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Phase Fraction Calculation in Multiphase Steel Microstructures Through Semantic Segmentation 10

Phase Fraction Calculation in Multiphase Steel Microstructur...

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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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Microwave Marvels: Design and Computational Analysis of an Ultra-thin Metamaterial Absorber for C, X and K Band 6

Microwave Marvels: Design and Computational Analysis of an U...

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6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Tasnim, Nowshin Rattre, Naziya Hasan Shawon, Sarowar Morshed Alam, Mohammed Shamsul Alam, Touhidul Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh International Islamic University Chittagong Dept. of Computer Science & Engineering Chittagong4318 Bangladesh University of Science & Technology Chittagong Dept. of Computer Science & Engineering Chattogram4202 Bangladesh Universiti Kebangsaan Malaysia Bangi43600 UKM Malaysia

ISBN: (纸本)9798350385779

Metamaterial absorbers are an advancement in material science as they provide more advantages in comparison to conventional materials. Achieving miniaturization with a multilayered structure is the primary challenge here. In this study, a novel metamaterial absorber design is proposed for microwave application. The proposed design is shaped as a squad of four segments where each segment contains an octagonal split ring and a central ring. The multi-layered design of the absorber helps to secure a high absorption rate. An extensive range of research is done to determine particular measurement units, and geometric shapes with the help of CST Studio Suit 2019. Here both ground and patch are designed using copper and the separator is designed with Roger. Moreover, the thickness of the absorber is only 3.4 mm which ensures its ultra-thin and lightweight nature. With an elaborated numerical analysis, it has been shown that the proposed absorber provides 90% to 99% absorption at the C, X, and K bands. Moreover, a concise computational analysis is accomplished with FR4 lossy, Rogers RT6010LM lossy, and Rogers RT5870LM lossy as dielectric constant. The key contribution of this research is the high absorption rate, coupled with minimal thickness and simple structure. © 2024 IEEE.

关键词： Metamaterials

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Evaluation of Machine Learning Models in Student Academic Performance Prediction 5

Evaluation of Machine Learning Models in Student Academic Pe...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Sandeepa, A.G.R. Mohottala, Sanka General Sir John Kotelawala Defence University Dept. of Computer Engineering Ratmalana Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331530983

This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach. © 2025 IEEE.

关键词： Students

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Multiple Disease Prediction Based on User Symptoms using Machine Learning Algorithms 2

Multiple Disease Prediction Based on User Symptoms using Mac...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Pentela, Ujwala Meesala, Anjali Nirupama Karingula, Deekshitha Seelamsetti, Nandini Krishna Veerlapalli, Swapna Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350347456

Every day, many individuals encounter different illnesses. The prognosis of a disease is the most pivotal part of treatment. Enormous increase in healthcare and medical data enabled accurate medical data analysis, which aids in early sickness discovery and beforehand patient care. This study focuses on performing research on the enormous medical data by exercising numerous supervised classification algorithms like Decision Tree, Support Vector Machine, K- Nearest Neighbor, Logistic Regression, Naive Bayes and Random Forest to anticipate the most probable disease grounded on the symptoms and also to directly prognosticate the possibility of whether or not the person might be suffering from that particular illness. Based on the symptoms, the model uses the results of the supervised classification algorithms and gives a final validation indicating the disease that the existent might be suffering. On combining the prognostications from all the below classification algorithms, the model returns a more accurate verification when compared to the prognostications made by individual models. This study enhances the swiftness of decision-making and can reduce the rate of false cons. It helps healthcare associations make better decisions about how to proceed with early patient care. It also aids healthcare professionals in developing further effective ways of treating patients. © 2023 IEEE.

关键词： Support vector machines

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Empowering Low-Resource Language Translation: Methodologies for Bhojpuri-Hindi and Marathi-Hindi ASR and MT 21

Empowering Low-Resource Language Translation: Methodologies ...

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21st International Conference on Spoken Language Translation, IWSLT 2024

作者： Anand, Harpreet Singh Dash, Amulya Ratna Sharma, Yashvardhan Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798891761414

This paper presents the methodologies implemented for the Automatic Speech Recognition and Machine Translation for the language pairs Bhojpuri-Hindi and Marathi-Hindi for the Dialectal and Low-Resource shared task proposed by The International Conference on Spoken Language Translation (IWSLT) for 2024. The implemented method uses the transcriptions generated through a fine-tuned Whisper models(for Marathi-Hindi) and vakyansh-wav2vec model (for Bhojpuri-Hindi) and generates the translations using fine-tuned NLLB(No Language Left Behind) Models for both the tasks. The selection of more accurate translation is done through sentence-embeddings generated using the MuRIL(Multilingual Representations for Indian Languages)(Khanuja et al., 2021) model for the Marathi-Hindi task. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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Text-Conditioned Image Synthesis - A Review

Text-Conditioned Image Synthesis - A Review

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Saha, Piyush Ghosh, Sumon Bhandari, Dinabandhu Calcutta Institute of Engineering and Management Dept. of Electrical Engineering Kolkata India Heritage Institute of Technology Dept. of Computer Applications Kolkata India Heritage Institute of Technology Dept. of Computer Science Kolkata India

ISBN: (纸本)9798350314144

With the advent of Generative Adversarial Networks and diffusion models, image synthesis conditioned on text description has been an active area of research. Generative Adversarial Networks are a flexible and intuitive way of conditional image synthesis and significant progress has been made in the last few years regarding visual realism, diversity, and semantic alignment. In recent years, Diffusion probabilistic models have been shown to perform better in the task of image synthesis and have been used extensively for the task of text-to-image synthesis, significantly improving visual realism, semantic alignment, and generation of high-resolution images. However, the field still faces many challenges such as the generation of high-resolution images with multiple objects and the development of suitable and reliable evaluation metrics. In this review, we contextualize the state-of-the-art text-conditioned image generation models, and critically examine current evaluation strategies, architectures, model training, and datasets. This review complements previous surveys on text-conditioned image synthesis which we believe will help researchers to further advance this field. © 2023 IEEE.

关键词： Generative adversarial networks

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A Secure and Efficient Group Authentication Framework for Blockchain-Enabled Vehicular Edge Computing Systems 3

A Secure and Efficient Group Authentication Framework for Bl...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Meena, Pawan Dash, Suryansh Sachin Monika Rani, Poonam Netaji Subhas University of Technology Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798331519582

Maintaining privacy and trust within Vehicular Edge Computing (VEC) systems requires proper authentication methods that ensure safety and effectiveness in dynamic settings. Current authentication solutions face three main drawbacks including excessive computational costs together with complex identity management systems and large security exposure. The proposed lightweight Multi-Entity Authenticated Group Key Agreement protocol serves blockchain-based VEC networks according to this research. MAGKA enhances authentication through bulk certification removal and reduces reliance on identity authentication and provides seamless vehicle traffic across edge networks. The solution provides VEC authentication with robust security and scalability demonstrated through security analysis of its ability to withstand attacks. © 2025 IEEE.

关键词： Ad hoc networks

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Cost-aware and Time-Varying Request Rate-based Dynamic Cache Provisioning over CCDN 17

Cost-aware and Time-Varying Request Rate-based Dynamic Cache...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Gupta, Rohit Kumar Mazumdar, Arka Prokash Malaviya National Institute of Technology Jaipur Manipal University Jaipur Dept. of Computer Science & Engineering India Malaviya National Institute of Technology Jaipur Dept. of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350307672

Today's Internet users depend on Cloud-based Content Delivery Network (CCDN) providers to efficiently use resources and minimize content delivery latency. However, time-varying request rates make incoming requests unpredictable, resulting in higher miss rates in CCDNs. Thus, dynamically allocating cache instances to mitigate request rate spikes is necessary to maintain Quality of Service (QoS). Therefore, by utilizing the Request Count Window (RCW) cache and a dynamic instantiation mechanism, this article aims to efficiently manage a changing request window and store objects according to a caching policy. To improve cache performance, we propose a new dynamic selective caching policy that caches only the most frequently requested objects over the past L requests. To further enhance dynamic cache provisioning and reduce the duration of resource allocation, a variable time slot mechanism based on the current request rate is introduced. Finally, we present a cost model that accounts for caching and delivery costs due to missed occurrences. The proposed method is extensively compared with the Least Recently Used (LRU) caching policy and the RCW cache with selective caching based on Kth request cache insertion policies. Experimental results demonstrate that the proposed method outperforms the LRU, selective caching on the values 1 and 2 of K by 22.04%, 12.11%, and 9.31%, and by 29.50%, 16.71%, and 3.03% in terms of the average total cost using permanent cache sizes of 2 and 5, respectively. © 2023 IEEE.

关键词： Dynamics

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A Semantic Driven Framework for Energy Efficient Task Scheduling in Dynamic Cloud Environment 4

A Semantic Driven Framework for Energy Efficient Task Schedu...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Aarav Kannan, J. Amshavalli, R.S. Venkateswara Reddy, D. Varma, Vegesna Surya Vinay Kavitha, G. Srm Institute of Science and Technology Dept of Biomedical Engineering Chennai India Sathyabama Institute of Science and Technology Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

The rapid expansion of cloud computing has led to increased energy consumption, raising concerns about its environmental impact. Despite advancements in scheduling algorithms, existing methods face critical challenges, including suboptimal energy efficiency, limited adaptability to dynamic cloud environments, and insufficient context-aware decision-making. Recent techniques, such as Meta Reinforcement Learning, Genetic Algorithms, and Energy-Aware Scheduling, show potential but often struggle with high computational complexity, static resource allocation, or scalability issues. These shortcomings result in inefficient resource utilization, elevated energy usage, and difficulties in meeting Service Level Agreement (SLA) requirements. To address these challenges, this study introduces the Energy Efficient Semantic Scheduling Framework (ESSF), a novel approach combining semantic reasoning and energy profiling to optimize task scheduling in dynamic cloud environments. The framework integrates three key components: (1) Dynamic Energy Profiling (DEP) for real-time energy monitoring, (2) Semantic Resource Matching (SRM) utilizing knowledge graphs for context-aware allocation, and (3) Adaptive Workload Balancing (AWB) to dynamically redistribute tasks for improved resource utilization and energy efficiency. Experiments using the Google Cluster Trace Dataset demonstrate that ESSF reduces energy consumption by up to 25% compared to traditional methods while achieving 90% resource utilization and 97% SLA compliance. These results highlight the potential of the proposed framework to overcome existing limitations, offering a scalable, adaptive, and environmentally sustainable solution for task scheduling in cloud computing. © 2025 IEEE.

关键词： Cloud Computing Dynamic Energy Profiling Energy Efficiency Google Cluster Trace Dataset Knowledge Graphs Semantic Scheduling

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An Ensemble Learning Approach to Detect the Presence of Diabetic Retinopathy based on Inception Module 2

An Ensemble Learning Approach to Detect the Presence of Diab...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Devi, T. Aswini Yamana, Sathwik Harshankar Reddy, G. Sangita, Vedh Sivaji Kumar Kulkarni, Swapneel Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665456302

In recent times, Diabetic Retinopathy (DR) has become a complication of major degree for diabetic patients, in which the blood vessels present in the eye retina are severely damaged. This in most cases has lead to a loss of vision as observed from records of DR patients, and if left untreated, can cause blindness. As per the estimates of the World Health Organization, DR will impact around 224 million people by 2040. This research study presents a convolutional neural network (CNN) model, followed by employing ensemble learning over various ML algorithms through max voting, for the task of image classification for detecting Diabetic Retinopathy using color fundus images, with the goal of improving the accuracy and efficiency of the diagnostic process. The proposed model is designed to address the challenges of real-world data, such as variability in the appearance of images that belong to the same class and class imbalance. The dataset used for this study is EYEPACS, which is widely used for training and testing models for detecting Diabetic Retinopathy. The Metrics that are used for measuring the performance of the proposed model are accuracy, precision, and F1-S core. This study obtains an accuracy of 95.4 percent. © 2023 IEEE.

关键词： Blood vessels

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