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Sentiment analysis (SA) is the study of people's emotions and attitudes toward a particular topic. It is beneficial for monitoring and analyzing social media text in order to gather public opinion. Despite the fact that there are SA applications for monolingual text such as English and non-English languages like Hindi, Chinese and French, the Malay language has far fewer works, not to mention the mixed language such as Malay-English (also known as Manglish). Other than comments and posts from websites and social media, the emoji used by internet users can also help to provide better insights into how they truly feel about a particular topic. Our work focuses on Malay-English mixed language comments and posts on how Malaysians feel about daily new cases of Covid-19 in Malaysia. We proposed a neural network framework to perform SA on languages spoken by Malaysians, namely Malay, English, and Malay-English, by also taking into account the emoji used by internet users. The data was pre-processed to remove noises and then transformed into word vector representation using word embedding technique. Then we propose a framework that involves training and testing mixed language textual data along with emoji analysis by using bidirectional Long Short Term Memory (biLSTM) neural network. To compare with the proposed method, several machine learning models and Long Short Term Memory (LSTM) with word vectorization was used. Finally, compared to the machine learning model such as Naïve Bayes and Logistic Regression, neural networks such as LSTM, the proposed method; biLSTM with tuned hyper-parameter for Malay-English mixed language achieved the highest accuracy of 76.6%, and macro F1-score of 69.6%. [ABSTRACT FROM AUTHOR]

Optical CDMA is a technique based on the same principle of CDMA, in which each individual user is assigned by a unique code. OCDMA consists of distributing the power of the transmitted signal across an enormously wider bandwidth which is necessary to transmit the information. In fact, OCDMA offers a significant role in optical fiber communication due to several attractive characteristics such as dynamic bandwidth assignment, high security, low cost, low interference, and flexible quality. On the other hand, WDMA is a technique that provides a division of the optical channel into subchannels. In this paper, an investigation of the performance of a hybrid WDMA-OCDMA system based on Laguerre Gaussian modes has been illustrated. The analysis is done by using 2D ZCC/MD code according to its success demonstrated in various investigations. 2D ZCC/MD code is distinguished by a property of zero cross-correlation which affords a capability in order to reduce receiver noise and Multiple Access Interference (MAI). In addition, 2D ZCC/MD code proved an ability to improve the system performance in terms of cardinality and other factors that can affect the system. The transmission is done over multi-mode fiber (MMF) which can be applied in a short haul-local area network. Furthermore, the system is implemented based on Three different Laguerre Gaussian modes (LG 01, LG 02, and LG 03). The evaluation of the system performance is done based on Bit Error Rate (BER), Q-Factor, and eye diagrams measurement. [ABSTRACT FROM AUTHOR]