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Plants are living organisms that can hear and recognize the environment around them but cannot communicate to inform their needs. Thus, in the past, humans thought that it was impossible to communicate with plants. However, in this modern era, humans can be able to communicate with these plants. In this paper, we propose a model that can interact (Chat) with plants cultivated in the automated farm system based on Internet of Things (IoT) and Fuzzy Logic. According to the communication of plants and humans, we apply a chatbot algorithm for sending/receiving messages between users and automated smart farming. The messages are processed by the natural language processing (NLP) to parse and interpret the meaning of the conversation. The experimented plant in this paper is orchid, namely Dendrobium Sonia (Bomjo). The result from the evaluation shows that the average accuracy (Harmonic mean) of chatting between the user and the orchid is equal to 0.71, the precision and recall are 0.75 and 0.6 respectively.

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This work presents a technique for classifying X-ray images of the chest (CXR) by applying deep learning-based techniques. The CXR will be classified into three different types, i.e. (i) normal, (ii) COVID-19, and (iii) pneumonia. The classification challenge is raised when the X-ray images of COVID-19 and pneumonia are subtle. The CXR images of the chest are first proceeded to be standardized and to improve the visual contrast of the images. Then, the classification is performed by applying a deep learningbased technique that binds two deep learning network architectures, i.e., convolution neural network (CNN) and long short-term memory (LSTM), to generate a hybrid model for the classification problem. The deep features of the images are extracted by CNN before the final classification is performed using LSTM. In addition to the hybrid models, this work explores the validity of image pre-processing methods that improve the quality of the images before the classification is performed. The experiments were conducted on a public image dataset. The experimental results demonstrate that the proposed technique provides promising results and is superior to the baseline techniques.

Feature-based opinion mining is a technique that identifies positive and negative polarity based on object features. The mining technique is different from traditional opinion mining as the feature-based technique does not only examine and summarizes the overall opinion of each review. In smart-phone market, the overall review may not be a practical recommendation for customers to choose their phones. It is essential to summarize opinions of users based on each features of smart-phones. This will be advantage for customers who want to buy smart-phones and smart-phone companies that can use this information to improve features of their smart-phone. In this paper, we propose a method for mining opinions on smart-phone reviews written in Thai. The method summarizes positive and negative polarity of each feature of smart-phones. In this paper, first, smart-phone reviews are collected from smart-phone pages on Facebook using Facebook Graph API. Second, the review dataset are clean and then perform word segmentation using a Thai segmentation technique. Then finding similarity word of each feature is performed as the same feature may write the different words. Finally, each feature is decided to be either negative or positive by considering polarity of words which are after the feature. From the experimental result, it was shown than the proposed method gives 70.17% of accuracy.