Iszaidy Ismail
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Iszaidy Ismail
Official Name
Ismail, Iszaidy
Alternative Name
Ismail, I.
Iszaidy, I.
Ismail, Iszaidy
Iszaidy E.
Main Affiliation
Scopus Author ID
56024209200

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Unauthorized parking notification system

2023 , Loo Ka Chun , Iszaidy Ismail , Ruzelita Ngadiran , Md Mostafijur Rahman

This paper focuses on development of an parking notification system on Raspberry Pi. In parking system, Automatic License Plate Recognition (ALPR) is becoming an increasingly practical security solution, while security and possession are the most discussed issues nowadays. However, similar systems on the market currently only focus on security, which only provides authorization at carpark entrance to prevent unauthorized personnel from entering the compound. There may be an infringement of ownership happens, where a parking lot owned by a person occupied by irresponsible car owner. Besides, although there are subscription-based services available for ALPR, but most of them are expensive due to their deeply customized high accuracy, and may be unaffordable to everyone. Most carpark systems also lack of the ability to send notification to lot owner with unauthorized vehicle information. Therefore, this study is aimed to design a system that able to check authorization at both entrance site and parking lot site, to implement an open-source solution to the system, and to equip notification ability to the system. In this study, license plate detection/localization was implemented to get the Region of Interest (ROI) from input images. License plate character recognition was then executed to perform authorization checking with database. After the authorization checking is completed, the result with relevant information will be sent as notification to parking lot owners. The performance of plate detection algorithms will be evaluated based on their accuracy. The plate detection algorithm with Haar Cascade Classifier had produced a high segmentation accuracy, which is 96.875%. Meanwhile, for the overall system accuracy (also known as OCR/plate recognition accuracy) had achieved 71.875% for Malaysian License Plate. In conclusion, a system with ALPR and notification abilities that emphasis on both security and possession is successfully developed.

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Publication

An Analysis of Background Subtraction on Embedded Platform Based on Synthetic Dataset

2021-03-01 , Iszaidy Ismail , Ruzelita Ngadiran , R Badlishah Ahmad , Ramli N. , Jais M.I. , Vijayasarveswari Veeraperumal

Background subtraction is a preliminary technique used for video surveillance and a widely used approach for indexing moving objects. Arange of algorithms have been introduced over the years, and it might be hard to implement the algorithms on the embedded platform because the embedded platform comes up with limited processing power. The goal of this study is to provide a comparative analysis of available background subtraction algorithms on the embedded platform:-Raspberry Pi. The algorithms are compared based on the segmentation quality (precision, recall, and f-measure) and hardware performance(CPU usage and time consumption) using a synthetic video from BMC Dataset with different challenges like normal weather, sunny, cloudy, foggy and windy weather.

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Video size comparison for embedded vehicle speed detection & travel time estimation system by using Raspberry Pi

2017-03-06 , Iszaidy Ismail , Alias A. , Ruzelita Ngadiran , R Badlishah Ahmad , Mohd Ilman Jais , Shuhaizar Daud

As traffic continues to grow up, the issue regarding the road accident also growing quickly. The accident occurred due to the high speed of vehicles on the road. This paper proposed a vehicle speed detection and travel time estimation system using Raspberry Pi to estimate the speed of passing vehicles through this system. The system is designed to detect the moving vehicles and calculate its velocity. The system used OpenCV as an image processing software to detect and track the moving vehicles. Several types of capturing size of the video are used in this system to check and measure the performance of the embedded board.

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Threading implementation on different hardware for travel time estimation purpose

2017-03-06 , Iszaidy Ismail , Ruzelita Ngadiran , R Badlishah Ahmad , Mohd Ilman Jais , Shuhaizar Daud

The travel time estimation is one of traffic management system which provide time taken from one point to another point. Travel time estimation system consists of an embedded platform with image sensor for detecting and tracking the vehicle. Due to limited resources of embedded board, it makes challenging to measure the travel time especially for fast moving vehicle. Capturing system required a high capturing rate of the camera to capture most current frame for fast moving vehicle. Threading is implemented in this system to improve embedded board resource utilization and input-output latency between camera and embedded board. In this paper, the threading technology is applied to two types of Raspberry Pi model and the performance of the embedded board is recorded and analyzed.

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T-way Test Suite Generation Strategy based on Ant Colony Algorithm to Support T-way Variable Strength

2021-03-01 , Nuraminah Ramli , Rozmie Razif Othman , Hendradi R. , Iszaidy Ismail

T-way test suite generation strategy based on Ant Colony algorithm (TTSGA) has been developed to support t-way variable strength testing which tackles exhaustive testing issues. It employs the ant colony optimization algorithm to generate near-optimal number of test suite size. Even though the test suite size is smaller than exhaustive testing, the strategy covers every possible combination of interacting parameters. The strategy has been evaluated by using benchmarked experiments. Results obtained were compared with other existing strategies that support variable strength. It was found that TTSGA produces comparable results with other existing strategies especially for higher strength configurations. Two non-parametric tests, which are Wilcoxon Rank and Friedman test, have been conducted to analyze the results statistically between TTSGA and HSS as only both strategies have complete experiments results. Although the results shows that there is no significant difference of test suite size among them, TTSGA is in the first rank in the Friedman test.

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IOT Smart Guidance Parking Search System for Open Space Parking Area

2021-07-26 , Iszaidy Ismail , Ruzelita Ngadiran , Nazren A.R.A. , Wafi N.M. , Ramli N. , Vijayasarveswari Veeraperumal , Leow W.Z.

Open parking facilities can be automated and parking spaces can be easily operated by the implementation of IoT technology (Internet of Things). In this article, we present the evolution and prototyping of the open space smart guidance-parking search system, an IoT-based smart parking search system. The Smart Guidance Parking Searches System consists of i) An IOT module to monitor the availability of a parking slot and to update the parking lot status; and (ii) A web-based software allows users to view parking spaces available for a specific open space area. This paper addresses the existing system, device description, its functional specifications, the methods, and technologies used, the development/deployment of prototypes, along with the findings from the demonstration. This device serves as a guide for the user/driver to search for the parking slot occupancy in open/outdoor environments.

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Background Subtraction Algorithm Comparison on the Raspberry Pi Platform for Real Video Datasets

2022-01-01 , Iszaidy Ismail , Ruzelita Ngadiran , Ramli N. , Nazren A.R.A. , Nasruddin M.W. , Jais M.I.

Background subtraction is an advanced method used for video monitoring and is commonly used for indexing of moveable objects. Over the years, several algorithms have been implemented and the implementation of algorithms on the embedded platform can be difficult because the embedded platform has minimal computing resources. The purpose of this study is to conduct a comparative review of background subtraction algorithms available on the embedded platform: Raspberry Pi. The algorithms are compared using a real video dataset based on segmentation accuracy (precision, recall, and f-measure) and hardware efficiency (CPU utilization and time consumption).

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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.

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Machine learning algorithms for optic pathway disease diagnostics: a review

2020 , Abu, Masyitah , Nik Adilah Hanin Zahri , Amiza Amir , Iszaidy Ismail

Most of people are unaware that some of the indicators of optic pathway diseases such as stroke or tumor can be detected from the loss part of human vision, or referred as visual field defect. Ophthalmologist will manually examine the site, size and margin of the lesion from patient’s visual field points mapped by Humphrey Field Analyzer. Different site, size and margin of lesion indicates different type of defects and disease that associated with it. Therefore, an effective automated detection mechanism of multi class visual field defect is in demand to help decision making by ophthalmologist. In this paper, we review multiple techniques of supervised and unsupervised learning method for detection of optic pathway disease.

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Tomato Diseases Classification Using Extreme Learning Machine

2023-10-06 , Xian T.S. , Ruzelita Ngadiran , Iszaidy Ismail , Amiza Amir , Siti Zuraidah Ibrahim , Taha T.B.

Plant disease classification is essential to the agriculture industry. In this work, a tomato disease classification using Extreme Learning Machine (ELM) with image-based features. Extreme Learning Machine (ELM), a classification algorithm with a single layer feed-forward neural network where it can be combined with few activation functions. The image features as the input where the image is pre-processed via HSV colour space and extracted using Haralick textures, colour moments and HSV histogram. The features are then loaded in the ELM classifier to perform the ELM training and testing. The accuracy result of ELM classification is then analysed after the validation. The dataset used for disease detection is tomato plant leaves which is a subset of the Plant-Village dataset. The results produced from the ELM demonstrate an accuracy of around 84.94% which is comparable to classifiers such as the Support Vector Machine and Decision Tree.

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