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Automated and accurate detection of power quality (PQ) events is important from the point of view of safety as well as maintaining the reliability of the power transmission and distribution network. However, detection of multiple PQ events in a noisy environment is challenging task. Another important issue is the choice of meaningful features that can directly influence the accuracy of PQ detection. Considering these two aforesaid facts, this paper presents a novel framework for automated classification of PQ signals in a noisy environment employing cross Stockwell Transform (XST). The XST proposed in this paper has better noise suppression capability compared to conventional Stockwell Transform. Here, XST was used to convert 1D PQ signals to 2D time–frequency (T–F) images. To improve the accuracy of PQ detection, an automated feature extraction method employing deep learning is implemented in this work. The noise free T–F images obtained using XST were fed as inputs to several pre-trained convolutional neural networks (CNNs) for deep feature extraction. Transfer learning technique was implemented to reduce the computational cost. The extracted deep features were further undergone selection using one-way analysis of variance test followed by false discovery rate correction. The statistically significant deep features were subsequently fed to three benchmark machine learning classifiers for classification of PQ signals. In addition, tests were also carried out on real-life PQ signals to verify the practicability of the proposed framework. Investigations revealed that the proposed method returned mean accuracy of 99.72% and 96.45% for classification of simulated and real-life PQ signals, respectively. [ABSTRACT FROM AUTHOR]

With the gradual growth of deep learning in machine vision, efficient extraction of 3D point clouds becomes significant. The raw data of the 3D point cloud are sparse, disordered, and immersed in noise, which makes it difficult to classify and segment. Whether 3D point clouds can be classified and segmented or not, the local feature is an essential ingredient. Therefore, this paper proposes a GateNet-based PointNet++ network (G-PointNet++). G-PointNet++ extracts local features more accurately than PointNet++ by suppressing irrelevant features and emphasizing important features. Meanwhile, it refines the feature adaptively. Besides, the SENet and attention mechanism are introduced into PointNet++. G-PointNet++ was evaluated on the public ModelNet dataset, ShapeNet dataset, and S3DIS dataset, and its effectiveness in classification and segmentation tasks was verified. In the classification task, G-PointNet++ achieves an overall classification accuracy (OA) of 95.5% on ModelNet10 and 93.3% on ModelNet40. In the segmentation task, the mIoU of G-PointNet++ reaches 85.5% on ShapeNet. These experiments show that G-PointNet++ achieves better performance and saves more time than PointNet++, and its overall accuracy is higher than that of PCT network on ModeNet40. [ABSTRACT FROM AUTHOR]

With the increased use of social network sites, such as Twitter, attackers exploit these platforms to spread counterfeit content. Such content can be fake advertisements or illegal content. Classifying such content is a challenging task, especially in Arabic. The Arabic language has a complex structure and makes classification tasks more difficult. This paper presents an approach to classifying Arabic tweets using classical machine learning (non-deep machine learning) and deep learning techniques. Tweets corpus were collected through Twitter API and labelled manually to get a reliable dataset. For an efficient classifier, feature extraction is applied to the corpus dataset. Then, two learning techniques are used for each feature extraction technique on the created dataset using N-gram models (uni-gram, bi-gram, and char-gram). The applied classical machine learning algorithms are support vector machines, neural networks, logistics regression, and naïve Bayes. Global vector (GloVe) and fastText learning models are utilised for the deep learning approaches. The Precision, Recall, and F1-score are the suggested performance measures calculated in this paper. Afterwards, the dataset is increased using the synthetic minority oversampling technique class to create a balanced dataset. After applying the classical machine learning models, the experimental results show that the neural network algorithm outperforms the other algorithms. Moreover, the GloVe outperforms the fastText model for the deep learning approach. [ABSTRACT FROM AUTHOR]

With the advancement of smartphone technology, the development of mobile applications is rapidly growing. These apps are designed to help mobile users with a variety of everyday tasks, such as e-commerce and online services. Because these applications are widely used, they are susceptible to malicious user attacks. As a result, new challenges have emerged, such as the inability to differentiate between benign and malicious malware applications. This paper proposes two methods for classifying mobile applications into either benign or malicious: 1D convolution (Conv1d) and long short-term memory (LSTM). The suggested approaches use two encoding techniques, namely binary and text encoding, which were applied to the Android permissions of each application. In addition, the support vector machine and K-nearest-neighbor classifiers are reported as well. The two primary approaches were tested on the well-known CICMalDroid2020 dataset. Conv1d and LSTM with text encoding performed the best in terms of precision and accuracy (98.16%, 97.72%, and 96.63%, 96.69%, respectively). When compared with the Mal-Prem dataset, the Conv1d on binary classification beat the LSTM model. [ABSTRACT FROM AUTHOR]

In the modern era, breast cancer (BC) is one of the most prevalent diseases affecting the lifespan of women. Single nucleotide polymorphism (SNP) elucidates an enormous proportion of the hazard in women with a solid family history. Different types of human disorders have been analyzed using Machine Learning methods to locate the vital SNP. The identification of an optimal feature set is the primary constraint in the existing methods owing to the ill effects of multidimensionality. Thus, a novel Bio-Inspired Ensemble Feature Selection (BIEFS) technique has been proposed in this paper to identify the most relevant SNP for accurate classification of BC. An initial feature subset is generated from each base feature selector such as Membership Weight Salp Swarm Algorithm (MWSSA), Crossover Horse Herd Optimization (CHHO), and Levy Mutation Manta-Ray Foraging Optimization (LMMRFO). Then the proposed BIEFS technique obtains the optimized weight of each feature subset through the mutation operator. Finally, the Self-Organizing Deep Auto-Encoder (SODAE) is employed for BC classification. A Gene Expression Omnibus (GEO) dataset is used to assess the proposed methodology. Simulation results validate that the proposed methodology attains a maximum accuracy of 98.75% as compared to the conventional techniques. [ABSTRACT FROM AUTHOR]

Skin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer cases is a skin cancer). Such an increase can be attributed to changes in our social and lifestyle habits coupled with devastating man-made alterations to the global ecosystem. Despite such a notable increase, diagnosis of skin cancer is still challenging, which becomes critical as its early detection is crucial for increasing the overall survival rate. This calls for advancements of innovative computer-aided systems to assist medical experts with their decision making. In this context, there has been a recent surge of interest in machine learning (ML), in particular, deep neural networks (DNNs), to provide complementary assistance to expert physicians. While DNNs have a high processing capacity far beyond that of human experts, their outputs are deterministic, i.e., providing estimates without prediction confidence. Therefore, it is of paramount importance to develop DNNs with uncertainty-awareness to provide confidence in their predictions. Monte Carlo dropout (MCD) is vastly used for uncertainty quantification; however, MCD suffers from overconfidence and being miss calibrated. In this paper, we use MCD algorithm to develop an uncertainty-aware DNN that assigns high predictive entropy to erroneous predictions and enable the model to optimize the hyper-parameters during training, which leads to more accurate uncertainty quantification. We use two synthetic (two moons and blobs) and a real dataset (skin cancer) to validate our algorithm. Our experiments on these datasets prove effectiveness of our approach in quantifying reliable uncertainty. Our method achieved 85.65 ± 0.18 prediction accuracy, 83.03 ± 0.25 uncertainty accuracy, and 1.93 ± 0.3 expected calibration error outperforming vanilla MCD and MCD with loss enhanced based on predicted entropy. [ABSTRACT FROM AUTHOR]

Improving and enhancing the effectiveness of software vulnerability detection methods is urgently needed today. In this study, we propose a new source code vulnerability detection method based on intelligent and advanced computational algorithms. It's a combination of four main processing techniques including (i) Source Embedding, (ii) Feature Learning, (iii) Resampling Data, and (iv) Classification. The Source Embedding method will perform the task of analyzing and standardizing the source code based on the Joern tool and the data mining algorithm. The Feature Learning model has the function of aggregating and extracting source code attribute based on node using machine learning and deep learning methods. The Resampling Data technique will perform equalization of the experimental dataset. Finally, the Classification model has the function of detecting source code vulnerabilities. The novelty and uniqueness of the new intelligent cognitive computing method is the combination and synchronous use of many different data extracting techniques to compute, represent, and extract the properties of the source code. With this new calculation method, many significant unusual properties and features of the vulnerability have been synthesized and extracted. To prove the superiority of the proposed method, we experiment to detect source code vulnerabilities based on the Verum dataset, details of this part are presented in the experimental section. The experimental results show that the method proposed in the paper has brought good results on all measures. These results have shown to be the best research results for the source code vulnerability detection task using the Verum dataset according to our survey to date. With such results, the proposal in this study is not only meaningful in terms of science but also in practical terms when the method of using intelligent cognitive computing techniques to analyze and evaluate source code has helped to improve the efficiency of the source code analysis and vulnerability detection process. [ABSTRACT FROM AUTHOR]

Heartbeat classification plays an important role in identifying cardiac arrhythmias. Although automated heartbeat classification approaches have been broadly reported, they still suffer from some disadvantages, such as the inescapable domain knowledge and human ingenuity for hand-crafted heartbeat feature engineering, and the slow learning speed for iterative deep learning heartbeat classification approach. To address the above issues, for the first time, this paper proposes a novel multilayer extreme learning machine (ML-ELM) heartbeat classification approach for feature representation and classification of the heartbeat signals. Different from the iterative optimization scheme in traditional neural networks, ELM autoencoder (ELM-AE) is employed in ML-ELM for random mapping of the input weights and non-iterative learning is implemented for output weights. To realize unsupervised self-encoding heartbeat feature mapping, the output of ELM-AE is consistent with the incoming heartbeat signal, and the optimization objective is to minimize the reconstruction error. Heartbeat feature extraction is efficiently performed by layer-by-layer ELM-AE stacking. In the stage of heartbeat classification, to solve the unstable performance caused by random parameter initialization in ELM hidden nodes, bagging-based ensemble learning is employed to combine several ELM classifiers and thus develops a well-performed heartbeat classification approach. The proposed approach is applied to two-lead ECG signals, which is obtained from the MIT-BIH arrhythmia public dataset. The experimental results show that the ELM-AE based feature extraction can effectively characterize the characteristics of the heartbeat signal with high efficiency compared with other state-of-the-art approaches. Applying the ensemble decision fusion to two leads, the final classification accuracy reaches 99.41%. [ABSTRACT FROM AUTHOR]

Cardiac-related disorders are rapidly growing throughout the world. Accurate classification of cardiovascular diseases is an important research topic in healthcare. During COVID-19, auscultating heart sounds was challenging as health workers and doctors wear protective clothing, and direct contact with patients can spread the outbreak. Thus, contactless auscultation of heart sound is necessary. In this paper, a low-cost ear contactless stethoscope is designed where auscultation is done with the help of a bluetooth-enabled micro speaker instead of an earpiece. The PCG recordings are further compared with other standard electronic stethoscopes like Littman 3 M. This work is made to improve the performance of deep learning-based classifiers like recurrent neural networks (RNN) and convolutional neural networks (CNN) for different valvular heart problems using tuning of hyperparameters like learning rate of optimizers, dropout rate, and hidden layer. Hyper-parameter tuning is used to optimize the performances of various deep learning models and their learning curves for real-time analysis. The acoustic, time, and frequency domain features are used in this research. The investigation is made on the heart sounds of normal and diseased patients available from the standard data repository to train the software models. The proposed CNN-based inception network model achieved an accuracy of 99.65 ± 0.06% on the test dataset with a sensitivity of 98.8 ± 0.05% and specificity of 98.2 ± 0.19%. The proposed hybrid CNN-RNN architecture attained 91.17 ± 0.03% accuracy on test data after hyperparameter optimization, whereas the LSTM-based RNN model achieved 82.32 ± 0.11% accuracy. Finally, the evaluated results were compared with machine learning algorithms, and the improved CNN-based Inception Net model is the most effective among others. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. [ABSTRACT FROM AUTHOR]

The concurrence of state-of-the-art Industrial 5G, Cyber-Physical Systems, Smart-Systems, Industrial Internet of Things, and Additive Manufacturing paves the next-level digital remodeling. However, the transfiguration unwittingly tailpiece an operational onus on the smart-environment operators. The multiplicity and classes of IoT devices operating in the intelligent environment are myriad. The characterization of ingress network traffic and the accurate classification of devices is necessary to efficiently manage the devices and offer cutting-edge security solutions and quality of Service (QoS). The paper addresses these challenges by offering a novel intelligent framework for traffic classification leveraging behavioral attributes of IoT traffic. The paper's contributions to the research community are fourfold. Firstly, the paper proposes a novel IoT classification framework based on Stack-Ensemble for real-time high-volume IoT traffic. The experimental results indicate that the proposed novel Stack Ensemble model can extract the best out of base models and demonstrate an accuracy of 99.94%. The intelligent models are evaluated over multiple dimensions to project the isometric view of the model performance and the experimental results. To achieve that goal, all the performance metrics that most researchers most often miss have been elucidated. Secondly, the paper comprehends the flow-level statistical characteristics of IoT devices. Third, the paper offers the distributed, scalable, and portable framework architecture. The architecture is horizontally scalable, distributing the computational load. The framework offers an end-to-end industry-grade machine-learning pipeline and triumphs the vulnerabilities of existing solutions. Finally, the paper discusses the statistical insights into the intelligent model and the results of the experimentation study. The proposed work paves the opportunities for researchers, smart-environment operators, and developers to unfold the architecture and supplement the security solutions against cyber-attacks. [ABSTRACT FROM AUTHOR]

This paper proposes a novel deep learning-based approach for financial chart patterns classification. Convolutional neural networks (CNNs) have made notable achievements in image recognition and computer vision applications. These networks are usually based on two-dimensional convolutional neural networks (2D CNNs). In this paper, we describe the design and implementation of one-dimensional convolutional neural networks (1D CNNs) for the classification of chart patterns from financial time series. The proposed 1D CNN model is compared against support vector machine, extreme learning machine, long short-term memory, rule-based and dynamic time warping. Experimental results on synthetic datasets reveal that the accuracy of 1D CNN is highest among all the methods evaluated. Results on real datasets also reveal that chart patterns identified by 1D CNN are also the most recognized instances when they are compared to those classified by other methods. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is one of the most common inflammatory neurological diseases in young adults. There are three types of MS: (1) In relapsing remitting MS (RRMS), people have temporarily periods of relapses (attacks) for days or weeks, and then symptoms seem to disappear (remitting stage). (2) In secondary progressive MS (SPMS), symptoms worsen more steadily over time. Attacks (relapses) may occur time to time but the disease can progress in non-attack periods. It is estimated that half of the RRMS patients progress to SPMS in 10 years. (3) Primary progressive MS (PPMS) is characterized by slowly worsening symptoms from the beginning, with no relapses or remissions. For PPMS patients, disability progresses slowly. Researchers have found out that in the first year, MS causes more damage than following 5–10 years. Therefore, early diagnosis is vital. In this context, deep learning models started to be popular for assisting identification/diagnosis/classification of MS patients using magnetic resonance imaging (MRI). This paper provides an in-depth review of deep learning approaches for identification and classification of MS using brain MRI images. We discuss recent trends of deep learning methods for MS identification under three categories: CNN models, hybrid models (CNN with a classifier) and deep transfer learning models. Existing deep learning algorithms are analyzed and compared according to their architecture, image modality, pre-processing, feature extraction, classifier, dataset, categories and accuracy. This survey paper would provide a valuable source for researchers who are interested in state-of-the-art deep learning methods for MS identification using MRI images. [ABSTRACT FROM AUTHOR]

Financial time series have been extensively studied within the past decades; however, the advent of machine learning and deep neural networks opened new horizons to apply supercomputing techniques to extract more insights from the underlying patterns of price data. This paper presents a tri-state labeling approach to classify the underlying patterns in price data into up, down and no-action classes. The introduction of a no-action state in our novel approach alleviates the burden of denoising the dataset as a preprocessing task. The performance of our labeling algorithm is experimented with using machine learning and deep learning models. The framework is augmented by applying the Bayesian optimization technique for the selection of the best tuning values of the hyperparameters. The price trend prediction module generates the required trading signals. The results show that the average annualized Sharpe ratio as the trading performance metric is about 2.823, indicating the framework produces excellent cumulative returns. [ABSTRACT FROM AUTHOR]

The monitoring of physical activities and recognition of motion disorders belong to important diagnostical tools in neurology and rehabilitation. The goal of the present paper is in the contribution to this topic by (1) analysis of accelerometric signals recorded by wearable sensors located at specific body positions and by (2) implementation of deep learning methods to classify signal features. This paper uses the general methodology to analysis of accelerometric signals acquired during cycling at different routes followed by the global positioning system. The experimental dataset includes 850 observations that were recorded by a mobile device in the spine area (L3 verterbra) for cycling routes with the different slope. The proposed methodology includes the use of deep learning convolutional neural networks with five layers applied to signal values transformed into the frequency domain without specification of any signal features. The accuracy of discrimination between different motion patterns for the uphill and downhill cycling and recognition of 4 classes associated with different route slopes was 96.6% with the loss criterion of 0.275 for sigmoidal activation functions. These results were compared with those evaluated for selected sets of features estimated for each observation and classified by the support vector machine, Bayesian methods, and the two-layer neural network. The best cross-validation error of 0.361 was achieved for the two-layer neural network model with the sigmoidal and softmax transfer functions. Our methodology suggests that deep learning neural networks are efficient in the assessment of motion activities for automated data processing and have a wide range of applications, including rehabilitation, early diagnosis of neurological problems, and possible use in engineering as well. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment classification has been widely used by researchers as a fine-grained sentiment classification task to predict the sentiment polarity of specific aspect words in a given sentence. Previous studies have shown relatively good experimental results using graph convolutional networks, so more and more approaches are beginning to exploit sentence structure information for this task. However, these methods do not link aspect word and context well. To address this problem, we propose a method that utilizes a hierarchical multi-head attention mechanism and a graph convolutional network (MHAGCN). It fully considers syntactic dependencies and combines semantic information to achieve interaction between aspect words and context. To fully validate the effectiveness of the method proposed in this paper, we conduct extensive experiments on three benchmark datasets, which, according to the experimental results, show that the method outperforms current methods. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) techniques have been used in an extensive range of applications in the field of structural and multidisciplinary optimization over the last few years. This paper presents a survey of this wide but disjointed literature on ML techniques in the structural and multidisciplinary optimization field. First, we discuss the challenges associated with conventional optimization and how Machine learning can address them. Then, we review the literature in the context of how ML can accelerate design synthesis and optimization. Some real-life engineering applications in structural design, material design, fluid mechanics, aerodynamics, heat transfer, and multidisciplinary design are summarized, and a brief list of widely used open-source codes as well as commercial packages are provided. Finally, the survey culminates with some concluding remarks and future research suggestions. For the sake of completeness, categories of ML problems, algorithms, and paradigms are presented in the Appendix. [ABSTRACT FROM AUTHOR]

In this world, travelling is the daily routine to reach the desired places such as schools, hospitals, offices and so on. Everyone makes use of their vehicles like two-wheelers or four-wheelers to reach the destination. Vehicle condition plays a major role which includes many parameters such as tyre condition, engine overheating, low fuel mileage. This paper concentrates on the examination of tyre track design for deformities. The mileage of a vehicle relies upon different components. However, it depends on different parameters like street condition, tyre weight and numerous others. For smooth driving and for safe traffic reasons, the auspicious maintenance of tyres plays a vital task. Consequently, the likelihood of prediction of tyre imperfections is assumed to play a vital role to fleet proprietors, government and tyre makers. Researchers make use of recent technologies such as machine learning and deep learning concepts to provide a solution to various real-life problems. The creation of a weighted quality-based convolutional neural network with the purpose of discovery of imperfections on tyres by the examination of pictures of tyre track designs is being proposed. The proposed system with the accessible data-set made public by different tyre producers is included for evaluation. The consequences of our tests demonstrate that it is conceivable to predict the deformities and durability of a tyre from its track designs through a weighted quality-based convolutional neural network. The proposed methodology is used to identify the group of feature values from the tyre image. These kinds of feature values are utilized to implement the dissimilarity of the feature related pixels. The number of epochs is varied as a callback mechanism has been used to validate the proposed model. Binary accuracy and mean absolute error are the best metrics to evaluate the performance which are discussed in the results section. Also, precision, recall and F-measure are used as validation parameters for the proposed model. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour concerning three aspects: the probability of single and consecutive missed payments for credit card customers, the purchasing behaviour of customers, and grouping customers based on a mathematical expectation of loss. Two models are developed: the first provides the probability of a missed payment during the next month for each customer, which is described as Missed payment prediction Long Short Term Memory model (MP-LSTM), whilst the second estimates the total monthly amount of purchases, which is defined as Purchase Estimation Prediction Long Short Term Memory model (PE-LSTM). Based on both models, a customer behavioural grouping is provided, which can be helpful for the bank's decision-making. Both models are trained on real credit card transactional datasets. Customer behavioural scores are analysed using classical performance evaluation measures. Calibration analysis of MP-LSTM scores showed that they could be considered as probabilities of missed payments. Obtained purchase estimations were analysed using mean square error and absolute error. The MP-LSTM model was compared to four traditional well-known machine learning algorithms. Experimental results show that, compared with conventional methods based on feature extraction, the consumer credit scoring method based on the MP-LSTM neural network has significantly improved consumer credit scoring. [ABSTRACT FROM AUTHOR]

Prediction of human physical activities has become a necessity for some applications that come with the development of wearable and portable hardware such as smartwatches and smartphones. The task of Human Activity Recognition (HAR) is to recognize human physical activities, e.g., walking, sitting, and running, using the data collected from sensors, e.g., accelerometers and gyroscope. HAR is commonly applied on smart systems, such as smartphones, to serve the understanding of a user's behaviors and provide assistance to the user because of the rapid development of ubiquitous computing technology in recent years. Thus, predicting activities, such as standing, walking, sitting, during the day have become a popular topic in machine and deep learning. The aim of this study is to predict the user's activities based on context information gathered by sensors such as gyroscopes and accelerometers. The conducted classification algorithms extract features from training data and learn a classification model based on the features to predict activity. In this paper, various classical machine and deep learning techniques have been studied and compared for human activity recognition. A comparative analysis is performed between techniques in order to select the classifier with the best recognition performance. Experimental results show that established Deep Neural Network (DNN) model achieved an accuracy of up to 96.81% and mean absolute error of up to 0.03 on publicly available UCI-HAR dataset. This method has given the best performance between conducted classification methods in this study to predict human activity. [ABSTRACT FROM AUTHOR]

With the increasing popularity of user-generated content on social media, the number of toxic texts is also on the rise. Such texts cause adverse effects on users and society at large, therefore, the identification of toxic comments is a growing need of the day. While toxic comment classification has been studied for resource-rich languages like English, no work has been done for Roman Urdu despite being a widely used language on social media in South Asia. This paper addresses the challenge of Roman Urdu toxic comment detection by developing a first-ever large labeled corpus of toxic and non-toxic comments. The developed corpus, called RUT (Roman Urdu Toxic), contains over 72 thousand comments collected from popular social media platforms and has been labeled manually with a strong inter-annotator agreement. With this dataset, we train several classification models to detect Roman Urdu toxic comments, including classical machine learning models with the bag-of-words representation and some recent deep models based on word embeddings. Despite the success of the latter in classifying toxic comments in English, the absence of pre-trained word embeddings for Roman Urdu prompted to generate different word embeddings using Glove, Word2Vec and FastText techniques, and compare them with task-specific word embeddings learned inside the classification task. Finally, we propose an ensemble approach, reaching our best F1-score of 86.35%, setting the first-ever benchmark for toxic comment classification in Roman Urdu. [ABSTRACT FROM AUTHOR]

The oddity in large information is rising step by step so that the current programming instruments faces trouble in supervision of huge information. Moreover, the pace of the irregularity information in the immense datasets is a key imperative to the exploration business. Along these lines, this paper proposes a novel method for taking care of the large information utilizing Spark structure. The proposed method experiences two stages for arranging the enormous information, which includes highlight choice and arrangement, which is acted in the underlying hubs of Spark engineering. The proposed improvement calculation is named Rider Chaotic Biography streamlining (RCBO) calculation, which is the incorporation of the Rider Optimization Algorithm (ROA) and the standard confused biogeography-based-advancement (CBBO). The proposed RCBO-profound stacked auto-encoder utilizing Spark structure successfully handles the large information for achieving powerful huge information arrangement. Here, the proposed RCBO is utilized for choosing reasonable highlights from the monstrous dataset. Besides, the profound stacked auto-encoder utilizes RCBO for preparing so as to characterize colossal datasets. In this research we focused on problem of supervision related to big information of The Cover type Data in UCI machine learning repository. The dataset describes the forest cover set data to predict the forest cover type from cartographic variables. The dataset is multivariate in nature with number of web hits 263,361. The number of instances is 581012 with 54 numbers of attributes and the task associated for the dataset is classification. The examination of the proposed RCBO-profound stacked auto-encoder-based Spark structure utilizing the UCI AI datasets uncovered that the proposed technique beat different strategies, by procuring maximal exactness of 86.71%, dice coefficient of 92.7%, affectability of 75.2% and explicitness of 95.4% separately. [ABSTRACT FROM AUTHOR]

In this paper, a novel Hybrid Deep Ensemble (HDE) is proposed for automatic speech disfluency classification on a sparse speech dataset. Categorizations of speech disfluencies for diagnosis of speech disorders have so long relied on sophisticated deep learning models. Such a task can be accomplished by a straightforward approach with high accuracy by the proposed model which is an optimal combination of diverse machine learning and deep learning algorithms in a hierarchical arrangement which includes a deep autoencoder that yields the compressed latent features. The proposed model has shown considerable improvement in downgrading processing time overcoming the issues of cumbersome hyper-parameter tuning and huge data demand of the deep learning algorithms with high classification accuracy. Experimental results show that the proposed Hybrid Deep Ensemble has superior performance compared to the individual base learners, and the deep neural network as well. The proposed model and the baseline models were evaluated in terms of Cohen's kappa coefficient, Hamming loss, Jaccard score, F-score and classification accuracy. [ABSTRACT FROM AUTHOR]

Extensive use of multimedia services and Internet Data Center applications demand distributed deployment of these applications. It is implemented using edge computing with server clusters. To increase the availability of the services, applications are deployed redundantly in server clusters. In this situation, an efficient server allocation strategy is essential to improve execution fairness in server cluster. Categorizing the incoming traffic at server cluster is desired for the improvement of QoS. The traditional traffic classification models categorize the incoming traffic according to their applications' type. They are ineffective in selection of suitable server, as they do not consider the characteristics of the server. Hence this paper proposes a classifier to assist the dispatcher to distribute the requests to appropriate server in server cluster. The proposed deep learning classification model based on incoming traffic characteristics and server status is reinforced with extended labelling using correlation based approach. The experimental results of the proposed classifier have shown considerable performance enhancement in terms of classification measures and waiting time of the requests compared to existing machine learning models. [ABSTRACT FROM AUTHOR]

Informal short texts on the web are rich in emotions as they often reflect unfiltered immediate reactions to breaking news events. The emotion density, however, stands in contrast to its poverty of linguistic contexts and features for emotion classification. This paper tackles that challenge by proposing orthographic features based on orthographic code mixing and code-switching for both non-ML and ML approaches. Our results show that orthographic features routinely outperform grammatical features for emotion classification for short texts in all approaches as expected. Orthographic features were also shown to make more significant contributions, especially in terms of precision and in formal texts when state of the art deep learning algorithms are applied. This result confirms the effectiveness of the orthographic change feature to the task of emotion classification. These results are argued to be applicable to all languages because of the common code-shifting in languages with non-Latin orthographies, and the use of non-letter symbols in all languages. [ABSTRACT FROM AUTHOR]

The aim of this study is to improve the classification efficiency of advanced methods using a multilayered dictionary learning framework. This paper presents the new idea of "multilayered K-singular value decomposition (MLK-SVD)" dictionary learning as a multilayer method of classification. This method starts by building a sparse representation at the patch level and relies on a hierarchy of learned dictionaries to output a global sparse representation for the whole image. In this research using class labels of training data, the label information is associated with each dictionary item (columns of the dictionary matrix) to enforce discrimination in sparse codes during the dictionary learning process. Also, this algorithm instead of learning one shallow dictionary learned multiple levels of dictionaries. The proposed formulation of deep dictionary learning provides the basis to develop more efficient dictionary learning algorithms. It relies on a succession of sparse coding and pooling steps in order to find an efficient representation of the data for classification. The performance of the proposed method is evaluated on MNIST and CIFAR-10 datasets, and results show that this method can help in advancing the state of the art. [ABSTRACT FROM AUTHOR]

Classification of hyperspectral image (HSI) is widely used for the study of remotely sensed images. Convolutional Neural Networks (CNNs) are one of the most commonly chosen deep learning algorithms for visual data analysis. The HSI classification framework based on the CNN is presented in this paper. Since the imbalance between the high dimension of HSI input data and the limited amount of labeled training data would induce overfitting, current convolutional networks are fairly superficial for HSI classification. To stop the limited efficiency of feature learning, a new HSI classification network called Residual Spectral Spatial-Channel Attention Network (RSS-CAN) is proposed. By utilizing the "shortcut connection" framework, RSS-CAN can use deeper layers to extract more succinct and efficient features. Furthermore, attention mechanism is used to emphasize meaningful features. In addition, we revised an HSI dataset called Shandong Feicheng. The resolution and pixel quantity of this dataset are significantly greater. In order to check its variety, it has been contrasted with state-of-the-art approaches. Experimental results with widely used hyperspectral image datasets demonstrate that, our proposed method has achieved better performance in comparison with state-of-the-art classifiers and conventional deep learning-based classifiers. [ABSTRACT FROM AUTHOR]

The protocol reverse engineering technique can be used to extract the specification of an unknown protocol. However, there is no standardized method, and in most cases, the extracting process is executed manually or semiautomatically. Since only frequently seen values are extracted as fields from the messages of a protocol, it is difficult to understand the complete specification of the protocol. Therefore, if the information about the structure of an unknown protocol could be acquired in advance, it would be easy to conduct reverse engineering. As such, one of the most important techniques for classifying unknown protocols is a feature extraction algorithm. In this paper, we propose a new feature extraction algorithm based on average histogram for classification of an unknown protocol and design unknown protocol classifier using deep belief networks, one of deep learning algorithms. In order to verify the performance of the proposed system, we performed the training using eight open protocols to evaluate the performance using unknown data. Experimental results show that the proposed technique gives significantly more reliable results of about 99% classification performance, regardless of the strength of the modification of the protocol. [ABSTRACT FROM AUTHOR]

Nowadays, big data plays a substantial part in information knowledge analysis, manipulation, and forecasting. Analyzing and extracting knowledge from such big datasets are a very challenging task due to the imbalance of data distribution, which could lead to a biased classification results and wrong decisions. The standard classifiers are not capable of handling such datasets. Hence, a new technique for dealing with such datasets is required. This paper proposes a novel classification framework for big data that consists of three developed phases. The first phase is the feature selection phase, which uses the Whale optimization algorithm (WOA) for finding the best set of features. The second phase is the preprocessing phase, which uses the SMOTE algorithm and the LSH-SMOTE algorithm for solving the class imbalance problem. Lastly, the third phase is WOA + BRNN algorithm, which is using the Whale optimization algorithm for training a deep learning approach called bidirectional recurrent neural network for the first time. Our proposed algorithm WOA-BRNN has been tested against nine highly imbalanced datasets one of them is big dataset in terms of area under curve (AUC) against four of the most common use machine learning algorithms (Naïve Bayes, AdaBoostM1, decision table, random tree), in addition to GWO-MLP (training multilayer perceptron using Gray Wolf Optimizer), then we test our algorithm over four well-known datasets against GWO-MLP and particle swarm optimization (PSO-MLP), genetic algorithm (GA-MLP), ant colony optimization (ACO-MLP), evolution strategy (ES-MLP), and population-based incremental learning (PBIL-MLP) in terms of classification accuracy. Experimental results proved that our proposed algorithm WOA + BRNN has achieved promising accuracy and high local optima avoidance, and outperformed four of the most common use machine learning algorithms, and GWO-MLP in terms of AUC. [ABSTRACT FROM AUTHOR]

Cloud machine learning (CML) techniques offer contemporary machine learning services, with pre-trained models and a service to generate own personalized models. This paper presents a completely unique emotional modeling methodology for incorporating human feeling into intelligent systems. The projected approach includes a technique to elicit emotion factors from users, a replacement illustration of emotions and a framework for predicting and pursuit user's emotional mechanical phenomenon over time. The neural network based CML service has better training concert and enlarged exactness compare to other large scale deep learning systems. Opinions are important to almost all human activities and cloud based sentiment analysis is concerned with the automatic extraction of sentiment related information from text. With the rising popularity and availability of opinion rich resources such as personal blogs and online appraisal sites, new opportunities and issues arise as people now, actively use information technologies to explore and capture others opinions. In the existing system, a segmentation ranking model is designed to score the usefulness of a segmentation candidate for sentiment classification. A classification model is used for predicting the sentiment polarity of segmentation. The joint framework is trained directly using the sentences annotated with only sentiment polarity, without the use of any syntactic or sentiment annotations in segmentation level. However the existing system still has issue with classification accuracy results. To improve the classification performance, in the proposed system, cloud integrate the support vector machine, naive bayes and neural network algorithms along with joint segmentation approaches has been proposed to classify the very positive, positive, neutral, negative and very negative features more effectively using important feature selection. Also to handle the outliers we apply modified k-means clustering method on the given dataset. It is used to cloud cluster the outliers and hence the label as well as unlabeled features is handled efficiently. From the experimental result, we conclude that the proposed system yields better performance than the existing system. [ABSTRACT FROM AUTHOR]