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p-Index From 2021 - 2026
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All Journal Indonesian Journal of Data and Science
Dima Genemo, Musa
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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[WITHDRAWN] Automatic Face Mask Detection on Gates to Combat Spread Of Covid_19 Dima Genemo, Musa
Indonesian Journal of Data and Science Vol. 3 No. 3 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i3.51

Abstract

The COVID-19 pandemic has spread across the globe, hitting almost every country. To stop the spread of the COVID-19 pandemic, this article introduces face mask detection on a gate to assure the safety of Instructors and students in both class and public places. This work aims to distinguish between faces with masks and without masks. A deep learning algorithm You Only Look Once (YOLO) V5 is used for face mask detection and classification. This algorithm detects the faces with and without masks using the video frames from the surveillance camera. The model trained on over 800 video frames. The sequence of a video frame for face mask detection is fed to the model for feature acquisition. Then the model classifies the frames as faces with a mask and without a mask. We used loss functions like Generalize Intersection of Union for abjectness and classification accuracy. The datasets used to train the model are divided as 80% and 20% for training and testing, respectively. The model has provided a promising result. The result found shows accuracy and precision of 95% and 96%, respectively. Results show that the model performance is a good classifier. The successful findings indicate the suggested work's soundness.
Federated Learning for Bronchus Cancer Detection Using Tiny Machine Learning Edge Devices Dima Genemo, Musa
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.116

Abstract

In deep learning, acquiring sufficient data is crucial for making informed decisions. However, due to concerns regarding security and privacy, obtaining enough data for training models in the era of deep learning is challenging. There is a growing need for machine learning (ML) solutions that can derive accurate conclusions from small data while preserving privacy. Smartphones, which are widely used and generate large amounts of data, can serve as an excellent source for data generation. One suitable approach for regularly evaluating real-world data from edge devices is Tiny Machine Learning (TinyML). With the increasing number of edge devices involved in transmitting private data, it's vital to have a method that allows computations to be performed on edge devices and pushed to the edge rather than over the network. Considering these obstacles, the combination of TinyML edge devices and Federated Learning can be applied in the early treatment of Bronchus Cancer. Under the framework of federated learning, local edge devices are trained independently and then integrated into the server without exchanging edge device data. This approach enables the creation of secure models without sharing information, resulting in a highly efficient solution with enhanced data security and accessibility. This article provides a comprehensive discussion of the key challenges addressed in recent literature, accompanied by an extensive examination of relevant studies. Additionally, a novel model based on edge devices and federated learning is proposed.
Dynamic Background Subtraction in Moving Object Detection on Modified FCM-CS Algorithm Dima Genemo, Musa
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.162

Abstract

This study uses deep learning for background subtraction in video surveillance. Scanned images often have unwanted background elements, making it difficult to separate objects from their backgrounds accurately. This affects how items are distinguished from their backgrounds. To solve this problem, this article introduces a model called the Improved Fuzzy C Means Cosine Similarity (FCM-CS). This model is designed to identify moving foreground objects in surveillance camera footage and address the associated challenges. The effectiveness of this model is evaluated against the current state-of-the-art, validating its performance. The results demonstrate the remarkable performance of the model on the CDnet2014 dataset
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