Ahmad Ashraf Abdul Halim
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Preferred name
Ahmad Ashraf Abdul Halim
Official Name
Ahmad Ashraf, Abdul Halim
Alternative Name
Abdul Halim, Ahmad Ashraf
Halim, Ahmad Ashraf Abdul
Halim, A. A.A.
Main Affiliation
Scopus Author ID
57200985251
Researcher ID
AGO-8881-2022

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Magnetohydrodynamic Stagnation-point Flow towards a Permeable Stretching/Shrinking Sheet with Slip and Heat Generation/Absorption Effects

2023-11-10 , Fatinnabila Kamal , Ahmad Ashraf Abdul Halim , Wan Mohd Khairy Adly Wan Zaimi

This study of the magnetohydrodynamic (MHD) stagnation-point flow towards a permeable stretching/shrinking sheet in the presence of slip and heat generation/absorption effects is considered. The governing equations in the form of partial differential equations are transformed into a system of ordinary differential equations by using similarity transformation, and then solved numerically using bvp4c function in Matlab software. The variations of the numerical solutions for the skin friction coefficient and the local Nusselt number as well as velocity and temperature profiles are obtained for several values of the governing parameters. It is found that the solution is unique for the stretching case whereas dual (first and second) solutions exist for the shrinking case in certain range of parameters.

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ENG and NZRI Characteristics of Decagonal-Shaped Metamaterial for Wearable Applications

2020-08-01 , Kabir Hossain , Thennarasan Sabapathy , Soh Ping Jack , Muzammil Jusoh , Fazilah, Ainur Fasihah Mohd , Ahmad Ashraf Abdul Halim , Raghava N.S. , Podilchak S.K. , Schreurs D. , Abbasi Q.H.

A decagonal-shaped split ring resonator metamaterial based on a wearable or textile-based material is presented in this work. Analysis and comparison of various structure sizes are compared considering a compact 6\times 6\ \mathrm{m}\mathrm{m}{2} metamaterial unit cell, in particular, where robust transmission-reflection (RTR) and Nicolson-Ross-Weir (NRW) methods have been performed to extract the effective metamaterial parameters. An investigation based on the RTR method indicated an average bandwidth of 1.39 GHz with a near-zero refractive index (NZRI) and a 2.35 GHz bandwidth when considering epsilon negative (ENG) characteristics. On the other hand, for the NRW method, approximately 0.95 GHz of NZRI bandwidth and 2.46 GHz of ENG bandwidth have been observed, respectively. These results are also within the ultra-wideband (UWB) frequency range, suggesting that the proposed unit cell structure is suitable for textile UWB antennas, biomedical sensors, related wearable systems, and other wireless body area network communication systems.

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Existing and emerging breast cancer detection technologies and its challenges: A review

2021-11-01 , Ahmad Ashraf Abdul Halim , Allan Melvin Andrew , Mohd Najib Mohd Yasin , Abd Rahman M.A. , Muzammil Jusoh , Vijayasarveswari Veeraperumal , Hasliza A Rahim @ Samsuddin , Illahi U. , Abdul Karim M.K. , Scavino E.

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

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Internet of things technology for greenhouse monitoring and management system based on wireless sensor network

2017 , Ahmad Ashraf Abdul Halim

The rapid development of agrotechnology is playing an important role in the production of greenhouse plantation for cultivating high value fruits, flowers or vegetables. It is imperative to constantly monitor these high value crops optimal requirements at every phase of the plant growth cycle to maintain the best quality production. However, traditional manual inspection, data collection and control method for large-scale greenhouse plantation deemed inefficient with high costs, time consuming and laborious. This project introduces a scheduler to enhance greenhouse management by taking into considerations the different phases of plant growth. The scheduling concept is also a contribution to this research projects implemented and it is believed there is no specific study on scheduling concepts in the automation system according to specific cycles and phases in the crop. Measuring several points in a greenhouse are required to trace down the local climate parameters to ensure the automation system works properly. Cabling would make the measurement system expensive and vulnerable in a large greenhouse plantation. Moreover, the cabled measurement points are complicated and difficult to maintain and relocate once they are installed. Thus, a Wireless Sensor Network (WSN) consisting of small-size wireless sensor nodes based on ZigBee technology is an attractive and cost-efficient option to build the required system. The system is used to sense and monitor the temperature, humidity, light, soil moisture and carbon dioxide which are essential in the photosynthesis process. The scheduler is build using Visual Basic C# to analyse, display and control the actuators in real-time.

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UWB-Based Early Breast Cancer Existence Prediction Using Artificial Intelligence for Large Data Set

2023-01-01 , Ahmad Ashraf Abdul Halim , Vijayasarveswari Veeraperumal , Allan Melvin Andrew , Mohd Najib Mohd Yasin , Mohd Zamri Zahir Ahmad , Hossain K. , Bari B.S. , Fatinnabila Kamal

Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.

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Development of statistically modelled feature selection method for microwave breast cancer detection

2025 , Vijayasarveswari Veeraperumal , Norfadila Mahrom , Rafikha Aliana A Raof , Phak Len Al Eh Kan , Muhammad Amiruddin Ab Razak , Bavanraj Punniya Silan , Yusnita Rahayu , Mohd Wafi Nasrudin , Ahmad Ashraf Abdul Halim , Nuraminah Ramli

Microwave technology is very promising tool for breast cancer detection. Microwave transmits and receives UWB signals. UWB signals carries information of the breast cancer. UWB signals need to be pre-processed in order to remove irrelevant and redundant features. Feature extraction and feature selection methods are mostly used to remove the unwanted features. In this paper, a statistically modelled feature selection (SMFS) method is proposed for microwave breast cancer detection. Initially, performance of different feature extraction and feature selection method are analysed using Anova test (p-value) and machine learning (SVM, DT, PNN, NB) accuracy. The best feature extraction and feature selection methods are combined and tested. Based on the performance of feature extraction and feature selection method, Combined Neighbour Component Analysis (feature selection) and Statistical features (feature extraction) are combined and tested. This method is able to achieve up to 85%. The result proves two stage methods are able to improve the accuracy compared to single stage method. Therefore, SMFS is able to detect breast cancer efficiently.

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