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All Journal Journal La Multiapp The Indonesian Journal of Computer Science
Abdulkareem, Sabah A.
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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System for Visually Disabled through Wearables Utilizing Arduino and Ultrasound Abdulkareem, Sabah A.; Mohammed, Hind I.; Mahdi, Abbas Alaa
Journal La Multiapp Vol. 5 No. 4 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i4.1230

Abstract

Blindness and other vision impairment is on the rise with more than 2.2 billion people worldwide are affected including children, elder persons, pregnant women, chronically ill and disabled persons who experience difficulties in mobility and being independent. Some of the conventional assistances like usage of white cane or a guide dog lacks the ability to cater all the needs of the blind people. The present research outlines a wearable system with Arduino and ultrasound equipment to improve the walking ability of the persons with vision impairment. From the use of the proposed system, there is the potentiality of detecting obstacles in real time and also determine the location hence minimizing the dependence on other help. The system consists of two wearable components: a glove and a belt which contain ultrasonic sensors, GPS module and GSM module and a vibration motor. The glove senses the objects that are in front of the user while the belt detects stairs or any other raised ground. The method used here was the development and calibration of these components separately then brought together to form a coherent entire system where all the component was precise and reliable. The findings show that the proposed system is successful to identify obstacles on its path before the user comes close to them and gives out alerts through sound and touch. GPS and GSM modules provide an extra layer of security to the kids by allowing a tracking of their location in real time.
Classifying Digital Medical Images for Breast Cancer Prediction Using Machine Learning I. Mohammed, Hind; Abdulkareem, Sabah A.; Ahmed, Shaimaa Khamees
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4392

Abstract

Statistics show that among the 1.67 million cancer reported cases worldwide, breast cancer is the most common cancer among women and constitutes the largest burden of the disease in developing countries. However, if detected early enough, it can be managed. Mammography is one of the best ways to identify and diagnose breast abnormalities among various medical imaging modalities. It typically detects signs and symptoms of breast cancer, including microcalcifications, lumps, nodules, architectural abnormalities, asymmetry, bilateral asymmetry, etc. These features can be benign or cancerous when they appear in the breast. Researchers have focused on creating fully automated computer-aided design methods to help radiologists combat this type of cancer. Artificial Intelligence( AI) -based algorithms have been essential in creating systems that allow for automated diagnosis, rapid response, and low mortality. In this work, several machine learning methods were compared—such as logistic regression, naive Bayesian Gaussian algorithms, support vector machines (SVM), linear support vector machines (SVM), and artificial neural networks (ANN). Processing time and accuracy were the main evaluation metrics where naive Bayes outperformed SVM, followed by linear SVM and logistic regression, with ANNs failing in accuracy. These results highlight how naive Bayes algorithms can help in early detection of breast cancer, leading to faster and more efficient treatments and ultimately better patient care.
Co-Authors Ahmed, Shaimaa Khamees I. Mohammed, Hind Mahdi, Abbas Alaa Mohammed, Hind I.