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
    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
    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
    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
    MOVE Mobility Model in GreedLea Routing Protocol for Internet of Vehicle (IoV) Network
    ( 2021-07-26)
    Normaliza Omar
    ;
    Naimah Yaakob
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Zulkifli Husin
    Internet of Vehicles (IoV) is a broad variety of mobile transmission purposes for file sharing [l]-[5]. There are still debates on the viability of purposes using end to end multi-hop communication, since the significant number of high mobility nodes involved in the networks. The main issue is the efficiency of IoV routing protocols in cities and highways can meet the ideal delay and throughput for such purposes. In particular, it is not usually a challenge to locate a node to hold a message in urban daytime situations, where vehicles are tightly packed. Since fewer number of vehicles are running in highway scenarios and cities at night, and it might not be possible to set up end-to-end roads. In general, each protocol offered a performance evaluation in contradiction of some other protocols, giving considerable importance to a detailed performance evaluation of each protocol type. After such an assessment, it was found that geocast routing would perform best in urban areas. GreedLea routing protocol is develop to overcome the current routing protocol drawback. The development of GreedLea routing protocol involved Greedy Perimeter Stateless Routing (GPSR) and reinforcement learning method in order to deliver better performance compared to current existing routing protocol. Urban environments without obstacles has been simulated using actual maps for example intersection density. In order to measure efficiency, the metrics are: average delivery rate, average delay, average length of path and overhead. From the analysis, it shows that GreedLea offers better performance compared to GPSR for both city and highway scenario. The first section in your paper.
      1
  • Publication
    Route Beaconing (RouteBea) Process in GreedLea Routing Protocol for Internet of Vehicle (IoV) Network Environment
    ( 2021-06-11)
    Normaliza Omar
    ;
    Naimah Yaakob
    ;
    Saidahmed M.E.E.
    ;
    Zulkifli Husin
    Practically, vehicles tend to travel in long distances. As a result, a vehicle might attach to different network scenarios and topologies. This unique behavior in IoV brings the attention for a robust routing protocol design. For example, a vehicle that runs the same routing protocol, while it moves from one geographical area to another, it experiences different network topology requirements, and thus, the performance of the routing protocol contrasts. Consequently, the performances of the network drop. Considering a huge number of vehicles join in these networks with their high mobility, there are still having problem due to the viability of applications via different network topology. Traffic management problems come up as number of vehicle has been growing at an exponential rate. In order to make life easier, emergency response to road accidents, speed limits, and pollution checks should be considered to be observed. The common applied to this problem are observing of vehicle's speed via CCTV cameras, speed trackers and periodic pollution checks. However, these approaches be inclined to fail as a large number of vehicles need to be observed. Therefore, GreedLea routing protocol has been develop to overcome the problem of monitoring the traffic condition and traffic congestion. In GreedLea routing protocol, path interval is provided by the host to other vehicle to update the condition of traffic and routes in certain area. The details about GreedLea routing protocol has been described in following section. The performance of the GreedLea routing in different speed and distance has been analyze and presented in result and discussion section. In the result, it shows that the performance of GreedLea increase in the packet delivery ratio (PDR) which is packet loss is less than 0.1% and reducing protocol overhead by approximately 20-60% as the vehicle's speed increase for beaconing intervals in the range of 1-3 seconds.
      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
    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.
      3  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