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All Journal Konstruksi: Publikasi Ilmu Teknik, Perencanaan Tata Ruang dan Teknik Sipil Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer
Israa Shakir Seger
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering

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Enhancing Depth Consistency in Augmented and Diminished Reality : Techniques and Evaluations Using RGB Imagery Israa Shakir Seger; Amjad Mahmood Hadi; Alaa Abd Ali Hadi
Konstruksi: Publikasi Ilmu Teknik, Perencanaan Tata Ruang dan Teknik Sipil Vol. 3 No. 1 (2025): Januari: Konstruksi: Publikasi Ilmu Teknik, Perencanaan Tata Ruang dan Teknik S
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/konstruksi.v3i1.685

Abstract

Augmented Reality (AR) applications are rapidly gaining popularity across various industries, including education and marketing. By integrating real-world environments with virtual objects, AR enhances user understanding and information display for products. This paper explores Diminished Reality (DR) techniques, which are used to visually remove real objects from AR environments. Despite growing interest, much of the DR research predominantly focuses on maintaining consistency between real and virtual elements, particularly in texture handling on marker areas. Our study addresses the preservation of depth consistency using edge detection and planar segmentation to construct a depth map, essential for developing effective DR methods. We introduce a two-stage process involving depth mask construction, each stage equipped with error measurement for iterative refinement. Our proposed techniques, Planarity and Boundary Depth, are evaluated on a dataset of high-quality RGB images captured by digital cameras. Experimental results validate the effectiveness of our methods across various performance metrics, confirming the practicality of our approach in enhancing AR experiences.
Achieving High Accuracy in Breast Cancer Diagnosis with CNN Israa Shakir Seger; Amjad Mahmood Hadi
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 4 (2024): Agustus: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i4.240

Abstract

Breast cancer is a prevalent disease among women that can lead to fatalities. Using deep learning methods to detect and classify tumors can aid in the diagnostic process. Tumors can be classified as either malignant or benign, and doctors require an accurate diagnostic system to distinguish between the two. Even specialists can find it challenging to identify tumors, emphasizing the need for an automated diagnostic system for diagnosing and treating tumors. This study aims to enhance the efficiency of breast cancer diagnosis by implementing a deep convolutional neural network (DCNN). The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used in the main trials. The CNN technique utilized in this study exhibits superior performance compared to existing methods and achieves a 99.70% accuracy rate in detecting breast cancer.
Co-Authors Alaa Abd Ali Hadi Amjad Mahmood Hadi