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

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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Enhancing Train Transportation in Sri Lanka: A Smart IOT based Multi-Subsystem Approach using MQTT

arXiv

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arXiv 2025年

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka

This research proposes a system as a solution for the challenges faced by Sri Lanka’s historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study’s data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka’s railway infrastructure. © 2025, CC BY.

关键词： Global positioning system

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Weakly Supervised Road Mark Detection for Advanced Driver Assistance Systems in Consumer Automotive technology

Weakly Supervised Road Mark Detection for Advanced Driver As...

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Consumer technology (ICCT-Pacific), International Conference on

作者： Xiu-Zhi Chen Xiao-Wei Huang Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Road mark detection is a critical component of Advanced Driver Assistance Systems (ADAS), ensuring accurate lane-keeping, navigation, and driver safety. However, the high cost of acquiring fully annotated datasets for supervised learning poses a significant challenge for real-world applications. This paper proposes a novel weakly supervised learning method for road mark detection tailored to consumer automotive technology. Leveraging weak annotations, the method efficiently learns to detect and classify road marks with minimal human intervention. The approach was evaluated on the publicly available CeyMo dataset, achieving a classification accuracy of 99.3% and a localization accuracy of 71.5%, demonstrating its effectiveness and practicality. The proposed method reduces the dependency on fully annotated data while maintaining competitive performance, making it a scalable solution for consumer-level ADAS applications. This work contributes to advancing cost-effective, reliable, and scalable ADAS technologies, paving the way for broader adoption in intelligent vehicles.

关键词： Location awareness Accuracy Weak supervision Intelligent vehicles Roads Supervised learning Safety Reliability Advanced driver assistance systems Automotive engineering

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CAM-Based High Efficient Weakly Supervised Multi-Object Multi-Class Detection Method

CAM-Based High Efficient Weakly Supervised Multi-Object Mult...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Xiu-Zhi Chen Hsiao-Wei Huang Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Two approaches are available for WSOD: MIL and CAM. MIL-based methods are capable of addressing multiple targets and multiple class problems. However, these methods require the input of region proposals. In contrast, CAM-based methods are straightforward and more efficient, yet they are constrained by limited localization accuracy and are likely to be applicable only to single target classes. This study proposes a CAM-based WSOD method that is capable of addressing multi-object, multi-class tasks with high efficiency, which could prove beneficial for the development of specific consumer electronic applications, such as smart cameras and smart home devices. The proposed method was evaluated on two public datasets, demonstrating that it is significantly more efficient than the baseline method.

关键词： Location awareness Electric potential Accuracy Smart cameras Object detection Smart homes Computational efficiency Proposals Consumer electronics Automotive engineering

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Single-Phase Open-Circuit Fault-Tolerant Control of Three-Phase PMSM Drives 14

Single-Phase Open-Circuit Fault-Tolerant Control of Three-Ph...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Minato, Yuichiro Nakata, Yuki Itoh, Jun-Ichi Technology Development Center Research and Development Headquarters Murata Machinery Ltd Kyoto Japan Nagaoka University of Technology Dept. of Electrical Electronics and Information Engineering Niigata Japan

ISBN: (纸本)9798331516116

This paper proposes a Fault-tolerant control method for a standard three-phase permanent magnet synchronous motor (PMSM) under a single-phase open-circuit fault condition. The method for driving a three-phase PMSM from a standstill using a three-leg, two-level inverter configuration is proposed. Experimental results show that 65% of the motor's rated speed and 50% of its rated torque maintains. © 2025 IEEE.

关键词： Traction motors

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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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Video Deepfake Detection: Current Trends, Methodologies, and Future Directions 2

Video Deepfake Detection: Current Trends, Methodologies, and...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Ansar, H. Kumar, Mathusoothana S Noorul Islam Centre for Higher Education Dept. of Computer Science & Engineering Tamil Nadu India Noorul Islam Centre for Higher Education Department of Information Technology Tamil Nadu India

ISBN: (纸本)9798331505745

Deepfake technology has emerged as a significant challenge in the domain of multimedia processing, particularly due to its capability to create highly realistic manipulated videos that can mislead viewers and compromise security, privacy, and public trust. While deepfake technologies have legitimate applications in fields such as entertainment, education, and healthcare, their misuse for malicious purposes such as misinformation and identity fraud necessitate the development of robust detection methods. This survey provides a comprehensive analysis of the state-of-the-art approaches in deepfake face detection within video data, categorizing them into three major sections: Hybrid Methods, Ensemble Methods, and Recurrent Methods. Hybrid methods integrate multiple machine learning models to enhance the accuracy of spatial and temporal feature extraction, offering a balanced approach to detecting subtle manipulations in deepfake videos. Ensemble methods leverage the combined strengths of diverse classifiers to improve robustness and adaptability in varying video contexts. Recurrent methods utilize recurrent neural networks to capture temporal dependencies across video frames, addressing challenges in detecting complex and sequential manipulations. The review highlights that while these approaches demonstrate significant progress, challenges remain in generalizing detection models to novel and highly advanced deepfake techniques. This work concludes by identifying the limitations in existing research and suggesting future directions for improving the robustness and scalability of deepfake detection systems. © 2025 IEEE.

关键词： Recurrent neural networks

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Homomorphic Encryption in Quantum Computing

Homomorphic Encryption in Quantum Computing

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Devices for Integrated Circuit (DevIC)

作者： Surojit Mondal Bikash Debnath Jadav Chandra Das Debashis De Dept. of Computer Science & Engineering Jadavpur University Kolkata India Dept. of Computer Science & Engineering Asansol Engineering College West Bengal India Dept. of Information Technology MAKAUT Kolkata India Dept. of Computer Science and Engineering MAKAUT Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

Homomorphic encryption (HE) is a groundbreaking cryptographic method that allows calculations on encrypted data without the need for decryption, thus safeguarding privacy and security. This article investigates how to implement homomorphic encryption with QISKIT, a free-to-use quantum computing framework. The research investigates the theoretical underpinnings of HE, its use in quantum computing, and its importance for privacy-preserving calculations. Moreover, the study covers adder circuits based on quantum logic gates, their realization with QISKIT, and the prospects for improving computational security in cloud environments. The results stress the practical viability of HE in quantum environments, underscoring its possible uses in secure data processing and privacy-enhancing technologies.

关键词： Data privacy Cloud computing Quantum computing technology Quantum mechanics Logic gates Cryptography Homomorphic encryption Adders Stress

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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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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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