Zulkifli Husin
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Preferred name
Zulkifli Husin
Official Name
Zulkifli, Husin
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Husin, Zulkifli
Husin, Z.
Husin, Zulkifli Bin
Main Affiliation
Scopus Author ID
57201059019
Researcher ID
EXV-4088-2022

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Feasibility Study of Fish Disease Detection using Computer Vision and Deep Convolutional Neural Network (DCNN) Algorithm

2022-01-01 , Muhammad Luqman Yasruddin , Muhammad Amir Hakim Ismail , Zulkifli Husin , Tan Wei Keong

Detection of diseased fish at an early stage is necessary to prevent the spread of the disease. However, detecting fish diseases still uses a manual process and requires a high level of expertise that can be prone to human error. The ability of automatic detection of these fish diseases is much needed to help and to prevent losses of economic in the aquaculture industry. Therefore, this paper aims to detect disease of fish using computer vision and deep convolutional neural network (DCNN) algorithm. One Thousand and Two Hundred fish samples images were selected is namely diseased fish and healthy fish, which is determined by expert of fish diseases according to the specific of characteristics of fish diseases. The fish images went through the DCNN classifier and successfully achieved a satisfying mean average precision (mAP) with 0.237. The result shows that the computer vision integrated with the DCNN algorithm can efficiently predict fish disease.

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Automated tomato grading system using Computer Vision (CV) and Deep Neural Network (DNN) Algorithm

2022-01-01 , Tan Wei Keong , Muhammad Amir Hakim Ismail , Zulkifli Husin , Muhammad Luqman Yasruddin

The tomato grading is based on the skin colour at the grading stage. The evaluation of the colour used to classify tomatoes is very important, and the current methods of identifying and determining tomato varieties are still manual and prone to human error. The ability to automate tomato grading helps the food industry determine colour grades during the evaluation phase. Therefore, Computer Vision (CV) and Deep Neural Network (DNN) are utilised to grade tomatoes by determining their maturity colour. Three hundred tomatoes were selected and its maturity level are assigned by expertise. The tomato images are captured, processed and passed to the DNN classifier to determine the tomato grade. The proposed DNN classifier achieved the mAP percentage of 95.52%. This shows that the computer vision built into the DNN algorithm can provide an efficient implementation for predicting tomato grade.

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Recent technology for food and beverage quality assessment : A review

2022-06 , Wei Keong Tan , Zulkifli Husin , Muhammad Luqman Yasruddin , Muhammad Amir Hakim Ismail

Food and beverage assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. These assessments had become crucial, especially in the issues of adulteration, replacement, and contamination that happened in artificial adjustment relating to the quality, weight and volume. Thus, this review will examine and describe features recently applied in image, odour, taste and electromagnetic, relevant to the food and beverages assessment. This review will also compare and discuss each technique and provides suggestions based on the current technology. This review will deliberate technology integration and the involvement of deep learning to enable several types of current technologies, such as imaging, odour and taste senses, and electromagnetic sensing, to be used in food evaluation applications for inspection and packaging.

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Stacked ensemble learning based on deep transfer learning models for food ingredient classification and food quality determination

2024 , T. W. Keong , Zulkifli Husin , Muhammad Amir Ismail , Muhammad Luqman Yasruddin

Food safety is critical in protecting consumers from foodborne diseases. The public currently classifies and determines food ingredients and their quality based on appearance, aroma, and other characteristics. Existing food inspection machines often focus on single characteristics, resulting in incomplete and inaccurate information. Hence, developing methods that analyse multiple characteristics is necessary for high-accuracy classification. This research proposed an effective stacked ensemble deep transfer learning algorithm using eight popular transfer learning algorithms as a base classifier and combining them with the Adaptive Neuro-Fuzzy Inference System as a meta-classifier to analyse imaging, odour, and capacitive sensing approaches. Twenty-four food samples classified according to freshness, maturity, ripeness, and disease levels were analysed using the proposed stacked ensemble EfficientNet algorithm, achieving the highest accuracy rate of 0.916 and 0.933 in food ingredient classification and quality determination, respectively. This research demonstrated the system’s reliability for deployment in classifying food ingredients in dishes.

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Automated Trading System for Forecasting the Foreign Exchange Market Using Technical Analysis Indicators and Artificial Neural Network

2022-01-01 , Muhammad Amir Hakim Ismail , Muhammad Luqman Yasruddin , Zulkifli Husin , Tan Wei Keong

The article discusses an automated trading system for forecasting foreign exchange markets that utilise Technical Analysis (TA) indicators and Artificial Neural Networks (ANN). Manual traders are usually swayed by their emotions, resulting in a catastrophic loss. As a result, this research will focus on developing an automated trading system that operates independently of human emotions. We provide a strategy for forecasting the movement of the foreign exchange market that incorporates TA indicators and the ANN system. The article examines TA indicators and the ANN system in automated trading systems to achieve accurate foreign exchange price forecasts. The experimental results on the Pound-Dollar (GBP/USD) exchange rate demonstrate that the combination of the TA indicators and the ANN system effectively provides information for forecasting the GBP/USD exchange rate. The performance of the suggested method is examined, revealing that it is capable of forecasting foreign exchange market movement utilising TA indicators and an ANN system.

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