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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing"

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Enhancing Precision Medicine through artificial neural networks for Phenotyping and Risk Prediction of Rare Genetic Disorders

Enhancing Precision Medicine through Artificial Neural Netwo...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International conference on

作者： Seeniappan Kaliappan V. Balaji S. Socrates Nagendar Yamsani Department of Mechatronics Engineering KCG College of Technology Karapakkam Chennai Tamil Nadu India Department of Mechanical Engineering Loyola Institute of Technology Chennai Tamil Nadu India Department of Mechanical Engineering Velammal Institute of Technology Chennai Tamil Nadu India School of Computer Science & Artificial Intelligence SR University Warangal Telangana

In this study, we present an innovative approach to enhance precision medicine for rare genetic disorders by leveraging a comprehensive artificial neural Network (ANN) architecture. This architecture amalgamates three pivotal algorithms: Convolutional neural Network (CNN) for image analysis, Recurrent neural Network (RNN) for sequential clinical data, and Dense neural Network (DNN) for feature integration and risk prediction. CNNs are specialized for efficient grid-like data handling, making them ideal for image analysis and pattern recognition. RNNs, on the other hand, excel in processing sequential data due to their inherent ability to retain information from previous inputs. DNNs play a fundamental role in capturing intricate patterns and relationships in the data. Our approach strategically integrates these algorithms into a cohesive ANN framework, enabling a comprehensive analysis of genetic data, which is critical in the domain of rare genetic disorders. By leveraging the capabilities of CNN, RNN, and DNN, a comprehensive strategy emerges, enabling accurate risk prediction and phenotype analysis. This approach significantly contributes to the progression of precision medicine, specifically tailored for rare genetic disorders.

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Enhanced Multi-Class Weather image Classification Using Data Augmentation and Dynamic Learning on Pre-Trained Deep neural Net

Enhanced Multi-Class Weather Image Classification Using Data...

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Emerging Techniques in Computational Intelligence (ICETCI), International conference on

作者： Dibyendu Chattoraj Abhiroop Chatterjee Susmita Ghosh Ashish Ghosh Emmett J. Ientilucci Jadavpur University India IIIT Bhubaneswar India Rochester Institute of Technology USA

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

The present work employs the VGG16 deep neural network as a foundational model for weather image classification. Experiments have been carried out on a Weather Phenomena Dataset (WEAPD) having 11 distinct classes. The article exploits data augmentation along with suitable hyperparameter tuning (specifically using dynamic learning rate) on transfer learnt VGG16. An overall best accuracy of 92.10% establishes the effectiveness of the proposed method. The performance of the proposed strategy is also compared with those of the state-of-the-art methods. This research contributes in advancing the understanding and accuracy of weather event classification, holding potential implications for various applications in meteorology and related fields.

关键词： Training Accuracy Transfer learning artificial neural networks Data augmentation image augmentation Tuning Computational intelligence Meteorology image classification

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A Double-Edged Sword: The Power of Two in Defending Against DNN Backdoor Attacks

A Double-Edged Sword: The Power of Two in Defending Against ...

引用

European Signal processing conference (EUSIPCO)

作者： Quentin Le Roux Kassem Kallas Teddy Furon Thales DIS La Ciotat France Inria Rennes France Inria INSERM & IMT Atlantique Brest France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Backdoor attacks on deep neural networks work by injecting them with a malicious behavior during training. Such behavior can then be activated at test-time using cleverly-crafted triggers. Defending against backdoors is key in machine learning security in order to safeguard the trust between model providers and users. This paper demonstrates the open problem of back-door defense performance against a representative selection of backdoor attacks, with a main focus on input purification (a valuable defense category in black-box contexts where all DNN inputs are preprocessed in the hope of erasing a potential trigger). We show that current defenses are adversary-aware and dataset-dependent. They typically focus on patch-based attacks and simpler image classification datasets. This brittleness when using stand-alone defenses highlights the cat-and-mouse game currently affecting the backdoor literature. In this context, we propose a two-defense strategy using existing methods as a palliative solution while waiting for future developments.

关键词： Training Purification Closed box artificial neural networks Machine learning Games Signal processing Robustness Security image classification

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image Quality Aware Deep Learning networks for Budgerigar Gender Recognition

Image Quality Aware Deep Learning Networks for Budgerigar Ge...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Hsiao, Michael A. Mooney, Kyle Wang, Jinhui Spanish Fort High School Spanish FortAL United States University of South Alabama MobileAL United States

ISBN: (纸本)9798331504847

Although traditional human image recognition suggests that higher resolution images will yield better results than lower resolution images because they hold more details, the results of this study prove that low-resolution images may have their own niche in the training of artificial Intelligence (AI) models for more accurate and reliable image recognition. The purpose of this study is to investigate the impact of image resolution on the performance of an artificial neural Network (ANN), by case study on budgie gender identification. This study will be an important reference for the training of color-oriented models by encouraging low-power and small-size data storage due to low-resolution images, decreasing cost and resources of AI applications. In the study, multiple data sets of images of budgie ceres with varying image resolutions, ranging from 22×22 to 256×256 pixels, are used to train and validate neural networks and test the reliability. It finds that the peak and end accuracy of the models are both right skewed with respect to the pixel size distribution and high accuracy. High reliability can be associated with a low image resolution, 22×22, 25×25, and 32×32 pixels image data set. The highest accuracy is obtained with an optimized ratio of a number of pixels to a number of images in a training dataset to create a stable learning rate and prevent overfitting or underfitting too rapidly. © 2025 IEEE.

关键词： Deep neural networks

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Boosting neural networks to Decompile Optimized Binaries 22

Boosting Neural Networks to Decompile Optimized Binaries

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38th Annual Computer Security applications conference (ACSAC)

作者： Cao, Ying Liang, Ruigang Chen, Kai Hu, Peiwei Chinese Acad Sci IIE SKLOIS Beijing Peoples R China UCAS Sch CyberSecur Beijing Peoples R China BAAI Beijing Peoples R China

ISBN: (纸本)9781450397599

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

关键词： C++ (programming language)

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WiGNet: Windowed Vision Graph neural Network

WiGNet: Windowed Vision Graph Neural Network

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IEEE Workshop on applications of Computer Vision (WACV)

作者： Gabriele Spadaro Marco Grangetto Attilio Fiandrotti Enzo Tartaglione Jhony H. Giraldo University of Turin Italy LTCI Télécom Paris Institut Polytechnique de Paris France

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In recent years, Graph neural networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their practical applicability is hindered by the computational complexity of constructing the graph, which scales quadratically with the image size. In this paper, we introduce a novel Windowed vision Graph neural Network (WiGNet) model for efficient image processing. WiGNet explores a different strategy from previous works by partitioning the image into windows and constructing a graph within each window. Therefore, our model uses graph convolutions instead of the typical 2D convolution or self-attention mechanism. WiGNet effectively manages computational and memory complexity for large image sizes. We evaluate our method in the imageNet-1k benchmark dataset and test the adaptability of WiGNet using the CelebA-HQ dataset as a downstream task with higher-resolution images. In both of these scenarios, our method achieves competitive results compared to previous vision GNNs while keeping memory and computational complexity at bay. WiGNet offers a promising solution toward the deployment of vision GNNs in real-world applications. We publicly released the code and pre-trained models at https://***/EIDOSLAB/WiGNet.

关键词： Convolutional codes Computer vision Adaptation models Accuracy Computational modeling Computer architecture Benchmark testing Graph neural networks Computational complexity image classification

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Multimodal Emotion Detection Using Deep Learning

Multimodal Emotion Detection Using Deep Learning

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Data Science, Agents & artificial Intelligence (ICDSAAI), International conference on

作者： Rasika Mohitkar Department of Computer Science Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Emotion recognition is a vital component of human communication and has significant applications across various domains. The goal of this research is to integrate text and visual data to create an effective model for real-time multimodal emotion identification. The text analysis utilizes fastText due to its speed and effectiveness, while 2D Convolutional neural Network (CNN) is employed for image analysis to capture local features and handle content variations. The CMU-MOSEI dataset is used for testing and combining the datasets, and emotion predictions classified into six distinct categories. The study compares various approaches and emphasizes the advantages of multimodal affective computing systems, which achieve higher classification accuracy depending on factors like the number of emotions analyzed, extracted features, classification methods, and database consistency. By addressing challenges in understanding visual signals and emotional awareness, this research aims to advance emotion detection and recognition systems.

关键词： Deep learning Emotion recognition Visualization Text analysis image analysis Databases Feature extraction Real-time systems Convolutional neural networks Testing

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Research on HLS Implementation of Self-Calibrated Illumination neural Network: Low-Light image Enhancement

Research on HLS Implementation of Self-Calibrated Illuminati...

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IEEE Information Technology, Networking, Electronic and Automation Control conference

作者： Hongyu Wang Pingjuan Zhang School of Electrical and Electronic Engineering Anhui Science and Technology University Bengbu China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Low-light image enhancement is crucial in applications such as nighttime surveillance, autonomous driving, and medical imaging. Traditional image enhancement methods like histogram equalization and Laplacian transformation can improve image brightness and contrast to some extent but struggle with complex lighting variations and detail preservation. Recently, deep learning-based methods have demonstrated superior performance but typically rely on high-performance computing resources, making real-time processing on resource- constrained embedded devices challenging. The paper addresses this problem by proposing a Self-Calibrated Illumination neural Network (SCINN) based low-light picture enhancement method and implementing it with hardware acceleration through the use of High-Level Synthesis (HLS) tools. SCINN employs a self-calibration mechanism to adaptively adjust illumination parameters based on input image features, enhancing low-light images. In particular, SCINN is composed of three primary modules: image improvement, illumination correction, and feature extraction. It makes use of a Convolutional neural Network (CNN) to improve the original image, extract features, and produce illumination adjustment parameters. Experimental results demonstrate that SCINN excels in improving image brightness and contrast while preserving details. Compared to traditional methods, SCINN shows significantly better enhancement effects. Additionally, the hardware-accelerated implementation using HLS tools on an FPGA shows that SCINN achieves efficient image enhancement with low hardware resource consumption, significantly improving processing speed and meeting real-time requirements.

关键词： Surveillance neural networks Brightness Refining Lighting Feature extraction Real-time systems Convolutional neural networks image enhancement Hardware acceleration

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Lumbar Spine Segmentation Based on Sagittal View Using Convolutional neural Network

Lumbar Spine Segmentation Based on Sagittal View Using Convo...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Smita Singh Neeta Singh Naresh Kumar dept. of Computer Science and Engineering Gautam Buddha University Gautam Buddha Nagar India dept. of Information Technology Gautam Buddha University Gautam Buddha Nagar India dept. of Mathematical and Physical Sciences University of Nizwa Sultanate of Oman

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Spine diseases have taken rise in numbers these days There are many diseases affecting the Lumbar part of the Spine like stenosis, herniated discs, and many more. Sometimes condition of the patient becomes so bad that they need spine surgeries. artificial intelligence algorithms and various image processing methods can be efficiently used to study MRI and CT images and diagnose these diseases in the early stages. Traditional methods of image segmentation are having many drawbacks including time consuming, the accuracy of results not good as well as tough to segment complex structures. This study presents the Implementation of deep learning methods on a small dataset of $T 1$ weighted MRI images in sagittal view. $U$ net shows best result of DC 92.23, precision 92.14, and recall as 91.56 as compared to SVM and FCN.

关键词： Support vector machines Deep learning image segmentation Accuracy Magnetic resonance imaging Spine neural networks Surgery Convolutional neural networks Diseases

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Enhanced Cassava Plant Disease Detection and Classification using ANN

Enhanced Cassava Plant Disease Detection and Classification ...

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Emerging Systems and Intelligent Computing (ESIC), International conference on

作者： Sheetal Saharan R Sai Avinash Reddy Godfrey Winster S Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

To implement an artificial neural networks (ANNs) based Plant Leaf Disease Detection System using MATLAB. On time detection of plant diseases is a very important element in maintaining the health and continuity of crops to prevent the threaten that could harm agriculture engine. It is a system based on image processing to analyze plant leaves to detect diseases. Plant leaves are then captured as high-resolution images, and the key features (color, texture, shape) of the plant leaves are extracted to separate healthy plants from infected plans. Subsequently ANN model is trained with these extracted features to identify the type and level of disease. The model created is developed to detect different plant diseases through a dataset containing labeled images. MATLAB is used as a powerful computational environment to design the ANN, fine tune its parameters and test the operation accuracy. Farmers and agricultural experts can use the approach to identify plant diseases early on, which means they can benefit from timely intervention, reducing crop loss. This combined system linked to smart connected agricultural tools presents a highly scalable approach in monitoring and management of diseases in modern farming practices.

关键词： Plant diseases Analytical models image color analysis Computational modeling Crops artificial neural networks Feature extraction Mathematical models MATLAB Monitoring

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