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Anomaly analytics in data-driven machine learning applications
Machine learning is used widely to create a range of prediction or classification models. The quality of the machine learning (ML) models depends not...
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Progress on half a century of process modelling research in steelmaking: a review
Process modelling in steelmaking started from mid-sixties and witnessed rapid growth and wide spread applications during the last fifty years or so....
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MDEANet: modified detail-enhanced convolution and attention-based network for dehazing of remote sensing images
Image de-hazing aims to improve quality and restore clarity of hazy images. When airborne particles like dust and smoke absorb light, it can result...
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The effect of interactive tutorial information and purpose built virtual chemistry laboratory on students’ performance
Different Virtual Chemistry Laboratories (VCLs) have been developed where users can perform their experimental work to increase their learning for...
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QuMIN: quantum multi-modal data fusion for humor detection
Humour detection has attracted considerable attention due to its significance in interpreting dialogues across text, visual, and acoustic modalities....
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Using synthetic camera poses for camera calibration in soccer videos
Camera calibration is the process of estimating the parameters that describe the viewpoint of a camera. These parameters allow the map** of points...
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Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model
Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct...
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M2AST:MLP-mixer-based adaptive spatial-temporal graph learning for human motion prediction
Human motion prediction is a challenging task in human-centric computer vision, involving forecasting future poses based on historical sequences....
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Hop**-mean: an augmentation method for motor activity data towards real-time depression diagnosis using machine learning
The advances from the last few decades in the fields of ML (Machine Learning), DL (Deep Learning), and semantic computing are now changing the shape...
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Temporal analysis of computational economics: a topic modeling approach
This study offers a comprehensive investigation into the thematic evolution within computational economics over the past two decades, leveraging...
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Decentralized traffic management of autonomous drones
Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common...
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Chasing Pelican based Deep Learning for Multiple Object Detection from Single Input Trash Image
Waste management is essential for develo** a smart city to enhance the population's living standards. Harmonious and healthy living environments...
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Exploring the potential of Easy Language for enhancing website sustainability
Sustainable design principles have become increasingly important in website development, mainly focusing on reducing carbon emissions and energy...
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Analysis of recent techniques in marine object detection: a review
Marine Object Detection, leveraging computer vision, plays a vital role in detecting objects in marine environments ranging from marine organisms to...
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C-Hybrid-NET: A self-attention-based COVID-19 screening model based on concatenated hybrid 2D-3D CNN features from chest X-ray images
The outbreak of novel coronavirus (2019-nCOV, commonly known as COVID-19) was declared a global pandemic by the World Health Organization (WHO) in...
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Disaster assessment from social media using multimodal deep learning
Real-time global event detection particularly catastrophic events has benefited significantly due to the ubiquitous adoption of social media...
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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach
To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material...
