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检索条件"任意字段=4th Workshop on Artificial Neural Networks in Pattern Recognition"

共 1332 条记录，以下是11-20 订阅

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Assessment of Pharmaceutical Patent Novelty with Siamese neural networks 10th

Assessment of Pharmaceutical Patent Novelty with Siamese Neu...

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10th IAPR TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： El-Shimy, Heba Zantout, Hind Hassen, Hani Ragab Heriot Watt Univ Dubai U Arab Emirates

ISBN: (纸本)9783031206498;9783031206504

Patents in the pharmaceutical field fulfil an important role as they contain details of the final product that is the culmination of years of research and possibly millions of dollars of investment. It is crucial that both patent producers and consumers are able to assess the novelty of such patents and perform basic processing on them. In this work, we review approaches in the literature in patent analysis and novelty assessment that range from basic digitisation to deep learning-based approaches including natural language processing, image processing and chemical structure extraction. We propose a system that automates the process of patent novelty assessment using Siamese neural networks for similarity detection. Our system showed promising results and has a potential to improve upon the current patent analysis methods, specifically in the pharmaceutical field, by not just focusing on the task from a Natural Language Processing perspective, but also, adding image analysis and adaptations for chemical structure extraction.

关键词： Document analysis Siamese neural networks CNN LSTM Optical character recognition Pharmaceutical patents Chemical structure extraction

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A Novel Representation of Graphical patterns for Graph Convolution networks 10th

A Novel Representation of Graphical Patterns for Graph Convo...

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10th IAPR TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Benini, Marco Bongini, Pietro Trentin, Edmondo Univ Siena DIISM Siena Italy

ISBN: (纸本)9783031206498;9783031206504

In the context of machine learning on graph data, graph deep learning has captured the attention of many researcher. Due to the promising results of deep learning models in the most diverse fields of application, great efforts have been made to replicate these successes when dealing with graph data. In this work, we propose a novel approach for processing graphs, with the intention of exploiting the already established capabilities of Convolutional neural networks (CNNs) in image processing. To this end we propose a new representation for graphs, called GrapHisto, in the form of unique tensors encapsulating the features of any given graph to then process the new data using the CNN paradigm.

关键词： GrapHisto Graph neural Network Graph Convolution Network

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2024 4th International Conference on artificial Intelligence and Industrial Technology Applications

2024 4th International Conference on Artificial Intelligence...

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2024 4th International Conference on artificial Intelligence and Industrial Technology Applications, AIITA 2024

the proceedings contain 108 papers. the topics discussed include: fuzzy PID control based on genetic algorithm optimization inverted pendulum system;multi-task recognition of modulation types and arrival directions of underwater acoustic signals based on convolutional neural networks;localization and detection of underwater acoustic communication signals using convolutional recursive neural networks;online trajectory anomaly detection model based on graph neural networks and variational autoencoder;an improved dynamical variational autoencoder framework for predicting aero-engine remaining useful life;construction of digital twin workshop integrated with edge computing and deep learning;research on electromagnetic pulse signal detection method based on intelligent compressed sensing technology;and path planning and collision avoidance approach for a multi-agent system in grid environments.

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Explaining Network Decision Provides Insights on the Causal Interaction Between Brain Regions in a Motor Imagery Task 11th

Explaining Network Decision Provides Insights on the Causal ...

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Borra, Davide Ravanelli, Mirco Univ Bologna Dept Elect Elect & Informat Engn Guglielmo Marcon Cesena Campus Cesena Italy Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada Mila Quebec AI Inst Montreal PQ Canada

ISBN: (纸本)9783031716010;9783031716027

neural decoding widely exploits machine learning for classifying electroencephalographic (EEG) signals for brain-computer interface applications. Recent advancements in neural decoding regards the use of brain functional connectivity estimates as input features and the adoption of convolutional neural networks (CNNs) to realize decoders. Moreover, explainable artificial intelligence (XAI) approaches based on CNNs are growing interest in the neuroscience community, for validating the knowledge learned by networks and for using the decoder not only to classify the EEG but also to analyze it in a data-driven way, without a priori assumptions. However, the adoption of connectivity estimates for neural decoding is still in its infancy, as adopts non-directed connectivity measures, limits the analysis of few interactions/frequency ranges, and exploits classic machine learning approaches without exploring CNNs. Moreover, XAI approaches have never been applied to analyze EEG-based functional connectivity. To overcome these limitations, we design and apply a CNN for processing directed connectivity measures estimated via spectral Granger causality. the CNN automatically learns features in the frequency and spatial domains, and it is coupled with an explanation technique (DeepLIFT) for highlighting the most relevant connectivity inflow and outflow associated to each decoded brain state. Our approach is applied to motor imagery decoding, and achieves state-of-the-art performance compared to existing networks. DeepLIFT relevance representations match the directional interactions known occurring when imagining movements, validating the features related to the brain network, as learned by the CNN.

关键词： Electroencephalogram Functional connectivity Convolutional neural networks Explainable AI Motor imagery

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A Hybrid Approach for Denoising and recognition of Handwritten Characters Using Deblur GAN-CNN 4

A Hybrid Approach for Denoising and Recognition of Handwritt...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bharati, P. Vijaya Vignan's Institute of Engineering for Women Department of Computer Science and Engineering India

ISBN: (纸本)9798331518981

Handwritten Character recognition (HCR) is a critical area of research within pattern recognition and artificial intelligence, with applications spanning from document digitization, optical character recognition (OCR), and handwriting analysis. Despite significant technological advancements, accurate recognition of handwritten characters remains challenging due to variations in writing styles, sizes, and inherent *** paper presents a novel approach to HCR, utilizing a combination of Deblur Generative Adversarial networks (GAN) and Convolutional neural networks (CNN) to effectively address these challenges. Unlike traditional deblurring methods, which often introduce artifacts or blur the edges of characters, Deblur GAN is specifically trained to enhance the clarity of handwritten images while preserving essential character features. this allows the subsequent CNN to extract more accurate and discriminative features, leading to improved character *** extensive experimentation on benchmark datasets such as MNIST, an approach that significantly outperforms the existing methods is demonstrated in terms of recognition accuracy, particularly in scenarios involving blurred or indistinct handwriting. these findings highlight the potential of integrating deblurring techniques with deep learning models to address persistent challenges in handwritten character recognition, paving the way for enhanced automation and efficiency in text analysis tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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pattern recognition tools for output-based classification of synchronised Kuramoto states 26

Pattern recognition tools for output-based classification of...

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26th International Symposium on Mathematical theory of networks and Systems (MTNS)

作者： Alanazi, Faizah Mueller, Markus Townley, Stuart Univ Exeter Fac Environm Sci & Econ Ctr Environm Math Penryn TR10 9FE Cornwall England Northern Border Univ Coll Sci Ar Ar 91431 Saudi Arabia

Kuramoto oscillators are known to exhibit multiple synchrony where the states of individual oscillators synchronise in groups. We present a method for output-based classification of synchronised states in networks of Kuramoto oscillators using an artificial neural network for pattern recognition. Outputs of synchronised states are represented by spectrograms, in other words "fingerprint", on which an artificial neural network of stacked autoencoders is then trained to classify these fingerprints and thus the different types of synchrony. We illustrate the approach for a Kuramoto model with N = 4 oscillators which exhibits synchrony of five types. We provide performance metrics for learning and training data which demonstrat that the approach reaches high levels of reliability. Copyright (c) 2024 the Authors. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Kuramoto networks Synchrony Observability artificial neural networks pattern recognition

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Machine Learning for Clinical Score Prediction from Longitudinal Dataset: A Case Study on Parkinson's Disease 11th

Machine Learning for Clinical Score Prediction from Longitud...

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Ahmed, Nourin Kobti, Ziad Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada

ISBN: (纸本)9783031716010;9783031716027

Accurate prediction of Parkinson's disease (PD) progression is vital for personalized treatment and effective clinical trials. this study presents a machine learning approach to predict the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDSUPDRS) Part III scores, quantifying motor symptom progression in PD patients. Using the longitudinal Parkinson's Progression Markers Initiative (PPMI) dataset, we examined the impact of dataset format (wide vs. cross-sectional), dimensionality reduction techniques (PCA, NMF), and regression models (Linear Regression, Random Forest, XGBoost, SVR) on prediction performance. Our findings indicate that models trained on wide-format datasets consistently outperformed those on cross-sectional data. the combination of Nonnegative Matrix Factorization (NMF) and Support Vector Regression (SVR) achieved the best performance, with a mean absolute error (MAE) of 1.91 and R-2 of 0.83. these results underscore the importance of data arrangement and highlight NMF's effectiveness in feature extraction for longitudinal datasets.

关键词： Parkinson's Disease Machine Learning Longitudinal Data Dimensionality Reduction NMF SVR Prediction Models MDS-UPDRS III

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A Metaheuristic Optimization Based Deep Feature Selection for Oral Cancer Classification 11th

A Metaheuristic Optimization Based Deep Feature Selection fo...

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Halder, Akash Laha, Sugata Bandyopadhyay, Saptarshi Schwenker, Friedhelm Sarkar, Ram Jadavpur Univ Dept Comp Sci & Engn Kolkata India Ulm Univ Inst Neural Informat Proc Ulm Germany

ISBN: (纸本)9783031716010;9783031716027

Oral cancer is a serious hazard to world health, with many new cases recorded each year. Researchers have been concentrating on developing medical image analysis-based computer-aided diagnostic (CAD) systems for oral cancer. To this end, we propose a novel model that we name GrayWolf Optimization (GWO) based deep Feature Selection Network (GFS-Net). Initially, we use an attention-aided NASNet Mobile, a convolutional neural network (CNN) architecture, to extract features from the input images. Next, we use a metaheuristic-based optimization algorithm, called GWO, to get rid of the extraneous features obtained from the CNN model. For the final classification task, the K-Nearest-Neighbours (KNN) classifier is applied with this optimal feature set. Our model is evaluated on two publicly accessible oral cancer datasets, histopathologic oral cancer identification dataset and oral cancer (lips and tongue) dataset, that yields classification accuracies of 92.86% and 93.94%, respectively. the code and additional results are available at https://***/stellarsb7/GFS-Net.

关键词： Oral Cancer Deep Learning Feature Selection Gray Wolf Optimization Attention Mechanism Medical Image

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Deep Multi-label Classification of Personality with Handwriting Analysis 11th

Deep Multi-label Classification of Personality with Handwrit...

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11th IAPR TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Shamsabad, Marzieh Adeli Suen, Ching Yee Concordia Univ Ctr Pattern Recognit & Machine Intelligence CENPA Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9783031716010;9783031716027

Handwriting analysis has traditionally been used to infer personality traits from the stylistic features of writing. With advances in machine learning, the accuracy and applicability of these analyses have significantly improved. this paper presents a new multi-label classification approach to classify personality traits, such as Extraversion and Conscientiousness, into Low, Average, and High categories. this approach uses Binary Cross-Entropy with Logits Loss and Focal Loss to handle multi-label classification and class imbalance. Image segmentation techniques are also employed to enhance the handling of limited handwriting samples. the paper evaluates the complexity and performance of ResNet-50 and ResNet-101 architectures in recognizing complex handwriting patterns using three optimizers: SGD with momentum, Adam, and AdaBelief. the results demonstrate the efficacy of our proposed method, improving overall accuracy from 67.09% to 90.16% for ResNet-50 and from 69.43% to 90.07% for ResNet-101, with an overall AUC of 0.96. these improvements emphasize the model's capability for practical automated handwriting analysis.

关键词： Handwriting Multi-label classification Class Imbalance Binary Cross-Entropy with Logits Loss Focal Loss

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A Study on Miao Batik pattern Design Based on Style Transfer Algorithms 4

A Study on Miao Batik Pattern Design Based on Style Transfer...

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4th International Conference on artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Liu, Lin Sun, Xiaoyun Hubei University of Technology College of Art and Design Wuhan430068 China

ISBN: (纸本)9798331531225

the purpose of this research is to protect and conserve the Miao batik, a form of intangible cultural heritage, and to stimulate the creative evolution of its traditional *** investigates the use of artificial intelligence technologies for the digital preservation and creative redesign of Miao batik patterns. through the extraction and analysis of pattern elements, alongside the application of convolutional neural networks and style transfer algorithms, the research reimagines the features and aesthetics of these traditional patterns. the findings demonstrate that this approach not only supports the self-renewal and enhancement of the cultural value of Miao batik but also paves the way for its innovative evolution in a contemporary context. © 2024 IEEE.

关键词： Convolutional neural networks

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