Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

No abstract

Named Entity Recognition (NER) is an elementary tool for all application areas in Natural Language Processing (NLP) such as Automatic Summarization, Information Extraction, Information Retrieval, Text Mining, Machine Translation, Question Answering, and Genetics. NER is a task to discover and categorises the named entities (‘atomic elements’) in the text into predefined classes such as the names of persons, organizations, locations, terminologies of time, quantity and etc. Different languages may have different morphologies and thus involve dissimilar NER procedures. For example, an Arabic NER system cannot be practically used in processing Malay texts due to the different morphological features. The morphological features of every language are rich and complex and donates to the difficulties of implementing an actual method to develop the accurate NER system. In this paper, we review on three main techniques that commonly used to develop an NER system well-known as Rule-Based, Machine Learning, and Hybrid approach. This paper also highlights the features of each technique.

No abstract

No abstract