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The spread of social networking sites (SNSs) has led to the development of news and its spread. In terms of momentum produced content and topics, the gathered data from online services like twitter, are from a wide range of areas. Finding an abnormality pattern as well as content oriented planning in society, is the reason why event analysis can be of great importance. Event, in social networking sites is an interesting happening in real world which causes discussion on the topic about related issues by the users of SNSs like twitter, and it is released immediately after the discussion or with a delay. Event, changes the amount of textual data, in a way that it presents related topics in a specific time period. This event is identified by time and topic, and is related with entities like people and places. The computations related to event detection in real time is a big problem in this context. A model based on deep learning is presented in this paper. Firstly, according to the labeled data, classes are formed through classification. Later, in a flow manner, unlabeled data are presented to the model. The unlabeled data are divided into the present classes according to the model which they have been trained. If the data are higher than an identified threshold, they are assigned to a new class, and when the data are lower than the threshold, they are categorized as temporary event. Innovation of the proposed method is in two issues. First, the data in this model are semi-supervised; therefore, the labeled data are used in the first phase and the rest of the data are used in the second phase. In HAN classification phase, a module titled bag of sentence was produced for exact classification of sentences, and in the second phase the abstract concept of Virtual backbone was used to enhance precision of multi label clustering. Adding these two sections using the proposed method enhanced the precision of classification and purification of the data in unlabeled data. [ABSTRACT FROM AUTHOR]

In tourism-dependent cities, investigating the spatiotemporal distribution and dynamics of tourist flows is crucial for better urban planning in both steady and perturbed states. In recent years, researchers have started relying more on photo-based, geotagged social data, which offer insights about tourists, popular hotspots, and mobility patterns. However, distinguishing between tourists and locals from this data is problematic since residence information is often not provided. While previous studies rely on heuristic (e.g., period of stay) and probabilistic (Shannon entropy) approaches, this paper proposes a method for classifying tourists and residents based on machine learning (ML) algorithms and considering parameters that could explain the variability between the two (e.g., weather, mobility, and photo content). This approach was applied to Flickr users' geotagged photos taken in Tokyo's 23 special wards from July 2008 to December 2019. The results show that stacked ensemble (SE) models are superior to models based on five supervised-learning algorithms, including gradient boosting machine (GBM), generalized linear model (GLM), distributed random forest (DRF), deep learning (DL), and extremely randomized trees (XRT). Temporal entropy (TEN), mobility on workdays, and frequent visits to amusement venues and crowded places influenced how users were classified. While temporal distribution showed similar monthly/hourly patterns, spatial distribution varied. The proposed approach might pave the way for scholars to carry out future tourism research on different topics and subsequently support policymakers in the decision-making process, specifically in urban settings. [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]

Classification plays a crucial role in big data, especially in e-commerce operations. Deep learning (DL) research has become a new means to provide a better solution to the problem of classification. In this paper, a deep learning spiking neural network model with the objective of building and testing a more biologically plausible network is proposed. The model is simulated using NengoDL simulator along with TensorFlow in a python environment. The performance of the model is evaluated using FashionMNIST dataset owing to its complexity than the existing MNIST dataset. The results provided better performance, and advantages are viewed in terms of area efficiency due to the use of lesser number of neurons, increasingly biologically plausible network and ease of implementation in hardware like FPGA as SNN is involved. [ABSTRACT FROM AUTHOR]

Despite the rapid improvement in dental health over the last few decades, a significant portion of our population continue seek dental care every year. Estimates show that 13% of adults seek dental care for dental infection or fatigue within four years. The Social and individual burden of this disease can be reduced by its early detection. However, the symptoms of teeth infection in the early stages are not clear, hence, it would be relatively difficult to predict teeth infections based solely on human skills and experience. Big Data (BD) technologies have a great potential in transforming dental care, as they have revolutionized other industries. In addition to reducing cost, they could save millions of lives and improve patient outcomes. This paper proposes a novel integrated prediction model that extracts hidden knowledge from radiographic datasets containing a large volume of dental X-ray images and utilizes this knowledge to predict dental infections. Initially, preprocessing techniques using morphological skeleton and mean approach is applied to eliminate noise and enhance the images. Next, Multi Scale Segmented Region (MSR) approach, Watershed Approach (WA), Sobel edge Detection (SD), Histogram based Segmentation (HS), Trainable Segmentation (TS), Dual Clustering (DC), and Fuzzy C-Means clustering (FCM) are examined for image segmentation and feature extraction. Among these methods, MSR was selected for feature extraction since it outperformed other methods in terms of accuracy, specificity, precision, recall and F1-score. Then, a set of neural network classifiers are trained to identify patterns in the extracted optimized features and predict dental infections. For this purpose, we have examined Bacterial Optimized Recurrent Neural Networks (BORNN), Deep Learning Neural Networks (DANN), Genetic Optimized Neural Networks (GONN) and Adaptive Neural Networks Algorithm (ADNN). BORNN have shown maximum accuracy and Roc value (98.1% and 0.92 respectively), and minimum error values (MSE = 0.189, MAE = 0.143). The output of the proposed integrated prediction model is fed into a dental robot who proceeds with the treatment process with high accuracy and minimum delay. The proposed prediction model was implemented using a big data analytics tool called Apache SAMOA and experimental results showed its correctness and effectiveness. [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]

In severely imbalanced datasets, using traditional binary or multi-class classification typically leads to bias towards the class(es) with the much larger number of instances. Under such conditions, modeling and detecting instances of the minority class is very difficult. One-class classification (OCC) is an approach to detect abnormal data points compared to the instances of the known class and can serve to address issues related to severely imbalanced datasets, which are especially very common in big data. We present a detailed survey of OCC-related literature works published over the last decade, approximately. We group the different works into three categories: outlier detection, novelty detection, and deep learning and OCC. We closely examine and evaluate selected works on OCC such that a good cross section of approaches, methods, and application domains is represented in the survey. Commonly used techniques in OCC for outlier detection and for novelty detection, respectively, are discussed. We observed one area that has been largely omitted in OCC-related literature is its application context for big data and its inherently associated problems, such as severe class imbalance, class rarity, noisy data, feature selection, and data reduction. We feel the survey will be appreciated by researchers working in these areas of big data. [ABSTRACT FROM AUTHOR]

A general description of modern scientometry, its main tasks, and its research methods is presented. The issues of the application of conventional machine learning and deep learning algorithms as tools of artificial intelligence in the thematic classification of scientific literature are considered. The problems and limitations of the classification of literature by sections of science in the systems of indexing and citing of scientific information are outlined. The author presents a specific example of a deep learning application for by-article thematic classification based on convolutional neural networks that was designed by scientists from the United Arab Emirates and Jordan. The article emphasizes the importance of the use of deep learning applications and models for creating correct classifications of the scientific literature that correspond to the realities of the development of science and that are capable of increasing the accuracy of calculating scientometric indicators. [ABSTRACT FROM AUTHOR]

The current digitalized world has emerged with the advances of big data, such as online learning, business, marketing, etc. So reviewing the customer opinion in each field is more critical in enabling smart services. Several neural models are implemented diversely to characterize the customer opinion from the gained review of the user or customer. But, those models have met some difficulties detecting the aspect terms and sentiment values. So, this proposed article has planned to design a novel Strawberry Deep Belief Neural Mechanism (SDBNM) to classify the aspect and sentiment values from the trained datasets. In addition, to evaluate the performance of the designed approach, three datasets are adopted: product, trip review, and Twitter data. Initially, after the data training process, the error is extracted from the database in the preprocessing model of SDBNM. Hereafter, the error-free data is imported to the dense frame of the SDBNM to specify the aspect terms and sentiment values. At last, the parameters are estimated and compared with old approaches and have earned the best results for classification rate. [ABSTRACT FROM AUTHOR]