What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Data mining, Intrusion detection system, Artificial intelligence, Network security and Computer security.
His Data mining research includes elements of Data modeling, Change detection, Affinity propagation and Cluster analysis.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Pattern recognition.
His study in the field of Probabilistic classification also crosses realms of Social discrimination.
His Network security study which covers Anomaly detection that intersects with Support vector machine.
The concepts of his Computer security study are interwoven with issues in Protocol and False positive rate.
His most cited work include:
- CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles (194 citations)
- Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection (187 citations)
- An up-to-date comparison of state-of-the-art classification algorithms (151 citations)
What are the main themes of his work throughout his whole career to date?
Xiangliang Zhang spends much of his time researching Artificial intelligence, Data mining, Machine learning, Theoretical computer science and Cluster analysis.
The study incorporates disciplines such as Graph and Pattern recognition in addition to Artificial intelligence.
His Data mining research includes themes of Data modeling, Change detection and Data set.
He interconnects Crowdsourcing and Process in the investigation of issues within Machine learning.
His research investigates the connection between Theoretical computer science and topics such as Graph that intersect with problems in Algorithm.
His Intrusion detection system study combines topics from a wide range of disciplines, such as Anomaly detection and Network security.
He most often published in these fields:
- Artificial intelligence (33.60%)
- Data mining (23.60%)
- Machine learning (21.60%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (33.60%)
- Machine learning (21.60%)
- Theoretical computer science (12.40%)
In recent papers he was focusing on the following fields of study:
Xiangliang Zhang mainly focuses on Artificial intelligence, Machine learning, Theoretical computer science, Deep learning and Graph.
His study in Hypergraph extends to Artificial intelligence with its themes.
As part of his studies on Machine learning, Xiangliang Zhang often connects relevant areas like Crowdsourcing.
The Theoretical computer science study combines topics in areas such as Node, Embedding, Representation and Relation.
His Deep learning study incorporates themes from Matching, Algorithm, Probabilistic logic and Convolutional neural network.
His work carried out in the field of Graph brings together such families of science as Inference, Graph, Node, Convolution and Key.
Between 2019 and 2021, his most popular works were:
- Privacy Risk Analysis and Mitigation of Analytics Libraries in the Android Ecosystem (42 citations)
- The soundscape of the Anthropocene ocean. (10 citations)
- Multi-View Multiple Clusterings using Deep Matrix Factorization (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Xiangliang Zhang mainly investigates Artificial intelligence, Machine learning, Benchmark, Data science and Natural language processing.
Xiangliang Zhang has included themes like Algorithm, Process and Graph in his Artificial intelligence study.
His Algorithm research incorporates elements of Estimator, Probabilistic logic and Residual neural network.
He focuses mostly in the field of Graph, narrowing it down to matters related to Pattern recognition and, in some cases, Embedding.
His multidisciplinary approach integrates Machine learning and Multi instance multi label in his work.
His Benchmark study integrates concerns from other disciplines, such as Redundancy, Human–computer interaction and Reinforcement learning.
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