Asmaa Hekal Omar

Al-Azhar University

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Abeer S. Desuky

Al-Azhar University

Naglaa Mostafa

Agricultural Research Center

Sadiq Hussain

Dibrugarh University

Amany A. Naim

Al-Azhar University

Somia A. Shams

Al-Azhar University

Eman K. Elsayed

Al-Azhar University

Mohammad T. Abou-Kreisha

Al-Azhar University

Gaber A. Elsharawy

U.S. Air Force Institute of Technology

Samina Kausar

University of Health Sciences Lahore

Khadija Elsayed

Al-Azhar University

Cooperative Institutions

Al-Azhar University

Al Azhar University

U.S. Air Force Institute of Technology

United States Air Force

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Dibrugarh University

NFC Institute of Engineering and Technology

Central Cotton Research Institute

Balochistan University of Information Technology, Engineering and Management Sciences

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Single-Point Crossover and Jellyfish Optimization for Handling Imbalanced Data Classification Problem

IEEE Access (2022)

Abeer S. Desuky Yomna M. Elbarawy Samina Kausar Asmaa Hekal Omar Sadiq Hussain

The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others. The imbalanced datasets are balanced by applying resampling and various solutions are designed to tackle such datasets that mainly focus on class distribution issues. The imbalanced data is rebalanced using these methods. This paper introduces a technique for balancing data through two stages: first, oversampling methods are utilized in the process of rebalancing such imbalanced dataset using the single-point crossover to generate the new data of minority classes, second, it searches for an optimal subset of the imbalanced and balanced datasets by Jellyfish Search (JS) which is an optimization method. Experiments are performed on 18 real imbalanced datasets, and results are compared with famous oversampling methods and the recently published ACOR (Ant Colony Optimization Resampling) method in terms of different appraisal measurements. Higher performance is recorded by the proposed method and comparability with well-known and recent techniques.

Oversampling

Resampling

Comparability

10.1109/access.2022.3146424

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Citations (13)

Boosting with crossover for improving imbalanced medical datasets classification

Bulletin of Electrical Engineering and Informatics (2021)

Abeer S. Desuky Asmaa Hekal Omar Naglaa Mostafa

Due to the common use of electronic health databases in many healthcare services, healthcare data are available for researchers in the classification field to make diseases’ diagnosis more efficient. However, healthcare-medical data classification is most challenging because it is often imbalanced data. Most proposed algorithms are susceptible to classify the samples into the majority class, resulting in the insufficient prediction of the minority class. In this paper, a novel preprocessing method is proposed, using boosting and crossover to optimize the ratio of the two classes by progressively rebuilding the training dataset. This approach is shown to give better performance than other state-of-the-art ensemble methods, which is demonstrated by experiments on seven real-world medical datasets with different imbalance ratios and various distributions.

Boosting

Data pre-processing

Statistical classification

10.11591/eei.v10i5.3121

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Citations (6)

An enhanced Giza Pyramids construction for solving optimization problems

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (2024)

Asmaa Hekal Omar Naglaa Mostafa Abeer S. Desuky Lamiaa M. El Bakrawy

Many real-world optimization problems can be solved by various algorithms that are not fast in convergence or gain enough accuracy. Meta-heuristic algorithms are used to solve optimization problems and have achieved their effectiveness in solving several real-world optimization problems. Meta-heuristic algorithms try to find the best solution out of all available solutions in the possible shortest time. A good meta-heuristic algorithm is characterized by its accuracy, convergence speed, and ability to solve high dimensions’ problems. Giza Pyramids construction (GPC) has recently been introduced as a physics-inspired optimization method. This paper suggests an enhanced Giza Pyramids construction (EGPC) by adding a new parameter based on the step length of each individual and iteratively revises the individual’ position. The EGPC algorithm is suggested for improving the GPC exploitation and exploration. Experiments were performed on 23 benchmark functions and four IEEE CEC 2019 benchmarks to test the performance of the proposed EGPC algorithm. The experimental results show the high competitiveness of the EGPC algorithm compared to the basic GPC algorithm and another four well known optimizers in terms of improved exploration, exploitation, convergence’ rate, and the avoidance of local optima.

10.11591/ijece.v14i5.pp5672-5680

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Citations (0)

NEW CROSSOVER VIA HYBRID ANT COLONY SYSTEM WITH GENETIC ALGORITHM AND MAKING STUDY OF DIFFERENT CROSSOVER FOR TSP

Zenodo (CERN European Organization for Nuclear Research) (2021)

Asmaa Hekal Omar Amany A. Naim

Ant colony

10.5281/zenodo.6870621

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Citations (1)

Even-odd crossover: a new crossover operator for improving the accuracy of students’ performance prediction

Bulletin of Electrical Engineering and Informatics (2022)

Somia A. Shams Asmaa Hekal Omar Abeer S. Desuky Mohammad T. Abou-Kreisha Gaber A. Elsharawy

Prediction using machine learning has evolved due to its impact on providing valuable and intuitive feedback. It has covered a wide range of areas for predicting student’ performance. Instructors can track student’s dropout in a particular course at an early stage and try to improve students’ performance. The problem of students’ future performance prediction using advanced statistics and machine learning is a hard problem due to the imbalanced nature of the student data where the number of students who passed the exam is generally much higher than the number of students who failed the exam. This paper proposes a new type of crossover operator called Even-Odd crossover to generate new instances into the minority class to handle the imbalanced data problem. The experiments are implemented using three machine learning (ML) algorithms: random forest (RF), support vector machines (SVM), and K-Nearest-Neighbor (KNN) to ensure the efficiency of the proposed technique. The performance of the classifiers is evaluated using several performance measures. The efficient ability of the proposed method on solving the imbalance problem is proved by performing the experiments on 22 real-world datasets from different fields and four students’ datasets. The proposed Even-Odd crossover shows superior performance compared to state-of-the-art resampling techniques.

Resampling

Operator (biology)

Dropout (neural networks)

10.11591/eei.v11i4.3841

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Citations (2)

Smart Solution for STSP Semantic Traveling Salesman Problem via Hybrid Ant Colony System with Genetic Algorithm

International journal of intelligent engineering and systems (2020)

Eman K. Elsayed Asmaa Hekal Omar Khadija Elsayed

Travelling Salesman Problem (TSP) is one of the main and famous problems in finding the shortest path.But life is not ideal, so in this paper, we proposed design a Semantic Travelling Salesman Problems (STSP).The STSP enhances nodes by suitable Ontologies according to a real problem to be more flexible.Also, we proposed a hybrid model (ACSGA) combining the ant colony system (ACS) with the genetic algorithm (GA).That is to speed-up convergence and finds an effective path of a Semantic Travelling Salesman Problem STSP.The performance of the proposed hybrid model ACSGA was faster by 66% than other standard algorithms.And the distance decreases by 0.72%.We apply the proposed solution to the real problem with challenges as traffic overcrowding and weather changeable.So, the proposed STSP in this paper used traffics and weather Ontology as a case study.The proposed method modified the results according to time which decreases by 2.3%.

Ant colony

10.22266/ijies2020.1031.42

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Citations (7)