Papers by MOGEEB A . A . MOSLEH
Computing, Apr 1, 2024
Breast cancer is a primary cause of cancer-associated mortality among women globally, and early d... more Breast cancer is a primary cause of cancer-associated mortality among women globally, and early detection and personalized treatment are critical for improving patient outcomes. In this study, we propose an optimal framework for predicting breast cancer patient survivability using the GentleBoost algorithm and Bayesian optimization. The proposed framework combines the strengths of the GentleBoost algorithm, which is a powerful machine-learning algorithm for classification, and Bayesian optimization, which is a powerful optimization technique for hyperparameter tuning. We evaluated the proposed framework using the publicly available breast cancer dataset provided by The Surveillance, Epidemiology, and End Results (SEER) program and compared its performance with several popular single algorithms, including support vector machine (SVM), artificial neural network (ANN), and knearest neighbors (KNN). The experimental results demonstrate that the proposed framework outperforms these methods in terms of accuracy (mean= 95.16%, best = 95.35, worst = 95.1%, and SD = 0.008). The values of precision, recall, and f1-score of the best experiment were 92.3 %, 98.2 %, and 95.2 %, respectively, with hyperparameters of (number of learners = 246, learning rate = 0.0011, and maximum number of splits = 1240). The proposed framework has the potential to improve breast cancer patient survival predictions and personalized treatment plans, leading to the improved patient outcomes and reduced healthcare costs.
An Optimized Framework Based on Data Exploration and Dynamic Ensemble-Based Models for Breast Cancer Prediction
Computing, Jul 1, 2024
Masked Face Recognition Using Transfer Learning Approaches
Signals and communication technology, Nov 27, 2023
Mathematics, Apr 11, 2024
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Computational Intelligence and Neuroscience
The deaf-mutes population always feels helpless when they are not understood by others and vice v... more The deaf-mutes population always feels helpless when they are not understood by others and vice versa. This is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing “Within Blocks” and “Before Classifier” methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time are recorded to evaluate the models’ efficiency. The experimental results showed that CBAM-ResNet models achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. The CBAM-ResNet “Before Classifier” models are more efficient than “Within Blocks” CBAM-ResNet models. Thus, the best trained model of CB...
An Automatic Nuclei Cells Counting Approach Using Effective Image Processing Methods
2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP)
Manual counting of nuclei cells from histological images is considered tedious process, time-cons... more Manual counting of nuclei cells from histological images is considered tedious process, time-consuming and subjected to human errors. Therefore, automated the process of nuclei cells counting is become important and necessary for effective analyzing of histological images. Current systems and approaches of nuclei cells counting are based on color or grayscale images leading to inaccurate results and have several limitations. In this paper, we propose a novel accurate approach for automatic nuclei cells counting using effective image processing methods. The new techniques are designed based on image thresholding method, morphological image processing operations, and connected component algorithm. The new approach was evaluated experimentally on 37 images of a public data set of 100 histological images. The experimental results demonstrated that the approach achieved a high accuracy up to 89.5% compared with previous works. We concluded the effectiveness of the proposed approach for automatic counting of nuclei cells from histological images.
Scientific Programming, 2022
Developing an electronic voting system that meets the practical needs of administrators has been ... more Developing an electronic voting system that meets the practical needs of administrators has been a difficult task for a long time. Now, blockchain technologies solve this problem by providing a distributed ledger with immutable, encrypted, and secure transactions. Distributed ledger technologies are an interesting technological leap in the field of data innovation, transparency, and trustability. In public blockchain, distributed ledger technology is widely used. The blockchain technology can be used in an almost infinite number of ways to benefit from sharing economies. The purpose of this study is to assess how blockchain may be utilized to build electronic voting systems that can be used as a service. The purpose of electronic voting systems is explained in this article, as are the technological and legal limitations of employing blockchain as a service. Then, using blockchain as a foundation, we propose a new electronic voting system that fixes the flaws we observed. In general,...
Impact of applying governance on achieving administrative invention: a case study in Yemeni universities
International journal of business performance management, 2025
Technology is used widely to serve education. However progress in transferring note taking into d... more Technology is used widely to serve education. However progress in transferring note taking into digital form age is slow. The necessity for digital note taking into digital era become importance because information resources were increased extensively where traditional note become insufficient to process these amounts of information. Digital notes are editable, searchable, portable, readable, can be indexed, linked, etc. Massive tools developed to bridge the gap between paper-based and technology-based notes. Unfortunately, these note taking tool still inadequate to replace the traditional approaches of note taking. This study investigates the limitations of typical note taking systems and discusses the implications on the design of future note taking applications. Developing successful note taking applications is a challenge because of the complexity, technology learning dilemma, integrity, and inefficiency issues. These challenges are stated in thesis statement to shape the soluti...
An image processing approach for cephalometric measurements
... 77 5.3.2 Comparison of Verification for system Accuracy and Performance 79 ix Page 10. ... cr... more ... 77 5.3.2 Comparison of Verification for system Accuracy and Performance 79 ix Page 10. ... craniofacial growth and development. The measurement of the dimensions of the head is ... of sets of feature points called Cephalometric Landmark in hard and soft tissue. ...
Maintenance and monitoring of aquatic systems such lakes, reservoirs and river involves properly ... more Maintenance and monitoring of aquatic systems such lakes, reservoirs and river involves properly documented, valid, and comprehensible data archives. However, aquatic data are collected and kept separately, creating difficulties in data integration. For effective aquatic data management it is important to have databases metadata that have been validated. This study aims to discuss framework for aquatic data warehouse system using web services for sharing database components using standard format and common data exchange method to foster easier data integration and exchange. The key features of the data warehouse comprises of graphical user interface (GUI) developed using ASP.Net. XML to represent metadata for data exchange and transfer, Darwin Core for formatting ecological and biological data management for data exchange protocol in this study.
Reviewing and Classification of Software Model Checking Tools
Lecture Notes in Electrical Engineering, 2015
In this study, we provide historical accounts with an overview of essential research on model-che... more In this study, we provide historical accounts with an overview of essential research on model-checking development tools. This study has two main objectives; first, it is intended to investigate whether model checking still an active area; second, to classify existing model-checking tools by providing an illustration of each dimension scope, an analysis of similarities and differences among them, and a prediction of the future direction of typical model-checking tools. We found that existing model-checking tools show significant effects in automated system testing and verification. We also found that system testing and verification are still active areas of research. Current model-checking tools work efficiently on limited environment, and a lot of work need to perform for verifying the functional and nonfunctional attributes of complex systems. Despite the limitations of existing model-checking tools, universal model-checking tools can probably be developed if a good framework is established to fulfill the requirements of fully automated tools.
Complexity, 2021
Planar graphs play an effective role in many practical applications where the crossing of edges b... more Planar graphs play an effective role in many practical applications where the crossing of edges becomes problematic. This paper aims to investigate the complex q-rung orthopair fuzzy (CQROF) planar graphs (CQROFPGs). In a CQROFPG, the nodes and edges are based on complex QROF information that represents the uncertain knowledge in the range of unit circles in terms of complex numbers. The motivation in discussing such a topic is the wide flexibility of QROF information in the expression of uncertain knowledge compared to intuitionistic and Pythagorean fuzzy settings. We discussed the complex QROF graphs (CQROFGs), complex QROF multigraphs (CQROFMGs), and related terms followed by examples. Furthermore, the notion of strength and planarity index (PI) of the CQROFPGs is defined and exemplified followed by a study of strong and weak edges. We further defined the notion of complex QROF face (CQROFF) and complex QROF dual graph (CQROFDG) and exemplified these concepts. A study of isomorph...
Complexity, 2022
A personalized recommender system is broadly accepted as a helpful tool to handle the information... more A personalized recommender system is broadly accepted as a helpful tool to handle the information overload issue while recommending a related piece of information. This work proposes a hybrid personalized recommender system based on affinity propagation (AP), namely, APHPRS. Affinity propagation is a semisupervised machine learning algorithm used to cluster items based on similarities among them. In our approach, we first calculate the cluster quality and density and then combine their outputs to generate a new ranking score among clusters for the personalized recommendation. In the first phase, user preferences are collected and normalized as items rating matrix. This generated matrix is then clustered offline using affinity propagation and kept in a database for future recommendations. In the second phase, online recommendations are generated by applying the offline model. Negative Euclidian similarity and the quality of clusters are used together to select the best clusters for r...
Mathematical Problems in Engineering, 2021
This paper aims to propose a new methodology for spherical cubic fuzzy (SCF) multicriteria decisi... more This paper aims to propose a new methodology for spherical cubic fuzzy (SCF) multicriteria decision-making (MCDM) utilizing the TOPSIS method that uses incomplete weight information. At first, the maximum deviation model is suggested to determine the criteria of weight values. An MCDM methodology is introduced using SCF information, based on the proposed method. Also, to validate the effectiveness of the proposed information, a numerical example is given. Finally, a comprehensive and structured analysis of existing work in comparison with previous work is given.
Journal of Healthcare Engineering, 2021
Biosensor is a means to transmit some physical phenomena, like body temperature, pulse, respirato... more Biosensor is a means to transmit some physical phenomena, like body temperature, pulse, respiratory rate, electroencephalogram (EEG), electrocardiogram (ECG), and blood pressure. Such transmission is performed via Wireless Medical Sensor Network (WMSN) while diagnosing patients remotely through Internet-of-Medical-Things (IoMT). The sensitive data transmitted through WMSN from IoMT over an insecure channel is vulnerable to several threats and needs proper attention to be secured from adversaries. In contrast to addressing the security of all associated entities involving patient monitoring in the healthcare system or ensuring the integrity, authorization, and nonrepudiation of information over the communication line, no one can guarantee its security without a robust authentication protocol. Therefore, we have proposed a lightweight and robust authentication scheme for the network-enabled healthcare devices (IoMT) that mitigate all the identified weaknesses posed in the recent liter...
Detection of Prostate Cancer Using MRI Images Classification with Deep Learning Techniques
2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA)
Mathematical Problems in Engineering, 2021
In the domains of computational and applied mathematics, soft computing, fuzzy logic, and machine... more In the domains of computational and applied mathematics, soft computing, fuzzy logic, and machine learning (ML) are well-known research areas. ML is one of the computational intelligence aspects that may address diverse difficulties in a wide range of applications and systems when it comes to exploitation of historical data. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. The proposed research approach uses Linear Regression, Support Vector Regression, Ridge Regressor, Stochastic Gradient Boosting, XGBoost, Decision Tree, Random Forest Regressor, Multiple Linear Regression, and k-Nearest Neighbors A medical insurance cost dataset is acquired from the KAGGLE repository for this purpose, and machine learning methods are used to show how differ...
Journal of Mathematics, 2021
The strenuous mining and arduous discovery of the concealed community structure in complex networ... more The strenuous mining and arduous discovery of the concealed community structure in complex networks has received tremendous attention by the research community and is a trending domain in the multifaceted network as it not only reveals details about the hierarchical structure of multifaceted network but also assists in better understanding of the core functions of the network and subsequently information recommendation. The bipartite networks belong to the multifaceted network whose nodes can be divided into a dissimilar node-set so that no edges assist between the vertices. Even though the discovery of communities in one-mode network is briefly studied, community detection in bipartite networks is not studied. In this paper, we propose a novel Rider-Harris Hawks Optimization (RHHO) algorithm for community detection in a bipartite network through node similarity. The proposed RHHO is developed by the integration of the Rider Optimization (RO) algorithm with the Harris Hawks Optimiza...
Parasitized Cell Recognition Using AlexNet Pre-trained Model
2021 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA), 2021
Malaria is globally known as one of the most prevalent diseases that kill thousands of people eve... more Malaria is globally known as one of the most prevalent diseases that kill thousands of people every year. Plasmodium parasites are the product of malaria disease that infects the red blood cells of humans. These parasites are transmitted by a female mosquito class that is known as anopheles. The diagnostic process of malaria involves isolation and manual counts in microscopic bloodstreams of parasitized cells by medical practitioners. In large-scale screening, Malaria diagnostic accuracy is largely affected because of resource unavailability. In this paper, we proposed an intelligent diagnosis system using advanced techniques based on a deep learning algorithm precisely AlexNet pre-trained model. As the bright side of machine learning techniques, CNN has greatly led to numerous image recognition activities. This method shows encouraging results. In terms of accuracy, the proposed model achieved 97.33% in the validation phase. Therefore, in some places where there are no medical services, this approach can be widely used for diagnosing parasitized cells.
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Papers by MOGEEB A . A . MOSLEH