Article
Green & Sustainable Science & Technology
Qi He, Chenyang Yu, Wei Song, Xiaoyi Jiang, Lili Song, Jian Wang
Summary: Islands that possess both land and sea characteristics serve as the foundation for marine environment protection, ecological balance preservation, and sustainable economic and social growth. This paper proposes a method for constructing an island knowledge graph based on an entity dictionary and rule patterns. The results demonstrate that the model is able to effectively predict missing entities and enhance the island knowledge graph.
Article
Computer Science, Information Systems
Ibrahim A. Ahmed, Fatima N. AL-Aswadi, Khaled M. G. Noaman, Wafa' Za'al Alma'aitah
Summary: With the growth of data on the Web, the need for efficient methods to extract valuable information from the data has increased. Knowledge graphs provide an efficient and easy way to represent and organize data. The construction of Arabic Knowledge Graph (AKG) faces challenges due to limited Arabic data and lack of effective language processing tools. This research reviews KG construction best practices and discusses the challenges and potential solutions in constructing AKG.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Green & Sustainable Science & Technology
Jiyuan Tan, Qianqian Qiu, Weiwei Guo, Tingshuai Li
Summary: In this paper, a top-down approach was used to construct a knowledge graph of the urban traffic system, utilizing the model layer for knowledge reuse and sharing, and adopting a representation learning based knowledge reasoning model to improve the knowledge graph.
Article
Mathematics
Yong Chen, Xinkai Ge, Shengli Yang, Linmei Hu, Jie Li, Jinwen Zhang
Summary: This survey comprehensively reviews the related advances of multimodal knowledge graphs, including their construction, completion, and typical applications. The methods of named entity recognition, relation extraction, and event extraction are outlined for construction, while multimodal knowledge graph representation learning and entity linking are discussed for completion. The mainstream applications of multimodal knowledge graphs in various domains are summarized.
Article
Multidisciplinary Sciences
Meihong Wang, Linling Qiu, Xiaoli Wang
Summary: Knowledge graphs are widely used in artificial intelligence, but their open nature often results in incompleteness, requiring the construction of a more comprehensive knowledge graph. Link prediction is a fundamental task in knowledge graph completion, utilizing existing relations to infer new ones. KG-embedding models have significantly advanced the state of the art in recent years.
Article
Computer Science, Interdisciplinary Applications
Javier Castell-Diaz, Jose Antonio Minarro-Gimenez, Catalina Martinez-Costa
Summary: SNOMED CT postcoordination is an underused mechanism that can help to implement advanced systems for the automatic extraction and encoding of clinical information from text. We have implemented KGE4SCT, a method that suggests the corresponding SNOMED CT postcoordinated expression for a given clinical term, by leveraging on the SNOMED CT ontology and its graph-like structure and using knowledge graph embeddings (KGEs).
JOURNAL OF BIOMEDICAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Xiaojuan Zhao, Rong Jiang, Yue Han, Aiping Li, Zhichao Peng
Summary: The development of key technologies of knowledge graph (KG) has promoted the development of machine cognition technology, and cybersecurity KG (CKG) provides knowledge and intelligent reasoning support for cybersecurity in strong adversarial and high dynamic environment. Most CKG studies tend to construct a cybersecurity ontology first and then extract semantic triples based on ontology. It is suggested that the construction of CKG should follow the Open-domain KG.
COMPUTERS & SECURITY
(2024)
Article
Geosciences, Multidisciplinary
Xianming Tang, Zhiqiang Feng, Yitian Xiao, Ming Wang, Tianrui Ye, Yujie Zhou, Jin Meng, Baosen Zhang, Dongwei Zhang
Summary: This paper proposes an engineering-based method to construct a domain knowledge graph for tackling the challenges associated with data integration and smart application in the upstream petroleum industry. The constructed exploration and development knowledge graph is assembled from a multi-sourced heterogeneous database and millions of nodes, and two applications based on it are developed and validated for providing better knowledge services in the oil and gas industry.
GEOSCIENCE FRONTIERS
(2023)
Article
Geosciences, Multidisciplinary
Lei Zhang, Mingcai Hou, Anqing Chen, Hanting Zhong, James G. Ogg, Dongyu Zheng
Summary: This study constructed a knowledge system for fluvial facies and developed an algorithm model for sedimentary facies reasoning. The automated reasoning results showed an interpretation accuracy of about 90%. This model and algorithm provide an efficient and automated technology for the rapid and intelligent identification of sedimentary facies.
GEOSCIENCE FRONTIERS
(2023)
Article
Computer Science, Information Systems
Mingkang Da, Teng Zhong, Jiaqi Huang
Summary: Indoor fire is a significant disaster that threatens the safety of indoor people worldwide. Traditional studies based on numerical simulation cannot provide adequate support for decision-making in indoor fire scenarios. This study presents a preliminary attempt to construct a knowledge graph for indoor fire emergency evacuation. The proposed method and knowledge graph can represent complicated indoor fire events and support emergency evacuation.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2023)
Article
Computer Science, Information Systems
Diego Rincon-Yanez, Chahinez Ounoughi, Bassem Sellami, Tarmo Kalvet, Marek Tiits, Sabrina Senatore, Sadok Ben Yahia
Summary: Knowledge representation is vital for automated decision-making, and knowledge graphs have emerged as a popular form of representation. This paper proposes a method using knowledge graph embeddings to model international trade and explores the impact of embeddings on other algorithms.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Wenqing Yang, Xiaochao Li, Peng Wang, Jun Hou, Qianmu Li, Nan Zhang
Summary: This article introduces a scheme for constructing the defect knowledge graph of power equipment based on multi-cloud. The scheme is based on the State Grid Multi-cloud IoT architecture and employs ontology design and a knowledge graph reasoning method to achieve independent deployment on multiple clouds and enhance system flexibility and security.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Genet Asefa Gesese, Russa Biswas, Mehwish Alam, Harald Sack
Summary: Knowledge Graphs (KGs) are structured representations containing entities and relations, facilitating interconnectivity and interoperability between different resources in the Linked Data Cloud. However, KG applications face challenges such as high computational and storage costs, leading to the need for representing high-dimensional KGs in low-dimensional embedding spaces. This paper surveys KG embedding models that consider structured and unstructured information, and evaluates them empirically for link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiaoyan Chen, Freddy Lecue, Jeff Z. Pan, Shumin Deng, Huajun Chen
Summary: Data stream learning faces challenges in dealing with concept drifts, which can affect the accuracy of machine learning models over time. This paper proposes a novel approach to encode knowledge graph embeddings for ontology streams, aiming to address the issue of concept drifts. Experimental results show that the method provides accurate predictions for real-world scenarios such as air quality in Beijing and bus delay in Dublin.
JOURNAL OF WEB SEMANTICS
(2021)
Article
Chemistry, Multidisciplinary
Khalid Mahmood, Rahmah Mokhtar, Muhammad Ahsan Raza, A. Noraziah, Basem Alkazemi
Summary: Knowledge management in a structured system is a complex task that requires common, standardized methods. This research proposes an enhanced ecological and confined domain ontology construction (EC-DOC) scheme, which includes five important phases such as conceptualization and clustering. The EC-DOC scheme can provide accurate domain concepts and make them available in a preferred local language.
APPLIED SCIENCES-BASEL
(2023)
Article
Multidisciplinary Sciences
Bilal Abu-Salih, Pornpit Wongthongtham, Greg Morrison, Kevin Coutinho, Manaf Al-Okaily, Ammar Huneiti
Summary: This study develops several models for forecasting short-term renewable energy consumption and generation, and through the analysis of real-time energy data, demonstrates the superiority of the deep learning-based LSTM model in energy prediction.
Article
Computer Science, Information Systems
Bilal Abu-Salih, Dana Al Qudah, Malak Al-Hassan, Seyed Mohssen Ghafari, Tomayess Issa, Ibrahim Aljarah, Amin Beheshti, Sulaiman Alqahtani
Summary: Social media is a key component of the communication revolution, but its open environment and popularity create opportunities for cyber threats. Continuous introduction of new techniques and approaches is crucial to detect and prevent social spam and malicious activities. This article proposes a novel and effective approach to detect social spammers and demonstrates its effectiveness through experiments.
JOURNAL OF INFORMATION SCIENCE
(2022)
Editorial Material
Computer Science, Artificial Intelligence
Kit Yan Chan, Bilal Abu-Salih, Khan Muhammad, Vasile Palade, Rifai Chai
Article
Computer Science, Information Systems
Omar Alshaweesh, Mohammad Wedyan, Moutaz Alazab, Bilal Abu-Salih, Adel Al-Jumaily
Summary: Social distancing is crucial for preventing diseases, particularly respiratory illnesses like COVID-19. Medical authorities have used social distancing measures and monitoring to control the spread of the pandemic in various settings. This study presents a computer application that uses a fast and unobtrusive technique to monitor social distancing in closed areas, such as schools and kindergartens, with an accuracy exceeding 98.5%.
Article
Computer Science, Artificial Intelligence
Reem Qadan Al-Fayez, Marwan Al-Tawil, Bilal Abu-Salih, Zaid Eyadat
Summary: In recent years, the use of shared and published online data has become crucial for research and development in all fields, especially with the advancement of semantic technologies. However, most datasets in social and economic domains lack standardization. This paper proposes the GTD ontology (GTDOnto) to organize and model knowledge about global incidents, with the aim of providing machine-readable and interoperable controlled vocabularies for future incident descriptions and applications.
BIG DATA AND COGNITIVE COMPUTING
(2023)
Article
Automation & Control Systems
Bilal Abu-Salih, Pornpit Wongthongtham, Kevin Coutinho, Raneem Qaddoura, Omar Alshaweesh, Mohammad Wedyan
Summary: Floods are natural disasters that cause severe damage to urban areas. This paper proposes a data-driven model for detecting flood risk areas in road networks using machine learning techniques. The experiments show that an optimized ensemble classifier performs well with an average ROC AUC value of 90% and outperforms other classifiers. This framework could be applied to similar regions with road networks.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Multidisciplinary Sciences
Bilal Abu-Salih, Mohammad Alhabashneh, Dengya Zhu, Albara Awajan, Yazan Alshamaileh, Bashar Al-Shboul, Mohammad Alshraideh
Summary: The development of emotion detection technology has become a viable option in the corporate sector due to the widespread availability of social data. However, there is a lack of research comparing different emotion detection technologies and applying benchmark comparisons to social data. This study compares eight technologies and evaluates their performance using different datasets and evaluation metrics.
Article
Information Science & Library Science
Abeer F. F. Alkhwaldi, Anas Ali Al-Qudah, Hamood Mohammed Al-Hattami, Manaf Al-Okaily, Ahmad Samed Al-Adwan, Bilal Abu-Salih
Summary: The purpose of this study is to investigate the determinants that influence the intention of using digital payment systems among public sector employees in Jordan. The authors developed a new research model based on UTAUT2 and UA to understand the acceptance of JoMoPay system in Jordan. The results indicate that social influence, UA, performance expectancy, price value, and effort expectancy have a significant positive impact on the intention to use the JoMoPay system, while facilitating conditions do not.
GLOBAL KNOWLEDGE MEMORY AND COMMUNICATION
(2023)
Review
Computer Science, Theory & Methods
Bilal Abu-Salih, Muhammad AL-Qurishi, Mohammed Alweshah, Mohammad AL-Smadi, Reem Alfayez, Heba Saadeh
Summary: The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to provide better data representation and knowledge inference. However, due to a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper provides a comprehensive taxonomy and overview of healthcare KG construction, and critically evaluates current state-of-the-art techniques in terms of knowledge extraction methods, types of the knowledge base and sources, and evaluation protocols.
JOURNAL OF BIG DATA
(2023)
Article
Computer Science, Artificial Intelligence
Malak Al-Hassan, Bilal Abu-Salih, Ahmad Al Hwaitat
Summary: The lack of regulation and oversight on Online Social Networks has led to a rise in social spam, which aims to deceive and manipulate users through unsolicited and low-quality content. Social spam can cause harmful effects such as malware spread, phishing scams, and reputational damage. Machine learning techniques can detect social spammers by analyzing data patterns, but have limitations in terms of false positives and false negatives. Ontologies offer a solution by providing explicit modeling of domain knowledge to create rules for identifying social spammers. This paper addresses the deficiency in ontologies for conceptualizing domain-based social spam by designing a specific ontology called DSpamOnto that can identify social spammers based on their domain-specific behavior.
BIG DATA AND COGNITIVE COMPUTING
(2023)
Review
Computer Science, Artificial Intelligence
Kit Yan Chan, Bilal Abu-Salih, Raneem Qaddoura, Ala' M. Al-Zoubi, Vasile Palade, Duc-Son Pham, Javier Del Ser, Khan Muhammad
Summary: This paper presents an up-to-date survey on current state-of-the-art deployed deep neural networks (DNNs) for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.
Article
Mathematics, Interdisciplinary Applications
Ahmad Alqatawna, Bilal Abu-Salih, Nadim Obeid, Muder Almiani
Summary: This paper focuses on using time-series analysis techniques to forecast resource needs in logistics delivery companies. The study builds a model that optimizes order volume prediction and staffing requirements. Among the different methods tested, the SARIMAX model demonstrates superior accuracy in predicting order volumes and trends in different countries.
Article
Green & Sustainable Science & Technology
Pornpit Wongthongtham, Bilal Abu-Salih, Jeff Huang, Hemixa Patel, Komsun Siripun
Summary: Climate change is causing extreme weather events and increasing the scale and severity of floods. To effectively respond to these conditions and their impacts, government agencies need to provide more efficient asset management strategies. Existing research on water-sensitive urban design and rural drainage design only partially addresses this problem, as road networks typically serve diverse areas with different hydrology, geology, and climatic conditions.
Article
Construction & Building Technology
Kevin Coutinho, Pornpit Wongthongtham, Bilal Abu-Salih, Mousa A. Abu Saleh, Neeraj Kumari Khairwal
Summary: This paper explores the linkages between blockchain technology and energy systems, comparing blockchain power consumption with its advantages in renewable energy transitions through peer-to-peer energy trading.
FRONTIERS IN BUILT ENVIRONMENT
(2022)
Article
Computer Science, Information Systems
Bilal Abu-Salih
Summary: This study aims to design a domain ontology for the metaverse, providing a specification of relevant technologies and infrastructure. It establishes a cornerstone for future efforts in building extant versions of this ontology considering the evolution of relevant technologies.
FRONTIERS IN BIG DATA
(2022)
Article
Computer Science, Hardware & Architecture
Zihang Zhen, Xiaoding Wang, Hui Lin, Sahil Garg, Prabhat Kumar, M. Shamim Hossain
Summary: In this paper, a blockchain architecture based on dynamic state sharding (DSSBD) is proposed to solve the problems caused by cross-shard transactions and reconfiguration. By utilizing deep reinforcement learning, the number of shards, block spacing, and block size can be dynamically adjusted to improve the performance of the blockchain. The experimental results show that the crowdsourcing system with DSSBD has better performance in terms of throughput, latency, balancing, cross-shard transaction proportion, and node reconfiguration proportion, while ensuring security.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Gabriel F. C. de Queiroz, Jose F. de Rezende, Valmir C. Barbosa
Summary: Multi-access Edge Computing (MEC) is a technology that enables faster task processing at the network edge by deploying servers closer to end users. This paper proposes the FlexDO algorithm to solve the DAG application partitioning and offloading problem, and compares it with other solutions to demonstrate its superior performance in various test scenarios.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Shahid Latif, Wadii Boulila, Anis Koubaa, Zhuo Zou, Jawad Ahmad
Summary: In the field of Industrial Internet of Things (IIoT), networks are increasingly vulnerable to cyberattacks. This research introduces an optimized Intrusion Detection System based on Deep Transfer Learning (DTL) for heterogeneous IIoT networks, combining Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and ensemble techniques. Through rigorous evaluation, the framework achieves exceptional performance and accurate detection of various cyberattacks.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Rongji Liao, Yuan Zhang, Jinyao Yan, Yang Cai, Narisu Tao
Summary: This paper proposes a joint control approach called STOP to guarantee user-perceived deadline using curriculum-guided deep reinforcement learning. Experimental results show that the STOP scheme achieves a significantly higher average arrival ratio in NS-3.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Miguel Rodriguez-Perez, Sergio Herreria-Alonso, J. Carlos Lopez-Ardao, Raul F. Rodriguez-Rubio
Summary: This paper presents an implementation of an active queue management (AQM) algorithm for the Named-Data Networking (NDN) architecture and its application in congestion control protocols. By utilizing the congestion mark field in NDN packets, information about each transmission queue is encoded to achieve a scalable AQM solution.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Angel Canete, Mercedes Amor, Lidia Fuentes
Summary: This paper proposes an energy-aware placement of service function chains of Virtual Network Functions (VNFs) and a resource-allocation solution for heterogeneous edge infrastructures. The solution has been integrated with an open source management and orchestration project and has been successfully applied to augmented reality services, achieving significant reduction in power consumption and ensuring quality of service compliance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Sachin Kadam, Kaustubh S. Bhargao, Gaurav S. Kasbekar
Summary: This paper discusses the problem of estimating the node cardinality of each node type in a heterogeneous wireless network. Two schemes, HSRC-M1 and HSRC-M2, are proposed to rapidly estimate the number of nodes of each type. The accuracy and efficiency of these schemes are proven through mathematical analysis and simulation experiments.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Jean Nestor M. Dahj, Kingsley A. Ogudo, Leandro Boonzaaier
Summary: The launch of commercial 5G networks has opened up opportunities for heavy data users and highspeed applications, but traditional monitoring and evaluation techniques have limitations in the 5G networks. This paper presents a cost-effective hybrid analytical approach for detecting and evaluating user experience in real-time 5G networks, using statistical methods to calculate the user quality index.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Ali Nauman, Haya Mesfer Alshahrani, Nadhem Nemri, Kamal M. Othman, Nojood O. Aljehane, Mashael Maashi, Ashit Kumar Dutta, Mohammed Assiri, Wali Ullah Khan
Summary: The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. This study introduces a resource allocation framework that leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to improve energy efficiency. Through the use of a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG), the proposed approach optimizes user association, cache design, and transmission power control, resulting in enhanced energy efficiency and reduced time delays compared to existing methods.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Wu Chen, Jiayi Zhu, Jiajia Liu, Hongzhi Guo
Summary: With advancements in technology, large-scale drone swarms will be widely used in commercial and military fields. Current application methods are mainly divided into autonomous methods and controlled methods. This paper proposes a new framework for global coordination through local interaction.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Peiying Zhang, Zhihu Luo, Neeraj Kumar, Mohsen Guizani, Hongxia Zhang, Jian Wang
Summary: With the development of Industry 5.0, the demand for network access devices is increasing, especially in areas such as financial transactions, drone control, and telemedicine where low latency is crucial. However, traditional network architectures limit the construction of low-latency networks due to the tight coupling of control and data forwarding functions. To overcome this problem, researchers propose a constraint escalation virtual network embedding algorithm assisted by Graph Convolutional Networks (GCN), which automatically extracts network features and accelerates the learning process to improve network performance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Review
Computer Science, Hardware & Architecture
P. Anitha, H. S. Vimala, J. Shreyas
Summary: Congestion control is crucial for maintaining network stability, reliability, and performance in IoT. It ensures that critical applications can operate seamlessly and that IoT devices can communicate efficiently without overwhelming the network. Congestion control algorithms ensure that the network operates within its capacity, preventing network overload and maintaining network performance.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Shunmugapriya Ramanathan, Abhishek Bhattacharyya, Koteswararao Kondepu, Andrea Fumagalli
Summary: This article presents an experiment that achieves live migration of a containerized 5G Central Unit module using modified open-source migration software. By comparing different migration techniques, it is found that the hybrid migration technique can reduce end-user service recovery time by 36% compared to the traditional cold migration technique.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Fatma Foad Ashrif, Elankovan A. Sundararajan, Rami Ahmad, Mohammad Kamrul Hasan, Elaheh Yadegaridehkordi
Summary: This article introduces the development and current status of authentication protocols in 6LoWPAN, and proposes an innovative perspective to fill the research gap. The article comprehensively surveys and evaluates AKA protocols, analyzing their suitability in wireless sensor networks and the Internet of Things, and proposes future research directions and issues.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
Article
Computer Science, Hardware & Architecture
Pranjal Kumar Nandi, Md. Rejaul Islam Reaj, Sujan Sarker, Md. Abdur Razzaque, Md. Mamun-or-Rashid, Palash Roy
Summary: This paper proposes a task offloading policy for IoT devices to a mobile edge computing system, aiming to balance device utility and execution cost. A meta heuristic approach is developed to solve the offloading problem, and the results show its potential in terms of task execution latency, energy consumption, utility per unit cost, and task drop rate.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2024)
