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Abualigah L, Diabat A, Svetinovic D, Elaziz MA. Boosted Harris Hawks gravitational force algorithm for global optimization and industrial engineering problems. JOURNAL OF INTELLIGENT MANUFACTURING 2023; 34:2693-2728. [DOI: 10.1007/s10845-022-01921-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 01/31/2022] [Indexed: 09/02/2023]
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Detecting vulnerable software functions via text and dependency features. Soft comput 2023. [DOI: 10.1007/s00500-022-07775-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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3
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Şahin CB. Semantic-based vulnerability detection by functional connectivity of gated graph sequence neural networks. Soft comput 2023. [DOI: 10.1007/s00500-022-07777-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Hussain A, Riaz S, Amjad MS, Haq EU. Genetic algorithm with a new round-robin based tournament selection: Statistical properties analysis. PLoS One 2022; 17:e0274456. [PMID: 36083869 PMCID: PMC9462581 DOI: 10.1371/journal.pone.0274456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/28/2022] [Indexed: 11/19/2022] Open
Abstract
A round-robin tournament is a contest where each and every player plays with all the other players. In this study, we propose a round-robin based tournament selection operator for the genetic algorithms (GAs). At first, we divide the whole population into two equal and disjoint groups, then each individual of a group competes with all the individuals of other group. Statistical experimental results reveal that the devised selection operator has a relatively better selection pressure along with a minimal loss of population diversity. For the consisting of assigned probability distribution with sampling algorithms, we employ the Pearson’s chi-square and the empirical distribution function as goodness of fit tests for the analysis of statistical properties analysis. At the cost of a nominal increase of the complexity as compared to conventional selection approaches, it has improved the sampling accuracy. Finally, for the global performance, we considered the traveling salesman problem to measure the efficiency of the newly developed selection scheme with respect to other competing selection operators and observed an improved performance.
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Affiliation(s)
- Abid Hussain
- Department of Statistics, Govt. College Khayaban-e-Sir Syed, Rawalpindi, Pakistan
- * E-mail:
| | - Salma Riaz
- Department of Applied Mathematics & Statistics, Institute of Space Technology, Islamabad, Pakistan
| | - Muhammad Sohail Amjad
- School of Applied Sciences & Humanities, National University of Technology, Islamabad, Pakistan
| | - Ehtasham ul Haq
- Department of Mathematics and Statistics, International Islamic University, Islamabad, Pakistan
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Ewees AA, Al-qaness MA, Abualigah L, Elaziz MA. HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting. ENERGY CONVERSION AND MANAGEMENT 2022; 268:116022. [DOI: 10.1016/j.enconman.2022.116022] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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6
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Khodadadi N, Soleimanian Gharehchopogh F, Mirjalili S. MOAVOA: a new multi-objective artificial vultures optimization algorithm. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07557-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Baghdadi NA, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. Classification of breast cancer using a manta-ray foraging optimized transfer learning framework. PeerJ Comput Sci 2022; 8:e1054. [PMID: 36092017 PMCID: PMC9454783 DOI: 10.7717/peerj-cs.1054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.
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Affiliation(s)
- Nadiah A. Baghdadi
- College of Nursing, Nursing Management and Education Department, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amer Malki
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
| | - Hossam Magdy Balaha
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Yousry AbdulAzeem
- Computer Engineering Department, Misr Higher Institute for Engineering and Technology, Mansoura, Egypt
| | - Mahmoud Badawy
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mostafa Elhosseini
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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An Environment-Specific Prioritization Model for Information-Security Vulnerabilities Based on Risk Factor Analysis. ELECTRONICS 2022. [DOI: 10.3390/electronics11091334] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Vulnerabilities represent a constant and growing risk for organizations. Their successful exploitation compromises the integrity and availability of systems. The use of specialized tools facilitates the vulnerability monitoring and scanning process. However, the large amount of information transmitted over the network makes it difficult to prioritize the identified vulnerabilities based on their severity and impact. This research aims to design and implement a prioritization model for detecting vulnerabilities based on their network environment variables and characteristics. A mathematical prioritization model was developed, which allows for calculating the risk factor using the phases of collection, analysis, and extraction of knowledge from the open information sources of the OSINT framework. The input data were obtained through the Shodan REST API. Then, the mathematical model was applied to the relevant information on vulnerabilities and their environment to quantify and calculate the risk factor. Additionally, a software prototype was designed and implemented that automates the prioritization process through a Client–Server architecture incorporating data extraction, correlation, and calculation modules. The results show that prioritization of vulnerabilities was achieved with the information available to the attacker, which allows evaluating the overexposure of information from organizations. Finally, we concluded that Shodan has relevant variables that assess and quantify the overexposure of an organization’s data. In addition, we determined that the Common Vulnerability Scoring System (CVSS) is not sufficient to prioritize software vulnerabilities since the environments where they reside have different characteristics.
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An intelligent cybersecurity system for detecting fake news in social media websites. Soft comput 2022; 26:5577-5591. [PMID: 35469124 PMCID: PMC9021563 DOI: 10.1007/s00500-022-07080-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2022] [Indexed: 02/05/2023]
Abstract
People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on their websites; even websites can publish any news they want. However, thus before quotation any news from a website, there should be a close look at news resource ranking by using a trusted websites classifier, such as the website world rank, which reflects the repute of these websites. This paper uses the world rank of news websites as the main factor of news accuracy by using two widespread and trusted websites ranking. Moreover, a secondary factor is proposed to compute the news accuracy similarity by comparing the current news with fakes news and getting the possible news accuracy. Experiments results are conducted on several benchmark datasets. The results showed that the proposed method got promising results compared to other comparative methods in defining the news accuracy.
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Correlation-based modified long short-term memory network approach for software defect prediction. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09423-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06747-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model. MATHEMATICS 2021. [DOI: 10.3390/math9182321] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.
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A Novel Evolutionary Arithmetic Optimization Algorithm for Multilevel Thresholding Segmentation of COVID-19 CT Images. Processes (Basel) 2021. [DOI: 10.3390/pr9071155] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.
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