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Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. [ABSTRACT FROM AUTHOR]

Internet of Things (IoTs) has been playing an important role in many sectors, such as smart cities, smart agriculture, smart healthcare, and smart manufacturing. However, IoT devices are highly vulnerable to cyber-attacks, which may result in security breaches and data leakages. To effectively prevent these attacks, a variety of machine learning-based network intrusion detection methods for IoT networks have been developed, which often rely on either feature extraction or feature selection techniques for reducing the dimension of input data before being fed into machine learning models. This aims to make the detection complexity low enough for real-time operations, which is particularly vital in any intrusion detection systems. This paper provides a comprehensive comparison between these two feature reduction methods of intrusion detection in terms of various performance metrics, namely, precision rate, recall rate, detection accuracy, as well as runtime complexity, in the presence of the modern UNSW-NB15 dataset as well as both binary and multiclass classification. For example, in general, the feature selection method not only provides better detection performance but also lower training and inference time compared to its feature extraction counterpart, especially when the number of reduced features K increases. However, the feature extraction method is much more reliable than its selection counterpart, particularly when K is very small, such as K = 4 . Additionally, feature extraction is less sensitive to changing the number of reduced features K than feature selection, and this holds true for both binary and multiclass classifications. Based on this comparison, we provide a useful guideline for selecting a suitable intrusion detection type for each specific scenario, as detailed in Table 14 at the end of Sect. 4. Note that such the comparison between feature selection and feature extraction over UNSW-NB15 as well as theoretical guideline have been overlooked in the literature. [ABSTRACT FROM AUTHOR]

Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice. [ABSTRACT FROM AUTHOR]

Early diagnosis and treatment are the keys to managing patients with breast cancer. Identifying patients even before they present with symptoms is made possible through screening methods. Thermography is a tool for screening carcinoma breast to reduce the associated morbidity and mortality. This paper proposes a novel feature selection using a machine learning algorithm, namely, the Greedy search optimization algorithm. This algorithm is applied to compare various features selection techniques. These techniques are sequential backward (SBS), sequential forward feature selection, and exhaustive feature selection techniques. It is concluded from this comparison that sequential backward feature selection is the best technique for breast cancer diagnosis. Our average score of SBS comes 88.5714%, with a computational time of 87.4 s. For classification purposes, we have used an artificial neural network. The classification result varies according to the age group with the physiology of the breast. Considering this, we have selected features age-group wise by sequential backward technique. The classification accuracy of (20–40), (41–60), (61–80) years age group patients are 79.349%, 80.711%, and 74.76%. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices are widely used but also vulnerable to cyberattacks that can cause security issues. To protect against this, machine learning approaches have been developed for network intrusion detection in IoT. These often use feature reduction techniques like feature selection or extraction before feeding data to models. This helps make detection efficient for real-time needs. This paper thoroughly compares feature extraction and selection for IoT network intrusion detection in machine learning-based attack classification framework. It looks at performance metrics like accuracy, f1-score, and runtime, etc. on the heterogenous IoT dataset named Network TON-IoT using binary and multiclass classification. Overall, feature extraction gives better detection performance than feature selection as the number of features is small. Moreover, extraction shows less feature reduction compared with that of selection, and is less sensitive to changes in the number of features. However, feature selection achieves less model training and inference time compared with its counterpart. Also, more space to improve the accuracy for selection than extraction when the number of features changes. This holds for both binary and multiclass classification. The study provides guidelines for selecting appropriate intrusion detection methods for particular scenarios. Before, the TON-IoT heterogeneous IoT dataset comparison and recommendations were overlooked. Overall, the research presents a thorough comparison of feature reduction techniques for machine learning-driven intrusion detection in IoT networks. [ABSTRACT FROM AUTHOR]

Features of data are much critical to the classification. However, when only small data are available, suitable features can not be easily obtained, easily leading to the bad classification performance. This paper propose a novel approach to automatically learns features from the irrelevant domain with much discriminative features for the given classification task. It first computes as the learning objectives the central vectors of each class in the irrelevant domain, and then uses machine learning method to automatically learn features for each sample in the target domain from these objectives. The merits of our method lie in that unlike the transfer learning, our method does not require the similarity between two domains. It can learn features from much discriminative domains. Its learned features are not limited to its original ones, unlike feature selection and feature extraction methods, so that the classification performance with the learned features can be better. Finally, our method is much general, simple, and efficient. Lots of experimental results validated the proposed method. [ABSTRACT FROM AUTHOR]

With the advent of deep learning algorithms, the field of portable musical instrument recognition, i.e., musical recognition using mobile devices, has experienced substantial progress. Manual labeling, which is time-consuming, labor-intensive, and error-prone, has historically been used to classify instruments. Recent research, however, has concentrated on automating the classification process through the extraction of music properties. Nonetheless, due to the complicated interplay between the fundamental wave and harmonics in music, identifying important audio information remains difficult. This article describes the underlying ideas and implementation approach of portable musical instrument identification based on acoustic characteristics in detail. This paper proposes utilizing the Learning Vector Quantization (LVQ) neural network learning technique to extract acoustic components from music sources using the Short-Time Fourier Transform (STFT). In addition, this paper uses a feature selection strategy to pick the most informative features, lowering the dimensionality of the classifier's feature vector and improving training and recognition efficiency. The weighted recognition accuracy is 79.8% when all characteristics are picked, according to the experimental results. However, by decreasing the number of feature dimensions to 24, the system obtains its greatest weighted recognition rate of 81.2%, outperforming the performance with all features enabled by 1.3%. This illustrates how feature dimensionality reduction may increase recognition performance. However, decreasing the feature dimensions beyond 24 resulted in worse recognition accuracy, demonstrating the existence of an ideal feature dimensionality for each portable musical instrument category. A feature vector with 24 dimensions produces the greatest results for piano recognition, whereas a vector with 20 dimensions offers the maximum accuracy for cello recognition. These findings highlight the significance of feature selection in obtaining high accuracy rates for certain instrument types. [ABSTRACT FROM AUTHOR]

Considering that serious hand function damage will greatly affect the daily life of patients, its recovery mainly depends on the regular inspection and manual training of medical staff, and medical monitoring based on bioelectric signals can largely replace manual re-examination as autonomous rehabilitation technology. So, for the rationality of feature selection and the diversity of classifier design in the gesture recognition process based on electromyography (EMG) signals, this paper proposes a hand medical monitoring system based on feature selection method of feature subset average recognition rate and optimal machine learning algorithm selection, which mainly depends on the prediction of hand movement. At the same time, since most experiments are conducted in different non-public proprietary databases, the comparison between various gesture recognition methods can only be analyzed to a certain extent. Therefore, this paper uses the DB1 dataset in the large publicly available NinaPro database and combines with presently well-known 11 time-domain (TD) features and 5 frequency domain (FD) features, then uses the support vector machine (SVM) classifier to comparative analysis total 136 feature combinations under various feature numbers. Under the premise of ensuring the overall recognition rate of electromyography gesture, this method will be able to reduce the number of features in feature set, according to the change of the average remove redundant features, and construct an optimal reduced EMG feature set. Finally, through the four common hand motion classifiers based on machine learning: SVM, back propagation neural network, linear discriminant analysis, and K-nearest neighbor, this paper tests and verifies the separability of the optimal reduced EMG feature set, and based on this, selects the optimal hand motion classifier to build the optimal hand motion recognition system, improve the hand medical monitoring system, and provide technical reference for the construction of real-time medical monitoring system. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) contains hundreds of adjacent spectral bands, which can effectively differentiate the region of interest. Nevertheless, many irrelevant and highly correlated spectral bands lead to the Hughes phenomenon. Consequently, hyperspectral image dimensionality reduction is necessary to select the most informative and significant spectral band and eliminate the redundant spectral band. To this end, this paper represents an extensive and systematic survey of hyperspectral dimensionality reduction approaches for land use land cover (LULC) classification. Moreover, this paper reviewed the following important points: (1) hyperspectral imaging data acquisition methods, (2) the difference between hyperspectral and multispectral images, (3) hyperspectral image dimensionality reduction based on machine learning (ML) and deep learning (DL) techniques, (4) the popular benchmark hyperspectral datasets with the performance metrics for LULC classification, and (5) the significant challenges with the future trends for hyperspectral dimensionality reduction. [ABSTRACT FROM AUTHOR]

Industry 4.0 brings about a convergence between traditional industrial practices and modern computational techniques. The manufacturing sector is expected to make a greater number of decisions and actions based on the advanced computational analysis of the measurable machinery data. Predictive maintenance of the CNC lathe spindle unit helps the machine avoid unexpected downtimes and meet closer machining tolerances. In this paper, the vibration signals acquired from an experimental run-to-failure test rig are first processed to extract health degradation features, which are then subjected to a neighborhood component analysis-based regression feature selection criteria. Finally, the selected spindle health degradation features are used to train a support-vector machine (SVM) algorithm to evolve a remaining useful life (RUL) estimation model. The SVM hyperparameters that strongly affect the prediction performance are tuned using the Bayesian optimization approach. The evolved predictive model is tested using an independent lathe spindle health degradation data set to obtain the root-mean-square error for predicted and actual RUL. The overall predicted RUL is having an acceptable agreement with the actual RUL. An RMSE equal to 206.23 is obtained as a quantitative measure of prediction accuracy for the Bayesian optimized SVM model for the given dataset. In industrial practice, the evolved SVM predictive model can be employed for the real-time RUL estimation of a similar mechanical system. The proposed predictive model with the integrated feature extraction, selection, and prognosis algorithm can be employed on a real-time spindle health monitoring and predictive maintenance platform for maintenance decision-making. [ABSTRACT FROM AUTHOR]

The detection of stego images by using the steganalysis approach is one of the demanding task in the recent days. Because, it is used as an importer for the immoral activities by hiding the secrets in the messages. For this secret identification, the traditional works develop various steganographic techniques for steganalysis. But, it has some important drawbacks such as low detection percentage, inefficient results, and increased complexity. In order to solve these issues, this paper introduces a new steganalysis approach with an efficient feature selection and optimization techniques. The aim of this paper is to accurately detect the stego and clean images by implementing an efficient classification algorithm. Initially, a novel Coefficient based Walsh Hadamard Transform along with the Gray Level Co-occurrence Matrix (GLCM) is used for extracting the features of the image. Then, an efficient feature selection technique, namely, Pine Growth Optimization (PGO) is developed to select the optimal features from the extracted features. Finally, the Cross Integrated Machine Learning (CIML) classifier is implemented to classify the stego and clean images. The newness is provided during the feature extraction, selection and classification processes. In experiments, the performance results of the proposed steganalysis is evaluated and compared with the existing approaches by using different measures. [ABSTRACT FROM AUTHOR]

Diabetes retinopathy (DR) is one of the leading causes of blindness globally. Early detection of this condition is essential for preventing patients' loss of eyesight caused by diabetes mellitus being untreated for an extended period. This paper proposes the design of an augmented bioinspired multidomain feature extraction and selection model for diabetic retinopathy severity estimation using an ensemble learning process. The proposed approach initiates by identifying DR severity levels from retinal images that segment the optical disc, macula, blood vessels, exudates, and hemorrhages using an adaptive thresholding process. Once the images are segmented, multidomain features are extracted from the retinal images, including frequency, entropy, cosine, gabor, and wavelet components. These data were fed into a novel Modified Moth Flame Optimization-based feature selection method that assisted in optimal feature selection. Finally, an ensemble model using various ML (machine learning) algorithms, which included Naive Bayes, K-Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forests, and Logistic Regression were used to identify the various severity complications of DR. The experiments on different openly accessible data sources have shown that the proposed method outperformed conventional methods and achieved an Accuracy of 96.5% in identifying DR severity levels. [ABSTRACT FROM AUTHOR]

Ultrasonic testing (UT) is increasingly combined with machine learning (ML) techniques for intelligently identifying damage. Extracting significant features from UT data is essential for efficient defect characterization. Moreover, the hidden physics behind ML is unexplained, reducing the generalization capability and versatility of ML methods in UT. In this paper, a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efficiency of UT. Firstly, multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature space. Subsequently, a feature selection method based on model interpretable strategy (FS-MIS) is innovatively developed by integrating Shapley additive explanation (SHAP), filter method, embedded method and wrapper method. The most effective ML model and the optimal feature subset with better correlation to the target defects are determined self-adaptively. The proposed framework is validated by identifying and locating side-drilled holes (SDHs) with 0.5λ central distance and different depths. An ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by experiments. The optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival (ToAs) of the scattered waves emitted by adjacent SDHs. The experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67% with an average error of 0.25%, significantly improving the time resolution of UT signals. On this basis, the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC datasets. The imaging resolution is enhanced to 0.5λ by implementing the total focusing method (TFM). The relative errors of hole depths and central distance are no more than 0.51% and 3.57%, respectively. Finally, the superior performance of the proposed FS-MIS is validated by comparing it with initial feature space and conventional dimensionality reduction techniques. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) using inertial motion data has gained a lot of momentum in recent years both in research and industrial applications. From the abstract perspective, this has been driven by the rapid dynamics for building intelligent, smart environments, and ubiquitous systems that cover all aspects of human life including healthcare, sports, manufacturing, commerce, etc., which necessitate and subsume activity recognition aiming at recognizing the actions, characteristics, and goals of one or more agent(s) from a temporal series of observations streamed from one or more sensors. From a more concrete and seemingly orthogonal perspective, such momentum has been driven by the ubiquity of inertial motion sensors on-board mobile and wearable devices including smartphones, smartwatches, etc. In this paper we give an introductory and a comprehensive survey to the subject from a given perspective. We focus on a subset of topics, that we think are major, that will have significant and influential impacts on the future research and industrial-scale deployment of HAR systems. These include: (1) a comprehensive and detailed description of the inertial motion benchmark datasets that are publicly available and/or accessible, (2) feature selection and extraction techniques and the corresponding learning methods used to build workable HAR systems; we survey classical handcrafted datasets as well as data-oriented automatic representation learning approach to the subject, (3) transfer learning as a way to overcome many hurdles in actual deployments of HAR systems on a large scale, (4) embedded implementations of HAR systems on mobile and/or wearable devices, and finally (5) we touch on adversarial attacks, a topic that is essentially related to the security and privacy of HAR systems. As the field is very huge and diverse, this article is by no means comprehensive; it is though meant to provide a logically and conceptually rather complete picture to advanced practitioners, as well as to present a readable guided introduction to newcomers. Our logical and conceptual perspectives mimic the typical data science pipeline for state-of-the-art AI-based systems. [ABSTRACT FROM AUTHOR]

The recent trend in network intrusion detection leverages key features of machine learning (ML) algorithms to detect network traffic anomalies. Network traffic flows contain high dimensional features which significantly affect data-driven approaches. Therefore, the performance of ML-based approaches mainly depends on the appropriate set of features of network data. Different feature selection and extraction methods are extensively employed to attain the informative and compact set of features. Existing methods often suffer from achieving the expected performance due to the lacking of effectively removing redundant features as well as incorporating features with complementary information. In this paper, we present a cluster-based feature extraction method using Mahalanobis distance (cFEM) that clusters the correlated features and extracts new feature representations based on a distance metric. The extracted features on the transformed dimensions are employed to train different machine learning classifiers. We conducted extensive experiments using three renowned datasets. The results show that cFEM outperforms the state-of-the-art intrusion detection methods in several performance metrics such as detection rate (99.61%) and false alarm rate (0.26%). Further experiments on extracted features show that our extracted features are discriminative, free of redundancy, and able to capture complementary information. [ABSTRACT FROM AUTHOR]

Feature extraction, feature clustering, feature selection are suitable to enhance learning performance, reduce computational complexity, create better generalizable models, and reduce required storage. Although there are several feature reduction techniques, still it remains one of the research hot spots in the field of data mining and machine learning groups. Owl search algorithm (OSA) is one of the recent metaheuristic optimization algorithms that mimic the hunting mechanism of owls in dark. However, OSA suffers from the same problem faced by many other optimization algorithms and tends to fall in local optima and premature convergence. To overcome these problems, two improvements for OSA algorithm are proposed in this paper. The first improvement uses a fuzzy system which is responsible to tune a control parameter in the updating phase of OSA. The second improvement includes the fuzzy system along with modifying the updating equation of OSA to enhance the exploration activity. In addition, this paper presents a new feature extraction technique for regression problems based on the improved OSA, called Fuzzy Owl Clustering Dimension Reduction (FOCDR). We apply a method that uses three weighting methods (i.e., soft, hard, and mixed) to extract new features based on the generated clusters. The experiment is divided into two parts. In the first part, the performance of the OSA algorithm and two improvement versions are analyzed with ten benchmark functions. The results show that the proposed versions on average can improve the convergence rate by 6.75% and 14.2% compared to OSA in solving complex problems. The second part is conducted to show FOCDR's ability for feature selection problems. The effectiveness of FOCDR has been evaluated using four benchmark datasets and a real-world case study. [ABSTRACT FROM AUTHOR]

This paper analyses the performance of different types of Deep Neural Networks to jointly estimate age and identify gender from speech, to be applied in Interactive Voice Response systems available in call centres. Deep Neural Networks are used, because they have recently demonstrated discriminative and representation capabilities in a wide range of applications, including speech processing problems based on feature extraction and selection. Networks with different sizes are analysed to obtain information on how performance depends on the network architecture and the number of free parameters. The speech corpus used for the experiments is Mozilla's Common Voice dataset, an open and crowdsourced speech corpus. The results are really good for gender classification, independently of the type of neural network, but improve with the network size. Regarding the classification by age groups, the combination of convolutional neural networks and temporal neural networks seems to be the best option among the analysed, and again, the larger the size of the network, the better the results. The results are promising for use in IVR systems, with the best systems achieving a gender identification error of less than 2% and a classification error by age group of less than 20%. [ABSTRACT FROM AUTHOR]

Gearboxes are critical equipment in many industrial applications such as machine manufacturing, petrochemical industry, renewable energy, etc. However, due to their complex structure and regularly harsh working environment, gearboxes are inevitably prone to a variety of faults and defects during operation. Therefore, intelligent condition monitoring techniques are crucially important for early gear and bearing fault recognition and detection to avoid any industrial failure due to machine breakdowns. In this paper, an intelligent algorithm for gear and bearing fault diagnosis is suggested based on several approaches mainly: robust empirical mode decomposition (REMD), time domain features are used for the feature extraction step, while equilibrium optimizer (EO) in the feature selection. For feature classification, random forest (RF), ensemble tree (ET) and nearest neighbors (KNN) are chosen as classifiers. REMD is used to alleviate the mode mixing problem by monitoring the sifting process and selecting the optimal iteration number. EO is a recent optimization approach based on the laws of physical theory in nature. EO reduces the high-dimensional data problem, by filtering redundant features, and increasing model generalization efficiency by avoiding the over-fitting curse. The proposed approach is applied to real-time vibration signals from a healthy gearbox and four different faulty gear and bearing conditions. According to our approach, data signals are decomposed by REMD to several intrinsic mode functions (IMFs). Thereafter, time-domain features are computed for each IMF to construct the feature matrix for every gear and bearing health status. After that, EO is applied to every matrix in the feature selection step. Finally, RF, ET and KNN are used to calculate classification accuracy and give the confusion matrix. Compared to several feature selection techniques, experimental results prove the efficiency of the proposed approach in detecting, identifying, and classifying all gear and bearing defects even under different operating modes. [ABSTRACT FROM AUTHOR]

Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight. [ABSTRACT FROM AUTHOR]

In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions. Several review papers in the literature have focused on various ML, statistical, and deep learning-based methods used in stock market forecasting. However, no survey study has explored feature selection and extraction techniques for stock market forecasting. This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications. We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022. We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles. We also describe the combination of feature analysis techniques and ML methods and evaluate their performance. Moreover, we present other survey articles, stock market input and output data, and analyses based on various factors. We find that correlation criteria, random forest, principal component analysis, and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications. [ABSTRACT FROM AUTHOR]

With the emergence of big data era, the dimensions of data are enhanced exponentially and it becomes a difficult task to handle information of high dimensions in various sectors like text mining, machine learning and data analysis. Redundant and inappropriate feature enhances the complexities in dimensions that further results in poor performances. In the intrusion detection system, the feature selection is considered as one of the most significant processes to improve the performances of the system. Due to high dimensional data, there occurs a drop in accuracy and efficiency. To overcome such drawback, this paper proposes three major phases namely the data pre-processing, feature selection and classification phases. In data-pre processing phase, the input data comprising of various noise signals, high dimensional and redundant data, numerous irrelevant features etc. are extracted. The second phase involves the selection of features using cooperative and competitive (C2) search based learning algorithm. In the classification phase, the extracted features are classified optimally using Bonferroni based Hybrid k-nearest neighbour (B-HkNN) algorithm thereby obtaining an optimal intrusion detection system. Furthermore, the proposed approach based on intrusion detection system is evaluated by the standard CICIDS2017 and ADFA-LD datasets to determine the accuracy and efficiency of the system. [ABSTRACT FROM AUTHOR]

In past few decades, there was a tremendous enhancement in natural disaster and their effects on economy and population. An adverse events like floods, wildfires, cyclones, earthquakes, tsunamis, etc., are regarded as a natural disaster once it strikes the vulnerable population areas. An early tracking of susceptibility areas and immediate tracking of affected areas might help in facilitating rescue and early warnings to the public. To achieve autonomous natural disaster prediction, this paper makes use of current developments in remote sensing, which speed up the availability of aerial/satellite data and are reinforced by progress in the computing sector. The aerial/satellite imageries are employed for acquiring the data from areas of Queensland-Australia that are more prone to natural disaster in an eagle-eye perspective. Since there were several techniques employed so far for the automatic prediction of natural disaster susceptibilities, there were some limitations like reduced rate of accuracy and so on. So as to overcome these limitations, deep learning based automated process is employed for predicting the natural disaster areas and probability of event occurrences. The main intention of the work is to detect the natural disaster occurrence from the sensed data which aids in providing warning to public and to safeguard them by taking necessary actions. Initially, remote sensing data is pre-processed and the features are extracted using Adaptive linear Internal embedding algorithm-based feature extraction (ALIE-FE). The extracted features are selected using Recursive Wrapper-based feature subset selection. To estimate best fitness function and to enhance the prediction accuracy, the optimization process is carried using Bio-Inspired Squirrel Search Optimization algorithm (BI-SSOA). Finally, the classification is carried by means of Deep learning based Multi-layer Alex Net classifier (DBMLA) approach. The simulation is carried and the outcomes attained are estimated for predicting flood susceptibility and wildfire susceptibility. The proposed BISSOA-DBMLA offers sensitivity of 98%, specificity of 99%, and TSS of 97%. The proposed system offers 98.99% classification accuracy. Accuracy, sensitivity, specificity, TSS, and area under the curve (AUC) are used to evaluate the efficacy of the suggested system in light of the achieved results from other approaches. To demonstrate the efficacy of the suggested mechanism, the achieved results are compared with those of current approaches. [ABSTRACT FROM AUTHOR]

Predicting students' performance during their years of academic study has been investigated tremendously. It offers important insights that can help and guide institutions to make timely decisions and changes leading to better student outcome achievements. In the post-COVID-19 pandemic era, the adoption of e-learning has gained momentum and has increased the availability of online related learning data. This has encouraged researchers to develop machine learning (ML)-based models to predict students' performance during online classes. The study presented in this paper, focuses on predicting student performance during a series of online interactive sessions by considering a dataset collected using digital electronics education and design suite. The dataset tracks the interaction of students during online lab work in terms of text editing, a number of keystrokes, time spent in each activity, etc., along with the exam score achieved per session. Our proposed prediction model consists of extracting a total of 86 novel statistical features, which were semantically categorized in three broad categories based on different criteria: (1) activity type, (2) timing statistics, and (3) peripheral activity count. This set of features were further reduced during the feature selection phase and only influential features were retained for training purposes. Our proposed ML model aims to predict whether a student's performance will be low or high. Five popular classifiers were used in our study, namely: random forest (RF), support vector machine, Naïve Bayes, logistic regression, and multilayer perceptron. We evaluated our model under three different scenarios: (1) 80:20 random data split for training and testing, (2) fivefold cross-validation, and (3) train the model on all sessions but one which will be used for testing. Results showed that our model achieved the best classification accuracy performance of 97.4% with the RF classifier. We demonstrated that, under similar experimental setup, our model outperformed other existing studies. [ABSTRACT FROM AUTHOR]

With the development of science and technology, the classification of polarimetric SAR images has become an important part of the research of target recognition and image interpretation. However, for the research method is relatively simple and the accuracy is low, this paper carries out the work from the two aspects of feature extraction and feature classification of the ground object, and analyzes and studies the application and value of the polarimetric SAR system. The basic algorithm of polarization SAR image classification is proposed. A polarimetric SAR image feature classification method based on polarization target decomposition and support vector machine is proposed. Four kinds of scattering features and Freeman decomposition are obtained by Cloude decomposition. The simulation results show that the accuracy of using combined features is about 6.5% higher than that of single features. A polarization classification model based on polarization target decomposition and limit learning method is proposed. The simulation experiment shows ELM learning. The algorithm is indeed much faster than SVM learning. In this paper, a polarimetric SAR image classification method based on improved scattering mechanism coefficients is proposed, and the effectiveness of the polarimetric SAR image classification method based on improved scattering mechanism coefficients is verified. Experimental results show that after feature selection, the method of combining Freeman decomposition and Wishart classifier can get better classification results. [ABSTRACT FROM AUTHOR]

As basic research, it has also received increasing attention from people that the "curse of dimensionality" will lead to increase the cost of data storage and computing; it also influences the efficiency and accuracy of dealing with problems. Feature dimensionality reduction as a key link in the process of pattern recognition has become one hot and difficulty spot in the field of pattern recognition, machine learning and data mining. It is one of the most challenging research fields, which has been favored by most of the scholars' attention. How to implement "low loss" in the process of feature dimension reduction, keep the nature of the original data, find out the best mapping and get the optimal low dimensional data are the keys aims of the research. In this paper, two-dimensionality reduction methods, feature selection and feature extraction, are introduced; the current mainstream dimensionality reduction algorithms are analyzed, including the method for small sample and method based on deep learning. For each algorithm, examples of their application are given and the advantages and disadvantages of these methods are evaluated. [ABSTRACT FROM AUTHOR]

Nowadays, the economy of countries highly depends on the agriculture productivity which has a great effect on the development of human civilization. Sometimes, plant diseases cause a major reduction in agricultural products. This paper proposes a new approach for the automatic detection and classification of plant leaf diseases based on using the ELM deep learning algorithm on a real dataset of plant leaf images. The proposed approach uses the k-means clustering algorithm for image segmentation and applies the GLCM for feature extraction. The BDA optimization algorithm is employed for feature selection, and lastly the ELM algorithm is used for plant leaf diseases classification. The presented approach optimizes the input weights and hidden biases for ELM. The dataset used in this study includes 73 plant leaf images, such that we tested our approach on four diseases that usually affect plants, including: Alternaria alternata, Anthracnose, Bacterial blight, and Cercospora leaf spot. The experimental results show that the proposed approach has achieved encouraging results in terms of these classification measures: accuracy, error rate, recall, F score, and AUC which are 94%, 6%, 92%, 95%, and 96% respectively. Babu [ABSTRACT FROM AUTHOR]

Recently, with the advance in information technology, pure data-driven approaches such as machine learnings have been widely applied in status diagnosis. However, the accuracy of those predictions strongly relies on the original data, which largely depends on the selected sensors and signal features. Furthermore, for unsupervised machine learning schemes, although it could avoid the concern of labeling in training, it lacks a quantified evaluation of the prediction results. These concerns significantly limit the effectiveness of modern machine learning and thus should be investigated. Meanwhile, ball bearings are fundamental key machine elements in rotating machinery and their condition monitoring should be critical for both quality control and longevity assessment. In this paper, by utilizing ball bearing failure diagnosis as the main theme, the flow of feature selection and evaluation, as well as the evaluation flow for multiple failure diagnosis, is developed for accessing the status of bearings in their imbalance, lubrication, and grease contamination levels based on unsupervised machine learning. The experimental results indicated that with proper feature selection, the failure identification could be more definite. Finally, a novel model based on the second norm to quantify the classification level of each cluster in hyperspace is proposed as the measure for unsupervised machine learning as the basis for performance evaluation and optimization of unsupervised machine learning schemes and should benefit related machine reliability evaluation studies and applications. [ABSTRACT FROM AUTHOR]

Dimension reduction is a preprocessing step in machine learning for eliminating undesirable features and increasing learning accuracy. In order to reduce the redundant features, there are data representation methods, each of which has its own advantages. On the other hand, big data with imbalanced classes is one of the most important issues in pattern recognition and machine learning. In this paper, a method is proposed in the form of a cost-sensitive optimization problem which implements the process of selecting and extracting the features simultaneously. The feature extraction phase is based on reducing error and maintaining geometric relationships between data by solving a manifold learning optimization problem. In the feature selection phase, the cost-sensitive optimization problem is adopted based on minimizing the upper limit of the generalization error. Finally, the optimization problem which is constituted from the above two problems is solved by adding a cost-sensitive term to create a balance between classes without manipulating the data. To evaluate the results of the feature reduction, the multi-class linear SVM classifier is used on the reduced data. The proposed method is compared with some other approaches on 21 datasets from the UCI learning repository, microarrays and high-dimensional datasets, as well as imbalanced datasets from the KEEL repository. The results indicate the significant efficiency of the proposed method compared to some similar approaches. [ABSTRACT FROM AUTHOR]

Microstructure determines the most important factors that influence all aspects of the physical properties of the metal. Machine learning based systems allow us to look at the images to find the features of microstructure images which will be useful for classifying such images. These classification outcomes are the fundamental data for many material scientists. However, handcrafted feature vectors extracted by some means may involve a significant amount of irrelevant and redundant features, which may lead to misclassification of the microstructural images. In this paper, at first, a modified version of texture-based feature descriptor, Local Tetra Pattern (LTrP), which is named as Uniform variant of LTrP (ULTrP) is used to extract the features from the microstructural images. Then a feature selection algorithm based on Genetic Algorithm (GA), named as Diversification of Population (DP) in GA (DPGA), is proposed which is applied on ULTrP to remove the possible redundant features present therein. To assess fitness of the candidate solutions, instead of applying a learning algorithm, which is a common trend, the proposed DPGA uses an ensemble of three filter ranking methods. Impressive outcomes obtained by evaluating the proposed classification framework on a standard 7-class microstructural image dataset confirm its superiority over some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals play an important role in clinical diagnosis and cognitive neuroscience. Automatic classification of EEG signals is gradually becoming the research focus, which contains two procedures: feature extraction and classification. In the phase of feature extraction, a hybrid feature extraction method is proposed and the features are derived by performing linear and nonlinear feature extraction methods, which can describe abundant properties of original EEG signals. In order to eliminate irrelevant and redundant features, feature selection based on class separability is employed to select the optimal feature subset. In the phase of classification, this paper presents a novel ensemble extreme learning machine based on linear discriminant analysis. Linear discriminant analysis is used to transform training subsets that are generated by bootstrap method, through which we can increase the differences of basic classifiers and reduce generalization errors of ensemble extreme learning machine. Experiments on two different EEG datasets are conducted in this study. Class separability is investigated to verify the effectiveness of feature extraction methods. The overall classification results show that compared with other similar studies, the proposed method can significantly enhance the performance of EEG signals classification. [ABSTRACT FROM AUTHOR]

Iris image-based biometric systems are commonly used in applications that demand security, authentication, recognition and faster login access. In solving these real-time problems, the impact of soft computing techniques which employ cognitive skills is very high. Although this system has been commercialized, the scope for improvement is still plenty. This paper introduces the reader to different segments of an iris recognition system and reviews the techniques involved with each segment. It reports on how research articles validate the robustness of an iris-based recognition system. As these systems are fallible, it also shows the vulnerabilities associated with each segment and provides insights to develop much better intelligent and robust techniques which will make the system more accurate. This paper also shows that in spite of versatility of soft computing techniques, it is not fully exploited for iris recognition systems. The present challenges and directions for future research are also discussed. [ABSTRACT FROM AUTHOR]

Offline handwritten signature verification has been widely used for document forensics and biometrics, and it is a popular issue. Deep learning models have commonly been used to solve this problem. This research has two aims, and they are to present a high accuracy hybrid classification model for forensics and to collect and share a new handwritten signature dataset to contribute document forensics. In this paper, a novel deep signature verification model is presented. This method has three fundamental phases and they are deep feature generation using transfer learning, iterative minimum redundancy maximum relevance (IMRMR) feature selection, and classification phases. In the deep feature extraction phase, 13 pre-trained widely preferred convolutional neural networks (CNN) are selected. These are utilized as feature generators and 1000 features are extracted from each network. By merging the generated features, a feature vector with a length of 13,000 is created. This feature generation network is named Deep Feature Warehouse (DFW) since it uses 13 pre-trained deep feature extractors in the transfer learning model. The most valuable features of the DFW are selected by the proposed IMRMR method and the selected features are forwarded to the classifier. To test the proposed DFW and IMRMR based verification method, we collected a handwritten signature dataset and CEDAR dataset to obtain comparative results. The proposed DFW and ImRMR based document classification method reached 97.16 % classification accuracy on the collected dataset and 100 % accuracy on the CEDAR dataset. We have used two datasets to demonstrate the general classification ability of our proposal. The calculated results and findings obviously demonstrate the effectiveness of the proposed DFW and ImRMR image verification model. According to the results, our model has general success (it has developed on two datasets), and it is a lightweight machine learning model since it uses transfer learning for feature extraction. [ABSTRACT FROM AUTHOR]

Speech enhancement has substantial interest in the utilization of speaker identification, video-conference, speech transmission through communication channels, speech-based biometric system, mobile phones, hearing aids, microphones, voice conversion etc. Pattern mining methods have a vital step in the growth of speech enhancement schemes. To design a successful speech enhancement system consideration to the background noise processing is needed. A substantial number of methods from traditional techniques and machine learning have been utilized to process and remove the additive noise from a speech signal. With the advancement of machine learning and deep learning, classification of speech has become more significant. Methods of speech enhancement consist of different stages, such as feature extraction of the input speech signal, feature selection, feature selection followed by classification. Deep learning techniques are also an emerging field in the classification domain, which is discussed in this review. The intention of this paper is to provide a state-of-the-art summary and present approaches for using the widely used machine learning and deep learning methods to detect the challenges along with future research directions of speech enhancement systems. [ABSTRACT FROM AUTHOR]

Spike trains (STs) induced by external stimuli are complex and challenging to decode the embedded spatiotemporal information. Although various methods have been developed to characterize STs, few applications have been reported in tactile discrimination applications. In this paper, a neurocomputing method based on reference spike train (RST) is proposed to establish a neural computation scheme, on which existing ST metrics could be fed into various traditional models directly. Moreover, existing metrics in the field of statistics and vector measurement are introduced together to extract more discriminative features. With the binning technique and feature selection algorithm applied, the neural computation scheme is aimed at taking advantage of as maximal as possible information contained in tactile signals. Based on our designed artificial fingertip, the effect is validated by improving the recognition accuracy from 77.6% to 83.4% when it is applied to the discrimination of eight roughness surfaces. Furthermore, properties of RST, such as spike intervals and distributions, are evaluated and it is found that RSTs with uniform distribution perform the best for tactile discrimination. [ABSTRACT FROM AUTHOR]

Many fundamental problems in machine learning require some form of dimensionality reduction. To this end, two different strategies were used: manifold learning and feature selection. Manifold learning (or data embedding) attempts to compute a subspace from original data by feature recombination/transformation. Feature selection aims to select the most relevant features in the original space. In this paper, we propose a novel cooperative manifold learning-feature selection that goes beyond the simple concatenation of these two modules. Our basic idea is to learn an embedding (or the subspace) by computing a cascade of embeddings in which each embedding undergoes feature selection and elimination. We use filter approaches in order to efficiently select irrelevant features at any stage of the process. For a case study, our proposed framework was used with two typical linear embedding algorithms: local discriminant embedding (a supervised technique) and locality preserving projections (unsupervised technique) on four challenging face databases and it has been conveniently compared with other cooperative schemes. Moreover, a comparison with several state-of-the-art manifold learning methods is provided. As it is exhibited by our experimental study, the proposed framework can achieve superior learning performance with respect to classic cooperative schemes and to many competing manifold learning methods. [ABSTRACT FROM AUTHOR]

The goal of securing a network is to protect the information flowing through the network and to ensure the security of intellectual as well as sensitive data for the underlying application. To accomplish this goal, security mechanism such as Intrusion Detection System (IDS) is used, that analyzes the network traffic and extract useful information for inspection. It identifies various patterns and signatures from the data and use them as features for attack detection and classification. Various Machine Learning (ML) techniques are used to design IDS for attack detection and classification. All the features captured from the network packets do not contribute in detecting or classifying attack. Therefore, the objective of our research work is to study the effect of various feature selection techniques on the performance of IDS. Feature selection techniques select relevant features and group them into subsets. This paper implements Chi-Square, Information Gain (IG), and Recursive Feature Elimination (RFE) feature selection techniques with ML classifiers namely Support Vector Machine, Naïve Bayes, Decision Tree Classifier, Random Forest Classifier, k-nearest neighbours, Logistic Regression, and Artificial Neural Networks. The methods are experimented on NSL-KDD dataset and comparative analysis of results is presented. [ABSTRACT FROM AUTHOR]

Many fundamental problems in machine learning require some form of dimensionality reduction. To this end, two different strategies were used: Manifold Learning and Feature Selection. Manifold learning (or data embedding) attempts to compute a subspace from original data by feature recombination/transformation. Feature selection aims to select the most relevant features in the original space. In this paper, we propose a novel cooperative Manifold learning-Feature selection that goes beyond the simple concatenation of these two modules. Our basic idea is to transform a given shallow embedding to a deep variant by computing a cascade of embeddings in which each embedding undergoes feature selection and elimination. We use filter approaches in order to efficiently select irrelevant features at any stage of the process. For a case study, our proposed framework was used with two typical linear embedding algorithms: Local Discriminant Embedding (LDE) (a supervised technique) and Locality Preserving Projections (LPP) (unsupervised technique) on four challenging face databases and it has been conveniently compared with other cooperative schemes. Moreover, a comparison with several state-of-the-art manifold learning methods is provided. As it is exhibited by our experimental study, the proposed framework can achieve superior learning performance with respect to classic cooperative schemes and to many competing manifold learning methods. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a domain of study that focuses on identifying and classifying the ideas expressed in the form of text into positive, negative and neutral polarities. Feature selection is a crucial process in machine learning. In this paper, we aim to study the performance of different feature selection techniques for sentiment analysis. Term Frequency Inverse Document Frequency (TF-IDF) is used as the feature extraction technique for creating feature vocabulary. Various Feature Selection (FS) techniques are experimented to select the best set of features from feature vocabulary. The selected features are trained using different machine learning classifiers Logistic Regression (LR), Support Vector Machines (SVM), Decision Tree (DT) and Naive Bayes (NB). Ensemble techniques Bagging and Random Subspace are applied on classifiers to enhance the performance on sentiment analysis. We show that, when the best FS techniques are trained using ensemble methods achieve remarkable results on sentiment analysis. We also compare the performance of FS methods trained using Bagging, Random Subspace with varied neural network architectures. We show that FS techniques trained using ensemble classifiers outperform neural networks requiring significantly less training time and parameters thereby eliminating the need for extensive hyper-parameter tuning. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) is a powerful concept for reducing the dimension of big data in many applications. It has been used for the feature extraction of microarray gene expression data in numerous works. One of the merits of ICA is that a number of extracted features are always equal to the number of samples. When ICA is applied to microarray data, whenever, it faces the challenges of how to find the best subset of genes (features) from extracted features. To resolve this problem, in this paper, we propose a new (artificial bee colony) ABC-based feature selection approach for microarray data. Our approach is based on two stages: ICA-based extraction approach to reduce the size of data and ABC-based wrapper approach to optimize the reduced feature vectors. To validate our proposed approach, extensive experiments were conducted to compare the performance of ICA + ABC with the results obtained from recently published and other previously suggested methods of gene selection for Naïve Bayes (NB) classifier. To compare the performance of the proposed approach with other algorithms, a statistical hypothesis test was employed with six benchmark cancer classification datasets of the microarray. The experimental result shows that the proposed approach demonstrates an improvement over all the algorithms for NB classifier with a certain level of significance. [ABSTRACT FROM AUTHOR]

Traffic sign recognition (TSR) is one of the significant modules of an intelligent transportation system. It instantly assists the drivers to efficiently recognize the traffic sign. Recognition of traffic sign is a large-scale feature learning problem with different real-world appearances. The main goal of this paper is to develop an efficient TSR method, which can run on an ordinary personal computer (PC). In the proposed method, GIST descriptors of the traffic sign images are extracted and subjected to graph-based linear discriminant analysis to reduce the dimension. Moreover, it effectively learns the discriminative subspace through the graph structure with increased computational efficiency. An efficient TSR module is built by conducting series of experiments using support vector machine, extreme learning machine, and k-nearest neighbor (k-NN) classifiers on available public datasets. Our approach achieved the highest recognition accuracy of 96.33 and 97.79% using k-NN classifier for German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign Classification Benchmark (BelgiumTSC), respectively. Also it achieved 99.1% accuracy for a subcategory of GTSRB traffic signs and able to predict the class of unknown traffic sign within 0.0019 s on an ordinary PC. Hence, it can be used in real-world driver assistance system. [ABSTRACT FROM AUTHOR]

Recognizing an individual's preference state for potential romantic partners based on electroencephalogram (EEG) signals holds significant practical value in enhancing matchmaking success rates and preventing romance fraud. Despite some progress has been made in this field, challenges such as high-dimensional feature space and channel redundancy limited the technology's practical application. The aim of this study is to explore the most discriminative EEG features and channels, in order to enhance the recognition performance of the system, while maximizing the portable and practical value of EEG-based systems for recognizing romantic attraction. To achieve this goal, we first conducted an interesting simulated dating experiment to collect the necessary data. Next, EEG features were extracted from various dimensions, including band power and asymmetry index features. Then, we introduced a novel method for EEG feature and channel selection that combines the sequential forward selection (SFS) algorithm with the frequency-based feature subset integration (FFSI) algorithm. Finally, we used the random forest classifier (RFC) to determine a person's preference state for potential romantic partners. Experimental results indicate that the optimal feature subset, selected using the SFS-FFSI method, attained an average classification accuracy of 88.42%. Notably, these features were predominantly sourced from asymmetry index features of electrodes situated in the frontal, parietal, and occipital lobes. [ABSTRACT FROM AUTHOR]

In fault detection systems, a massive amount of data gathered from the life-cycle of equipment is often used to learn models or classifiers that aims at diagnosing different kinds of errors or failures. Among this huge quantity of information, some features (or sets of features) are more correlated with a kind of failure than another. The presence of irrelevant features might affect the performance of the classifier. To improve the performance of a detection system, feature selection is hence a key step. We propose in this paper an algorithm named STRASS, which aims at detecting relevant features for classification purposes. In certain cases, when there exists a strong correlation between some features and the associated class, conventional feature selection algorithms fail at selecting the most relevant features. In order to cope with this problem, STRASS algorithm uses k-way correlation between features and the class to select relevant features. To assess the performance of STRASS, we apply it on simulated data collected from the Tennessee Eastman chemical plant simulator. The Tennessee Eastman process (TEP) has been used in many fault detection studies and three specific faults are not well discriminated with conventional algorithms. The results obtained by STRASS are compared to those obtained with reference feature selection algorithms. We show that the features selected by STRASS always improve the performance of a classifier compared to the whole set of original features and that the obtained classification is better than with most of the other feature selection algorithms. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to build radiomic feature-based machine learning models to predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) for esophageal squamous cell carcinoma (ESCC) patients. Methods: A total of 112 ESCC patients who underwent nCRT followed by surgical treatment from January 2008 to December 2018 were recruited. According to pCR status (no visible cancer cells in primary cancer lesion), patients were categorized into primary cancer lesion pCR (ppCR) group (N = 65) and non-ppCR group (N = 47). Patients were also categorized into total pCR (tpCR) group (N = 48) and non-tpCR group (N = 64) according to tpCR status (no visible cancer cells in primary cancer lesion or lymph nodes). Radiomic features of pretreatment CT images were extracted, feature selection was performed, machine learning models were trained to predict ppCR and tpCR, respectively. Results: A total of 620 radiomic features were extracted. For ppCR prediction models, radiomic model had an area under the curve (AUC) of 0.817 (95% CI: 0.732–0.896) in the testing set; and the combination model that included rad-score and clinical features had a great predicting performance, with an AUC of 0.891 (95% CI: 0.823–0.950) in the testing set. For tpCR prediction models, radiomic model had an AUC of 0.713 (95% CI: 0.613–0.808) in the testing set; and the combination model also had a great predicting performance, with an AUC of 0.814 (95% CI: 0.728–0.881) in the testing set. Conclusion: This study built machine learning models for predicting ppCR and tpCR of ESCC patients with favorable predicting performance respectively, which aided treatment plan optimization. Clinical relevance statement: This study significantly improved the predictive value of machine learning models based on radiomic features to accurately predict response to therapy of esophageal squamous cell carcinoma patients after neoadjuvant chemoradiation therapy, providing guidance for further treatment. Key Points: • Combination model that included rad-score and clinical features had a great predicting performance. • Primary tumor pCR predicting models exhibit better predicting performance compared to corresponding total pCR predicting models. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area. [ABSTRACT FROM AUTHOR]

The combination of radiomics and artificial intelligence has emerged as a strong technique for building predictive models in radiology. This study aims to address the clinically important issue of whether a radiomic profile can predict the overall survival (OS) time of glioblastoma multiforme (GBM) patients having gross tumor resection (GTR) through pre-operative structural magnetic resonance imaging (MRI) scans. A retrospective analysis was made using data of glioma patients made publicly available by the University of Pennsylvania. The radiomic characteristics were extracted from pre-operative structural multiparametric MRI (mpMRI) sequences after pre-processing and 3D segmentation using deep learning (DL). After removing irrelevant features, regression models based on machine learning (ML) were developed by considering selected features to predict the OS time of GBM patients within a period of days only. The patients were divided into three survivor groups depending on their projected survival time. To validate the significance of the selected feature set, statistical analysis was performed. As many as 494 patients were considered to improve survival prediction (SP) by using more effective feature extraction and selection techniques. The ridge regressor acquired the highest SpearmanR Rank correlation of 0.635 with an accuracy of 69%, the greatest of all the previous works for categorical predictions of such patients. The researchers in the past who used radiomic characteristics for the OS prognosis of GBM patients could yield limited results only. However, the current research work recorded an enhanced accuracy and SpearmanR rank for the three survivor classes of GBM patients using ML, feature selection, and radiomics. The significance of this work lies in the selection of patients with GTR and the extraction of radiomic characteristics through the use of radiomics and artificial intelligence. [ABSTRACT FROM AUTHOR]

Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study. [ABSTRACT FROM AUTHOR]

Stock values are predicted using the market's biggest issues. Stock price data is hard to predict due to its distinct traits and volatility. Online news and comments reflect investor sentiment and thoughts on stocks, which may assist predict stock prices. Most authors lately recommended a statistical and environmental measurement-based technique for anticipating machine learning or stock price changes. These models struggle with changing data. We propose a hybrid machine learning technique for long-term stock market price trend prediction (LT-SMF). We employed improved butterfly optimization (IBO) to remove artefacts from input data. Scaling, polarising, and variation percentage are used to find valuable qualities. Second, a brown Planthopper optimization (BPO) approach reduces data dimensionality for optimal feature selection. To forecast stock market price variations, a hybrid FEL-DNN was utilized. Using 11 stock market indices and social media data, evaluate the LT-SMF model. Simulation performance was compared to state-of-the-art models for mean square error, mean bios error, mean absolute error, root mean square error, accuracy, precision, recall, and F-measure. The proposed FEL-DNN classifier outperforms the current state-of-the-art CNN3D, CNN3D − DR, LSTM − D, CNN3D + LSTM, CNN3D − D + LSTM, and CNN3D − DR + LSTM classifiers by 38.67%, 41.71%, 39.32%, 36.04%, 41.13%, and 43.43% respectively in terms of accuracy in the social media data. [ABSTRACT FROM AUTHOR]

The growing demand for flaxseed as a source of healthy edible oil mandates the need for adopting novel strategies for preserving its quantity and quality. Mechanical damage during harvest and handling is one of the important threats that can adversely affect the quality and viability of flaxseeds. Currently, mechanical damage assessment in grains is mainly performed by human visual inspection, which is a subjective and time-consuming procedure. In this study, the authors propose to utilize radiographic imaging with the machine and deep learning tools to characterize the mechanical damage in flaxseeds intelligently. Images were acquired under four levels of mechanical damage, and two strategies were used to discriminate seeds' damage: pattern recognition and convolutional neural network (CNN). In the former case, 69 morphological, color, and texture features were extracted. Various classifiers, namely, linear discriminant analysis (LDA), K-nearest neighbors (KNN), support vector machines (SVM), and decision trees were used for the analysis. SVM provided the best performance with a classification accuracy of 87.4%. Furthermore, the analysis of variance (ANOVA) F-test feature selection algorithm was utilized, and the 17 most effective features were selected to be used with an SVM classifier to classify seeds with 88.4% accuracy. In the case of CNN-based classifiers, six state-of-the-art architectures were employed including EfficientNet-B0, VGG19, Resnet18, MobileNet-v2, Inception-v3, and Xception. Among them, EfficientNet-B0 provided superior performance with a classification accuracy of 91.0%. The developed models' high accuracy confirms the capabilities of radiographic imaging and artificial intelligence tools for rapid, reliable, and automated assessments of mechanical damage in flaxseeds. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a neurological illness that affects individuals at the later stage of life. Most patients complain of voice or speech abnormalities during the nascent stage of this disease, and it is difficult to recognize these abnormalities. This creates a need for a speech signal-based Parkinson's detection system to aid clinicians in the diagnosis process. A hybrid Parkinson's disease detection system has been proposed in this research work. Two speech datasets have been used in the design of this system: The first is an Italian Parkinson's Voice & Speech dataset, and the other is Mobile Device Voice Recordings at King's College London dataset. Seventeen acoustic features have been generated from the voice samples available in the datasets using Parselmouth library. In addition, based on the significance of features, the eight most significant features have been used in the design of the model. These features have been selected using genetic algorithm method. Four classifiers, k-nearest neighbors, XGBoost, random forest, and logistic regression, have been used during classification stage. The accuracy, sensitivity, f-measure, specificity, and precision parameters have been used for the analysis of the designed system. The combination of a genetic algorithm-based feature selection approach and logistic regression classifier has given 100% accuracy on Italian Parkinson's Voice & Speech dataset. The same feature extraction and classifier combination on the Mobile Device Voice Recordings at King's College London dataset have attained an accuracy level of 90%. Results have shown that the proposed system has outperformed the system found in the literature. [ABSTRACT FROM AUTHOR]

Nowadays, with the advances and use of technological possibilities and devices, the number of digital images is increasing gradually. Computer-aided classification of image types is widely applied in many applications such as medicine, security, and automation. The feature extraction and selection stages have great importance in terms of improving the classification performance as sub-stages of the pattern recognition process. Researchers apply different feature extraction methods for their works due to the requirements. In this study, a novel pattern recognition framework combining diverse and large-scale handcrafted feature extraction methods (shape-based and texture-based) and the selection stage on images is developed. Genetic algorithms are also used for feature selection. In the experimental studies, Flavia leaf recognition, Caltech101 object classification image datasets, and five supervised classification models (random forest, ECOC-SVM, k-nearest neighbor, AdaBoost, classification tree) with different parameters' values are used. The experimental results show that the proposed method achieves 98.39% and 82.77% accuracy rates on Flavia and Caltech101 datasets with the ECOC-SVM model, respectively. The proposed framework is also competitive with the existing state-of-the-art methods in the related literature. [ABSTRACT FROM AUTHOR]