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Ahmed Wasif Reza
East West University

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An integrated machine learning model for indoor network optimization to maximize coverage Ahmed Wasif Reza; Abdullah Al Rifat; Tanvir Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp394-402

Abstract

Indoor network optimization is not a simple task due to the obstacles, interference, and attenuation of the signal in an environment. Intense noises can affect the intelligibility of the signal and reduce the coverage strength significantly which results in a poor user experience. Most of the existing works are associated with finding the location of the devices via different mathematical and generic algorithmic approaches, but very few are focused on implying machine learning algorithms. The purpose of this research is to introduce an integrated machine learning model to find maximum indoor coverage with a minimum number of transmitters. The users in the indoor environment also have been allocated based on the most reliable signal strength and the system is also capable of allocating new users. K-means clustering, K-nearest neighbor (KNN), support vector machine (SVM), and Gaussian Naïve Bayes (GNB) have been used to provide an optimized solution. It is found that KNN, SVM, and GNB obtained maximum accuracy of 100% in some cases. However, among all the algorithms, KNN performed the best and provided an average accuracy of 93.33%. K-fold cross-validation (Kf-CV) technique has been added to validate the experimental simulations and re-evaluate the outcomes of the machine learning models.
An internet of things-based automatic brain tumor detection system Md. Lizur Rahman; Ahmed Wasif Reza; Shaiful Islam Shabuj
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.pp214-222

Abstract

Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
Pre-trained deep learning models in automatic COVID-19 diagnosis Ahmed Wasif Reza; Md Mahamudul Hasan; Nazla Nowrin; Mir Moynuddin Ahmed Shibly
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1540-1547

Abstract

Coronavirus Disease (COVID-19) is a devastating pandemic in the history of mankind. It is a highly contagious flu that can spread from human to human without revealing any symptoms. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. This study presented an alternative way to identify COVID-19 patients by doing an automatic examination of chest X-rays of the patients. To develop such an efficient system, six pre-trained deep learning models were used. Those models were: VGG16, InceptionV3, Xception, DenseNet201, InceptionResNetV2, and EfficientNetB4. Those models were developed on two open-source datasets that have chest X-rays of patients diagnosed with COVID-19. Among the models, EfficientNetB4 achieved better performances on both datasets with 96% and 97% of accuracies. The empirical results were also exemplary. This type of automated system can help us fight this dangerous virus outbreak.
Modeling and classification of Departmental Business Processes of a Bangladeshi University Tahmina Akter Tisha; Mir Moynuddin Ahmed Shibly; Rashedul Amin Tuhin; Ahmed Wasif Reza
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1113-1121

Abstract

Business process modeling (BPM) is a field of computer science that can be used by every organization to maintain its workflow pattern. Adopting this can significantly improve the workflow and can identify problems with the workflow in terms of resource optimization. In this article, the idea of representing the business processes of a Bangladeshi Educational Institute using the business process model and Notation 2.0 has been presented. In this case study, a business process model for the information system at the departmental level of East West University (EWU) has been designed after analyzing 15 key business processes by interviewing stakeholders. After classifying the created as-is business process models based on two criteria- order of actor participation and participation of external entities/departments, two areas of optimization in the workflow pattern have been proposed, load optimization and online automation. This documented model of the business processes has multi-purpose uses. It can be used for resource management, as a guide for stakeholders to better understand a business process, and as a guideline for new employees. This study has shown that by adopting business process modeling, an educational institution could march toward a better and enhanced workflow pattern by identifying problems in it.
Co-Authors Abdullah Al Rifat Md Mahamudul Hasan Md. Lizur Rahman Mir Moynuddin Ahmed Shibly Mir Moynuddin Ahmed Shibly Nazla Nowrin Rashedul Amin Tuhin Shaiful Islam Shabuj Tahmina Akter Tisha Tanvir Ahmed