The proceedings contain 138 papers. The topics discussed include: efficient parking lot management system for parking attendants based on real-time impulsive sound detection and voice command recognition;impact of PON...
ISBN:
(纸本)9798350309249
The proceedings contain 138 papers. The topics discussed include: efficient parking lot management system for parking attendants based on real-time impulsive sound detection and voice command recognition;impact of PON network range and laser power on GPON and XGSPON coexistence system;enhancing breast cancer classification using ensemble techniques and feature selection algorithms;diabetic retinopathy detection using modified U-Net architecture and artificial metaplasticity algorithm;brain tumor classification using DenseNet and U-net convolutional neuralnetworks;a comparative analysis of branch-cut and quality-guided algorithms for inSAR interferogram;classification of multi-view mammogram images using a parallel pre-trained models system;and deep learning approaches for plant diseases identification and classification: a comprehensive review.
neuralnetworks have achieved remarkable success in various applications such as image classification, speech recognition, and natural language processing. However, the growing size of neuralnetworks poses significan...
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ISBN:
(纸本)9783031490071;9783031490088
neuralnetworks have achieved remarkable success in various applications such as image classification, speech recognition, and natural language processing. However, the growing size of neuralnetworks poses significant challenges in terms of memory usage, computational cost, and deployment on resource-constrained devices. Pruning is a popular technique to reduce the complexity of neuralnetworks by removing unnecessary connections, neurons, or filters. In this paper, we present novel pruning algorithms that can reduce the number of parameters in neuralnetworks by up to 98% without sacrificing accuracy. This is done by scaling the pruning rate of the models to the size of the model and scheduling the pruning to execute throughout the training of the model. Code related to this work is openly available.
Target contour extraction is a key task in the field of imageprocessing, which is of great significance for applications such as image segmentation, object detection, and scene understanding. Traditional methods are ...
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We are training neuralnetworks to predict house rents using artificial intelligence and deep learning, which is being used in the real estate and financial industries. Real estate agents, financial institutions, and ...
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Pattern recognition has been evolving to include problems posed by new sceneries containing a high number of pattern components. processing this volume of information allows a more exact classification in wider types ...
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ISBN:
(纸本)9783031477645;9783031477652
Pattern recognition has been evolving to include problems posed by new sceneries containing a high number of pattern components. processing this volume of information allows a more exact classification in wider types of applications;however, some of the difficulties of this scheme is the maintenance of numerical precision and mainly the reduction of the execution time. During the last 15 years, several Machine Learning solutions have been implemented to reduce the number of pattern components to be analyzed, such as artificialneuralnetworks. Deep learning is an appropriate tool to accomplish this task. In this paper, a convolutional neural network is implemented for recognition and classification of human activity signals and digital images. It is achieved by automatically adjusting the parameters of the neural network through genetic algorithms using a multiprocessor and GPU platform. The results obtained show the reduction of computational costs and the possibility of better understanding of the solutions provided by Deep Learning.
In recent years, convolutional neuralnetworks (CNNs) have become the core of many artificial intelligence applications, especially in fields such as image recognition and speech recognition. Deploying convolutional n...
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Unsupervised learning of discrete representations in neuralnetworks (NNs) from continuous ones is essential for many modern applications. Vector Quantisation (VQ) has become popular for this, in particular in the con...
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ISBN:
(纸本)9783031723315;9783031723322
Unsupervised learning of discrete representations in neuralnetworks (NNs) from continuous ones is essential for many modern applications. Vector Quantisation (VQ) has become popular for this, in particular in the context of generative models, such as Variational Auto-Encoders (VAEs), where the exponential moving average-based VQ (EMA-VQ) algorithm is often used. Here, we study an alternative VQ algorithm based on Kohonen's learning rule for the Self-Organising Map (KSOM;1982). EMA-VQ is a special case of KSOM. KSOM is known to offer two potential benefits: empirically, it converges faster than EMA-VQ, and KSOM-generated discrete representations form a topological structure on the grid whose nodes are the discrete symbols, resulting in an artificial version of the brain's topographic map. We revisit these properties by using KSOM in VQ-VAEs for imageprocessing. In our experiments, the speed-up compared to well-configured EMA-VQ is only observable at the beginning of training, but KSOM is generally much more robust, e.g., w.r.t. the choice of initialisation schemes (Our code is public: https://***/IDSIA/kohonen-vae. The full version with an appendix can be found at: https://***/abs/2302.07950).
The disk diffusion tests used in clinical microbiology laboratories to guide antibiotic therapy decisions and monitor antibiotic resistance patterns in bacterial populations, provides valuable information for selectin...
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A widely studied problem in computer science is the restoration, segmentation, and classification of images, which involves imageprocessing, computer vision, and machine learning techniques. Deep learning has made si...
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The proceedings contain 158 papers. The topics discussed include: duct inspection and monitoring robot;deep learning-based approaches for preventing and predicting wild animals disappearance: a review;classification a...
ISBN:
(纸本)9798350394528
The proceedings contain 158 papers. The topics discussed include: duct inspection and monitoring robot;deep learning-based approaches for preventing and predicting wild animals disappearance: a review;classification and tracking of items on a moving conveyor belt using convolutional networks and imageprocessing;critical analysis of the 220/110/20 kV Sardanesti power substation from Romania in the context of identification elements of instability and insecurity;machine learning based collaborative prediction of SSD failures in the cloud;the impact of explainable ai on low-accuracy models: a practical approach with movie genre prediction;utilizing transfer learning-based algorithms for breast ultrasound data in multi-instance classification;predictive maintenance model-based on multi-stage neural network systems for wind turbines;and using teaching learning-based optimization with convolutional neural network to detect pneumonia based on chest X-Ray images.
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