Machine Learning

The detection and recognition of road traffic signs and panel guide content has become challenging in recent years. Few studies have been made to solve these two issues at the same time, especially in the Arabic language. Additionally, the limited number of datasets for traffic signs and panel guide content makes the investigation more interesting. In our work, we propose a Deep Active Learning Approach for Traffic Sign and Panel Guide Arabic-Latin Text Content Annotation in Natural Scene Images.

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This is the dataset we collected for the article "Scalable Undersized Dataset RF Classification: Using Convolutional Multistage Training". 17 objects were collected in the laboratory and scanned using a 'cw radar' setup featuring 2x UWB antennas (1 transmit antenna, 1 receive antenna), inside anechoic chamber. There was no clutter added in the experiment.

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This dataset includes real-world Channel Quality Indicator (CQI) values from UEs connected to real commercial networks in Greece. In total, we collected CQI data from 74 cars that drive through a specific road in the city of Volos, Greece. This dataset is part of our following work:

 

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This cherry tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of cherry trees, including stress analysis and prediction. An orchard of cherry trees is considered in the area of Western Macedonia, where 577 cherry trees were recorded in a full crop season starting from Jul. 2021 to Jul. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos.

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179 Views

Mental health greatly affects the quality of life. The ability to detect and classify multiple levels of stress is therefore imperative. The aim of this work is to develop machine learning models for detection and multiple level classification of stress through ECG and EEG signals for both unspecified and specified genders. The models for the detection of stress from ECG are developed for real-world use, while the models based on ECG and EEG for the detection and multiple level classification of stress are devised towards clinical use.

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This dataset contains 10,532 issues extracted from Github and Sourceforge. The dataset is for generating linguistic patterns to identify concurrency bug reports automatically.

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The cold start problem is a significant challenge in recommendation systems. Traditional methods are ineffective when the amount of interaction data is small. Further, as meta-learning has achieved increasingly remarkablesuccess in few-shot classification, some studies in recent years has abstracted cold-start recommendations into few-shot problems and applied meta-learning-based approaches, but mostly, simple transplants of generic approaches have been adopted.

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The evolution of the Industrial Internet of Things (IIoT) introduces several benefits, such as real-time monitoring, pervasive control and self-healing. However, despite the valuable services, security and privacy issues still remain given the presence of legacy and insecure communication protocols like IEC 60870-5-104. IEC 60870-5-104 is an industrial protocol widely applied in critical infrastructures, such as the smart electrical grid and industrial healthcare systems.

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338 Views

Nowadays, more and more machine learning models have emerged in the field of sleep staging. However, they have not been widely used in practical situations, which may be due to the non-comprehensiveness of these models' clinical and subject background and the lack of persuasiveness and guarantee of generalization performance outside the given datasets. Meanwhile, polysomnogram (PSG), as the gold standard of sleep staging, is rather intrusive and expensive. In this paper, we propose a novel automatic sleep staging architecture calle

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33 Views

Due to the smaller size, low cost, and easy operational features, small unmanned aerial vehicles (SUAVs) have become more popular for various defense as well as civil applications. They can also give threat to national security if intentionally operated by any hostile actor(s). Since all the SUAV targets have a high degree of resemblances in their micro-Doppler (m-D) space, their accurate detection/classification can be highly guaranteed by the appropriate deep convolutional neural network (DCNN) architecture.

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