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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"

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Scalable machine learning Frameworks for Large-Scale Multimodal image and Speech Signal processing 8

Scalable Machine Learning Frameworks for Large-Scale Multimo...

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8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Dankan Gowda, V. Pathak, Disha Prasad, K.D.V. Srinivas, Ved Manu, Y.M. Reddy, N. Sudhakar Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Symbiosis Centre for Management Studies-Hyderabad Mulshi Pune Maharashtra Lavale412115 India Symbiosis Institute of Business Management Hyderabad Pune India Thiagarajar School of Management Department of Human Resources Tamil Nadu Madurai India Adichunchanagiri University Bgs Institute of Technology Department of Computer Science and Engineering Nagamangala Taluk B.G.Nagara Karnataka Mandya District India Mohan Babu University School of Engineering Department of Ece Tirupati India

ISBN: (纸本)9798350376425

The machine learning algorithm proposed in this paper is suitable for Big Data multimodal datasets and in particular for integrating image and speech data. Preliminary feature extraction is based on convolutional neural network (CNN) for image analysis and recurrent neural network (RNN) for speech processing, while the fusion was done in a fusion layer. For assessment, the system's performance is compared on the aspect of accuracy and latency, computational overhead, and resource consumption. The above experiment results prove that the proposed framework reduces the latency to 120ms and the required computational power, but the training and validation set accuracies as high as 90% and 87% are higher than the corresponding values of basic models such as CNN and hybrids of CNN+LSTM. It also illustrates the system's scalability: inference time of less than 2s in the range of moderate batch size, and lower memory consumption (110 MB) in comparison with the baseline models. The characteristics of the model mean that it takes less resources to process data, making the model ideal for use in applications where there is a need for processing of data in real time. © 2024 IEEE.

关键词： Convolutional neural networks

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Review of Dental Diagnosis by Deep learning Models: Trends, applications and Challenges 14

Review of Dental Diagnosis by Deep Learning Models: Trends, ...

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14th International conference on Emerging Ubiquitous Systems and Pervasive networks / 13th International conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Bouali, Rime Mahboub, Oussama Lazaar, Mohamed ENSA Abdemalek Essaadi University in Tetouan Morocco ENSIAS Mohammed V University in Rabat Morocco

Recently, deep learning (DL) has become increasingly important in the field of image processing, and its application in dental radiology has become particularly relevant. DL-based image recognition has become a key component of image interpretation in dental radiology, making it possible to automate diagnosis and treatment planning. These techniques can be applied to areas such as caries, tooth detection, periodontal disease, oral lesions, age determination, gender estimation, and osteoporosis. In addition, recent studies have shown that DL algorithms in these areas have high accuracy, with some studies achieving accuracy rates of over 90%. Such high accuracy can significantly improve the efficiency and accuracy of dental diagnostics and treatment planning. Thus, using DL in dental imaging may become an everyday tool in dentistry with the current advances in applications. In this paper, recent applications and potential challenges of using deep convolutional neural network architectures in dental image analysis will be reviewed. © 2024 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Fundamental Considerations on Representation learning for Multimodal processing

Fundamental Considerations on Representation Learning for Mu...

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Human Interface and the Management of Information (HIMI) part of International conference on Human-Computer Interaction (HCII)

作者： Jin'no, Kenya Izumi, Masato Okamoto, Saki Dai, Mizuki Takahashi, Chisato Inami, Tatsuro Tokyo City Univ Tokyo 1588557 Japan Tokyo City Univ Grad Sch Tokyo 1588557 Japan

ISBN: (纸本)9783031351310;9783031351327

In recent years, there has been an extremely active research boom in the fields of machine learning, particularly in artificial neural networks (ANNs). This boom was triggered by the extremely high performance of an image classification system based on cellular neural networks (CNNs) proposed in 2012. This is not only because of the high performance, but also because the proposed system uses GPGPU to reduce various computational costs, the ReLU function to prevent gradient vanish phenomena, and Dropout to realize a regularization. The availability of very easy-to-use deep learning modules such as TensorFlow and PyTorch also contributed to the rise of this type of research. As a result, various systems have been proposed and a variety of applications have been proposed. For example, the recent image generation model is a good example. On the other hand, theoretical analysis of such artificial neural networks is completely insufficient, and there is no rigorous explanation as to why each system works well. In deep learning to date, emphasis has been placed on improving the quality of output data, and little consideration has been given to the quality of information represented by latent variables from which features in the input information are extracted. On the other hand, improvement of representation learning by contrast learning, in which latent variable representations of similar input data in the same modality are brought closer together, has begun to take place. In this study, we assume a multimodal environment and examine the possibility of bringing latent variable representations close to similar input data in various modalities.

关键词： representation learning latent variables neural networks

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Effective Brain Tumor Detection using Convolutional neural networks 10

Effective Brain Tumor Detection using Convolutional Neural N...

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17th INDIACom;2023 10th International conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Narang, Aman Rajpoot, Aman Singh Jayaraman, Ramkumar Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9789380544472

In the recent scenario, the move from machine learning to deep learning has played a significant role in various applications such as, healthcare, automation, robotics, etc. Considerign the healthcare, diagnosing diseases, like brain tumor, brest cancer, etc. using Magnetic Resonance Imaging (MRI) images, is a complex process because the images contain various cell/image pixel divisions to identify the condition of the disease. Based on the complexity of the disease, identifying and diagnosing the tumour becomes a challenging task as it may lead to an increase in the medical survey on tumour disease based on the number of patients. In this paper, a Deep learning (DL) - Convolutional neural networks (CNN) based approach is proposed with the integration of data augmentation and image processing. The proposed approach provides classification of MRI images to diagnose the tumour location on the dense areas based on small tumour cells. The effectiveness of the proposed approach is analyzed based on the data accuracy as it achieves 96% with low complexity rate compared to the existing models such as, CNN and Artificial neural networks (ANN) models. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Convolutional neural networks

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Challenges and Opportunities in Integrating machine learning with Medical Imaging: A Comprehensive Review 2

Challenges and Opportunities in Integrating Machine Learning...

引用

2nd International conference on Informatics, ICI 2023

作者： Chaudhary, Manish Agrawal, Himanshu Jaypee Institute of Information Technology Computer Science and Engineering & Information Technology UP Noida India

ISBN: (纸本)9798350343830

In recent years, there has been a significant increase in the usage of electronic medical records and diagnostic imaging, which coincides with the enormous success that machine learning algorithms have had in performing image identification jobs in recent years. This article is an introduction to machine learning techniques as they are applied to medical image processing, with a specific focus on convolution neural networks as well as the clinical aspects of the issue. In this era of medical big data, one of the benefits of machine learning is that critical hierarchal linkages within the data may be discovered algorithmically rather than having to painstakingly hand-craft features. It saves a significant lot of time and effort. We cover major research areas and applications in medical image classification, localization, detection, segmentation, and registration sectors. In conclusion, we will discuss research challenges, emerging trends, and probable future possibilities. © 2023 IEEE.

关键词： Medical imaging

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Gaussian noise image recognition based on convolutional neural networks 5

Gaussian noise image recognition based on convolutional neur...

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5th International conference on Computer Vision, image and Deep learning, CVIDL 2024

作者： Huang, Chengqiang Yang, Yanjun Zunyi Normal University Zunyi China

ISBN: (纸本)9798350373820

Gaussian noise is one of the main noise sources of digital images. In this paper, the idea of judging before processing is adopted to improve the efficiency of Gaussian noise removal. First, the Pascal VOC dataset is taken as a clean image, and the Gaussian noise image dataset is obtained by adding Gaussian noise with random density. Then, various conventional and lightweight convolutional neural network models are constructed, and the constructed datasets are imported for training, validating and testing. Experimental results show that Vgg-16 achieves the highest classification accuracy of 0.9929. Due to its high computational complexity, VGG-16 is only applicable to scenarios that do not require mobile applications such as servers and hosts. SqueezeNet achieves the second-highest classification accuracy of 0.9906 with relatively low computational complexity, making it suitable for portable consumer electronics such as smartphones and smartwatches. Gaussian noise image recognition is realized based on conventional and lightweight convolutional neural network models, which provides a technical basis for efficient Gaussian denoising. © 2024 IEEE.

关键词： Convolutional neural networks

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Empirical Analysis of Hybrid Classical Variational Quantum neural networks for Target Classification from SAR Data 4th

Empirical Analysis of Hybrid Classical Variational Quantum ...

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4th International conference on machine Intelligence and Signal processing, MISP 2022

作者： Gajjar, Pranshav Saxena, Aayush Ranpariya, Divyesh Shah, Pooja Das, Anup Institute of Technology Nirma University Ahmedabad India SRM Institute Of Science and Technology Chennai India Space Applications Centre ISRO Ahmedabad India

ISBN: (纸本)9789819900848

The advancements in the field of quantum machine learning and the ability of quantum-enhanced networks to acquire a higher effective dimension are promising for remote sensing applications and synthetic aperture radar (SAR) data. This paper aims to incorporate variational quantum circuits with existing classification-based deep learning state-of-the-art architectures. Tests are proposed by using ResNets, DenseNets, and a SqueezeNet-based convolutional neural network. Various dressed quantum circuits associated with these encoders are created and tested for novel applicability analysis. A famous publicly available X-Band SAR image-based dataset called moving and stationary target acquisition and recognition (MSTAR) is utilized to demonstrate image-centric target classification. The utility of quantum-enhanced and hybrid classical-quantum transfer learning models is provided. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

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neural Network Deep Supervised learning Algorithm Based on Multimodal Data 3

Neural Network Deep Supervised Learning Algorithm Based on M...

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3rd International conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Fu, Yunxia College of Humanities and Teachers Heilongjiang University of Technology Heilongjiang Jixi China

ISBN: (纸本)9798331508456

In response to the problems of difficulty in information fusion, complex feature learning and difficulty in deep network training in multimodal data, a deep supervised learning algorithm is introduced. Through a multi-level supervision mechanism, it aims to improve the performance and generalization ability of neural networks in multimodal data processing. First, a deep supervised learning framework based on multi-modal information is proposed to jointly learn and fuse features of different modalities. Then, a hierarchical supervision mechanism is designed to introduce supervision signals at different levels of the network to enhance the model's understanding and utilization of the characteristics of each modality. Next, the improved Convolutional neural Network (CNN) and LSTM (Long Short-Term Memory) structures are used to process image and text modal data. Finally, in order to avoid information loss, adversarial training is adopted to ensure that the fused features have good robustness and optimize the gradient flow during the training process. The multi-modal + text combination solution that introduces text information has significantly improved the model accuracy. The accuracy of some samples is as high as 0.95, far exceeding the effect of using images or multi-modal images alone. The multi-modal data processing framework based on deep supervised learning proposed in this study effectively solves the difficulties in information fusion and feature learning. Especially when processing heterogeneous data such as images and text, the generalization ability and robustness of neural networks are significantly improved. This method provides new ideas for the promotion of multimodal learning in practical applications. © 2025 IEEE.

关键词： Data curation

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Using Ensemble Convolutional neural Network to Detect Deepfakes Using Periocular Data

Using Ensemble Convolutional Neural Network to Detect Deepfa...

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Intelligent Systems conference (IntelliSys)

作者： Johnson, David Yuan, Xiaohong Roy, Kaushik North Carolina Agr & Technol State Univ Greensboro NC 27411 USA

ISBN: (纸本)9783031477232;9783031477249

Deepfakes are manipulated or altered images, or video, that are created using deep learning models with high levels of photorealism. A popular method of deepfake creation is using convolutional neural networks (CNN). Deepfakes created using CNN comparatively show high qualities of realism, yet oftentimes leave artifacts and distortions in the generated media that can be detected using machine learning and deep learning algorithms. In recent years, there has been an influx of periocular image and video data because of the increased usage of face masks. By wearing masks, much of what is used for facial recognition is hidden, leaving only the periocular region visible to an observer. This loss of vital information leads to easier misidentification of media, allowing deepfakes to less likely be identified as fake. In this work, feature extraction methods, such as Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and CNN, are used to train an ensemble deep learning model to detect deepfakes in videos on a frame-by-frame level based on the periocular region. Our proposed model is able to distinguish original and manipulated images with averaged accuracy of 98.9 percent, which is an improvement to previous works by combining SIFT and HOG for deepfake detection in convolutional neural networks.

关键词： Deepfake detection CNN Deep neural network Computer vision Scale invariant feature transform Histogram of oriented gradients

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image Recognition Methods Based on Deep learning 1

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3rd International conference on 3D Imaging Technologies - Multidimensional Signal processing and Deep learning (3DIT-MSP and DL)

作者： Zhang, Zehua Univ Calif San Diego 9500 Gilman Dr La Jolla CA 92093 USA

ISBN: (数字)9789811924484

ISBN: (纸本)9789811924484;9789811924477

With the development of science and technology worldwide, computer technology has gradually been widely used in various fields like the military and biomedical domains. In this situation, people's requirements for image recognition are increasing. However, the efficiency of manual image recognition is very low, and image recognition has always been a weak field in computer technology. This paper will introduce various deep learning models such as feedforward neural networks, convolutional neural networks, and recurrent neural networks. We will talk about how these models work and then compare and analyze these different networks. In this paper, we will try to train a convolutional neural network from a data set. The "dogs-vs-cats" data set from Kaggle will be used. In the "dogs-vs-cats" experiment part, some basic tasks in image processing will be discussed, such as feature extraction method, which is one of the most important steps in each link of image processing. Finally, the machine should be able to distinguish between pictures of dogs and cats. Moreover, we will also discuss some preliminary applications of deep learning in image recognition.

关键词： Deep learning image recognition machine learning

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