Egyptian Informatics Journal最新文献

{"title":"Addressing label noise in leukemia image classification using small loss approach and pLOF with weighted-average ensemble","authors":"Md. Tarek Aziz ,&nbsp;S.M. Hasan Mahmud ,&nbsp;Kah Ong Michael Goh ,&nbsp;Dip Nandi","doi":"10.1016/j.eij.2024.100479","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100479","url":null,"abstract":"<div><p>Machine learning (ML) and deep learning (DL) models have been extensively explored for the early diagnosis of various cancer diseases, including Leukemia, with many of them achieving significant performance improvements comparable to those of human experts. However, challenges like limited image data, inaccurate annotations, and prediction reliability still hinder their broad implementation to establish a trustworthy computer-aided diagnosis (CAD) system. This paper introduces a novel weighted-average ensemble model for classifying Acute Lymphoblastic Leukemia, along with a reliable Computer-Aided Diagnosis (CAD) system that combines the strengths of both ML and DL approaches. Initially, a variety of filtering methods are extensively analyzed to determine the most suitable image representation, with subsequent data augmentation techniques to expand the training data. Second, a modified VGG-19 model was proposed with fine-tuning that was utilized as a feature extractor to extract meaningful features from the training samples. Third, A small-loss approach and probabilistic local outlier factor (pLOF) have been developed on the extracted features to address the label noise issue. Fourth, we proposed an weighted-average ensemble model based on the top five models as base learners, with weights calculated based on their model uncertainty to ensure reliable predictions. Fifth, we calculated Shapley values based on cooperative game theory and performed feature selection with different feature combinations to determine the optimal number of features using SHAP. Finally, we integrate these strategies to develop an interpretable CAD system. This system not only predicts the disease but also generates Grad-CAM images to visualize potential affected areas, enhancing both clarity and diagnostic insight. All of our code is provided in the following repository: <span>https://github.com/taareek/leukemia-classification</span><svg><path></path></svg></p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000422/pdfft?md5=c7485c79886026d6f9e82b0fb4f76cd0&pid=1-s2.0-S1110866524000422-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140879044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative survey on LEACH successors clustering algorithms for energy-efficient longevity WSNs","authors":"Mai Hussain Ahmed Hussain ,&nbsp;Bassem Mokhtar ,&nbsp;Mohamed R.M. Rizk","doi":"10.1016/j.eij.2024.100477","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100477","url":null,"abstract":"<div><p>Wireless Sensor Network (WSN) has appeared as a significant study field integrated with much research in the last two decades. In response to the widespread use of this technology in numerous applications, such as military operations, medical care, automated processes, urban domain and so on. WSN handles conditions where it is challenging or impossible for humans to perform measurement tasks physically and effectively. WSNs are fast-expanding networks incorporating many data communications streams. A WSN is made up of a wide amount of economic sensing device nodes with low energy requirements that have been irregularly placed in a particular region. These sensor node devices periodically sense data and record values before sending them to the sink node (or base station) through other sensor nodes. Some concerns should be addressed, such as preserving the sensor nodes' activity and load balancing as much as possible by wisely distributing the overall energy and the significant duplication problem of generated data where some sensor nodes in the monitor region may provide similar data. These are the WSN's most fundament l issues, necessitating the development of new routing and clustering algorithms. Research has proposed several routing algorithms to respond to these challenges and several optimization methods to decide the best route between the broadcaster and reception nodes. LEACH and its various versions with hierarchical clustering are widely utilized to reduce energy consumption, optimize performance, and lengthen the network's longevity. In this survey, We present an in-depth evaluation of LEACH descendant clustering protocols to respond to the previous challenges. We suggest several optimization methods to decide the best route between the broadcaster and reception nodes. Our qualitatively comparative study and analysis organize LEACH-based routing algorithms into five categories: algorithms for optimizing CH selection, algorithms for optimizing data transmission, algorithms for optimizing both CH selection &amp; data transmission, algorithms executed by fuzzy logic approach, and algorithms that use external energy sources to maximize network energy. Moreover, the survey compares these clustered routing techniques based on these criteria. An examination of algorithms is provided, including information on their goals, categories, strategies, assessments, effectiveness, advantages, and disadvantages. This survey gives academics technical guidance regarding the best way to improve algorithms for routing. The publication concludes with suggestions for additional areas of WSN to investigate.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000409/pdfft?md5=fe0c8a1723649d437f75892182ae01c8&pid=1-s2.0-S1110866524000409-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140843080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Email spam detection by deep learning models using novel feature selection technique and BERT","authors":"Ghazala Nasreen,&nbsp;Muhammad Murad Khan,&nbsp;Muhammad Younus,&nbsp;Bushra Zafar,&nbsp;Muhammad Kashif Hanif","doi":"10.1016/j.eij.2024.100473","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100473","url":null,"abstract":"<div><p>Due to the influx of advancements in technology and the increased simplicity of communication through emails, there has been a severe threat to the global economy and security due to upsurge in volume of unsolicited During the training of models, high-dimensional and redundant datasets may reduce the classification results of the model due to high memory costs and high computation. An important data processing technique is feature selection which helps in selecting relevant features and subsets of information from the dataset. Therefore, choosing efficient feature selection techniques is very important for the best performance of classification of a model. Moreover, most of the research has been performed using traditional machine learning techniques, which are not enough to deal with the huge amount of data and its variations. Also,<!--> <!-->spammers are becoming smarter with technological advancement. Therefore, there is a need for hybrid techniques consisting of deep learning and conventional algorithms to cope with these problems. We have proposed a novel scheme in this paper for email spam detection, which will result in an improved feature selection approach from the original dataset and increase the accuracy of the classifier as well. The literature has been studied to explore the efficient machine learning models that have been applied by different researchers for email spam detection and feature selection to acquire the best results. Our method, GWO-BERT, has given remarkable results with deep learning techniques such as CNN, biLSTM and LSTM. We have compared our models with RF and LSTM and used dataset: “Lingspam,” which is a publicly available dataset. With different experiments, our technique, GWO-BERT, obtained 99.14% accuracy, which is almost equal to 100 percent.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000367/pdfft?md5=6c116c46366f074ba8f51cb6e2b18e31&pid=1-s2.0-S1110866524000367-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140816512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molding robust S-box design based on linear fractional transformation and multilayer Perceptron: Applications to multimedia security","authors":"Adil Waheed ,&nbsp;Fazli Subhan ,&nbsp;Mazliham Mohd Su'ud ,&nbsp;Muhammad Mansoor Alam","doi":"10.1016/j.eij.2024.100480","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100480","url":null,"abstract":"<div><p>This study introduces a novel and refined approach for generating exceptionally efficient S-boxes. The proposed methodology employs a hybrid approach that combines linear fractional transformation (LFT) with a multilayer perceptron (MLP) architecture. This method makes use of a perceptron with three layers: input, hidden, and output. Each layer's neuron count is fine-tuned to conform to the S-box layout. In addition, a threshold nonlinear transformation is utilized to increase nonlinearity, and a novel algorithm for boosting nonlinearity is introduced. The utilization of both LFT and MLP approaches has led to the development of S-boxes that possess nearly ideal average nonlinearity values, surpassing those that have been presented in literature. Notably, one S-box achieved an exceptional nonlinearity score of 114.50. Furthermore, to demonstrate how well the S-box works, this study also employs it in an image encryption application.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000434/pdfft?md5=3acbbc8ed84be3e3a1296e29c371ccff&pid=1-s2.0-S1110866524000434-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140816513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CoTNeT: Contextual transformer network for encrypted traffic classification","authors":"Hong Huang ,&nbsp;Ye Lu ,&nbsp;Shaohua Zhou ,&nbsp;Xingxing Zhang ,&nbsp;Ze Li","doi":"10.1016/j.eij.2024.100475","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100475","url":null,"abstract":"<div><p>As network infrastructures continue to grow and traffic encryption technologies evolve at a rapid pace, the task of classifying encrypted traffic has become significantly more intricate. These emerging encryption methods render conventional approaches ineffectual in discerning traffic types, consequently posing novel challenges to network security and administration. Evidently, conventional traffic classification techniques are inadequate when it comes to encrypted traffic. Consequently, researchers have turned to machine learning and deep learning models to address this challenge, achieving remarkable results in this domain. Nonetheless, contemporary deep learning models exhibit a propensity to overly depend on self-attention mechanisms while processing 2D feature maps. This mechanism typically focuses only on individual query-key pairs, neglecting the rich contextual information among adjacent keys, thereby limiting their performance in encrypted traffic classification. To address this limitation, our study examines an innovative approach called CoTNet. The CoT module is integrated into the ResNet model to more comprehensively exploit the contextual associations among input keys. This innovation engenders a sturdier and more potent classification model, adept at comprehensively capturing the inherent patterns and correlated information within input features. The suggested method enhances the ResNet model by substituting the conventional 3x3 convolution operations with CoT modules, thereby more effectively harnessing the contextual associations among input keys. In particular, we integrate self-attention mechanisms at various model levels to more thoroughly capture the inherent patterns and correlated information within input features. Experimental results on two real-world datasets show that CoTNet outperforms multiple state-of-the-art methods in the encrypted traffic classification task.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000380/pdfft?md5=f028923ea9da9ba5fb91d9045eff67b1&pid=1-s2.0-S1110866524000380-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140813606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification feature selection and dimensionality reduction based on logical binary sine-cosine function arithmetic optimization algorithm","authors":"Xu-Dong Li,&nbsp;Jie-Sheng Wang,&nbsp;Yu Liu,&nbsp;Hao-Ming Song,&nbsp;Yu-Cai Wang,&nbsp;Jia-Ning Hou,&nbsp;Min Zhang,&nbsp;Wen-Kuo Hao","doi":"10.1016/j.eij.2024.100472","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100472","url":null,"abstract":"<div><p>Arithmetic optimization algorithm (AOA) is a <em>meta</em>-heuristic algorithm inspired by mathematical operations. AOA has been diffusely used for optimization issues on continuous domains, but few scholars have studied discrete optimization problems. In this paper, we proposed Binary AOA (BAOA) based on two strategies to handle the feature selection problem. The first strategy used S-shaped and V-shaped shift functions to map continuous variables to discrete variables. The second strategy was to combine four logical operations (AND, OR, XOR, XNOR) on the basis of the transfer function, and constructed a parameter model based on the sine and cosine function. An enhanced logic binary sine–cosine function arithmetic optimization algorithm (LBSCAOA) was proposed to realize the position update of variables. Its purpose was to improve the algorithm's global search capabilities and local exploitation capabilities. In the simulation experiments, 20 datasets were selected to testify the capability of the proposed algorithm. Since KNN had the advantages of easy understanding and low training time complexity, this classifier was selected for evaluation. The performance of the improved algorithm was comprehensively evaluated by comparing the average classification accuracy, the average number of selected features, the average fitness value and the average running time. Simulation results showed LBSCAOA with V-Shaped “V4” stood out among many improved algorithms. On the other hand, LBSCAOA with V-Shaped “V4” was used as a representative to compare with other typical feature selection algorithms to verify its competitivenes.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000355/pdfft?md5=5944a52cdf2755a375e3c04882b20ef5&pid=1-s2.0-S1110866524000355-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140632962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cerebral palsy-affected individuals' brain-computer interface for wheelchair movement in an indoor environment using mental tasks","authors":"Jayabrabu Ramakrishnan","doi":"10.1016/j.eij.2024.100470","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100470","url":null,"abstract":"<div><p>The technique of measuring brain signals or activities by placing electrodes on the scalp of human beings is called Electroencephalogram (EEG). Brain-computer interface (BCI) is a technique to capture brain signals and translate them into control signals to run external devices. With the combination of these two techniques, we can create BCI using brain signals. Methods: In this study, the author considered conducting two types of methods offline and online both in the indoor environment using the Fast Fourier Transform (FFT) technique with Feed Forward Neural Network trained with Bat optimization algorithm (FFNNBOA). The study was carried out on two different age groups between 30 to 45 years and 46 to 60 years with four different tasks. Based on the execution of the four different tasks concerning two different age groups, the accuracy obtained during classification is 94.35 % and 93.76 % for offline and online modes. Results: The results it is observed that the classification accuracy for the age group belonging 46 to 60 is comparably higher than that of the conventional classification model. The offline and online tests were conducted for both age groups persons and obtained the recognizing accuracy of 95 %, 93.25 %, and 93.75 %, 91.75 % for the two modes. This study confirms that the performances of the subjects belonging to age groups 30 to 45 are higher than the age groups belonging to 46 to 60 in terms of classification, offline, and online mode. Finally, this study also identified that subject S4 from the 30 to 45 age group showed 100 % accuracy in both offline and online signal acquisition.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000331/pdfft?md5=26f28f6caaf909563e346c6f084b7016&pid=1-s2.0-S1110866524000331-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140622017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An automated quantitative investment model of stock selection and market timing based on industry information","authors":"Minshi Liu ,&nbsp;Weipeng Sun ,&nbsp;Jiafeng Chen ,&nbsp;Menglin Ren","doi":"10.1016/j.eij.2024.100471","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100471","url":null,"abstract":"<div><p>As a growth asset, many listed companies have distinct characteristics of their respective industries. The listed companies in the same industry have a link effect, which can provide a wealth of information for stock operations. In this paper, a complete automated quantitative investment model is developed based on statistical models that use industry information to select stocks and time the market, thereby bringing out the synergistic effect of the system and ensuring maximum returns on investment. Risk control is taken into consideration in our model, a risk control factor is designed by measuring volatility of stock prices to determine the buying volume and the timing of stop loss, effectively safeguarding capital security. The latest industry classification results published by the China Securities Regulatory Commission are used as the basis for the industry classification. After data preprocessing, there are 70 sub-categories in 18 major categories of industry. We take the stock price of the 70 sub-categories from January 1, 2012 to January 1, 2022 as our research data. The back testing results show that positive returns are obtained in all industries except for six in our model. The average annualized rate of return is 11.10 %, which is higher than the stock indexes of the same period and far higher than the investment model of bank savings. Additionally, in accordance with a real trading system, the experiment simulates the inclusion of all transaction fees in the trading process, demonstrating the practical application value of our expert system.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000343/pdfft?md5=248586e104598256bbe7bc6c16207a98&pid=1-s2.0-S1110866524000343-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140622016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ODRNN: Optimized deep recurrent neural networks for automatic detection of Leukaemia","authors":"K. Dhana Shree ,&nbsp;S. Logeswari","doi":"10.1016/j.eij.2024.100453","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100453","url":null,"abstract":"<div><p>Leukaemia, a kind of cancer that may occur in individuals of all ages, including kids and adults, is a significant contributor to worldwide death rates. This illness is currently diagnosed by manual evaluation of blood samples obtained using microscopic imaging, which is frequently slower, lengthy, imprecise. Additionally, inspection under a microscope, leukemic cells look and develop similarly to normal cells, making identification more difficult. Convolutional Neural Networks (CNN) for Deep Learning has provided cutting-edge techniques for picture classification challenges throughout the previous several decades, there is still potential for development with regard to performance, effectiveness, and learning technique. As a consequence, the study provided a unique deep learning approach known as Optimized Deep Recurrent Neural Network (ODRNN) for identifying Leukaemia sickness by analysing microscopic images of blood samples. Deep recurrent neural networks (DRNN) are used in the recommended strategy for diagnosing Leukaemia, then the Red Deer Optimization algorithm (RDOA) applies to optimize the weight gained by DRNN. The mass of DRNN from RDOA will be tuned on the deer roaring rate behavior. The model that has been proposed is evaluated on two openly accessible Leukaemia blood sample datasets, AML, ALL_IDB1 and ALL_IDB2. It is possible to create an accurate computer-aided diagnosis for Leukaemia malignancy by using the proposed deep learning model, which shows encouraging results. The research work uses statistical metrics related to disease including specificity, recall, accuracy, precision and F1 score to assess the effectiveness of the proposed model for identification and classification. The proposed method achieves highly impressive results, with scores of 98.96%, 99.85%, 99.98%, 99.23%, and 99.98%, respectively.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000161/pdfft?md5=353f12748e755e8b0dcbc35593394ae1&pid=1-s2.0-S1110866524000161-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140542582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosted Nutcracker optimizer and Chaos Game Optimization with Cross Vision Transformer for medical image classification","authors":"Ahmed F. Mohamed ,&nbsp;Amal Saba ,&nbsp;Mohamed K. Hassan ,&nbsp;Hamdy.M. Youssef ,&nbsp;Abdelghani Dahou ,&nbsp;Ammar H. Elsheikh ,&nbsp;Alaa A. El-Bary ,&nbsp;Mohamed Abd Elaziz ,&nbsp;Rehab Ali Ibrahim","doi":"10.1016/j.eij.2024.100457","DOIUrl":"https://doi.org/10.1016/j.eij.2024.100457","url":null,"abstract":"<div><p>This paper presents an alternative breast cancer classification method based on enhancing the efficiency of the Nutcracker optimizer (NO) algorithm using Chaos Game Optimization (CGO). In addition, we use the Cross Vision Transformer to extract features from breast images. After that, the relevant features are allocated using the modified version of NO based on CGO. This modification aims to enhance the exploration ability of the NO algorithm to discover the region of a feasible solution (an optimal subset of features). The performance of the developed model is validated by using twelve functions from the CEC2022 benchmark and comparing the results with traditional CGO and NO algorithms. In addition, to assess the applicability of the developed technique, a set of three datasets, and the results were compared with other techniques. The results illustrate the high ability of the developed method to enhance the detection of breast cancer and find the optimal solution of CEC2022 functions according to different performance measures.</p></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110866524000203/pdfft?md5=7449499fe303096c88eb601d9737fe9a&pid=1-s2.0-S1110866524000203-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140535106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}