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查看斯高帕斯 (Scopus) 概要
黃 智謙
Associate Professor
Associate Professor
,
Faculty of Applied Sciences
之前聯繫機構
Associate Professor
,
Faculty of Applied Sciences
電話
8599 6875
電子郵件
cwong
mpu.edu
mo
h-index
227
引文
8
h-指數
按照存儲在普爾(Pure)的出版物數量及斯高帕斯(Scopus)引文計算。
2005
2025
每年研究成果
概覽
指紋
網路
研究成果
(56)
新聞/媒體
(4)
類似的個人檔案
(6)
指紋
查看啟用 CHI HIM WONG 的研究主題。這些主題標籤來自此人的作品。共同形成了獨特的指紋。
排序方式
重量
按字母排序
Computer Science
Gray Code
100%
Blockchain
88%
Gray Code Order
51%
Markov Decision Process
51%
Language Modeling
48%
Decision-Making
47%
Authentication
44%
Case Study
44%
Digital Transformation
44%
Large Language Model
44%
Microservice Architecture
44%
Random Function
44%
Internet-Of-Things
40%
And-States
38%
User Experience
33%
Teaching and Learning
33%
Greedy Algorithm
33%
Learning System
25%
Machine Learning
25%
Smart Contract
24%
Financial Literacy
22%
Fingerprint Authentication
22%
Experimental Paradigm
22%
Text Mining
22%
mobile commerce
22%
Feedback Function
22%
Cloud Migration
22%
Network Performance
22%
Technology Usage
22%
Architecture Perspective
22%
Group Interview
22%
Implementation Detail
22%
Cryptocurrency
22%
Trading System
22%
Gamification
22%
Performance Evaluation
22%
Fast Algorithm
22%
Software Architecture
22%
Lexicographic Order
22%
Random Graphs
22%
Lines of Code
22%
Convolutional Neural Network
22%
Learning Technology
22%
Feasible Solution
22%
0-1 knapsack problem
22%
Big Data
22%
Password
22%
Neural Network Application
22%
Intelligent Decision Making
22%
Microservice
22%
Engineering
Construction Sequence
66%
Binary String
44%
Markov Decision Process
44%
Recursive Algorithm
44%
Airlines
33%
Search Algorithm
22%
Convolutional Neural Network
22%
Greedy Algorithm
22%
Fast Algorithm
22%
Real Number
22%
Data Security
22%
Data Flow
22%
Hypercube
22%
Exhaustive Search
22%
Robotic Process Automation
22%
Flight Data
22%
Sensitivity Parameter
22%
Experimental Result
22%
Optimality
22%
Observables
22%
Recursive
22%
Iterative Algorithm
22%
Customer Experience
11%
Simulation Experiment
11%
Computation Time
11%
Improve Efficiency
11%
Regression Coefficient
11%
Mean Value
11%
Nodes
11%
Streamlines
11%
Resource Utilisation
11%
K-Means Classification Algorithm
11%
Proposed Recommender System
11%
Network Model
11%
User Profile
11%
Customer Satisfaction
11%
Essential Requirement
11%
Partially Observable Markov Decision Process
11%
Observability
7%