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All Journal Bulletin of Electrical Engineering and Informatics
Ahmad Hijazi, Mohd Hanafi
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Electrical & Electronics Engineering

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The development and usability test of an automated fish counting system based on CNN and contrast limited histogram equalization Leong, Jing Mei; Ahmad Hijazi, Mohd Hanafi; Saudi, Azali; Kim On, Chin; Fui Fui, Ching; Haviluddin, Haviluddin
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5840

Abstract

The aquaculture industry has rapidly grown over the year. One pertinent aspect is the ability of the aquaculture farm management to accurately count the fish populations to provide effective feeding and the control of breeding density. The current practice of counting the fish manually increased the hatchery workers workload and led to inefficiency. The presented work proposed an intelligent, web-based fish counting system to assist hatchery workers in counting fish from images. The methodology consists of two phases. First, an intelligent fish counting engine is developed. The captured image was first enhanced using the contrast limited adaptive histogram equalization. A deep learning architecture in the form of you only look once (YOLO)v5 is used to generate a model to identify and count fish on the image. Second, a web-based application is developed to implement the developed fish counting engine. When applied to the test data, the developed engine recorded a precision of 98.7% and a recall of 65.5%. The system is also evaluated by hatchery workers in the University Malaysia Sabah fish hatchery. The results of the usability and functionality evaluations indicate that the system is acceptable, with some future work suggested based on the feedback received.
Multitask deep learning for sentiment analysis with sarcasm detection in bilingual code-mixed social media content Md Suhaimin, Mohd Suhairi; Wibowo, Adi; Moung, Ervin Gubin; Anthony, Patricia; Ahmad Hijazi, Mohd Hanafi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10935

Abstract

Sentiment analysis in social media often hindered by sarcasm, which can reverse text meaning, and bilingual code-mixing, which adds complexity in non-English primary context. Existing approaches extract separate features for each language and translate them into a single language, resulting in the loss of contextual meaning and omission of crucial features. This paper proposes a multitask learning model for sentiment analysis with sarcasm detection tailored to bilingual code-mixed social media content. A hybrid feature engineering technique is integrated into a multitask deep learning architecture designed to capture the nuances of sentiment and sarcasm while addressing the complexities of processing bilingual code-mixed content. The hybrid technique combines domain-knowledge-based natural language processing (NLP) with a deep learning-based embedding approach. It includes rule-based preprocessing, normalization, spellchecking, feature extraction and selection, and feature representation. The engineered features are integrated into a multitask deep learning network using bidirectional long short-term memory (Bi-LSTM) combined with gated recurrent units (GRU). Using a public dataset that contains bilingual code-mixed social media content related to public security, our proposed model achieved a higher F1score compared to two baseline models that employ single task and multitask approaches.
Co-Authors Adi Wibowo Anthony, Patricia Fui Fui, Ching Haviluddin Haviluddin Kim On, Chin Leong, Jing Mei Md Suhaimin, Mohd Suhairi Moung, Ervin Gubin Saudi, Azali