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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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DGNN-MN: Dynamic Graph Neural Network via memory regenerate and neighbor propagation
Dynamic Graph Neural Network (DGNN) models have been widely used for modelling, prediction and recommendation tasks in domains such as e-commerce and...
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Vector-based uncertain ordered density weighted averaging: a family of incentive-oriented aggregation operators
Incentive is a common phenomenon in the process of decision management. It is important and necessary to integrate incentive requirement into the...
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Optimal analysis and design of large-scale problems using a Modified Adolescent Identity Search Algorithm
This research uses a new method called the Modified Adolescent Identity Search Algorithm (MAISA) to solve optimization problems. The identity of a...
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Class feature Sub-space for few-shot classification
Few-shot learning is used in the development of models that can acquire novel class concepts from limited training samples, facilitating rapid...
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A multiobjective multiperiod portfolio selection approach with different investor attitudes under an uncertain environment
Though there are several studies on uncertain single-period portfolio selection, the uncertain multiperiod portfolio selection literature is still in...
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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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XK-III: A Spherical Robot with Redundant Degrees of Freedom
The spherical robot XK-III, designed with redundant degrees of freedom, addresses the limitations of existing pendulum spherical robot structures by...
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De-confounding representation learning for counterfactual inference on continuous treatment via generative adversarial network
Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are...
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A revolutionary RPL-based IoT routing protocol for monitoring building structural health in smart city domain utilizing equilibrium optimizer algorithm
The Internet of Things (IoT) has been the subject of recent studies and is expected to be extremely important to the future growth of the Internet....
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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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Role of Artificial Intelligence in the crime prediction and pattern analysis studies published over the last decade: a scientometric analysis
Crime is the intentional commission of an act usually suspected as socially detrimental and specifically defined, forbidden, and punishable under...
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New quantum color image watermarking technique (NQCIWT)
The crazy, unconscious use of the Internet, and the increase in cybercrime and hacking, which resulted in the loss of a large number of sensitive...
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Multi-source-free Domain Adaptive Object Detection
To enhance the transferability of object detection models in real-world scenarios where data is sampled from disparate distributions, considerable...
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Exploiting Diffusion Prior for Real-World Image Super-Resolution
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution....
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Attention-based cross-frequency graph convolutional network for driver fatigue estimation
Fatigue driving significantly contributes to global vehicle accidents and fatalities, making driver fatigue level estimation crucial....
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Explainable dating of greek papyri images
Greek literary papyri, which are unique witnesses of antique literature, do not usually bear a date. They are thus currently dated based on...
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Addressing the traveling salesperson problem with frequency fitness assignment and hybrid algorithms
The traveling salesperson problem (TSP) is one of the most iconic hard optimization tasks. With frequency fitness assignment (FFA), a new approach to...
