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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 36, No 2: November 2024" : 64 Documents clear
Improved search method for classified reusable components on cloud computing Rawashdeh, Adnan; Alkasassbeh, Mouhammd; Dwairi, Radwan; Abu-Salem, Hani; Al-Mattarneh, Hashem
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1092-1104

Abstract

Expanding development environments to accommodate huge amounts of reusable components along with associated maintenance and evolution responsibilities has become difficult and costly for software organizations to cope with, while benefits are limited to owner organizations. The challenge of organizing reusable assets so that finding the right component needed has always been a big challenge. The literature of software reuse lacks a comprehensive search method that is efficient and covers the entire system development lifecycle (SDLC). This research work attempts to make an efficient use of the cloud computing advantages and thus, encourages the migration of reusable components to the clouds. The maintenance, the search process and cost-related problems encountered with traditional in-house development environments can be resolved conclusively on the cloud. This research work proposes a multi-classification and clusters approach to migrate reusable components to the cloud. Accordingly, it applies indexing process to classified reusable components achieving efficient search. In addition, the proposed approach adopts a comprehensive SDLC-based classification to organize reusable components so that searching and finding an appropriate component becomes an easy task due to the fact it is bound to the particular undergoing phase. Cloud computing provides more storage and resources with low cost, compared to traditional in-house development environments.
Auto digitization of aerial images to map generation from UAV feed Kannan, Raju Jagadeesh; Yadav, Karunesh Pratap; Sreedevi, Balasubramanian; Chelliah, Jehan; Muthumarilakshmi, Surulivelu; Jeyapriya, Jeyaprakash; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1338-1346

Abstract

Nowadays the rapid growth of unmanned aerial vehicles (UAVs) bridges the space between worldly and airborne photogrammetry as well as allow flexible acquisition of great solution images. In the case of natural disasters such as floods, tsunamis, earthquakes, and cyclones, their effects are most often felt in the micro-spaces and urban environments. Therefore, rescuers have to go around to get to the victims. This paper presents an auto digitization of aerial images to map generation from UAV feed at night time. In case of a power outage and an absence of alternative light sources, rescue operations are also slowed due to the darkness caused by the lack of electricity and the inability to light additional sources. In other words, to save lives, we need to know about all essential large-scale feature spaces in the dark so that we can use this information in times of disaster. The research proposed a soft framework for crisis mapping to aid in mapping the state of the aerial landscape in disaster-stricken areas, allowing strategic rescue operations to be more effectively planned.
Stochastic geometry-based resource allocation scheme over cellular shotgun systems Gomaa, Ibrahim G.; Abdelaziz, Amr M.; Elbayoumy, Ashraf D.; Elsayed, Rania A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp913-922

Abstract

This paper presents a resource allocation scheme that fulfills the maximum possible aggregate rate of the capacity region by targeting the corner points of the multiple-input multiple-output multiple access channel. This corner points of the channel’s capacity region are attainable whenever each user’s transmission has minimum possible interference among other users. This work aims to investigate the non-singularity of such situations by the exploitation of users’ geographic location seeking the opportunity of getting users’ transmission spatially multiplexed. The developed model demonstrates that similar results can be achieved with partial channel state information knowledge under certain conditions throughout the operational signal to noise ratio range. The proposed resource allocation scheme is designed for a shotgun cellular system with a random distribution of users over a circular coverage area. The proposed model uses stochastic geometry to prove that when number of users grows up within the coverage area, the probability of achieving the corner points sum rate increases rapidly. The developed model was evaluated, and the results show that for a circular coverage area with a radius of 10 km, the probability of having users whose transmissions can be spatially multiplexed with minimum interference increases as the number of users grows to 300 users.
Convolutional neural networks breast cancer classification using Palestinian mammogram dataset Saadah, Hanin; Owda, Amani Yousef; Owda, Majdi
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1149-1162

Abstract

Breast cancer is widespread across the globe. It’s the primary cause of death in cancer fatalities. According to the Palestinian Ministry of Health annual report, it ranked as the third reported death of all reported cancer deaths in the West Bank. Mammogram screening is the most common technique to diagnose breast abnormalities, but there is a challenge in the lack of skilled experts able to accurately interpret mammograms. Machine learning plays an important role in medical image processing particularly in early detection when the treatment is less expensive and available. In this paper we proposed different convolutional neural network (CNN) models to detect breast abnormalities with promising results. Six CNN models were used in this research on a unique (first-hand) dataset collected from the Palestinian Ministry of Health. The models are VGG16, VGG19, DenseNet121, ResNet50, Xception, and EfficientNetB7. Consequently, DenseNet121 outperformed other models with 0.83 and 0.85 for testing accuracy and area under curve (AUC) respectively. As a future work, the outperformed model can be combined with other patient data like genetic information, medical history, and lifestyle factors to evaluate the risk of developing specific diseases. This would increase the survival rate and enable proactive measures.

Page 7 of 7 | Total Record : 64

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