Wei Emma Zhang – författare

The Internet of Vehicles (IoV) is referred to as an efficient and inevitable convergence of the Internet of Things, intelligent transportation systems, edge / fog and cloud computing, and big data, all of which could be intelligently harvested for the cooperative vehicular safety and non-safety applications as well as cooperative mobility management. A secure and low-latency communication is, therefore, indispensable to meet the stringent performance requirements of the safety-critical vehicular applications.

Whilst the challenges surrounding low latency are being addressed by the researchers in both academia and industry, it is the security of an IoV network which is of paramount importance, as a single malicious message is perfectly capable enough of jeopardizing the entire networking infrastructure and can prove fatal for the vehicular passengers and the vulnerable pedestrians.

This book thus investigates the promising notion of trust in a bid to strengthen the resilience of the IoV networks. It not only introduces trust categorically in the context of an IoV network, i.e., in terms of its fundamentals and salient characteristics, but further envisages state-of-the-art trust models and intelligent trust threshold mechanisms for segregating both malicious and non-malicious vehicles. Furthermore, open research challenges and recommendations for addressing the same are discussed in the same too.

The Internet of Vehicles (IoV) is referred to as an efficient and inevitable convergence of the Internet of Things, intelligent transportation systems, edge / fog and cloud computing, and big data, all of which could be intelligently harvested for the cooperative vehicular safety and non-safety applications as well as cooperative mobility management. A secure and low-latency communication is, therefore, indispensable to meet the stringent performance requirements of the safety-critical vehicular applications.

Whilst the challenges surrounding low latency are being addressed by the researchers in both academia and industry, it is the security of an IoV network which is of paramount importance, as a single malicious message is perfectly capable enough of jeopardizing the entire networking infrastructure and can prove fatal for the vehicular passengers and the vulnerable pedestrians.

This book thus investigates the promising notion of trust in a bid to strengthen the resilience of the IoV networks. It not only introduces trust categorically in the context of an IoV network, i.e., in terms of its fundamentals and salient characteristics, but further envisages state-of-the-art trust models and intelligent trust threshold mechanisms for segregating both malicious and non-malicious vehicles. Furthermore, open research challenges and recommendations for addressing the same are discussed in the same too.

The text highlights a comprehensive survey that focuses on all security aspects and challenges facing the Internet of Things systems, including outsourcing techniques for partial computations on edge or cloud while presenting case studies to map security challenges. It further covers three security aspects including Internet of Things device identification and authentication, network traffic intrusion detection, and executable malware files detection.

This book:

It will serve as an ideal text for senior undergraduate and graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

The text highlights a comprehensive survey that focuses on all security aspects and challenges facing the Internet of Things systems, including outsourcing techniques for partial computations on edge or cloud while presenting case studies to map security challenges. It further covers three security aspects including Internet of Things device identification and authentication, network traffic intrusion detection, and executable malware files detection.

This book:

It will serve as an ideal text for senior undergraduate and graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos. 

In this book, the authors first address the research issues by providing a motivating scenario, followed by the exploration of the principles and techniques of the challenging topics. Then they solve the raised research issues by developing a series of methodologies. More specifically, the authors study the query optimization and tackle the query performance prediction for knowledge retrieval. They also handle unstructured data processing, data clustering for knowledge extraction. To optimize the queries issued through interfaces against knowledge bases, the authors propose a cache-based optimization layer between consumers and the querying interface to facilitate the querying and solve the latency issue. The cache depends on a novel learning method that considers the querying patterns from individual’s historical queries without having knowledge of the backing systems of the knowledge base. To predict the query performance for appropriate query scheduling, the authors examine the queries’ structural and syntactical features and apply multiple widely adopted prediction models. Their feature modelling approach eschews the knowledge requirement on both the querying languages and system.

To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities. They use natural language processing techniques to pre-process the source codes and obtain the natural language elements. Then they apply traditional knowledge extraction techniques to extract knowledge. For the data from programming question-answering communities, the authors make the attempt towards building programming knowledge base by starting with paraphrase identification problems and develop novel features to accurately identify duplicate posts. For domain specific knowledge extraction, the authors propose to use a clustering technique toseparate knowledge into different groups. They focus on developing a new clustering algorithm that uses manifold constraints in the optimization task and achieves fast and accurate performance.

For each model and approach presented in this dissertation, the authors have conducted extensive experiments to evaluate it using either public dataset or synthetic data they generated.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024.

The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: -

Part I : Applications; Data mining.

Part II : Data mining foundations and algorithms; Federated learning; Knowledge graph.

Part III : Graph mining; Spatial data mining.

Part IV : Health informatics.

Part V : Multi-modal; Natural language processing.

Part VI : Recommendation systems; Security and privacy issues.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024.

The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: -

Part I : Applications; Data mining.

Part II : Data mining foundations and algorithms; Federated learning; Knowledge graph.

Part III : Graph mining; Spatial data mining.

Part IV : Health informatics.

Part V : Multi-modal; Natural language processing.

Part VI : Recommendation systems; Security and privacy issues.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024.

The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: -

Part I : Applications; Data mining.

Part II : Data mining foundations and algorithms; Federated learning; Knowledge graph.

Part III : Graph mining; Spatial data mining.

Part IV : Health informatics.

Part V : Multi-modal; Natural language processing.

Part VI : Recommendation systems; Security and privacy issues.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024.

The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: -

Part I : Applications; Data mining.

Part II : Data mining foundations and algorithms; Federated learning; Knowledge graph.

Part III : Graph mining; Spatial data mining.

Part IV : Health informatics.

Part V : Multi-modal; Natural language processing.

Part VI : Recommendation systems; Security and privacy issues.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024.

The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: -

Part I : Applications; Data mining.

Part II : Data mining foundations and algorithms; Federated learning; Knowledge graph.

Part III : Graph mining; Spatial data mining.

Part IV : Health informatics.

Part V : Multi-modal; Natural language processing.

Part VI : Recommendation systems; Security and privacy issues.