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All Journal Bulletin of Electrical Engineering and Informatics
Ali Makki Sagheer
Al-Qalam University College
Electrical & Electronics Engineering

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Design a mobile application for vehicles managing of a transportation issue Seddiq Q. Abd Al-Rahman; Sameeh Abdulghafour Jassim; Ali Makki Sagheer
Bulletin of Electrical Engineering and Informatics Vol 10, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i4.2918

Abstract

The movement of people between cities is leading to a recovery in the economy that transportation companies have begun to dominate. These companies start providing the best services to customers and promoting them through workers to earn money properly. From this basis, this paper presents a system designed to manage a company that transports people and goods between a group of cities. Database management was used across the web to enable data exchange between workers. The database is designed to be accessible to workers. It has also been suggested that the elliptic curve can be used to generate public and private keys for all parties while the company's management generates a prime number every day to ensure the confidentiality of the exchanged data. In this proposal, the rivest-shamir-adleman (RSA) algorithm is used to encrypt transferred data. It uses technology to exchange information if the recipient is not connected to the network. The proposed system performs a good service for the company’s management in securing the transferred data where smartphone applications are designed to work on it.
Enhancement of digital signature algorithm in bitcoin wallet Farah Maath Jasem; Ali Makki Sagheer; Abdullah M. Awad
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2339

Abstract

Bitcoin is a peer-to-peer electronic cash system largely used for online financial transactions. It gained popularity due to its anonymity, privacy, and comparatively low transaction cost. Its wallet heavily relies on elliptic curve digital signature algorithm (ECDSA). Weaknesses in such algorithms can significantly affect the safety and the security of bitcoin wallets. In this paper, a secure key management wallet was designed based on several changes in the wallet parts. In the cold wallet, we employed an image-based passphrase to achieve a strong entropy source of master seed. The hot wallet, the proposed Key_Gen algorithm is modifying to the key generation step of the ECDSA that it is to generate a fresh key pair at each transaction. The final part ensures recovering all keys on both hot and cold wallets without daily backups in case of losing the wallet. The findings prove that the proposed cold wallet is resisting against a dictionary attack and overcoming the memorizing problem. The proposed hot wallet model acquires good anonymity and privacy for bitcoin users by eliminating transaction likability without additional cost. The execution time for signing a transaction of the proposed model is~70 millisecond, which is then important in the bitcoin domain.
Breast cancer segmentation using K-means clustering and optimized region-growing technique Srwa Hasan Abdulla; Ali Makki Sagheer; Hadi Veisi
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3458

Abstract

Breast cancer is one of the major causes of death among women, and early detection may decrease the aggressiveness of the disease. The goal of this paper is to create an automated system that can classify digital mammogram images into benign and malignant. This paper presents a new detection technique of micro-calcifications in mammogram images. An automated technique for identifying breast microcalcifications (MCs) proposed utilizing two-level segmentation processes, first crop the breast area from the image using k-means clustering, then, an optimized region growing (ORG) approach has been used, where multi-seed points and thresholds are generated optimally depending on the color values of the image pixels. Then the texture features are extracted based on Haralick definitions of texture analysis. In addition, three features (cross-correlation coefficient, pearson correlation, and average area of segmented spots) are obtained from the segmented image. Support vector machine (SVM) classifier evaluate the efficiency of the system utilizing the images from the digital database for screening mammography (DDSM) dataset. The results were obtained by utilizing 355 images for training and 85 images for testing. The proposed system's sensitivity reached up to 97.05%, the specificity obtained is 98.52%, and accuracy is 98.2%.
Co-Authors Abdullah M. Awad Farah Maath Jasem Hadi Veisi Sameeh Abdulghafour Jassim Seddiq Q. Abd Al-Rahman Srwa Hasan Abdulla