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This paper examines the work practices involved in making data legible to machines and machine output legible to humans. The study is based on ethnographic research of a team of art experts at DNArt – a data classification system that features a growing database of art images, a classification scheme, a similarity matching algorithm, and a website that together serve as a consumer judgment device in an emerging online market for art. I analyze interactions from meeting observations, interviews, documentation, and online interaction data to show how non-technical art experts explain and repair sociotechnical breakdowns – when their expectations for similarity between art images and artists differ from the similarity relations produced by the algorithm. By repairing breakdowns, the art experts construct the algorithm anew, as a legitimate revealer of similarity in art. In doing so, the team's repair work is folded back into the black box of the algorithm, rendering it invisible and unacknowledged, sometimes even by the experts themselves. [ABSTRACT FROM AUTHOR]

Wavelet neural networks (WNN) have been applied in many fields to solve regression as well as classification problems. After the advent of big data, as data gets generated at a brisk pace, it is imperative to analyze it as soon as it is generated owing to the fact that the nature of the data may change dramatically in short time intervals. This is necessitated by the fact that big data is all pervasive and throws computational challenges for data scientists. Therefore, in this paper, we built an efficient Scalable, Parallelized Wavelet Neural Network (SPWNN) which employs the parallel stochastic gradient algorithm (SGD) algorithm. SPWNN is designed and developed under both static and streaming environments in the horizontal parallelization framework. SPWNN is implemented by using Morlet and Gaussian functions as activation functions. This study is conducted on big datasets like gas sensor data which has more than 4 million samples and medical research data which has more than 10,000 features, which are high dimensional in nature. The experimental analysis indicates that in the static environment, SPWNN with Morlet activation function outperformed SPWNN with Gaussian on the classification datasets. However, in the case of regression, there is no clear trend was observed. In contrast, in the streaming environment i.e., Gaussian outperformed Morlet on the classification and Morlet outperformed Gaussian on the regression datasets. Overall, the proposed SPWNN architecture achieved a speedup of 1.22 - 1.78. [ABSTRACT FROM AUTHOR]

The layout, scale and spatial form of urban employment centers are important guidelines for the rational layout of public service facilities such as urban transportation, medical care, and education. In this paper, we use Internet cell phone positioning data to identify the workplace and residence of users in the Beijing city area and obtain commuting data of the employed to measure the employment center system in Beijing. Firstly, the employment density distribution is generated using the data of the working places of the employed persons, and the employment centers are identified based on the employment density of Beijing. Then, we use the business registration data of employment centers to measure the industrial diversity within the employment centers by using the ecological Shannon Wiener Diversity Index, and combine the commuting links between employment centers and places of residence to measure the energy level of each employment center, analyze the hinterland and sphere of influence of each center, and finally using the industrial diversity index of employment centers and the average commuting time of employed persons, combined with the K-Means clustering algorithm, to classify the employment centers in Beijing. The employment center identification and classification method based on big data constructed in this study can help solve the limitations of the previous employment center system research in terms of center identification and commuting linkage measurement due to large spatial units and lack of commuting data to a certain extent. The study can provide reference for the regular understanding and technical analysis of employment centers and provide help for the employment multi-center system in Beijing in terms of quantifying the employment spatial structure, guiding the construction of multi-center system, and adjusting the land use rules. [ABSTRACT FROM AUTHOR]

This research paper focuses on the challenges associated with learning classifiers from large-scale, highly imbalanced datasets prevalent in many real-world applications. Traditional algorithms learning often need better performance and high computational efficiency when dealing with imbalanced data. Factors such as class imbalance, noise, and class overlap make it demanding to learn effective classifiers. In this study, we propose a novel self-paced ensemble framework for classifying imbalanced data. The framework employs under-sampling to self-harmonize data hardness and build a robust ensemble. Extensive experimental testing demonstrates promising results in handling overlapping classes and skewed distributions while maintaining computational efficiency. The self-paced ensemble method addresses the challenges of high imbalance ratios, class overlap, and noise presence in large-scale imbalanced classification problems. By incorporating the knowledge of these challenges into our learning framework, we establish the concept of classification hardness distribution, and the self-paced ensemble is a revolutionary learning paradigm for massive imbalance categorization, capable of improving the performance of existing learning algorithms on imbalanced data and providing better results for future applications. [ABSTRACT FROM AUTHOR]

One of the greatest challenges of our time is monitoring the rapid environmental changes taking place worldwide at both local and global scales. This requires easy-to-use and ready-to-implement tools and services to monitor and quantify aspects of bio- and geodiversity change and the impact of land use intensification using freely available and global remotely sensed data, and to derive remotely sensed indicators. Currently, there are no services for quantifying both raster- and vector-based indicators in a "compact tool". Therefore, the main innovation of ESIS/Imalys is having a remote sensing (RS) tool that allows for RS data processing, data management, and continuous and discrete quantification and derivation of RS indicators in one tool. With the ESIS/Imalys project (Ecosystem Integrity Remote Sensing—Modelling and Service Tool), we try to present environmental indicators on a clearly defined and reproducible basis. The Imalys software library generates the RS indicators and remote sensing products defined for ESIS. This paper provides an overview of the functionality of the Imalys software library. An overview of the technical background of the implementation of the Imalys library, data formats and the user interfaces is given. Examples of RS-based indicators derived using the Imalys tool at pixel level and at zone level (vector level) are presented. Furthermore, the advantages and disadvantages of the Imalys tool are discussed in detail in order to better assess the value of Imalys for users and developers. The applicability of the indicators will be demonstrated through three ecological applications, namely: (1) monitoring landscape diversity, (2) monitoring landscape structure and landscape fragmentation, and (3) monitoring land use intensity and its impact on ecosystem functions. Despite the integration of large amounts of data, Imalys can run on any PC, as the processing and derivation of indicators has been greatly optimised. The Imalys source code is freely available and is hosted and maintained under an open source license. Complete documentation of all methods, functions and derived indicators can be found in the freely available Imalys manual. The user-friendliness of Imalys, despite the integration of a large amount of RS data, makes it another important tool for ecological research, modelling and application for the monitoring and derivation of ecosystem indicators from local to global scale. [ABSTRACT FROM AUTHOR]

Ephemeral Fast Radio Bursts (FRBs) must be powered by some of the most energetic processes in the Universe. That makes them highly interesting in their own right, and as precise probes for estimating cosmological parameters. This field thus poses a unique challenge: FRBs must be detected promptly and immediately localised and studied based only on that single millisecond-duration flash. The problem is that the burst occurrence is highly unpredictable and that their distance strongly suppresses their brightness. Since the discovery of FRBs in single-dish archival data in 2007, detection software has evolved tremendously. Pipelines now detect bursts in real time within a matter of seconds, operate on interferometers, buffer high-time and frequency resolution data, and issue real-time alerts to other observatories for rapid multi-wavelength follow-up. In this paper, we review the components that comprise a FRB search software pipeline, we discuss the proven techniques that were adopted from pulsar searches, we highlight newer, more efficient techniques for detecting FRBs, and we conclude by discussing the proposed novel future methodologies that may power the search for FRBs in the era of big data astronomy. [ABSTRACT FROM AUTHOR]

In recent years, various studies have been conducted on SVMs and their applications in different area. They have been developed significantly in many areas. SVM is one of the most robust classification and regression algorithms that plays a significant role in pattern recognition. However, SVM has not been developed significantly in some areas like large-scale datasets, unbalanced datasets, and multiclass classification. Efficient SVM training in large-scale datasets is of great importance in the big data era. However, as the number of samples increases, the time and memory required to train SVM increase, making SVM impractical even for a medium-sized problem. With the emergence of big data, this problem becomes more significant. This paper presents a novel distributed method for SVM training in which a very small subset of training samples is used for classification, which reduces the problem size and thus the required memory and computational resources. The solution of this problem almost converges to standard SVM. This method includes three steps: first, detecting a subset of distributed training samples, second, creating local models of SVM and obtaining partial vectors, and finally combining the partial vectors and obtaining the global vector and the final model. In addition, the datasets which suffer from unbalanced number of samples and tend to the majority class, the proposed method balances the samples of the two classes and it can be used in unbalanced datasets. The empirical results show that using this method is efficient for large-scale problems. [ABSTRACT FROM AUTHOR]

This paper targets the area of optimizing machine learning (ML) training data by constructing compact data. The methods of optimizing ML training have improved and become a part of artificial intelligence (AI) system development. Compact data learning (CDL) is an alternative practical framework to optimize a classification system by reducing the size of the training dataset. CDL originated from compact data design, which provides the best assets without handling complex big data. CDL is a dedicated framework for improving the speed of the machine learning training phase without affecting the accuracy of the system. The performance of an ML-based arrhythmia detection system and its variants with CDL maintained the same statistical accuracy. ML training with CDL could be maximized by applying an 85% reduced input dataset, which indicated that a trained ML system could have the same statistical accuracy by only using 15% of the original training dataset. [ABSTRACT FROM AUTHOR]

Quantification of all types of uncertainty helps to establish reliability in any analysis. This research focuses on uncertainty in two attribute levels of wetland classification and creates visualization tools to guide analysis of spatial uncertainty patterns over several scales. A novel variant of confusion matrix analysis compares the Cowardin and Hydrogeomorphic wetland classification systems, identifying areas and types of misclassification for binary and multivariate categories. The specific focus on uncertainty in the paper refers to categorical consistency, that is, agreement between the two classification systems, rather than comparing observed data to ground truth. Consistency is quantified using confusion matrix analysis. Aggregation across progressive focal windows transforms the confusion matrix into a multiscale data pyramid for quick determination of where attribute uncertainty is highly variant, and at what spatial resolutions classification inconsistencies emerge. The focal pyramids summarize precision, recall, and F1 scores to visualize classification differences across spatial scales. Findings show that the F1 scores appear most informative on agreement about wetlands misclassification at both coarse and fine attribute scales. The pyramid organizes multi-scale uncertainty in a single unified framework and can be "sliced" to view individual focal levels of attribute consistency. Results demonstrate how the confusion matrix can be used to quantify the percentage of a study area in which inconsistencies occur reflecting wetland presence and type. The research provides confusion metrics and display tools to focus attention on specific areas of large data sets where attribute uncertainty patterns may be complex, thus reducing land managers' workloads by highlighting areas of uncertainty where field checking might be appropriate, and improving analytics by providing visualization tools to quickly see where such areas occur. [ABSTRACT FROM AUTHOR]

Twitter produces a massive amount of data due to its popularity that is one of the reasons underlying big data problems. One of those problems is the classification of tweets due to use of sophisticated and complex language, which makes the current tools insufficient. We present our framework HTwitt, built on top of the Hadoop ecosystem, which consists of a MapReduce algorithm and a set of machine learning techniques embedded within a big data analytics platform to efficiently address the following problems: (1) traditional data processing techniques are inadequate to handle big data; (2) data preprocessing needs substantial manual effort; (3) domain knowledge is required before the classification; (4) semantic explanation is ignored. In this work, these challenges are overcome by using different algorithms combined with a Naïve Bayes classifier to ensure reliability and highly precise recommendations in virtualization and cloud environments. These features make HTwitt different from others in terms of having an effective and practical design for text classification in big data analytics. The main contribution of the paper is to propose a framework for building landslide early warning systems by pinpointing useful tweets and visualizing them along with the processed information. We demonstrate the results of the experiments which quantify the levels of overfitting in the training stage of the model using different sizes of real-world datasets in machine learning phases. Our results demonstrate that the proposed system provides high-quality results with a score of nearly 95% and meets the requirement of a Hadoop-based classification system. [ABSTRACT FROM AUTHOR]

Big data is a large set of data that is analyzed with the calculation to manifest myriad sources. Big data is capable of handling various challenges to processing huge amounts of data. To handle issues based on large-scale databases, a MapReduce framework is employed which provides robust and simple infrastructure for huge datasets. This paper proposes a novel Elastic collision seeker optimization based Faster R-CNN (ECSO-FRCNN) classifier for efficient big data classification. The proposed ECSO-FRCNN classifier is capable of handling missing attributes, and incremental learning and improves training performance effectively. As the proposed technique deals with large data samples, it necessitates the inclusion of the MapReduce framework. The adaption of MapReduce design in big data classification prevents the classification results from uncertainties such as data redundancy, misclassification, and storage issues. The proposed method is examined with three standard datasets, namely the skin segmentation dataset, mushroom dataset, and localization dataset, collected from the University of California, UCI machine learning repository. Finally, extensive experimental analysis is carried out for various parameters to depict the efficiency of the system. [ABSTRACT FROM AUTHOR]

In the first-tier cities, subway has become an important carrier and life focus of people's daily travel activities. By studying the distribution of POIs of public service facilities around Metro Line 10, using GIS to quantitatively analyze the surrounding formats of subway stations, discussing the functional attributes of subway stations, and discussing the distribution of urban functions from a new perspective, this paper provided guidance and advice for the construction of service facilities. [ABSTRACT FROM AUTHOR]

Data play an indispensable role in transport modelling. The availability of data from non-conventional sources, such as mobile phones, social media, and public transport smart cards, changes the way we conduct mobility analyses and travel forecasting. Existing studies have demonstrated the multitude and varied applications of these emerging data in transport modelling. The transferability of current research and further endeavours depend mostly on the availability of these data. Therefore, the openness or public availability of the prominent data for transport modelling needs to be adequately investigated. Such a discussion should also encompass these data's application aspects to provide a holistic overview. This paper defines a typology for the data classification based on a set of availability or openness attributes from the existing literature. Subsequently, we use the developed typology to classify the prominent transport data into four categories: (i) Commercial data, (ii) Inaccessible data, (iii) Gratis and accessible data with restricted use, and (iv) Open data. Using this typology, we conclude that the public data, which refer to the data that are accessible and free of cost, are a superset of open data. Further, we discuss the applications and limitations of the selected data in transport modelling and highlight in which task(s) certain data excel. Lastly, we synthesise our review using a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis to bring out the aspects relevant to data owners and data consumers. Public availability of data can help in various modelling steps such as trip generation, accessibility, destination choice, route choice, network modelling. Complementary datasets such as General Transit Feed Specification (GTFS) and Volunteered Geographic Information (VGI) increase the usability of other data. Thus, modellers can gain from the positive cascade effect by prioritising these data. There is also a potential for data owners to release proprietary data, such as mobile phone data, with restricted-use licenses after addressing privacy risks. Our study contributes by dealing with two problems at the same time. On the one hand, the paper analyses existing data based on their potential for mobility studies. On the other hand, we classify them based on how open they are. Hence, we identify the most promising public data for developing the next generation of transport models. [ABSTRACT FROM AUTHOR]

With the development of computer science and information technology, human society is gradually stepping into the Internet and big data. The medical and health industry can realize the integration and readjustment of existing resources, improve the operation efficiency of the industry, and tap the huge potential of the industry with the support of big data technology. However, the medical data in the new era has the characteristics of massive, high latitude, complex structure, and complex information, which is not conducive to the direct classification of health data. The preprocessing of health data can improve the quality of dataset, reduce the size of data, and improve the efficiency and accuracy of data classification. Based on this and according to the characteristics of health dataset and the existing pretreatment technology, this paper analyzes and improves the algorithm of abnormal data detection and data protocol in the process of reprocessing data cleaning. This paper analyzes and studies feature selection algorithms based on Bayesian inference algorithm and focuses on feature selection algorithms based on random forest. In order to solve the problem that the original algorithm ignored the relationship between the importance degrees of each feature in a single tree, a feature importance degree calculation method based on local importance degree was proposed. Through experimental analysis and comparison, the improved algorithm can select better feature subset and improve the performance of the classification model. Then, TAN classifier, BAN classifier, and MBN classifier were constructed based on preprocessed hypothyroidism data, and the performances of these four classifiers were compared through experiments. The final results show that BAN classifier has the best average classification effect. [ABSTRACT FROM AUTHOR]

Big data in medical diagnosis can provide abundant value for clinical diagnosis, decision support and many other applications, but obtaining a large number of labeled medical data will take a lot of time and manpower. In this paper, a classification model based on semi-supervised learning algorithm using both labeled and unlabeled data is proposed to process big data in medical diagnosis, which includes structured, semi-structured and unstructured data. For the medical laboratory data, this paper proposes a self-training algorithm based on repeated labeling strategy to solve the problem that mislabeled samples weaken the performance of classifiers. Aiming at medical record data, this paper extracts features with high correlation of classification results based on domain expert knowledge base first, and then chooses the unlabeled medical record data with the highest confidence to expand the training set and optimizes the performance of the classifiers of tri-training algorithm, which uses supervised learning algorithm to train three basic classifiers. The experimental results show that the proposed medical diagnosis data classification model based on semi-supervised learning algorithm has good performance. [ABSTRACT FROM AUTHOR]

This research is devoted to the determination of hidden dependencies between the flow of particles that come from the Sun and precipitation-induced floods in the United Kingdom (UK). The analysis covers 20 flood events during the period from October 2001 to December 2019. The parameters of solar activity were used as model input data, while precipitations data in the period 10 days before and during each flood event were used as model output. The time lag of 0–9 days was taken into account in the research. Correlation analysis was conducted to determine the degree of randomness for the time series of input and output parameters. For establishing a potential causative link, machine learning classification predictive modeling was applied. Two approaches, the decision tree, and the random forest were used. We analyzed the accuracy of classification models forecast from 0 to 9 days in advance. It was found that the most important factors for flood forecasting are proton density with a time lag of 9, differential proton flux in the range of 310–580 keV, and ion temperature. Research in this paper has shown that the decision tree model is more accurate and adequate in predicting the appearance of precipitation-induced floods up to 9 days ahead with an accuracy of 91%. The results of this study confirmed that by increasing technical capabilities, using improved machine learning techniques and large data sets, it is possible to improve the understanding of the physical link between the solar wind and tropospheric weather and help improve severe weather forecasting. [ABSTRACT FROM AUTHOR]

This paper considers the learning of Boolean rules in disjunctive normal form (DNF, ORof-ANDs, equivalent to decision rule sets) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. We also consider the fairness setting and extend the formulation to include explicit constraints on two different measures of classification parity: equality of opportunity and equalized odds. Column generation (CG) is used to efficiently search over an exponential number of candidate rules without the need for heuristic rule mining. To handle large data sets, we propose an approximate CG algorithm using randomization. Compared to three recently proposed alternatives, the CG algorithm dominates the accuracy-simplicity trade-off in 8 out of 16 data sets. When maximized for accuracy, CG is competitive with rule learners designed for this purpose, sometimes finding significantly simpler solutions that are no less accurate. Compared to other fair and interpretable classifiers, our method is able to find rule sets that meet stricter notions of fairness with a modest trade-off in accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we present the relationship and place of the big data modeling process and the construction of predictive models in the analysis of economic and financial information. An effective methodology for creating models in the analysis of financial data to predict the possibility of bankruptcy is presented. The methodology is applied to a public dataset of financial data for Taiwanese companies. The results of the application of the methodology are compared with those obtained in scientific literature from 2022. [ABSTRACT FROM AUTHOR]

The endemic growth of online reviews has attracted the users to post their opinions in the social network. This growth made the classification of sentiment process an interesting domain in both industrial and academic research. Various sentiment classification techniques are developed to perform sentiment analysis, but the acquisition of sentiment grade is not precisely performed. Hence, this paper devises novel optimization driven classifier for classifying the sentiment grades. Here, the reviews are considered wherein the features are mined using reviews. In addition, significant features, like, SentiWordNet-based features, statistical features, context-based features, and Term Frequency-Inverse Document Frequency (TF-IDF)-based features are obtained from review. These features are adapted in Hierarchical Attention Network (HAN) to categorize sentiment grade. The training of HAN is performed using the proposed Competitive Swarm Water Wave Optimization (CSWWO) algorithm. The developed CSWWO algorithm is newly designed by integrating the Competitive Swarm Optimizer (CSO) and Water Wave Optimization (WWO) technique. Thus, the proposed CSWWO-based HAN model categorizes the sentiment into five classes, poor, better, good, very good, and excellent. At last, data stream handling is performed by concept drift detection and prototype-based adaption. Hence, the proposed CSWWO-based HAN offered enhanced performance with maximal accuracy of 93%, minimal True Positive Rate (TPR) of 93.9%, and maximal True Negative Rate (TNR) of 91.7%. [ABSTRACT FROM AUTHOR]

This paper proposes an effective classification method named Rider Chicken Optimization Algorithm-based Recurrent Neural Network (RCOA-based RNN) to perform big data classification in spark architecture. Initially, the input data are collected from the network by the master node and then forwarded to the slave node. These nodes are responsible for storing the data and performing computations. The features are effectively selected in the slave node using the proposed RCOA. The selected features are forwarded to the master node. The big data classification is achieved in the master node by using the RNN classifier, and the training of the classifier is done using the proposed RCOA algorithm, which is the integration of the Rider optimization algorithm (ROA) with the standard Chicken Swarm Optimization (CSO). The experimentation is done by using the Switzerland dataset, Cleveland dataset, Hungarian dataset and Skin disease dataset, in which the proposed RCOA-based RNN attained better performance based on the quantitative properties, such as sensitivity, accuracy and specificity with the values of 9.3E+01%, 9.4E+01% and 9.3E+01% using Hungarian dataset. The existing learning methods failed to address the complex classification problems at a reasonable time, which is overcome by the proposed method. [ABSTRACT FROM AUTHOR]

Using big data-assisted machine learning methods in animal science has received increasing attention in recent years since they extract useful insights from large-scale animal datasets. Especially, animal activity recognition is the task of identifying the actions performed by animals and can provide rich insight into their health, welfare, reproduction, survival, foraging, and interaction with humans/other animals. This paper aims to propose a new solution for this purpose by building a machine learning model that classifies the actions of horses based on big sensor data. Unlike the previous studies, our study is original in that it compares the accuracies of per-subject (personalized) and cross-subject (generalized) models. It is the first study that especially compares different ensemble learning algorithms for horse activity recognition in terms of classification accuracy, including bagging trees, extremely randomized trees, random forest, extreme gradient boosting, light gradient boosting, gradient boosting, and categorical boosting. The purpose of the study is to classify five horse activities: walking, standing, grazing, galloping, and trotting. The experimental results showed that our solution achieved very good performance (94.62%) on average on a real-world dataset. Furthermore, the results also showed that our method outperformed the state-of-the-art methods on the same dataset. [ABSTRACT FROM AUTHOR]

Lung tumor is a complex disease caused due to the irregular growth of lung cells. A key factor in effective treatment planning is the early detection of lung tumor. Visual similarity between benign and malignant nodules, heterogeneity and low contrast variation are the factors that make accurate cancerous lesion recognition, a very challenging task. In this paper, Optimized Deep convolutional neural network (DCNN) and Fuzzy C‐means with Equilibrium optimizer (FCM‐EO) is proposed for classification and segmentation of CT lung images. The proposed architecture is comprised of four phases such as preprocessing, feature extraction, classification and segmentation. In preprocessing, the weighted mean histogram analysis (WMHA) is utilized to enhance the quality of images and noise removal. Hybrid Dual tree‐complex wavelet transform (DT‐CWT) with Gabor filter is proposed in feature extraction to extract the features from the preprocessed images. DCNN model is designed to classify the original images into benign and malignant images. The weight of DCNN model is updated using the Enhanced black widow optimization algorithm (EBWOA). In segmentation, FCM‐EO is introduced to identify the tumor regions and remove the outliers from the malignant images. LIDC‐IDRI dataset is utilized for the experimental analysis and MATLAB is the implementation tool. The simulation analysis is performed for both the classification and segmentation processes. Accuracy, specificity, sensitivity, precision, F‐measure, FROC, DSC, MCC, and IoU are evaluated for both these processes. The experimental results showed the proposed framework is efficient for the identification of tumor from the CT lung images. [ABSTRACT FROM AUTHOR]

Feature selection is a common dimension reduction approach for processing high-dimensional big data, but it often involves multiple conflicting feature subsets evaluation objectives which are difficult to balance . To reach a compromise among various feature subset evaluations in feature selection and optimize the performance of subset, this paper proposed a subset evaluation multi-objective optimization based feature selection framework and focused on the application of multi-objective particle swarm optimization( MOPSO) in feature subset evaluation. The framework used sparsity, classification ability and information loss to design multi-objective optimization functions. Then it optimized the weight vectors of the features based on multi-objective optimization algorithm, and selected the " knee" of Pareto solution set as optimal vector. Finally, the framework realized feature selection based on weight vector ranking. This paper designed experiments to compare the performance of MOPSO based feature selection( FS_MOPSO) with four classical methods. The results on several standard data sets show that, FS_MOPSO shows higher classification accuracy in low dimensional space while ensuring less information loss. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet in modern society has promoted the development of many different network platforms. In the context of big data, many types of multimodal data such as pictures, videos, and texts are generated in the platform. Through the analysis of multimodal data, we can provide better services for users. The traditional big data analysis platform cannot achieve a completely stable state for the analysis of multimodal data. The construction of multimodal intelligent platform can achieve efficient analysis of relevant data, so as to create greater economic benefits for the society. This paper mainly studies the historical development trend of big data multimodal intelligence technology and the data processing method of multimodal intelligence technology applied to network expression classification, including data acquisition, storage, and analysis. Finally, it studied the fusion algorithm between multimodal data and visual design, as well as the classification of network expression and the application result analysis of visual design in big data multimodal intelligence technology. [ABSTRACT FROM AUTHOR]

A distributed denial of service (DDoS) attack is the most destructive threat for internet-based systems and their resources. It stops the execution of victims by transferring large numbers of network traces. Due to this, legitimate users experience a delay while accessing internet-based systems and their resources. Even a short delay in responses leads to a massive financial loss. Numerous techniques have been proposed to protect internet-based systems from various kinds of DDoS attacks. However, the frequency and strength of attacks are increasing year-after-year. This paper proposes a novel Apache Kafka Streams-based distributed classification approach named KS-DDoS. For this classification approach, firstly, we design distributed classification models on the Hadoop cluster using highly scalable machine learning algorithms by fetching data from Hadoop distributed files system (HDFS). Secondly, we deploy an efficient distributed classification model on the Kafka Stream cluster to classify incoming network traces into nine classes in real-time. Further, this distributed classification approach stores highly discriminative features with predicted outcomes into HDFS for creating/updating models using a new set of instances. We implemented a distributed processing framework-based experimental environment to design, deploy, and validate the proposed classification approach for DDoS attacks. The results show that the proposed distributed KS-DDoS classification approach efficiently classifies incoming network traces with at least 80% classification accuracy. [ABSTRACT FROM AUTHOR]

Distributed denial of service (DDoS) is an immense threat for Internet based-applications and their resources. It immediately floods the victim system by transmitting a large number of network packets, and due to this, the victim system resources become unavailable for legitimate users. Therefore, this attack is claimed to be a dangerous attack for Internet-based applications and their resources. Several security approaches have been proposed in the literature to protect Internet-based applications from this type of threat. However, the frequency and strength of DDoS attacks are increasing day-by-day. Further, most of the traditional and distributed processing frameworks-based DDoS attack detection systems analyzed network flows in offline batch processing. Hence, they failed to classify network flows in real-time. This paper proposes a novel Spark Streaming and Kafka-based distributed classification system, named by SSK-DDoS, for classifying different types of DDoS attacks and legitimate network flows. This classification approach is implemented using a distributed Spark MLlib machine learning algorithms on a Hadoop cluster and deployed on the Spark streaming platform to classify streams in real-time. The incoming streams consume by Kafka's topic to perform preprocessing tasks such as extracting and formulating features for classifying them into seven groups: Benign, DDoS-DNS, DDoS-LDAP, DDoS-MSSQL, DDoS-NetBIOS, DDoS-UDP, and DDoS-SYN. Further, the SSK-DDoS classification system stores formulated features with their predicted class into the HDFS that will help to retrain the distributed classification approach using a new set of samples. The proposed SSK-DDoS classification system has been validated using the recent CICDDoS2019 dataset. The results show that the proposed SSK-DDoS efficiently classified network flows into seven classes and stored formulated features with the predicted value of each incoming network flow into HDFS. [ABSTRACT FROM AUTHOR]

Two new hybrid neural architectures combining morphological neurons and perceptrons are introduced in this paper. The first architecture, called Morphological - Linear Neural Network (MLNN) consists of a hidden layer of morphological neurons and an output layer of classical perceptrons has the capability of extracting features. The second architecture, called Linear-Morphological Neural Network (LMNN) is composed of one or several perceptron layers as a feature extractor, it is then followed by an output layer of morphological neurons for non-linear classification. Both architectures are trained by stochastic gradient descent. One of the main contributions of this paper is to show that the morphological layer offers a greater capacity to extract features than the perceptron layer. This claim is supported both theoretically and experimentally. We prove that the morphological layer possesses a greater capacity per computation unit to segment the 2D input space than the perceptron layer. In other words, adding more hyper-boxes produces more response regions than adding hyperplanes. From an empirical point of view, we test the two new models on 25 standard datasets at low dimensionality and one big data dataset. The result is that MLNN requires a lesser number of learning parameters than the other tested architectures while achieving better accuracies. [ABSTRACT FROM AUTHOR]

Background: Computer-aided diagnosis (CAD) can assist physicians in effective and efficient diagnostic decision-making. CAD systems are currently essential tools in some areas of clinical practice. In addition, it is one of the established fields of study in the interface of medicine and computer science. There are, however, still some critical challenges that CAD systems face.Methods: This paper first describes a new literature review protocol, the Dynamic PRISMA approach based on the well-known PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) approach. This new approach enhances the traditional approach by integrating a feedback mechanism module. As a result of the literature review, this paper identifies seven major challenges that occur today in CAD and inhibit the next major developments.Results: The seven challenges described in this paper involve some technical weaknesses in the interface of medicine and computer science. These challenges are related to various algorithmic limitations, the difficulty of medical professionals to adopt new systems, problems when dealing with patient data, and the lack of guidelines and standardization regarding many aspects of CAD. This paper also describes some of the recent research developments towards these challenges.Conclusion: If these seven key challenges are addressed properly, then the ways for dealing with them will become the R&D pillars needed to bring CAD to the next level. This would require additional well-coordinated collaboration between researchers and practitioners in the fields of medicine and computer science. [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]

Scholarly articles are a great source of knowledge. Learning from them like E-learning requires automatic approaches to build concept-maps, learning paths, etc., as these sources are monotonically increasing and are big too. These sources have multi-domain, variety, huge volumes, which are, in fact, Big Data's characteristics. Thus data from different domains have to be handled together, especially in the E-learning systems. This paper presents a new approach for concept extraction and semantically clustering and classification of these e-learning concepts using fuzzy membership values. Scholarly articles from different domains are taken for our experimental work, and we tested on BBC datasets with 100 documents and 650 documents. Since the number of domains is known, and all concepts are stored, we have done both clustering and classification for testing our fuzzy-based semantic system. We have used logistics regression, Support Vector Machine (SVM) with Linear kernel, Polynomial kernel, Radial Basis Function (RBF) Kernel, Sigmoid kernel to obtain maximum accuracy up to 94% to 96% for all data sets. In clustering, using K-Means, we got precision up to 93%. The system can be used to generate adaptive learning paths, concept map extraction; Big-Data based E-Learning portals. [ABSTRACT FROM AUTHOR]

Hidden and unknown pattern are extracted from large data sets by performing several combinations of techniques from database and machine learning. Data mining plays a significant role for handling a huge amount of data. Data mining deals with heterogeneity, privacy and correctness of data. Moreover, medical data mining is tremendously important research area and significant attempts are made in this area in recent years because inaccuracy in medical data systems may cause seriously disingenuous medical treatments. Medical data sets should be analyzed using suitable mining algorithms. To perform related operations, techniques of data mining have been used in developing medical systems for prediction of diseases through a set of medical data set. This paper reviews state of the art data mining algorithms for predicting different diseases and to analyze the performance of classification techniques i.e. Naive Bayes (NB), J48, REF Tree, Sequential Minimal Optimization (SMO), Multi-Layer Perceptron and Vote on different data sets of different diseases i.e. chronic kidney disease (CKD), heart disease, liver and diabetes. The experimental setup for performance evaluation of various algorithms using disease data sets retrieved from UCI respiratory has been made in WEKA tool. Values of different parameters i.e. correctly classified instances, precision, recall and F-Measure, time taken are analyzed by applying different classification algorithms. [ABSTRACT FROM AUTHOR]

Quantity and quality of grain are both closely related to national development and social stability. Grain is lost during storage due to mildew and insects. Detection of damaged grain kernels not only can reduce the loss of grain, but also protect human beings from diseases caused by damaged grain. Therefore, research on the automatic detection of damaged grain is of continued urgency. In this paper, we propose a framework combining spectrogram generative adversarial network and progressive neural architecture search (SPGAN-PNAS) to detect and classify mildew-damaged wheat kernels (MDK), insect-damaged wheat kernels (IDK) and undamaged wheat kernels (UDK). First, the spectrogram generative adversarial network (SPGAN) is designed to enlarge the data set. Second, we apply progressive neural architecture search (PNAS) to generate network structure to classify three types of wheat kernels. An F1 of 96.2% is obtained using the proposed method with 5-fold cross-validation. The results are superior to the classical neural networks for detection and classification of damaged wheat kernels. Experimental results show that the structure of SPGAN-PNAS is feasible and effective. • A novel framework SPGAN-PNAS was proposed for detection of damaged wheat kernels. • Neural architecture search applied to detection and classification of damaged wheat. • Good and rapid results for detection and classification of damaged wheat kernels. • Method particularly suitable for large data sets. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, data privacy protection has become a valuable topic in the field of data publication. Unfortunately, traditional methods of privacy protection, k-anonymity, and its extensions are not absolutely secure as an adversary with background knowledge can determine the owner of a record. The emergence of differential privacy provides a reasonable alternative for privacy security, but the existing solutions ignore the correlation between sensitive attributes and other attributes. In this paper, we propose a new differential privacy scheme based on quasi-identifier classification for big data publication (DP-QIC). It is a new data publishing scheme based on the obfuscation of attribute correlation. We innovatively present quasi-identifier classification based on sensitive attributes and the privacy ratio for evaluating the data set vulnerability. DP-QIC achieves data privacy-protecting through four steps: data collection, grouping and shuffling, generalization, merging, and noise adding, which retains the overall statistical characteristics of the data set. Moreover, the exponential mechanism and the Laplace mechanism are integrated to ensure higher flexibility and a stronger level of privacy protection, so DP-QIC can be used for privacy processing of different data groups in future development. Finally, we have compared the performance of our scheme with the other two famous schemes in the industry. Experimental results demonstrate that DP-QIC has obvious advantages in data utility, privacy protection, and processing efficiency. [ABSTRACT FROM AUTHOR]

The paper describes a manifold learning-based algorithm for big data classification and reduction, as well as parameter identification in real-time operation of a power system. Both black-box and gray-box settings for SCADA- and PMU-based measurements are examined. Data classification is based on diffusion maps, where an improved data-informed metric construction for partition trees is used. Data classification and reduction is demonstrated on the measurement tensor example of calculated transient dynamics between two SCADA refreshing scans. Interpolation/extension schemes for state extension of restriction (from data to reduced space) and lifting (from reduced to data space) operators are proposed. The method is illustrated on the single-phase Motor D example from a very detailed WECC load model, connected to the single bus of a real-world 441-bus power system. [ABSTRACT FROM AUTHOR]

The problem of risk classification and prediction, an essential research direction, aiming to identify and predict risks for various applications, has been researched in this paper. To identify and predict risks, numerous researchers build models on discovering hidden information of a label (positive credit or negative credit). Fuzzy logic is robust in dealing with ambiguous data and, thus, benefits the problem of classification and prediction. However, the way to apply fuzzy logic optimally depends on the characteristics of the data and the objectives, and it is extraordinarily tricky to find such a way. This paper, therefore, proposes a general membership function model for fuzzy sets (GMFMFS) in the fuzzy decision tree and extend it to the fuzzy random forest method. The proposed methods can be applied to identify and predict the credit risks with almost optimal fuzzy sets. In addition, we analyze the feasibility of our GMFMFS and prove our GMFMFS‐based linear membership function can be extended to a nonlinear membership function without a significant increase in computing complex. Our GMFMFS‐based fuzzy decision tree is tested with a real dataset of US credit, Susy dataset of UCI, and synthetic datasets of big data. The results of experiments further demonstrate the effectiveness and potential of our GMFMFS‐based fuzzy decision tree with linear membership function and nonlinear membership function. [ABSTRACT FROM AUTHOR]

Inner ear balance problems are common worldwide and are often difficult to diagnose. In this study we examine the classification of patients with inner ear balance problems and controls (people not suffering from inner ear balance problems) based on data derived from the stabilogram signals and using machine learning algorithms. This paper is a continuation for our earlier paper where the same dataset was used and the focus was medically oriented. Our collected dataset consists of stabilogram (a force platform response) data from 30 patients suffering from Ménière's disease and 30 students called controls. We select a wide variety of machine learning algorithms from traditional baseline methods to state-of-the-art methods such as Least-Squares Support Vector Machines and Random Forests. We perform extensive and carefully made parameter value searches and we are able to achieve 88.3% accuracy using k -nearest neighbor classifier. Our results show that machine learning algorithms are well capable of separating patients and controls from each other. [ABSTRACT FROM AUTHOR]

Highlights • A method was to classify train stopping at red aspects using industrial data. • A hybrid of physical model and statistical model was used and validated. • The industry can understand and mitigate SPAD risks better based on this research project. Abstract The paper describes a novel technique to reliably classify whether or not a train approaching a signal at red actually came to a stand before the signal cleared (red aspect approaches) using the data from conventional signalling systems. This data is limited to the times at which the signal aspect changes and the times at which the train enters and leaves the signalling section ('berth') in advance of the signal. Knowing the percentage of red aspect approaches is potentially important for understanding the likelihood of a signal being passed at danger (SPAD) at individual signals and also for normalisation of SPAD data, both locally and nationally, for trending and benchmarking. The industry currently uses the number of red aspect approaches based on driver surveys as estimates, which are considered to have significant shortcomings. The development of the classification model is described together with the validation procedures. The techniques presented in this paper allow red approach rates to be reliably determined without the need for complex integration of signalling system and on-train data recorder data. The initial study of 94 million train approaches shows that 5% of them stopped at red aspects. It is also highlighted that there is a large variation in the red aspect approach rates between signalling areas and between individual signals. SPAD risk assessment at individual signals could be significantly enhanced by the ability to estimate red aspect approach rates using the techniques described. [ABSTRACT FROM AUTHOR]

Highlights • Novel feature selection approaches based on Binary Dragonfly Algorithm (BDA) are proposed. • Eight time varying S-shaped and V-shaped transfer functions are proposed. • The leverage of using time-varying transfer functions on exploration and exploitation behaviors is investigated. • Extensive tests are made to assess the proposed algorithms on the datasets to prove their merits. Graphical abstract Abstract The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that was shown to have excellent performance for numerous optimization problems. In this paper, a wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm (BDA). The key component of the BDA is the transfer function that maps a continuous search space to a discrete search space. In this study, eight transfer functions, categorized into two families (S-shaped and V-shaped functions) are integrated into the BDA and evaluated using eighteen benchmark datasets obtained from the UCI data repository. The main contribution of this paper is the proposal of time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation. During the early stages of the optimization process, the probability of changing the position of an element is high, which facilitates the exploration of new solutions starting from the initial population. On the other hand, the probability of changing the position of an element becomes lower towards the end of the optimization process. This behavior is obtained by considering the current iteration number as a parameter of transfer functions. The performance of the proposed approaches is compared with that of other state-of-art approaches including the DA, binary grey wolf optimizer (bGWO), binary gravitational search algorithm (BGSA), binary bat algorithm (BBA), particle swarm optimization (PSO), and genetic algorithm in terms of classification accuracy, sensitivity, specificity, area under the curve, and number of selected attributes. Results show that the time-varying S-shaped BDA approach outperforms compared approaches. [ABSTRACT FROM AUTHOR]

Abstract The paper reports an experimentally identified list of benchmark data sets that are hard for representative classification and feature selection methods. This was done after systematically evaluating a total of 48 combinations of methods, involving eight state-of-the-art classification algorithms and six commonly used feature selection methods, on 129 data sets from the UCI repository (some data sets with known high classification accuracy were excluded). In this paper, a data set for classification is called hard if none of the 48 combinations can achieve an AUC over 0.8 and none of them can achieve an F-Measure value over 0.8; it is called easy otherwise. A total of 15 out of the 129 data sets were found to be hard in that sense. This paper also compares the performance of different methods, and it produces rankings of classification methods, separately on the hard data sets and on the easy data sets. This paper is the first to rank methods separately for hard data sets and for easy data sets. It turns out that the classifier rankings resulting from our experiments are somehow different from those in the literature and hence they offer new insights on method selection. It should be noted that the Random Forest method remains to be the best in all groups of experiments. [ABSTRACT FROM AUTHOR]

In this paper, a new hybridization of supervised principal component analysis (SPCA) and stochastic gradient descent techniques is proposed, and called as SGD- SPCA, for real large datasets that have a small number of samples in high dimensional space. SGD-SPCA is proposed to become an important tool that can be used to diagnose and treat cancer accurately. When we have large datasets that require many parameters, SGD-SPCA is an excellent method, and it can easily update the parameters when a new observation shows up. Two cancer datasets are used, the first is for Leukemia and the second is for small round blue cell tumors. Also, simulation datasets are used to compare principal component analysis (PCA), SPCA, and SGD-SPCA. The results show that SGD-SPCA is more efficient than other existing methods. [ABSTRACT FROM AUTHOR]

This paper takes education data mining as the research theme, mine the existing massive education big data, compares the analysis methods of existing data models, and proposes an improved random forest reference model. The information gain of various features is calculated by introducing the feature weighting system, and the evaluation index is used to improve the existing data analysis. The simulation results show that the improved model is highly efficient as compared to the existing models for classification. In order to resolve the performance bottleneck of a single node in multiple data classification tasks in the era of big data, a classification and prediction model of graduates' large-scale employment data, based on distributed improved RF algorithm, is proposed. The MapReduce distributed computing framework is used to complete the serial writing and deserialization loading of the training model between the local disk and the distributed file system, and realizing the distributed expansion of the large-scale data classification model based on the improved RF model. [ABSTRACT FROM AUTHOR]

Big data classification problems have drawn great attention from diverse fields, and many classifiers have been developed. Among those classifiers, the extended belief rule-based system (EBRBS) has shown its potential in both big data and multiclass situations, while the time complexity and computing efficiency are two challenging issues to be handled in EBRBS. As such, three improvements of EBRBS are proposed first in this paper to decrease the time complexity and computing efficiency of EBRBS for multiclass classification under the assumption of large amount of data, including the strategy to skip rule weight calculation, a simplified evidential reasoning algorithm, and the domain division-based rule reduction method. This turns out to be a micro version of the EBRBS, called Micro-EBRBS. Moreover, one of commonly used cluster computing, named Apache Spark, is then applied to implement the parallel rule generation and inference schemes of the Micro-EBRBS for big data multiclass classification problems. The comparative analyses of experimental studies demonstrate that the Micro-EBRBS not only can obtain a desired accuracy but also has the comparatively better time complexity and computing efficiency than some popular classifiers, especially for multiclass classification problems. [ABSTRACT FROM AUTHOR]

Intelligent computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. CAD systems could provide physicians with a suggestion about the diagnostic of heart diseases. The objective of this paper is to review the recent published preprocessing, feature extraction and classification techniques and their state of the art of phonocardiogram (PCG) signal analysis. Published literature reviewed in this paper shows the potential of machine learning techniques as a design tool in PCG CAD systems and reveals that the CAD systems for PCG signal analysis are still an open problem. Related studies are compared to their datasets, feature extraction techniques and the classifiers they used. Current achievements and limitations in developing CAD systems for PCG signal analysis using machine learning techniques are presented and discussed. In the light of this review, a number of future research directions for PCG signal analysis are provided. [ABSTRACT FROM PUBLISHER]

In recent years, an enormous amount of research has been carried out on support vector machines (SVMs) and their application in several fields of science. SVMs are one of the most powerful and robust classification and regression algorithms in multiple fields of application. The SVM has been playing a significant role in pattern recognition which is an extensively popular and active research area among the researchers. Research in some fields where SVMs do not perform well has spurred development of other applications such as SVM for large data sets, SVM for multi classification and SVM for unbalanced data sets. Further, SVM has been integrated with other advanced methods such as evolve algorithms, to enhance the ability of classification and optimize parameters. SVM algorithms have gained recognition in research and applications in several scientific and engineering areas. This paper provides a brief introduction of SVMs, describes many applications and summarizes challenges and trends. Furthermore, limitations of SVMs will be identified. The future of SVMs will be discussed in conjunction with further applications. The applications of SVMs will be reviewed as well, especially in the some fields. [ABSTRACT FROM AUTHOR]

To identify the data examples of rare categories that form small compact clusters in large data sets, existing approaches mostly require enough labeled data examples as a training set to learn a classifier, assuming that the rare-category clusters are spherical or nearly spherical. Nonetheless, a large enough training set is usually difficult to obtain in practice, and rare categories in many real-world applications often form small compact clusters with arbitrary shapes. In this paper, we investigate how to identify all data examples of a rare category with an arbitrary shape based on only one seed (i.e., a labeled rare-category data example). Instead of finding a compact and spherical local region around the seed, we locally explore the data set from the seed by continuously searching and visiting the $k$ k -nearest neighbors of each newly visited data example. The local exploration connects the data examples in the objective rare category by the relationship of $k$ k -nearest neighbors, and meanwhile, suspected external data examples are filtered out if they are not close enough to any visited data example. Experimental results on both synthetic and real-world data sets are conducted, and the results verify the effectiveness and efficiency of our approach. [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]

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]

Graph-based semi-supervised learning is the problem of propagating labels from a small number of labelled data points to a larger set of unlabelled data. This paper is concerned with the consistency of optimization-based techniques for such problems, in the limit where the labels have small noise and the underlying unlabelled data is well clustered. We study graph-based probit for binary classification, and a natural generalization of this method to multi-class classification using one-hot encoding. The resulting objective function to be optimized comprises the sum of a quadratic form defined through a rational function of the graph Laplacian, involving only the unlabelled data, and a fidelity term involving only the labelled data. The consistency analysis sheds light on the choice of the rational function defining the optimization. [ABSTRACT FROM AUTHOR]