Rapid advancements in the internet of things (IoT) are driving massive transformations of health care, which is one of the largest and critical global industries. Recent pandemics, such as coronavirus 2019 (COVID-19), include increasing demands for ubiquitous, preventive, and personalized health care to be provided to the public at reduced risks and costs with rapid care. Mobile crowdsourcing could

The accuracy of the prediction of stock price fluctuations is crucial for investors, and it helps investors manage funds better when formulating trading strategies. Using forecasting tools to get a predicted value that is closer to the actual value from a given financial data set has always been a major goal of financial researchers and a problem. In recent years, people have paid particular attention

In this study, we set up a scalable framework for large-scale data processing and analytics using the big data framework. The popular classification methods are implemented, tuned, and evaluated by using intrusion datasets. The objective is to select the best classifier after optimizing the hyper-parameters. We observed that the decision tree (DT) approach outperforms compared with other methods in

Cardiac diseases constitute a major root of global mortality and they are likely to persist. Electrocardiogram (ECG) is widely opted in clinics to detect countless heart illnesses. Numerous artifacts interfere with the ECG signal, and their elimination is vital to allow medical specialists to acquire valuable statistics from the ECG. The utmost artifact that is added to the ECG signal is power line

Geological hazards (geohazards) are geological processes or phenomena formed under external-induced factors causing losses to human life and property. Geohazards are sudden, cause great harm, and have broad ranges of influence, which bring considerable challenges to geohazard prevention. Monitoring and early warning are the most common strategies to prevent geohazards. With the development of the internet

In image registration, the search space used to compute the optimal transformation between the images depends on the group of pixels in the vicinity. Favorable results can be achieved by significantly increasing the number of neighboring pixels in the search space; however, this strategy increases the computational load, thus making it challenging to realize the most desirable solution in a reasonable

Surface reconstruction technology based on cloud data has broad prospects in the fields of reverse engineering, cultural heritage protection, and smart city construction. This article studies the surface reconstruction pattern recognition technology based on scattered point cloud data. The candidate feature points are extracted according to the surface variation, and the precise method of point cloud

Accurate detection of malignant tumor on lung computed tomography scans is crucial for early diagnosis of lung cancer and hence the faster recovery of patients. Several deep learning methodologies have been proposed for lung tumor detection, especially the convolution neural network (CNN). However, as CNN may lose some of the spatial relationships between features, we plan to combine texture features

Spider monkey optimization (SMO) algorithm imitates the spider monkey's fission–fusion social behavior. It is evident through literature that the SMO is a competitive swarm-based algorithm that is used to solve difficult real-life problems. The SMO's search process is a little bit biased by the random component that drives it with high explorative searching steps. A hybridized SMO with a memetic search

Along with the country's comprehensive strength, the people's wealth is also more and more substantial, in every aspect of life has been significantly improved, people also pay more attention to environmental protection. The harm of environmental pollution should not be underestimated. Once the environmental pollution accident occurs, it must be handled promptly, or a series of consequences are very

Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous

Customer patronage behavior has been widely studied in market share modeling contexts, which is an essential step toward estimating retail sales and finding new store locations in a competitive setting. Existing studies have conducted surveys to estimate merchants' market share and factors of attractiveness to use in various proposed mathematical models. Recent trends in Big Data analysis allow us

The Recommendation system relies on feedback and personal information collected from users for effective recommendation. The success of a recommendation system is highly dependent on storing and managing sensitive customer information. Users refrain from using the application if there is a threat to user privacy. Several works that were performed to protect user privacy have paid little attention to

The main goal in the influence maximization problem (IMP) is to find k minimum nodes with the highest influence spread on the social networks. Since IMP is NP-hard and is not possible to obtain the optimum results, it is considered by heuristic algorithms. Many strategies focus on the power of the influence spread of core nodes to find k influential nodes. Most of the core detection-based methods concentrate

Cybersecurity protects and recovers computer systems and networks from cyber attacks. The importance of cybersecurity is growing commensurately with people's increasing reliance on technology. An anomaly detection-based network intrusion detection system is essential to any security framework within a computer network. In this article, we propose two models based on deep learning to address the binary

An exponential progression in the miniaturization of communicating devices has proliferated the generation of a large volume of data termed as “big data.” The technological advancements in the micro-electro/mechanical system has made it possible to design the low-cost, low-power consuming artificial intelligence (AI)-based wireless sensor nodes to gather the big data belonging to various attributes

At present time, an effective tool becomes essential to forecast business failure as well as financial crisis on small- to medium-sized enterprises. This article presents a new optimal feature selection (FS)-based classification model for financial crisis prediction (FCP). The proposed FCP method involves data acquisition, preprocessing, FS, and classification. Initially, the financial data of the

The new and integrated area called Internet of Things (IoT) has gained popularity due to its smart, objects, services and affordability. These networks are based on data communication, augmented reality (AR), and wired and wireless infrastructures. The basic objective of these network is data communication, environment monitoring, tracking, and sensing by using smart devices and sensor nodes. The dAR

Drought is the primary and dominant natural cause of stress on vegetation, and thus, it needs our full attention. Current understanding of drought across extensive spatial measures, around the world, is considerably limited. As case studies to evaluate the feasibility of utilizing space-based solar-induced chlorophyll fluorescence (SIF) across extensive spatial measures, here, we have used data from

The development of big data analytics is creating new opportunities to advance financial management, financial technology, insurance technology, wealth management technology, and investments. This special issue focuses on the applications of big data in finance. A bibliometric analysis method is employed in this editorial review to visualize and highlight the current trends and the future research

This article aims at identifying appropriate models for analyzing large datasets to serve a twofold goal: first, to better understand the dynamics impacting innovative startups' performance and their managerial practice and, second, to detect their patterns of failure. Therefore, we investigate the interaction of economic–financial, context, and governance dimensions of 4185 Italian innovative startups

Limit order books (LOBs) have been widely adopted as a trading mechanism in global securities markets, and the degree of LOB transparency is one of the most studied topics in market design. In the past, this issue was mainly researched through the comparison of LOB transparency in a market before and after a policy change, although such instances were rare and occurred decades ago. This article analyzes

At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. A diverse set of data classifier approaches were presented for forecasting the financial crisis of an institution using the past data. An essential process toward the design of a precise financial crisis prediction (FCP) approach comprises

In insurance business, product sales can be realized over a variety of channels such as independent agencies, or bank branches. In 2017, 55% of premium production was generated over insurance agencies in Turkey making independent agency evaluation prominent in the domain. Unfortunately lacking attention from the scientific community, agency evaluation problem is usually tackled in the industry by utilizing

Product affinity segmentation discovers groups of customers with similar purchase preferences for cross-selling opportunities to increase sales and customer loyalty. However, this concept can be challenging to implement efficiently and effectively for actionable strategies. First, the nature of skewed and sparse product-level data in the clustering process results in less meaningful solutions. Second

Recently, emotion recognition in conversation (ERC) has become more crucial in the development of diverse internet of things devices, especially closely connected with users. The majority of deep learning-based methods for ERC combine the multilayer, bidirectional, recurrent feature extractor and the attention module to extract sequential features. In addition to this, the latest model utilizes speaker

The internet of things (IoT) is permeating our daily lives through continuous environmental monitoring and data collection. The promise of low latency communication, enhanced security, and efficient bandwidth utilization lead to the shift from mobile cloud computing to mobile edge computing. In this study, we propose an advanced deep reinforcement resource allocation and security-aware data offloading

Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem

Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved

In recent years, big data became a hard challenge. Analyzing big data needs a lot of speed precision combination. In this article, we describe a deep learning-based method to deal with big data with a focus on precision and speed. In our case, the data are images that are the hardest type of data to manipulate because of their complex structure that needs a lot of computation power. Besides, we will

Conflict resolution is one of the central tasks during the control of air traffic. In this article, we examined the problem of conflict resolution for unmanned aerial vehicle (UAV) integration into national airspace system and presented an approach based on satisficing game theory to conflict resolution for cooperative UAVs and manned aircraft sharing airspace. This approach ensured the priority of

As high-throughput approaches in biological and biomedical research are transforming the life sciences into information-driven disciplines, modern analytics platforms for big data have started to address the needs for efficient and systematic data analysis and interpretation. We observe that radiobiology is following this general trend, with -omics information providing unparalleled depth into the

This special issue was open for submissions in the field of big data in business. Accordingly, this special issue includes five contributions to the fields of business process innovation in the big data era, unstructured big data analytical methods in firms, online analytical processing approach for business intelligence in big data, geospatial insights for retail recommendation using similarity measures

While recent debate recognizes the importance of big data (BD) and knowledge management (KM) in firm performance, there has been a paucity of literature regarding big data analytics technological (BDAT) and knowledge exploration–exploitation capabilities (KEEC) in the context of business process innovation (BPI). This study aims to identify whether BD and KM can be established in these emerging issues

Firms face challenging analytical tasks at the advent of a growing amount of unstructured big data (BD). These data lead to radical shifts in their analytical strategies and market insights. Yet, the particular types of analytical methods remain in the literature still loosely scattered. This work stresses the unstructured BD analytics, first by capturing their unique characteristics and then by proposing

Online analytical processing (OLAP) approach is widely used in business intelligence to cater the multidimensional queries for decades. In this era of cutting-edge technology and the internet, data generation rates have been rising exponentially. Internet of things sensors and social media platforms are some of the major contributors, leading toward the absolute data boom. Storage and speed are the

Recommending a retail business given a particular location of interest is nontrivial. Such a recommendation process requires careful study of demographics, trade area characteristics, sales performance, traffic, and environmental features. It is not only human effort taxing but often introduces inconsistency due to subjectivity in expert opinions. The process becomes more challenging when no sales

Research has shown that the use of big data can modify operational processes in organizations. However, little research has been conducted on overcoming resistance to the process changes needed for adoption of big data technologies. In this article, we address this gap in the literature by investigating the impact of interactive data visualization on decision-making around operational process changes

To deal with a large amount of redundant data in the indirect category database and inefficient redundancy elimination of the existing methods, we proposed an indirect category data transfer learning algorithm based on regularization discrimination. First of all, we denoised indirect category data, calculated the objective function of distance between the source domain and the target domain, and established

We study a family of pattern-detection problems in vertex-colored temporal graphs. In particular, given a vertex-colored temporal graph and a multiset of colors as a query, we search for temporal paths in the graph that contain the colors specified in the query. These types of problems have several applications, for example, in recommending tours for tourists or detecting abnormal behavior in a network

Many real-world graphs are temporal, for example, in a social network, persons only interact at specific points in time. This temporal information directs dissemination processes on the graph, such as the spread of rumors, fake news, or diseases. However, the current state-of-the-art methods for supervised graph classification are mainly designed for static graphs and may not capture temporal information

Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medical records. A crucial requirement for this task is to effectively model the high-dimensional, noisy, and temporal electronic health record (EHR) data. Existing studies fulfill this requirement by applying recurrent neural networks with attention mechanisms

Shapelets are discriminative segments used to classify time-series instances. Shapelet methods that jointly learn both classifiers and shapelets have been studied in recent years because such methods provide both interpretable results and superior accuracy. The partial area under the receiver operating characteristic curve (pAUC) for a low range of false-positive rates (FPR) is an important performance

Tensor decomposition has been shown, time and time again, to be an effective tool in multiaspect data mining, especially in exploratory applications where the interest is in discovering hidden interpretable structure from the data. In such exploratory applications, the number of such hidden structures is of utmost importance since incorrect selection may imply the discovery of noisy artifacts that

Physics-based simulations are often used to model and understand complex physical systems in domains such as fluid dynamics. Such simulations, although used frequently, often suffer from inaccurate or incomplete representations either due to their high computational costs or due to lack of complete physical knowledge of the system. In such situations, it is useful to employ machine learning (ML) to