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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Harini, M. Janalyn maroula, L. Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

The Virtual Trial Room is an innovative augmented reality (AR) system that enables users to virtually try on apparel, enhancing the online shopping experience by simulating a physical fitting room. This technology leverages advanced computer vision, 3D modeling and machine learning to deliver an accurate and immersive experience. Data augmentation techniques are utilized to expand the training dataset, ensuring more precise clothing placement on different body types. The results demonstrate that our integrated approach, which combines AR and machine learning, significantly outperforms traditional virtual try-on systems, achieving enhanced user satisfaction, fit accuracy and personalized recommendations. The convolutional neural network (CNN) used in the system accurately detects body and facial landmarks, allowing for real-time garment fitting and recommendations. © 2025 IEEE.

关键词： Convolutional neural networks

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Credit Risk Assessment using Ensemble Models and Explainable AI 3

Credit Risk Assessment using Ensemble Models and Explainable...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srikanteswara, Ramya Naghera, Krinal Kukkaje, Shashank Balnad Kumar, Adarsh Shrey, P. Dept of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9798331527549

New challenges are still there in the credit risk management in this context of the financial system, primarily because of the lack of good models for prognosis as well as low interpretability. To address this issue, this research brings together CatBoost, XGBoost, and LightGBM to improve the prospect of identifying high-risk borrowers. A minimal level of Explainable AI (XAI) is utilized to make confirmations that the model follows them and to build confidence in the outcome. Moreover, based on a difference to basic models, the obtained ROC-AUC score equals 0.9693, proving the feasibility of the proposed ensemble approach in questions of impact developing default risks. The end product is therefore a robust method with ensemble modeling and compliant with regulatory requirements in operational financial environments. © 2025 IEEE.

关键词： Credit Risk Credit Risk and Machine Learning Algorithms Ensemble Learning and Risk Interpretation in Financial Institutions Explainable AI and Credit Risk Management Risk Models

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ViTYoga: Vision Transformer for Real-Time Yoga Pose Estimation and Analysis

ViTYoga: Vision Transformer for Real-Time Yoga Pose Estimati...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Wadawadagi, Ramesh Ajay, V.G. Desai, Vinod Varon, E. Patil, Shantakumar B. Sai Vidya Institute of Technology Dept. of Computer Science and Engg. Bangalore560064 India

ISBN: (纸本)9798331538538

Recognizing and analyzing yoga poses is a critical aspect of yoga practices and is vital when it is supported by technology. The importance of yoga arises from its ability to promote mental and physical well-being, stress reduction, im-proved focus, emotional stability, resilience, spiritual growth, and a life-centered perspective. As the popularity of yoga continues to grow, technological advancements are becoming increasingly vital to assist yoga instructors and practitioners in monitoring and enhancing their practice. This work elucidates the significance of automated yoga pose detection and its potential benefits for enhancing self-correction, providing real-time feedback, and maximizing yoga benefits. The proposed work utilizes Vision Transformers (ViT) for real-time detection and monitoring of yoga poses, referred to as ViTYoga. The proposed ViTYoga employs ViT model, which is pre-trained on the COCO-pose dataset and further fine-tuned on Yoga Pose Image datasets that comprise specific yoga postures. The performance assessment of proposed ViTYoga is compared and contrasted with other baseline techniques. The experimental results reveal that ViTYoga outperforms several SOTA techniques. Moreover, the proposed design offers a lightweight model that can be deployed as a mobile application and used by yoga practitioners to perform yoga at their convenience. © 2025 IEEE.

关键词： COCO-Pose dataset Deep Learning Deep Vision Vision Transformers Yoga pose estimation

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model Machine learning algorithms Remote sensing images

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IOT Based Multicrop Soil Moisture Control and Monitoring System 1

IOT Based Multicrop Soil Moisture Control and Monitoring Sys...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Barhate, Madhusudan Desai, Devam Mishra, Gaurav Phadke, Gargi Das, Siuli Bhuran, Supriya Yogesh Ramrao Adik Institute of Technology D.Y. Patil Deemed to be University Dept. of Instrumentation Engg Dept. of Computer Engg Dept. of Computer Science and Engg Navi Mumbai India

ISBN: (纸本)9798350355611

Agriculture is crucial for the economy, with China and India leading in production. Conventional irrigation methods require precise timing and quantity, varying for different crops. This paper proposes an IoT-enabled intelligent irrigation system to enhance crop productivity. The system uses a moisture sensor to activate the pump motor, and IoT technology enables remote monitoring through a web interface or mobile app. The system stores moisture data customized to specific crop requirements, reducing human intervention while ensuring optimal water supply. © 2025 IEEE.

关键词： Moisture control

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Enhancing Livestock Supply Chains with Blockchain Traceability from Source to Market: A Survey 5

Enhancing Livestock Supply Chains with Blockchain Traceabili...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dahariya, Tukendra Kumar Sharma, Suraj Ray, Niranjan Kumar Guru Ghasidas Vishwavidyalaya Dept. Computer Science & Engineering Chhattisgarh Bilaspur India Kalinga Institute of Industrial Technology School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

This document provides an in-dept. analysis of how blockchain technology can greatly improve transparency, traceability, and accountability in fish and livestock supply chains, presenting the potential to transform this industry. Utilizing blockchain's decentralized framework, which guarantees a secure and unchangeable record of transactions, problems associated with inefficiencies, fraud, and data manipulation in conventional supply chains can be efficiently resolved. This survey analyzes the present condition of blockchain applications in fish and livestock management, emphasizing their advantages and drawbacks. Furthermore, it highlights significant technical and regulatory challenges that impede wider acceptance, while recognizing potential future research avenues that could enhance processes from the production stage to the retail stage. The results intend to assist stakeholders in developing supply chains that are more sustainable, efficient, and transparent, fostering trust and complying with regulatory requirements. © 2025 IEEE.

关键词： Records management

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System 'Stationary 3-Axis Fire-Emergency Response Base' 8

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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8th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Kundu, Shyantan Dey, Swarajit Abdullha, Md. Mustafa Sen, Gobinda Gangopadhyay, Malay Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India Institute of Engineering & Management Dept. of Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named 'Stationary 3-Axis Fire-Emergency Response Base' (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management. © 2025 IEEE.

关键词： Fire hazards

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Comparative Analysis of Machine Learning Methods in Climate Data Extraction and Forecasting

Comparative Analysis of Machine Learning Methods in Climate ...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Khajgiwale, Parth Ajit Naveen, Atul Jethani, Nishtha Shitole, Ajitkumar International Institute of Information Technology Dept. of Computer Engineering Hinjewadi Pune India

ISBN: (纸本)9798331507541

Climate forecasting plays a critical role in understanding and mitigating the impacts of climate change. Advances in machine learning (ML) have significantly enhanced the accuracy of climate projections, particularly when dealing with large and complex datasets derived from regional climate models (RCMs) and global climate models (GCMs). This study investigates the application of various ML techniques for predicting precipitation based on temperature data extracted from climate datasets. We evaluate a range of regression models, including linear models and advanced ensemble methods such as Gradient Boosting and XGBoost, along with deep learning approaches like Convolutional Neural Networks (CNNs). Through a rigorous evaluation of these models, we focus on their performance as measured by Mean Squared Error (MSE) to identify the most effective techniques for capturing the intricate relationships within climate data. By analyzing existing studies and performance metrics, we aim to provide insights into the capabilities and limitations of these ML models in improving climate predictions. Furthermore, this research highlights the need for incorporating additional climate variables and optimizing model configurations to enhance predictive accuracy in climate forecasting. © 2025 IEEE.

关键词： Convolutional neural networks

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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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Optimizing Data Movement in Heterogeneous Computing: A LASSA-based Approach for Efficient Nucleation List Precomputation 3

Optimizing Data Movement in Heterogeneous Computing: A LASSA...

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

作者： Bhowmik, Biswajit Girish, K.K. Pandey, Himanshu Prabhanjans, Piyus National Institute of Technology Karnataka 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 Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

In the rapidly evolving landscape of heterogeneous computing, the efficiency of data movement between CPUs and GPUs can make or break system performance. Despite advancements in parallel processing, existing methods for managing data transfers - particularly in GPU offloading scenarios - suffer from significant inefficiencies. These inefficiencies are particularly evident in nucleation list precomputation for non-equilibrium solidification models, where redundant data movements and complex dynamic work-sharing in OpenMP lead to significant performance overhead. To tackle this issue, this paper proposes a novel solution that integrates the Location-Aware Heap Static Single Assignment (LASSA) algorithm into the compilation process. This approach identifies and eliminates redundant memory copy operations, optimizing data transfers and reducing overhead. The findings reveal a dramatic performance boost, with up to a 9.6-fold increase in efficiency. By addressing the specific challenges of nucleation list precomputation, this work provides valuable insights into optimizing data movement in heterogeneous computing environments, paving the way for enhanced performance in parallel programming models. © 2025 IEEE.

关键词： Cellular Automata Models Heterogeneous Computing Nucleation List Precomputation OpenMP Performance Issues System Efficiency

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