Title: COMPARATIVE STUDY OF SUPERVISED MACHINE LEARNING MODELS FOR MULTICLASSIFICATION IN BUG REPORT DOMAIN
<br>Cover Date: 2023-12-01
<br>Cover Display Date: December 2023
<br>DOI: 10.24507/icicel.17.12.1365
<br>Description: The challenge in this study was to multiclassify bug reports, and the proposed method attempted to assign bug reports into three categories: real-bug, enhancement, and task. The dataset that is used in this study was obtained from the Bugzilla system and was connected to the opensource Firefox browser. Our approach began with bug report pre-processing. It was driven by replacing contractions, tokenization, spelling correction, punctuation and stop-word removal, CamelCase processing, and stemming and lowercase conversion, in that order. We compared two features of bug reports (i.e., unigram words only and unigram together with CamelCase words). The pre-processed bug reports were afterwards formatted in a vector space model format, with each term weighed using a term weighting scheme. In addition, term frequency (tf) and term frequency-inverse gravity moment (tf-igm) used to assign weight for each term were examined in this research. Following that, the vector of bug reports was utilized to build the multi-classifier models. Logistic Regression, Multinomial Naïve Bayes, eXtreme Gradient Boosting, Linear Support Vector Machines, Random Forest, and Neural Networks were all evaluated. Finally, it was determined that the Linear Support Vector Machine classifier was the most suitable model for our dataset.
<br>Citations: 1
<br>Aggregation Type: Journal
<br>-------------------
<br><br><br>Title: A Comparative Study of Multi-class Sentiment Classification Models for Hotel Customer Reviews
<br>Cover Date: 2023-01-01
<br>Cover Display Date: 2023
<br>DOI: 10.1109/RI2C60382.2023.10355942
<br>Description: This research intends to provide a comparative analysis of multi-class sentiment classification models for hotel customer reviews. This study utilized a dataset of hotel reviews downloaded from the TripAdvisor website. These hotel reviews were composed in English and were based on a five-star rating scale. The rating of each hotel reflected its class. There was a comparison between transformer-based sentiment classification algorithms (such as BERT) and traditional sentiment classification algorithms. The traditional algorithms for sentiment classification in this study can be machine learning (e.g. Multinomial Naïve Bayes, Random Forest, and Support Vector Machines with linear kernel function) and deep learning (e.g. Convolutional Neural Networks). After evaluation via recall, precision, F1, accuracy, and AUC, the BERT model outperformed other models such as Multinomial Nave Bayes, Random Forest, and Support Vector Machines, and Convolutional Neural Networks.
<br>Citations: 0
<br>Aggregation Type: Conference Proceeding
<br>-------------------
<br><br><br>Title: Fast boundary extraction of color image using negative divergence of a normal compressive vector field
<br>Cover Date: 2022-01-01
<br>Cover Display Date: 2022
<br>DOI: 10.14456/easr.2022.19
<br>Description: Among low level gradient-based edge detection techniques, boundary extraction algorithms based on particle motion yield superior continuous edge map results. However, these methods sequentially tracing particle trajectories to obtain continuous edges can be slow in the case of images having a number of objects or spurious edges. In order to accelerate such an edge tracking process, this paper proposes the use of negative divergence of a normal compressive vector field to enable fast edge detection. By exploiting the compressive property and negative divergence of the normal compressive vector field, prominent edges can be rapidly detected in a raster scan manner. The remaining incomplete or broken boundaries are later fixed using the boundary extraction algorithm based on particle motion. Image segmentation performance of the proposed algorithm was evaluated using the BSDS500 benchmark dataset with the F-measures for ODS and OIS, with average precision and computation time used as performance measurements. Experimental results indicated that the proposed algorithm provided results comparable to those of the well-known low-level methods, while the average computation time was drastically reduced by a factor of 2 when compared to that of the original particle motion based method.
<br>Citations: 0
<br>Aggregation Type: Journal
<br>-------------------
<br><br><br>Title: Chatting with Plants (Orchids) in Automated Smart Farming using IoT, Fuzzy Logic and Chatbot
<br>Cover Date: 2019-01-01
<br>Cover Display Date: 2019
<br>DOI: 10.25046/aj040522
<br>Description: 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.
<br>Citations: 10
<br>Aggregation Type: Journal
<br>-------------------
<br><br><br>Title: Automated Smart Farming for Orchids with the Internet of Things and Fuzzy Logic
<br>Cover Date: 2018-12-20
<br>Cover Display Date: 20 December 2018
<br>DOI: 10.23919/INCIT.2018.8584881
<br>Description: In this paper, we propose an automated smart farming for Orchids (Dendrobium Sonia "Bomjo") cultivation by applying Fuzzy logic and Internet of things (IoT) to control all the essential environment variables inside a greenhouse. Sensors for capturing environments are temperature, humidity, light, and soil moisture. The actuators consist of fogs, light bulbs (heaters), fans, sprinkler pumps, LEDs, and motors for controlling plastic curtains. The proposed system can automatically control the growth factors of orchids' inflorescences. The results show that orchids can thrive constantly by the average growth rate about 27.38 cm. per a week.
<br>Citations: 13
<br>Aggregation Type: Conference Proceeding
<br>-------------------
<br><br><br>Title: A novel and simple management system of interactive system using google street view
<br>Cover Date: 2015-01-01
<br>Cover Display Date: 2015
<br>DOI: 10.1007/978-3-319-10774-5_17
<br>Description: The Google street View (GSV) is used for many applications such as using for driver decision support system, collecting and merging the image for special purpose. The interactive system can be created by GSV. However, the most difficulty is the great image volumes. Moreover, for the reality, the map of interactive system ought to like with the physical place. This research proposed the management system created by the simple concept of database and designed by the simple color menu concept. Additionally, the map is constructed by the adjacent in the directed graph theory. This concept will demonstrate with the interactive system of the small pagoda in Thailand. The administrator management is easy and simple to manage this system, and the user or visitor feels like stay in the physical place and attracts to go to that place with more satisfied contentment.
<br>Citations: 0
<br>Aggregation Type: Book Series
<br>-------------------
<br><br><br>