IEEE International Conference on Advanced Computational Intelligence 

About: IEEE International Conference on Advanced Computational Intelligence is an academic conference. The conference publishes majorly in the area(s): Artificial neural network & Feature extraction. Over the lifetime, 857 publications have been published by the conference receiving 3489 citations.

Feature selection approach based on whale optimization algorithm

Abstract: In this paper, a feature selection system is introduced applies the whale optimization algorithm (WOA). WOA is a recently introduced meta-heuristic optimization algorithm that mimics the natural behavior of the humpback whales. The proposed model applies the wrapper-based method to reach the optimal subset of features. This technique was applied to find the best feature subset that maximizes the accuracy of the classification while preserving the minimum number of features. The proposed model is compared with the particle swarm optimization (PSO) and genetic algorithm (GA) using a number of assessment indicators on 16 different data-sets from UCI data repository. The results demonstrate the advantage of the introduced algorithm compared to the other optimizers.

A survey of control algorithms for Quadrotor Unmanned Helicopter

Abstract: Basic structure of Quadrotor Unmanned Helicopter (QUH) and its practical application values are introduced as well as several control algorithms, such as, intelligent PID algorithm, linear quadratic LQR algorithm, H∞ loop-shaping algorithm, sliding mode control algorithm, feedback linearization control algorithm, adaptive control algorithm and backstepping design algorithm and then analyses merits and drawbacks of above control algorithms. At last, our research prospects the major research aspects and development direction on future researches of QUH.

Crack detection in concrete surfaces using image processing, fuzzy logic, and neural networks

Abstract: Automation in structural health monitoring has generated a lot of interest in recent years, especially with the introduction of cheap digital cameras. This paper presents fuzzy logic and artificial neural network based models for accurate crack detection on concrete. Features are extracted from digital images of concrete surfaces using image processing which incorporates the edge detection technique. The properties of extracted features are fed into the models for detecting cracks. Two kinds of approaches have been implemented in this study: the image approach which classifies an image as a whole, and the object approach which classifies each component or object in an image into cracks and noise. The models have been tested on 205 images and evaluated on the basis of five measures of performance.

Fuzzy rule based systems for interpretable sentiment analysis

Abstract: Sentiment analysis, which is also known as opinion mining, aims to recognise the attitude or emotion of people through natural language processing, text analysis and computational linguistics. In recent years, many studies have focused on sentiment classification in the context of machine learning, e.g. to identify that a sentiment is positive or negative. In particular, the bag-of-words method has been popularly used to transform textual data into structured data, in order to enable the direct use of machine learning algorithms for sentiment classification. Through the bag-of-words method, each single term in a text document is turned into a single attribute to make up a structured data set, which results in high dimensionality of the data set and thus negative impact on the interpretability of computational models for sentiment analysis. This paper proposes the use of fuzzy rule based systems as computational models towards accurate and interpretable analysis of sentiments. The use of fuzzy logic is better aligned with the inherent uncertainty of language, while the “white box” characteristic of the rule based learning approaches leads to better interpretability of the results. The proposed approach is tested on four datasets containing movie reviews; the aim is to compare its performance in terms of accuracy with two other approaches for sentiment analysis that are known to perform very well. The results indicate that the fuzzy rule based approach performs marginally better than the well-known machine learning techniques, while reducing the computational complexity and increasing the interpretability.

Safety helmet wearing detection based on image processing and machine learning

Abstract: Safety helmet wearing detection is very essential in power substation. This paper proposed a innovative and practical safety helmet wearing detection method based on image processing and machine learning. At first, the ViBe background modelling algorithm is exploited to detect motion object under a view of fix surveillant camera in power substation. After obtaining the motion region of interest, the Histogram of Oriented Gradient (HOG) feature is extracted to describe inner human. And then, based on the result of HOG feature extraction, the Support Vector Machine (SVM) is trained to classify pedestrians. Finally, the safety helmet detection will be implemented by color feature recognition. Compelling experimental results demonstrated the correctness and effectiveness of our proposed method.