Article
Computer Science, Artificial Intelligence
Wei Yun, Xuan Zhang, Zhudong Li, Hui Liu, Mengting Han
Summary: Knowledge modeling is a crucial step in building knowledge-based applications, with ontology-based and non-ontology knowledge modeling being key aspects. Systematically summarizing methods, processes, and techniques of knowledge modeling can help developers improve efficiency, choose appropriate modeling methods, and advance future research in the field.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Review
Computer Science, Artificial Intelligence
Amirhossein Zaji, Zheng Liu, Gaozhi Xiao, Jatinder S. Sangha, Yuefeng Ruan
Summary: Precision farming has gained significant attention in recent years due to advances in sensing technologies and deep learning algorithms. This paper presents a survey of publications that utilized deep learning techniques to address challenges in wheat production. The authors propose an ontology-based knowledge management system to highlight the objectives, algorithms, models, frameworks, datasets, and results of these publications. The study demonstrates that deep learning algorithms offer a more robust, accurate, and cost-effective approach for measuring wheat traits compared to traditional machine learning techniques.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu
Summary: This survey provides a comprehensive review of recent developments in heterogeneous graph embedding methods and techniques. It introduces the basic concepts of heterogeneous graphs and discusses the unique challenges they pose for embedding. The state-of-the-art methods are systematically surveyed and categorized based on the information they use to address these challenges. The paper also explores the real-world applicability of different embedding methods and presents successful systems. Open-source code, graph learning platforms, and benchmark datasets are summarized to facilitate future research and applications in this area.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Interdisciplinary Applications
Sandya De Alwis, Ziwei Hou, Yishuo Zhang, Myung Hwan Na, Bahadorreza Ofoghi, Atul Sajjanhar
Summary: This survey explores the impact of IoT and relevant technologies on smart farming, focusing on data collection, decision making, and challenges. It discusses the various types and applications of big data in smart farming and introduces key big data and machine learning techniques.
COMPUTERS IN INDUSTRY
(2022)
Article
Computer Science, Artificial Intelligence
Yaozu Wu, Yankai Chen, Zhishuai Yin, Weiping Ding, Irwin King
Summary: Due to advancements in biomedical technologies, large amounts of relational data have been collected for biomedical research. Biomedical graphs, a popular representation of this data, can capture complex biomedical systems. However, traditional graph analysis methods face difficulties when handling high-dimensional and sparsely interconnected biomedical data. To address these issues, graph embedding methods have gained attention. These methods convert graph-based data into low-dimensional vector space, which is used for downstream biomedical tasks. This article focuses on graph embedding techniques in the biomedical domain, introducing recent developments, methodologies, tasks, datasets, and implementations, while discussing limitations and potential solutions.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Rui Bing, Guan Yuan, Mu Zhu, Fanrong Meng, Huifang Ma, Shaojie Qiao
Summary: Graph Neural Networks (GNNs) have achieved excellent performance in graph representation learning and have attracted plenty of attention. Most GNNs focus on learning embedding vectors of homogeneous graphs, but in real-world scenarios, entities and their interactions often form heterogeneous graphs with rich information. Therefore, advancing heterogeneous graph representation learning is beneficial for complex network analysis performance.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Yousra Fettach, Mounir Ghogho, Boualem Benatallah
Summary: Studies on the relationship between education and employability play a crucial role for policy makers, training institutions, companies, and students. Utilizing knowledge graphs to represent large-scale data can facilitate a better understanding of job market needs and address the skill mismatch between education and the job market. This paper provides a comprehensive review of the applications of knowledge graphs in education and employability, offering insights into promising research directions.
Article
Computer Science, Theory & Methods
Eva Papadogiannaki, Sotiris Ioannidis
Summary: The adoption of network traffic encryption is on the rise, providing privacy protection for users but also increasing the potential for malicious activities to be hidden through encryption. Research and review are needed to address the adaptability challenges between emerging encryption technologies and traditional traffic processing systems.
ACM COMPUTING SURVEYS
(2021)
Review
Engineering, Electrical & Electronic
Ayele Gobezie Chekol, Marta Sintayehu Fufa
Summary: This paper provides an extensive review of existing next location prediction approaches, covering basic definitions and concepts, data sources, methods, and applications. It highlights the challenges in next location prediction, such as heterogeneous data, users' random movement behavior, and the time sensitivity of trajectory data. The paper also offers important insights for future research directions.
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
(2022)
Review
Computer Science, Interdisciplinary Applications
Yume Matsushita, Dinh Tuan Tran, Hirotake Yamazoe, Joo-Ho Lee
Summary: This paper discusses techniques for using deep learning for gait analysis in case of limited data availability, reviews recent studies on clinical applications of deep learning in gait analysis, and provides an overview of publicly available gait databases for different sensing modalities.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2021)
Article
Computer Science, Hardware & Architecture
May Altulyan, Lina Yao, Xianzhi Wang, Chaoran Huang, Salil S. Kanhere, Quan Z. Sheng
Summary: This article provides a comprehensive review of recommender systems for the Internet of Things (RSIoT), including the related techniques, applications, and limitations. It also proposes a reference framework to guide future research and practices.
Review
Biochemistry & Molecular Biology
Alfonso Monaco, Ester Pantaleo, Nicola Amoroso, Antonio Lacalamita, Claudio Lo Giudice, Adriano Fonzino, Bruno Fosso, Ernesto Picardi, Sabina Tangaro, Graziano Pesole, Roberto Bellotti
Summary: High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous omic data requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. This review introduces and describes common machine learning methodologies, including deep learning, applied to various genomics tasks, highlighting the power of machine learning in handling big data and how these methods can be relevant in cases with large amounts of multimodal genomic data available.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Review
Computer Science, Theory & Methods
Pian Qi, Diletta Chiaro, Antonella Guzzo, Michele Ianni, Giancarlo Fortino, Francesco Piccialli
Summary: This paper presents a systematic literature review on model aggregation in federated learning, summarizing the proposed and applied techniques, and providing a valuable resource for researchers and practitioners.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2024)
Review
Computer Science, Artificial Intelligence
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
Summary: Uncertainty quantification (UQ) methods are essential in reducing uncertainties in optimization and decision making processes. Bayesian approximation and ensemble learning techniques are widely used types of UQ methods.
INFORMATION FUSION
(2021)
Article
Computer Science, Theory & Methods
Yisheng Song, Ting Wang, Puyu Cai, Subrota K. Mondal, Jyoti Prakash Sahoo
Summary: This survey investigates the latest advances in few-shot learning and provides a fair comparison of the strengths and weaknesses of existing work. By elaborating and contrasting relevant concepts, the prior knowledge is extracted and summarized in the form of a pyramid. In-depth analysis and discussions are presented for each subsection, highlighting the important application of FSL in computer vision.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Muhammad Wasim, Muhammad Nabeel Asim, Muhammad Usman Ghani, Zahoor Ur Rehman, Seungmin Rho, Irfan Mehmood
MULTIMEDIA TOOLS AND APPLICATIONS
(2019)
Article
Mathematical & Computational Biology
Muhammad Nabeel Asim, Muhammad Wasim, Muhammad Usman Ghani Khan, Waqar Mahmood
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
(2018)
Article
Computer Science, Interdisciplinary Applications
Muhammad Wasim, Muhammad Nabeel Asim, Muhammad Usman Ghani Khan, Waqar Mahmood
JOURNAL OF BIOMEDICAL INFORMATICS
(2019)
Article
Computer Science, Information Systems
Muhammad Nabeel Asim, Muhammad Wasim, Muhammad Usman Ghani Khan, Nasir Mahmood, Waqar Mahmood
Article
Computer Science, Information Systems
Muhammad Wasim, Waqar Mahmood, Muhammad Nabeel Asim, Muhammad Usman Ghani
Proceedings Paper
Computer Science, Information Systems
Muhammad Sajjad, Muhammad Wasim, Muzammil Shahbaz, Kashif Saghar, Usman Ghani Khan
2018 INTERNATIONAL CONFERENCE ON FRONTIERS OF INFORMATION TECHNOLOGY (FIT 2018)
(2018)
Review
Computer Science, Theory & Methods
Muhammad Wasim, Muhammad Sajjad, Farheen Ramzan, Usman Ghani Khan, Waqar Mahmood
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2018)
