Rough set based multi-agent system cooperation for industrial supervisory interface system

In this paper, rough set theory is introduced into the interface multi-agent system （MAS） for industrial supervisory system. Taking advantages of rough set in data mining, a cooperation model for MAS is built. Rules for avoiding cooperation conflict are deduced. An optimization algorithm is used to enhance security and real time attributes of the system. An application based on the proposed algorithm and rules are given.

Computer-generated Conversation Using Newspaper Headline

In this paper, the authors propose a method that incorporates mechanisms for handling ambiguity in speech and the ability of humans to create associations, and for formulating conversations based on rule base knowledge and common knowledge. Go beyond the level that can be achieved, using only conventional natural language processing and vast repositories of sample patterns. In this paper, the authors propose a method for computer conversation sentences generated using newspaper headlines as an example of how the common knowledge and associative ability are applied.

SSC:Gesture-based game for initial dementia examination

This paper presents a novel system assisting medical dementia examination in a joyful way: the object just needs to play a popular game SSC against the computer during the examination. The SSC game’s target is to detect the player’s reacting capability, which is related closely with dementia. Our system reaches this target with some advantages: there are no temporal and spatial constraints at all. There is no cost, and it can even improve people’s mental status. Hand talk technology and EHMM gesture recognition approach are employed to realize the human computer interface. Experiments showed that this system can evaluate people’s reacting capability effectively and is helpful for initial dementia examination.

Intelligent Machine Learning Based EEG Signal Classification Model

In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and computer.BCI systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,etc.Electroencephalography(EEG)is a technique commonly applied in capturing brain signals.It is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and cost-effective.EEG classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and classification.With this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI system.Independent Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG information.Besides,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG signals.Moreover,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was conducted.The outcomes were examined under distinct aspects.The experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.