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
    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
    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.
  • 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.
  • 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.
  • 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.