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• Newbie study using standard ML techniques with HAR Application and Discussions. • Activities found in Literature with the corresponding reference. • Co-occurrences between activities and sensors with the corresponding reference. • Summary and comparison among the different datasets found in Literature. • Summary of the experimentation settings with performance scores found in Literature. Human Activity Recognition (HAR) is an essential area of research related to the ability of smartphones to retrieve information through embedded sensors and recognize the activity that humans are performing. Researchers have recognized people's activities by processing the data received from the sensors with Machine Learning Models. This work is intended to be a hands-on survey with practical's tables capable of guiding the reader through the sensors used in modern smartphones and highly cited developed machine learning models that perform human activity recognition. Several papers in the literature have been studied, paying attention to the preprocessing, feature extraction, feature selection, and classification techniques of the HAR system. In addition, several summary tables illustrating HAR approaches have been provided: most popular human activities in the literature with paper references, the most popular datasets available for download (Analyzing their characteristics, such as the number of subjects involved, the activities recorded, and the sensors with online-availability), co-occurrences between activities and sensors, and a summary table showing the performance obtained by researchers. =The paper's goal is to recommend, through the discussion phase and thanks to the tables, the current state of the art on this topic. [ABSTRACT FROM AUTHOR]

• Pinch force strength is accurately predicted using sEMG. • Different pinch movements are accurately classified using sEMG. • Similarity of EMG features is evaluated. • An EMG feature-to-image method is proposed. The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning. The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R 2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers. The proposed methodology has the potential to realize simultaneous force and movement control of prosthetic hand. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the world's population. Seizure detection and classification are difficult tasks and are ongoing challenges in biomedical signal processing to enhance medical diagnosis. This paper presents and highlights the unique frequency and amplitude information found within multiple seizure types, including their morphologies, to aid the development of future seizure classification algorithms. Whilst many published works in the literature have reported on seizure detection using electroencephalogram (EEG), there has yet to be an exhaustive review detailing multi-seizure type classification using EEG. Therefore, this paper also includes a detailed review of multi-seizure type classification performance based on the Temple University Hospital Seizure Corpus (TUSZ) dataset for focal and generalised classification, and multi-seizure type classification. Deep learning techniques have a higher overall average performance for focal and generalised classification compared to machine learning techniques, whereas hybrid deep learning approaches have the highest overall average performance for multi-seizure type classification. Finally, this paper also highlights the limitations of the TUSZ dataset and suggests some future work, including the curation of a standardised training and testing dataset from the TUSZ that would allow a proper comparison of classification methods and spur advancement in the field. [ABSTRACT FROM AUTHOR]

Automatic fault diagnosis in many systems is performed via the analysis of vibration data in the time or frequency domain. In the literature, many approaches for extracting featured from signals for fault diagnosis have been proposed. In this paper, we apply a variety of transform functions including Fourier, Wavelet, etc. to extract features from the vibration data, which includes the statistical features and some features automatically extracted via Convolutional Neural Networks (CNNs). For each of these feature extraction approaches, a learning algorithm is trained to diagnose the faults and their results are aggregated in an ensemble machine learning algorithm. The weights of the base learner algorithms are optimized via an evolutionary algorithm to achieve the best-weighted voting scheme. The architecture of CNNs has a significant effect on the performance of the algorithm, thus, in this paper, an evolutionary algorithm is proposed to find the best architecture for CNNs in fault diagnosis. The CNNs are trained via gradient descent algorithms which suffer from getting stuck in local optima. To manage this, we propose an evolutionary algorithm that benefits from the speed of gradient descent and the global search of evolutionary algorithms. The proposed algorithm is tested on a number of benchmark problems and the experimental results are presented. • An ensemble algorithm is proposed for fault diagnosis. • Different feature extractors are exploited. • An evolutionary training algorithm is proposed. [ABSTRACT FROM AUTHOR]

• The feature representation of the network was extracted from a randomly searched sequence of vertices. • In networks of scientific documents, representation learning needs to consider similarity and citation relationships. • This paper proposed a method to search and learn multiple paths connecting similar documents in a scientific document network. • From scientific papers and patent datasets, we found empirical evidence that our algorithm is suitable for representation learning. • This paper can contribute to preparing the basis for inducing innovation in science and technology from scientific documents. A network is a structure that can represent an organic relationship of observations. Network representation learning has the advantage of extracting latent features in a network. In recent years, various algorithms have been developed to embed network representation in a low-dimensional feature space. Conventional algorithms learn a sequence of vertices that are randomly searched from a network and convert a vertex into a vector. This study focused on the representation learning of document networks. Thus, this study proposes a scalable network representation learning algorithm that preserves the structure and content of the document network and reduces data sparsity. To do this, we used the shortest walks to extract a sequence of vertices that reflected semantic similarity from a network. The shortest walk is a method for finding several shortest paths that connect similar vertices. After this, the sequence of vertices was inputted into the language model, assuming that the sequence was a sentence. Scientific papers and patent datasets provided empirical evidence for the validity of our approach. The former, the bibliography network of scientific paper, consists of 17,716 nodes and 105,734 edges. The latter is a patent describing a technology for energy, representing a network of 24,894 nodes and 28,314 edges. The results showed that the proposed algorithm exhibited the lowest error of the tested methods in multi-class classification and link prediction tasks. The results of these tasks provided evidence that our algorithm is suitable for extracting feature representations of document networks. [ABSTRACT FROM AUTHOR]

• The global and multi-scale local causal features are extracted to interpret the model and improve diagnosis accuracy. • The performance of the initial model can be improved by adopting an extensible framework to add enhancement nodes. • The sample incremental learning is developed to update incrementally based on the previous models without retraining. • Two experiments on bearing components validate the effectiveness and superiority of the proposed method. Intelligent fault diagnosis (IFD) plays a significant function in ensuring the reliable operation of mechanical equipment. However, most existing IFD methods are trained in batch learning way and the feature extraction process is unexplainable and it is challenging to satisfy the complex diagnosis requirements. Thus, this paper proposes a causal broad learning model (CBLM) guided by global and multi-scale local causal features for incremental machinery fault diagnosis. Firstly, rich global and multi-scale local causal features are extracted for training CBLM. Then, incremental learning capability is developed to update model by expanding or modifying the original weights, considerably reducing the time consumption and greatly improving the computational efficiency. When the initial model has inadequate nodes for feature learning, CBLM performs structural incremental learning by adding extra nodes to improve the diagnostic performance. As new samples with various fault degrees are entered, sample incremental learning is built to rapidly update based on previous model without retraining. Finally, two experimental results on bearings indicate that CBLM improves the testing accuracy of the initial model by 0.13% to 52.22% and 1.95% to 57.64%, respectively, and remains above 97.22% and 99.77% for the updated models. The training time consumption is reduced by 25.06 s to 133.0 s, 20.78 s and 141.9 s, respectively, and the subsequent models update time are only about 12 s and 10 s. Consequently, the proposed CBLM is an effective online IFD method because it is obviously superior to the existing IFD methods in regards to time consumption and diagnosis accuracy. [ABSTRACT FROM AUTHOR]

Predictions of the oil price are critical to support intelligent decision-making for individual investors, governments, and corporations, but a challenging task since there are complex nonlinear and random fluctuations involved in series. Additionally, most existing methods mainly focus on the data-layer fitting, and inherit uninterpretable design flowcharts and unclear layer functions. Based on the fact that living things evolve numerous advanced cognition processes after long-term interactions with environments, it is a promising problem to design bionic deep prediction networks. In this paper, an a uditory-inspired m ulti- f eature c ollaboration network (AMFC-Net) is explored to predict the oil price, and includes the feature extraction, brain memory, and comprehensive prediction blocks. Specifically, through imitating that complex sounds are captured by auditory canals and converted into electrical impulses, the first block adopts four typical activation functions to extract multi-group complementary features for detecting nonlinear changes. Through imitating that electrical impulses are transformed to the left and right hemispheres for handling and analyzing, the second block realizes gating and cooperation mechanisms to learn long short-term dependencies and highlight core information. Through imitating that information is sent to higher cerebral cortices for sensing the environment, the third block maps relationships from features to targets for producing final predictions. In summary, multi-channel convolution operations establish the imitations of both form (clear functional layer) and spirit (layer-by-layer collaboration), which not only ensure the prediction effectiveness but also improve the AMFC-Net interpretability. Three experiments (comparison with 12 machine learning methods), three experiments (comparison with 8 hybrid methods under decomposition plus ensemble framework), and seven discussions under two real-world oil price datasets all indicate that the proposed AMFC-Net is superior and suitable to predict the oil price. [ABSTRACT FROM AUTHOR]

For the purpose of managing financial risk and making investment decisions, interval stock price forecasting is essential. Currently, decomposition integration frameworks are widely used in point-valued stock price forecasting studies, mainly focusing on mining internal information. However, point forecasts are difficult to adequately capture price uncertainty and may suffer from loss of volatile information. Therefore, this paper proposes an enhanced interval-valued decomposition integration model for stock price prediction based on comprehensive feature extraction and optimized deep learning. Firstly, the interval variational modal decomposition with feedback mechanism (FIVMD) is proposed to extract internal features and can decompose interval values into interval trend and residual. FIVMD not only solves the interval decomposition challenge, but also helps to improve the internal feature extraction capability. Secondly, while considering the influencing factors more comprehensively, appropriate feature selection and compression techniques can effectively achieve external feature extraction, obtain the best influencing factors, and improve the modeling capability of high-dimensional data. Finally, the final prediction results are obtained by modeling the interval trend and residuals separately through the optimization algorithm and deep learning model to improve the prediction accuracy. The results of the empirical analysis reveal that the proposed interval decomposition integrated model has the smallest of the three evaluation metrics, where the values of interval mean average percentage errors (IMAPE) are 1.8188%, 1.1244%, 1.9001%, and 2.1542% respectively. This shows that the model is significantly more accurate and stable than the other comparative models, and it is a successful model for predicting interval-valued stock prices. [ABSTRACT FROM AUTHOR]

At present, the serious challenge of human-robot collaboration is how to establish an interaction interface with multi-directional interactivity and robustness. This paper applies surface electromyography (sEMG) signals as the interface medium, and proposes a robust 3D arm strength estimation model based on hybrid deep learning networks (R3DNet) to build a stable interaction interface and achieve reliable human-robot collaboration performance in 3D operation scenarios. R3DNet adopts the modular design idea and includes a feature extraction module, a regression estimation module, and a robust enhancement module. In the feature extraction module, an autoencoder structure-based network and a new loss function are proposed to extract the fusion features of multi-channel sEMG signals. In the regression estimation module, an arm strength model based on transfer learning algorithms is proposed. This model considers the similarity of arm strength estimation in each direction and realizes the arm strength estimation from one to three dimensions through the fine-tune strategy. The robust enhancement module adopts a residual structure and aims to address the problem of poor model robustness due to non-ideal conditions such as electrode displacement, muscle fatigue, muscle time-varying characteristics and differences in muscle characteristics among subjects. Further, an improved adaptive admittance control method is proposed to help the robot to adjust collaborative behavior according to the changing arm strength of the human tutor. The results of comparison experiments and multi-point flexible assembly experiments verify the validity and engineering realizability of R3DNet for human-robot collaboration. [ABSTRACT FROM AUTHOR]

Animation drama and movies are important entertainment sources for children and young people. However, many of these entertainment videos introduce ideas such as violence, fighting, abuse, and car accidents, which are not suitable for children and young people. In order to prevent children from viewing these videos, different countries' government bodies put restrictions on age on these videos. Violence detection is mostly done using manual inspection, which is time and resource consuming and cannot achieve a very good result. Therefore, artificial technologies such as deep learning and machine learning can detect violence in a video. Bleeding is one of the distinct characteristics of violent actions presented in such videos. This paper aims to propose a system that can detect a given video's blood in real-time. Researchers have proposed different image processing-based techniques to detect violence, but their proposed systems lack precision and real-time detection. To this end, a deep learning-based approach is proposed to detect violence in a given video and images. A Faster R-CNN model is modified due to the sophisticated characteristics of the violence in the cartoon and animation images to achieve the highest performance. The backbone of the proposed violence detection model is changed to a modified RegNet model to extract the features from the frame. The standard inner lateral connection is replaced with the modulated deformable convolutional (MDC) layer to extract deformable feature maps. A novel distributed attention module (DAM) is proposed on the feature pyramid network to improve the performance of the feature extraction. The multiscale Region of interest (ROI) Align is adapted to improve the detection performance of violence in a different scenario. Moreover, the classification approach is integrated with the detection model to classify the different levels of violence in a given frame. We have explored state-of-the-art object detection methods such as Faster R-CNN, Cascade R-CNN, yolov3-spp, SSD, FCOS, and yolov5 to detect violence in a particular frame of a video. We compared the different models' performances and found out that the modified Faster R-CNN is appropriate for real-time blood detection in a given video or image with high accuracy. This study makes a significant contribution to detecting violence in animation videos which will help different platforms and government bodies to regulate entertainment content. [ABSTRACT FROM AUTHOR]

The human skin, the largest organ with multiple layered functionalities, houses melanocytes in the deeper strata of its epidermis. These cells can be adversely impacted by ultraviolet radiation, thereby instigating melanoma, the deadliest form of skin cancer. Failure to detect melanoma at an early stage can potentially lead to metastasis, forming complex tumors in other tissues. Despite substantial efforts, visual inspections can occasionally overlook melanoma cases due to inherent subjectivity. To surmount this challenge, an automated detection system is necessary. Recent attempts to establish such a system have predominantly employed "push-through" strategies involving deep (neural) networks and their ensembles, which however necessitate significant computational resources. This paper presents a novel approach, amalgamating a conventional machine learning technique, Bag of Visual Words, with a pretrained deep neural network for comprehensive deep feature extraction from enhanced input image patches. The proposed method, assessed on the ISIC Challenge 2017 dataset, surpassed all other entries on the challenge leader-board, registering an accuracy of 96.2% in the task of lesion classification. • Novel method for melanoma detection combining Bag of Visual Words with a deep network. • Patch-based feature extraction from lesion images integrated into BoVW with K-means. • Successful image enhancement technique for feature extraction from lesion images. • Efficient framework: modular architecture, easy updates, less training delay. [ABSTRACT FROM AUTHOR]

The presence of Beggiatoa Spp. indicates anoxic conditions or 'poor condition' in marine sediments beneath aquaculture pens, resulting from organic enrichment. Currently, the most efficient approach to estimate Beggiatoa Spp. coverage, and thus the extent of the issue, involves video surveys which are scored by human observers for presence of this bacteria. However, this approach is highly time-consuming and relies heavily on the expertise and experience of the individuals involved, thus affecting its accuracy. Machine learning-based computer vision techniques, such as Convolutional Neural Networks (CNNs), offer the potential for automated estimation of Beggiatoa Spp. coverage. However, most existing machine learning methods focus solely on the estimation of the coverage via presence, absence of a single type of Beggiatoa Spp.. These approaches typically rely on binary classification to distinguish the object from the background when estimating coverage. Nevertheless, the inclusion of subordinate categories within high-level classifications poses a great challenge for accurately estimating their coverage rates. In this paper, an adaptive ensemble learning approach was proposed to estimate Beggiatoa Spp. coverage. Unlike other approaches, the proposed approach is capable in adaptively extracting and fusing features from underwater images and accurately estimating the coverages of multiple types of Beggiatoa Spp. through ensemble learning. Experimental results demonstrated that our proposed approach outperforms other approaches in terms of both effectiveness and efficiency in estimating Beggiatoa Spp. coverage. • Adaptive feature extraction and fusion enhance relevant and informative features' identification. • Adaptive feature extraction and fusion enhance feature representation and reduce time cost. • Flexible ensemble learning framework improves accuracy, robustness, and efficiency. • Flexible ensemble learning framework can be extended to other models or applications. • Novel machine learning-based approach estimates subordinate categories of Beggiatoa Spp. coverage. [ABSTRACT FROM AUTHOR]

• A class incremental learning framework is proposed for open sets. • The framework is implemented based on multiple one-class classifiers (OCCs). • An improved deep SVDD is proposed as the basic OCC of the framework. • Several tricks (e.g., feature fusion, band attention) are used to improve OCC. • HSI combined with this method has broad application prospects in seed industry. Rapid and non-destructive classification of seed varieties is one of the important goals pursued by modern seed industry. Due to the large number of maize varieties in practice, collecting training samples that exhaust all varieties to train a classifier is extremely difficult. Therefore, maize seed classification in the real world faces the challenge of variety renewal and rejection of unknown varieties. This paper proposes an end-to-end trainable incremental learning (IL) framework based on hypercube data. This method achieves class-IL via learning one-class classifier (OCC) incrementally, and directly uses raw data as input without additional data preprocessing and feature extraction. The OCC is a dual deep support vector data description, which makes full use of spectral and spatial information to establish an exclusive hypersphere for a specific variety to receive the variety and reject unknown varieties. To remove the interference of redundant bands, a band attention and sparse constraint module is added to automatically assign the weights of redundant bands to zero, thereby maximally improving the performance of the model. Moreover, a new loss function is defined to alleviate the difficulty of parameter updating after sparse constraint. Experimental results on our open set indicate that the accuracy of the proposed method for receiving known varieties and rejecting unknown varieties are both above 91 %, which has significant advantages over the other two state-of-the-art IL methods. In the future, the corresponding OCCs can also be deleted from the whole framework according to the varieties eliminated by the government to reduce computational overhead and inter-class interference. Overall, the proposed method can perform both IL and open set recognition. [ABSTRACT FROM AUTHOR]

The continuous malicious software (malware) attacks on smartphones pose a serious threat to the security of users, especially the dominant platform Android. Data-driven methods based on machine learning algorithms are a promising way to defend against that. In this paper, we explore the limitations of this kind of methods in improving the performance of malware detection, e.g., considering each feature in isolation and relying on balanced class data, and propose a multi-model ensemble framework MEFDroid by combining individual predictors, where hybrid deep learning based feature extraction methods are adopted to learn meaningful features from raw data. Besides, a novel fusion scheme is exploited to fuse multi-level representations and mine their correlations to maximize the utilization of original information. To evaluate the effectiveness of the MEFDroid framework, we conduct a step-by-step model justification experiment to investigate how the proposed algorithms (i.e., ESAES, EDAES and EDAFS) enhance the malware detection performance gradually. We also compare the proposed algorithms with classical machine learning methods, conventional sampling solutions for the imbalance problem. Besides, we investigate the reliability of our proposed methods using another public dataset. Our extensive experimental results demonstrate that the target model EDAFS in MEFDroid outperforms others in terms of most metrics, which means that it is an effective alternative solution to detect Android malware. [ABSTRACT FROM AUTHOR]

Due to the presence of redundant and irrelevant features in large-dimensional biomedical datasets, the prediction accuracy of disease diagnosis can often be decreased. Therefore, it is important to adopt feature extraction methodologies that can deal with problem structures and identify underlying data patterns. In this paper, we propose a novel approach called the Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine (ACO-KSELM) to accurately predict different types of skin cancer by analyzing high-dimensional datasets. To evaluate the proposed ACO-KSELM method, we used four different skin cancer image datasets: ISIC 2016, ACS, HAM10000, and PAD-UFES-20. These dermoscopic image datasets were preprocessed using Gaussian filters to remove noise and artifacts, and relevant features based on color, texture, and shape were extracted using color histogram, Haralick texture, and Hu moment extraction approaches, respectively. Finally, the proposed ACO-KSELM method accurately predicted and classified the extracted features into Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Actinic Keratosis (ACK), Seborrheic Keratosis (SEK), Bowen's disease (BOD), Melanoma (MEL), and Nevus (NEV) categories. The analytical results showed that the proposed method achieved a higher rate of prediction accuracy of about 98.9%, 98.7%, 98.6%, and 97.9% for the ISIC 2016, ACS, HAM10000, and PAD-UFES-20 datasets, respectively. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing, wafer map defect pattern provides critical information for facility maintenance and yield management, so the classification of defect patterns is one of the most important tasks in the manufacturing process. In this paper, we propose a novel way to represent the shape of the defect pattern as a finite-dimensional vector, which will be used as an input for a neural network algorithm for classification. The main idea is to extract the topological features of each pattern by using the theory of persistent homology from topological data analysis (TDA). Through some experiments with a simulated dataset, we show that the proposed method is faster and much more efficient in training with higher accuracy, compared with the method using convolutional neural networks (CNN) which is the most common approach for wafer map defect pattern classification. Moreover, it was shown that our method outperforms the CNN-based method when the number of training data is not enough and is imbalanced. • A novel approach for wafer map defect pattern classification is proposed. • This new method is based on the theory from topological data analysis. • The proposed method is accurate and computationally efficient. • This algorithm is fast enough to provide a real-ti me prediction. • This method is robust against a small and imbalanced dataset. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is an irreversible disease that damages blood vessels and results in permanent visual impairment. Timely detection and proper diagnosis can mitigate severe eye impacts and slow DR progression. Color fundus images are commonly used by eye specialists to diagnose DR. However, manual operations are tedious, laborious, time-consuming, and error-prone. As a result, automated techniques are gaining increased attention in the medical domain due to their ability to precisely analyze unstructured clinical notes. In this paper, a novel Gannet-optimized deep belief network-based wavelet kernel extreme learning machine (GO-DBN-WKELM) technique is proposed for detecting DR occurrences and assessing its progressive stages. The GO-DBN-WKELM approach detects and classifies DR severities into six distinct classes: no DR (i.e., normal), early DR, mild non-proliferative DR, moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR. Initially, detailed information from the original datasets is extracted by reducing feature dimensions using the Deep Belief Network (DBN) model. Subsequently, the extracted images are analyzed by the proposed GO-DBN-WKELM classification model, which effectively detects and accurately classifies fundus images based on their severities into distinct classes. The enhanced detection performance is primarily due to the application of the GO algorithm with a wavelet kernel extreme learning machine (WKELM). The GO algorithm not only optimizes the kernel parameters of the WKELM but also increases the convergence speed of the classifier. The proposed classifier is analyzed using three different types of datasets, namely MESSIDOR, DIARETDB1, and IDRiD datasets. The effectiveness of the proposed classification model is determined by evaluating its ability to detect DR in terms of diverse performance metrics, such as accuracy, precision, recall, and F-measure. Simulation results reveal that the proposed GO-DBN-WKELM classifier achieved a high accuracy rate of approximately 98% for the MESSIDOR dataset and 97.8% for the DIARETDB1 dataset. These outcomes demonstrate the potential of the GO-DBN-WKELM approach in detecting and classifying diabetic retinopathy severities, offering an efficient, automated alternative to manual methods that can aid eye specialists in providing timely diagnoses and interventions for patients with this condition. [ABSTRACT FROM AUTHOR]

Nowadays, insider attacks are emerging as one of the top cybersecurity threats. However, the detection of insider threats is a more arduous task for many reasons. A significant cause is the availability of various data types related to insider activities and their possible behavioral drift. Another major reason is that threat activities rarely happen within any organizational environment and usually remain submerged within a massive amount of normal activities thereby creating data imbalance issues. Any insider threat event requires three major components to get materialized: proper motivation, suitable opportunity and a minimum skill set. The simultaneous occurrence of all these elements is rarely found in organizational environment compared to regular activity traits, and the data imbalance thus caused makes accurate detection of threat activities quite challenging. Existing insider threat detection techniques are mainly divided into statistical rule-based, machine learning-based, and deep learning-based methods. Although recent deep learning methods have been found to extract intrinsic behavioral properties from users' activity patterns more effectively than traditional rule-based and machine-learning methods by utilizing their multilayer architecture. But sporadic approaches prioritize critical sections of activity patterns in their detection scheme. Also, rare methods focused on taking advantage of multiple deep learning-based feature extraction models together in their detection process. Finally, rare methods have adequately focused on data imbalance issues, especially over the unequal proportion of different categories of threat instances. In this paper, we proposed an insider threat detection approach using an ensemble of stacked-LSTM and stacked-GRU-based attention models. Our models are first trained on the user's single-day sequential activity logs. Then a stacked ensemble of trained attention models is used to extract the user's single-day activity information in the form of the feature vector, which is finally used for classification. To address the data imbalance issues, we propose a new equally-weighted random sampling approach for balancing the population of the different categories of threat patterns. We randomly undersample the nonmalicious instances followed by random oversampling of the different categories of threat instances in an equally-weighted manner so that the training models can learn the behavioral characteristics of the different types of insider activity patterns without getting biased towards any particular type, which is a major limitation of random oversampling and random undersampling-based techniques. Experiments have been performed on the different versions of the CMU CERT insider threat datasets. For robust evaluation, stratified division-based train-test sets have been used based on different categories of insider activities. An average AUC of 0.99 on CMU CERT v4.2 and v5.2 datasets and 0.97 on its v6.2 dataset shows the robustness of the proposed approach in detecting insider threats. • Stacked ensemble of attention models-based insider threat detection approach. • Broad categorization of user activity improves insider threat detection process. • Equally weighted random sampling (EWRS) technique used for handling data imbalance. • Stratified division of insider threat dataset is used for evaluation purpose. • AUC score of 0.99 achieved on CMU CERT v4.2, v5.2 and 0.97 on v6.2 datasets. [ABSTRACT FROM AUTHOR]

We are living in an era in which daily interaction between individuals and businesses involves sending, uploading, and sharing videos as a means of communication and advertising. However, many users are unaware of the risks associated with opening a malicious video file, it is thus no surprise that cyber-criminals have taken advantage of this situation and adopted this attack vector in recent years. MP4 is one of the most commonly used video formats, and its properties make it well-suited for software vulnerability exploitation across multiple platforms, which can ultimately lead to a cyberattack. Due to their deterministic, signature-based technique, antivirus software solutions are limited in their ability to detect unknown malware, let alone zero-day attacks. Machine learning (ML) algorithms have been effective in detecting known and unknown malware across various file formats, domains, and platforms. ML algorithms' performance relies heavily on the feature extraction methodology. However, to the best of our knowledge, there is no designated and specialized feature extraction methodology for MP4 files which generates a set of features for the task of unknown MP4 file malware detection. In this paper, we present three innovative and efficient feature extraction methodologies for unknown MP4 file malware detection. Two of them are file structure-based and one is knowledge-based. The methodologies are evaluated in a series of five experiments using six ML algorithms and 177 different datasets which represent different configurations of feature extraction, representation, and selection. The datasets are based on a representative collection of 6,229 files − 5,066 benign (∼81.3 %) files and 1,163 malicious files (∼18.7 %). The first three experiments demonstrate the methodologies' discrimination and generalization capabilities across multiple configurations, in terms of known and unknown MP4 file malware detection. The fourth experiment shows that applying principal component analysis (PCA) on the features suggested by the methodologies can improve time and space complexity and feature resilience while maintaining strong detection and generalization capabilities. In the fifth experiment, the methodologies' best performing configuration is compared to state-of-the-art, generic feature extraction methodologies, such as n -grams, MinHash, and representation and transfer learning (using a CNN), in the task of unknown MP4 file malware detection. The results show that our best performing configuration outperforms all other state-of-the-art feature extraction methodologies with an AUC, TPR, and FPR of 0.9951, 0.976, and 0.0 respectively. [ABSTRACT FROM AUTHOR]

• Diabetic retinopathy (DR) detection from Fundus Images has been proposed. • Parallel CNN with fewer parameters and layers for distinctive feature extraction. • Extreme Learning Machine (ELM) technique was utilized for the DR classification. • The model demonstrated robustness for different types of datasets. • Accuracy upto 97.27 % was achieved and outscored state-of-the-art models. Diabetic retinopathy (DR) is an incurable retinal condition caused by excessive blood sugar that, if left untreated, can result in even blindness. A novel automated technique for DR detection has been proposed in this paper. To accentuate the lesions, the fundus images (FIs) were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE). A parallel convolutional neural network (PCNN) was employed for feature extraction and then the extreme learning machine (ELM) technique was utilized for the DR classification. In comparison to the similar CNN structure, the PCNN design uses fewer parameters and layers, which minimizes the time required to extract distinctive features. The effectiveness of the technique was evaluated on two datasets (Kaggle DR 2015 competition (Dataset 1; 34,984 FIs) and APTOS 2019 (3,662 FIs)), and the results are promising. For the two datasets mentioned, the proposed technique attained accuracies of 91.78 % and 97.27 % respectively. However, one of the study's subsidiary discoveries was that the proposed framework demonstrated stability for both larger and smaller datasets, as well as for balanced and imbalanced datasets. Furthermore, in terms of classifier performance metrics, model parameters and layers, and prediction time, the suggested approach outscored existing state-of-the-art models, which would add significant benefit for the medical practitioners in accurately identifying the DR. [ABSTRACT FROM AUTHOR]

• Extraction of image features for some classifiers. Selection of the five most successful classifiers for Phase-1 and Phase-2. Designing a system using majority voting and creating a theoretical framework. Designing the graphical user interface application. The COVID-19 pandemic has been affecting the world since December 2019, and nowadays, the number of infected is increasing rapidly. Chest X-ray images are clinical adjuncts that can be used in the diagnosis of COVID-19 disease. Because of the rapid spread of COVID-19 disease worldwide and the limited number of expert radiologists, the proposed method uses the automatic diagnosis method rather than a manual diagnosis method. In the paper, COVID-19 Positive/Negative (2275 Positive, 4626 Negative) and Normal/Pneumonia (2313 Normal, 2313 Pneumonia) are diagnosed using chest X-ray images. Herein, 80 % and 20 % of the images are used in the training and validation set, respectively. In the proposed method, six different classifiers are trained using chest X-ray images, and the five most successful classifiers are used in both phases. In Phase-1 and Phase-2, image features are extracted using the Bag of Features method for Cosine K-Nearest Neighbor (KNN), Linear Discriminant, Logistic Regression, Bagged Trees Ensemble, Medium Gaussian Support Vector Machine (SVM), excluding SqueezeNet Deep Learning (K = 2000 and K = 1500 for Phase-1 and Phase-2, respectively). In both phases, the five most successful classifiers are determined, and images classify with the help of the Majority Voting (Mathematical Evaluation) method. The application of the proposed method is designed for users to diagnose COVID-19 Positive, Normal, and Pneumonia. The results show that accuracy values obtained by Majority Voting (Mathematical Evaluation) method for Phase-1 and Phase-2 are equal to 99.86 % and 99.28 %, respectively. Thus, it indicates that the accuracy of the whole system is 99.63 %. When we analyze the classification performance metrics for Phase-1 and Phase-2, Specificity (%), Precision (%), Recall (%), F 1 Score (%), Area Under Curve (AUC), and Matthews Correlation Coefficient (MCC) are equal to 99.98–99.83–99.07–99.51–0.9974–0.9855 and 99.73–99.69–98.63–99.23–0.9928–0.9518, respectively. Moreover, if the classification performance metrics of the whole system are examined, it is seen that Specificity (%), Precision (%), Recall (%), F 1 Score (%), AUC, and MCC are 99.88, 99.78, 98.90, 99.40, 0.9956, and 0.9720 , respectively. When the studies in the literature are examined, the results show that the proposed model is better than its counterparts. Because the best performance metrics for the dataset used were obtained in this study. In addition, since the biphasic majority voting technique is used in the study, it is seen that the proposed model is more reliable. On the other hand, although there are tens of thousands of studies on this subject, the usability of these models is debatable since most of them do not have graphical user interface applications. Already, in artificial intelligence technologies, besides the performance of the developed models, their usability is also important. Because the developed models can generally be used by people who are less knowledgeable about artificial intelligence. [ABSTRACT FROM AUTHOR]

The curse of dimensionality is a common problem in classification tasks. However, feature selection is an exciting approach to deal with this type of problem by searching for a suboptimal feature set, either by eliminating irrelevant attributes, those with redundant information, or even both. Furthermore, although the use of machine learning techniques for the classification of drum cymbals is found in the literature, little attention has been paid to approaches that focus on classifying these instruments according to their bronze alloys. This paper aims to explore and evaluate the temporal information retrieved from audios, using TSFEL (Time Series Feature Extraction Library) as a tool to extract 18 temporal attributes, three feature selection approaches to assess these features, and logistic regression as a classifier. To this end, 276 audios referring to four drum cymbals of three different bronze alloys were captured in the studio through a sound acquisition procedure that took into account environment and microphone variations. Hence, one expects to find an optimal subset of features that contains enough information from the audios to achieve the best classification performance in the proposed problem. The experimental results show that a feature selection approach sequentially based on L 1 Regularization and Correlation Analysis was able to find a subset consisting of 5 attributes that achieved an average accuracy of 97.08%. • The computational model can identify the cymbals' bronze alloys through their sound. • A standardized impact procedure is developed for the audio signals acquisition. • Feature selection could reduce the dimensionality up to 70%, improving accuracy. • Logistic Regression with feature selection achieved high material classification rate. • High accuracy is attained even considering different sound capture perspectives. [ABSTRACT FROM AUTHOR]

The goal of few-shot learning is to use limited labeled samples to complete independent classification tasks. The feature extractor of few-shot learning needs to have a stronger feature expression ability to generalize in unseen novel classes. To further enhance the expressive ability, in this paper, we propose an inherited feature extraction method, named Base and Meta Feature Extraction (BMFE). Base feature represents the task-irrelevant classification information of each sample. Meta feature obtained by the proposed Triplet Meta-train Mechanism (TMM) inherits the classification information and also contains the task-related meta information of each sample. We concatenate both the base and meta features to complementarily express the rich information of each sample. Besides, instead of relying on limited support samples to obtain the prototype, we propose a novel unsupervised prototype correction module, named Prototype Self-updating (PSU). All unlabeled query samples in a few-shot test task participate in the iterative updating of each prototype in the task without training. Extensive experiments prove that our overall method can obtain richer features by BMFE and more accurate prototypes by PSU. Our overall method outperforms state-of-the-art methods on miniImageNet and tiredImageNet datasets, and especially under the 1-shot case we obtains 78.45% and 81.21% classification accuracy respectively. • BMFE can obtain the complementary features representation of FSL. • TMM mechanism can better perform meta-training of BMFE. • PSU can correct prototypes without training process. • The proposed CFPSU gets outstanding results on two standard datasets. [ABSTRACT FROM AUTHOR]

Disease diagnosis and prediction methods in biotechnology and medicine have significantly advanced over time. Consequently, analyzing raw gene expression is crucial for identifying diseases such as cancer. Interestingly, microarrays are a tool that records gene expression from deoxyribonucleic acid (DNA) or ribonucleic acid. This technique exhibits intriguing characteristics, such as generating high-dimensional data with a small sample size. However, in the case of such dataset, the classification model is prone to overfitting. This limitation can be overcome by reducing the dimensions of the microarray datasets to a reasonable number. Machine learning (ML)-based data reduction has recently achieved considerable attention in genomic research. Therefore, this review examines recent studies that present state-of-the-art data reduction and classification algorithms for microarray gene expression data to diagnose tumors and analyzes their performance. To the best of our knowledge, this is the first review that provides a comprehensive view of data preprocessing, dimensionality reduction, including feature (i.e., gene) selection, feature extraction, and their hybrid, and ML algorithms. The paper is structured as follows. First, this review summarizes several data preprocessing methods applied to gene expression datasets. Then, a detailed review of various ML-based feature selection algorithms, including filter, wrapper, embedded, ensemble, and hybrid algorithms, is discussed. These algorithms are examined under three main classes—supervised, unsupervised, and semisupervised ML. Next, the feature extraction and hybrid of feature extraction and selection algorithms are thoroughly reviewed. Furthermore, a detailed review of broadly applied ML algorithms to simplify tumor and nontumor classification using microarray datasets is presented. Finally, the challenges and open questions related to gene expression datasets for accurate cancer classification and detection are highlighted. [Display omitted] • A comprehensive review on microarray gene expression reduction is proposed. • A novel taxonomy of up-to-date gene reduction algorithms is presented. • The strength and limitations of each type of gene reduction algorithms are provided. • Provide an outline of the accurately used type of machine learning algorithms. • Analyze the performance of the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Zero-shot learning enables the recognition of classes not seen during training through the use of semantic information comprising a visual description of the class either in textual or attribute form. Despite the advances in the performance of zero-shot learning methods, most of the works do not explicitly exploit the correlation between the visual attributes of the image and their corresponding semantic attributes for learning discriminative visual features. In this paper, we introduce an attention-based strategy for deriving features from the image regions regarding the most prominent attributes of the image class. In particular, we train a Convolutional Neural Network (CNN) for image attribute prediction and use a gradient-weighted method for deriving the attention activation maps of the most salient image attributes. These maps are then incorporated into the feature extraction process of Zero-Shot Learning (ZSL) approaches for improving the discriminability of the features produced through the implicit inclusion of semantic information. For experimental validation, the performance of state-of-the-art ZSL methods was determined using features with and without the proposed attention model. Surprisingly, we discover that the proposed strategy degrades the performance of ZSL methods in classical ZSL datasets (AWA2), but it can significantly improve performance when using face datasets. Our experiments show that these results are a consequence of the interpretability of the dataset attributes, suggesting that existing ZSL datasets attributes are, in most cases, difficult to be identifiable in the image. Source code is available at https://github.com/CristianoPatricio/SGAM. • An attention-based method for improving the performance of zero-shot face recognition. • Derivation of Attribute Attention Maps (AAM) for the most salient image attributes. • Extensive visual analysis of the AAM obtained from both CelebA and ZSL datasets. [ABSTRACT FROM AUTHOR]

The growing rate of malware and its complexity demands a new approach to detecting evolving malware instead of relying only on high-level features such as opcodes, API calls, control flow graphs, etc. Moreover, extracting these features is an expensive and time-consuming because it requires disassembling or code execution. This paper presents a new malware detection architecture using image analysis & machine learning, requiring significantly fewer resources and does not depend upon code disassembling or execution. The collected binary samples are systematically labeled using the AVClass tool and clustering method based on similar characteristics of them. The labeled malicious program is visualized into grayscale images to extract the local and global textural features. The local textural features are extracted using SIFT, KAZE, and ORB descriptors, and global features are extracted using GIST, Hu Moments, and HOG. A bag of visual words (BoVW) algorithm is designed to select low-dimensional features and construct a local feature map of malware grayscale images. The feature maps from different image descriptors are stacked and used to train five machine learning algorithms, namely, k-Nearest Neighbor (k-NN), Support Vector Machine(SVM), Random Forest (RF), Naive Bayes(NB), and ExtraTree classifier. Two datasets are used for evaluations — the public MalImg dataset of 9339 samples of 25 different families and 690 real-world malware of 22 families collected on honeypots in the wild. Intensive experiments are performed for image descriptors, image ratios, vocabulary size, and computational time as a mean time to detection(MTTD) to devise the best detector. The proposed method obtained test accuracy of 98.34% with stacked global features and 98.23% with stacked local features. Test accuracy of 92.75% with low false-positive rates is obtained for real-world recent malware datasets. Experiment results reveal the efficacy of the proposed method in detecting polymorphic obfuscated malware. Finally, a comparison with other similar malware detection systems is presented. • A new architecture of malware detection and classification using machine learning. • Novel use of hybrid textural features and bag of visual words. • A single image descriptor has difficulty capturing complex patterns of malware. [ABSTRACT FROM AUTHOR]

Machine learning has become an appealing signature-less approach to detect and classify malware because of its ability to generalize to never-before-seen samples and to handle large volumes of data. While traditional feature-based approaches rely on the manual design of hand-crafted features based on experts' knowledge of the domain, deep learning approaches replace the manual feature engineering process by an underlying system, typically consisting of a neural network with multiple layers, that perform both feature learning and classification altogether. However, the combination of both approaches could substantially enhance detection systems. In this paper we present an hybrid approach to address the task of malware classification by fusing multiple types of features defined by experts and features learned through deep learning from raw data. In particular, our approach relies on deep learning to extract N-gram like features from the assembly language instructions and the bytes of malware, and texture patterns and shapelet-based features from malware's grayscale image representation and structural entropy, respectively. These deep features are later passed as input to a gradient boosting model that combines the deep features and the hand-crafted features using an early-fusion mechanism. The suitability of our approach has been evaluated on the Microsoft Malware Classification Challenge benchmark and results show that the proposed solution achieves state-of-the-art performance and outperforms gradient boosting and deep learning methods in the literature. • It presents an hybrid system for malware classification. • It provides a detailed description of hand-crafted and deep features. • It presents an effective mechanism to fuse hand-crafted and deep features. • It successfully combines the benefits of feature engineering and deep learning. • It performs an exhaustive evaluation of the features on the Microsoft benchmark. [ABSTRACT FROM AUTHOR]

Dysphagia is a widespread swallowing disorder. One of the most widely used clinical assessments is the bedside swallowing disorder screening, including the water swallow test. This method can only diagnose the disease according to the choking situation of the liquid, and the aspiration may appear during the drinking water test. In this paper, an automatic diagnostic system is designed according to the patient's performance in pronunciation. To diagnose dysphagia, we treat it as a binary classification task. We use a throat vibrator to collect the vibration signals of the subjects during pronunciation for clear speech signals. Feature engineering techniques, including feature extraction, data balancing and dimensionality reduction are implemented to extract the features for classification. Three popular classifiers are integrated to make the final predictions. Experimental results show that the integrated classifier performs better with accuracy = 72.09%, sensitivity = 63.64% and specificity = 80.95% respectively. Different from the traditional clinical ways, a new machine learning based swallowing disorders detection system is proposed with vibration signals of throat acquired for high-quality speech analysis. It may help to realize a portal and low-cost telemedicine device for dysphagia diagnosis at home. • We achieve an early screening of dysphagia based on speech analysis. • We use a new data acquisition system to collect clear throat vibration. • We design a new integrated classifier that performs well. • A new group of speech features is tested and performs better than previous methods. [ABSTRACT FROM AUTHOR]

• 3 layer neural networks outperform other ML algorithms in automated assessments. • In subjective assignments, deep neural networks can improve score prediction. • Word embeddings and linguistic feature results in more precise text mining models. • Extraction and selection of appropriate features can improve results. Machine learning (ML) has recently gained popularity in a variety of domains for automating tasks. This has also been used in the evaluation of student responses/ assignments. This paper presents a qualitatively enhanced methodology for automated score prediction of subjective assignments. To consider all the major aspects of a human grader, 21 linguistic features related to syntactical, grammatical, sentimental, and readability are analyzed from the assignments. This study makes use of the ASAP competition dataset from Kaggle. The evaluation metric used is Quadratic Weighted Kappa (QWK), which measures the agreement between the human graded score and the predicted score. The effect of appropriate feature selection has been observed using Mutual Information Regression. Four ML algorithms are investigated on identified linguistic features. 3 Layer Neural Network with feature selection performed well among all chosen ML algorithms with average QWK of 0.678. To include the benefits of deep learning, a new hybrid model (LF-BiLSTM-att-FS) is proposed that combines a higher level deep neural network (DNN) with the selected features. Pre-trained Glove embedding is used to include contextual information of the text. The proposed model results demonstrated an improvement in overall accuracy, with an average QWK value of 0.768. [ABSTRACT FROM AUTHOR]

• We propose an end-to-end automated cryptocurrency trading system. • We detect and remove the periods with unusual price movements. • We use various classification algorithms for the prediction of price direction. • The outlier detection step significantly increases return on investment. • During the highly volatile periods the trading system becomes more profitable. The cryptocurrency market, which has a rapidly growing market size, attracts the increasing attention of individual and institutional investors. While this highly volatile market offers great profit opportunities to investors, it also brings risks due to its sensitivity to speculative news and the unpredictable behaviour of major investors that can cause unsual price movements. In this paper, we argue that rapid and high price fluctuations or unusual patterns that occur in this way may negatively affect the functionality of technical signals that constitute a basis for feature extraction in a machine learning (ML)-based trading system and this may cause the generalization of the model to deteriorate. To address this problem, we propose an end-to-end ML-based trading system including a time series outlier detection module that detects the periods in which unusual price formations are observed. The training of the classification algorithms for the price direction prediction task was performed on the remaining data. We present the results related to the accuracy of the classification models as well as the simulation results obtained using the proposed system for real time trading on the historical data. The findings showed that the outlier detection step significantly increases return on investment for the machine learning-based trading strategies. Besides, the results showed that during the highly volatile periods the trading system becomes more profitable compared to the baseline model and buy&hold strategy. [ABSTRACT FROM AUTHOR]

Recently the most infectious disease is the novel Coronavirus disease (COVID 19) creates a devastating effect on public health in more than 200 countries in the world. Since the detection of COVID19 using reverse transcription-polymerase chain reaction (RT-PCR) is time-consuming and error-prone, the alternative solution of detection is Computed Tomography (CT) images. In this paper, Contrast Limited Histogram Equalization (CLAHE) was applied to CT images as a preprocessing step for enhancing the quality of the images. After that, we developed a novel Convolutional Neural Network (CNN) model that extracted 100 prominent features from a total of 2482 CT scan images. These extracted features were then deployed to various machine learning algorithms — Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Finally, we proposed an ensemble model for the COVID19 CT image classification. We also showed various performance comparisons with the state-of-art methods. Our proposed model outperforms the state-of-art models and achieved an accuracy, precision, and recall score of 99.73%, 99.46%, and 100%, respectively. • Achieved an accuracy of 99.73% and a recall of 100%. • Enhance the quality of the CT scan images using CLAHE. • Build a novel CNN for extracting the most relevant features from the CT scan images. • Develop a soft voting ensemble learning model for improving the performance. [ABSTRACT FROM AUTHOR]

Complex welding processes and harsh industrial environment inevitably affect the tailored welding quality of large workpieces, and remain the major cause of surface defects. In this paper, an online defect detection method combining the improved incremental learning algorithm and the network-based recognition algorithm is proposed to improve the performance of real-time processing. The incremental learning algorithm, called generalized incremental two-dimensional principal component analysis (GI2DPCA), rapidly extracts the relevant pattern features from weld surface images. The principal components of weld images are incrementally estimated in a recursive form, rather than by directly solving the image covariance matrices. The GI2DPCA reduces the influence of irrelevant feature changes on the convergence rates of principal components. To improve the classification speed, the artificial feedforward neural network (FNN), instead of various complex deep learning networks, is applied to identify defects online by assigning the features extracted from images to weld classes. The proposed algorithm is evaluated on 3600 classified weld images, collected from different sources, and tested on the actual running platform. The experimental results indicate that the proposed algorithm is capable of enhancing the performances of the convergence rate and the classification rate, and the average processing speed also meets the real-time requirements for online defect detection. • Online detection by applying incremental learning approach to infer weld defects. • Implementation of a generalized incremental 2DPCA in a recursive form. • Validation of the detection from the weld dataset and the actual running videos. • Easy expansion by incorporating more defect classes with different weld widths. [ABSTRACT FROM AUTHOR]

Hierarchical multi-label text classification (HMTC) has been gaining popularity in recent years thanks to its applicability to a plethora of real-world applications. The existing HMTC algorithms largely focus on the design of classifiers, such as the local, global, or a combination of them. However, very few studies have focused on hierarchical feature extraction and explore the association between the hierarchical labels and the text. In this paper, we propose a L abel-based A ttention for H ierarchical Multi-label Text C lassification Neural N etwork (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels. Besides, hierarchical information is shared across levels while preserving the hierarchical label-based information. Separate local and global document embeddings are obtained and used to facilitate the respective local and global classifications. In our experiments, LA-HCN outperforms other state-of-the-art neural network-based HMTC algorithms on four public HMTC datasets. The ablation study also demonstrates the effectiveness of the proposed label-based attention module as well as the novel local and global embeddings and classifications. By visualizing the learned attention (words), we find that LA-HCN is able to extract meaningful information corresponding to the different labels which provides explainability that may be helpful for the human analyst. • Proposed label-based attention can improve performance and provide interpretability. • Information can be shared across hierarchical layers while avoiding error propagation. • Respective document embedding is generated for local and global classification to prevent error propagation. [ABSTRACT FROM AUTHOR]

Feature selection in real-world data mining problems is essential to make the learning task efficient and more accurate. Identifying the best feature selection algorithm, among the many available, is a complex activity that still relies heavily on human experts or some random trial-and-error procedure. Thus, the automated machine learning community has taken some steps towards the automation of this process. In this paper, we address the metalearning challenge of recommending feature selection algorithms by proposing a novel meta-feature engineering model. Our model considers a broad collection of meta-features that enable the study of the relationship between the dataset properties and the feature selection algorithm performance in terms of several criteria. We arrange the input meta-features into eight categories: (i) simple, (ii) statistical, (iii) information-theoretical, (iv) complexity, (v) landmarking, (vi) based on symbolic models, (vii) based on images, and (viii) based on complex networks (graphs). The target meta-features emerge from a multi-criteria performance measure, based on five individual performance indexes, that assesses feature selection methods grounded in information, distance, dependence, consistency, and precision measures. We evaluate our proposal using a recently developed framework that extracts the input meta-features from 213 benchmark datasets, and ranks the assessed feature selection algorithms, to fill in the target meta-features in meta-bases. This evaluation uses five state-of-the-art classification methods to induce recommendation models from meta-bases: C4.5, Random Forest, XGBoost, ANN, and SVM. The results showed that it is possible to reach an average accuracy of up to 90% applying our meta-feature engineering model. This work is the first to use an extensive empirical evaluation to provide a careful discussion of the strengths and limitations of more than 160 meta-features. These meta-features, while designed to aid the task of feature selection algorithm recommendation, can readily be employed in other metalearning scenarios. Therefore, we believe our findings are a valuable contribution to the fields of automated machine learning and data mining, as well as to the feature extraction and pattern recognition communities. • A novel meta-feature engineering model recommends feature selection algorithms. • The proposal obtains promising results from 213 datasets with hit rates of up to 90%. • Some simple, landmarking, image, and graph-based input meta-features highlighted. • A multi-criteria performance measure rigorously assesses candidate algorithms. • Chains of binary or multiclass classifiers can efficiently rank candidate algorithms. [ABSTRACT FROM AUTHOR]

Due to the actual operation of the wind turbine, the collected fault data sets are limited, and it is difficult to realize fault diagnosis through the correlation between variables. To solve this problem, a fault diagnosis method based on virtual expansion and spherical mapping model is proposed in this paper. Firstly, Hermite interpolation is applied to discrete wind power data samples to obtain an interpolation curve about the characteristics of the sample, and a synchronous sampling method is adopted for the interpolation curve to construct a virtual sample. Then, the features of the virtual sample are mapped to a three-dimensional space. Define the spherical data model and perform spherical fitting in a three-dimensional coordinate system. Finally, feature extraction is performed on the fitted spherical surface for training and testing extreme learning machine (ELM). The distribution law of fault data in the spherical model is summarized. Using the data generated based on the Bootstrap method as a control group, comparative experiments were carried out in back-propagation neural network (BP), Probabilistic Neural Network (PNN), General Regression Neural Network (GRNN), and Support Vector Machine (SVM), which verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Integration of deterministic and multiple kernel learning approach. • Feature vectors are determined based on holistic and local appearance. • Hidden layer parameters are analytically designed rather than random selection. • Resultant Kernel function is linear combination of pre-specified kernels. • Applicable for classification problems containing heterogeneous data. The Extreme learning machine (ELM) designed by Huang et al. is proved to be a fast and good classifier over a decade, but existing ELM is non-deterministic in nature as well as kernel dependent and needs attention to optimize the selection of kernels. In ELM feature space is obtained with the help of single kernel function. The choice of kernel depends on perceptiveness of classification problem. So a generalized framework with deterministic nature along with optimized kernel is ought to be designed that can be applied to large domain of real world heterogeneous pattern classification problems. This paper presents a deterministic extreme learning machine for neural network with feedforward architecture which is formulated with multiple kernel learning. We further enhance this approach by incorporating Gray level co-occurrence matrix (GLCM) for multi-modal feature extraction. Two formulation of kernel extreme learning machine are introduced, with target kernel function as a linear combination of different base kernels. The first one is based on deterministic multiple kernel learning while the second one uses deterministic multiple kernel learning along with GLCM for extracting the invariant feature vectors. The performance of proposed algorithms are analyzed on pattern recognition problem for face recognition by changing the number of training set, types of kernel used and coefficients used for combining base kernels. The superior recognition rate is achieved for prominent multi-class face databases, when compared with contemporary methods that prove the efficacy of proposed algorithms. [ABSTRACT FROM AUTHOR]