Traitement Du Signal最新文献_第5页

A Hybrid Diabetic Retinopathy Neural Network Model for Early Diabetic Retinopathy Detection and Classification of Fundus Images 用于早期糖尿病视网膜病变检测和眼底图像分类的混合型糖尿病视网膜病变神经网络模型

IF 1.9 4区计算机科学

Traitement Du Signal Pub Date : 2023-12-30 DOI: 10.18280/ts.400631

Jayant Shimpi, Poonkuntran Shanmugam

引用次数: 0

Advancing Cephalometric Soft-Tissue Landmark Detection: An Integrated AdaBoost Learning Approach Incorporating Haar-Like and Spatial Features 推进头颅软组织地标检测：融合 Haar 类和空间特征的 AdaBoost 综合学习方法

IF 1.9 4区计算机科学

Traitement Du Signal Pub Date : 2023-12-30 DOI: 10.18280/ts.400649

Said Elaiwat, Mohammad Azad, Mohammad Khursheed Alam, Marwan Abo-zanona, Bassam Elzaghmouri, Hani Omar

引用次数: 0

Optimizing Water Body Detection in Southeast Hubei Using PCA on Landsat ETM+ Imagery 在 Landsat ETM+ 影像上使用 PCA 优化鄂东南地区的水体探测

IF 1.9 4区计算机科学

Traitement Du Signal Pub Date : 2023-12-30 DOI: 10.18280/ts.400612

Xiaohong Xiao, Jiangang Xie, Bin Cai

引用次数: 0

Detection and Classification of Plant Stress Using Hybrid Deep Convolution Neural Networks: A Multi-Scale Vision Transformer Approach 使用混合深度卷积神经网络检测和分类植物应力：多尺度视觉变换器方法

IF 1.9 4区计算机科学

Traitement Du Signal Pub Date : 2023-12-30 DOI: 10.18280/ts.400625

Bhargavi Thokala, Sumathi Doraikannan

引用次数: 0

Advancements in Lossless and Reversible Compression of Digital Pathology Images via Auto-Recursive Set Partitioning in Hierarchical Trees and Wavelet Decomposition 通过层次树和小波分解中的自动递归集分割实现数字病理图像无损和可逆压缩的进展

IF 1.9 4区计算机科学

Traitement Du Signal Pub Date : 2023-12-30 DOI: 10.18280/ts.400632

Goh Jee Yuan, Afzan Adam, Mohammad Kamrul Hasan, Z. Alyasseri, Mohammad Faizal Ahmad Fauzi, Elaine Wan Ling Chan

引用次数: 0

Enhanced Material Classification via MobileSEMNet: Leveraging MobileNetV2 for SEM Image Analysis 通过 MobileSEMNet 增强材料分类：利用 MobileNetV2 进行 SEM 图像分析

IF 1.9 4区计算机科学

Traitement Du Signal Pub Date : 2023-12-30 DOI: 10.18280/ts.400638

Cihat Aydin

引用次数: 0

Personalized Learning Pathway Generation for Online Education Through Image Recognition 通过图像识别生成在线教育的个性化学习路径

IF 1.9 4区计算机科学

Traitement Du Signal Pub Date : 2023-12-30 DOI: 10.18280/ts.400640

Jie Yan, Na Wang, Yiming Wei, Menglu Han

引用次数: 0

Leveraging Deep Learning for Identification of Illicit Images in Digital Forensic Investigations 利用深度学习识别数字取证调查中的非法图像

IF 1.9 4区计算机科学

Traitement Du Signal Pub Date : 2023-12-30 DOI: 10.18280/ts.400617

M. Eriş, Mustafa Kaya

引用次数: 0

Support Vector Machines Optimisation for Face Recognition Using Sparrow Search Algorithm 基于麻雀搜索算法的人脸识别支持向量机优化

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400519

Wenli Lei, Yang Lei, Bin Li, Kun Jia

{"title":"Support Vector Machines Optimisation for Face Recognition Using Sparrow Search Algorithm","authors":"Wenli Lei, Yang Lei, Bin Li, Kun Jia","doi":"10.18280/ts.400519","DOIUrl":"https://doi.org/10.18280/ts.400519","url":null,"abstract":"In the realm of face recognition utilising Support Vector Machines (SVM), the adaptivity of the penalty parameter c and the kernel function g is often found lacking, leading to suboptimal recognition rates. To address this issue, an approach harnessing the Sparrow Search Algorithm (SSA) for SVM parameter optimisation has been proposed. Traditional methods such as grid and random search, alongside other swarm intelligence optimisation algorithms like Particle Swarm Algorithm (PSO) and Differential Evolutionary Algorithm (DE), were surpassed by the capabilities of the SSA in numerous applications. Cross-validation (CV) was employed, with the SVM model training recognition accuracy serving as the SSA fitness value. Upon achieving optimal fitness values, the best combination of hyperparameters was ascertained. The overarching aim was to deploy the SSA for global optimisation of SVM's penalty parameters and kernel function, ensuring the derivation of the globally optimal solution for the ultimate classifier model in face recognition tasks. An empirical analysis conducted on the ORL standard face database revealed that the proposed method outperformed the PSO, DE, Gray Wolf Algorithm (GWO), and Enhanced Gray Wolf Algorithm (EGWO), registering an average accuracy of 95.1%. This starkly contrasts with the 81.9% accuracy of the traditional SVM. Such results demonstrate the method's efficacy in enhancing recognition performance, offering a novel avenue to elevate the accuracy of conventional SVM-based face recognition.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models 利用先进的YOLOv8对象识别模型在法医图像中实时检测和识别嫌疑人

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400521

Serkan Karakuş, Mustafa Kaya, Seda Arslan Tuncer

{"title":"Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models","authors":"Serkan Karakuş, Mustafa Kaya, Seda Arslan Tuncer","doi":"10.18280/ts.400521","DOIUrl":"https://doi.org/10.18280/ts.400521","url":null,"abstract":"Rapid advancements in artificial intelligence, machine learning, deep learning, coupled with easy access to high-capacity processing hardware, expansive organized datasets, and the evolution of artificial intelligence algorithms, have extensively influenced numerous fields. Digital Forensics is one such discipline where the application of artificial intelligence has been significantly amplified in recent years. The analysis of extensive image and video files derived from forensic evidence presents challenges in terms of time efficiency and accuracy. To surmount these challenges, artificial intelligence models can be employed to perform identification and classification processes on these data, thus expediting the resolution of forensic cases with enhanced precision. In the current study, state-of-the-art pre-trained YOLOv8 object recognition models - nano, small, medium, large, and extra-large - were utilized. These models were trained on the Wider-Face dataset with the objective of identifying suspects from images and videos sourced from digital materials in the field of digital forensics. The models achieved mean Average Precision (mAP) values of 97.513%, 98.569%, 98.763%, 98.775%, and 99.032% respectively. The YOLOv8 architecture demonstrated superior performance, outperforming the YOLOv5 architecture by a margin of 7.1% to 8.8%. To aid digital forensic experts in the detection and identification of suspicious individuals, a desktop application capable of real-time image analysis was developed.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136067781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0