MIKE 2023: International Conference on Mining Intelligence and Knowledge Exploration

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Example submission areas include, but are not limited to: Evolutionary Computation We focus on the following topics, but strictly not limited to: Evolutionary Computation Design of new and improved evolutionary algorithms Design of new search operators Diversity control Hybrid approaches and memetic algorithms Local search methods Automatic algorithm configuration and design Applications of metaheuristics to combinatorial optimization problems Development of tailored evolutionary algorithms for specific applications Knowledge Exploration in IoT We focus on the following topics, but strictly not limited to: Physical: world event processing and understanding: novel data collection, deep learning, real-time decision making, event processing, extracting information from large data sets); Machine learning and deep learning on sensor data: deep learning models for multimodal sensing and processing, multi-sensor fusion with deep learning); IoT: reliability, adaptability, and dependability; Technical assessment of emerging IoT standards; New hardware and system design to enable machine learning on sensor data; Sensor data related issues (e.g., structure, management, methods, tools, analysis, etc.); Anomaly detection; Data mining of large scale urban networks and big data; Human/social behavior mining in urban environments; Environmental modelling based on data mining methods. Artificial intelligence We focus on the following topics, but strictly not limited to: Artificial intelligence and philosophy Automated reasoning and inference Cognitive aspects of AI Common-sense reasoning Constraint processing Heuristic search Intelligent interfaces Intelligent robotics Knowledge representation Multi-agent systems Reasoning under uncertainty or imprecision Applications of AI Machine Learning We focus on the following topics, but strictly not limited to: Decision tree learning Association rule learning Artificial neural networks Deep learning Support vector machines Bayesian learning Reinforcement learning Representation learning Similarity and metric learning Sparse dictionary learning Rule-based machine learning Learning classifier systems Applications of ML Applications of ML to various interdisciplinary domains Modelling Material Properties, Predicting Material Properties Predicting Mechanical Properties Thermal Properties of Fluids Design Materials Optimizing Material properties Application of Neural Networks Fuzzy Logic Fuzzy Systems Computational Intelligence Data Mining and Information Retrieval We focus on the following topics, but strictly not limited to: Data and knowledge creation and discovery Data and knowledge processing, modelling, mapping, Data and knowledge search, interoperability, exchange and integration Data visualization Databases, indexing and query processing Data mining: machine learning and deep learning Behaviour analytics Business analytics Customer analytics Financial analytics Information retrieval and extraction Big data infrastructure and visualization Scalable computing and high-performance computing for big data Parallel and distributed data mining techniques Computational intelligence based theories and methods Statistical and mathematical techniques Large-scale optimization Data characteristics and complexities Bioinformatics, computational biology, health and medical analytics Medical Image Analysis, Pattern Recognition and Computer Vision We focus on the following topics, but strictly not limited to: Visual search Computer vision theory Physics-based vision and shape from-X 3D computer vision Vision for graphics Vision for robotics Vision for web Computational photography, sensing and display Document and handwriting analysis Image and video analysis Segmentation, grouping and shape representation Motion and tracking Content-based retrieval of image and video RGBD sensor and analytics Recognition: detection, categorization, indexing Face and gesture recognition Action recognition Biometrics Statistical learning based applications Deep learning based applications Speech / Signal Processing We focus on the following topics, but strictly not limited to: Basics of oral communication: modelling of production and perception processes; phonetics and phonology; syntax; cognitive aspects Models and tools for language learning: functional organization and developmental models of human language capabilities; acquisition and rehabilitation of spoken language; Speech signal analysis Speech recognition Language identification Speaker recognition Speech synthesis Multimodal human computer interface: speech in combination with gesture, handwriting etc Forensic voice comparison Forensic analysis of disputed utterances Speaker identification by earwitness Text Mining and Natural Language Processing We focus on the following topics, but strictly not limited to: Named entity recognition Part-of-speech tagging Relationship extraction Parsing Sentence breaking Word segmentation Morphological segmentation Topic segmentation and recognition Automatic summarization Coreference resolution Machine translation Natural language generation Natural language understanding Question answering Sentiment analysis Word sense disambiguation Intelligent Security Systems We focus on the following topics, but strictly not limited to: Blockchain in Security Device log analysis Correlation of events posing security threats Deep packet analysis Network and system behaviour analysis User behaviour modelling and analysis Intrusion detection Intelligent security systems Cryptanalysis Steganalysis Social media analysis for security Smart and Intelligent Systems We focus on the following topics, but strictly not limited to: Smart Cities Smart Transportation Intelligent V2V and V2I Communication Vehicular Networks Data Analytics for Real-Time Systems System Assisted Driving/ Recommendation Engine Deep Learning for smart systems</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2023-04-18</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th 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