Allan Melvin Andrew
Thumbnail Image
Preferred name
Allan Melvin Andrew
Official Name
Allan Melvin, Andrew
Alternative Name
Andrew, Allan Melvin
Andrew, Allan M.
Melvin, A. A.
Main Affiliation
Scopus Author ID
36469871200
Researcher ID
GDH-3820-2022

Research Output

Filters

5
5
Reset filters

Settings
Now showing 1 - 5 of 5
  • Publication
    The effect of stearic acid and different loading treated and untreated EFB fiber on the tensile, structural and chemical properties of polypropylene/ recycled acrylonitrile butadiene rubber/ empty fruit bunch composites
    ( 2021-05-24)
    Ragunathan Al Santiagoo
    ;
    Hong T.W.
    ;
    Nik Zainab Nik Azizan
    ;
    Allan Melvin Andrew
    ;
    Wahab F.
    ;
    Nor Amirah Abu Seman @ Haji Ahmad
    ;
    Othman N.S.
    A new developed of polymer materials made up of polypropylene (PP), recycled nitrile gloves (NBRr) and empty fruit bunch (EFB) fiber was fabricated. Recycled nitrile glove was blended together with PP plastic to reduce the use of petroleum based and to solve the issue of waste discarded at the landfill. EFB fiber was incorporated to lower the production cost of composite. Due to incompatibility of different phases, stearic acid treatment was used to improve the EFB fiber polarity. Thus, the effect of different EFB loading for untreated and treated stearic acid of PP/NBRr/EFB composite was studied. In this work, all the materials were mixed well using a heated two roll mill with temperature 180 °C within 9 minutes. The EFB loading were varied from 0 to 30 phr. The analysis of tensile properties was tested followed the ASTM D638, Type IV, testing procedure, to analyze tensile strength, Young’s modulus and elongation at break. The structural properties of the fracture sample surfaces were observed using Scanning Electron Microscope (SEM) and the chemical properties was analyzed using Fourier Transform Infra-Red (FTIR). Composites with stearic acid treatment have shown higher tensile strength compared to untreated EFB fiber. Besides, the micrograph structure surface from scanning electron microscopy analysis showed better fiber and matrix interaction, as the treated-EFB fiber is well encapsulated with the PP matrix. From the FTIR analysis, the intensity peak of OH for PP/NBRr/EFB/SA was reduced due to the removal O-H bond of cellulose from fibers during the surface modification of fibers.
  • Publication
    Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction
    ( 2020-08-01)
    Vijayasarveswari V.
    ;
    Allan Melvin Andrew
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Rafikha Aliana A Raof
    ;
    Mohd Najib Mohd Yasin
    ;
    R Badlishah Ahmad
    ;
    Khatun S.
    ;
    Hasliza A Rahim @ Samsuddin
    Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multistage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.
  • Publication
    Compatibilizers Effect on Recycled Acrylonitrile Butadiene Rubber with Polypropylene and Sugarcane Bagasse Composite for Mechanical Properties
    ( 2023-10-16)
    Zainal M.
    ;
    Ragunathan Al Santiagoo
    ;
    Razi Ahmad
    ;
    Mohammed S.A.
    ;
    Abdul Ghapar Ahmad
    ;
    Vikneswaran Vijean
    ;
    Wan Amiza Amneera Wan Ahmad
    ;
    Allan Melvin Andrew
    Compatibilizers effect on recycled acrylonitrile butadiene rubber (NBRr) with polypropylene (PP) and sugarcane bagasse (SCB) composite for mechanical properties is evaluated. Trans-Polyoctylene Rubber (TOR) and Bisphenol a Diglycidyl Ether (DGEBA) are used as compatibilizers in this study. Three (3) different composites (80/20/15, 60/40/15, and 40/60/15), with fixed filler (15 phr) and compatibilizers (10 phr) content, were carried out. These composites were arranged via melt mixing technique utilizing a heated two-roll mill at a temperature of 180 C for 9 minutes employing a 15-rpm rotor speed. Tensile and morphological properties were evaluated. The result shown average tensile strength dropped by 48.50% as the recycle NBR content rises 20 phr. Nevertheless, subsequent compatibilization reveals that the compositesâ tensile properties were all greater than control composites. The morphology discovered validates the tensile properties, indicating a stronger interaction between the PP/SCB and recycle NBR composites with the addition of compatibilizer DGEBA.
  • Publication
    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.
      1
  • Publication
    Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver
    ( 2022-11-01)
    Halim A.A.A.
    ;
    Allan Melvin Andrew
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Najib Mohd Yasin
    ;
    Muzammil Jusoh
    ;
    Vijayasarveswari Veeraperumal
    ;
    Abd Rahman M.A.
    ;
    Zamin N.
    ;
    Mary M.R.
    ;
    Khatun S.
    Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.
      2  20