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

共 21063 条记录，以下是4931-4940 订阅

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Disease Classification Model Using Capsule networks Based on White Blood Cells

Disease Classification Model Using Capsule Networks Based on...

引用

Emerging Technology (INCET), International conference for

作者： Prajwal.S. Gaikwad Rakesh K. Deshmukh Priya Pise Shrikant D. Bhopale Kalinga University Raipur Dept. of Computer Engineering AISSMS's Institute of Information Technology Pune India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr.D.Y. Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

As a substitute for recurrent neural networks (RNNs), Deep Learning (DLs) has shown promise in text processing in the last several years. In this study, we looked at the recently announced capsule networks (CapsNets), which are gaining a lot of interest since they outperform Deep Learning (DLs) in image analysis, and we found that they function well for language categorization and sentiment analysis in some instances. Our experimental findings demonstrate that the suggested well-tuned CapsNets model may serve as a suitable (and, in some cases, superior and more cost-effective) alternative to sentence categorization models based on RNNs and DLs. We ran a battery of tests with randomly shuffled test data to see whether CapsNets could learn the sequence of words sequentially. Overall, our CapsNets model outperforms DL and RNN models in terms of classification performance and resilience to adversarial assaults.

关键词： Deep learning White blood cells Sentiment analysis Recurrent neural networks image analysis Batteries Text processing

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Document-Level Relation Extraction using Deep Learning and Natural Language processing

Document-Level Relation Extraction using Deep Learning and N...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Yadvendra Prasad Dwivedi Jay Kumar Vagairya Somesh Kumar ABV-Indian Institute of Information Technology and Management Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Human Action Recognition (HAR) is crucial for monitoring elderly individuals living alone, ensuring timely assistance during distress. This paper presents a HAR framework leveraging image processing and artificial intelligence to enhance performance across key benchmarks, including accuracy, computation speed, memory efficiency, and practical usability. By utilizing skeletal data, recurrent neural networks, and gait classification techniques, the approach achieves improved results on RGB video inputs. The proposed method reduces computation time significantly using Divide and Conquer, Sliding Window, and spatial aspects of Long Short-Term Memory (LSTM) architecture while maintaining low resource requirements for native device compatibility. Testing on the Fall Detection and NTU-RGB datasets demonstrates its effectiveness in handling real-time detection with reduced processing times compared to existing methods.

关键词： Training Accuracy Recurrent neural networks Transfer learning Streaming media Vectors Computational efficiency Human activity recognition Usability Long short term memory

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image Analysis Aided Freshness Classification of Pool Barb Fish (Puntius sophore) 2nd

Image Analysis Aided Freshness Classification of Pool Barb F...

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2nd International conference on Emerging Trends in Expert applications and Security, ICE-TEAS 2023

作者： Upadhyay, Aniruddh Dev Choudhury, Tanupriya Sarkar, Tanmay Bansal, Nikunj Khurana, Madhu Uttarakhand Dehradun248007 India Adjunct Professor CSE Dept. Graphic Era Hill University Uttarakhand Dehradun248002 India The AI University CutbankMT59427 United States Department of Food Processing Malda Polytechnic West Bengal Malda732102 India Gloucestershire College Gloucester United Kingdom

ISBN: (纸本)9789819919451

Fish is a very nutritious dish that is consumed worldwide as a complete meal. This causes a rise in fish production and storage. Freshness of fish that is stored in ice boxes and deep freezers can degrade very quickly. A stale fish can cause great harm to a human ingesting it as it may carry many diseases. This paper aims to present a hybrid model that can recognise Puntius (commonly Puti Fish) as fresh or stale by using an image and certain values graded on scale of 10 like color, texture, etc. This mainly aims to a non-destructive approach to classify fishes as fresh or stale. The model comprises of a CNN for processing images and a Dense network that extracts information from the numerical data. These features are combined and are then further passed onto another dense neural network that performs the final classification task. We were able to achieve 96–98% accuracy with this model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fish

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A Learning Method for Feature Correspondence with Outliers 26

A Learning Method for Feature Correspondence with Outliers

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26th International conference on Pattern Recognition / 8th International Workshop on image Mining - Theory and applications (IMTA)

作者： Yang, Xu Zeng, Shao-Feng Han, Yu Lu, Yu-Chen Liu, Zhi-Yong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Feature correspondence is an important topic in many computer vision or robot vision tasks. Different from traditional optimization based matching method, in the last two years, researchers are finally able to solve the matching process in a learning manner. As a representative method, SuperGlue achieves superior performance in many real-world tasks, but it still has problems in dealing with outlier features. Targeting at the outlier problem, this paper improves SuperGlue by introducing a deep learning based feature correspondence method, which consists of the pruned attentional graph neural network and the improved matching layer for the outlier problem. Experiments on real world images validate the effectiveness of the proposed method.

关键词： Learning systems Deep learning Computer vision Robot vision systems Graph neural networks Pattern recognition Task analysis

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Research on the Enhanced Target Detection Model of Drone Aerial images Based on AIGC

Research on the Enhanced Target Detection Model of Drone Aer...

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Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Weijie You School of Art and Design Fuzhou University of Foreign Languages and Trade Fuzhou Fujian China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper proposes an image enhancement and target detection model based on AI GC (artificial intelligence generated content). The model innovatively combines GAN and self-attention mechanism, and effectively improves the quality of aerial images through processing steps such as super-resolution, denoising and contrast enhancement. The generator performs image enhancement through a deep convolutional network, and the self-attention mechanism focuses on the key areas in the image to restore more details and textures. Experimental results show that compared with traditional image enhancement methods, the AIGC method shows superior performance in different scenarios. Specifically, after AIGC enhancement processing, the mean average precision (mAP) of target detection is improved by 18.5%, the recall rate is improved by 15.2 % , and the F1-score reaches 90.4 % . Furthermore, the results show that the proposed model is effective in complicated background and low light environment, and the PSNR and structure similarity (SSIM) are improved by 12 dB and 0.18. It is proved that the AIGC technique can greatly improve the performance of the VA V, and it is an effective way to solve the problem of the complicated scene.

关键词： Training Superresolution Object detection Generative adversarial networks Generators Robustness Satellite images artificial intelligence image enhancement Drones

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Encoding Generative Adversarial networks for Defense Against image Classification Attacks 9th

Encoding Generative Adversarial Networks for Defense Against...

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9th International Work-conference on the Interplay Between Natural and artificial Computation (IWINAC)

作者： Perez-Bravo, Jose M. Rodriguez-Rodriguez, Jose A. Garcia-Gonzalez, Jorge Molina-Cabello, Miguel A. Thurnhofer-Hemsi, Karl Lopez-Rubio, Ezequiel Univ Malaga Dept Comp Languages & Comp Sci Malaga Spain Inst Invest Biomed Malaga IBIMA Malaga Spain

ISBN: (纸本)9783031065279;9783031065262

image classification has undergone a revolution in recent years due to the high performance of new deep learning models. However, severe security issues may impact the performance of these systems. In particular, adversarial attacks are based on modifying input images in a way that is imperceptible for human vision, so that deep learning image classifiers are deceived. This work proposes a new deep neural network model composed of an encoder and a Generative Adversarial Network (GAN). The former encodes a possibly malformed input image into a latent vector, while the latter generates a reconstructed image from the latent vector. Then the reconstructed image can be reliably classified because our model removes the deleterious effects of the attack. The experiments carried out were designed to test the proposed approach against the Fast Gradient Signed Method attack. The obtained results demonstrate the suitability of our approach in terms of an excellent balance between classification accuracy and computational cost.

关键词： Generative Adversarial networks Adversarial attack Fast Gradient Signed Method attack

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7th International conference on Emerging applications of Information Technology, EAIT 2022

7th International Conference on Emerging Applications of Inf...

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7th International conference on Emerging applications of Information Technology, EAIT 2022

ISBN: (纸本)9789811951909

The proceedings contain 57 papers. The special focus in this conference is on Emerging applications of Information Technology. The topics include: A Method of Genome Sequence Comparison Based on a New Form of Fuzzy Polynucleotide Space;identification of Humans by Using Machine Learning Models on Gait Features;efficient Heart Disease Prediction Using Modified Hybrid Classifier;an Unstructured Mammogram Analysis for Feasible Classification and Detection of Breast Cancer Using a Convolutional Approach;Emotion Recognition from EEG Data Using Hybrid Deep Learning Approach;colon Cancer Prediction with Transfer Learning and K-Means Clustering;lightweight Authentication Protocol for E-Healthcare Systems Using Fuzzy Commitment Scheme;performance Analysis of Machine Learning Algorithms for Prediction of Cerebral Attack (Stroke);breast Cancer Detection Using Transfer Learning Techniques in Convolutional neural networks;improving Mental Health Through Multimodal Emotion Detection from Speech and Text Data Using Long-Short Term Memory;a Machine Learning-Based Prediction Model for Fetal Health Assessment;A Smart System for Assessment of Mental Health Using Explainable AI Approach;binary Classification of Thyroid Using Comprehensive Set of Machine Learning Algorithms;Interpretability Approaches of Explainable AI in Analyzing Features for Lung Cancer Detection;impregnable Healthcare Ecosystem Using Blockchain and artificial Intelligence Approaches;RIWT Generative Feedback Residual Network for Secure Clinical Data Communication in Healthcare Unit;blockchain-Based Smart Integrated Healthcare System;Designing a Secure Robust Medical image Authentication Based on Watermarking Using the ED-DWT and Encryption;IoT-Based Secure Blockchain Framework for Patient Record Management Using MPRESENT Lightweight Block Cipher;hybridized Support Vector Machine and Adaboost Technique for Malaria Diagnosis;feather-Light Vessel Segregation Model.

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Advancing spiking neural networks for sequential modeling with central pattern generators 24

Advancing spiking neural networks for sequential modeling wi...

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Proceedings of the 38th International conference on neural Information processing Systems

作者： Changze Lv Dongqi Han Yansen Wang Xiaoqing Zheng Xuanjing Huang Dongsheng Li School of Computer Science Fudan University Microsoft Research Asia

ISBN: (纸本)9798331314385

Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible. However, applying SNNs to sequential tasks, such as text classification and time-series forecasting, has been hindered by the challenge of creating an effective and hardware-friendly spike-form positional encoding (PE) strategy. Drawing inspiration from the central pattern generators (CPGs) in the human brain, which produce rhythmic patterned outputs without requiring rhythmic inputs, we propose a novel PE technique for SNNs, termed CPG-PE. We demonstrate that the commonly used sinusoidal PE is mathematically a specific solution to the membrane potential dynamics of a particular CPG. Moreover, extensive experiments across various domains, including time-series forecasting, natural language processing, and image classification, show that SNNs with CPG-PE outperform their conventional counterparts. Additionally, we perform analysis experiments to elucidate the mechanism through which SNNs encode positional information and to explore the function of CPGs in the human brain. This investigation may offer valuable insights into the fundamental principles of neural computation. Our code is available at https://***/microsoft/SeqSNN.

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Analysis of artificial Intelligence Based Human Expression 4

Analysis of Artificial Intelligence Based Human Expression

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4th International conference on Smart Electronics and Communication, ICOSEC 2023

作者： Nikhil, Bandaru Anitha, J. Manasa, B. Srikanth, Kolli. Kumar, K. Anil Rajeswaran, N. Birla Institute of Technology and Science-Pilani Dept. of Mechanical Engg. Hyderabad India Malla Reddy Engineering College Dept. of Cse Hyderabad India JNTU-GURAJADA Vizianagaram Dept. of It Andhra pradesh India

ISBN: (纸本)9798350300888

Facial Emotion Recognition (FER) is a current area of research in computer vision and machine learning. This study looks into how well Convolutional neural networks (CNNs) can identify face expressions of human emotion. CNNs have proven to perform exceptionally well in a range of computer vision applications, including image categorization, object recognition, and face recognition. To categorize the seven fundamental human emotions-anger, disgust, fear, happiness, sadness, surprise, neutral- a deep learning model is suggested utilizing CNNs. Convolutional and pooling layers are interspersed with fully connected layers for classification in the proposed model. The FER2013 dataset, which includes more than 35,000 photos, is the one utilized for training and evaluation. The performance of the proposed model was evaluated and compare it with the state-of-the-art approaches in terms of accuracy, precision, recall, and F1 score. The experimental findings show that the state-of-the-art performance in facial emotion recognition achieved by the proposed CNN-based model. © 2023 IEEE.

关键词： Emotion Recognition

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Fertilizer Recommendation for Rice Crop based on NPK Nutrient deficiency using Deep neural networks and Random Forest Algorithm

Fertilizer Recommendation for Rice Crop based on NPK Nutrien...

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International Symposium on artificial Intelligence and Signal processing (AISP)

作者： Tanneru Bhavya Ramyalaxmi Seggam Ravi Kumar Jatoth Electronics and Communication Engineering National Institute of Technology Warangal

Nutrient deficiency has a noteworthy impact on agriculture which results in reduced plant quality in turn reduced crop yield. A plant can have Multiple deficiencies at the same time so there is need of suggesting an appropriate Fertilizer considering all nutrient deficiencies in it. In this paper we suggested suitable fertilizer using random forest for detected nutrient deficiency from leaf images using different neural networks. We trained our dataset using four different Convolution neural networks and selected the network with highest accuracy and then the output of this network is sent to the trained model of Random Forest which suggests the suitable fertilizer. Since Potassium(K), Phosphorous(P) and Nitrogen(N) are three key nutrients required for Rice plant we considered the image dataset having leaves with N, P, K deficiencies.

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