Titlebook: ICT Innovations 2023. Learning: Humans, Theory, Machines, and Data; 15th International C Marija Mihova,Mile Jovanov Conference proceedings

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書目名稱ICT Innovations 2023. Learning: Humans, Theory, Machines, and Data被引頻次




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書目名稱ICT Innovations 2023. Learning: Humans, Theory, Machines, and Data讀者反饋




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國家明智

MakedonASRDataset - A Dataset for?Speech Recognition in?the?Macedonian Languages paper provides the first publicly available dataset consisting of audio segments and appropriate textual transcription in the Macedonian language. It is appropriately preprocessed and prepared for direct utilization in the automatic speech recognition pipelines. The dataset was created by students

確認(rèn)

ASSAY

Implementation of?the?Time Series and?the?Convolutional Vision Transformers for?Biological Signal Prtively new transformer architecture for accomplishing this task in the domain of biological signal processing. Several preceding studies of blood pressure estimation solely for PPG signals have had success with CNN and LSTM neural networks. In this study two types of transformer variants are conside

嫻熟

The Curious Case of?Randomness in?Deep Learning Models for?Heartbeat Classificationsults by more than 15% on the heartbeat classification performance. Furthermore, we address a research question to evaluate the impact level of random values in the initialization of model parameters experimenting on the classification of ventricular heartbeats in electrocardiogram training and eval

貪心

modest

A Review of?Dew and?Edge Computing: Two Sides of?a?Modern Internet of?Things Solutionhe Internet of Things. In this paper, we analyze the requirements of post-cloud architectures to build such a solution, which clarify the main differences between dew and edge computing approaches. The analysis includes energy consumption, communication and processing requirements, latency, and thro

RLS898

充氣球

ERUPT

Enhancing Knee Meniscus Damage Prediction from?MRI Images with?Machine Learning and?Deep Learning TeMRI) scans. We utilized the MRNet dataset, and processed it with different approaches, using a one-dimensional grayscale, RGB, and segmented images, complemented with features extracted using Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) techniques. Our objective