Search

This paper proposes a hybrid approach integrating the expert knowledge judgment approach using the Cognitive Pairwise Comparison (CPC) to the Deep Learning, a modern classification approach, for astronomic object classification. The astronomic data with ten thousand samples retrieved from Sloan Digital Sky Survey Sky Server Data Release 15 (SDSS SkyServer DR 15) are used for this study. The CPC is an approach to elicit and encode expert knowledge in the format of a Pairwise Opposite Matrix (POM) to evaluate expert preferences for the features. A forward feature selection algorithm taking the expert choices using CPC for the ordered features is used for the feature selection for the deep learning algorithm to build a heuristic training model based on the astronomic data. Whilst the accuracy of the case of improper feature selection is just 37.1%, the proposed hybrid approach can obtain a very high accuracy of 97.9% for the classification of the astronomic object using the eight scaled features (u, g, r, i, z redshift, ra, dec). To extend this research, the proposed CPC can be used as a human-centered tool to be applied to other areas of data sciences. [ABSTRACT FROM AUTHOR]

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

The cardiovascular disease is the one of the major cause of death in the today's world. The Holter recorder is used to monitor the Electrocardiogram (ECG) signal to record the heart activity which is a popular method to detect the disease. Unfortunately, finding professionals to examine a big volume of ECG data takes up far too much medical time and money. The machine learning-based methods for recognising ECG characteristics have important role for extraction of features and ECG signal classification for Arrhythmia detection. The traditional processed used for the cardiac treatment have certain disadvantages, such as the need to understand the exact cardiac problem to recognize the disease and long learning curve to study the arrhythmia process manually. In this study twelve different heartbeat micro-classes are used with MIT-BIH Arrhythmia database for the classification. An efficient and robust virtually 12-layer deep traditionally one-dimensional CNN (convolutional neural network) is used with ELM (Extreme learning Machine) is used to propose the classification procedure. In the experiment different heart beat characteristics are identified, and the wavelet self-adaptive threshold denoising approach is applied by a combined algorithm using ELM and CNN. The results reveal that the model described in this paper performing better than the random forest, BP neural network, and other GA networks in terms of accuracy, sensitivity, resilience, and anti-noise capabilities. In this work ELM based classification on Convolutional Neural Network is developed for arrhythmia detection from the ECG signal. Its precise classification effectively conserves medical resources, which benefits clinical practise. In this study the classification result produces 98.82% accuracy which is quite satisfactory with compared with other similar types of algorithms used classification results. [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]

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

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]

Accurate classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI), especially distinguishing the progressive MCI (pMCI) from stable MCI (sMCI), will be helpful in both reducing the risk of converting into AD and also releasing the burden on the family and even the society. In this study, a novel deep belief network (DBN) based multi-task learning algorithm is developed for the classification issue. In particular, the dropout technology and zero-masking strategy are exploited for getting over the overfitting problem and also enhancing the generalization ability and robustness of the model. Then, a new framework based on the DBN-based multi-task learning is established for accurate diagnosis of AD. After MRI preprocessing, not only the principal component analysis is utilized to reduce the feature dimension, but also multi-task feature selection approach is introduced to select the feature set related to all tasks as a result of taking the internal relevancy among multiple related tasks into consideration. Using data from the ADNI dataset, our method achieves satisfactory results in six tasks of health control (HC) vs. AD, HC vs. pMCI, HC vs. sMCI, pMCI vs. AD, sMCI vs. AD and sMCI vs. pMCI with the accuracies are 98.62%, 96.67%, 92.31%, 91.89%, 99.62% and 87.78%, respectively. Experimental results demonstrate that the DBN-based MTL algorithm developed in this study is an effective, superior and practical method of AD diagnosis. [ABSTRACT FROM AUTHOR]

There is a broad consensus that stroke rehabilitation interventions within the first 3–6 months after stroke can result in maximizing post-stroke recovery. However, in middle- and low-income countries, shortage of trained physiotherapists, expenses and anxiety associated with therapist-assisted exercise sessions have been observed to be major impediments to effective post-stroke recovery. A possible solution is deployment of machine learning/deep learning-based approaches, which can play a significant role in providing effective rehabilitation assistance at affordable costs. A deep learning-based system to assist the patient in identifying completion status of twelve hand and arm rehabilitation exercises has been presented in this study. The exercise completion status identification problem is formulated as a multi-class classification problem with the limb movement data collected for each exercise using accelerometers along the x-, y- and z-axes of motion serving as the input. A convolutional neural network-based deep learning model is then used to identify the exercise completion status. Performance of the proposed system is compared to that of models generated using four popular machine learning algorithms: decision tree, support vector machines, linear discriminant and naïve Bayes classifiers. Effectiveness of dimensionality reduction and feature selection on the performance of these systems is also evaluated, resulting in a total of 24 models evaluated on four performance measures: accuracy, precision, recall and F1-score. The proposed deep learning-based system is observed to be successful in identifying the completion of rehabilitation exercises with an accuracy in excess of 98%, indicating the effectiveness of such systems in post-stroke rehabilitation therapy. [ABSTRACT FROM AUTHOR]