Chin Poo Lee
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View article: Optimizing Kubernetes with Multi-Objective Scheduling Algorithms: A 5G Perspective
Optimizing Kubernetes with Multi-Objective Scheduling Algorithms: A 5G Perspective Open
This review provides an in-depth examination of multi-objective scheduling algorithms within 5G networks, with a particular focus on Kubernetes-based container orchestration. As 5G systems evolve, efficient resource allocation and the opti…
View article: VLMT: Vision-Language Multimodal Transformer for Multimodal Multi-hop Question Answering
VLMT: Vision-Language Multimodal Transformer for Multimodal Multi-hop Question Answering Open
The increasing availability of multimodal data across text, tables, and images presents new challenges for developing models capable of complex cross-modal reasoning. Existing methods for Multimodal Multi-hop Question Answering (MMQA) ofte…
View article: Adaptive control techniques for improving anti-lock braking system performance in diverse friction scenarios
Adaptive control techniques for improving anti-lock braking system performance in diverse friction scenarios Open
Anti-lock braking systems (ABS) enhance vehicle safety by preventing wheel lock-up, but their effectiveness depends on tire-road friction. Traditional braking systems struggle to maintain effective performance due to the risk of wheel lock…
View article: KDViT: COVID-19 diagnosis on CT-scans with knowledge distillation of vision transformer
KDViT: COVID-19 diagnosis on CT-scans with knowledge distillation of vision transformer Open
This paper introduces Knowledge Distillation of Vision Transformer (KDViT), a novel approach for medical image classification. The Vision Transformer architecture incorporates a self-attention mechanism to autonomously learn image structur…
View article: LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest
LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest Open
Sign language recognition presents significant challenges due to the intricate nature of hand gestures and the necessity to capture fine-grained details. In response to these challenges, a novel approach is proposed—Lightweight Attentive V…
View article: Multilingual Question Answering for Malaysia History with Transformer-based Language Model
Multilingual Question Answering for Malaysia History with Transformer-based Language Model Open
In natural language processing (NLP), a Question Answering System (QAS) refers to a system or model that is designed to understand and respond to user queries in natural language. As we navigate through the recent advancements in QAS, it c…
View article: Guest Editorial: Special issue on advances in representation learning for computer vision
Guest Editorial: Special issue on advances in representation learning for computer vision Open
Deep learning has been a catalyst for a transformative revolution in machine learning and computer vision in the past decade. Within these research domains, methods grounded in deep learning have exhibited exceptional performance across a …
View article: DBDC-SSL: Deep Brownian Distance Covariance With Self-Supervised Learning for Few-Shot Image Classification
DBDC-SSL: Deep Brownian Distance Covariance With Self-Supervised Learning for Few-Shot Image Classification Open
Few-shot image classification remains a persistent challenge due to the intrinsic difficulty
\nfaced by visual recognition models in achieving generalization with limited training data. Existing methods
\nprimarily focus on exploiting marg…
View article: NegCosIC: Negative Cosine Similarity-Invariance- Covariance Regularization for Few-Shot Learning
NegCosIC: Negative Cosine Similarity-Invariance- Covariance Regularization for Few-Shot Learning Open
Few-shot learning continues to pose a challenge as it is inherently difficult for visual recognition models to generalize with limited labeled examples. When the training data is limited, the process of training and fine-tuning the model w…
View article: Ensemble CNN-ViT Using Feature-Level Fusion for Gait Recognition
Ensemble CNN-ViT Using Feature-Level Fusion for Gait Recognition Open
Individual deep learning models showcase impressive performance; however, the capacity of a single model might fall short in capturing the full spectrum of intricate patterns present in the input data. Thus, relying solely on a single mode…
View article: SFT: Few-Shot Learning via Self-Supervised Feature Fusion With Transformer
SFT: Few-Shot Learning via Self-Supervised Feature Fusion With Transformer Open
The few-shot learning paradigm aims to generalize to unseen tasks with limited samples.
\nHowever, a focus solely on class-level discrimination may fall short of achieving robust generalization,
\nespecially when neglecting instance divers…
View article: MaxMViT-MLP: Multiaxis and Multiscale Vision Transformers Fusion Network for Speech Emotion Recognition
MaxMViT-MLP: Multiaxis and Multiscale Vision Transformers Fusion Network for Speech Emotion Recognition Open
Vision Transformers, known for their innovative architectural design and modeling capabilities, have gained significant attention in computer vision. This paper presents a dual-path approach that leverages the strengths of the Multi-Axis V…
View article: QARR-FSQA: Question-Answer Replacement and Removal Pretraining Framework for Few-Shot Question Answering
QARR-FSQA: Question-Answer Replacement and Removal Pretraining Framework for Few-Shot Question Answering Open
In Natural Language Processing, creating training data for question answering (QA) systems
\ntypically requires significant effort and expertise. This challenge is amplified in few-shot scenarios where
\nonly a limited number of training s…
View article: PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition
PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition Open
Facial Expression Recognition (FER) is a machine learning problem that deals with recognizing human facial expressions. While existing work has achieved performance improvements in recent years, FER in the wild and under challenging condit…
View article: UniRaG: Unification, Retrieval, and Generation for Multimodal Question Answering With Pre-Trained Language Models
UniRaG: Unification, Retrieval, and Generation for Multimodal Question Answering With Pre-Trained Language Models Open
Multimodal Question Answering (MMQA) has emerged as a challenging frontier at the intersection of natural language processing (NLP) and computer vision, demanding the integration of diverse modalities for effective comprehension and respon…
View article: TransKGQA: Enhanced Knowledge Graph Question Answering With Sentence Transformers
TransKGQA: Enhanced Knowledge Graph Question Answering With Sentence Transformers Open
Knowledge Graph Question Answering (KGQA) plays a crucial role in extracting
\nvaluable insights from interconnected information. Existing methods, while commendable, face challenges
\nsuch as contextual ambiguity and limited adaptability …
View article: MPNet-GRUs: Sentiment Analysis With Masked and Permuted Pre-Training for Language Understanding and Gated Recurrent Units
MPNet-GRUs: Sentiment Analysis With Masked and Permuted Pre-Training for Language Understanding and Gated Recurrent Units Open
Sentiment analysis, a pivotal task in natural language processing, aims to discern opinions and
\nemotions expressed in text. However, existing methods for sentiment analysis face various challenges such
\nas data scarcity, complex languag…
View article: SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition
SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition Open
Hand gesture recognition (HGR) is a rapidly evolving field with the potential to revolutionize human–computer interactions by enabling machines to interpret and understand human gestures for intuitive communication and control. However, HG…
View article: EnViTSA: Ensemble of Vision Transformer with SpecAugment for Acoustic Event Classification
EnViTSA: Ensemble of Vision Transformer with SpecAugment for Acoustic Event Classification Open
Recent successes in deep learning have inspired researchers to apply deep neural networks to Acoustic Event Classification (AEC). While deep learning methods can train effective AEC models, they are susceptible to overfitting due to the mo…
View article: Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach
Scheduling Scientific Workflow in Multi-Cloud: A Multi-Objective Minimum Weight Optimization Decision-Making Approach Open
One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a colle…
View article: Plant-CNN-ViT: Plant Classification with Ensemble of Convolutional Neural Networks and Vision Transformer
Plant-CNN-ViT: Plant Classification with Ensemble of Convolutional Neural Networks and Vision Transformer Open
Plant leaf classification involves identifying and categorizing plant species based on leaf characteristics, such as patterns, shapes, textures, and veins. In recent years, research has been conducted to improve the accuracy of plant class…
View article: PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition
PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition Open
Facial Expression Recognition (FER) is a machine learning problem that deals with recognizing human facial expressions. While existing work has achieved performance improvements in recent years, FER in the wild and under challenging condit…
View article: HGR-ViT: Hand Gesture Recognition with Vision Transformer
HGR-ViT: Hand Gesture Recognition with Vision Transformer Open
Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they f…
View article: Fine-Tuned Temporal Dense Sampling with 1D Convolutional Neural Network for Human Action Recognition
Fine-Tuned Temporal Dense Sampling with 1D Convolutional Neural Network for Human Action Recognition Open
Human action recognition is a constantly evolving field that is driven by numerous applications. In recent years, significant progress has been made in this area due to the development of advanced representation learning techniques. Despit…
View article: Recent Advances in Traffic Sign Recognition: Approaches and Datasets
Recent Advances in Traffic Sign Recognition: Approaches and Datasets Open
Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their abili…
View article: 3D Shape Generation via Variational Autoencoder with Signed Distance Function Relativistic Average Generative Adversarial Network
3D Shape Generation via Variational Autoencoder with Signed Distance Function Relativistic Average Generative Adversarial Network Open
3D shape generation is widely applied in various industries to create, visualize, and analyse complex data, designs, and simulations. Typically, 3D shape generation uses a large dataset of 3D shapes as the input. This paper proposes a vari…
View article: Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer
Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer Open
Gait recognition, the task of identifying an individual based on their unique walking style, can be difficult because walking styles can be influenced by external factors such as clothing, viewing angle, and carrying conditions. To address…
View article: Enhanced Traffic Sign Recognition with Ensemble Learning
Enhanced Traffic Sign Recognition with Ensemble Learning Open
With the growing trend in autonomous vehicles, accurate recognition of traffic signs has become crucial. This research focuses on the use of convolutional neural networks for traffic sign classification, specifically utilizing pre-trained …
View article: Three-dimensional shape generation via variational autoencoder generative adversarial network with signed distance function
Three-dimensional shape generation via variational autoencoder generative adversarial network with signed distance function Open
Mesh-based 3-dimensional (3D) shape generation from a 2-dimensional (2D) image using a convolution neural network (CNN) framework is an open problem in the computer graphics and vision domains. Most existing CNN-based frameworks lack robus…