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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control)
Muayad Sadik Croock
University of Technology-Iraq
Computer Science & IT Electrical & Electronics Engineering

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

 1 
Early detection of breast cancer using mammography images and software engineering process Muayad Sadik Croock; Saja Dhyaa Khuder; Ayad Esho Korial; Sahar Salman Mahmood
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14718

Abstract

The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Software engineering model based smart indoor localization system using deep-learning Zainab Mohammed Resan; Muayad Sadik Croock
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14318

Abstract

During the last few years, the allocation of objects or persons inside a specific building is highly required. It is well known that the global positioning system (GPS) cannot be adopted in indoor environment due to the lack of signals. Therefore, it is important to discover a new way that works inside. The proposed system uses the deep learning techniques to classify places based on capturing images. The proposed system contains two parts: software part and hardware part. The software part is built based on software engineering model to increase the reliability, flexibility, and scalability. In addition, this part, the dataset is collected using the Raspberry Pi III camera as training and validating data set. This dataset is used as an input to the proposed deep learning model. In the hardware part, Raspberry Pi III is used for loading the proposed model and producing prediction results and a camera that is used to collect the images dataset. Two wheels’ car is adopted as an object for introducing indoor localization project. The obtained training accuracy is 99.6% for training dataset and 100% for validating dataset.
A developed GPS trajectories data management system for predicting tourists' POI Rula Amjed Hamid; Muayad Sadik Croock
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.13006

Abstract

One of the areas that have challenges in the use of internet of things (IoT) is the field of tourism and travel. The issue here is how to employ this technology to serve the tourism and managing the produced data. This work is focus on the use of tourists' trajectories that are collected from global positioning system (GPS) mobile sensors as a source of information. The aim of work is to predict preferred tourism places for tourists by tracking tourists' behavior to extract the tourism places that have been visited by such tourists. Density based clustering algorithm is mainly used to extract stay points and point of interest (POI). By projecting GPS location (for user and places) on the Google map, the type and name of places favored by the tourists are determined. K nearest neighbor (KNN) algorithm with haversine distance has been adopted to find the nearest places for tourists. The evaluation of the obtained results shows superior and satisfactory performance that can reach the objective behind this work.
Co-Authors Ayad Esho Korial Muntadher Naeem Yasir Rawan Ali Taaban Rula Amjed Hamid Sahar Salman Mahmood Saja Dhyaa Khuder Zainab Mohammed Resan