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
    Velocity Based Performance Analysis of GreedLea Routing Protocol in Internet of Vehicle (IoV)
    ( 2024-12-01)
    Normaliza Omar
    ;
    Naimah Yaakob
    ;
    Mohamed Elshaikh Elobaid Said Ahmed
    ;
    Zulkifli Husin
    ;
    Iszaidy Ismail
    ;
    Alaa KY Dafhalla
    Intelligent routing protocols for IoV have also been made possible by the convergence of IoT and machine learning algorithm. In order to make informed routing decisions, these intelligent routing protocols examine data gathered from IoT devices like vehicle sensors and traffic monitoring systems using machine learning algorithms. Moreover, as the number of vehicles increases and road networks become more complex, traditional routing protocols for ad hoc networks are being replaced by more advanced and efficient protocols. The purpose of this study is to concentrate on these unique qualities of IoVs network scenario. A combined routing method has been developed to construct periodic connectivity and find routes on-demand in order to save route data as graphs. The simulation's findings show that GreedLea routing protocol outperforms GPSR and AODV routing protocols in terms of delay and packet delivery ratio (PDR). The results demonstrate that the average AODV latency is significantly higher when there are fewer vehicles on the network. This is due to the fact that connections are frequently lost at higher speeds and lower densities, and re-establishing new channels takes a lot of time. As the number of vehicles rises, efficiency improves and the wait gets shorter. The average latency, yet, keeps increasing as vehicle density increases due to the additional overheads related with routing.
      3  23
  • 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.
      10  27
  • 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.
      4  18
  • 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
    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
    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.
      6  16
  • 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.
      4  27
  • 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.
      15  11
  • 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