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10th International Conference on Applied System Innovation, ICASI 2024

作者： Swain, Bishal Ranjan Cheoi, Kyung Joo Ko, Jaepil Kumoh National Institute of Technology Dept. of Computer & Ai Convergence Engineering Gumi Korea Republic of Chungbuk National University Dept. of Computer Science Cheonju Korea Republic of

ISBN: (纸本)9798350394924

The characterization of a material is defined by its transformation kinetics which is measured using the fraction of volume transformed. In common situations, a polycrystalline parent phase is transformed into various sub-phase constituent regions based on the desired material property. Characterization of such multiphase regions include accurate identification of phase and calculation of the phase fraction. To this end, we propose using semantic segmentation to identify and calculate the phase fraction in steel microstructures. We assign each pixel in an image to accomplish both phase identification and segmentation which we perform using enhanced ELU-Net coupled with improved combined loss function. Though scalable experiments we demonstrate more accurate phase fraction calculation via superior semantic segmentation by achieving an overall 8.6% performance increase over baseline with mean phase fraction error of 5.69%. © 2024 IEEE.

关键词： Semantic Segmentation

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Land Cover Clustering for Change Detection using Deep Belief Network

Land Cover Clustering for Change Detection using Deep Belief...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Sivasankari, A. Jayalakshmi, S. Vistas Dept. of Computer Science Chennai600117 India Vistas Dept. of Computer Applications Chennai600117 India

ISBN: (纸本)9781665484251

Change detection in recent era plays a major role in the remote sensing areas that includes land cover and water bodies. The application of change detection in remote sensing is considered complicated due to continuous interaction of humans with nature. Further, the change detection in land cover and water bodies plays a major role in global change. Manual change detection process via experts consumes more time to survey the land, whereas detection of changes in land cover and water bodies via an artificial intelligence (AI) model enables faster change detection. However, multi-sensor and multi-temporal satellite data calibration poses a serious problem in remote-sensing application. It makes the entire clustering process a difficult one and hence the accuracy of change detection degrades. In this paper, we apply the task of unsupervised learning to a deep learning model namely deep belief network. Here, the land cover images are clustered using the unlabeled data, where the algorithms are trained in unsupervised manner. The intelligent decisions are made available via deep belief network that clusters the uncategorized the land covers and water bodies images from the satellite image database. The dataset includes Landsat sensors' datasets in dry semi-arid river basin in Western India. The performance of the unsupervised deep belief network is evaluated in terms of various performance metrics that includes accuracy, precision, recall, f-measure, geometric mean, peak signal noise ratio and mean average percentage error. © 2022 IEEE.

关键词： Change detection

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An Abnormalities Detection Model of a Machine Using Spectrogram

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Transactions of the Korean Institute of Electrical Engineers 2022年第9期71卷 1274-1279页

作者： Kim, Youngjun Lee, Seok-Pil Dept. of Electronic Engineering Sangmyung University Korea Republic of Dept. of Computer Science Sangmyung University Korea Republic of

There are many methods for diagnosing abnormal conditions of machines. Among them we use a method using sound for detecting abnormalities of machines. Experimental data sets were collected at approximately 30 minutes intervals for 2 weeks. The collected data sets are converted into spectrogram images expressed by time, frequency and amplitude with a 5 second time step. In this paper, we propose a learning model created by combining Conv1D for image processing and LSTM for time series data processing to detect abnormal conditions of machines. The comparison test with the existing model combining CNN, Conv1D and GRU shows our method has a promising result. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Long short-term memory

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Group-based Random Access with Age of Information Minimization in 6G umMTC 14

Group-based Random Access with Age of Information Minimizati...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Youn, Jiseung Park, Joohan Ahn, Seyoung Kim, Soohyeong Cho, Sunghyun Major in Bio-Artificial Intelligence Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of

ISBN: (纸本)9798350313277

6G has defined ultra-massive machine type communication (umMTC) as a key scenario in response to the advancement of IoT technologies. The presence of a large number of machine-type communication devices (MTCDs) in umMTC calls for the research of efficient random access (RA) schemes. To address the problem of high collision rates in dense networks, a group-based RA approach has been introduced. In the group-based RA approach, we design an optimal congestion control method to minimize the Age of Information (AoI). Since AoI and collision rate are in a trade-off relationship, we aim to maximize the successful RA rate by iteratively searching for the optimal congestion control factor. Simulation results represent that the proposed scheme achieves a 10% collision rate and an average AoI of less than 100 age, enabling the 90% successful RA rate in 100,000 MTCDs deployment scenario. © 2023 IEEE.

关键词： AoI Group-based random access umMTC

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Towards an integration management maturity 49

Towards an integration management maturity

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49th Euromicro Conference on Software engineering and Advanced applications, SEAA 2023

作者： Hyrynsalmi, Sonja M. Olsson, Helena Holmstrom Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350342352

As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside *** research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration *** address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management. © 2023 IEEE.

关键词： Integration

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Free Space Optical Communication Using Principles of Quantum Mechanics 4

Free Space Optical Communication Using Principles of Quantum...

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4th International Conference on Innovations in computer science, ICONICS 2024

作者： Angabeen, Tooba Asghar, Fatima Ali Zafar, Roohi Malik, Tahir Khan, Muhammad Mubashir Kamran, Muhammad NED University of Engineering and Technology Dept. of Physics Karachi Pakistan NED University of Engineering and Technology Dept. of Telecommunications Engineering Karachi Pakistan NED University of Engineering and Technology Dept. of Computer Science and Information Technology Karachi Pakistan

ISBN: (纸本)9798331504342

It all begun by the early 1970s through, the work of Stephen Wiesner and Gilles Brassard, who introduced the concept of quantum conjugate coding, and it has now stretched to a level where today's agile communication systems constitute a substantial challenge for data security and safety. A number of research has been performed to achieve secure communication among legitimate parties in which QKD (Quantum Key Distribution) is prominent. Quantum Key Distribution (QKD), also called Quantum Cryptography, enables two communicating parties to share secret cryptographic keys with security that is theoretically guaranteed by the laws of quantum mechanics, assuming ideal conditions. However, practical implementations may be vulnerable to certain attacks, and the security of QKD can be compromised by imperfections in hardware, side-channel attacks, or limitations in real-world setups. In a practical QKD setup, two stations send and receive encoded quantum particles and some classical information. The most rudimentary and well-known QKD protocol is BB84, developed by Bennet and Brassard in 1984. We have developed the simulation and achieved the secret shared key with and without an intruder. © 2024 IEEE.

关键词： Optical communication

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AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation 4

AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentat...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kolekar, Samruddhi Maheshkumar Yadav, Agnesh Chandra Yadav, Suchita Dash, Daba Prasad Thaper Institue of Engineering and Technology Dept. Electrical Engineering Patiala India India Instite of Technology Patna Dept. Electrical Engineering Patna India Bennett University Dept. of Computer Science and Engineering Gr. Noida India

ISBN: (纸本)9798350364729

Accurate segmentation of brain tumors in MRI images is crucial for diagnosis, treatment planning, and disease monitoring. Traditional manual segmentation methods are time- consuming and prone to variability, while standard deep learning architectures such as U-Net face challenges in capturing the complex, heterogeneous structures of brain tumors. In this study, we proposed AttRes-UNet, a novel dual-model architecture combining Attention U-Net and Residual U-Net to enhance segmentation accuracy. Attention Gates allow the model to focus on critical tumor regions, while Residual Blocks improve deep feature learning, enabling the model to handle diverse tumor morphologies with greater precision. We evaluated the model using the BraTS 2021 dataset, achieving high performance with an accuracy of 0.996, a dice coefficient of 0.8340, and an Intersection over Union of 0.8210. These results demonstrate the effectiveness of AttRes-UNet in improving segmentation accuracy, making it a valuable tool for automated brain tumor delineation, ultimately reducing manual workload and improving clinical outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A Feedback-based Decision-Making Mechanism for Actor-Critic Deep Reinforcement Learning

A Feedback-based Decision-Making Mechanism for Actor-Critic ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Yang, Guang Geng, Ziye Li, Jiahe Zhao, Yanxiao Abdelwahed, Sherif Luo, Changqing Virginia Commonwealth University Dept. of Computer Science RichmondVA United States Virginia Commonwealth University Dept. of Electrical and Computer Engineering RichmondVA United States

ISBN: (纸本)9798350362480

Deep reinforcement learning (DRL) has achieved remarkable success in solving sequential decision-making problems across various domains. However, a critical challenge is sample inefficiency, especially in real-world environments with high-dimensional solution spaces due to continuous state and action spaces. Although off-policy actor-critic algorithms have been proposed to mitigate this issue, the gains in sample efficiency remain limited, as decision-making in these algorithms relies solely on the policy function that might not always yield optimal actions. To bridge the gap, we design a novel feedback-based decision-making mechanism (FADA) that incorporates a feedback mechanism into the actor-critic framework to enhance decision-making robustness. Specifically, FADA utilizes feedback from the value function (critic) to calibrate the decisions produced by the policy function (actor). More concretely, FADA comprises four integrated modules: a decision-space expansion module (DEM) to produce a pool of candidate actions, a critic-guided evaluation module (CGEM) that estimates the efficacy of the candidate actions, an adaptive selection module (ASM) that adaptively selects a set of elite actions based on the estimated efficacy and samples the final action, and an iterative refinement module (IRM) that improves the quality of elite actions. We evaluate our approach on multiple tasks in the DeepMind Control Suite, and the experimental results demonstrate a significant improvement in sample efficiency. © 2024 IEEE.

关键词： Deep reinforcement learning

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Forecasting Traffic Air Pollution Increases from Urban Transportation Using Machine Learning in Metropolitan Cities-A Review

Forecasting Traffic Air Pollution Increases from Urban Trans...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Hari Naga Vijaya Bhaskar V. Valli Mayil Dept. of Computer Science and Applications KL University.

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Rapid urban growth and industrial expansion in Indian cities have led to a 30% increase in vehicles over the past 5–10 years, causing a rise in air pollution and health risks. Existing methods to predict air quality face challenges like limited data, inaccurate models, slow predictions, and changing environmental conditions. This review explores how machine learning, including deep learning and hybrid models, can improve pollution forecasting. It also highlights issues such as unreliable sensors, high computing costs, and difficulty in understanding model results. Finally, the study suggests future directions for better air quality management using AI-driven predictions and policy improvements.

关键词： Deep learning Reviews Atmospheric modeling Computational modeling Transportation Predictive models Air pollution Data models Forecasting Public healthcare

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Tender Coconut Classification using Decision Tree and Deep Learning Technique 10

Tender Coconut Classification using Decision Tree and Deep L...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Bhat, Ishani Umadevi, V. Jagadeesh, Nishchitha Bhat, Savithri Shenoy, Rashmi S Pes University Dept. of Computer Science Bengaluru India B.MS. College of Engineering Dept. of Computer Science Bengaluru India B.MS. College of Engineering Dept. of Biotechnology Bengaluru India

ISBN: (纸本)9781665490993

A number of industry sectors such as Food & nutraceuticals, Pharma, Irrigation, Transport and Automobile use coconut and its parts for varied products. As different sectors make use of different type and parts of coconut, an automated separation of coconut Would aid in faster processing. Thus the major issue faced by these sectors is the automated sorting of coconut into different categories such as Non-Tender Coconut, Tender Whole coconut, Tender coconut cut With flesh and Without flesh. In this Work We have trained and tested a Decision Tree and DenseNet model to automatically classify coconut images into various categories. For Decision Tree 93% of accuracy Was obtained While the advanced Deep learning model, DenseNet, gave 100% accuracy. This showed that the Deep Learning models such as DenseNet perform better as compared to conventional machine learning algorithms such as Decision Tree. © 2023 IEEE.

关键词： Decision trees

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