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All Journal International Journal of Electrical and Computer Engineering Buletin Ilmiah Sarjana Teknik Elektro
Beulah Jackson
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Computer Science & IT Electrical & Electronics Engineering

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Performance analysis of smart optimization antenna for wireless networks Jacob Abraham; Kannadhasan Suriyan; Beulah Jackson; Mahendran Natarajan; Thanga Mariappan Lakshmanaperumal
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5222-5231

Abstract

Antenna design has significantly advanced as a result of the widespread need for wireless communications and data substitution through wireless devices. The research article's goal is to provide a conceptual framework, difficulties, and opportunities for a source as well as a general overview of the antenna used in wireless communications applications. In this proposed research, we will go over a variety of topics related to mobile communication and fifth generation (5G) technologies, including its pros and benefits. A thorough comparison between the expected properties of the antennas and each generation, from 1st generation (1G) to 5G, is also included. This article also provides an overview of the investigated 5G technologies and various antenna designs.
Effective Analysis of Machine Learning Algorithms for Breast Cancer Prediction Vanitha M; V Anitha; Beulah Jackson; Anne Jenefer F
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.13663

Abstract

Early prognosis of Breast Cancer (BC) is significantly important to cure the disease easily so it is essential to develop methods that is able to aid doctors to get precise prognosis. Hence, a BC prognosis methodology is proposed utilizing Machine Learning (ML) approaches. The target of this paper is to utilize classification techniques to classify tumor types, or benign and malignant cells, using 569 samples from Wisconsin Diagnostic Breast Cancer (WDBC) database. Initially, preprocessing is employed to enhance the data’s quality, which includes data cleaning and min-max normalization. It improves the input breast cancer data's quality, accuracy, and suitability for further analysis. Followed by preprocessing, the ML approaches such as K-Nearest Neighbour (KNN), Random Forest (RF) and Support Vector Machine (SVM) methods are analyzed for the classification of BC data. Each algorithm offers a distinct approach to classification by capturing local patterns in data and handles high-dimensional spaces along with nonlinear boundaries through kernel tricks. The developed work is implemented in python software and comparative analysis is done with traditional methods. The outcomes demonstrates that the proposed KNN classifier shows better performance interms of precision, recall, F1-score with an accuracy of 96.49%, ensuring the earliest diagnosis of breast cancer compared with SVM and RF. This comparative approach enhances the reliability of the proposed methodology and supports the selection of the best-performing algorithm offering valuable insights for real-world clinical decision support systems.
Co-Authors Anne Jenefer F Jacob Abraham Kannadhasan Suriyan Mahendran Natarajan Thanga Mariappan Lakshmanaperumal V Anitha Vanitha M