检索结果-内蒙古大学图书馆

Exploring optimizer efficiency for facial expression recognition with convolutional neural networks

JOURNAL OF ENGINEERING-JOE 2025年第1期2025卷

作者： Madni, Syed Hamid Hussain Pathmanatan, Lokessh A. L. Faheem, Muhammad Shahzad, Hafiz Muhammad Faisal Shah, Sajid Univ Southampton Malaysia Sch Elect & Comp Sci Johor Baharu Malaysia Univ Teknol Malaysia Fac Comp Skudai Johor Malaysia Univ Vaasa Sch Technol & Innovat Vaasa Finland VTT Tech Res Ctr Finland Ltd Espoo Finland Univ Sargodha Dept Comp Sci Sargodha Pakistan

It's widely accepted that human expressions, considering for roughly sixty percent of all daily interactions, are among the most authentic forms of communication. Numerous studies are being conducted to explore the importance of facial expressions and the development of machine-assisted recognition techniques. Significant progress is being made in facial and expression recognition, largely due to the rapid growth of machine learning and computer vision. A variety of algorithmic approaches and methods exist for detecting and recognizing facial expressions and features. This study investigates various optimization algorithms used with convolutional neural networks for facial expression recognition. The primary focus is on Adam, RMSProp, stochastic gradient descent and AdaMax optimizers. A comprehensive comparison is being made, examining the key aspects of each optimizer, including its advantages and disadvantages. Furthermore, the study also incorporates findings from recent studies that used these optimizers in various applications, highlighting their performance in terms of training time and precision. The aim is to illuminate the process of selecting a suitable optimizer for specific applications, analysing the trade-offs between training speed and higher accuracy levels. Moreover, this study provides a deeper analysis of the role optimizers play in machine learning-based facial expression recognition models. The discussion of the technical challenges posed by these optimizers and future improvements for achieving much more optimal results concludes the study.

关键词： image classification image processing image recognition optimisation convolution neural network machine leanring

来源：评论

学校读者我要写书评

暂无评论

A Cnn Model for Computationally Efficient Haze Removal in Railway Monitoring images

A Cnn Model for Computationally Efficient Haze Removal in Ra...

引用

International Conference on Knowledge and Smart Technology (KST)

作者： Pitchayapatchaya Srikram Atid Aumchoo Anurak Khamchaimongkhon Panrawee Rungskunroch Dept. Computer engineering RMUTT Pathum thani Thailand Dept. Industrial Engineering RMUTT Pathum thani Thailand

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Haze degrades the accuracy of computer vision algorithms for railway monitoring applications. This study proposed a dehazing algorithm emphasizing image quality, performed on GPUs and CPUs rather than on the IoT or low power devices, as a CNN-based haze removal model for an edge device. Our optimized CNN model utilized the ADD operation to relieve concatenation between two convolutional layers. The model was tested on a TFlite file, with results showing acceptable SSIM and PSNR image quality assessment values. The average computational time was $\mathbf{2 1 0}$ milliseconds on an Intel Xeon processor while a Raspberry Pi 4 Model B, operating as an offline edge inference device, achieved a processing time of $\mathbf{2 8 0}$ milliseconds per image.

关键词： image quality Performance evaluation Computational modeling image edge detection Tiny machine learning Rail transportation Computational efficiency Convolutional neural networks Monitoring Testing

来源：评论

学校读者我要写书评

暂无评论

ElectroCom61: A multiclass dataset for detection of electronic components

引用

DATA IN BRIEF 2025年 59卷 111331页

作者： Sayeedi, Md. Faiyaz Abdullah Osmani, Anas Mohammad Ishfaqul Muktadir Rahman, Taimur Deepti, Jannatul Ferdous Rahman, Raiyan Islam, Salekul United Int Univ Dept Comp Sci & Engn Dhaka Bangladesh North South Univ Dept Elect & Comp Engn Dhaka Bangladesh

In contemporary industrial, robotics, and technical education settings, the efficient detection and sorting of electronic components play a pivotal role in advancing automation and increasing efficiency in these sectors. To address this need, we present "ElectroCom61," a comprehensive multi- class object detection dataset encompassing 61 commonly used electronic components. Our dataset, sourced from the electronic components collection at United International University (UIU) in Dhaka, Bangladesh, comprises 2121 meticulously annotated images. We ensured that these images reflect real-world conditions, incorporating varied lighting, backgrounds, distances, and camera angles to bolster the potential machine learning model's robustness. We also divided the dataset into training, validation, and test sets to facilitate deep learning model development. Additionally, we conducted minimal pre-processing to optimise model training and performance. "ElectroCom61" stands as a valuable asset for developing cutting-edge electronic component detection systems, with far-reaching applications in both education and industry. Its potential applications span from interactive educational tools to e-waste management systems and streamlined inventory management processes in electronic manufacturing and automation. The code for technical validation of this dataset is available on GitHub: https: //***/faiyazabdullah/ElectroCom61 (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/)

关键词： Electronic component Deep learning image processing Object detection Computer vision E-waste management Industrial manufacturing Industrial automation

来源：评论

学校读者我要写书评

暂无评论

GRIPLAB 1.0: GRID image processing LABORATORY FOR DISTRIBUTED machine vision applications

GRIPLAB 1.0: GRID IMAGE PROCESSING LABORATORY FOR DISTRIBUTE...

引用

17th IEEE International Workshop on Enabling Technologies - Infrastructure for Collaborative Enterprises

作者： Crisafi, A. Giordano, D. Spampinato, C. Univ Catania Dept Informat & Telecommun Engn I-95125 Catania Italy

ISBN: (纸本)9780769533155

Computational grids have become an imperative rising platform for high-performance computing. However, the grid and the grid applications development are still far from being affirmed, which is mainly due to the undeveloped grid-enabled computing environments. For that reason in this paper we propose a toolbox, called GrIPLab 1.0 (Grid image processing Laboratory), that aims at providing high performance image-processing platform in a grid computing environment by using the GLite middleware developed in the EGEE project. GrIPLab 1.0 is a combination of vision algorithms (the most common and some novel approaches) on which complex distributed vision applications can be modeled as a simple sequence of choices in a user friendly interface. Therefore, the main advantage of the presented dynamic Grid toolbox is that provides a novel and comfortable access for scientific software developers and users without prior knowledge of Grid technologies or even the underlying architecture. In this paper, we discuss the infrastructure that provides flexible and useful mechanism to achieve series image processing operations and we analyze the advantages of using such a system.

关键词： Grid gLite machine vision

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool for Non-Invasive Medical Diagnostics 3

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool fo...

引用

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

作者： Ghosh, Aniruddha Pal, Sayan Snigdha Das, Sumedha Bhattacharya, Spandan Bhattacharya, Arijit De, Nilanjana Sahana, Sudipta University of Engineering and Management Kolkata Computer Science and Engineering(Artificial Intelligence and Machine Learning) Institute of Engineering & Management West Bengal Kolkata India

ISBN: (纸本)9798331523893

Thermography is an innovative technique that creates a heatmap of different portions of the human body by using infrared (IR) radiation where color scales vary for various temperature regions. This work's scope aims to extend thermal imaging applications with machine learning algorithms in conjunction with Azure API services to analyze thermal images and return a preliminary diagnosis. The project consists of generating heatmaps for better visualization results, analyzing them through image processing techniques, and feeding the information to Azure's Custom vision API to detect anomalies and classify health conditions and finally producing a proper report. This research allows individuals and healthcare professionals to have a quick, non-invasive diagnostic tool that empowers health monitoring and preventive care. It combines the capabilities of contemporary technologies with uses directed toward health practice, leading to inimical patient outcomes and true practices. © 2025 IEEE.

关键词： Anomaly Detection Custom vision API Predictive Analytics Thermography

来源：评论

学校读者我要写书评

暂无评论

Retinal image Analysis for Detection of Diabetic Macular Edema Using Transfer Learning

Retinal Image Analysis for Detection of Diabetic Macular Ede...

引用

Conference on Data Science and machine Learning applications (CDMA)

作者： Tayyaba Asim Shahzad Akbar Usama Shahzore Amjad Rehman Tanzila Saba Noor Ayesha Riphah College of Computing Riphah International University Faisalabad Pakistan AIDA Lab. CCIS Prince Sultan University Riyadh Saudi Arabia Zhengzhou University Zhengzhou China Henan RP China

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Diabetic Macular Edema (DME) is a disease of the eye's retina and it's a major factor of causing vision problems and leads towards blindness if it is undiagnosed. Early detection of DME can prevent vision loss and may reduce diabetic-related problems like cardiovascular issues. Therefore, this study presents a method for detecting DME through Optical Coherence Tomography (OCT) and fundus images using transfer learning. The proposed method is based on four stages: Pre-processing, augmentation, segmentation through DeepLabV3+, and binary classification of DME by using two (02) publicly available datasets; the first dataset is Messidor-2, which contains fundus images, and the second dataset is Retinal images of Optical Coherence Tomography (OCT). In the Messidor-2 dataset, the total number of images is 1744, and Retinal OCT images dataset consists of 84,495 images total, separated into four categories (CNV, DME, DRUSEN, NORMAL). In the proposed method, Convolutional Neural Networks (CNN) architectures ResNet50 and VGG-19 have been used for the detection of the DME. Convolutional Neural Networks (CNNs) have been extensively used in medical imaging analysis and classification. Using the well-known ResNet50 architecture for classification of each dataset, the proposed model yielded an accuracy of 98.79%, 99% of F1 Score, 98.43% of Precision, and recall of 98.89%. By using VGG-19, the proposed model gives an accuracy of 98.81%, 98.94% of Fl Score, 98.1 % of Precision and recall of 98.73%. When both models (ResNet50 and VGG-19) were compared, the VGG-19 gave the best accuracy.

关键词： image segmentation Accuracy Optical coherence tomography Transfer learning Computer architecture Retina Diabetes Topology Convolutional neural networks Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Imperceptible Transfer Attack on Large vision-Language Models

Imperceptible Transfer Attack on Large Vision-Language Model...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiaowen Cai Daizong Liu Runwei Guan Pan Zhou School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wangxuan Institute of Computer Technology Peking University Beijing China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In spite of achieving significant progress in recent years, Large vision-Language Models (LVLMs) are proven to be vulnerable to adversarial examples. Therefore, there is an urgent need for an effective adversarial attack to identify the deficiencies of LVLMs in security-sensitive applications. However, existing LVLM attackers generally optimize adversarial samples against a specific textual prompt with a certain LVLM model, tending to overfit the target prompt/network and hardly remain malicious once they are transferred to attack a different prompt/model. To this end, in this paper, we propose a novel Imperceptible Transfer Attack (ITA) against LVLMs to generate prompt/model-agnostic adversarial samples to enhance such adversarial transferability while further improving the imperceptibility. Specifically, we learn to apply appropriate visual transformations on image inputs to create diverse input patterns by selecting the optimal combination of operations from a pool of candidates, consequently improving adversarial transferability. We conceptualize the selection of optimal transformation combinations as an adversarial learning problem and employ a gradient approximation strategy with noise budget constraints to effectively generate imperceptible transferable samples. Extensive experiments on three LVLM models and two widely used datasets with three tasks demonstrate the superior performance of our ITA.

关键词： Visualization Noise Adversarial machine learning Acoustics Speech processing

来源：评论

学校读者我要写书评

暂无评论

Advanced Medical image processing for Lung Cancer Detection using machine Learning Algorithms

Advanced Medical Image Processing for Lung Cancer Detection ...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： P. Karthika T. Papitha Christobel Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram Chennai

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

One of the main causes of cancer-related deaths is lung cancer, and increasing survival rates requires early detection. The use of sophisticated machine learning (ML) algorithms to improve the identification of lung cancer from chest X-rays and CT images is investigated in this study. vision Transformers (ViT), Reinforcement Learning (RL), Generative Adversarial Networks (GANs), Meta-Learning, and Ensemble Learning are some of the state-of-the-art methods we use. Compared to conventional CNNs, By capturing long-range dependencies in medical pictures, vision Transformers (ViT) can improve accuracy by up to 85–96% compared to typical CNN models, especially when it comes to feature extraction and categorization. The optimization of diagnostic workflows using Reinforcement Learning (RL) results in a 90% improvement in decision-making efficiency and adaptive learning capabilities, which greatly enhance real-time picture analysis. Training datasets can be enhanced with Generative Adversarial Networks (GANs), which create realistic synthetic images and increase model generalization by 90–97%. This is crucial in situations where data is scarce or unbalanced. For uncommon or underrepresented cancer cases, meta-learning improves classification accuracy by 90% by allowing models to learn from sparsely labeled data. Ensemble learning reduces bias and variation by combining many models using approaches like XGBoost, Bagging, and Stacking, increasing total accuracy by 80–95%. When compared to conventional methods, key performance indicators including precision, recall, and F1 score demonstrate significant gains, with sensitivity rising by up to 90% and specificity improving by 85%. These cutting-edge algorithms greatly improve the detection of lung cancer, facilitating quicker and more precise diagnoses and assisting clinicians in making decisions that will benefit patients.

关键词： Metalearning Computer vision Adaptation models machine learning algorithms Accuracy Lung cancer Reinforcement learning Transformers Generative adversarial networks Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

image processing : : machine vision applications /

Image processing : : Machine vision applications /

引用

2008年

作者： Fofi David.

来源：评论

学校读者我要写书评

暂无评论

Improvement of image Classification Technique Using Transfer Learning

Improvement of Image Classification Technique Using Transfer...

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Devanand Bhonsle Ruhi Uzma Priyanka Prasad Rushikesh Bankar Manjushree Nayak Sampada Vishwas Massey Tanu Rizvi Eiectrical Engineering Shri Shanakaracharya Technical Campus Bhilai India Electrical Engineering Anjuman College of Engineering & Technology Nagpur India Artificial Intelligence and Machine Learning Shri Shanakaracharya Institute of Professional Management & Tech Bhilai India Electronics & Telecommunication Engineering G. H. Raisoni Institute of Engineering and Technology Nagpur India Computer Science Engineering NIST University Berhampur Odisha India Computer Science Engineering Shri Shanakaracharya Technical Campus Bhilai India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

image classification is one of the fundamental task in the digital image processing and computer vision. It is very essential to get high accuracy in it which requires substantial computational resources and a large dataset for the training using deep neural networks. Transfer learning technique is one of the solution which mitigate various challenges by leveraging pre-trained models on large datasets. In this paper; the effectiveness of the transfer technique is exhibited for the purpose of enhancing the image classification. A comprehensive survey has been done of the recent advancement for the purpose to investigate the strategies of transfer learning technique including feature extraction, fine tuning and domain adaption. Experimental results exhibits the efficacy of the transfer learning in the improvement of classification accuracy with less computational complexity. This paper also discusses practical consideration, challenges and future research scope for the utilization of transfer learning for image classification applications.

关键词： Training Surveys Adaptation models Accuracy Computational modeling Transfer learning Lighting License plate recognition Tuning image classification

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：