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共 274 条记录，以下是11-20 订阅

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

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

引用

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

A Metaheuristic Optimization Based Deep Feature Selection fo...

引用

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

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

引用

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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Palmprint Classification via Filter Faces and Feature Extraction 11th

Palmprint Classification via Filter Faces and Feature Extrac...

引用

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

作者： Chen, Guang Yi Krzyzak, Adam Valev, Ventzeslav Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada Bulgarian Acad Sci Inst Math & Informat Sofia 1113 Bulgaria

ISBN: (纸本)9783031716010;9783031716027

Palmprint classification is a popular method for today's biometrics, and it can be combined with iris or fingerprint to identify a person's identification. In this paper, we propose a novel method for palmprint classification by using filter faces and feature extraction. We extract such features as histogram of oriented features (HOG) and local binary patterns (LBP). We select to use both nearest neighbor (NN) classifier and collaborative representation classifier (CRC) for palmprint classification. Experiments demonstrate that filter faces always improve the classification accuracy for palmprint classification no matter what features are used and how much noise is present in palmprint images. Based on our experiments, the HOG features are very robust to noise whereas the LBP features are very sensitive to noise. As a result, it is preferable to use the HOG features for noisy palmprint images and the LBP features for clean palmprint images to improve classification accuracy.

关键词： Histogram of oriented features (HOG) Local binary patterns (LBP) Filter faces (FF) Collaborative representation classifier (CRC) Nearest neighbor classifier (NN)

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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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Assessment of Pharmaceutical Patent Novelty with Siamese neural networks 10th

Assessment of Pharmaceutical Patent Novelty with Siamese Neu...

引用

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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Leveraging LSTM Embeddings for River Water Temperature Modeling 11th

Leveraging LSTM Embeddings for River Water Temperature Model...

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

作者： Fankhauser, Benjamin Bigler, Vidushi Riesen, Kaspar Univ Bern Inst Comp Sci Bern Switzerland Bern Univ Appl Sci Inst Optimisat & Data Anal Biel Switzerland

ISBN: (纸本)9783031716010;9783031716027

River water temperature modeling is a major task in climate research. State-of-the-art methods for water temperature modeling deploy a transductive design, which makes it difficult to generalize to unseen water stations during test time. In the present paper, we isolate one of the common building blocks - a central LSTM, trained for each water station - and propose an embedding scheme in order to increase both the prediction accuracy and the amount of shared parameters and thus the generalization. The proposed embeddings are learned during training time. In an empirical evaluation we show that our method is able to reduce the RMSE by about five percentage points compared to the state-of-the-art reference method while decreasing the tuneable parameters by several orders of magnitude. We also provide a sample analysis of the embedding space of the catchment area of one specific river. Looking at the results of this qualitative analysis, we come to the conclusion that deploying an embedding in water temperature models is not only convincing to decrease the RMSE of water temperature predictions, but also enables better explainable deep learning models. Moreover, the proposed embedding technique opens up various unexplored applications in water temperature research.

关键词： Water Temperature Embedding LSTM Recurrent neural Network

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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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Vision Transformer Features-Based Leukemia Classification 11th

Vision Transformer Features-Based Leukemia Classification

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

作者： Ben-Suliman, Karima Krzyzak, Adam Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9783031716010;9783031716027

Acute Lymphoblastic Leukemia (ALL) is a disease that is caused by the uncontrollable growth of immature and malignant White Blood Cells (WBCs) which are called lymphoblasts. It occurs when the bone marrow contains 20% or more lymphoblasts. Therefore, Leukemia is diagnosed by counting White Blood Cells (WBCs) in the microscopic smears of bone marrow and blood. There are several attempts for effective leukemia classification with the help of computer aided-based methods by analyzing microscopic smear images. However, how to further improve the classification accuracy is still challenging. Therefore, in this paper, we propose an end-to-end framework to classify ALL. First, we train a more robust specific pre-trained Vision Transformer (ViT) trained on the same type of data, ALL-IDB1. Then, we examine the extracted features of Deep ViT across layers and utilize these features as dense descriptors to make predictions. Based on the observation in this paper, we find and empirically demonstrate that such features, when extracted from the ViT model, consistently improve the classification performance. Moreover, we observe that the pre-trained ViT which is fine-tuned on specific dataset is more effective than the general images dataset. We show that our proposed method achieves an average accuracy of 98.75% which is a competitive result with recent state-of-the-art by a large margin.

关键词： Lung cancer

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Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via neural networks 11th

Multi-modal Decoding of Reach-to-Grasping from EEG and EMG v...

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

作者： Borra, Davide Fraternali, Matteo Ravanelli, Mirco Magosso, Elisa 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

Convolutional neural networks (CNNs) have revolutionized motor decoding from electroencephalographic (EEG) signals, showcasing their ability to outperform traditional machine learning, especially for Brain-Computer Interface (BCI) applications. By processing also other recording modalities (e.g., electromyography, EMG) together with EEG signals, motor decoding improved. However, multi-modal algorithms for decoding hand movements are mainly applied to simple movements (e.g., wrist flexion/extension), while their adoption for decoding complex movements (e.g., different grip types) is still under-investigated. In this study, we recorded EEG and EMG signals from 12 participants while they performed a delayed reach-to-grasping task towards one out of four possible objects (a handle, a pin, a card, and a ball), and we addressed multi-modal EEG+EMG decoding with a dual-branch CNN. Each branch of the CNN was based on EEGNet. The performance of the multi-modal approach was compared to mono-modal baselines (based on EEG or EMG only). The multi-modal EEG+EMG pipeline outperformed the EEG-based pipeline during movement initiation, while it outperformed the EMG-based pipeline in motor preparation. Finally, the multi-modal approach was capable of accurately discriminating between grip types widely during the task, especially from movement initiation. Our results further validate multi-modal decoding for potential future BCI applications, aiming at achieving a more natural user experience.

关键词： EEG EMG Multi-modal motor decoding Reach-to-grasping Convolutional neural networks Brain-Computer Interfaces

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