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检索条件"任意字段=International Conference on Advances in Computer Science and Engineering"

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Offset-Fed Defected Ground Structure (DGS)-Based Metamaterial-Inspired Asymmetric SRR for 5G Terminal Applications 3rd

Offset-Fed Defected Ground Structure (DGS)-Based Metamateria...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Meenambal, B. Vasudevan, K. Uma Maheshwari, G. Sherene, J. Department of Electronics and Communication Engineering Thiagarajar College of Engineering Tamil Nadu Madurai625015 India Department of Computer Science and Engineering Kamaraj College of Engineering and Technology Tamil Nadu Virudhunagar626001 India

ISBN: (纸本)9789819719426

In this research, a low-profile metamaterial-inspired dual-band antenna is proposed for 5G communication which covers n78 band of Laptop and Mobile Terminal applications and 5G IOT Wireless Avionics Intra Communication (WAIC) applications. The designed antenna consists of rectangular Split Ring Resonator (SRR) as radiating element and partial rectangular ground plane with defected ground structure (DGS) is achieved by Complementary Split Ring Resonator (CSRR). It has been designed on Rogers RO4350 substrate which has a thickness of 0.8 mm, with the relative dielectric constant value Ɛr = 3.66 and with the loss tangent value tan δ = 0.004. The results indicate that metamaterial structure improves the gain, bandwidth and radiation efficiency of the antenna. The simulated 10 dB impedance bandwidth covers 5G-n78 band with the center frequency of 3.5 GHz and WAIC band with the center frequency of 4.3 GHz. The proposed antenna operates with high radiation efficiency, better gain and covered required bandwidth with linear polarisation. A reduced dimension of 0.25λ0 × 0.35λ0 × 0.009λ0 is achieved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G mobile communication systems

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Comparative Analysis of Deep Learning Based Algorithms for Road Feature Extraction

Comparative Analysis of Deep Learning Based Algorithms for R...

引用

Disruptive Technologies (ICDT), international conference on

作者： Jyoti Pruthi Sunita Dhingra Computer Science and Engineering Maharishi Dayanand University Rohtak Haryana India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Extraction of road features from remote sensing images is popular research and getting accurate road features has great practical applications such as urban planning, autonomous navigation, and disaster management. Accurate road feature extraction is challenging due to variations in road surfaces, occlusions, shadows, and complex environmental conditions. Recent advances in deep learning methods have shown great performance in road feature extraction compared with machine learning. In this paper, we evaluate and compare the performance of state-of-art road feature extraction including FCN, Unet, Segner, DeepLabv3+, and DCS-TransUpernet. This analysis is performed on the publicly available Deepglobe road extraction dataset, which consists of high-resolution satellite images. We explored the strengths and limitations of these models to understand the advancements made in road feature extraction and found that transformer-based models can capture more road information. This result sets a benchmark for future research in this domain.

关键词： Deep learning Measurement Analytical models Accuracy Convolution Roads Urban planning Feature extraction Transformers Satellite images

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CLIMB: Imbalanced Data Modelling Using Contrastive Learning with Limited Labels 25th

CLIMB: Imbalanced Data Modelling Using Contrastive Learning ...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Alsuhaibani, Abdullah Razzak, Imran Jameel, Shoaib Wang, Xianzhi Xu, Guandong University of Technology Sydney School of Computer Science Sydney Australia The University of New South Wales Sydney Australia University of Southampton Southampton United Kingdom Islamic University of Madinah Medina Saudi Arabia The Education University of Hong Kong HK Hong Kong

ISBN: (纸本)9789819605729

Machine learning classifiers typically rely on the assumption of balanced training datasets, with sufficient examples per class to facilitate effective model learning. However, this assumption often fails to hold. Consider a common scenario where the positive class has only a few labelled instances compared to thousands in the negative class. This class imbalance, coupled with limited labelled data, poses a significant challenge for machine learning algorithms, especially in the ever-growing data landscape. This challenge is further amplified when dealing with short text datasets, as these inherently provide less information for computational models to leverage. While techniques like data sampling and fine-tuning pre-trained language models exist to address these limitations, our analysis reveals their inconsistencies in achieving reliable performance. We propose a novel model that leverages contrastive learning within a two-stage approach to overcome these challenges. Our proposed framework involves unsupervised Fine-Tuning of a language model to learn representation on short text followed by fine-tuning on a few labels integrated with GPT-generated text using a novel contrastive learning algorithm designed to effectively model short texts and handle class imbalance simultaneously. Our experimental results demonstrate that the proposed method significantly outperforms established baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Agri-Ontology: A Novel Ontology Framework for Agricultural Domain

Agri-Ontology: A Novel Ontology Framework for Agricultural D...

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Electronics and Renewable Systems (ICEARS), international conference on

作者： R. Deepa Venkatesh Jayaraman Senduru Srinivasulu B. Umamageswari Surendran R Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science & Technology Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The agricultural information system deals with massive amounts of data from heterogeneous sources. It helps the farmers gain accurate information by providing better insights. A significant issue in agricultural data is its unstructured form, which can be resolved through ontology—a novel framework known as AgriOntology is proposed to address this issue for the Agrovoc dataset. The data in the unstructured form is initially pre-processed through the Natural Language Processing approach to generate features for ontology construction. The BERT and Jaccard similarity are applied to establish a relationship between the features used to construct the Agri-Ontology. Post ontology construction, the BiGAN framework is used to analyze the performance. The performance of the constructed ontology is observed to have an accuracy of 94.64%, which is higher than the existing models.

关键词： Renewable energy sources Accuracy Ontologies Feature extraction Natural language processing Data models Cleaning Data mining Meteorology Information systems

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Artificial Intelligence, Intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach

Artificial Intelligence, Intelligent Manufacturing, and Sust...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sathika Begum Mohammad Shabi Bhuvaneshwari M S. Kanimozhi S Pavithra Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry.

关键词： Reviews Quality control Production Fabrics Manufacturing Quality assessment Artificial intelligence Sustainable development Textile industry Defect detection

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Overhead Reduction in Intelligent Reflecting Surface-aided Wireless Networks: A Review

Overhead Reduction in Intelligent Reflecting Surface-aided W...

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international conference on Information Networking

作者： The Vi Nguyen Thi My Tuyen Nguyen Chunghyun Lee Yunseong Lee Anh Tien Tran Sungrae Cho Department of Computer Science Engineering Chung-Ang University Seoul Republic of Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Reconfigurable intelligent surfaces (RISs) have shown great promise in enhancing coverage, spectral, and energy efficiency in the future 6 G wireless networks. Because of the passive nature of the surface, there is no signal processing unit for channel state information acquisition. In addition, due to the limited computational capabilities at the RIS nodes, the optimized RIS phase shifts must be computed and fed back from the base station (BS) before the data transmission. Therefore, overhead occurred in the channel estimation and feeding back phases pose major challenges to realizing this technology. This paper provides an overview of recent advances in overhead reduction in RIS-aided networks.

关键词： Reviews Wireless networks Channel estimation Reconfigurable intelligent surfaces Signal processing MIMO Energy efficiency Data communication Surface treatment Channel state information

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Improving Adaptive Runoff Forecasts in Data-Scarce Watersheds Through Personalized Federated Learning 27th

Improving Adaptive Runoff Forecasts in Data-Scarce Watershe...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Xie, Zaipeng Zhang, Xiangqin Wang, Yunfei Jie, Xuanyao Fang, Wenhao Cai, Yanping Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031781827

Runoff forecasting plays a crucial role in water resource management and flood mitigation, but it often faces significant challenges due to data deficiency and decentralized datasets. Inadequate hydrological data in many watersheds hinders the development of accurate prediction models, while organizational barriers and concerns about data privacy lead to information silos, preventing effective collaborative modeling. To address these issues, we propose FedHydroDSW, a novel personalized federated learning framework for runoff forecasting in data-scarce watersheds. Our approach enables decentralized model training across multiple organizations without requiring direct data sharing, thus preserving data privacy. FedHydroDSW incorporates unique model similarity metrics and parameter adaptation methods, utilizing insights from data-rich areas to refine forecasts in data-scarce regions. Experiments on the CAMELS dataset show that FedHydroDSW significantly outperforms standalone and conventional federated learning methods, with an average 30.1% increase in NSE, and reductions of 28% in RAE and 26.6% in RMSE across multiple data-scarce basins. By enabling secure cross-silo learning, our FedHydroDSW strategy paves the way toward more equitable runoff forecasting globally. The customized training and pattern recognition techniques represent major progress in deploying federated intelligence for impactful hydrological predictions. Our code is publicly available at https://***/xyjie37/FedHydroDSW. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Understanding Inflicted Injuries in Young Children: Toward an Ontology Based Approach 24th

Understanding Inflicted Injuries in Young Children: Toward ...

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24th international conference on Knowledge engineering and Knowledge Management, EKAW 2024

作者： Maikore, Fatima Mazumdar, Suvodeep Offiah, Amaka Hughes, Anthony Roychowdhury, Sneha Hocking, Katie Lanfranchi, Vitaveska Department of Computer Science University of Sheffield Sheffield United Kingdom Information School University of Sheffield Sheffield United Kingdom School of Medicine and Population Health Sheffield United Kingdom Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom

ISBN: (纸本)9783031777912

Investigations of children experiencing inflicted injuries is often initiated once admitted into the emergency department for injuries, and involves understanding the complex interactions between a range of different factors such as health conditions, fracture biomechanics, family history, carer accounts and so on. In this position paper, we propose the use of ontologies for capturing case details of such radiology investigations in order to create an initial knowledge base of retrospective cases, to be further expanded in the future. We discuss how we developed our ELECTRICA (ELEctronic knowledge base for Clinicians, Trainers and Researchers in Child Abuse) ontology and the different components of the ontology. We are currently using this ontology to create a knowledge base, to be used as a vision demonstrator to access larger datasets. In the longer term, we would like to use the knowledge base to support clinicians and radiologists in making decisions on current cases by offering a mechanism for searching historical cases, identifying similar cases and offering insights on risk factors that may have been missed during investigations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： child abuse inflicted injury non-accidental injury ontology development

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AI-Driven Early Prediction of Pulmonary Fibrosis Using Deep Learning

AI-Driven Early Prediction of Pulmonary Fibrosis Using Deep ...

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international conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Gayathri S S. Barath G. Ramesh Surendran R K S. Balamurugan Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India School of Computer Science Takshashila University Tindivanam Taluk Ongur Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Karpaga Vinayaga College of Engineering and Technology Chengalpattu Tamil Nadu India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The development of scar tissue (fibrosis) in the lungs is a hallmark of pulmonary fibrosis, a progressive lung disease. impairing its ability to function properly. Progressive nature leading to declining lung function and yearly, 13 to 20 out of 100,00 people effecting by pulmonary fibrosis throughout the world. Present technologies for prediction of pulmonary fibrosis are limited to predictive accuracy, availability of high-quality data and early detection. The recommended technique with a high-resolution computed Tomography (HRCT) scan along with U-Net for better classification of HRCT signals and diagnosis of disease accurately. The results accomplished by this method are remarkable achievements in terms of precision recall, accuracy and F1-Score ranging from 0.96 to 0.98.

关键词： Deep learning Accuracy Image resolution Lungs Computed tomography Pulmonary diseases Prediction algorithms Robustness Distance measurement Diabetes

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An Effective Prediction of Heart Diseases Using Machine Learning Techniques 5th

An Effective Prediction of Heart Diseases Using Machine Lear...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Obulesu, O. Yakshitha, Koulampeta Vuyyuru, Sanjana Meenakshi, Tumkur Vaishnavi, Pujala Department of Computer Science and Engineering G. Narayanamma Institute of Technology and Science Telangana Hyderabad India

ISBN: (纸本)9789819780426

The heart plays a crucial role in the survival of living organisms by serving as the central pump that circulates blood throughout the body. Heart disease is often considered one of the most life-threatening conditions in humans due to its significant impact on health and mortality rates. The healthcare sector accumulates massive volumes of healthcare data, but regrettably, much of this valuable information often remains untapped, preventing the industry from uncovering hidden insights that could enhance decision-making processes. Hidden patterns and relationships are often left undiscovered and underutilized. The use of advanced machine learning techniques offers a promising solution to address the complexities involved in predicting heart diseases. It represents a crucial challenge within the realm of clinical data analysis, as accurately identifying and forecasting heart-related conditions is of paramount importance for improving healthcare outcomes. Early prediction of heart attack may save many lives hence preventing these has become more than necessary. Machine learning (ML) has the potential to provide highly efficient solutions for decision- making and precise predictive capabilities. In the field of machine learning, there are several classification models such as Logistic Regression, K- nearest neighbors (K-NN), and Support Vector Machine (SVM) that have proven effective in achieving the goal of predicting and diagnosing heart diseases. Decision Tree Classifier, in particular, serves as a valuable decision support system for detecting and forecasting heart diseases and heart attacks in individuals by utilizing risk factors associated with heart disease. Datasets containing medical parameters play a pivotal role in this process, as they are processed through various machine learning algorithms. These algorithms help uncover correlations among the different attributes present in the dataset using standard machine learning techniques. The overarching aim of t

关键词： Adversarial machine learning

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