrent neural network (RNN) for detecting money laundering in banking transactions. The proposed framework exercises standalone RNN models such as LSTM, GRU, BiLSTM, and stacked RNN models for the detection. Additionally, the effectiveness of hybrid ensemble models combining RNNs with XGBoosts is investigated. The evaluation achieves standard performance metrics, with the stacked RNN model achieving 92% accuracy. Surpassing it, the ensemble model achieves an impressive 95%. These results underscore the superiority of hybrid ensemble models over standalone RNNs, particularly in accurately detecting money laundering activities. © 2024 IEEE.

关键词： Banking Deep Learning FinTech Fraud Detection Money Laundering Recurrent Neural Network

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Intelligent Fraud Detection Techniques in Credit Card and Internet Banking 3

Intelligent Fraud Detection Techniques in Credit Card and In...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Goudar, Santosh Iranagouda Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Lab Department of Computer Science and Engineering Surathkal Mangalore India

ISBN: (纸本)9798331528201

In an era where financial services are easily accessible, financial fraud has emerged as a widespread threat, aimed at unlawfully seizing assets from individuals, businesses, and institutions. Crimes like credit card and internet banking fraud present serious risks to the financial ecosystem. Criminals exploit card information using techniques such as identity theft, skimming, and phishing, causing substantial economic losses each year. Similarly, the rise of internet banking fraud, where unauthorized access to accounts often results from weaknesses in authentication systems, has further disrupted the financial sector. Detecting such fraudulent activities is especially difficult due to the ever-changing tactics of perpetrators, the large volume of transaction data, and technical challenges like data imbalance and concept drift. This paper examines the landscape of financial fraud, with a specific emphasis on credit card and internet banking fraud. It begins by providing an overview of the issue and its impact on the modern economic environment, followed by an exploration of various fraud detection methods. Additionally, the paper addresses key challenges in fraud detection and highlights the need for innovative approaches to effectively combat the growing threat of financial crime. © 2024 IEEE.

关键词： Phishing

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Quantum-Enhanced Deep Q Learning with Parametrized Quantum Circuit 4

Quantum-Enhanced Deep Q Learning with Parametrized Quantum C...

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4th IEEE International Conference on VLSI Systems, Architecture, Technology and Applications, VLSI SATA 2024

作者： Manjunath, T.D. Bhowmik, Biswajit National Institute Of Technology Karnataka Maharshi Kanad Qc Lab Brics Lab Department Of Computer Science And Engineering Mangalore Surathkal575025 India

ISBN: (数字)9798350362268

ISBN: (纸本)9798350362268

Quantum Computing (QC) is fastly growing to replace current computing systems for many high-performance applications. Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning, i.e., Quantum Machine Learning (QML), will enhance the performance in multiple ways. Subsequently, Deep Q learning is a prominent approach to reinforcement learning used in solving complex applications for a desired performance. But this performance can be improved using a quantum computer. This paper proposes a quantum-enhanced Deep Q Learning algorithm to see its advantages over its classical counterparts. We implement our approach using parametrized quantum circuit (PQC). The evaluation of the proposed method achieves the metrics rewards and episodes up to 500 points and 1200, respectively. The performance shows that it collects roughly four times more rewards in a given time and takes significantly fewer episodes to converge compared to the classical approach. © 2024 IEEE.

关键词： Reinforcement learning

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Big Data Insights: Pioneering Changes in FinTech 9

Big Data Insights: Pioneering Changes in FinTech

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Anusha Hegde, H. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Lab Department of Computer Science And Engineering Mangalore Surathkal India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394474

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. © 2024 IEEE.

关键词： Data Analytics

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