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Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency. Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions. We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analyzed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance. The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet. To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilized in real-time. [ABSTRACT FROM AUTHOR]

Highlights • Mammographic masses classification is dominated by ANN, SVM and KNN. • Grammars and syntactic approaches have not been used so far. • Genetic algorithm was the most used technique to reduce feature dimensionality. • Most of the published papers are database-dependent. • Results are not comparable due to the lack of a common benchmark. Abstract Breast cancer is one of the most common and deadliest cancers that affect mainly women worldwide, and mammography examination is one of the main tools to help early detection. Several papers have been published in the last decades reporting on techniques to automatically recognize breast cancer by analyzing mammograms. These techniques were used to create computer systems to help physicians and radiologists obtain a more precise diagnosis. The objective of this paper is to present an overview regarding the use of machine learning and pattern recognition techniques to discriminate masses in digitized mammograms. The main differences we found in the literature between the present paper and the other reviews are: 1) we used a systematic review method to create this survey; 2) we focused on mass classification problems; 3) the broad scope and spectrum used to investigate this theme, as 129 papers were analyzed to find out whether mass classification in mammograms is a problem solved. In order to achieve this objective, we performed a systematic review process to analyze papers found in the most important digital libraries in the area. We noticed that the three most common techniques used to classify mammographic masses are artificial neural network, support vector machine and k-nearest neighbors. Furthermore, we noticed that mass shape and texture are the most used features in classification, although some papers presented the usage of features provided by specialists, such as BI-RADS descriptors. Moreover, several feature selection techniques were used to reduce the complexity of the classifiers or to increase their accuracies. Additionally, the survey conducted points out some still unexplored research opportunities in this area, for example, we identified that some techniques such as random forest and logistic regression are little explored, while others, such as grammars or syntactic approaches, are not being used to perform this task. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing is a recent model in which a group of mobile users uses their smart devices such as smartphones or wearable devices to cooperatively perform a large-scale sensing task. In this paper, a novel model will be introduced for recognizing/classifying human activities that were collected from sensor units on the chest, legs, and arms. The proposed model employed the k -Nearest Neighbor ( k -NN) classifier which is one of the most common classifiers. k -NN has only one parameter, k , to determine the number of selected nearest neighbors to the test or unknown samples for predicting the class labels of the unknown samples. Searching for the value of k which has a great impact on the classification performance is difficult especially with high dimensional data. This paper employs the Particle Swarm Optimization (PSO) algorithm to search for the optimal value of the k parameter in the k -NN Classifier. This paper shows first experimentally how the PSO in the proposed algorithm searches for the optimal value of k parameter to reduce the misclassification rate of the k -NN classifier. Then, in the second experiment, ten standard datasets are utilized to benchmark the performance of the proposed algorithm. For verification, the results of the PSO- k NN algorithm are compared with two well-known algorithms: Genetic Algorithm (GA) and Ant Bee Colony Optimization (ABCO). In the third experiment, the proposed PSO- k NN algorithm was employed for recognizing human activities. The experimental results proved that the PSO- k NN algorithm is able to find the optimal or near optimal value(s) of the k parameter which enhances the accuracy of k -NN classifier. The results also demonstrated lower error rates compared when GA and ABCO algorithms. [ABSTRACT FROM AUTHOR]

• Adaptive Dimensionality Reduction for the selection of relevant spectral bands. • Selecting the most relevant spectral bands using limited number of training samples. • Semi Supervised 3D Convolutional Neural Network for image classification. • Extracting deep spectral and spatial features based on convolutional encoder-decoder. • Enhancing image classification compared to well-established deep learning methods. This paper proposes a novel approach based on adaptive dimensionality reduction (ADR) and a semi-supervised 3-D convolutional neural network (3-D CNN) for the spectro-spatial classification of hyperspectral images (HSIs). It tackles the problem of curse of dimensionality and the limited number of training samples by selecting the most relevant spectral bands. The selected bands should be informative, discriminative and distinctive. They are fed into a semi-supervised 3-D CNN feature extractor, then a linear regression classifier to produce the classification map. In fact, the proposed semi-supervised 3-D CNN model seeks to extract the deep spectral and spatial features based on convolutional encoder-decoder to enhance the HSI classification. It uses several 3-D convolution and max-pooling layers to extract these features from the selected relevant bands. The main advantage of the proposed approach is to reduce the high dimensionality of HSI, preserve the relevant spectro-spatial information and enhance the classification using few labeled training samples. Experimental studies are carried out on three real HSI data sets: Indian Pines, Pavia University, and Salinas. The obtained results show that the proposed approach performs better than other deep learning-based methods including CNN-based methods, and significantly improves the classification accuracy of HSIs. [ABSTRACT FROM AUTHOR]

Highlights • Convolutional Neural Networks (CNN) is widely used in fashion image recognition. • Apparel needs to be classified considering its hierarchical structure. • Hierarchical Convolutional Neural Networks (H CNN) is proposed using VGGNet. • Proposed model achieves better results than the base model. Abstract Deep learning can be applied in various business fields for better performance. Especially, fashion-related businesses have started to apply deep learning techniques on their e-commerce such as apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The most important backbone of these applications is the image classification task. However, apparel classification can be difficult due to its various apparel properties, and complexity in the depth of categorization. In other words, multi-class apparel classification can be hard and ambiguous to separate among similar classes. Here, we find the need of image classification reflecting hierarchical structure of apparel categories. In most of the previous studies, hierarchy has not been considered in image classification when using Convolutional Neural Networks (CNN), and not even in fashion image classification using other methodologies. In this paper, we propose to apply Hierarchical Convolutional Neural Networks (H CNN) on apparel classification. This study has contribution in that this is the first trial to apply hierarchical classification of apparel using CNN and has significance in that the proposed model is a knowledge embedded classifier outputting hierarchical information. We implement H CNN using VGGNet on Fashion-MNIST dataset. Results have shown that when using H CNN model, the loss gets decreased and the accuracy gets improved than the base model without hierarchical structure. We conclude that H CNN brings better performance in classifying apparel. [ABSTRACT FROM AUTHOR]

The Faster Region-based Convolutional Network (Faster R-CNN) was recently proposed achieving outstanding performance for object detection. Specially, a Region Proposal Network (RPN) is designed to efficiently predict region proposals with a wide range of scales and aspect ratios in Faster R-CNN. Nevertheless, once the number and quality of region proposals generated by RPN are not ideal the object detection performance of Faster R-CNN is affected. In this paper, multiple strategies are applied to address these limitations and improve RPN. Hence, a novel architecture for region proposal generation is presented which is named as Multi-strategy Region Proposal Network (MSRPN). Four improvements are presented in MSRPN. Firstly, a novel skip-layer connection network is designed for combining multi-level features and boosting the ability of pooling layers. Thereupon, the quality of region proposals is strengthened. Secondly, improved anchor boxes are introduced with adaptive aspect ratio and evenly distributed interval of selected scales. In this way, the number of predicted region proposals for detection is seriously reduced and the efficiency of object localization is increased. Particularly, the capability of small object detection is enhanced by applying the first and second improvements. Thirdly, classification layer and regression layer are unified as a single convolutional layer. Furthermore, the model complexity of output layer is reduced. Thus, the speed of training and testing is accelerated. Fourthly, the bounding box regression part of multi-task loss function in RPN is improved. Consequently, the performance of bounding box regression is promoted. In the experiment, MSRPN is compared with the Fast Region-based Convolutional Network (Fast R-CNN), Faster R-CNN, Inside-Outside Net (ION), Multi-region CNN (MR-CNN) and HyperNet approaches. MSRPN achieves the state-of-the-art mean average precision (mAP) of 78.9%, 74.8% and 32.1% on PASCAL VOC 2007, 2012 and MS COCO data sets with the deep VGG-16 model, surpassing other five object detection methods. Simultaneously, the above experiment results are obtained by MSRPN with only 150 region proposals per image. Additionally, MSRPN gets excellent performance on small object detection. Furthermore, MSRPN runs at 6 fps which is faster than other methods. In conclusion, the MSRPN method can provide important support for the intelligent object detection systems. [ABSTRACT FROM AUTHOR]

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets. [ABSTRACT FROM AUTHOR]

A smart camera is a vision system capable of extracting application-specific information from the captured images. The paper proposes a decentralized and efficient solution for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark-EXT. The former is an existing dataset, that allowed us to exhaustively compare with previous works. The latter dataset has been created in the context of this research, accumulating data across various seasons of the year, to test our approach in particularly challenging situations, exhibiting occlusions, and diverse and difficult viewpoints. This dataset is public available to the scientific community and is another contribution of our research. Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets. The performance of our proposed CNN architecture on the parking lot occupancy detection task, is comparable to the well-known AlexNet, which is three orders of magnitude larger. [ABSTRACT FROM AUTHOR]

Presently, the amount of data occurring in several business and academic areas such as ATM transactions, web searches, and sensor data are tremendously and continuously increased. Classifying as well as recognizing patterns among these data in a limited memory space complexity are very challenging. Various incremental learning methods have proposed to achieve highly accurate results but both already learned data and new incoming data must be retained throughout the learning process, causing high space and time complexities. In this paper, a new neural learning method based on radial-shaped function and discard-after-learn concept in the data streaming environment was proposed to reduce the space and time complexities. The experimental results showed that the proposed method used 1 to 95 times fewer neurons and 1.2 to 2,700 times faster than the results produced by MLP, RBF, SVM, VEBF, ILVQ, ASC, and other incremental learning methods. It is also robust to the incoming order of data chunks. [ABSTRACT FROM AUTHOR]

• A novel dataset including combinations of RIR and BAR individuals has been created. • 20 characteristic egg features have been used to classify egg quality and HU. • Eggs have been classified as excellent quality and very good quality by ML models. • HU at the 24th week and the 32nd week have been classified by ML models. With the increase in the world population, the nutritional needs of people have been increased. The demand for eggs which is one of the most important food sources has been increased over years. Therefore, it is very important to inform people about egg quality in order to be prepared for adverse situations such as substitution, mislabeling, and fraud. In this study, it is aimed to specify egg quality without using haugh unit (HU). Besides, another aim is to find how much information HU carries about the specification of egg quality. A dataset including 20 features related to eggs taken from 438 chickens created by Poultry Research Institute (PRI) has been analyzed. An application that can classify egg qualities as very good and excellent using machine learning (ML) models like decision tree (DT), linear discriminant analysis (LDA), logistic regression (LR), naïve bayes (NB), support vector machines (SVM), K-nearest neighboring (KNN), random forest (RF) and artificial neural networks (ANN) has been developed in this study. In addition to that, HU at the 24th week and the 32nd week which are the most important classifier to determine egg qualities have been classified as low informative, medium informative, and high informative by the developed application. Egg quality has the best been classified by LR model based on accuracy and Matthews correlation coefficient (MCC) values as 98.6% and 0.96, respectively. HU at the 24th week has the best been classified by RF based on accuracy and MCC as 96.8% and 0.93, respectively. HU at the 32nd week has the best been classified by RF based on accuracy and MCC as 95.1% and 0.92, respectively. This paper mainly focuses on the classification of egg quality based on not only HU but also egg characteristic features and the importance of the informative feature of HU to classify egg quality. [ABSTRACT FROM AUTHOR]

Over the last decade, the ever increasing world-wide demand for early detection of breast cancer at many screening sites and hospitals has resulted in the need of new research avenues. According to the World Health Organization (WHO), an early detection of cancer greatly increases the chances of taking the right decision on a successful treatment plan. The Computer-Aided Diagnosis (CAD) systems are applied widely in the detection and differential diagnosis of many different kinds of abnormalities. Therefore, improving the accuracy of a CAD system has become one of the major research areas. In this paper, a CAD scheme for detection of breast cancer has been developed using deep belief network unsupervised path followed by back propagation supervised path. The construction is back-propagation neural network with Liebenberg Marquardt learning function while weights are initialized from the deep belief network path (DBN-NN). Our technique was tested on the Wisconsin Breast Cancer Dataset (WBCD). The classifier complex gives an accuracy of 99.68% indicating promising results over previously-published studies. The proposed system provides an effective classification model for breast cancer. In addition, we examined the architecture at several train-test partitions. [ABSTRACT FROM AUTHOR]

The production of a motion picture is an expensive, risky endeavor. During the five-year period from 2008 through 2012, approximately 90 films were released in the United States with production budgets in excess of $100 million. The majority of these films failed to recoup their production costs via gross domestic box office revenues. Existing decision support systems for pre-production analysis and green-lighting decisions lack sufficient accuracy to meaningfully assist decision makers in the film industry. Established models focus primarily upon post-release and post-production forecasts. These models often rely upon opening weekend data and are reasonably accurate but only if data up until the moment of release is included. A forecast made immediately prior to the debut of a film, however, is of limited value to stakeholders because it can only influence late-stage adjustments to advertising or distribution strategies and little else. In this paper we present the development of a model based upon a dynamic artificial neural network (DAN2) for the forecasting of movie revenues during the pre-production period. We first demonstrate the effectiveness of DAN2 and show that DAN2 improves box-office revenue forecasting accuracy by 32.8% over existing models. Subsequently, we offer an alternative modeling strategy by adding production budgets, pre-release advertising expenditures, runtime, and seasonality to the predictive variables. This alternative model produces excellent forecasting accuracy values of 94.1%. [ABSTRACT FROM AUTHOR]

• Deep learning algorithm for identification of type and flight mode of detected drones. • Novel deep learning algorithm for detection of multiple drones in radio frequency domain. • Results of three indoor experiments concerning collecting and analyzing radio signals from drones. • Public available database with radio signals of drones from two different radio frequency bands. Unmanned aerial systems, especially drones have gone through remarkable improvement and expansion in recent years. Drones have been widely utilized in many applications and scenarios, due to their low price and ease of use. However, in some applications drones can pose a malicious threat. To diminish risks to public security and personal privacy, it is necessary to deploy an effective and affordable anti-drone system in sensitive areas to detect, localize, identify, and defend against intruding malicious drones. This research article presents a new publicly available radio frequency drone dataset and investigates detection and identification methodologies to detect single or multiple drones and identify a single detected drone's type. Moreover, special attention in this paper has been underlined to examine the possibility of using deep learning algorithms, particularly fully connected deep neural networks as an anti-drone solution within two different radio frequency bands. We proposed a supervised deep learning algorithm with fully-connected deep neural network models that use raw drone signals rather than features. Regarding the research results, the proposed algorithm shows a lot of potentials. The probability of detecting a single drone is 99.8%, and the probability of type identification is 96.1%. Moreover, the results of multiple drones detection demonstrate an average accuracy of 97.3%. There have not been such comprehensive publications, to this time, in the open literature that have presented and enlightened the problem of multiple drones detection in the radio frequency domain. [ABSTRACT FROM AUTHOR]

• A novel neural network-based method for multi-label graph node classification. • Use one-dimensional convolution modules for node representation learning. • Use attention mechanism to capture node-label dependencies during training. • Extensive experiments manifest the superiority of the proposed method. Learning with graph structured data is of great significance for many practical applications. A crucial and fundamental task in graph learning is node classification. In reality, graph nodes are often encoded with various attributes. In addition, the task is usually multi-labeled in nature. In this paper, we tackle the problem of multi-label graph node classification, by leveraging structure, attribute and label information simultaneously. Specifically, to obtain rational node feature representations, we propose an intuitive yet effective graph convolution module to aggregate local attribute information of a given node. Moreover, the homophily hypothesis motivates us to build a label attention module. By exploiting both input and output contextual representations, we utilize the additive attention mechanism and build a label-aware representation learning framework to measure the compatibility between pairs of node embeddings and label embeddings. The proposed novel neural network-based, multi-label classification method has been verified by extensive experiments conducted on five public-available benchmark datasets, including both attributed and non-attributed networks. The results demonstrate the effectiveness of the proposed model with respect to micro-F1, macro-F1 and Hamming loss, comparing with several state-of-the-art methods, including two relational neighbor classifiers and several popular graph neural network models. [ABSTRACT FROM AUTHOR]

• This paper imparts a road map for the researchers of biometric recognition system. • Highlights both Unimodal and multimodal biometric system. • Covering diagrammatic view and major characteristics of all the biometric traits. • Focus is given to the feature extraction methods, selection of the classifier. • Datasets used and the accuracy achieved to explore the main ideas of the authors. Biometrics is the branch of science that deals with the identification and verification of an individual based on the physiological and behavioral traits. These traits or identifiers are permanent, unique and can separate one individual from another. Biometric recognition systems integrate complex definitional, technological and operational selection under various contexts. The systems are not going to replace the authentication tools and technologies, but the combination of biometric approaches and authentication methods to help in improving the security aspects of the applications where user cooperation can be inferred. Biometric based recognition methods and tools have become popular for the development of many useful, challenging and widely accepted applications such as security issues, surveillance, forensic investigations, fraudulent technologies, identity access management and access control. These systems also help to identify an individual in group of industrial networks, home/office building and control system. For the successful implementation of the biometric systems, deep artificial neural networks are in great demand. These systems can be built up either on the single modality or multiple modalities. This article explicates the comprehensive and deep survey that compactly and systematically summarizes the literature work done on unimodal and multimodal biometric systems and analyzes the feature extraction techniques, classifiers, datasets, results, efficiency and reliability of the system with high and multi-dimensional perspectives. This article also justifies in detail the classical methods, influential methods and taxonomy based on the biometric attributes. The goal is to aware the researchers of this area regarding various dimensions for the development of biometric systems to enhance the security aspects. The article begins with the fundamentals, types, need of system, challenges, uncertainties, motivations and then to the survey work. The tabular representation prepares for each biometric trait shows the author, year, major findings and results achieved with the synthesis analysis and the evaluation. The article finally ends up with the 3D biometric, a future perspective and concluding remarks. [ABSTRACT FROM AUTHOR]

• A novel method for automatic detection of Parkinson disease and severity prediction. • 1D-Convnet architecture specifically designed to process signals from foot sensors. • Parkinsonian gate is detected without manual feature extraction. • We achieved improved accuracy compared to recent state-of-the art algorithms. Diagnosing Parkinson's disease is a complex task that requires the evaluation of several motor and non-motor symptoms. During diagnosis, gait abnormalities are among the important symptoms that physicians should consider. However, gait evaluation is challenging and relies on the expertise and subjectivity of clinicians. In this context, the use of an intelligent gait analysis algorithm may assist physicians in order to facilitate the diagnosis process. This paper proposes a novel intelligent Parkinson detection system based on deep learning techniques to analyze gait information. We used 1D convolutional neural network (1D-Convnet) to build a Deep Neural Network (DNN) classifier. The proposed model processes 18 1D-signals coming from foot sensors measuring the vertical ground reaction force (VGRF). The first part of the network consists of 18 parallel 1D-Convnet corresponding to system inputs. The second part is a fully connected network that connects the concatenated outputs of the 1D-Convnets to obtain a final classification. We tested our algorithm in Parkinson's detection and in the prediction of the severity of the disease with the Unified Parkinson's Disease Rating Scale (UPDRS). Our experiments demonstrate the high efficiency of the proposed method in the detection of Parkinson disease based on gait data. The proposed algorithm achieved an accuracy of 98.7%. To our knowledge, this is the state-of-the-start performance in Parkinson's gait recognition. Furthermore, we achieved an accuracy of 85.3% in Parkinson's severity prediction. To the best of our knowledge, this is the first algorithm to perform a severity prediction based on the UPDRS. These results show that the model is able to learn intrinsic characteristics from gait data and to generalize to unseen subjects, which could be helpful in a clinical diagnosis. [ABSTRACT FROM AUTHOR]

• A new model for acoustic scene classification is proposed. • A relationship between time-frequency resolution and receptive field is modeled. • A fine-resolution feature with semantic information is used for classification. • The proposed method can customize feature representations in various resolutions. • Compared to other deep CNNs, the proposed model reduces computational complexity. Convolutional neural networks with spectrogram feature representation for acoustic scene classification are attracting more and more attentions due to its favorable performance. However, most of the existing methods are still restricted to the tradeoff between the minimum coverage area across time-frequency feature representation, i.e. time-frequency feature resolution, and the depth of CNN models. Thus, it is unfeasible to improve the performance by simply deepening networks. In this paper, fine-resolution convolutional neural network (FRCNN) is proposed to embrace the progress in very deep architecture, feature fusion and convolutional operation. Specifically, lateral construction is applied to generate a fine-resolution feature map with semantic information, and depth-wise separable convolution is utilized to reduce the number of trainable parameters. Extensive experiments demonstrate that the proposed FRCNN exhibits high performance on several metrics, with low computational complexity. [ABSTRACT FROM AUTHOR]