Vol. 13 No. 03 (2024) | International Journal of Engineering and Computer Science

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Vol. 13 No. 03 (2024)

Published: 2024-03-14

Articles

Breast Cancer Detection Using Machine Learning

Satakshi singh, Dr. Kavitha R

Pages No. 26039-26048

Breast Cancer Detection Using Machine Learning

Satakshi singh, Dr. Kavitha R

In today's Breast cancer detection becomes more accurate through the use of machine learning algorithms, which examine complex patterns found in patient data. The early diagnosis that these models provide could lead to better patient outcomes and prompt actions. The goal of this study paper is to increase the accuracy and efficiency of diagnostic processes by presenting a thorough investigation of the use of machine learning approaches for breast cancer diagnosis. We investigate how well Support Vector Machines (SVM), Random Forest, and Neural Networks differentiate between benign and malignant cases using a variety of datasets. To improve the discriminatory strength of our models, we also incorporate sophisticated feature extraction methods like the Wavelet Transform and Gabor Filter. The outcomes of the experiment show the promise of various machine learning techniques, with SVM showing a noteworthy accuracy rate. In addition, the research adds to our understanding of decision-making processes by offering insights into how interpretable the models are. Our research highlights the importance of using machine learning to detect breast cancer, providing a viable path forward for improving early diagnosis and treatment planning that follows.

Keywords—Breast cancer, Machine Learning, Accuracy, Efficiency, Support Vector Machines (SVM), Random Forest, Neural Networks, Feature Extraction, Wavelet Transform, Gabor Filter, Interpretability, Early Diagnosis, Treatment Planning.

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Abstract 148 | PDF DOI https://doi.org/10.18535/ijecs/v13i03.4794

Enlighten Minds Initiative: Bridging Educational Gaps Through Vocational Training

Rakshith N, Sameer kumar, B Arjun, Nikhil Devraj Gowda, Harshit Agarwal, Dr.Febin Prakash

Pages No. 26049-26056

Enlighten Minds Initiative: Bridging Educational Gaps Through Vocational Training

Rakshith N, Sameer kumar, B Arjun, Nikhil Devraj Gowda, Harshit Agarwal, Dr.Febin Prakash

The Enlighten Minds Initiative (EMI) is a forward-thinking NGO dedicated to enhancing access to education and vocational training for disadvantaged students worldwide. Our mission is to bridge the gap in educational opportunities by establishing a platform that connects compassionate donors with students facing financial constraints. Through strategic partnerships with local educational institutions and vocational training providers, EMI identifies deserving students who lack the resources to pursue their educational goals. At the core of our initiative is an innovative web application designed to streamline the donation process. Donors can explore student profiles, gain insight into their educational aspirations, and provide direct support. EMI places a strong emphasis on transparency and accountability, ensuring that donors receive regular updates on student progress and the utilization of funds.

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Abstract 111 | PDF DOI https://doi.org/10.18535/ijecs/v13i03.4804

Predictive Analysis in Healthcare

Soumalya Ghosh Dastider, Dr Kavitha R

Pages No. .26058-26067

Predictive Analysis in Healthcare

Soumalya Ghosh Dastider, Dr Kavitha R

This research paper explores the transformative role of predictive analysis in healthcare, with a specific focus on predicting and managing Parkinson's disease as a case study. By integrating advanced analytics and machine learning techniques, we delve into the intricate landscape of healthcare data to forecast patient outcomes, optimize resource allocation, and enhance overall healthcare decision-making. Through an exhaustive examination of literature and scientific inquiry, we illuminate the promising applications of predictive analysis in addressing the chronic and complex nature of neurodegenerative diseases like Parkinson's. The paper also discusses the challenges, ethical considerations, and future directions within the realm of predictive analysis in healthcare, offering a comprehensive perspective on its potential to revolutionize patient care and public health outcomes.

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Abstract 113 | PDF DOI https://doi.org/10.18535/ijecs/v13i03.4795

Developing Zero-Cost Solutions for Testing Autonomous Vehicle Software

Karthikeyan Palanichamy

Pages No. 26068-26100

Developing Zero-Cost Solutions for Testing Autonomous Vehicle Software

Karthikeyan Palanichamy

As the development of autonomous vehicles accelerates, the need for robust and cost-effective testing solutions becomes paramount. This paper explores the concept of zero-cost solutions for testing autonomous vehicle software, aiming to reduce development expenses while maintaining high testing efficacy. We examine various methodologies, tools, and strategies that leverage existing resources to create effective testing frameworks without significant financial investment. Case studies and practical implementations illustrate the feasibility and benefits of these approaches in real-world scenarios.

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Abstract 95 | PDF DOI https://doi.org/10.18535/ijecs/v13i03.4806

Metropolitan Flood Risk Characterization Using Remote Sensing, GIS, and Fuzzy Logic (RS-GIS-Fl) Approach: Suleja, Nigeria

Atemoagbo, Oyarekhua Precious, Martins, Yusuf Otache, Animashaun, Iyanda Murtala, Chukwu, Samuel Emeke

Pages No. 26101-26112

Metropolitan Flood Risk Characterization Using Remote Sensing, GIS, and Fuzzy Logic (RS-GIS-Fl) Approach: Suleja, Nigeria

Atemoagbo, Oyarekhua Precious, Martins, Yusuf Otache, Animashaun, Iyanda Murtala, Chukwu, Samuel...

Climate change exacerbates extreme events like floods, droughts, heatwaves, cyclones, hurricanes, tornadoes, and wildfires, affecting human populations and environments worldwide. Despite varying impacts across regions, no area is immune to climate change consequences. To mitigate these effects, this study employs Geographic Information System (GIS), Remote Sensing, and Fuzzy Logic System to map flood vulnerability zones in Suleja Local Government, Niger State, North Central Nigeria. The research integrates satellite and GIS datasets to prepare Suleja's Flood Zonation Mapping. Rainfall data from Nigeria Meteorological Agency (NiMET), Climate Hazards Group InfraRed Precipitation with Station Data (CHRPS), Global Satellite Mapping of Precipitation (GSMaP), and parameters like Slope, Elevation, Nearness to Water Bodies, Land Use Land Cover, and Drainage Density are utilized to identify flood vulnerability. The flood vulnerability map categorizes the area into five zones: Very Low Risk (40.9%), Low Risk (18.9%), Medium Risk (27.6%), High Risk (7.1%), and Very High Risk (5.5%). Considering the stochastic nature of flood characteristics, this study emphasizes the importance of integrating hydro-climatic and physical catchment parameters, leveraging bias-adjusted satellite data to minimize uncertainty. Effective flood management requires quantifying risk, susceptibility, and hazard components to establish a robust control framework. This research contributes to the development of data-driven flood mitigation strategies in vulnerable regions like Suleja. By harnessing GIS, Remote Sensing, and Fuzzy Logic System, policymakers and stakeholders can make informed decisions to protect communities and infrastructure from flood risks.

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Abstract 41 | PDF DOI https://doi.org/10.18535/ijecs/v13i03.4798