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All Journal International Journal of Electrical and Computer Engineering Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science
Mohammad Khalaf Rahim Al-juaifari
University of Kufa
Computer Science & IT Electrical & Electronics Engineering

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Power enhancement based link quality for wireless mesh network Mohammad Khalaf Rahim Al-juaifari; Hussain Mumtaiz Alshamy; Noor Hassan Abed Khammas
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1388-1394

Abstract

Energy consumption of wireless network communication is still a big issue and a lot of research papers have proposed many solutions to increase node life time. The WMN architecture is made up of a fixed and mobile component, whereas the wireless mesh networks (WMNs) are multi-hop wireless networks with instant deployment, self-healing, self-organization and self-configuration features. The reduction in the distance by a factor of two can result in at least four times more powerful signals at the receiver. This paper presents suggestions that the links are more reliable without the increase in power of the transmitter in individual nodes. As a result, the present simulations networks are nine mobile nodes for considering coverage issues of the service area. The analytic results show that the link power node for direct communication between two nodes with long distance consuming more power than it is cleared. The improvement in the network performance for maintaining is available and this solution can be used to implement mobility in such case with low power region for the wireless mesh networks.
New method for route efficient energy calculations with mobile-sink for wireless sensor networks Mohammad Khalaf Rahim Al-juaifari; Jammel Mohammed Ali Mohammed Mona; Zainab Abd Abbas
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp365-374

Abstract

Despite proposing a number of algorithms and protocols, especially those related to routing, for the purpose of reducing energy consumption in wireless sensor networks, which is one of the most important issues facing this type of network. In this research paper, energy consumption and cost are calculated taking into account energy consumption and the amount of data transferred to a thousand nodes through specific paths towards the mobile sink. The proposed model simulated by sending various amounts of data with specific path to know the energy consumption of each track and the network life time with 250, 500, and 1000 bits. Cost calculated using various weight for each track of these paths and the coefficient of movement time and path loss factor and others related to the transmission and receiving circuits. And finally, the results compared with a previous method it showed the efficiency of our method used and calculating 1000 nodes with various amount of bits to show the experimental results. Deep learning used to remember each and every path of each position or nearby to avoid calculation cost later.
A Hybrid Weighted Soft-Voting Ensemble with Integrated Preprocessing for Breast Cancer Classification on the WDBC Dataset Mohammad Khalaf Rahim Al-Juaifari; Israa Mohammed Rahi Alabudy; Ammar Rasoul Mohammad Alsaachi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.14587

Abstract

Breast cancer continues to be one of the major causes of deaths due to cancer amongst women around the globe, requiring an effective method of diagnosis. In this research, a machine learning model pipeline that uses a hybrid weighted soft-voting based ensemble method on breast tumor classification for binary data is presented using the Wisconsin Diagnostic Breast Cancer Dataset (WDBC). Data preprocessing includes balancing the classes using Synthetic Minority Over-sampling Technique (SMOTE), removing the highly correlated attributes, and reducing dimensionality through Principal Component Analysis (PCA). Using stratified 10-fold cross-validation, an ensemble yielded 99.3% accuracy and 100% recall for the malignant class on PCA-transformed data, which was better than any individual classifier. Logistic Regression had good performance too, with 98.83% accuracy and 99.81% ROC AUC, which shows that our data can be nearly linearly separable. The feature importance analysis showed that “worst concave points” and “mean radius” were the most important features, and this makes sense from a medical perspective. Overall, this work presents an effective methodology for diagnosing breast cancer.
Co-Authors Ammar Rasoul Mohammad Alsaachi Hussain Mumtaiz Alshamy Israa Mohammed Rahi Alabudy Jammel Mohammed Ali Mohammed Mona Noor Hassan Abed Khammas Zainab Abd Abbas