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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Muhanad Tahrir Younis
Mustansiriyah University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Solving flexible job-shop scheduling problem using harmony search-based meerkat clan algorithm Muna Mohammed Jawad; Muhanad Tahrir Younis; Ahmed T. Sadiq
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp423-431

Abstract

The classical job shop scheduling (JSS) problem can be extended by allowing processing of an operation by any machine from a given set. This type of scheduling is known as flexible job shop scheduling (FJSS) problem. It incorporates all the difficulties and complexities of its predecessor classical problem. However, it is more complex as it is required to determine the assignment of operations to the machine. Swarm intelligence techniques proved their effectiveness in solving a wide range of complex NP-Hard real world problems. One of these techniques is the meerkat clan algorithm (MCA) that has been successfully applied to various optimization problems. This paper presents a modified MCA for solving the FJSS problem. The modification is based on using harmony search (HS). The introduction of HS provides more exploitation and intensification. HS generates various solutions, which are provided to the MCA. As a result, the exploitation of the local optimum is increased, which in turn increases the convergence rate. The experimental results show that the improved method achieves higher quality schedules. Additionally, the convergence rate is speeded up compared with the standalone algorithm. This gives the proposed method the superiority over the original algorithm.
An accurate Alzheimer's disease detection using a developed convolutional neural network model Muhanad Tahrir Younis; Younus Tahreer Younus; Jamal Naser Hasoon; Ali Hussain Fadhil; Salama A. Mostafa
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3659

Abstract

Alzheimer's disease indicates one of the highest difficult to heal diseases, and it is acutely affecting the elderly normal lives and their households. Early, effective, and accurate detection represents an important blueprint for minimizing Alzheimer's progression risk. The modalities of brain imaging can assist in identifying the abnormalities associated with Alzheimer's disease. This research presents a developed deep learning scheme, which is designed and implemented to classify the brain images into multiclass, namely very mild, moderate, mild, and non-demented. The proposed convolutional neural network (CNN) based detection model attained a high performance with an accuracy of 99.92%, considerably enhancing the results achieved via the pre-trained 16 layers in the visual geometric group (VGG16) model and the other related learning models. Consequently, this developed model can assist medical personnel by providing a facilitating tool to identify Alzheimer's disease stage and establishing a suitable medical treatment platform.
Co-Authors Ahmed T. Sadiq Ali Hussain Fadhil Jamal Naser Hasoon Muna Mohammed Jawad Salama A. Mostafa Younus Tahreer Younus