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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

共 6128 条记录，以下是251-260 订阅

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Detection of Corn Leaf Infection Using CNN with Various Optimizers 2

Detection of Corn Leaf Infection Using CNN with Various Opti...

引用

2nd International conference on Artificial Intelligence and machine learning applications, AIMLA 2024

作者： Sreekanth, G.R. Latha, R.S. Roja, R. Sankarnanth, S. Gayathri, A. Kongu Engineering College Department of Computer Science and Engineering Erode India Kongu Engineering College Department of Computer Technology Erode India

ISBN: (纸本)9798350349221

Corn, a grain categorized within the grass family, stands as a fundamental staple crop globally. It plays a crucial role in supplying sustenance for both humans and livestock, in addition to serving as a raw material for various industries. In 2017, Africa produced 7.4% of the 1135 million tons produced worldwide in 40 million hectares. Throughout its growing process, corn plant is impacted by numerous illnesses. Lack of early detection of its diseases may result in a loss in productivity and even crop failure. It is proposed to analyze the leaves of the corn plant for identifying the diseases by exploiting deep learning methods. A wide range of deep learning algorithms are being utilized to detect the corn leaf with infection with good range of accuracy. To categorize the leaf, image processing models are mostly used for these kind of disease detections. Since it has in-built convolutional layer, CNN can reduce the image with high dimensionality without losing any kind of information. In this proposed work CNN with Adam optimizer has been implemented for better results and achieved 91.68 % of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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AI Innovator: Text to image Generation using GAN

AI Innovator: Text to Image Generation using GAN

引用

2024 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Nimbarte, Mrudula Hussain, Alfiya Budhlani, Kanak Bansod, Chirag Ramdham, Mayur Kanhe, Om Khullar, Amit S.B. Jain Institute of Technology Management and Research Department of Computer Science and Engineering MS Nagpur India

ISBN: (纸本)9798350348460

This review provides a comprehensive review of the latest approaches and advances in text-to-image processing using artificial neural networks (GANs). The work under review uses GAN architecture, where generators and classifiers work together to extract corresponding images from descriptions. This research includes a comprehensive review of the GAN framework, delving into the complexities of the generator and discrimination during training. The project solves problems related to converting text input into visual images using a combination of natural language processing and computer vision. This article reviews the main concepts of the architecture as well as the use of advanced neural networks for text understanding and image synthesis. Particular attention is paid to the optimization process the generator uses to improve the quality and integrity of the resulting images. Additionally, the review highlights the evolution of different labels and discusses their implications for real-world applications. © 2024 IEEE.

关键词： Adversarial Training Computer Vision Generative Adversarial networks (GANs) image Synthesis Multimodal learning Natural Language processing (NLP) neural Network Architecture Text to image Generation

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Evaluating Deep learning Approaches for Enhanced Underwater image Quality 4

Evaluating Deep Learning Approaches for Enhanced Underwater ...

引用

4th International conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Vairagar, Shubhangi Gupta, Praveen Sharma, R.P. Csmu Department of Computer Engineering Navi Mumbai India Dr. D. Y. Patil Institute of Technology Pimpri Pune India

ISBN: (纸本)9798331529635

This research investigates the application of deep learning techniques, including Deep Convolutional neural networks (DCNNs) and Conditional Generative Adversarial networks (CGANs), for enhancing the quality of underwater images (UWIE). Underwater imaging presents significant challenges due to factors such as low light conditions, color distortion, and limited visibility. This study aims to improve the quality of underwater images by addressing these challenges through advanced image processing techniques. The performance of different UWIE methods is evaluated using metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Underwater image Quality Metrics (UIQM). The findings of this research contribute to the advancement of underwater imaging technology, enabling improved image quality for various applications, including marine biology, underwater exploration, and underwater archaeology. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Advanced Methods for Tomato Leaf Disease Detection: A Convolutional neural

Exploring Advanced Methods for Tomato Leaf Disease Detection...

引用

2024 International conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Gupta, Aman Tiwari, Shashank Arora, Yojna Sharma, Avinash Kumar Sharda University Dept. of Computer Science Greater Noida India

ISBN: (纸本)9798350378092

CNN(Convolutional neural Network) establishes an ongoing, Cutting-edge method in image processing with significant potential and encouraging outcomes. Having demonstrated its effectiveness across diverse applications, Convolutional neural Network has now made significant inroads into the agricultural domain as well. We conducted a survey of 38 research studies that employed Convolutional neural Network techniques to address a range of research challenges related to tomato *** explored the realms of tomato plant research where Convolutional neural networks were implemented, delving into the employed data preprocessing techniques, as well as the utilization of transfer learning and augmentation *** analyzed dataset details such as the sources utilized, the total number of images, the number of classes, and the train-test-validation ratio employed. Furthermore, we conducted comparisons across different Convolutional neural Network architectures and discussed the resulting *** findings indicated that Convolutional neural networks consistently outperformed other image processing techniques. However, it was noted that the performance of Convolutional neural networks is heavily influenced by the quality and characteristics of the dataset used. © 2024 IEEE.

关键词： Convolutional neural networks

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Unravelling Visual Narratives with Deep learning for image Caption Generation 1

Unravelling Visual Narratives with Deep Learning for Image C...

引用

1st IEEE International conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Yoga, M. Ramyasri, M.M. Bhavatharini, N. Harini, M. NivethaSri, A. Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

image Caption Generation (ICG), situated at the confluence of computer vision and natural language processing, empowers machines to comprehend visual content and express it in human-like language. This research offers a comprehensive overview of key concepts, methodologies, and challenges in ICG. The process involves developing algorithms for the automatic generation of contextually relevant captions, utilizing deep neural networks for feature extraction, and employing natural language processing techniques for coherent composition. Recent advancements, particularly in convolutional neural networks for image processing and recurrent neural networks for language modelling, have significantly elevated the performance of image captioning systems. The study delves into the core components of an ICG system, including pre-processing techniques for image data, feature extraction mechanisms, and the integration of language models. Attention mechanisms, a key innovation in this field, enable the model to focus on relevant image regions while generating captions, closely mirroring human attention patterns. Despite notable progress, ICG faces several challenges, such as handling diverse and complex visual scenes, ensuring cross-modal coherence between images and captions, and addressing biases present in training data. Ethical considerations, particularly in applications like automated content generation, are also discussed. The study concludes by highlighting potential future directions in ICG research, including the incorporation of multimodal learning approaches, enhancing the interpretability of generated captions, and addressing societal concerns related to bias and fairness. As ICG continues to evolve, it holds promise for various applications, ranging from accessibility for the visually impaired to improving content indexing and retrieval in multimedia databases. The research also underscores the significance of the accuracy attainments, showcasing the success of the pr

关键词： Feature extraction

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Ch4os: Discretized Generative Adversarial Network for Functionality-Preserving Evasive Modification on Malware 33rd

Ch4os: Discretized Generative Adversarial Network for Functi...

引用

33rd International conference on Artificial neural networks and machine learning (ICANN)

作者： Molloy, Christopher Alaca, Furkan Ding, Steven H. H. Queens Univ Kingston ON K7L 3N6 Canada

ISBN: (纸本)9783031723551;9783031723568

Rapid advancements in Artificial Intelligence (AI) have led to applications in varying domains. Due to the exponential growth of cyberspace in recent years, the domain of cybersecurity has seen substantial integrations of AI to aid in handling large amounts of data. The discipline of malware analysis within cybersecurity has leveraged AI to develop advanced analysis techniques. Within malware analysis, AI has been applied to both malware detection and evasive malware generation. Adversarial learning on Malware (ALM) is the study of evasive modifications that is focused on AI-based detection tools. Most of the existing ALM evasive modification methods produce samples that are not valid executables. Solutions that produce effective valid executables are limited to injecting random code from a finite set of benign samples. Instead of using known code, we aim to optimize the injected bytes to increase evasion probability through adversarial learning. We propose Ch4os, a malware modification system trained in a Generative Adversarial Network setting. We introduce the Valid machine Code Execution (VaME) activation function, guaranteeing functionality of modified malware samples while preserving differentiability of the learning process. As well, to address the challenge of learning efficiency and stability, we introduce the Binary Copier Pre-training (BCP) method. We conduct experiments on a dataset of chronologically separated malware for a simulated real-world detection scenario and show Ch4os can generate 152% more evasive samples compared to the state-of-the-art.

关键词： machine learning Adversarial learning Malware Analysis

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Interpretation of Diabetic Foot Ulcer image Classification Using Layer Attribution Algorithms 19th

Interpretation of Diabetic Foot Ulcer Image Classification ...

引用

19th International conference on Soft Computing Models in Industrial and Environmental applications, SOCO 2024

作者： Arkah, Zinah Mohsin Pontes, Beatriz Rubio, Cristina University of Seville Seville Spain University of Information Technology and Communications Baghdad Iraq

ISBN: (纸本)9783031750120

The rise of deep learning has led to the development of intricate neural networks with diverse layers for analyzing various data types. While these deep neural networks excel in tasks involving unstructured data such as images, text, and audio, their inherent complexity poses challenges in terms of interpretability. Unlike traditional machine learning models (e.g., decision trees, random forests) that are considered white-box models and are easy to interpret, deep neural networks often function as black-box models. Ensuring interpretability of these models is essential to comprehend the reasoning behind predictions and the contributing factors. This research employs Captum, a Python library, to elucidate predictions made by a PyTorch-based diabetic foot ulcer (DFU) image classification network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Analysis of Different Object Detection Techniques Used in image processing

Analysis of Different Object Detection Techniques Used in Im...

引用

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

作者： Gour, Sonam Dubey, Shirish Mohan Sharma, Gaurav Joshi, Neetu Poornima College of Engineering Department of Computer Engineering Jaipur India

ISBN: (纸本)9798350373783

Computer vision research uses self-driving systems, robot surveillance, and science interpretation. A plethora of applications, including robotics, self-driving systems, video surveillance, and scene interpretation, have spurred intensive research in computer vision throughout the past ten years. Visual recognition systems have attracted a lot of research attention and include localization, detection, picture categorization, and detection. Given the significant developments in neural networks, especially deep learning, it performs remarkably well. One area where a lot of success with computer vision is needed is object recognition. The purpose of this work is to conduct a methodical investigation into the relevance of object detection and its applications in the field of computer vision. This work gives a thorough introduction to object detection, as well as various approaches, computer vision basics, and applications, which will be useful to the image processing and computer vision research communities. © 2024 IEEE.

关键词： machine vision

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Leveraging Deep learning for Early Stage Diabetic Retinopathy Detection: A Novel CNN and Transfer learning Comparision 2

Leveraging Deep Learning for Early Stage Diabetic Retinopath...

引用

2nd International conference on Artificial Intelligence and machine learning applications, AIMLA 2024

作者： Manohar, Rayavarapu Aarthi, M. Shilpa Ancy Jenifer, J. Karunya Institute of Technology and Sciences CSE Department Coimbatore India

ISBN: (纸本)9798350349221

Diabetic retinopathy (DR) is a contributor, to vision impairment highlighting the need, for accurate and efficient screening methods. While traditional techniques are prevalent, deep learning approaches offer promising alternatives. In this research paper, A new Convolutional neural Network (CNN) design was proposed and that focuses on detecting Diabetic Retinopathy (DR) from retinal images. The evaluation of the effectiveness of the proposed CNN is comparing it with two known trained models, namely VGG 19 and ResNet 101 which are commonly used in image classification tasks. The proposed CNN model has the highest performance with respect to the other two models was based on the performance metrics(Accuracy, Precision, Recall, F1-score). The paper gives an overview of how the proposed CNN model is built. © 2024 IEEE.

关键词： Convolutional neural networks

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Click-Through Rate Prediction Based on Filtering-Enhanced with Multi-head Attention 33rd

Click-Through Rate Prediction Based on Filtering-Enhanced wi...

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33rd International conference on Artificial neural networks and machine learning (ICANN)

作者： Yao, Meihan Zhang, Shuxi Lv, Lang Chen, Jianxia Lu, Mengyu Jiang, Gaohang Xiao, Liang Song, Zhina Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China

ISBN: (纸本)9783031723551;9783031723568

Click-Through Rate (CTR) prediction is a crucial task in recommend systems (RSs), particularly in large-scale industrial applications. CTR models based on graph neural networks (GNNs) is currently the mainstream technology, however, it also encounters challenges in terms of feature interactions and user interests. In the context of RSs, the individual attribute information associated with each entity holds significant importance beyond the inherent interactions between users and items. Rational utilization and capturing of dependencies among these attributes can substantially enhance the predictive accuracy of the model. Therefore, we propose two attention mechanisms of interactions, including internal interaction and external interaction, to handle the information exchange within the attributes of either users or items individually, as well as the interactions between the attributes of users and items. Simultaneously, we introduce a multi-head attention (MHA) mechanism for the interaction selection, to capture features from different dimensions. Furthermore, the behavioral data reflecting user interests unavoidably contains noise, we attenuates noise by utilizing fast Fourier transform (FFT) filtering algorithms to transfer information from the spatial domain to the frequency domain. Experimental results on three benchmarks show that our model outperforms existing SOTA models.

关键词： Click-Through Rate Filtering Algorithm Graph neural networks Feature Interaction

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