57 research outputs found
RF_MISMOC: Improvement of MISMOC graph based classification algorithm
No full textAbstractIn recent years, the graph mining has gained much attention in the area of data mining. A novel technique called mining interesting substructures in molecular data for classification (MISMOC) is one of the most efficient algorithms in graph based classification. In this paper, we propose a novel technique called RF_MISMOC (Relative Frequency MISMOC) for computing interestingness of patterns by considering relative frequency of patterns in each class. In addition, we have improved the performance of the base algorithm by selecting equal numbers of interesting indicator patterns of classes and also determining optimum threshold value for selection of indicator patterns. The experimental results demonstrate that, the proposed algorithm has improved efficiency of the base algorithm
SARF: Smart Activity Recognition Framework in Ambient Assisted Living
No full textHuman activity recognition in Ambient Assisted Living(AAL) is an important application in health care systems andallows us to track regular activities or even predict these activitiesin order to monitor healthcare and find changes in patterns andlifestyles. A review of the literature reveals various approachesto discovering and recognizing human activities. The presence ofa vast number of activity recognition issues and approaches hasmade it difficult to make adequate comparisons and accurateassessment. Introducing the five basic components of activityrecognition in the smart homes as a famous environment toremote monitoring of patients and independent living for elderly,the present paper proposes SARF framework to classify each ofactivity recognition approaches and then it is evaluated basedon the proposed classification by some proposed measures. UsingSARF proposed framework can play an effective role in selectingthe appropriate method for human activity recognition in smarthomes and beneficial in analysis and evaluation of differentmethods for various challenges in this field.</p
Augmented feature-state sensors in human activity recognition
No full textNowadays, Human Activity Recognition (HAR) has gain a lot of interest because of demand growth in many applications particularly in smart homes as a fundamental task. This problem is typically addressed as a supervised learning problem with the goal of learning the mapping of extracted related features out of sensors data to the underlying human activities. Most of the proposed methods for HAR do not consider important information such as time domain features explicitly for activity modeling. In this paper, Augmented Feature-StAte (Statistical-Activity context) Sensors (AFSSs)are proposed to incorporate combination of important statistical features and activity context information. To evaluate the proposed AFSSs, they are applied in four benchmark and popular probabilistic graphical activity recognition algorithms including Naïve Bayesian classifiers (nBCs), Hidden Markov Models (HMMs), Hidden Semi Markov Models (HSMMs) and Linear-Chain Conditional Random Fields (LCCRFs). The experiments are performed on three well-known and real-world datasets in this field. The results show that the proposed AFSSs improve the classification performance particularly in terms of Fl-Score, accuracy and robustness.</p
Detecting and investigating crime by means of data mining: a general crime matching framework
No full textAbstractData mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods. The wide range of data mining applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify crime characteristics. Actually crime analysis includes exploring and detecting crimes and their relationships with criminals. The high volume of crime datasets and also the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. Identifying crime characteristics is the first step for developing further analysis. The knowledge that is gained from data mining approaches is a very useful tool which can help and support police forces. An approach based on data mining techniques is discussed in this paper to extract important entities from police narrative reports which are written in plain text. By using this approach, crime data can be automatically entered into a database, in law enforcement agencies. We have also applied a SOM clustering method in the scope of crime analysis and finally we will use the clustering results in order to perform crime matching process
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