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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

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Beyond the Pixels: Detecting Originality and Assessing Product Condition using Digital image processing Techniques

Beyond the Pixels: Detecting Originality and Assessing Produ...

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International conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Ishit Arhatia Bryan Abraham Department of Computing Technologies SRM Institute of Science and Technology India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

This paper introduces a novel approach using digital image processing for product management, focusing on authenticity verification and quality assessment. Advanced algorithms distinguish genuine from fraudulent products and evaluate their condition. The study highlights the potential of digital image processing to enhance supply chain security and consumer trust, promising significant business impacts by reducing counterfeit circulation, improving brand reputation, and optimizing inventory management

关键词： Computers Digital images Supply chains Focusing Data integration Inventory management Real-time systems

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Efficiency of deep neural networks for reinforced concrete shear walls failure mode detection 27

Efficiency of deep neural networks for reinforced concrete s...

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27th International Computer conference, Computer Society of Iran, CSICC 2022

作者： Barkhordari, Mohammad Sadegh Massone, Leonardo M. Department Of Civil And Environmental Engineering Iran University Of Chile Department Of Civil Engineering Santiago Chile

ISBN: (纸本)9781665480277

After a severe earthquake, it is critical to check the health of hospitals, buildings, fire stations, to name but a few. New technologies such as deep learning algorithms, image processing, and signal processing methods have become more appealing in such quick applications because of their higher reliabilities and efficiency than traditional visual methods. On the other hand, reinforced concrete shear walls are the key structural elements of the most buildings as lateral force resisting systems in seismic areas. Determining the failure mode of reinforced concrete shear walls is a crucial task that can help engineers and designers choose the best retrofitting options. The goal of this research is to assess the ability of deep neural networks for predicting the failure mode of the reinforced concrete shear walls. Furthermore, the SHapley Additive exPlanation approach is used to demonstrate the relevance and contribution of the factors that influence the reinforced concrete shear walls failure mode determination. © 2022 IEEE.

关键词： Deep learning Visualization Buildings Neurons Signal processing algorithms Signal processing Concrete

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249709

The proceedings contain 113 papers. The special focus in this conference is on Applied Technologies. The topics include: Automatic Evaluation of Physiotherapy Activities Using Deep Learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering Visual Deficiencies in Pilots Candidates Using Data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital image processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical image Segmentation;access Control Through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science Database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised Machine Learning Model Using Facial Recognition;crime Data Analysis Using Machine Learning Models;Gene Therapy as a Solution to Genetic Diseases Through DNA Manipulation;Vulnerability of CAPTCHA systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep Learning algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives Through the Production of Cyclopentenol.

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Synergizing Support Vector Machines and image processing for Superior Skin Cancer Detection : A Crammer-Singer Approach

Synergizing Support Vector Machines and Image Processing for...

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International conference on Communication and Electronics systems (ICCES)

作者： S. Divya S. Thilagavathi Department of Computer Sciene Sri Krishna Adithya College of Arts and Science Coimbatore India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Skin cancer, which primarily impacts skin exposed to ultraviolet (UV) rays against the sun, represents dangerous to the most significant organs in the human body, the skin. Usually, a spot, lump, or mole that appears on the skin is the primary suspicion of skin cancer. However, each of these can undergo changes in coloring or shape as time passes. Recovery for skin cancer is mostly possible if the disease is discovered early. Numerous medical diagnostic methods, such as Dermoscopy, biopsy, and ocular examination of the affected area, are useful in helping anticipate the development of skin cancer. However, these approaches have the disadvantage of delivering erroneous results because it is extremely difficult to distinguish between normal and malignant skin. Therefore, the drawback of these diagnostic procedures is that machine learning algorithms are currently used together with image processing techniques to examine the images for the purpose of precisely identifying skin cancer. The current research employs the ISIC dataset to develop a novel model for skin cancer classification that combines image processing techniques with advanced machine learning methods, including Crammer-Singer Support vector machine learning algorithms. The categorization of skin cancer begins with preprocessing the input image, which includes hair removal using a morphological filter and image enhancement using a median filter to minimize noise and increase image clarity. The ABCD approach is used to segment lesion images by evaluating them for asymmetry, border irregularity, color variability, and diameter. The crammer-Singer SVM algorithm is then used with these images to classify skin lesions into various types such as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and squamous cell carcinoma (SCC), leveraging its robust multi-class handling capabilities. The system achieve

关键词： Support vector machines image segmentation Machine learning algorithms Accuracy Filtering algorithms Skin Classification algorithms Lesions image enhancement Skin cancer

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

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Low Power Consumption Online Testing Technique for Wearable Wireless Body Area Networks 37

Low Power Consumption Online Testing Technique for Wearable ...

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37th conference on Design of Circuits and Integrated Circuits (DCIS)

作者： Lasaga Arana, Inigo Mikel Ramirez Barcenas, Alberto Garcia-Valderas, Mario Lopez-Ongil, Celia Univ Carlos III Madrid Dept Elect Technol Leganes Spain

ISBN: (数字)9781665459501

ISBN: (纸本)9781665459501

Internet of Things (IoT) is a growing field of research and in real-life applications. Cyber-physical systems are aimed to monitor physical variables, processing data, applying artificial intelligent algorithms, and responding in real time for further actions. For example, on-the-edge medical monitoring of chronic illnesses or supervising of an industrial process can help to early detect small anomalies or misfunctions, preventing catastrophic events or assuring minimum quality thresholds. These systems require continuous monitoring, data collection, signal processing and analysis, which demand smart sensors, efficient wireless communications, and powerful processing modules. This is especially critical in wearable body area networks (BAN) where size, weight and power consumption are key elements in the system's usability and attractiveness. Dependability becomes a big concern in the design and deployment of wearable devices that can be considered unsupervised wireless nodes, with high complexity in architecture and functionality. This work proposes the integration of a software online testing procedure for monitoring intra-node faults appearance, detecting the presence of errors, reporting their effects, and performing a graceful degradation when possible, maintaining a low power consumption. The online testing proposal has been implemented and validated in a real system, Bindi from the UC3M4Safety team. The effect on the power consumption has been measured showing a very low effect on the overall power budget.

关键词： IoT CPS W2BAN Online testing Dependability On-the-edge computing Power consumption Affective Computing

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Abnormal Behavior Detection in Electronic-Exam Videos Using BeatGAN 8

Abnormal Behavior Detection in Electronic-Exam Videos Using ...

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8th International Iranian conference on Signal processing and Intelligent systems, ICSPIS 2022

作者： Atabay, Habibollah Agh Hassanpour, Hamid Shahrood University of Technology Image Processing & Data Mining Lab Shahrood Iran

ISBN: (纸本)9781665476232

Abnormal Behavior Detection is a core ability to recognize cheating in electronic exams (e-exams), especially in the scenes where a fraudulent candidate hides unauthorized resources from the view of the proctor. This work utilizes the BeatGAN algorithm to detect behavioral abnormalities in the skeleton-based motion information of candidates during remote e-exams. We examine the algorithm on the previously published dataset of skeleton features extracted from e-exam videos. The results show that BeatGAN outperforms previously investigated algorithms in sequence anomaly detection using the raw joint-coordinate, implicit bone-length-and-angle and explicit bone-length feature types. But, in event anomaly detection, BeatGAN does not improve the state-of-the-art results on the dataset. © 2022 IEEE.

关键词： Anomaly detection

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A Comparative Analysis of Branch-Cut and Quality-Guided algorithms For inSAR Interferogram

A Comparative Analysis of Branch-Cut and Quality-Guided Algo...

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International conference on image and Signal processing and their Applications (ISPA)

作者： Saoussen Djeddi Tarek Bentahar Atef Bentahar Riad Saidi Hanane Djellab Laboratory of Mathematics Informatics and Systems (LAMIS) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Laboratory of Electrical Engineering-Telecommunications (LABGET) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Dept Commun Base Science and Technology Mustapha Ben Boulaid Batna2 University Batna Algeria LTI Laboratory of Guelma Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Interferometric synthetic aperture radar (inSAR) phase image is a key for the digital elevation model of earth mapping. To achieve this three-dimensional reconstruction, phase unwrapping process must be performed whitch is used to calculate the accurate elevations from the wrapped phase map. For the noise-free images, this process is just a simple integration of the wrapped gradient. But in reality, there is no phase image without noise, therefore the phase unwrapping have to be adaptive with a strong immunity to noise. Several adaptive algorithms have been proposed in such area where Goldstein’s branch-cut and Flynn’s quality-guided are the widely known in the path-following category, they are the most used methods and all other propositions are just enhancements or hybridizations. In this paper, we analyze the performance of each one and provide the substantial difference between them. Both algorithms are implemented using simulated and real inSAR data of different patterns and are analyzed under several relevant criteria.

关键词： image quality Earth Noise Signal processing algorithms Adaptive algorithms Noise measurement Synthetic aperture radar

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition

A Review on the Applications of Machine Learning and Deep Le...

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Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Ketan Wanjari Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Sawangi (Meghe) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition.

关键词： Deep learning Ethics Data privacy image recognition Machine learning algorithms Reviews Social networking (online) Vectors Security Stakeholders

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Toward Consistency in Writing Proficiency Assessment: Mitigating Classification Variability in Developmental Education 17

Toward Consistency in Writing Proficiency Assessment: Mitiga...

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17th International conference on Computer Supported Education, CSEDU 2025

作者： Da Corte, Miguel Baptista, Jorge University of Algarve Faro Portugal INESC-ID Lisboa Lisbon Portugal

ISBN: (纸本)9789897587467

This study investigates the adequacy of Machine Learning (ML)-based systems, specifically ACCUPLACER, compared to human rater classifications within U.S. Developmental Education. A corpus of 100 essays was assessed by human raters using 6 linguistic descriptors, with each essay receiving a skill-level classification. These classifications were compared to those automatically generated by ACCUPLACER. Disagreements among raters were analyzed and resolved, producing a gold standard used as a benchmark for modeling ACCUPLACER'S classification task. A comparison of skill levels assigned by ACCUPLACER and humans revealed a "weak" Pearson correlation (ρ = 0.22), indicating a significant misplacement rate and raising important pedagogical and institutional concerns. Several ML algorithms were tested to replicate ACCUPLACER'S classification approach. Using the Chi-square (χ2) method to rank the most predictive linguistic descriptors, Naïve Bayes achieved 81.1% accuracy with the top-four ranked features. These findings emphasize the importance of refining descriptors and incorporating human input into the training of automated ML systems. Additionally, the gold standard developed for the 6 linguistic descriptors and overall skill levels can be used to (i) assess and classify students' English (L1) writing proficiency more holistically and equitably;(ii) support future ML modeling tasks;and (iii) enhance both student outcomes and higher education efficiency. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Natural language processing systems

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