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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Perfecting a Video Game with Game Metrics Indonesian Journal of Electrical Engineering and Informatics (IJEEI)

Norhafizah Ramli

Universiti Teknologi Malaysia

Author-ID : 2406435

Computer Science & IT Electrical & Electronics Engineering Engineering

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Sentiment analysis of informal Malay tweets with deep learning Ong Jun Ying; Muhammad Mun'im Ahmad Zabidi; Norhafizah Ramli; Usman Ullah Sheikh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Twitter is an online microblogging and social-networking platform which allows users to write short messages called tweets. It has over 330 million registered users generating nearly 250 million tweets per day. As Malay is the national language in Malaysia, there is a significant number of users tweeting in Malay. Tweets have a maximum length of 140 characters which forces users to stay focused on the message they wish to disseminate. This characteristic makes tweets an interesting subject for sentiment analysis. Sentiment analysis is a natural language processing (NLP) task of classifying whether a tweet has a positive or negative sentiment. Tweets in Malay are chosen in this study as limited research has been done on this language. In this work, sentiment analysis applied to Malay tweets using the deep learning model. We achieved 77.59% accuracy which exceeds similar work done on Bahasa Indonesia.

A deep learning AlexNet model for classification of red blood cells in sickle cell anemia Hajara Aliyu Abdulkarim; Mohd Azhar Abdul Razak; Rubita Sudirman; Norhafizah Ramli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively.

Co-Authors Anuar Ahmad Chuan Mu Wen Hajara Aliyu Abdulkarim Ivin Amri Musliman Mohd Azhar Abdul Razak Muhammad Mun'im Ahmad Zabidi Muhammad Mun’im Zabidi Nor Hafizah Ngajikin Ong Jun Ying Rubita Sudirman Suhaila Isaak Usman Ullah Sheikh Yusmeeraz Yusof

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)