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IEEE Transactions on Emerging Topics in Computational Intelligence
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Year: 2024 | Volume: 8 | Issue: 1
IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information
Publication Year: 2024,Page(s):C2 - C2
IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information
Year: 2024 | Volume: 8 | Issue: 1
Deep Contrastive Representation Learning With Self-Distillation
Zhiwen Xiao;Huanlai Xing;Bowen Zhao;Rong Qu;Shouxi Luo;Penglin Dai;Ke Li;Zonghai Zhu
Publication Year: 2024,Page(s):3 - 15
Cited by: Papers (53)
Recently, contrastive learning (CL) is a promising way of learning discriminative representations from time series data. In the representation hierarchy, semantic information extracted from lower levels is the basis of that captured from higher levels. Low-level semantic information is essential and should be considered in the CL process. However, the existing CL algorithms mainly focus on the sim...Show More
Joint Multi-View Unsupervised Feature Selection and Graph Learning
Publication Year: 2024,Page(s):16 - 31
Cited by: Papers (12)
Despite significant progress, previous multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize either cluster structure or similarity structure to guide the feature selection, which neglect the possibility of a joint formulation with mutual benefits. Second, they often learn the similarity structure by either global structure learning or ...Show More
Improved Differentiable Architecture Search With Multi-Stage Progressive Partial Channel Connections
Publication Year: 2024,Page(s):32 - 43
Cited by: Papers (6)
Neural architecture search has attracted great attention in the research community and has been successfully applied in the industry recently. Differentiable architecture search (DARTS) is an efficient architecture search method. However, the networks searched by DARTS are often unstable due to the large gap in the architecture depth between the search phase and the verification phase. In addition...Show More
Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks
Publication Year: 2024,Page(s):44 - 54
Cited by: Papers (6)
Despite their immense success in numerous fields, machine and deep learning systems have not yet been able to firmly establish themselves in mission-critical applications in healthcare. One of the main reasons lies in the fact that when models are presented with previously unseen, Out-of-Distribution samples, their performance deteriorates significantly. This is known as the Domain Generalization ...Show More
TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation
Bingzhi Chen;Yishu Liu;Zheng Zhang;Guangming Lu;Adams Wai Kin Kong
Publication Year: 2024,Page(s):55 - 68
Cited by: Papers (79)
Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry. In recent years, convolutional encoder-decoder solutions have achieved substantial progress in the field of automatic medical image segmentation. Due to the inherent bias in the convolution operations, prior models mainly focus on local visual cues formed by the neigh...Show More
A Cross-Level Interaction Network Based on Scale-Aware Augmentation for Camouflaged Object Detection
Publication Year: 2024,Page(s):69 - 81
Cited by: Papers (3)
Camouflaged object detection (COD), with the task of separating the camouflaged object from its color/texture similar background, has been widely used in the fields of medical diagnosis and military reconnaissance. However, the COD task is still a challenging problem due to two main difficulties: large scale-variation for different camouflaged objects, and extreme similarity between the camouflage...Show More
A Swarm Intelligence Assisted IoT-Based Activity Recognition System for Basketball Rookies
Yu Zhou;Ruiqi Wang;Yufan Wang;Shilong Sun;Jiafeng Chen;Xiao Zhang
Publication Year: 2024,Page(s):82 - 94
Cited by: Papers (6)
Recent years have witnessed many applications of wearable sensor technology and machine learning on smart sports training, e.g., basketball. For rookie players, the fundamental skills are very important and can be improved using a scientific auxiliary training system, the core of which is human activity recognition (HAR) technique. For basketball players, one complete action is usually performed i...Show More
An Hybrid Local Search for the Direct Aperture Optimisation Problem
Mauricio Moyano;Guillermo Cabrera-Guerrero;Gonzalo Tello-Valenzuela;Carolina Lagos
Publication Year: 2024,Page(s):95 - 109
Cited by: Papers (3)
Radiotherapy is a cancer treatment that uses high levels of radiation to destroy cancerous cells and shrink tumours while minimising harm to surrounding organs at risk (OARs). One of the techniques used in radiotherapy is Intensity Modulated Radiation Therapy (IMRT). Usually, the IMRT problem is approached sequentially, that is, we first need to determine the set of beam angles from which radiatio...Show More
Evolutionary Multitasking With Solution Space Cutting for Point Cloud Registration
Yue Wu;Peiran Gong;Maoguo Gong;Hangqi Ding;Zedong Tang;Yibo Liu;Wenping Ma;Qiguang Miao
Publication Year: 2024,Page(s):110 - 125
Cited by: Papers (12)
Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of its robustness to the initial pose and flexibility in objective function design. However, most evolving registration methods cannot tackle the local optimum well and they have rarely investigated the success ratio, whic...Show More
Generalizable Segmentation of COVID-19 Infection From Multi-Site Tomography Scans: A Federated Learning Framework
Weiping Ding;Mohamed Abdel-Basset;Hossam Hawash;Mahardhika Pratama;Witold Pedrycz
Publication Year: 2024,Page(s):126 - 139
Cited by: Papers (2)
COVID-19-like pandemics are a major threat to the global health system that causes a lot of deaths across ages. Large-scale medical images (i.e., X-rays, computed tomography (CT)) dataset is favored to the accuracy of deep learning (DL) in the screening of COVID-19-like pneumonia. The cost, time, and efforts for acquiring and annotating, for instance, large CT datasets make it impossible to obtain...Show More
Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals
Publication Year: 2024,Page(s):140 - 149
Cited by: Papers (4)
Typical convolutional neural networks (CNNs) are widely used to recognize a user's stress state using the functional near-infrared spectroscopy (fNIRS), which is the latest brain imaging technology. fNIRS signals are usually fed into CNN models in the form of high-dimensional image data. However, this approach is not easy to achieve high classification accuracy because of physiological noises in b...Show More
Model Sparsification for Communication-Efficient Multi-Party Learning via Contrastive Distillation in Image Classification
Kai-Yuan Feng;Maoguo Gong;Ke Pan;Hongyu Zhao;Yue Wu;Kai Sheng
Publication Year: 2024,Page(s):150 - 163
Cited by: Papers (4)
Multi-party learning allows all parties to train a joint model under legal and practical constraints without private data transmission. Related research can perform multi-party learning tasks on homogeneous data through deep networks. However, due to the heterogeneity of data from different parties and the limitation of computational resources and costs, traditional approaches may affect the effec...Show More
3D Skeleton-Based Human Motion Prediction Using Dynamic Multi-Scale Spatiotemporal Graph Recurrent Neural Networks
Publication Year: 2024,Page(s):164 - 174
Cited by: Papers (4)
A dynamic multi-scale spatiotemporal graph recurrent neural network (DMST-GRNN) model has been introduced, which is leveraged to use human motion prediction on a 3D skeleton-based human activity dataset. It offers a multi-scale approach to spatial & temporal graphs using multi-scale graph convolution units (MGCUs) to describe the human body's semantic interconnection. The proposed DMST-GRNN is an ...Show More
A Hypersphere Information Granule-Based Fuzzy Classifier Embedded With Fuzzy Cognitive Maps for Classification of Imbalanced Data
Publication Year: 2024,Page(s):175 - 190
Cited by: Papers (2)
In this article, a hypersphere information granule-based fuzzy classifier integrated with Fuzzy Cognitive Maps (FCM), named FCM-IGFC, is proposed for the classification of imbalanced data. The proposed FCM-IGFC is structured by sequentially linking a Takagi-Sugeno-Kang (TSK) fuzzy system (FCM-TSK) – which is embedded with FCMs – and a rule-based fuzzy system (IGFS) based on hypersphere information...Show More
A Secure Federated Data-Driven Evolutionary Multi-Objective Optimization Algorithm
Publication Year: 2024,Page(s):191 - 205
Cited by: Papers (3)
Data-driven evolutionary algorithms usually aim to exploit the information behind a limited amount of data to perform optimization, which have proved to be successful in solving many complex real-world optimization problems. However, most data-driven evolutionary algorithms are centralized, causing privacy and security concerns. Existing federated Bayesian optimization algorithms and data-driven e...Show More
Super Neurons
Serkan Kiranyaz;Junaid Malik;Mehmet Yamac;Mert Duman;Ilke Adalioglu;Esin Guldogan;Turker Ince;Moncef Gabbouj
Publication Year: 2024,Page(s):206 - 228
Cited by: Papers (5)
Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neurons that yield an elegant level of diversity; however, like its predecessor, conventional Convolutional Neural Networks (CNNs), they still have a common drawback: localized (fixed) kernel operations. This severely limits ...Show More
Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion
Chin-Teng Lin;Haichao Zhang;Liang Ou;Yu-Cheng Chang;Yu-Kai Wang
Publication Year: 2024,Page(s):229 - 239
Cited by: Papers (1)
The performance of agents is highly influenced by multiple factors, including ability, decision, and states. Trust modeling is widely used to boost the performance of multiagent teaming (MAT). However, most existing trust models rely on statistical methods or preset parameters to assess the trust value in the MAT scenario. In this article, an adaptive trust model is proposed to evaluate comprehens...Show More
In real-world applications that involve complex data dependencies, it would be essential to proceed with machine learning tasks in an adaptable manner. This article presents a novel Mutually Adaptable Learning (MAL) approach that allows for, on the one hand, extracting the most crucial information from data, and on the other hand, maximally utilizing it through model learning, in a mutually adapta...Show More
Adaptive Indoor People-Counting System Based on Edge AI Computing
Mao-Hsu Yen;Bor-Shyh Lin;Yu-Lun Kuo;I-Jung Lee;Bor-Shing Lin
Publication Year: 2024,Page(s):255 - 263
Cited by: Papers (3)
Since the outbreak of COVID-19 in 2019, people counting in confined spaces has become essential for controlling the flow of people and reducing viral spread. Many people-counting systems use a fisheye lens to achieve a wide viewing range. However, the images captured with a fisheye lens can have different sizes and deformations due to different camera heights, which consequently affects the accura...Show More
Self-Learning Modeling in Possibilistic Model Checking
Publication Year: 2024,Page(s):264 - 278
Cited by: Papers (3)
Generalized possibility Kripke structure (GPKS) plays a key role in modeling fuzzy systems for possibilistic model checking. However, it is unrealistic, in practice, to produce manually a large number of fuzzy states of GPKSs and transition relations over states space. In this article, we develop an online supervised learning algorithm for GPKS to learn its fuzzy states and possibilistic transitio...Show More
Multiplierless Implementation of Fitz-Hugh Nagumo (FHN) Modeling Using CORDIC Approach
Publication Year: 2024,Page(s):279 - 287
Cited by: Papers (5)
The study, simulation, and implementation of neural behavior in the human brain are central goals of neuromorphic engineering. By integrating various scientific fields, we present a hardware solution based on neuronal cell mechanisms that can emulate such a nature-inspired system. This article presents a Fitz-Hugh Nagumo (FHN) neuron implemented using COordinate Rotation DIgital Computer (CORDIC),...Show More
MG-GCN: Multi-Granularity Graph Convolutional Neural Network for Multi-Label Classification in Multi-Label Information System
Publication Year: 2024,Page(s):288 - 299
Cited by: Papers (13)
Recently, many researchers have applied Graph Convolutional Neural Networks (GCN) to multi-label learning tasks by establishing relations among labels. However, these researchers have only used GCN as an auxiliary module and have lacked exploration of object relations, which has limited the inference capability of GCN. Applying GCN to multi-label classification in information systems poses signifi...Show More
Tensor-Based Traffic Recovery of Industrial Network by BCD-Inspired Neural Approximation to Latent Nonlinearity and Sparsity
Publication Year: 2024,Page(s):300 - 312
Due to the massive network data created by the industrial Internet of Things, tensor as the compact and multi-dimensional representation is widely employed to model the industrial network traffic. The unstable data acquisition often results in parts of entities' loss in the traffic tensor, which is conventionally solved by the tensor completion algorithms based on linear algebra and low rankness. ...Show More
Contact Information
Editor-in-Chief
Yiu-Ming Cheung
Hong Kong Baptist University, Department of Computer Science
Hong Kong
China
ymc@comp.hkbu.edu.hkContact Information
Editor-in-Chief
Yiu-Ming Cheung
Hong Kong Baptist University, Department of Computer Science
Hong Kong
China
ymc@comp.hkbu.edu.hkIEEE Account
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