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The Utilization of Artificial Intelligence and Deep learning for Identifying Vehicle Wheels 4th

The Utilization of Artificial Intelligence and Deep Learning...

引用

4th International conference on Digital Technologies and applications (ICDTA)

作者： Maaroufi, Mohammed Mouhcine Stour, Laila Agoumi, Ali Demori, Julien Hassan II Univ Casablanca Fac Sci & Technol Mohammedia Proc & Environm Engn Lab BP 146 Mohammadia Morocco Hassania Sch Publ Works Civil Engn Hydraul Environm & Climate Lab Casablanca Morocco Macq Mobil Evere Belgium

ISBN: (纸本)9783031686498;9783031686504

The technology using artificial intelligence (AI) allows, through the preprocessing of video and image data, their transformation into traffic information, the recognition in abusive conditions of elements, the specification of the different characteristics of the vehicle (type, brand, model, color), object detection and tracking, automatic calibration and positioning. Algorithms for performance evaluation can evaluate recognition quality and detect malfunctions due to external factors, reduced lighting conditions, or incorrect camera installation. This article discusses the application of AI and deep learning techniques to identify vehicle wheels. It highlights the preprocessing of image and video data to extract traffic information, detect vehicle attributes, and perform object tracking. The study focuses on understanding AI and convolutional neural networks (CNNs) for image processing, emphasizing the significance of feature extraction algorithms. The research outlines the methodology for creating a dataset of annotated wheel images, selecting the YOLO detection algorithm, and using the Darknet Github framework for training and testing CNNs. It discusses the training process, including configuration, data augmentation, and evaluation metrics like Mean Average Precision (MAP) and Intersection Over Union (IOU). Finally, the article concludes with insights into the practical applications of the developed wheel detection system, suggesting future improvements and potential integrations with other projects.

关键词： Smart mobility artificial intelligence deep learning convolutional neural networks wheel

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Precision diagnosis of brain tumors: An overview of advanced machine learning techniques 1st

Precision diagnosis of brain tumors: An overview of advanced...

引用

1st International conference on applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Shivani Agrawal, Krishna Kant Agrawal, Gaurav School of Computer Science and Engineering Galgotias University Uttar Pradesh India

ISBN: (纸本)9781032874036

This paper provides a comprehensive examination of deep learning techniques for brain tumor segmentation in magnetic resonance imaging (MRI) data, delving into the various methods utilized. It begins with an overview of traditional segmentation techniques, emphasizing the limitations in accurately capturing tumor boundaries and appearances. Deep learning, particularly convolutional neural networks (CNNs), is introduced as an effective alternative due to its ability to handle complex image features. The review offers an in-depth analysis of recent advancements in deep learning-based brain tumor segmentation, highlighting various convolutional neural network (CNN) architectures such as U-Net and DeepMedic. It discusses the efficacy, computational efficiency, and robustness of these methods across different tumor types and MRI modalities. The paper addresses key challenges and future directions in the field, including the need for large, annotated datasets, improving model interpretability, integrating with clinical workflows, and mitigating class imbalance and data variability. The paper concludes by highlighting the transformative potential of deep learning in enhancing brain tumor segmentation, leading to more accurate diagnosis and treatment planning. Researchers aim to advance understanding of tumor biology, predict treatment outcomes, and ultimately improve patient care in neuro-oncology by harnessing the capabilities of deep learning. © 2025 the Author(s).

关键词： Convolutional neural networks

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A Multi Model Deep learning Framework for Sarcasm Detection in Regional Social Media: An Overview 1

A Multi Model Deep Learning Framework for Sarcasm Detection ...

引用

1st International conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Bhadgale, Anilkumar M. Maheswari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350355611

Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful approach for addressing these challenges, particularly in regional social media, where language variations and local idiomatic expressions complicate the detection process. This survey explores the latest developments in multi-model deep learning frameworks for sarcasm detection, focusing on their application in regional social media. The survey begins by reviewing foundational techniques in sarcasm detection, including traditional machine learning approaches that rely on handcrafted features. These methods, although effective in certain contexts, often fail to capture the subtleties of sarcasm in informal, region-specific languages. The advent of deep learning has led to significant advancements, particularly through models like Convolutional neural networks (CNNs), Recurrent neural networks (RNNs), and Transformers. These architectures, combined with Natural Language processing (NLP) techniques, have enhanced the ability to identify sarcasm through text analysis. However, single-modal approaches focusing solely on text fail to fully capture sarcasm's multimodal nature, especially on platforms where users often express themselves through a combination of text, images, emojis, and video. This has led to the development of multi-model frameworks that integrate various data modalities, such as text, image, and user behaviour, to better understand the context of sarcastic expressions. In regional social media, where local language and cultural symbols play a crucial role, these multi-model approaches prove even more valuable. This survey highlights key multi-model frameworks, emphasizing their use in regional settings. By examining datasets, model architectures, and evaluation metrics, the survey underscores the importance of combining textual and non-textual

关键词： Convolutional neural networks

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AR-Base Educational Mobile Application for Pet Care Management

AR-Base Educational Mobile Application for Pet Care Manageme...

引用

2024 International conference on Computer and applications, ICCA 2024

作者： Herath, H.M.V.P. Jayawardhana, A.L. Pietersz, N.J. Siriwardana, Samanthi E.R Manathunga, Kalpani Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350367560

This research proposes an innovative mobile application designed to enhance pet care management by assisting dog owners in understanding and addressing various aspects of dog care. The application incorporates three key components: Augmented Reality (AR) guidance for capturing dog skin conditions, image processing to determine dog skin conditions, breed, life stage, and body condition, and video analysis to assess dog behaviors. By integrating advanced machine learning models, including DenseNet121, Xception, and YOLO v5 algorithm, along with Convolutional neural networks (CNN), the application aims to provide accurate health assessments and recommendations. The goal of this research is to enhance pet care practices through the use of innovative technology, enabling users to make informed decisions regarding their dogs' health, nutrition, behaviors, and overall well-being. © 2024 IEEE.

关键词： Convolutional neural networks

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Volumetric Feature Extraction from 2D images Using Cubixels 23

Volumetric Feature Extraction from 2D Images Using Cubixels

引用

23rd IEEE International conference on machine learning and applications, ICMLA 2024

作者： Aburass, Sanad Rumman, Maha Abu Luther College Department of Computer Science DecorahIA United States Maharishi International University Department of Computer Science FairfieldIA United States

ISBN: (纸本)9798350374889

This paper introduces innovative volumetric feature extraction methods for 2D images using Cubixels and the Volume of the Void (VoV) metric. Cubixels transform traditional pixels into three-dimensional entities, capturing more detailed spatial and color relationships by assigning RGB values to the dimensions of width, length, and height. The VoV metric quantifies spatial gaps between adjacent Cubixels, providing valuable insights into textural characteristics and edge acuity. We developed two methods: the first focuses on calculating VoV between Cubixels, while the second integrates gradient and curvature features to enhance geometric feature extraction. These methods were applied to the CIFAR-10 and MNIST datasets and evaluated using four convolutional neural network (CNN) models incorporating residual connections, spatial dropout, and attention mechanisms. Our experimental results demonstrate significant improvements in model performance, including higher validation accuracy and reduced overfitting, as indicated by the Overfitting Index. This study underscores the potential of volumetric feature extraction in advancing image processing and computer vision, paving the way for more sophisticated and context-aware AI applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Analyzing the Characteristics of Gradient Descent and Non-Gradient Descent-Based Algorithms in neural Network learning 4

Analyzing the Characteristics of Gradient Descent and Non-Gr...

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4th IEEE International conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Kim, HyunJung Konkuk University dept. of SANG-HUH COLLEGE Seoul Korea Republic of

ISBN: (纸本)9798350395914

In this paper, a comparative analysis of optimization algorithms for artificial neural networks widely used in the fields of machine learning and deep learning was conducted. Beyond the conventional backpropagation algorithm utilizing gradient descent, we investigated the characteristics of various optimization methods for neural networks, including alternative non-gradient descent approaches such as the Forward-Forward (FF) algorithm and Genetic Algorithm (GA). Our analysis revealed diverse performances of each algorithm contingent upon the characteristics of the image dataset and the problem at hand. Furthermore, by scrutinizing the features and limitations of each algorithm, we provided considerations for practical applications. These findings offer crucial guidelines for selecting the optimal weight update methods tailored to specific problem domains. © 2024 IEEE.

关键词： Contrastive learning

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive learning

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BSMO: Enhancing Multitask learning Through Batch Swapping Optimization

BSMO: Enhancing Multitask Learning Through Batch Swapping Op...

引用

conference on Real-Time image processing and Deep learning

作者： Spencer, Zephaniah Schirner, Gunar Tabkhi, Hamed UNC Charlotte 9201 Univ City Blvd Charlotte NC 28223 USA Northeastern Univ 360 Huntington Ave Boston MA 02115 USA

ISBN: (纸本)9781510673878;9781510673861

With the advent of deep learning, there has been an ever-growing list of applications to which Deep Convolutional neural networks (DCNNs) can be applied. The field of Multi-Task learning (MTL) attempts to provide optimizations to many-task systems, improving performance by optimization algorithms and structural changes to these networks. However, we have found that current MTL optimization algorithms often impose burdensome computation overheads, require meticulously labeled datasets, and do not adapt to tasks with significantly different loss distributions. We propose a new MTL optimization algorithm: Batch Swapping with Multiple Optimizers (BSMO). We utilize single-task labeled data to train on a multi-task hard parameter sharing (HPS) network through swapping tasks at the batch level. This dramatically increases the flexibility and scalability of training on an HPS network by allowing for per-task datasets and augmentation pipelines. We demonstrate the efficacy of BSMO versus current SO TA algorithms by benchmarking across contemporary benchmarks & networks.

关键词： Deep learning Multi-Task learning Real-Time Vision

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Text Extraction from Document images using CNN and LSTM

Text Extraction from Document Images using CNN and LSTM

引用

2023 International conference on Sustainable Communication networks and Application, ICSCNA 2023

作者： Shinde, Sandip Bhosle, Sanket Bomble, Gaurav Bhosale, Shourya Bokil, Siddhant Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350313987

This research study proposes an advanced system that combines image processing techniques with deep learning models to achieve precise text extraction from images. Our approach in this paper involves several key steps. First, we apply preprocessing techniques to enhance the image quality and clarity of text regions. Next, the proposed method employs image processing algorithms to identify and isolate text regions within the image. To recognize and transcribe the extracted text, the proposed method utilizes deep learning models, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs). To train the deep learning model in the proposed method, we generate a labeled dataset by annotating text regions in images. The proposed model highlights the effectiveness of integrating image processing and deep learning techniques in achieving accurate and efficient text extraction from images. The system's performance and flexibility make it a valuable tool for applications that require reliable text extraction capabilities. © 2023 IEEE.

关键词： activation function Convolutional neural Network (CNN) deep learning Recurrent neural Network (RNN) text extraction from images

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Deep learning for Sentiment Analysis with Adaptive neural networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th International conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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