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This in-depth study journey explores the context of natural language processing and text analysis in dementia detection, revealing their importance in a variety of fields. Beginning with an examination of the widespread and influence of text data. The dataset utilised in this study is from TalkBank's DementiaBank, which is basically a vast database of multimedia interactions built with the goal of examining communication patterns in the context of dementia. The various communication styles dementia patients exhibit when communicating with others are seen from a unique perspective by this specific dataset. Thorough data preprocessing procedures, including cleansing, tokenization, and structuring, are undertaken, with a focus on improving prediction capabilities through the combination of textual and non-textual information in the field of feature engineering. In the subsequent phase, the precision, recall, and F1-score metrics of Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forest, and Artificial Neural Networks (ANN) are assessed. Empirical facts are synthesized using text analysis methods and models to formulate a coherent conclusion. The significance of text data analysis, the revolutionary potential of natural language processing, and the direction for future research are highlighted in this synthesis. Throughout this paper, readers are encouraged to leverage text data to embark on their own adventures in the evolving, data-centric world of dementia detection. [ABSTRACT FROM AUTHOR]

Glaucoma is an asymptotic condition that damages the optic nerves of a human eye. Glaucoma is frequently caused due to abnormally high pressure in an eye that leads to permanent blindness. Detecting glaucoma at an initial phase has the possibility of curing this disease, but diagnosing accurately is considered as a challenging task. Therefore, this paper proposes a novel method known as a glaucoma detection system that performs the diagnosis of glaucoma by exploiting the prescribed characteristics. The significant intention of this paper involves diagnosing the glaucoma disease present at the top optical nerve of a human eye. The proposed glaucoma detection has used four different phases namely data preprocessing or enhancement phase, segmentation phase, feature extraction phase, and classification phase. Here, a novel classifier named fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) is developed for the effective classification of glaucoma-infected images from the normal image. Finally, the experimental analysis for the proposed approach and various other techniques are performed, and the accuracy rate while diagnosing glaucoma achieved is 98.75%. [ABSTRACT FROM AUTHOR]

In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods. [ABSTRACT FROM AUTHOR]

Bone age assessment (BAA) is a method or technique that helps in predicting the age of a person whose age is unavailable and can also be used to find growth disorders if any. The automated bone age assessment system (ABAA) depends heavily on the efficiency of the feature extraction stage and the accuracy of a successive classification stage of the system. This paper has presented the implementation and analysis of feature extraction methods like Bag of features (BoF), Histogram of Oriented Gradients (HOG), and Texture Feature Analysis (TFA) methods on the segmented phalangeal region of interest (PROI) images and segmented radius-ulna region of interest (RUROI) images. Artificial Neural Networks (ANN) and Random Forest classifiers are used for evaluating classification problems. The experimental results obtained by BoF method for feature extraction along with Random Forest for classification have outperformed preceding techniques available in the literature. The mean error (ME) accomplished is 0.58 years and RMSE value of 0.77 years for PROI images and mean error of 0.53 years and RMSE of 0.72 years was achieved for RUROI images. Additionally results also proved that prior knowledge of gender of the person gives better results. The dataset contains radiographs of the left hand for an age range of 0-18 years. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is one of the long-lasting Diabetic retinal disorders that leads to vision impairment eventually blindness in most of the working-age population. The process of classifying the severity level of DR has been a great challenging task as the lesion features are hard to analyze. The screening process requires an effective detection method to classify the subtle pathologies of the retina. Deep neural architectures play a vital role in diagnosing eye disease and helps ophthalmologists to provide timely treatment. This paper proposes an efficient, optimized deep neural network with Chronological Tunicate Swarm Algorithm (CTSA) for classifying the severity of DR. Initially, the retinal images captured through the low-quality fundus photography are preprocessed and then subjected to the segmentation process. First, the optic disc and the blood vasculatures are segmented using a U-Net and sparse Fuzzy C-means-based hybrid entropy model. The lesion area is then detected using the Gabor filter banks, and then the features are extracted. The final classification process takes place using a deep Stacked Autoencoder (SAE) jointly optimized with a bio-inspired Tunicate Swarm Algorithm based on the chronological concept. The presented model achieved an average accuracy, sensitivity, specificity and F1-Score values of 95.9%, 88.07%, 96.80% and 85.26% for the DIARETDB0 database and 95.48%, 93.29%, 91.89% and 90.53% for the DIARETDB1 database. The experimental outcome demonstrates the effectiveness and the robustness of the proposed method in the DR classification task. [ABSTRACT FROM AUTHOR]

Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]

Image classification, which aims at assigning a semantic category to images, has been extensively studied during the past few years. More recently, convolution neural network arises and has achieved very promising achievement. Compared with traditional feature extraction techniques (e.g., SIFT, HOG, GIST), the convolutional neural network can extract features from image automatically and does not need hand designed features. However, how to further improve the classification algorithm is still challenging in academic research. The latest research on CNN shows that the features extracted from middle layers is representative, which shows a possible way to improve the classification accuracy. Based on the observation, in this paper, we propose a method to fuse the latent features extracted from the middle layers in a CNN to train a more robust classifier. First, we utilize the pretrained CNN models to extract visual features from middle layer. Then, we use supervised learning method to train classifiers for each feature respectively. Finally, we use the late fusion strategy to combine the prediction of these classifiers. We evaluate the proposal with different classification methods under some several images benchmarks, and the results demonstrate that the proposed method can improve the performance effectively. [ABSTRACT FROM AUTHOR]

An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at the same time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster–Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSL-KDD. Our proposed method indicates the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure. [ABSTRACT FROM AUTHOR]

Chromosome classification and karyotype establishment are important procedures for genetic diseases diagnosis. Various computer-aided systems have been developed to automate this tedious and time consuming task, which is performed manually in most cytogenetic laboratories. This paper provides a comprehensive review of past and recent research in the area of automatic chromosome classification systems. We start by reviewing methods for feature extraction, followed by a neural network based chromosome classifiers survey. We sum-up various techniques and methods in this area of research and discuss important issues and outcomes within each study for both chromosome feature extraction and classification. Although the ANN based chromosome classifiers are the main topic of this survey, a number of classifiers based on other algorithms are exposed to give an overall idea about additional techniques employed in chromosome classification. [ABSTRACT FROM AUTHOR]

Chinese Sign Language (CSL) offers the main means of communication for the hearing impaired in China. Sign Language Recognition (SLR) can shorten the distance between the hearing-impaired and healthy people and help them integrate into the society. Therefore, SLR has become the focus of sign language application research. Over the years, the continuous development of new technologies provides a source and motivation for SLR. This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR). With a thorough review of superior methods from 2000 to 2019 in CSLR researches, various techniques and algorithms such as scale-invariant feature transform, histogram of oriented gradients, wavelet entropy, Hu moment invariant, Fourier descriptor, gray-level co-occurrence matrix, dynamic time warping, principal component analysis, autoencoder, hidden Markov model (HMM), support vector machine (SVM), random forest, skin color modeling method, k-NN, artificial neural network, convolutional neural network (CNN), and transfer learning are discussed in detail, which are based on several major stages, that is, data acquisition, preprocessing, feature extraction, and classification. CSLR was summarized from some aspect as follows: methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets. The advantages and limitations of different CSLR approaches were compared. It was found that data acquisition is mainly through Kinect and camera, and the feature extraction focuses on hand's shape and spatiotemporal factors, but ignoring facial expressions. HMM and SVM are used most in the classification. CNN is becoming more and more popular, and a deep neural network-based recognition approach will be the future trend. However, due to the complexity of the contemporary Chinese language, CSLR generally has a lower accuracy than other SLR. It is necessary to establish an appropriate dataset to conduct comparable experiments. The issue of decreasing accuracy as the dataset increases needs to resolve. Overall, our study is hoped to give a comprehensive presentation for those people who are interested in CSLR and SLR and to further contribute to the future research. [ABSTRACT FROM AUTHOR]

Supervised learning techniques require labeled examples that can be time consuming to obtain. In particular, deep learning approaches, where all the feature extraction stages are learned within the artificial neural network, require a large number of labeled examples to train the model. Various data augmentation techniques can be performed to overcome this issue by taking advantage of known variations that have no impact on the label of an example. Typical solutions in computer vision and document analysis and recognition are based on geometric transformations (e.g. shift and rotation) and random elastic deformations of the original training examples. In this paper, we consider Generative Adversarial Networks (GAN), a technique that does not require prior knowledge of the possible variabilities that exist across examples to create novel artificial examples. In the case of a training dataset with a low number of labeled examples, which are described in a high dimensional space, the classifier may generalize poorly. Therefore, we aim at enriching databases of images or signals for improving the classifier performance by designing a GAN for creating artificial images. While adding more images through a GAN can help, the extent to which it will help is unknown, and it may degrade the performance if too many artificial images are added. The approach is tested on four datasets on handwritten digits (Latin, Bangla, Devanagri, and Oriya). The accuracy for each dataset shows that the addition of GAN generated images in the training dataset provides an improvement of the accuracy. However, the results suggest that the addition of too many GAN generated images deteriorates the performance. [ABSTRACT FROM AUTHOR]

Fingerprint pattern recognition and classification can be of assistance in the research on human personality. In some previous studies, fingerprints were classified into four categories to speed up recognition, but the method of that classification is not suitable for researching the diversity of human personalities. Therefore, in this paper, fingerprint patterns were classified into six types and the accuracy of the recognition was improved to facilitate the research on human personality characteristics. Based on this idea, a six-category fingerprint database is annotated manually and a convolutional neural network (CNN) is proposed for identifying real fingerprint patterns. The new CNN consists of four convolutional layers, three max-pooling layers, two norm layers, and three fully connected layers. The best accuracy the model achieved was 94.87% for a six-category fingerprint database and 92.9% accuracy for a four-category fingerprint database. The results of experimental tests show that the proposed model can recognize the pattern features from a large fingerprint database using the automatic learning and feature extraction abilities of the CNN to get a greater accuracy than in previous experiments. [ABSTRACT FROM AUTHOR]

Human physical action classification is an emerging area of research for human-to-machine interaction, which can help to disable people to interact with real world, and robotics application. EMG signals measure the electrical activity muscular systems, which involved in physical action of human. EMG signals provide more information related to physical action. In this paper, we proposed deep transfer learning-based approach of human action classification using surface EMG signals. The surface EMG signals are represented by time–frequency image (TFI) by using short-time Fourier transform. TFI is used as input to pre-trained convolutional neural network models, namely AlexNet and VGG16, for deep feature extraction, and support vector machine (SVM) classifier is used for classification of physical action of EMG signals. Also, the fine-tuning of the pre-trained AlexNet model is also considered. The experimental results show that deep feature extraction and SVM classification method and fine-tuning have obviously improved the classification accuracy when compared with various results from the literature. The 99.04% accuracy score is obtained with AlexNet fc6 + AlexNet fc7 + VGG16 fc6 + VGG16 fc7 deep feature concatenation and SVM classification. 98.65% accuracy score is performed by fine-tuning of the AlexNet model. We also compare the obtained results with some of the existing methods. The comparisons show that the deep feature concatenation and SVM classification method provide better classification accuracy than the compared methods. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Classification of alcoholic electroencephalogram (EEG) signals is a challenging job in biomedical research for diagnosis and treatment of brain diseases of alcoholic people. The aim of this study was to introduce a robust method that can automatically identify alcoholic EEG signals based on time-frequency (T-F) image information as they convey key characteristics of EEG signals. In this paper, we propose a new hybrid method to classify automatically the alcoholic and control EEG signals. The proposed scheme is based on time-frequency images, texture image feature extraction and nonnegative least squares classifier (NNLS). In T-F analysis, the spectrogram of the short-time Fourier transform is considered. The obtained T-F images are then converted into 8-bit grayscale images. Co-occurrence of the histograms of oriented gradients (CoHOG) and Eig(Hess)-CoHOG features are extracted from T-F images. Finally, obtained features are fed into NNLS classifier as input for classify alcoholic and control EEG signals. To verify the effectiveness of the proposed approach, we replace the NNLS classifier by artificial neural networks, k-nearest neighbor, linear discriminant analysis and support vector machine classifier separately, with the same features. Experimental outcomes along with comparative evaluations with the state-of-the-art algorithms manifest that the proposed method outperforms competing algorithms. The experimental outcomes are promising, and it can be anticipated that upon its implementation in clinical practice, the proposed scheme will alleviate the onus of the physicians and expedite neurological diseases diagnosis and research. [ABSTRACT FROM AUTHOR]

A computerized coconut tree detection system can help dendrologists and laypersons in identifying coconut trees based on three morphological parameters including height, inclination, and orientation. These three parameters help to determine the health and the nature of growth of coconut trees which influences the design and use of robots for harvesting coconuts. Deep learning is a powerful tool used for feature extraction as it is better in extracting deeper details (features) in an image. In this research work, a new Modified Inception Net based Hyper Tuning Support Vector Machine classification method named MIN-SVM is proposed for coconut tree classification based on three morphological parameters including height, inclination and orientation. The features from the pre-processed coconut tree images were extracted using four distinct Convolutional Neural Network models including Visual Geometry Group, Inception Net, ResNet, and MIN-SVM. These extracted features were then classified using a Machine Learning model named Support Vector Machine (SVM). The MIN-SVM have achieved a remarkable accuracy of 95.35 percent as contrasted to Visual Geometry Group (91.90%), Inception Net (81.66%), and ResNet (71.95%). The features extracted from Modified Inception Net fitted good with SVM classifier. Experimental results show that MIN-SVM can be powerful computerized automated system to identify coconut trees based on height, inclination, and orientation. [ABSTRACT FROM AUTHOR]

Biometric identification depends on the statistical analysis of the unique physical and behavioral characteristics of individuals. However, a unimodal biometric system is susceptible to different attacks such as spoof attacks. To overcome these limitations, we propose a multimodal biometric authentication system based on deep fusion of electrocardiogram (ECG) and finger vein. The proposed system has three main components, which are biometric pre-processing, deep feature extraction, and authentication. During the pre-processing, normalization and filtering techniques are adapted for each biometric. In the feature extraction process, the features are extracted using a proposed deep Convolutional Neural Network (CNN) model. Then, the authentication process is performed on the extracted features using five well-known machine learning classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Random Forest (RF), Naive Bayes (NB), and Artificial Neural Network (ANN). In addition, to represent the deep features in a low-dimensional feature space and speed up the authentication task, we adopt Multi-Canonical Correlation Analysis (MCCA). We combine the two biometric systems based on ECG and finger vein into a single multimodal biometric system using feature and score fusion. The performance of the proposed system is tested on two finger vein (TW finger vein and VeinPolyU finger vein) databases and two ECG (MWM-HIT and ECG-ID) databases. Experimental results reveal improvement in terms of authentication performance with Equal Error Rates (EERs) of 0.12% and 1.40% using feature fusion and score fusion, respectively. Furthermore, the authentication with the proposed multimodal system using MCCA feature fusion with a KNN classifier shows an increase of accuracy by an average of 10% compared with those of other machine learning algorithms. Therefore, the proposed biometric system is effective in performing secure authentication and assisting the stakeholders in making accurate authentication of users. [ABSTRACT FROM AUTHOR]

Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional Siamese network to solve visual tracking by directly predicting the target bounding box in an end-to-end manner. We first reformulate the visual tracking task as two subproblems: a classification problem for pixel category prediction and a regression task for object status estimation at this pixel. With this decomposition, we design a simple yet effective Siamese architecture based classification and regression framework, termed SiamCAR, which consists of two subnetworks: a Siamese subnetwork for feature extraction and a classification-regression subnetwork for direct bounding box prediction. Since the proposed framework is both proposal- and anchor-free, SiamCAR can avoid the tedious hyper-parameter tuning of anchors, considerably simplifying the training. To demonstrate that a much simpler tracking framework can achieve superior tracking results, we conduct extensive experiments and comparisons with state-of-the-art trackers on a few challenging benchmarks. Without bells and whistles, SiamCAR achieves leading performance with a real-time speed. Furthermore, the ablation study validates that the proposed framework is effective with various backbone networks, and can benefit from deeper networks. Code is available at https://github.com/ohhhyeahhh/SiamCAR. [ABSTRACT FROM AUTHOR]

Cancer disease is assumed as a gathering of diseases which is initiated because of uncontrolled cell growth. An early analysis of Lung Nodules (LN) can possibly enhance the prognosis and in future can save numerous lives every year. In the proposed research work, the LN detection from the ELCAP lung image database is analyzed by image segmentation and classification techniques. Initially, the exact portion of the lung image is achieved and then it is subjected to pre-processing where the image contrast level is enhanced by the imadjust function of MATLAB. Next to that, the potential nodules are segmented by the Fuzzy C-Means (FCM) and then some features are extracted for effective classification. Based on the selected or extracted feature sets, the images are classified as two types (nodule detected and normal lung) by the proposed classifier i.e. Artificial Neural Network (ANN) with weight optimization. The performances of the proposed algorithm and classifier are tested on the chosen datasets in terms of sensitivity, specificity and accuracy. The results demonstrate that ANN with Oppositional based Ant Lion Optimization (OALO) algorithm achieves high accuracy and less execution time compared to existing algorithms. [ABSTRACT FROM AUTHOR]

The automatic assessment of the aesthetic value of an image is a task with many applications but really complex and challenging, due to the subjective component of the aesthetics for humans. The computational systems that carry out this task are usually composed of a set of ad hoc metrics proposed by the researchers and a machine learning system. We propose a new approach that fully automates the metrics creation process, its filtering and adjustment without human subjectivity. Thus, it does not depend on the authors' human aesthetic intuitions. Our proposal is therefore based on the integration of two machine learning algorithms: CNN, which works as a feature extractor, and Correlation by Genetic Search (CGS)—a novel regression method, working as a supervised learning method. CGS is based on the creation of an adjusted linear regression model using Pearson's correlation as a measure of performance in an evolutionary process. Experiments were conducted on a very well-known aesthetics database called "Photo.net" with more than a million images from over 400,000 users. The comparison of results with other approaches using the same dataset demonstrates that the fusion of CNN transfer learning features with this specific machine learning method has achieved robust and significantly better results than other state-of-the-art methods and hybrid approaches in terms of AUROC (0.93), accuracy (0.93) and Pearson's correlation value (0.94). [ABSTRACT FROM AUTHOR]

A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with the dilemma of manual inspection of the massive volume of frames. This article gives an overview of different available alternatives for automated inspection, detection, and classification of various GI abnormalities. Also, this work elaborates techniques associated with content-based image retrieval and automated systems for summarizing endoscopic procedures. In this survey, we perform a comprehensive review of feature extraction techniques and deep learning methods which were specifically developed for automatic analysis of endoscopic videos. In addition, we categorize features extraction techniques according to image processing domains and further we classify them based on their visual descriptions. We also review hybrid feature extraction techniques which are developed by the fusion of different kind of basic descriptors. Moreover, this survey covers various endoscopy data-sets available for the bench-marking of vision based algorithms. On the basis of literature, we explain emerging trends in computerized analysis of endoscopy. We also survey important issues, challenges, and future research directions to the development of computer-assisted systems for detection of maladies and interactive surgery in the GI tract. [ABSTRACT FROM AUTHOR]

Huge amount of time series data over several domains such as engineering, biomedical and finance, demands the development of efficient methods for the problem of time series classification. The classification of univariate and multivariate time series together using a single architecture is a very difficult task. In this work, a bio-inspired convolutional spiking neural network (CSNN) is proposed for both univariate and multivariate time series. For this, first we develop a simple transformation to convert raw time series sequences into matrices. The CSNN is a three staged framework which include convolutional feature extraction, spike encoding using soft leaky integrate and fire (Soft-LIf) and classification. As spikes generated are differentiable, thus the learning algorithm for CSNN uses error-backpropagation with cyclical learning rates (CLR) and RMSprop optimizer. Additionally, validation based stopping rules are employed to overcome the overfitting which also provides a set of parameters associated with low validation set loss. Thereafter, to demonstrate the accuracy and robustness of proposed CSNN model, we have used University of California (UCR) univariate as well as University of East Anglia (UEA) multivariate datasets to perform the experiments. Moreover, we conduct comparative empirical performance evaluation with benchmark methods and also with recent deep networks proposed for time series classification. Our results reveal that proposed CSNN advances the baseline methods by achieving higher performance accuracy for both univariate and multivariate datasets. It is shown that the CLR with RMSprop optimizer is able to achieve faster convergence, however CLR and adaptive rates are considered competitive to each other. In addition, we also address the optimal model selection and study the effects of different factors on the performance of proposed CSNN. [ABSTRACT FROM AUTHOR]

Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning-based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called "Deep Packet," can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset. [ABSTRACT FROM AUTHOR]

We proposed a one-dimensional convolutional neural network (CNN) model, which divides heart sound signals into normal and abnormal directly independent of ECG. The deep features of heart sounds were extracted by the denoising autoencoder (DAE) algorithm as the input feature of 1D CNN. The experimental results showed that the model using deep features has stronger anti-interference ability than using mel-frequency cepstral coefficients, and the proposed 1D CNN model has higher classification accuracy precision, higher F-score, and better classification ability than backpropagation neural network (BP) model. In addition, the improved 1D CNN has a classification accuracy rate of 99.01%. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) are increasingly being used in hyperspectral image (HSI) classification. However, most pooling methods are non-overlap pooling and ignore the influence of neighboring pixels on image characteristics, thereby limiting network classification accuracy. This work presents a deep CNN that is based on overlap pooling; in this model, non-overlap pooling is replaced with overlap pooling to improve the accuracy of feature extraction. However, overlap pooling introduces additional noise while improving feature accuracy. We have found that different combinations of max pooling and mean pooling can effectively solve the problem and significantly improve classification performance. The best pooling combination (max–mean–mean) for HSI classification is obtained after verification through experiments. A rectified linear unit activation function layer and the softmax loss classification model are combined to improve overall classification accuracy. Experiments on three HSI data sets, namely, Indian Pines, Salinas and Pavia University, show that the CNN model can increase overall accuracy to 95.66, 97.8 and 97.48%, respectively. Compared with deep network models such as deep belief network and non-overlap CNN, the proposed model has significantly improved the classification accuracy, and thus verifying the high accuracy of feature extraction of overlap pooling in CNN. [ABSTRACT FROM AUTHOR]

In order to develop an efficient angiosperm-genus classification system, we first collected petal image of Hibiscus, Orchis, and Prunus, by digital camera, and remove the backgrounds by region-growing method. Next, we proposed a novel feature-extraction method, which combined most abundant color index (MACI) and introduced the fractional Fourier entropy (FRFE). Third, we submitted the 41 features to a single-hidden layer feedforward neural-network (SLFN), with weight decay (WD) to avoid overfitting. The 10 × 10-fold cross validation showed our method achieved an overall accuracy of 98.92%. Without weight decay, the overall accuracy decreased to 95.50%. Our experiments validated that optimal decay factor is 0.1, and optimal number of hidden neurons is 15. This proposed method is excellent. It performs better than six state-of-the-art approaches and AlexNet. The weight decay helps to enhance generalization of our classifier. [ABSTRACT FROM AUTHOR]