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Graphical representation of crossover and mutation in GA. (A) shows two parent chromosomes. The second row shows the children's chromosomes after crossover (B) and after mutation (C). https://doi.org/10.1371/journal.pone.0219683.g001
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The diagnosis and prognosis of patients with severe chronic disorders of consciousness are still challenging issues and a high rate of misdiagnosis is evident. Hence, new tools are needed for an accurate diagnosis, which will also have an impact on the prognosis. In recent years, functional Magnetic Resonance Imaging (fMRI) has been gaining more an...
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... this contributes to a large step in the search for a better solution, a mutation only modifies individual chromosomes and contributes to a local search for a better solution. See Fig 1 for a graphical representation of crossover and mutation. Now we turn to the specific parameters we use in our approach. ...
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... L. , (C. L. , (Wutzl et al., 2019), (Yeh et al., 2021), , and (Bucklin et al., 2023) are based on their datasets. ...
Sleep is vital for people's physical and mental health, and sound sleep can help them focus on daily activities. Therefore, a sleep study that includes sleep patterns and disorders is crucial to enhancing our knowledge about individuals' health status. The findings on sleep stages and sleep disorders relied on polysomnography and self-report measures, and then the study went through clinical assessments by expert physicians. However, the evaluation process of sleep stage classification and sleep disorder has become more convenient with artificial intelligence applications and numerous investigations focusing on various datasets with advanced algorithms and techniques that offer improved computational ease and accuracy. This study aims to provide a comprehensive, systematic review and meta-analysis of the recent literature to analyze the different approaches and their outcomes in sleep studies, which includes works on sleep stages classification and sleep disorder detection using AI. In this review, 183 articles were initially selected from different journals, among which 80 records were enlisted for explicit review, ranging from 2016 to 2023. Brain waves were the most commonly employed body parameters for sleep staging and disorder studies. The convolutional neural network, the most widely used of the 34 distinct artificial intelligence models, comprised 27%. The other models included the long short-term memory, support vector machine, random forest, and recurrent neural network, which consisted of 11%, 6%, 6%, and 5% sequentially. For performance metrics, accuracy was widely used for a maximum of 83.75% of the cases, the F1 score of 45%, Kappa of 36.25%, Sensitivity of 31.25%, and Specificity of 30% of cases, along with the other metrics. This article would help physicians and researchers get the gist of AI's contribution to sleep studies and the feasibility of their intended work.
... In most of the cases, it is not appropriate to use exhaustive search for the selection of the optimal feature subset. At present, many technologies have been applied to feature selection, such as the global, greedy, heuristic, and random search methods [15]. Although the global search can accurately find the optimal solution, it is limited by the lower dimensional data, while for high-dimensional data, the time complexity is unacceptable. ...
Mining user data and constructing web user profiles of older adults from the perspective of elderly services is conducive to understanding their behavioral habits, needs, and usage preferences on the web, which provides more targeted elderly care services. In this paper, IGA-SOMK + + , which is a novel clustering method for constructing web user profiles of older adults, is proposed based on the China Family Panel Studies (CFPS) survey data, which include 6596 older adults aged greater than 60 years. The selected data aspects include basic information, work situation, health situation, living habits, and web use services. To describe the web user profiles of older adults, a hybrid method based on improved genetic algorithm (IGA) feature selection, self-organizing feature maps (SOM), and K-means + + is proposed. Data on older adults’ web use behaviors are first processed, and IGA is used for feature selection based on the adaptive crossover and mutation probabilities. SOM is then used to determine the initial center vectors of K-means + + for further clustering, which is referred to as SOMK + + (SOM-K-means + +). The results of IGA-SOMK + + are compared with those of the state-of-the-art methods, including the K-means, mini batch K-means, Agnes, K-modes, FCM, K-means + + , SOMK + + , and IHPSO-KM. In addition, the significance and robustness of IGA-SOMK + + are analyzed. The experimental results show that the IGA feature selection reduces the influence of the redundant feature factors and improves the performance of the clustering algorithm. SOMK + + overcomes the sensitivity of K-means to initial cluster centers. Moreover, IGA-SOMK + + has the best clustering effect among the compared algorithms in terms of silhouette coefficient (SC), calinski-harabaz (CH) index, and davies-bouldin (DB) metrics. For example, it increases the SC from 0.280 to 0.629. Finally, by analyzing the results, the user group of older adults is segmented to perform the deep mining of CFPS data, which verifies the feasibility of the user profile model. This paper summarizes the basic situation of the current web access of older adults in China in terms of web use services, as well as the importance of the web in their lives and in the information channels. It also provides suggestions for the current problems of older adults in accessing the web.
... With the proliferation of data and the consequent development of ML, the use of GA for feature selection increased in the last decade. Numerous recent contributions can be found, for instance, aimed at improving the method in presence of vast amounts of data [30,31], or applying the method in several different real-world scenarios, including medicine [32], economy [33], image processing [34], remote sensing [35] and sociology [36], just to mention a few. However, in this work, we match the GA with another evolutionary algorithm, Genetic Programming (GP), obtaining an integrated, and purely evolutionary, method that is able to perform feature selection and at the same time induce good models using the selected features. ...
We present SLUG, a recent method that uses genetic algorithms as a wrapper for genetic programming and performs feature selection while inducing models. SLUG was shown to be successful on different types of classification tasks, achieving state-of-the-art results on the synthetic datasets produced by GAMETES, a tool for embedding epistatic gene–gene interactions into noisy datasets. SLUG has also been studied and modified to demonstrate that its two elements, wrapper and learner, are the right combination that grants it success. We report these results and test SLUG on an additional six GAMETES datasets of increased difficulty, for a total of four regular and 16 epistatic datasets. Despite its slowness, SLUG achieves the best results and solves all but the most difficult classification tasks. We perform further explorations of its inner dynamics and discover how to improve the feature selection by enriching the communication between wrapper and learner, thus taking the first step toward a new and more powerful SLUG.
... Bag-of-words features are representations of source code text that describe the occurrence of source code words within a source code file [23]. Traditional filter-based Features Selection: The filter methods [24] are generally used for feature selection. In the filter approach, the features are pre-selected during the preprocessing procedure with regard to a predefined relevance measure which is independent of the performance of the learning algorithm. ...
... Obviously, the GA has the highest accuracy due to several reasons. First, GA is efficiently used to determine the minimal subset of features for which the different classes are best distinguished during classification [24]. Second, GA is repetitively performed over multiple generations, and reproduction is performed to get individuals that are the 10 VOLUME 4, 2016 ...
Source code authorship attribution is the task of identifying who develops the code based on learning based on the programmer style. It is one of the critical activities which used extensively in different aspects such as computer security, computer law, and plagiarism. This paper attempts to investigate source code authorship attribution by capturing natural language aspects of the code rather than only using minimal set of syntactic and stylistic code features as explored in the previous literature. It proposes an evolutionary feature selection model to improve the accuracy of authorship attribution by implementing two language models (uni-gram and bi-gram). The proposed approach uses K-Nearest Neighbor as a classifier and Genetic Algorithm as a feature selection technique. Two experiments have been demonstrated on a public Authorship Attribution dataset on GitHub, the experiments include various evolutionary feature selection models. Notably, the obtained results in both experiments were compared with the related studies, and show a significant improvement in terms of accuracy.
... The process of choosing the optimal features from GA is passed through the following procedures [53]: randomly selecting the GA's population and generating the individuals from them. Then, create new generations from the crossover and mutation techniques as shown in Fig. 8 [54]. Finally, evaluate all individuals to choose the best one for the subsequent iteration from the fitness value computed from Eq. (15) ...
Today, investigating gait recognition as a biometric technology has become necessary, especially after the COVID-19 pandemic broke out in the world. This paper proposes a deep structure procedure for precise human identification from the walking manner quickly, as each human has a unique way of walking. The proposed generative adversarial network (GAN) generates gait images from pedestrians of the CASIA and OU-ISIR gait datasets for normalizing the number of frames in each class in both datasets. Then, we extract the gait features from the normalized datasets by the pretrained convolutional neural network (CNN) models; AlexNet, Inception, VGG16, VGG19, ResNet, and Xception, and balance them using the synthetic minority oversampling technique (SMOTE) to enhance the recognition results. Several feature selectors are selecting the best features, including particle swarm optimization (PSO), grey wolf optimization (GWO), Chi-square, and genetic models. Finally, the proposed deep neural network recognizes the gait images precisely. Several performance assessment measures were generated to assess the model's quality, including accuracy, precision, sensitivity, specificity, false negative rate (FNR), intersection of union (IoU), and time. Experimental results showed that the inception model with genetic feature selector performed better in all used datasets than the current studies and was more robust against any change, achieving 99.3%, 99.1%, and 99.09% accuracy in identifying CASIA-B, CASIA-A, and OU-ISIR datasets, respectively in low time.
... Silveira et al. [20] obtained an RF classifier and the model gave 90% accuracy. In [21] considered RF classifier. The model classified the sleep accurately for about 85.95% of the sleep data. ...
Sleep deeply influences human physiological behaviors. Sleep stage classification is a principal metric for evaluating the quality of sleep. Therefore, it is important to come up with an automatic sleep staging algorithm. The present study presents an automatic sleep staging method that majorly consists of three stages: data processing, feature extraction, and classification. In addition, we presented a proper feature screening technique to discriminate the changes in sleep-level information. Finally, we used the ISRUC-Sleep dataset and raw data from the healthy controlled subjects and subjects with sleep problems to perform model evaluation purposes. The model achieved the highest classification accuracy performance of 98.73% and 96.53% using subgroup-II and subgroup-III respectively, using SVM classification models. In addition, the overall accuracy and kappa coefficient of the proposed method were superior to those of state-of-the-art methods centered on the different machine learning classifiers. In this paper, automatic sleep data staging was realized, effectively improving the accuracy.
... The selection of characteristic wavelengths (holding sample-specific or component-specific information) can improve the performance of prediction or classification algorithms since it eliminates the uninformative variables and improves the performance of the models. Several methods have been developed and can be found in the literature [30]. The two selection procedures performed for the selection of the independent variables on our data were a PCA and a GA. ...
In this study, a multivariate analysis combined with near-infrared (NIR) spectroscopy was employed to classify intact grape berries based on the rootstock x cover crops combination. NIR spectra were collected in diffuse reflection mode using a TANGO FT-NIR spectrometer (Bruker, Germany) with 8 cm−1 resolution and 64 scans in the wave number range of 4000–10,000 cm−1. The chemometric analyses were performed with the statistical software R version 4.2.0 (2022-04-22). Elimination of uninformative variables was accomplished with a PCA and a genetic algorithm (GA). The discrimination performance of a linear discriminant analysis (LDA) model was not enhanced with either a PCA- or a GA-based selection. A multiclass classification model was built with an artificial neural network (ANN). The best fit multiclass classification model on test data was obtained with the GA-ANN model that gave a classification accuracy of close to 80% for samples belonging to the four classes. These results demonstrate that NIR spectroscopy could be used as a rapid method for the classification of berries based on their rootstock x cover-crops combination.
... Additionally, our article focused how the best way of screening features so that it helps us to discriminate the similar characteristics of the sleep stages, and it alternatively assists proper way of diagnosis various sleep-related diseases. We used the Online Streaming Feature Selection (OSFS) technique for feature selection [21][22][23]. ...
... To alleviate the convergence problems, researchers have frequently applied evolutionary computation (EC) techniques to feature selection tasks since they are able to thoroughly investigate a solution space due to their global searching characteristics. The most representative EC techniques applied to feature selection are genetic algorithms (GA) [15], genetic programming (GP) [16], ant colony optimization (ACO) [17], particle swarm optimization (PSO) [18], differential evolution (DE) [19] and artificial bee colony (ABC) [20], just to name a few. ...
Feature selection typically aims to select a feature subset that maximally contributes to the performance of a further process (such as clustering, classification and regression). Most of the current feature selection methods handle all features in the dataset with the same importance while evaluating the possible feature subsets in a solution space. However, this case may not be appropriate since each feature in a dataset comes with its own impact and importance. In particular, each feature may provide a different cost to achieve some specific purposes. To address this issue, we introduce an improved multi-objective artificial bee colony-based cost-sensitive subset selection method which simultaneously tries to minimize two main conflicting objectives: the classification error rate and the feature cost. According to the results on well-known benchmarks, the proposed cost-sensitive subset selection approach outperforms the recently introduced multi-objective variants of the artificial bee colony, particle swarm optimization and genetic algorithms. To the best of our knowledge, this work is one of the earliest studies on multi-objective cost-sensitive subset selection in the literature.
... This helps a lot for analyzing the sleep abnormality, and it is also more popular for its easier recording facility. In general, EEG signals are combinations of different waveforms, which help to characterize the different sleep stages with different frequency bands such as delta band (0-4 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), spindle (12)(13)(14), sawtooth (2)(3)(4)(5)(6), and k-complex (0.5-1.5 Hz). Finally, the scoring and decisions are taken by the sleep experts through proper interpretation of the quantitative and visual analysis of collected sleep recordings. ...
... Xiaojin Li et al. introduced the hybrid model for identifying the irregularities that occurred in different stages of sleep during the night, and extracted features were forwarded into random forest classifiers. It has been reported that overall classification accuracy has reached 85.95% [25]. ...
Sleep is a basic requirement of human life. It is one of the vital roles in human life to maintain the proper mental health, physical health, and quality of life. In this proposed research work, we conduct an automated sleep staging classification system to a proper investigation of irregularities that occurred during sleep based on single-channel electroencephalography (EEG) signal using machine learning techniques. The major advantage of this proposed research work over the standard polysomnography method are: (1) It measures the sleep irregularities during sleep by considering three different medical condition subjects of different gender with different age groups. (2) One more important objective of this proposed sleep study is that here we obtain different session recordings to investigate sleep abnormality patterns, which can help to find better diagnosis toward the treatment of sleep-related disorder. (3) In the present work, we have obtained two different time-framework epochs from individual subjects to check which window size is more effective toward identification on sleep irregularities. The present research work based on a two-state sleep stage classification problem based on a single channel of EEG signal was performed in a different stepwise manner such as the acquisition of data from participated subjects, preprocessing, feature extraction, feature selection, and classification. We obtained the polysomnographic data from the ISRUC-Sleep data repository for measuring the performances of the proposed framework, where the sleep stages are visually labeled. The obtained results demonstrated that the proposed methodologies achieve a high classification accuracy of 99.46 and 97.46% using SVM and DT classifiers, respectively, and which support sleep experts to accurately measure the irregularities that occurred during sleep and also help the clinicians to evaluate the presence and criticality of sleep-related disorders.
