Zulkifli Husin
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Zulkifli Husin
Official Name
Zulkifli, Husin
Alternative Name
Husin, Zulkifli
Husin, Z.
Husin, Zulkifli Bin
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Scopus Author ID
57201059019
Researcher ID
EXV-4088-2022

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  • Publication
    Study on leave image processing with application in herbal classification and early detection of chili plant disease
    ( 2016)
    Zulkifli Husin
    Herbs have been widely used in food preparation, medicine and cosmetic industry. Knowing which herbs to be used would be very important in these applications. The current way of identification and determination of the types of herbs however, is still being done manually and prone to human error. Designing a convenient and automatic recognition system of herbs species is essential since this will improve herb species classification efficiency. Chili (Capsicum Annum and Capsicum Frutescen) is an important fruiting vegetable used in majority of Asian dishes. Chili cultivation has been a very difficult and meticulous task due to its vulnerability to various attacks frommicro-organisms, bacterial disease and pests which leave distinguished marks on leaves, stems or fruits. Current manual method applies pesticides and chemicals indiscriminately throughout the farm. To improve the process, development of an automated disease detection is essential. There are a few research that have been done in classification of the plant species using certain factors (leaf shape and size). The classification are accomplished through several image processing techniques. However, the literature shows that there are still a gap in classifying the herb plants species. Therefore, this research focuses on classification approach to the shape, texture features and colors of the herbs leaves. The combination of techniques used in morphology image processing i.e. SVD and skeleton would be able to classify the species of herb regardless of the shape and size. In addition, the techniques demonstrate the capability to detect early plant chili disease through leaf features inspection using HSV colour model technique. The proposed herbs species recognition system employs neural networks algorithm and image processing techniques to perform classification on twenty herbs species. One hundred samples for each species went through the system and the recognition accuracy was at 98.9%. Most importantly the system is capable of identifying the herbs leaves species even though they are dried, wet, torn or deformed. Additionally, a novel method of early automatic recognition for plant chili disease based on color and texture features using a HSV color model and BPNN technique via intelligent decision support system is presented in this research. The proposed system employs image processing technique on one thousand chili plant samples and the recognition accuracy was at 97.7%. The efficiency and effectiveness of the proposed methods in recognizing herbs plant and detecting early plant chili disease are demonstrated by the experiments.
  • Publication
    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.
  • Publication
    Recent technology for food and beverage quality assessment: a review
    ( 2023-06-01)
    Tan W.K.
    ;
    Zulkifli Husin
    ;
    Yasruddin M.L.
    ;
    Ismail M.A.H.
    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.
      1
  • Publication
    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.
      1
  • Publication
    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.
      1
  • Publication
    Automated Chilli Pesticide Residues Detection Using Odour Gas Sensors (OGS) and Deep Learning (DL) Algorithm
    ( 2023-01-01)
    Tan W.K.
    ;
    Hakin Ismail M.A.
    ;
    Zulkifli Husin
    ;
    Yasruddin M.L.
    Detection of excessive pesticide residue detection is a serious problem for food regulators, suppliers, and consumers. It is very important to determine which chilli are contaminated with pesticides, and the current method of identifying and determining pesticide residues in chilli is still done using laboratory equipment. To overcome this problem, this study attempted to develop a method to detect pesticide residues in chilli samples using an eight different type of electronic nose based on a readily available metal oxide gas sensor. The proposed system used noise filtering, Long Short-Term Memory (LSTM) and Principal Component Analysis (PCA) algorithm along with a realtime data acquisition system that uses a computer to perform pesticide residue detection on the chilli sample. Two hundred forty samples of chilli sample with different pesticide concentrations went through the system and the accuracy rate achieved a success rate of 89.58% using the LSTM algorithm. The proposed method is expected to help the food processing industry to determine food contamination for producing clean and healthy food. The validation and feasibility of the proposed method for the determination of pesticide residues in chilli have been demonstrated by experiments.
      1
  • Publication
    Performance Analysis of GreedLea Routing Protocol in Internet of Vehicle (IoV) Network
    ( 2021-08-27)
    Normaliza Omar
    ;
    Naimah Yaakob
    ;
    Saidahmed M.E.E.
    ;
    Zulkifli Husin
    The Internet of Vehicles (IoV) network transforms smart life on the wheels through several connections between vehicles, highways, people and networks, providing a safer, more effective and more energy-efficient driving experience. In a specific field, the reliable arrival of independent vehicles and the typical enhancement of traffic safety change through a fast and consistent distribution of messages. It is important to disseminate messages between vehicles that make up the IoV network and to be exploit of the quick and effective transmission of multi-hop communication for the information broadcasting. This study introduces the standardization method and summarizes the primary technologies of IoV network. This study provides a set of traditional research developments, analyses key innovations to date and, eventually, proposes solutions to common use cases that could provide valuable references for the development and implementation of potential IoVs network. The simulation has been done using OMNET++ platform to evaluate the GreedLea routing protocol with the standard Greedy Perimeter Stateless Routing (GPSR) and Ad-hoc On-demand Distance Vector (AODV) routing protocol in IoV network scenario. In the performance analysis varied parameters for example direction, node and speed has been take into account. This study also proposed to evaluate GreedLea in a crowded city situation and in a highway situation to provide further realistic simulations. From the simulation results, it shown that the GreedLea presented better performance compared to the traditional GPSR and AODV in term of end-To-end latency, packet loss rate and path loss.
      1
  • Publication
    Feasibility Study of Beef Quality Assessment using Computer Vision and Deep Neural Network (DNN) Algorithm
    ( 2020-08-24)
    Tan Wei Keong
    ;
    Zulkifli Husin
    ;
    Hakim Ismail Muhammad Amir
    The beef quality relies upon the colour score of muscle during the grading stage. Colour scoring to be used in beef grading would be very critical and the current way of identification and determination of the quality of beef is still being done manually and susceptible to human error. The ability to automate the prediction of the beef quality will assist the meat industry through the grading phase to establish the colour score. Therefore, computer vision and deep neural network (DNN) were used to predict the beef quality by determining colour scores of beef muscle. Four hundred of beef rib-eye steaks were chosen and acquired for each image, which is the colour score of beef were assigned by expertise according to the standard colour cards. The image was processed and went through DNN classifier for determining beef quality. The proposed DNN classifier achieved the best performance percentage of 90.0%, showing that the computer vision integrated with the DNN algorithm can deliver an efficient implementation for predicting beef quality using colour scores of beef muscle.
      1
  • Publication
    Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection
    ( 2020-08-01)
    Muhamad Sani Mustafa
    ;
    Zulkifli Husin
    ;
    Tan Wei Keong
    ;
    Mavi Muhamad Farid
    ;
    Rohani S Mohamed Farook
    Plants such as herbs are widely used in the medical and cosmetic industry. Recognizing a species and detecting an early disease of a plant are quite challenging and difficult to implement as an automated device. The manual identification process is a lengthy process and requires a prior understanding about the plant itself, such as shape, odour, and texture. Thus, this research aimed to realize the computerized method to recognize the species and detect early disease of the herbs by referring to these characteristics. This research has been developed a system for recognizing the species and detecting the early disease of the herbs using computer vision and electronic nose, which focus on odour, shape, colour and texture extraction of herb leaves, together with a hybrid intelligent system that are involved fuzzy inference system, naïve Bayes (NB), probabilistic neural network (PNN) and support vector machine (SVM) classifier. These techniques were used to perform a convenient and effective herb species recognition and early disease detection on ten different herb species samples. The species recognition accuracy rate among ten different species using computer vision and electronic nose is archived 97% and 96%, respectively, in SVM, 98% and 98%, respectively, in PNN and both 94% in NB. In the early disease detection, the detection rate among ten different herb’s species using computer vision and electronic nose are 98% and 97%, respectively, in SVM, both 98% in PNN, 95% and 94%, respectively, in NB. Integrated three machine learning approaches have successfully achieved almost 99% for recognition and detection rate.
      1
  • Publication
    Design and Development of GreedLea Routing Protocol for Internet of Vehicle (IoV)
    ( 2020-03-20)
    Normaliza Omar
    ;
    Naimah Yaakob
    ;
    Zulkifli Husin
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    In Internet of Vehicle (IoV), each vehicle uses a routing protocol to find a path for sending its messages to the last destination. Nowadays, the studies of IoV routing protocols and their impact on the performances of network with different network scenarios has significantly developed a precise understanding of the requirements and goals for designing an IoV routing protocol. In IoV, topology of network diverse promptly which leads to the fragmentation of network, frequent route breakage, and packet loss. This paper discusses on the development of an integrated routing protocol for IoV scenario. Greedy Perimeter Stateless Routing (GPSR) and Reinforcement Learning (RL) is integrate to determine a route based on demand. Then, the mobility model has been designed to reduce road collision. Lastly, traffic management also been focused to deal with the loss, mobility and network delay to meet the application demands.
      1