The proceedings contain 30 papers. The topics discussed include: defect automatic detection for tire x-ray images using inverse transformation of principal component residual;co-channel interference mitigation on capa...
ISBN:
(纸本)9781467391870
The proceedings contain 30 papers. The topics discussed include: defect automatic detection for tire x-ray images using inverse transformation of principal component residual;co-channel interference mitigation on capacity and coverage in cellular systems;dyadic lifting wavelet based signal detection;multi-view matching points extraction algorithm based on union find sets;revocation basis and proofs access control for cloud storage multi-authority systems;a detection method for network security based on the combination of support vector machine;issues on critical objects in mining algorithms;motion background modeling based on context-encoder;application of intelligence of swarm in architecture;features selection for building an early diagnosis machine learning model for Parkinson's disease;latent fingerprint wavelet transform image enhancement technique for optical coherence tomography;synchronizing switching times of vacuum interrupters for medium voltage switchboards' techniques;performance analysis of the method for social search of information in university information systems;and modeling and data processing of information systems.
The proceedings contain 43 papers. The topics discussed include: application research of model-free reinforcement learning under the condition of conditional transfer function with coupling factors;expected regret min...
ISBN:
(纸本)9781450375511
The proceedings contain 43 papers. The topics discussed include: application research of model-free reinforcement learning under the condition of conditional transfer function with coupling factors;expected regret minimization for Bayesian optimization with student’s-t processes;a mining frequent itemsets algorithm in stream data based on sliding time decay window;experimental and theoretical scrutiny of the geometric derivation of the fundamental matrix;dual-precision deep neural network;annotating documents using active learning methods for a maintenance analysis application;offline handwritten Chinese character recognition based on improved Googlenet;a network combining local features and attention mechanisms for vehicle re-identification;and a spatial attention-enhanced multi-timescale graph convolutional network for skeleton-based action recognition.
The proceedings contain 39 papers. The topics discussed include: application research of model-free reinforcement learning under the condition of conditional transfer function with coupling factors;expected regret min...
ISBN:
(纸本)9781450375511
The proceedings contain 39 papers. The topics discussed include: application research of model-free reinforcement learning under the condition of conditional transfer function with coupling factors;expected regret minimization for Bayesian optimization with student’s-t processes;research on unbalanced data processing algorithm base Tomeklinks-Smote;a mining frequent itemsets algorithm in stream data based on sliding time decay window;experimental and theoretical scrutiny of the geometric derivation of the fundamental matrix;annotating documents using active learning methods for a maintenance analysis application;offline handwritten Chinese character recognition based on improved Googlenet;a network combining local features and attention mechanisms for vehicle re-identification;and a spatial attention-enhanced multi-timescale graph convolutional network for skeleton-based action recognition.
The proceedings contain 25 papers. The topics discussed include: cloud and mobile security: challenges and future research directions;DLP-technologies: new directions and trends;using fuzzy logic to evaluate trust in ...
The proceedings contain 25 papers. The topics discussed include: cloud and mobile security: challenges and future research directions;DLP-technologies: new directions and trends;using fuzzy logic to evaluate trust in e-commerce;gamification of teaching and learning activity: prospect and challenges of mobile game-based learning;ComboSplit: combining various splitting criteria for building a single decision tree;text classification using computational model of the cerebral cortex;restricted Boltzmann machines for modeling businesses;variables selection for multiclass SVM using the multiclass radius margin bound;on the enumeration of frequent patterns in sequences;predicting movie incomes using search engine query data;best-parameterized sigmoid ELM for benign and malignant breast cancer detection;inference engine for classification of expert systems using keyword extraction technique;comparison of classifiers for retinal pathology images using surf and bag-of-words model;content based video quality control for wide-area video surveillance systems;line detection by centre and width estimation;and interactive versus passive 2D face spoofing detection.
Electronic nose (e-nose) technology has become a powerful tool for identifying and evaluating complex scents in a variety of contexts, such as environmental monitoring, medical diagnostics, and food quality control. T...
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artificialintelligence Probabilistic Neural Networks are a type of neural network based on the theory of probability density functions, widely applicable in areas such as patternrecognition. Addressing the current i...
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Planning has achieved significant progress in recent years. Among the various approaches to scale up plan synthesis, the use of macro-actions has been widely explored. As a first stage towards the development of a sol...
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ISBN:
(纸本)9781467391870
Planning has achieved significant progress in recent years. Among the various approaches to scale up plan synthesis, the use of macro-actions has been widely explored. As a first stage towards the development of a solution to learn on-line macro-actions, we propose an algorithm to identify useful macroactions based on data mining techniques. The integration in the planning search of these learned macro-actions shows significant improvements over six classical planning benchmarks.
Aim of this work was to create an adaptive method allowing effective use of generator reactive power in coordinated voltage control system independently of its parameters and Automatic Voltage Controller (AVR) type. T...
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ISBN:
(纸本)9781467391870
Aim of this work was to create an adaptive method allowing effective use of generator reactive power in coordinated voltage control system independently of its parameters and Automatic Voltage Controller (AVR) type. Two methods were tested, one based on artificial neural networks classification and second based on modifying voltage limiter curve constructed on base of domain experts knowledge.
The use of artificial neural networks in many fields is still on the increase. The paper deals with application of neural networks as a data mining method to a prediction of the production line performance. Performanc...
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ISBN:
(纸本)9781467391870
The use of artificial neural networks in many fields is still on the increase. The paper deals with application of neural networks as a data mining method to a prediction of the production line performance. Performance of production line was defined by output indicators like number of finished products, flow time and work in progress production. Predictive model was implemented in the program STATISTICA Data Miner, therefore this paper brings also short overview of used options. The overall quality of learned networks was evaluated. PMML file was created for fast deployment to new data and better decision making. Neural networks provide an effective analyzing and diagnosing tool to understand and simulate the behavior of the plant, and can be used as a valuable performance assessment tool for decision makers.
Today, the increasing ease of publishing information online combined with a gradual shift of paradigm from consuming news via conventional media to non-conventional media calls for a computational and automatic approa...
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ISBN:
(纸本)9781450375511
Today, the increasing ease of publishing information online combined with a gradual shift of paradigm from consuming news via conventional media to non-conventional media calls for a computational and automatic approach to the identification of an article's legitimacy. In this study, we propose an approach for cross-domain fake news detection focusing on the identification of legitimate content from a pool of articles that are of varying degrees of legitimacy. We present a model as a proof of concept as well as data gathered from evaluating the model on Fake-News AMT, a dataset released for cross-domain fake news detection. The results of our model are then compared against a baseline model which has served as the benchmark for the dataset. We find all results in support of our hypothesis. Our proof-of-concept model has also outperformed the benchmark in the domains Technology and Entertainment as well as when it was run on the whole dataset at once.
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