检索结果-内蒙古大学图书馆

3rd IEEE International Conference on Intelligent Systems, Smart and Green Technologies, ICISSGT 2024

作者： Padmaja, Chinimilli Venkata Rama Lakshminarayana, S. Institute of Aeronautical Engineering Computer Science & Engineering Hyderabad India Andhra University Computer Science & Systems Engineering Visakhapatnam India

ISBN: (纸本)9798350304329

In recent years, language models have undergone significant advancements with models like GPT-3, showcasing impressive abilities in natural language processing and generation. However, these models often experience from issues such as generating biased or unreliable content, producing inappropriate responses, or failing to fulfill specific user needs. To address these challenges, prompt engineering emerged as a promising approach by providing explicit guidance to language models. It refers to the process of designing and constructing effective prompts or instructions to guide language models like GPT-3 in generating desired outputs. In this article an attempt was made to address the role of prompt engineering, its techniques, and its potential impact on enhancing the performance and reliability of language models. © 2024 IEEE.

关键词： ChatGPT Language Models Prompt engineering

来源：评论

学校读者我要写书评

暂无评论

DeepGAN: Utilizing generative adversarial networks for improved deep learning

引用

International Journal of Knowledge-Based and Intelligent engineering Systems 2024年第4期28卷 732-748页

作者： V, Edward Naveen A, Jenefa T.M, Thiyagu A, Lincy Taurshia, Antony Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Department of Computer Science and Engineering National Engineering College India

In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper addresses the existing challenges in synthesizing high-quality data and harnessing the capabilities of GANs for improved deep learning outcomes. Unlike traditional approaches that heavily rely on manually engineered data augmentation techniques, our work introduces a novel framework that leverages DeepGANs to autonomously generate diverse and high-fidelity data. Our experiments encompass a diverse spectrum of datasets, including images, text, and time series data. In the context of image classification tasks, we conduct experiments on the widely recognized CIFAR-10 dataset, which consists of 50,000 image samples. Our results demonstrate the remarkable efficacy of DeepGANs in enhancing model performance across various data domains. Notably, in image classification using the CIFAR-10 dataset, our innovative approach achieves an impressive accuracy of 97.2%. This represents a substantial advancement beyond conventional CNN models, underscoring the profound impact of DeepGANs in the realm of deep learning. In summary, this research sheds light on DeepGANs as a fundamental component in the pursuit of enhanced deep learning performance. Our framework not only overcomes existing limitations but also heralds a new era of data augmentation, with generative adversarial networks leading the way. The attainment of an accuracy rate of 97.2% on CIFAR-10 serves as a compelling testament to the transformative potential of DeepGANs, solidifying their pivotal role in the future of deep learning. This promises the development of more robust, adaptive, and accurate models across a myriad of applications, marking a significant contribution to the field. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

MLRT-UNet:An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation

引用

computer Modeling in engineering & sciences 2025年第4期143卷 413-448页

作者： Kaku Haribabu Prasath R Praveen Joe IR Department of Computer Science and Engineering RMK College of Engineering and TechnologyTiruvallur601206India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.

关键词： Thyroid nodules endocrine system multi-level relation transformer U-Net self-attention external attention co-operative transformer fusion thyroid nodules segmentation

来源：评论

学校读者我要写书评

暂无评论

Tribological Study of Modified Al-6013 Aluminum Alloys

引用

Journal of Bio- and Tribo-Corrosion 2024年第2期10卷 1-18页

作者： Sundaramoorthy, R. Sasidhar, Gollu Akhilesh, A.P. Vardhan, Aluri Harsha SIMCRASH Centre Department of Aeronautical Engineering Hindustan Institute of Technology & ampScience Tamilnadu Chennai India Department of Aeronautical Engineering Hindustan Institute of Technology & ampScience Tamilnadu Chennai India

This study explores the modification of Al-6013 aluminium alloys through the stir casting method, incorporating varying magnesium percentages (Al6013–2% Mg and Al6013–4% Mg). Utilizing a salt spray chamber, the corrosion resistance of the alloys was assessed over 48 h, offering insights into their durability under corrosive conditions. Additionally, wear tests were conducted for 40 min at 382 rpm, applying loads of 10 N, 20 N, and 30 N to evaluate the alloys’ performance under different Tribological conditions. The stir casting process effectively integrated magnesium into the Al-6013 alloy matrix, resulting in modified alloys with enhanced properties. The findings contribute valuable insights into the corrosion resistance and wear performance of the Al-6013 alloys, aiding in their potential application in industries where such characteristics are crucial. This work provides a basis for optimizing alloy composition to meet specific engineering requirements. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Aluminum alloys

来源：评论

学校读者我要写书评

暂无评论

Epileptic Seizure Analysis using Scalp EEG Signals with Deep Learning

Epileptic Seizure Analysis using Scalp EEG Signals with Deep...

引用

2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Madhavi Devi, B. Srilekha, Rayedi Harshitha, Gattineni Sushma, Burri Department of Computer Science and Engineering India Institute of Aeronautical Engineering Computer Science and Engineering India

ISBN: (纸本)9798350333602

There has been an increase in interest in using Electroencephalogram (EEG) data for emotion detection in recent years, with possible applications in industries including affective computing and medicine. It has been discovered that persons with depression, a common mental health disorder, have differential spatial responses to neurophysiological signals when exposed to positive and negative stimuli that differ from those in healthy people. Our investigation contrasts previous research in the field and focuses on important elements essential to emotion recognition, such as themes, extracted features, classifiers, and more. This research study develops an emotion activation curve to more clearly depict the activation process of emotions. The proposed study employs machine learning to classify emotions by extracting features from EEG signals. It leverages data from several experiments to train a suggested model, which is then tested for its effectiveness in detecting emotions. The main goal of our project is to identify emotions from brain signals by applying a novel adaptive channel selection method that can distinguish between how brain activity operates in various people and emotional *** results of our investigation show a significant improvement in the proposed method's ability to correctly categorize positive or negative emotions in depressed patients. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning-Based Class Imbalance-Aware Shoulder Implant Classification from X-Ray Images

引用

Journal of Bionic engineering 2024年第2期21卷 892-912页

作者： Marut Jindal Birmohan Singh Department of Computer Science and Engineering Sant Longowal Institute of Engineering and TechnologySangrur148106PunjabIndia

Total shoulder arthroplasty is a standard restorative procedure practiced by orthopedists to diagnose shoulder arthritis in which a prosthesis replaces the whole joint or a part of the *** is often challenging for doctors to identify the exact model and manufacturer of the prosthesis when it is *** paper proposes a transfer learning-based class imbalance-aware prosthesis detection method to detect the implant’s manufacturer automatically from shoulder X-ray *** framework of the method proposes a novel training approach and a new set of batch-normalization,dropout,and fully convolutional layers in the head *** employs cyclical learning rates and weighting-based loss calculation *** modifications aid in faster convergence,avoid local-minima stagnation,and remove the training bias caused by imbalanced *** proposed method is evaluated using seven well-known pre-trained models of VGGNet,ResNet,and DenseNet *** is performed on a shoulder implant benchmark dataset consisting of 597 shoulder X-ray *** proposed method improves the classification performance of all pre-trained models by 10–12%.The DenseNet-201-based variant has achieved the highest classification accuracy of 89.5%,which is 10%higher than existing ***,to validate and generalize the proposed method,the existing baseline dataset is supplemented to six classes,including samples of two more implant *** results have shown average accuracy of 86.7%for the extended dataset and show the preeminence of the proposed method.

关键词： Biomedical engineering Artificial intelligence Total shoulder arthroplasty Prosthesis identification Biomedical image classification

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

引用

CAAI Transactions on Intelligence Technology 2025年第1期10卷 72-93页

作者： Hazique Aetesam Suman Kumar Maji V.B.Surya Prasath Computer Science and Engineering Birla Institute of Technology MesraBiharIndia Computer Science and Engineering Indian Institute of Technology PatnaBiharIndia Department of Computer Science University of CincinnatiCincinnatiOhioUSA

Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.

关键词： hyper-laplacian prior hyperspectral images image restoration mixed noise variational approach

来源：评论

学校读者我要写书评

暂无评论

Investigating mobility patterns through crowd-sourced activity data

Advances in Transportation Studies

引用

Advances in Transportation Studies 2025年 65卷 253-270页

作者： Castiglione, M. Cipriani, E. Gemma, A. Nigro, M. Department of Civil Computer Science Aeronautical Technologies Engineering Roma Tre University Rome00146 Italy

Investigating trip purposes represents an important phase of travel demand modeling which allows to correctly infer mobility patterns and to better understand travel behavior. Until now, researchers collected information on the motivation for carrying out a trip mainly through travel surveys. However, traditional methods of acquiring this type of information are challenging and expensive to implement;therefore, they are typically performed infrequently and with low sampling rates. These two occurrences do not always allow for adequate representation of the heterogeneity of trip purposes. This paper aims to investigate trip purposes through the joint processing of GPS-based data, such as Floating Car Data (FCD), and aggregated activity data available through open-source platforms, such as Google Popular Times (GPT), which provide information on the daily distribution of visits in a certain activity venue as well as the average visit duration based on aggregated data obtained from users who share their mobile phones geotraces. Through the application of clustering techniques on a FCD dataset containing trips carried out between September and November 2020 in the EUR district of Rome, Italy, we classify the trips as Home-Work trips and Not Home-Work trips, obtaining a total of 96 Origin-Destination matrices (one for every 15 minute time interval). Not Home-Work trips are further examined, exploiting 6 patterns obtained through the clustering of GPT activity data, and classified according to the arrival time at destination and the duration of their stopover. The obtained Origin-Destination matrices have been compared to values found in literature in terms of spatial and temporal flexibility to validate the results. © 2025, Aracne Editrice. All rights reserved.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Predictive Analysis and Application of Various Machine Learning Algorithms to Forecast used Car Prices

Predictive Analysis and Application of Various Machine Learn...

引用

2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Srinivasan, Palayanur Reddy, R Obulakonda Sai, Kaki Anirudh Naidu, Jayanth Institute of Aeronautical Engineering Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350333602

India has been a huge market for secondhand market. The most popular second-hand items sold in India include used cars, furniture, electronic appliances, Mobile phones and etc. In general, a used car would cost lesser than a brand new car. Thus people prefer buying a used car. During the pandemic, the secondhand automobile industry in India has seen a sharp increase in popularity. Many people choose to purchase a used car rather than a brand-new one due to the difficulty of using public transit and the requirement for personal transportation. A used car was more cost-effective to purchase than a new one because of the economic uncertainty brought on by the pandemic and the increased prudence with which people approached their spending. Also, the pandemic's effects on the supply chain and production delays made the used automobile market more alluring to customers. In this paper, the comparative analysis of multiple machine learning algorithms enabled us to identify the most effective method for determining the value of secondhand automobiles. Last but not least, providing an interactive web application to the owners of the cars where they can enter the details get an overview at the price. This study has processed the proposed dataset using popular Machine learning algorithms. Feature selection with Random Forest Regression yield the best performance with r2-score = 0.9321 (accuracy of 93.2%) with MAE ∼= 70213.9685. This study aims to increase the reliability of prediction of used car prices. In particular, this study concentrates on the approach of selecting features. The precise selection of features definitely showed us a significant edge over previous works. © 2023 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Human Age Prediction Based on Gender Image Recognition 5

Human Age Prediction Based on Gender Image Recognition

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Vignesh, Bowgikar Keerthan, A V K Sai Shyamsunder, Bonkuru Satheesh Kumar, S. Computer Science and Information Technology Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331518097

Facial age and gender classification is a fundamental task for many applications, such as social media, security and personalized user experience. This project provides a detailed approach to age and gender classification based on CNNs, including model training and evaluation using the UTK Face dataset. The diversity and granularity of the datasets offered an excellent basis for training and evaluating models. Normalization and augmentation were added in our preprocessing pipeline to enhance the generalizability of this model among a wide variety of conditions, mitigating overfitting risks. Breathable CNN Model ArchitectureThe CNN model was designed in a deep architecture to incorporate several convolutional layers, pooling layerone after other. It helps the model to learn detailed complex patterns of face at different levels. We introduced techniques like batch normalization and dropout to improve training stability and mitigate overfitting. The CNN was shown to be correlated and robust by demonstrating a 95% accuracy for gender classification and an MAE of 95.04 for age estimation. The performance of the model was evaluated in a comprehensive manner using standard metrics like accuracy, precision, recall and F1-score. Ethics and Bias - This project also explored the ethics of using age and gender classifiers, investigating fairness in model performance across different demographic groups to identify potential biases. To summarize, this project demonstrates the use of CNNs for age and gender classification utilizing the UTK Face dataset, a practical approach with significant scope for real-world application. Future work will focus on refining the model to address even more diverse datasets and exploring seamless integration into real-time applications. © 2024 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：