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This paper presents a novel technique for the classification of Arabic sentences as Dialogue Acts, based on structural information contained in Arabic function words. It focuses on classifying questions and non-questions utterances as they are used in Conversational Agents. The proposed technique extracts function words features by replacing them with numeric tokens and replacing each content word with a standard numeric token. The Decision Tree has been chosen for this work to extract the classification rules. Experiments provide evidence for highly effective classification. The extracted classification rules will be embedded into a Conversational Agent called ArabChat in order to classify Arabic utterances before further processing on these utterances. This paper presents a complement work for the ArabChat to improve its performance by differentiating among question-based and non question-based utterances.

ABSTRACT

Air pollution is one of the most serious hazards to humans′ health nowadays, it is an invisible killer that takes many human lives every year. There are many pollutants existing in the atmosphere today, ozone being one of the most threatening pollutants. It can cause serious health damage such as wheezing, asthma, inflammation, and early mortality rates. Although air pollution could be forecasted using chemical and physical models, machine learning techniques showed promising results in this area, especially artificial neural networks. Despite its importance, there has not been any research on predicting ground-level ozone in Jordan. In this paper, we build a model for predicting ozone concentration for the next day in Amman, Jordan using a mixture of meteorological and seasonal variables of the previous day. We compare a multi-layer perceptron neural network (MLP), support vector regression (SVR), decision tree regression (DTR), and extreme gradient boosting (XGBoost) algorithms. We also explore the effect of applying various smoothing filters on the time-series data such as moving average, Holt-Winters smoothing and Savitzky-Golay filters. We find that MLP outperformed the other algorithms and that using Savitzky-Golay improved the results by 50% for coefficient of determination (R 2) and 80% for root mean square error (RMSE) and mean absolute error (MAE). Another point we focus on is the variables required to predict ozone concentration. In order to reduce the time required for prediction, we perform feature selection which greatly reduces the time by 91% as well as shrinking the number of features required for prediction to the previous day values of ozone, humidity, and temperature. The final model scored 98.653% for R 2 , 1.016 ppb for RMSE and 0.800 ppb for MAE.

Abstract-The Enhanced ArabChat is a complement of the previous version of ArabChat. This paper details an enhancements development of a novel and practical Conversational Agent for the Arabic language called the "Enhanced ArabChat". A conversational Agent is a computer program that attempts to simulate conversations between machine and human. Some of lessons was learned by evaluating the previous work of ArabChat . These lessons revealed that two major issues affected the ArabChat's performance negatively. Firstly, the need for a technique to distinguish between question and non-question utterances to reply with a more suitable response depending on the utterance's type (question and nonquestion based utterances). Secondly, the need for a technique to handle an utterance targeting many topics that require firing many rules at the same time. Therefore, in this paper, the "Enhanced ArabChat" will cover these enhancements to improve the ArabChat's performance. A real experiment has been done in Applied Science University in Jordan as an information point advisor for their native Arabic students to evaluate the Enhanced ArabChat.

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Abstract-The conversation automation/simulation between a user and machine evolved during the last years. A number of research-based systems known as conversational agents has been developed to address this challenge. A conversational Agent is a program that attempts to simulate conversations between the human and machine. Few of these programs targeted the mobilebased users to handle the conversations between them and a mobile device through an embodied spoken character. Wireless communication has been rapidly extended with the expansion of mobile services. Therefore, this paper discusses the proposing and developing a framework of a mobile-based conversational agent called Mobile ArabChat to handle the Arabic conversations between the Arab users and mobile device. To best of our knowledge, there are no such applications that address this challenge for Arab mobile-based users. An Android based application was developed in this paper, and it has been tested and evaluated in a large real environment. Evaluation results show that the Mobile ArabChat works properly, and there is a need for such a system for Arab users.

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