Applied Intelligence

In this paper, we study the Multi-Start Team Orienteering Problem (MSTOP), a mission re-planning problem where vehicles are initially located away from the depot and have different amounts of fuel. We consider/assume the goal of multiple vehicles ...

Computed tomography (CT) is an important technique that is widely used in disease screening and diagnosis. In order to assist doctors in diagnosis and treatment plans, an efficient and accurate automatic image segmentation technology is urgently ...

Audio-visual speech synthesis (AVSS) has garnered attention in recent years for its utility in the realm of audio-visual learning. AVSS transforms one speaker’s speech into another’s audio-visual stream while retaining linguistic content. This ...

Both wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and ...

Annotating accelerometer-based physical activity data remains a challenging task, limiting the creation of robust supervised machine learning models due to the scarcity of large, labeled, free-living human activity recognition (HAR) datasets. ...

Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, ...

To effectively address large-scale optimization problems, this paper proposes an evolutionary dynamic grouping (EDG) based cooperative co-evolution (CC) algorithm. In the proposed algorithm, a novel decomposition method is designed to generate the ...

Complex visual question answering holds the potential to enhance artificial intelligence proficiency in understanding natural language, stimulate advances in computer vision technologies, and expand the range of practical applications. However, ...$$$$

Accurate cell segmentation is a pivotal step throughout the cervical cancer treatment continuum, encompassing early screening, guiding treatment decisions, and assessing long-term prognosis. Currently, in clinical practice, pathologists rely on ...

In this paper, considering the effect of multiple delayed states on the reservoir itself, based on the advantage of the empirical wavelet transform, an improved ESN with multiple delayed states is proposed, called multi-state delayed echo state ...

Bayesian Networks (BN) are robust probabilistic graphical models mainly used with discrete random variables requiring discretization and quantization of continuous data. Quantization is known to affect model accuracy, speed and interpretability, ...

Weakly-supervised learning has emerged as a compelling method for object detection by reducing the fully annotated labels requirement in the training procedure. Recently, some works have treated the detection task as a classification task, ...

In the domain of flexible manufacturing, Deep Reinforcement Learning (DRL) has emerged as a pivotal technology for robotic assembly tasks. Despite advancements in sample efficiency and interaction safety through residual reinforcement learning ...$$$$$$

In 3D printing processes, there are many thermal stress related defects that can have a significant negative impact on the shape and size of the structure. Such anomalies in the heat transfer of the printing process need to be detected at an early ...

The global spread of epidemic lung diseases, including COVID-19, underscores the need for efficient diagnostic methods. Addressing this, we developed and tested a computer-aided, lightweight Convolutional Neural Network (CNN) for rapid and ...

Depth estimation is an essential component of computer vision applications for environment perception, 3D reconstruction and scene understanding. Among the available methods, self-supervised monocular depth estimation is noteworthy for its cost-...

The image captioning task is among the most important tasks in computer vision. Most existing methods mine more useful contextual information from image features. Similarly, to mine more contextual information, this paper proposes a visual ...

The scale of communities in real-world networks is often imbalanced, which has a significant impact on community detection performance. Existing approaches exhibit a trade-off between accuracy and computational cost, with global methods offering ...

Accurate grant classification of network traffic not only assists service providers in making acceptable allocations based on actual business demands, but also ensures service quality. To further improve the accuracy of traffic classification, we ...

Traffic flow prediction is the fundamental cornerstone of intelligent urban transportation systems. However, existing research has predominantly focused on exploring spatiotemporal dependencies within the spatial and temporal domains, often ...

In wireless networks, the effectiveness of beamforming and power allocation strategies is crucial in meeting the increasing data demands of users and ensuring rapid data transmission. Graph learning approaches have been developed to tackle complex ...

Deep learning models often suffer from degraded performance when the distributions of the training and testing data differ (i.e., domain shift). Domain generalization (DG) techniques can help improve the generalization performance for unseen ...

Rough set theory is a mathematical tool widely employed in various fields to handle uncertainty. Feature selection, as an essential and independent research area within rough set theory, aims to identify a small subset of important features by ...

Causal inference is considered a crucial topic in the medical field, as it enables the determination of causal effects for medical treatments through data analysis. However, the vast volume and complexity of medical data present significant ...

For multi-object behaviour recognition in classroom scenes, crowded objects have heavy occlusion, invisible keypoints, scale variation, which directly overwhelms the recognition performance. Due to the dense student objects and similar student ...${}^{\mathrm{}}$${}_{}$

In the real world, data often exhibits high-dimensional and complex characteristics. In addition, an object may correspond to multiple class labels. Therefore, filtering and processing such data has become a hot research topic. Multi-label feature ...

Writing essays is an important skill which enables one to clearly write the ideas and understanding of certain topic with the help of language articulation and examples. Writing essay is a skill so is the grading of those essays. It requires a lot ...

Binarization efficiently compresses full-precision convolutional neural networks (CNNs) to achieve accelerated inference but with substantial performance degradations. Self-knowledge distillation (SKD) can significantly improve the performance of ...

Sound Event Localization and Detection (SELD) is the task of spatial and temporal localization of various sound events and their classification. Commonly, multitask models are used to perform SELD. In this work, a deep learning network model named ...

Parametric methods are widely utilized in RGB-based human mesh recovery, relying on precise statistical human body model parameters that are challenging to obtain. In contrast, non-parametric transformer-based approaches excel but are applied only ...