检索结果-内蒙古大学图书馆

Application of computer Vision Approach for Automation and Classification of Fashion Store

Application of Computer Vision Approach for Automation and C...

2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adebayo, Aminat Abiodun, Moses Kazeem Awoniran, Ifeoluwa Temitayo Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Adeniyi, Jide Kehinde Rueben Aremu, Dayo Alhikman University Dept. of Computer Science Ilorin Nigeria Landmark University Dept. of Computer Science Omu-Aran Nigeria Bowen University Dept. of Computer Science Iwo Nigeria University of Ilorin Dept. of Computer Science Ilorin Nigeria

ISBN: (纸本)9798350358155

The fashion industry has long been interested in utilizing computer vision techniques to automate tasks such as recognizing different types of clothing items in images. This study proposed a novel convolutional neural network (CNN) architecture for the classification of fashion outfits into three categories: shoes, sunglasses, and trousers. The proposed CNN architecture is based on state-of-the-art deep learning techniques and is trained on a fairly large-scale dataset of fashion images. The effectiveness of the proposed CNN architecture is evaluated through extensive experiments and analysis. The result demonstrates that the proposed CNN architecture achieves a high accuracy rate of 0.99 on each diagonal value of the confusion matrix. This indicates that the proposed CNN is capable of accurately classifying each item type with high accuracy. Additionally, the study investigates the impact of various hyperparameters on the performance of the proposed CNN architecture and found that the model which uses a 7x7 filter size and 64 filter number yields higher accuracy compared to other filter combinations. The study demonstrates the potential of CNN in automating fashion item recognition, which can lead to improved efficiency and accuracy in the fashion industry. It can also form the basis for developing more advanced computer vision systems for the fashion industry. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Effectiveness of a Facial Recognition-Based Attendance Management System in a Real-World Setting 14

Evaluating the Effectiveness of a Facial Recognition-Based A...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nijgal, Dhamini R George, Stebin Subramanian, Parameswaran Christ University Dept. of Computer Science Bangalore India Christ University Dept. of Data Science Bangalore India

ISBN: (纸本)9798350335095

Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features. © 2023 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Comparison of MobileNet, ResNet50 and InceptionV3 for Efficient Plant Pathology Detection

A Comprehensive Comparison of MobileNet, ResNet50 and Incept...

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Jevoor, Susmita Girish, V. Chandan, N. Umamaheswari, D. Gondkar, Raju Ramakrishna Dept. Computer Science Bangalore India

ISBN: (纸本)9798331538538

Plant diseases have a strong impact on agricultural productivity due to economic factors and a reduction in crop quality. This work focuses on the classification of apple leaves into four classes: healthy, rust-infected, scab-infected, and infected by both diseases, using the Plant Pathology FGVC7-2020 dataset that contains 3,642 images in total. The work involves the analysis of three sophisticated deep learning architectures: ResNet50, MobileNet and *** turned out that MobileN et showed the highest performance, reaching a 92% accuracy rate;it was followed by ResNet50 with 75% accuracy and InceptionV3 at 73%, hence underlining its better generalization capability and efficiency in classifying. We discuss the proposed methodology, which includes data preprocessing techniques, experimental results and final conclusions, is discussed in detail. These results underline the fundamental importance of determining an appropriate neural network architecture for the recognition of plant diseases, which is of prime importance to improve agricultural productivity. © 2025 IEEE.

关键词： Crops

来源：评论

学校读者我要写书评

暂无评论

Controlling the Number of Sample-Contributive Vertices in Generalized Sampling of Graph Signals

Controlling the Number of Sample-Contributive Vertices in Ge...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yamashita, Keitaro Naganuma, Kazuki Ono, Shunsuke Dept. of Computer Science Institute of Science Tokyo Tokyo Japan

ISBN: (纸本)9798350368741

This paper proposes a method for sampling graph signals by designing a flexible sampling operator via a difference-of-convex (DC) based algorithm. Departing from conventional methods limited to bandlimited signals, our method extend the generalized sampling theory to handle graph signals beyond bandlimitedness. Our method aims to design a flexible sampling operator that mixes vertex values, while controlling the number of sample-contributive vertices. The operator design is formulated as a feasibility problem with an invertibility constraint for the best possible recovery and a constraint controlling the number of sample-contributive vertices. We reformulate the problem as a DC-like optimization problem by using the nuclear norm to obtain a tight relaxation of the invertibility constraint. To solve this problem, we present a DC-based algorithm. The effectiveness of our approach is demonstrated through sampling and recovery experiments on various graph signal models. © 2025 IEEE.

关键词： arbitrary prior difference-of-convex based algorithm generalized sampling theory graph signal processing

来源：评论

学校读者我要写书评

暂无评论

An Evolutionary Algorithm for the Travelling Sales Man problem 3

An Evolutionary Algorithm for the Travelling Sales Man probl...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Waseem, Shaik Mohammad Jeshwanth, N.L. Lingesh, A. Jayanth, Matta Bhargavi, C.H. Dhanwanth, Batini Dept. of Computer Science and Engineering Chennai India Dept. of Artificial Intelligence and Data Science Chennai India Sai Spurthi Institute of Technology Dept. of Computer Science and Engineering Gangaram Sathuaplly India Jönköping University Dept. of Ai Engineering Jönköping Sweden

ISBN: (纸本)9798331509675

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques. © 2025 IEEE.

关键词： Traveling salesman problem

来源：评论

学校读者我要写书评

暂无评论

Stable and Lightweight Deep Primal-Dual Unrolling for Constrained Image Restoration with Convolutional Sparse Coding

Stable and Lightweight Deep Primal-Dual Unrolling for Constr...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ueki, Takafumi Naganuma, Kazuki Ono, Shunsuke Dept. Computer Science Institute of Science Tokyo Tokyo Japan

ISBN: (纸本)9798350368741

This paper proposes an image restoration method using a convolutional sparse coding (CSC) unrolling network with a box constraint and total variation. Unlike conventional deep unrolling methods, the proposed method constructs an interpretable lightweight network with restoration stability. Specifically, we design a new constrained convex optimization problem that incorporates CSC, a box constraint, and total variation (TV). The box constraint ensures that the image values fall within a certain range, making the restoration process stable. In addition, combining total variation and CSC leads to high interpretability and representation with a small number of parameters. We develop an optimization algorithm based on the primal-dual splitting (PDS) method. Then, by unrolling the algorithm, we construct the proposed lightweight network. Experimental results demonstrate the superiority of the proposed method in image restoration accuracy and lightweightness of the proposed network in the number of parameters. © 2025 IEEE.

关键词： box constraint convolutional sparse coding deep unrolling Image restoration primal-dual splitting algorithm

来源：评论

学校读者我要写书评

暂无评论

Facial Expression Recognition with Transfer Learning: A Deep Dive 2

Facial Expression Recognition with Transfer Learning: A Deep...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Debroy, Moushreeta Thomas, K.T. Mathew, Lian Dept. of Data Science Lavasa Pune India

ISBN: (纸本)9798350384277

In the realm of affective computing, where the nuanced interpretation of facial expressions plays a pivotal role, this research presents a comprehensive methodology aimed at refining the precision of facial expression recognition on the CK+ (Cohn-Kanade Extended) dataset. Our method uses the robust DenseNet121 architecture that has been pretrained on the 'imagenet' dataset, and it leverages transfer learning on the foundational CK+ dataset. The model deftly handles issues with overfitting, normalization, and feature extraction that are present in facial expression detection on CK+. Our approach not only achieves an overall accuracy of 98%, with a 5.86% accuracy enhancement over the base paper on the CK+ dataset, but also shows remarkable precision, recall, and F1-score values for individual emotion classes. It is noteworthy that emotions such as anger, contempt, and disgust have precision rates that reach 100%. The study highlights the model's noteworthy input to affective computing and presents its possible real-world uses in emotion analysis on CK+ and human-computer interaction. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Moth flame optimization algorithm for efficient feature selection and hybrid classification technique for intrusion detection system

引用

Multimedia Tools and Applications 2025年 1-21页

作者： Keerthanarani, U. Selvakumar, V. Singaravelan, S. Raja, S. Edwin Thenmozhi, K. Shunmugam, D. Arun Dept. of Computer Science and Engineering P.S.R Engineering College Sivakasi India Dept.Of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai Avadi India Dept. Of Computer Science and Engineering Coimbatore Institute of Engineering and Technology Coimbatore India Dept. Of Electronics and Communication Engineering P.S.R Engineering College Sivakasi India

The rapid expansion of the Internet of Things (IoT) brings numerous benefits but further presents fresh difficulties, especially in terms of security. The distributed and interconnected nature of IoT devices makes them susceptible to various security threats. An Intrusion Detection System (IDS) plays a crucial role in safeguarding IoT networks by monitoring, detecting, and responding to potential security incidents. The Internet of Things (IoT) connects billions of devices, sensors, and systems, transforming industries and everyday life. However, this extensive interconnectivity also introduces significant security risks, making Intrusion Detection Systems (IDS) a critical component for safeguarding IoT environments. Feature selection is a crucial aspect of building effective Intrusion Detection Systems (IDS). Selecting relevant features not only helps in achieving better classification accuracy but also contributes to reducing false alarm rates. In the proposed system a moth flame optimizer is utilized to select the efficient features from the dataset, overall accuracy of over 97%. Further it is classified employing Random Forest and J48 classifier which attains high classification accuracy and low false alarm rate. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

ML-driven Emotion Identification for Feedback Analysis in E-learning Platforms

ML-driven Emotion Identification for Feedback Analysis in E-...

引用

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chitteti, Chengamma Kopparam, Runvika Ganesh, B.V.S.S. Sutraya, Sukerthi Kamakshi, V. Jangam, Shiridinath School of Computing Dept. of Data Science Tirupati India Dept. of Csse Tirupati India G. Narayanamma Institute of Technology and Science for Women Dept. of Data Science Hyderabad India

ISBN: (纸本)9798350373783

With an emphasis on the cognitive and emotional effects of e-learning platforms, this study fills a research gap concerning user experience studies. The study suggests a nonintrusive technique to evaluate the psychological impacts of e-learning platforms by considering feedback data of users in the form of videos and analyzes emotions from video and decides the user feedback level. This process automates the understanding of feedback in a short amount of time for anyone, without requiring specific knowledge of the system. Feedback analysis for E-Learning platforms using deep learning methods for facial expression analysis is proposed in this work. Convolutional Neural Networks (CNNs), a category of deep learning, has become increasingly popular for its ability to precisely analyze and comprehend emotions from facial photos. These models may efficiently learn and extract essential features by being trained on large datasets of tagged facial expression photos. This application takes video feedback as input for various E-learning platforms and analyzes emotions and displays the predicted emotions on video. A publicly accessible dataset is used to test the proposed technique, and the results show that it achieves 90.3% accuracy. This indicates that the approach is highly effective in accurately recognizing facial emotions. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Distributed Scheduler for Computing on the Edge 1

A Dynamic Distributed Scheduler for Computing on the Edge

引用

1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Hu, Fei Mehta, Kunal Mishra, Shivakant Almutawa, Mohammad University of Colorado Dept. of Computer Science United States Kuwait University Dept. of Computer Science Kuwait

ISBN: (纸本)9798350349658

Edge computing is crucial for IoT applications, especially those needing quick, private data handling. However, these applications are resource-intensive, and edge computing resources are limited compared to cloud capabilities. Efficiently using these limited resources is essential to meet all application demands, including latency, privacy, and cost. Due to the dynamic and hybrid nature of IoT environments, static scheduling systems often falls short in meeting these diverse constraints. This paper describes the design, implementation, and evaluation of a dynamic distributed scheduler for the edge. This scheduler schedules the execution of various computing tasks across all computing resources available at the edge in order to satisfy the diverse constraints of an IoT application. The key characteristic of this scheduler is that it constantly monitors the current state of the IoT infrastructure and dynamically adjusts the scheduling of various computing tasks based on the current environmental context as well as the current computing and network conditions. The scheduler utilizes a predictive profiling method to manage the hybrid and variable nature of edge computing resources and tasks. A prototype of this scheduler has been implemented and the paper demonstrates its practicality via an augmented reality application. © 2024 IEEE.

关键词： Augmented Reality Applications De-vice Profile Dynamic Distributed Scheduler Edge Computing Parallel Com-puting

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：