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Multispectral Satellite Data Classification Using Rank Correlation Similarity Index and Automatic Labeling Technique

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SN computer science 2024年第8期5卷 1073页

作者： Ain, Arindom Gogoi, Minakshi Chutia, Dibyajyoti Dept. of Computer Science and Engineering GIMT Under Assam Science and Technology University Assam Guwahati India Dept. of Computer Science and Engineering Girijananda Chowdhury University Assam Guwahati India NESAC Umium Shillong India

Multispectral satellite image classification is crucial in remote sensing and image analysis, serving various applications like water area extraction, urban region classification, and vegetation monitoring. Model-based classification, particularly supervised learning approaches, heavily relies on manually annotated training samples, which are labor-intensive and subject to human bias. However, solely depending on features derived from individual pixels may fail to capture spatial contextual information effectively and manage mixed pixels and various types of noise, resulting in classification inaccuracies. To address these challenges, this research proposes a novel unsupervised learning approach and automatic labeling technique (ALT). This approach utilizes collective information from groups of pixels instead of relying solely on individual pixels for identification and extraction of meaningful unique patterns which serves as the most significant factor for satellite image classification. A new similarity index, the patch similarity index (PSI), is generated by incorporating the rank correlation similarity index (RCSI). The PSI is used to determine the similarity factor both between the patches and within the patch. Subsequently, a clustering algorithm is employed to group pixels into four distinct classes: vegetation, water, built-ups, and open land. Additionally, ALT streamlines the post-classification process by reducing the need for manual intervention in mapping clusters to the actual class label. The proposed method offers a more accurate and reliable unsupervised classification for multispectral satellite imagery, promising enhanced efficiency and reduced manual labor. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automatic labeling technique Frequent masking Multispectral Patch similarity index Rank Correlation

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Optimizing Network Traffic Prediction for Network Digital Twins: The Impact of Look-Back Period and Forecast Horizon

Optimizing Network Traffic Prediction for Network Digital Tw...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Sengendo, John Granelli, Fabrizio University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350368369

Over recent years, many advancements in networks have taken place with now the advent of 6G. With these growing advancements, challenges in managing networks also emerge. Network Digital Twins (NDTs) is one of the potential technology in solving many of these challenges because of the capability to virtually replicate the physical network elements. One of the key challenges is predicting network traffic especially with the exponentially growing number of devices in the network. In this paper, we study and show how varying the 'look-back period' and 'forecast horizon' significantly affects traffic predictions. Look-back period or window size is how much of the historical data is used by a prediction algorithm to make predictions. Forecast horizon is how far in the future we can predict. These highly impact how accurately traffic is predicted in networks and as well significantly determine how well Digital Twins (DTs) for networks accurately reflect traffic of the physical network. © 2025 IEEE.

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Enhancing Domain-Specific ASR Performance Using Finetuning and Zero-Shot Prompting: A Study in the Medical Domain 6

Enhancing Domain-Specific ASR Performance Using Finetuning a...

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6th Doctoral Symposium on Intelligence Enabled Research, DoSIER 2024

作者： Maulik, Utsav Bandyopadhyay Mitra, Pabitra Sarkar, Sudeshna Dept. of Computer Science and Engineering IIT Kharagpur West Bengal India

Domain Adaptation has emerged as an important development in Speech Recognition systems for improving the transcription accuracy of the input audio. This study explores the enhancement of Domain-specific Automatic Speech Recognition performance through finetuning and postprocessing using Large Language Models, focusing specifically on the medical domain. We investigate how domain-specific finetuning and advanced text postprocessing techniques can significantly improve transcription accuracy in medical contexts, reducing errors in specialized terminology, acronyms, and abbreviations. Our findings highlight the benefits of integrating Large Language Model based postprocessing with Automatic Speech Recognition systems to achieve better results in complex domains. © 2025 Copyright for this paper by its authors.

关键词： Speech recognition

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Using Machine Learning Algorithms in Edge Intelligence for Ultra-Reliable and Low-Latency Communications in the Industrial Metaverse 29

Using Machine Learning Algorithms in Edge Intelligence for U...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Mohammadvand, Reza Mozayani, Naser Dept. Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331523114

Given the rapid advancements in technologies such as the metaverse and digital twins, the need for effective integration of communication, computation, and storage in complex systems and edge computing environments is becoming increasingly apparent. This paper proposes an architecture to support an industrial metaverse based on digital twins that optimizes resources by leveraging mobile edge computing and ultra-reliable low-latency communication. This architecture reduces latency by employing task offloading and storage methods on nearby edge servers, meeting the precise requirements of future metaverses in terms of reliability and latency minimization. The proposed model, utilizing reinforcement learning algorithms such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Dueling Double Deep Q-Network (Dueling DDQN), is capable of achieving adaptability in dynamic conditions and making intelligent decisions. The simulation results demonstrate that the proposed method reduces by more than 10% on average compared to the best method available in the literature and improves resource utilization efficiency. © 2025 IEEE.

关键词： Reinforcement learning

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Solving Task-Select Problems Using a Quantum Annealer

Solving Task-Select Problems Using a Quantum Annealer

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Mita, Koki Yachi, Yuta Fukada, Keisuke Togawa, Nozomu Dept. of Computer Science and Communications Engineering Waseda University Tokyo Japan

ISBN: (纸本)9798331521165

To tackle environmental and social challenges, we face numerous tasks that need to be solved. However, due to limited time and resources, it is unrealistic to solve all tasks. Therefore, prioritizing tasks to ensure efficient handling and meeting criteria is essential. When the number of tasks is large, prioritizing the optimal combination of tasks on classical computers becomes highly time-consuming. We define this prioritization as a combinatorial optimization problem, termed the task-select problem, and address it using a quantum annealer. A quantum annealer is specialized hardware designed to efficiently solve combinatorial optimization problems by transforming them into Quadratic Unconstrained Binary Optimization (QUBO) problems. In this paper, we propose a QUBO formulation for the task-select problem. Experimental evaluations demonstrate that our approach enables solving the task-select problem with up to 100 items using a real quantum annealer. © 2025 IEEE.

关键词： Ising model

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A New Three-phase Multi-mode AC/DC LLC Converter with Output-controlled Active Rectifier (with V2G and G2V functions) For Fast DC Charging Application 14

A New Three-phase Multi-mode AC/DC LLC Converter with Output...

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

作者： Xia, Xiaoyi Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798331516116

This paper proposes a new three-phase multi-mode AC/DC LLC resonant converter with an output-controlled active rectifier for electric vehicle (EV) fast DC charging applications. In the proposed approach, two low-frequency bidirectional switches at the primary side in each phase of the converter allow vehicle-to-grid (V2G, i.e. DC/AC mode) and grid-to-vehicle (G2V, i.e. AC/DC mode) modes. When the proposed converter operates in AC/DC mode, each phase consists of an integrated bridgeless boost power factor corrector and an LLC resonant converter at the primary side. Output voltage regulation is managed by switches on the high frequency transformer's secondary side output rectifier in each phase, with soft-switching operation achieved in all the switches. When the proposed three-phase converter operates in DC/AC mode, each phase consists of a half-bridge DC/DC resonant converter cascaded with a half-bridge grid-side inverter. The operation of the proposed converter is explained in this paper. Results from a 20kW, 480VLLrms/350Vdc, 130kHz design, and a hardware test on a 130kHz, 250V-output proof-of-concept prototype are presented to validate the functionality of the proposed converter. © 2025 IEEE.

关键词： Zero voltage switching

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Smart Plant Watering System for Advanced Irrigation 2

Smart Plant Watering System for Advanced Irrigation

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Pujitha, Pasupuleti Sai Bhoomika, H.R. Saideep, Konduri Rahul Reddy, T. Banshal, Sumit Kumar Karmakar, Mousumi Shekhar, R. Dept. of Computer Science & Engineering Alliance University Bangalore India

ISBN: (纸本)9798350383867

The Smart Plant Watering System is an Internet of Things (IoT) solution for automated plant irrigation. It combines an ESP8266 Wi-Fi module for real-time monitoring with soil moisture and DHT11 sensors for data collection. It makes use of tools such as Blynk and Thing Speak to facilitate data visualization and remote control. An online crop recommendation systems using ML approaches and written in Python makes recommendations for the best crops to plant depending on the analysis of sensor data. With its ability to maximize irrigation, reduce water use, and improve crop management, this system represents a major breakthrough in precision agriculture for increased productivity and environmental sustainability. © 2024 IEEE.

关键词： Soil moisture

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays 3

Explainable AI-based Detection and Interpretation of Abnorma...

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

作者： Muthukumar, Sai Smrithi Gandham, Bhavika Vijayekkumaran, Trisha Jyotsna, C. Aiswariya Milan, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523893

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes. © 2025 IEEE.

关键词： CheXpert Explainable AI Grad-CAM ResNet X-ray masks X-rays

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Enhancing Movie Recommendation Efficiency Using Ensemble Learning Techniques

Enhancing Movie Recommendation Efficiency Using Ensemble Lea...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Nitasha Pandey, Aniket Kumar Yadav, Ankit Mishra, Subrat Medhanshi Chandigarh University Dept. of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350376470

This research paper explores the 'Movie Dataset' to create a better movie recommendation. The data includes many features such as genre, ratings, players, and user preferences. Our main goal is to find the best combination model combining SVD (Singular Value Decomposition), SVD++ (SVD with Implicit Feedback) and KNN Baseline (K Nearest Neighbors with Baseline) recommendation algorithms to improve prediction accuracy and recommendations. The rapid expansion of digital media platforms has created a lot of information in the entertainment industry, making movie recommendations very important for the development of consumers. Through our research, we focus on the development of personalized movie recommendations and user satisfaction in the field of digital entertainment. © 2024 IEEE.

关键词： Collaborative filtering

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Dynamic Task Scheduling for Load Balancing in Cloud Environments to Enhance Resource Allocation and Performance Efficiency

Dynamic Task Scheduling for Load Balancing in Cloud Environm...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Khan, Mohammad Imran Sharma, Kapil Amity University Dept. of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350319019

Effective resource distribution and speed efficiency are key to giving people the best service in cloud computer settings. Dynamic task ordering is a key part of reaching these goals because it uses available resources to easily distribute jobs while balancing the load. Within this paper, we suggest a new way to handle dynamic task scheduling that will help make cloud settings better at allocating resources and optimizing speed. The suggested approach uses a dynamic scheduling program that considers things like available resources, job needs, and present system load to make smart schedule choices. With dynamic task assignment, the system can change to changing job conditions in real time, making better use of resources and reducing response times. The suggested method includes load balance, which spreads jobs fairly among available resources so that computer or server doesn't get too busy. A fair job division keeps the system running smoothly, even during times of high usage. To make the best use of resources and reduce energy use, the suggested method also includes techniques like virtual machine consolidation and transfer. There is better resource usage and energy efficiency in the system because tasks are centralized on fewer actual computers and resources are allocated based on job demand. Based on results from experiments, the suggested method is better at allocating resources and improving performance compared to standard scheduling methods. In terms of shorter reaction times, higher output, and better use of resources, the results show big gains. © 2024 IEEE.

关键词： Resource allocation

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