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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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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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Optimizing Feature Selection in Big Data: A Hybrid Spark and Fuzzy Approach

Optimizing Feature Selection in Big Data: A Hybrid Spark and...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Hada, Aman Singh Sahoo, Gyanaballav Samir Vamsi, Chinnapareddy Krishna Hegde, Anusha Bhowmik, Biswajit National Institute of Technology Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Department of Computer Science and Engineering Karnataka Mangalore575025 India

ISBN: (纸本)9798331517892

The exponential growth of big data presents both immense opportunities and significant challenges. While vast datasets hold the key to unlocking groundbreaking insights, efficiently extracting value requires sophisticated feature selection techniques. Traditional methods often struggle with the sheer volume and complexity of big data. This paper addresses this challenge by proposing a novel hybrid feature selection algorithm by leveraging Apache PySpark's distributed computing power. Combining a robust feature selection technique with a novel weighting scheme, our method outperforms existing hypercuboid and fuzzy Rough Set methods. The hybrid approach achieves superior accuracy of 72.1% with a reduced feature set, demonstrating its effectiveness in identifying salient features for big data analysis. © 2024 IEEE.

关键词： Data accuracy

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IoT-Enabled Smart Applications and Challenges 8

IoT-Enabled Smart Applications and Challenges

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Prathyusha, M.R. Bhowmik, Biswajit National Institute of Technology Karnataka Brics Laboratory Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350396638

Significant advances in the development of information and communication technology lead to collaborating computational entities into cyber-physical systems (CPS). A CPS serves as the foundation for many latest technology, e.g., the internet of things (IoT). Both IoT and CPS technologies provide several advantages that make our daily lives more simple and smart. These technologies are employed for a wide range of smart applications like smart city, smart home, smart transport, smart healthcare, etc., that can improve business, security, and health-related experiences. This paper primarily explores various IoT-enabled smart applications and examines their necessity, characteristics, and related issues. The paper also highlights various challenges of these smart applications followed by the state-of-the-art to address them. Each smart application promotes a sustainable, healthier, and comfortable life, including their future in the smart world. © 2023 IEEE.

关键词： Cyber Physical System

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Efficient Parallel Algorithm for Detecting Longest Flow Paths in Flow Direction Grids 3

Efficient Parallel Algorithm for Detecting Longest Flow Path...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Jayarukshi, Karunamuni Agarwal, Shivali Girish, K.K. Goudar, Santosh Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

High-performance computing (HPC) has transformed the capacity to address complex computational tasks across various scientific fields by enabling the efficient processing of large datasets and intricate simulations. In hydrological modeling, a critical task is identifying the longest flow channel within a catchment, which is essential for understanding water flow patterns and managing resources. However, existing geographic information system (GIS) algorithms for flow path identification often suffer from inefficiencies and inaccuracies. To address these challenges, this paper introduces innovative parallel methods utilizing Open Multi-Processing (OpenMP), a widely-used API that supports multi-platform shared-memory parallel programming. This approach optimizes the analysis of flow direction data, resulting in faster and more accurate identification of flow channels. The results demonstrate that the proposed method outperforms current approaches, offering substantial improvements in both performance and precision. These advancements have the potential to significantly enhance hydrological modeling practices and water resource management. © 2025 IEEE.

关键词： Water treatment

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum Machine Learning 3

A Comprehensive Survey on Breast Cancer Diagnostics: From Ar...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Reddy, Manideep Raya V S R S Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Patanjali CPS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

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. © 2025 IEEE.

关键词： Biopsy

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Dynamic Checkpointing: Fault Tolerance in High-Performance Computing 3

Dynamic Checkpointing: Fault Tolerance in High-Performance C...

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

作者： Bhowmik, Biswajit Verma, Tushar Dineshbhai, Navdeep Dadhania Reddy, Manideep Raya Vsrs Girish, K.K. National Institute of Technology Karnataka Maharshi Sushrut Cas Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad Qc Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528201

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. © 2024 IEEE.

关键词： Parallel processing systems

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IoT Systems and Battery-Based Energy Sources

IoT Systems and Battery-Based Energy Sources

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Sajja, Komal Sai Bhowmik, Biswajit Brics Laboratory National Institute of Technology Karnataka Surathkal Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350322309

The Internet of Things (IoT) has rapidly expanded over the past few years and has begun taking over many aspects of our lives, including hospitals, houses, roadways, and much more. Due to its expansion, IoT may become the future technology in the following years. IoT is a collection of real-world items equipped with electronics, software, networks, and sensors to collect and exchange data. To power IoT devices, there must be a proper understanding of the energy consumption of thesensors and the kind of energy sources to be used. This paper provides state-of-the-art battery-powered IoT systems. First, the study discusses IoT's architecture and various applications. The study then extends IoT energy resources. Next, the study examines the use of batteries in IoT systems, their advantages and disadvantages, followed by unravelling the ambient energy harvesting mechanism. Finally, we present related works on battery-based IoT systems. © 2023 IEEE.

关键词： Internet of things

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Leveraging Hybrid Modeling for Enhanced Runtime Prediction in Big Data Jobs

Leveraging Hybrid Modeling for Enhanced Runtime Prediction i...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Singh, Rahul Zadokar, Vivek Narayan Kumar, Sunil Doddamani, Sachin Shivaji Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798331517892

In an era of rapid data expansion, big data has significantly transformed various industries, redefining the processes of data processing, analysis, and utilization. The widespread adoption of digital technologies has driven this surge in big data, leading to an unprecedented accumulation of information from sources such as social media, sensors, and transactions. As big data evolves, it presents significant challenges and unique opportunities, necessitating innovative solutions to leverage its potential fully. One critical challenge in big data environments is accurately predicting job runtimes, essential for optimizing resource utilization and enhancing overall system performance. Current approaches, including analytical models and machine learning algorithms, often need help to manage the complexities of unstructured data and maintain interpretability effectively. This paper proposes a novel hybrid modeling approach that integrates the strengths of both techniques to improve job runtime predictions. The hybrid architecture combines an analytical model, which captures the intricate characteristics of jobs and execution environments, with a machine learning model trained to detect patterns and relationships in historical data. As demonstrated on real-world big datasets, the hybrid model achieves greater accuracy by merging these capabilities. Utilizing the flexible capabilities of PySpark and incorporating advanced feature engineering techniques, the model dynamically adapts to various dataset sizes and complexities, ensuring robust performance across different scenarios. © 2024 IEEE.

关键词： Prediction models

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Rice Cultivation and Its Disease Classification in Precision Agriculture

Rice Cultivation and Its Disease Classification in Precision...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kumar, Amit Bhowmik, Biswajit National Institute of Technology Brics Laboratory Department of Computer Science and Engineering Karnataka Bharat Mangalore India

ISBN: (纸本)9798350322309

Bharat is a cultivation-based country that cultivates various crops, including rice, in colossal capacity. Rice is one of the daily used crops across the country and other parts of the globe. Rice is cultivated in almost every state in Bharat. However, rice plant diseases severely degrade the quality and quantity of the crop. Rice plants can be affected by different conditions, e.g., foot rot, sheath blight, etc., causing a loss in the farming output. Various steps, including academic research, are practiced to detect rice diseases in their early stage to minimize the loss and increase the productivity of the agriculture sector. The schemes involve traditional and technology-based innovative solutions like agricultural cyber-physical systems (ACPS) or precision agriculture. This paper provides a state-of-the-art on rice crop cultivation, its disease classifications, etc. The paper depicts the importance of rice in daily life and how it plays a vital role in health. Later, this paper provides statistics on rice production in Bharat, followed by the export of rice to different countries. Subsequently, we offer a detailed analysis of rice cultivation, various diseases that can occur in rice plants and other prevention mechanisms for these diseases in the early stages of their life cycle. Finally, recent developments in precision agriculture are provided. © 2023 IEEE.

关键词： Precision agriculture

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