Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science
Sayed, Md Abu
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Modified back-line inset feed 1x4 array microstrip antenna for 5.8 GHz frequency band Hasan, Md Fazlul; Awang Mat, Dayang Azra; Sayed, Md Abu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp892-900

Abstract

This paper presents the design of 1x4 array microstrip antenna utilizing modified backline feeding technique at 5.8 GHz frequency band. The antenna, designed on flame retardant (FR-4) substrate with a dielectric constant of 4.4, aims to achieve reduced harmonics and mutual coupling between closely spaced antenna elements. The primary scope of the paper is investigating the performance of a single band microstrip antenna employing the proposed modified backline feeding method. Moreover, developed design came out with the result and critical analysis by various parameters such as, gain, return loss, voltage standing wave ratio (VSWR), and directivity. Therefore, the proposed design of microstrip antenna with backward linefeed (BLF) demonstrates a directivity of 10.29 dBi, return loss of -21.947 dB, and VSWR of 1.173; are significant improvement compared to recent literature shown in this paper. The adoption of proposed back line feeding technique (BLF) represents a promising alternative for addressing poor wireless connectivity issues in terms of antenna design, gain, and direction within microstrip technology.
Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images Miah, Jonayet; M Cao, Duc; Sayed, Md Abu; Taluckder, Md Siam; Haque, Md Sabbirul
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2208

Abstract

Brain tumors pose a significant global health challenge due to their high prevalence and mortality rates across all age groups. Detecting brain tumors at an early stage is crucial for effective treatment and patient outcomes. This study presents a comprehensive investigation into the use of Convolutional Neural Networks (CNNs) for brain tumor detection using Magnetic Resonance Imaging (MRI) images. The dataset, consisting of MRI scans from both healthy individuals and patients with brain tumors, was processed and fed into the CNN architecture. The SoftMax Fully Connected layer was employed to classify the images, achieving an accuracy of 98%. To evaluate the CNN's performance, two other classifiers, Radial Basis Function (RBF) and Decision Tree (DT), were utilized, yielding accuracy rates of 98.24% and 95.64%, respectively. The study also introduced a clustering method for feature extraction, improving CNN's accuracy. Sensitivity, Specificity, and Precision were employed alongside accuracy to comprehensively evaluate the network's performance. Notably, the SoftMax classifier demonstrated the highest accuracy among the categorizers, achieving 99.52% accuracy on test data. The presented research contributes to the growing field of deep learning in medical image analysis. The combination of CNNs and MRI data offers a promising tool for accurately detecting brain tumors, with potential implications for early diagnosis and improved patient care
Co-Authors Awang Mat, Dayang Azra Haque, Md Sabbirul Hasan, Md Fazlul M Cao, Duc Miah, Jonayet Taluckder, Md Siam