Processing Large Volumes Of Data Research Articles

A “big-data” algorithm for KNN-PLS

A well known issue regarding PLS lies in the difficulty to apprehend nonlinearities. As a solution, an extension of the method, “KNN-PLS”, was developed. However, this solution is based on a neighbourhood selection algorithm whose execution time is highly dependent on the size of the database, leading to prohibitive response times. This article proposes, as an alternative, a new method designed to process large data volumes: “parSketch-PLS”. This method combines a “big-data domain” neighbour selection method, called “parSketch”, and the PLS method. Essentially, this paper presents a feasibility study, regarding the adaptation of big-data principles for spectral datasets, in non-linear contexts. The parSketch method has not been studied in the context of chemometrics and considering the specific properties of spectral data. This method is based on the approximation of sample neighbourhoods, based on spectral distances. It is then necessary to investigate the relevance of these neighbourhoods for PLS models and predictions. This article compares PLS and KNN-PLS methods with the parSketch-PLS method. In this context, PLS allows to process large volumes of data quickly but performs poorly in prediction while the KNN-PLS method returns accurate predictions, yet with much higher computational time. This paper shows that the proposed pairing offers a good operational trade-off between prediction performances and computational cost. In addition a comprehensive study of the input parameters of parSketch-PLS is conducted. The objective is to understand the influence of these parameters on the prediction performances. This article proposes a framework to interpret the neighbourhoods returned by comparing their relative sizes with the evolution of performances and the input parameters of parSketch.

SIMBA: Scalable Inversion in Optical Tomography Using Deep Denoising Priors

Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables high-quality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixed-point convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.

Development of portable user-interactive holographic information collector for agricultural product markets

The efficient and accurate collection of agricultural product market information serves as the basis for the effective regulation of agricultural product markets. To achieve a comprehensive, accurate, and timely collection of agricultural product market information, this study puts forth a technique for collecting holographic information of agricultural product markets. A portable human-machine interaction device with an Advanced RISC (reduced instruction set computer) Machines (ARM)-based processor as the core is developed. Holographic information such as agricultural product market trading time, trading place, product name, price, and trading volume can be collected. Via embedded technology and component technology, innovative agricultural product market positioning, and matching, standardized collection, and data processing, and in combination with intelligent algorithms for analysis and early warning, a mobile application terminal for information collection is developed, namely, a holographic information collector for agricultural product markets (named Nongxincai). By using a layered structure, the hardware integrates microprocessor, storage, power, application and communication interface, and human-machine interaction modules. The device has the advantages of miniaturization, the whole-machine power consumption of less than 0.5 W, and continuous operating time of at least 10 h. A supporting software system for Nongxincai has also been developed, in which Microsoft Windows Mobile 6.5 is employed as the operating system, and the CPU frequency is up to 600 MHz. This configuration fully meets the computing requirements of map processing and large-volume data processing and has high compatibility. Nongxincai has been popularized and applied in 12 provinces/municipalities in China. It has played an important role in the monitoring and early warning of different varieties of agricultural products and target prices of soybean and cotton. Keywords: agricultural product market, holographic information, portable, user-interactive, information collector, monitoring and early warning DOI: 10.25165/j.ijabe.20201303.5845 Citation: Xu S W, Li D H, Zhang Y E, Chen W, Zhuang J Y, Liu J J, et al. Development of portable user-interactive holographic information collector for agricultural product markets. Int J Agric & Biol Eng, 2020; 13(3): 143–153.

Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons

In today’s data-driven world, the ability to process large data volumes is crucial. Key tasks, such as pattern recognition and image classification, are well suited for artificial neural networks (ANNs) inspired by the brain. Neuromorphic computing approaches aimed towards physical realizations of ANNs have been traditionally supported by micro-electronic platforms, but recently, photonic techniques for neuronal emulation have emerged given their unique properties (e.g. ultrafast operation, large bandwidths, low cross-talk). Yet, hardware-friendly systems of photonic spiking neurons able to perform processing tasks at high speeds and with continuous operation remain elusive. This work provides a first experimental report of Vertical-Cavity Surface-Emitting Laser-based spiking neurons demonstrating different functional processing tasks, including coincidence detection and pattern recognition, at ultrafast rates. Furthermore, our approach relies on simple hardware implementations using off-the-shelf components. These results therefore hold exciting prospects for novel, compact and high-speed neuromorphic photonic platforms for future computing and Artificial Intelligence systems.

Machine Learning Techniques: The Need of the Hour

Scientists, researchers and business analyst need to process large volume of data to convert the data to meaningful information. Historically all the professionals are doing this manually and later with the invention of computers by developing algorithms and coding programs. Eventually size of data is growing with the advancement of social economic developments and advancement of technological sectors. This posed greater challenge to the technocrats. So there was demand to automatically process and analyze the data. Scientists started working towards the direction of artificial intelligence (AI). They developed the branch of AI as machine learning (ML). Large number of areas are using these techniques and getting benefit of these models. Financial data analyst, business analyst, medical researchers, software professionals are applying machine learning to increase the speed of large amount of data for decision making by drawing patterns with minimal and no human intervention. The purpose of this paper is to discuss a few widely publicized ML algorithms and their advantages and benefits

High throughput image labeling on chest computed tomography by deep learning

When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy on test set across different tasks using a research database from multicenter clinical trials.

Seismology in the Cloud: A New Streaming Workflow

AbstractData-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. In this work, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.

A gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers

Software pipelines enable organizations to chain applications for adding value to contents (e.g., confidentially, reliability, and integrity) before either sharing them with partners or sending them to the cloud. However, the pipeline components add overhead when processing large volumes of data, which can become critical in real-world scenarios. This paper presents a gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers. In this model, the gears represent applications, whereas gearboxes represent software pipelines. This model was implemented as a collaborative system that automatically performs Gear up (by using parallel patterns) and/or Gear down (by using in-memory storage) until all gears produce uniform data processing velocities. This model reduces delays and bottlenecks produced by the heterogeneous performance of applications included in software pipelines. The new container tool capsule has been designed to encapsulate both the collaborative system and the software pipelines into a virtual container and deploy it on IT infrastructures. We conducted case studies to evaluate the performance of capsule when processing medical images and PDF repositories. The incorporation of a capsule to a cloud storage service for pre-processing medical imagery was also studied. The experimental evaluation revealed the feasibility of applying the gearbox model to the deployment of software pipelines in real-world scenarios as it can significantly improve the end-user service experience when pre-processing large-scale data in comparison with state-of-the-art solutions such as Sacbe and Parsl.

SDPBDVC: secure data processing on big data using visual cryptography

The rapid deployment of modern technologies such as iPhone, laptops, digital watch, social network, internet are resulted in handling of enormous amount of data in our day-to-day life. Since the data storage is increased abundantly and techniques to retrieve the relevant information and protection of data related issues to be focused efficiently to protect this big data. However various organisations and IT industries dealing with big data are faced with many security risks in storage and processing of data. Hence the protecting the big data is challenging task for the researchers. MapReduce is an efficient tool for processing large volume of data in a distributed environment. Security and secrecy of data are vital concerns while processing data carried out in MapReduce. In this article, the proposed secure data processing on big data using visual cryptography (SDPBDVC) technique ensures the protection of sensitive information by applying the visual cryptography technique and the processing done only by authorised entities.

Development of portable user-interactive holographic information collector for agricultural product markets

The efficient and accurate collection of agricultural product market information serves as the basis for the effective regulation of agricultural product markets. To achieve a comprehensive, accurate, and timely collection of agricultural product market information, this study puts forth a technique for collecting holographic information of agricultural product markets. A portable human-machine interaction device with an Advanced RISC (reduced instruction set computer) Machines (ARM)-based processor as the core is developed. Holographic information such as agricultural product market trading time, trading place, product name, price, and trading volume can be collected. Via embedded technology and component technology, innovative agricultural product market positioning, and matching, standardized collection, and data processing, and in combination with intelligent algorithms for analysis and early warning, a mobile application terminal for information collection is developed, namely, a holographic information collector for agricultural product markets (named Nongxincai). By using a layered structure, the hardware integrates microprocessor, storage, power, application and communication interface, and human-machine interaction modules. The device has the advantages of miniaturization, the whole-machine power consumption of less than 0.5 W, and continuous operating time of at least 10 h. A supporting software system for Nongxincai has also been developed, in which Microsoft Windows Mobile 6.5 is employed as the operating system, and the CPU frequency is up to 600 MHz. This configuration fully meets the computing requirements of map processing and large-volume data processing and has high compatibility. Nongxincai has been popularized and applied in 12 provinces/municipalities in China. It has played an important role in the monitoring and early warning of different varieties of agricultural products and target prices of soybean and cotton. Keywords: agricultural product market, holographic information, portable, user-interactive, information collector, monitoring and early warning DOI: 10.25165/j.ijabe.20201303.5845 Citation: Xu S W, Li D H, Zhang Y E, Chen W, Zhuang J Y, Liu J J, et al. Development of portable user-interactive holographic information collector for agricultural product markets. Int J Agric & Biol Eng, 2020; 13(3): 143–153.

4th Industrial Revolution as a Challenge to the University Community

Currently, one can observe accelerated implementation of high-tech solutions including those developed on the basis of artificial intelligence in various sectors of economy and public relations, in particular, in education. In this regard, the article discusses the main challenges that the 4th industrial revolution poses to the higher education system. These challenges include the automation of a number of professions; lack of competent personnel; digitalization of the educational process, etc. The prospects of introducing a blockchain to fix personal achievements in the context of the development of artificial intelligence technologies in particular, are highlighted. The possibilities of processing large volumes of data (Big Data) are described in order to create ratings of universities, departments and teaching staff. The experience of introducing artificial intelligence technologies into the creation of an individual educational path and the formation of a «digital footprint» at the STI University «20.35» is analyzed. Namely, the creation of a digital fingerprint and its application; building a student’s optimal development path; effective team building and improving the effectiveness of e-learning are considered. The conclusion is made about the need to preserve the universal basic component of higher education.

Ontologies for Liver Diseases Representation: A Systematic Literature Review.

Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to be clear, comprehensive, and with well-defined format. It characterizes several domains purposes description through structured and formalized languages. In various areas of research, it has become a significant way to realize successful and powerful accomplishments. Actually, medical ontologies were turned into an efficient application in medical domains. They also become a relevant approach to process large medical data volumes. Consequently, they are behaving as a support decision system in some cases. Also, they ensure diagnosis process acceleration and assistance. Additionally, they have been integrated especially to represent human healthcare concepts. For that reason, plenty of research works applied ontologies to design and treat liver diseases. In this article, we present a general overview of medical ontologies to stand for this type of disease. We expose and discuss these works in details by a complete comparison. Also, we show their performance to arrange clinical data and extract results.

Elements of digital energy in the control of the state of the network, built on measurements of auxiliary parameters

The article deals with the issue of transition of power engineering technologies to digital stations. An overview of the Siemens and ABB materials that de facto set standards for digital technology for energy. The creation of digital stations addresses issues that have not been addressed in this connection so far. Therefore, a triad was formed: digital control - equipment state monitoring - cybersecurity. Proceeding from such a triangle, energy in the digital incarnation acquires new faces, which opens up new possibilities for the use of diagnostics and data collection by sensors built on mediated data, and accordingly, the number of sensors increases significantly if we take into account the possibility of monitoring the primary technological processes. In this case, the amount of incoming data grows significantly. In accordance with current trends and increasing requirements for the quality of electricity in the network. One of the factors of the spoofed form of a grid on the network is the crown discharges that occur on the network and on the equipment, even during the interception to the digital station. Leading companies are developing devices and systems for diagnosing the presence of a corona discharge in the network according to non-electronics parameters. An example of such measurements is the ability to diagnose non-direct vemiers in the network. Such measurements should lead to an increase in data volume. Accordingly, the processing of such data arrays becomes possible with the use of digital technologies, which include the ability to process large volumes of data using the mathematical apparatus for processing large data - Data Mining. The results of processing by hardware (software) methods allows to determine or invent such data, which in direct measurements were not visible. Data Mining technologies allow expert estimates to be used on direct measurement data, which will inevitably lead to objectivity. Data Mining, in contrast to expert methods, finds objective patterns between different factors, thus minimizing the influence of the subjective human factor on decision making. The authors draw attention to the promising use of indirect measurement sensors in the power system, which, based on digital technologies, will provide an opportunity for a more profound analysis of real systems, which can lead to qualitative changes in the energy sector as a whole.

Investigating the Relationship between New Diagnostic Technology and Information Technology in Diagnosing Cancer: Focusing on Breast Cancer

Introduction: The use of computer-assisted diagnostic tools has increased dramatically in various fields of medicine, especially cancer. It is essential and inevitable to use these tools, especially when there is a need to process large volumes of data, but new diagnostic methods was based on the use of Nano-sensors that have many advantages and they can be used to diagnose breast cancer, one of the most common neoplastic malignancies in the world. The study aims to investigate the relationship between new diagnostic methods and information technology in cancer diagnosis by categorizing research topics and important known aspects of cancer related illness from 1984 to 2017.
 Methods: The evaluation conducted in March 2018 and used to access the articles on diagnostic and therapeutic methods from the PubMed, Medline, and Cochran library, WHO, Iranmedex and PsycINFO databases.
 Results: The findings of this study in two sections of cancer and biological sensors show that biomarkers detect disease, and health information technology is expanding and innovating new methods. This can play an important role in managing chronic diseases such as cancer.
 Conclusion: Primal diagnosis, reducing the time to diagnose and improving the quality of care during treatment, is one of the health’s information technology (IT) benefits of diagnosing cancer. In general, the results this study indicate that the use of information technology to increase the speed of effective therapeutic stages. Resulting in general health, as well as the relationship of information technology with diagnostic and therapeutic methods in our country can greatly increase the level of health and well-being and development, and create a new way of linking information technology and new diagnostic methods to cancer.

High-accuracy and video-rate lifetime extraction from time correlated single photon counting data on a graphical processing unit

Graphical Processing Units (GPUs) are a powerful alternative to central processing units, especially for data-parallel, video-rate processing of large data volumes. In the complex scenario of high-performance, multichannel Time Correlated Single Photon Counting (TCSPC), a huge amount of data is potentially generated by the acquisition system. Exploiting a dedicated, external, programmable elaboration unit enables a high degree of flexibility to perform different types of analysis. In this paper, we present a GPU-based application that leverages the common unified device architecture application programming interface for video-rate and accurate lifetime extraction from TCSPC data acquired at a rate of up to 10 Gbit/s.

Big data, key factor for the knowledge society

We are currently in an era of information explosion that affects our life in one way or another. Because of this, the transformation of huge databases into knowledge has become one of the tasks of greatest interest to society in general. Big Data was born as an instrument for knowledge due to the inability of current computer systems to store and process large volumes of data. The knowledge society arises from the use of technologies such as Big Data. The purpose of this article is to analyze the influence of Big Data on the knowledge society through a review of the state of the art supported by research articles and books published in the last 15 years, which allow us to put these two terms into context, understand their relationship and highlight the influence of Big Data as a generator of knowledge for today's society. The concept of Big Data, and its main applications to society will be defined. The concept of the Information Society is addressed and the main challenges it has are established. The relationship between both concepts is determined. And finally the conclusions are established. In order to reduce the digital divide, it is imperative to make profound long-term changes in educational models and public policies on investment, technology and employment that allow the inclusion of all social classes. In this sense, knowledge societies with the help of Big Data are called to be integrative elements and transform the way they are taught and learned, the way they are investigated, new social and economic scenarios are simulated, the brand decisions in Companies and share knowledge.

Forewarned is forearmed

PurposeInternet of Things (IoT) interconnects many heterogeneous devices to each other, collecting and processing large volumes of data for decision making without human intervention. However, the information security concern it brings has attracted quite a lot of attention, and, at this stage, the smart step would be to analyze the security issues of IoT platform and get to the state of readiness before embarking upon this attractive technology. The purpose of this paper is to address these issues.Design/methodology/approachIoT risk assessment through the application of the analytical hierarchy process (AHP), a favorite multi-criteria decision making technique, is proposed. The IoT risks are prioritized and ranked at different layers, before which a well-defined IoT risk taxonomy is defined comprising of 25 risks across six layers of the IoT model for developing control and mitigation plans for information security of IoT.FindingsPeople and processes layer, network layer and applications layer are the top three critical layers with risks like the lack of awareness, malware injection, malicious code injection, denial of service and inefficient policies for IoT practice get the highest priority and rank. Pareto analysis of the overall risk factors revealed that the top ten factors contribute to 80 percent of the risks perceived by information security experts.Research limitations/implicationsThe study focuses only on certain predefined constructs or layers of the IoT model traced from legacy studies. It is essential to re-look these constructs on a timely basis to prolong the results’ validity. The study’s empirical scope is confined only to the risk perception of select IoT experts and does not encompass a broader segment of the IoT ecosystem. Therefore, the risks assessment may not be sweeping to a bigger audience.Practical implicationsThe study implications are two-fold: one it consolidates the earlier siloed works to intensify the need for risk assessment in the IoT domain, and second the study brings yet another contextual avenue of extending the application AHP and Pareto principle combination. The paper also draws specific critical organizational interventions about IoT risks. A comprehensive approach to prioritizing and ranking IoT risks are present in this research paper.Originality/valueThe contribution of this study to the benchmarking of IoT risk assessment is two-fold. One, a comprehensive risk assessment taxonomy is proposed, and two, the risks are prioritized and ranked to give a convincing reference for the organizations while making information security plans for IoT technology.

CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection

Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks. CRED uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure. It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on individual stations. We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California. The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals. We also apply the model to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity. Our model is able to detect more than 800 microearthquakes as small as −1.3 ML induced during hydraulic fracturing far away than the training region. We compare the performance of the model with the STA/LTA, template matching, and FAST algorithms. Our results indicate an efficient and reliable performance of CRED. This framework holds great promise for lowering the detection threshold while minimizing false positive detection rates.

Quantitative assessment of the potential of retail outlets

Purpose of the study . Improving the speed and efficiency of decision-making on the development of sales of products by manufacturing companies based on the application of quantitative estimates of the potential of retail outlets, using automated methods of intellectual data analysis of Big Data. In order to create new mechanisms for the intellectual management of enterprises (economic agents) in the concept of the digital economy, it is necessary to simulate the processes of their interaction in the organizational market environment from the perspective of a multi-agent approach. The approach describes cybernetic mechanisms of interaction of agents with the ability to adapt to the needs of the population based on the analysis of market situations in order to develop intelligent management systems for enterprises to increase their market potential and competitiveness. Today the paradigm of open complex systems is used to simulate and analyze the mechanisms and processes of interaction of agents in the market environment. The main modern functioning of enterprises are corporate information systems, telecommunication networks, Internet technologies, mobile communication systems, Big Data, technologies of intellectual analysis, forecasting and machine learning. In addition, information for modeling, research and analysis of the market and the behavior of agents can be collected from open sources on the Internet. In the field of mass sales, the result of the introduction of innovative technologies is the support of decision-making in the process of managing the sales of goods and services in order to synthesize effective business-strategies for the production and sale of goods, aimed at increasing the company’s profits. From the point of view of the manufacturer, it is necessary to carry out strategic and tactical planning for the deployment and maintenance of the sales network through retail outlets that implement or have the potential to realize the company’s products. In the overwhelming majority of cases, experts make the collection and analysis of objective data on outlets, and the models for building quantitative estimates of the potential of outlets are used only the internal data on the sale of goods or partners. Materials and methods . The solution is based on the analog method of assessing the attractiveness of the outlet, used in marketing. Data mining techniques (various clustering methods) and the method of mathematical statistics - variance analysis are also used to solve this problem. Modern methods of processing large volumes of data and their intellectual analysis allow us to offer new methods for quantifying potential based on the analysis of the whole diversity of data stored in the open information sources. Results . A multi-stage method of quantitative assessment of the potential of retail outlets in ruble equivalent was proposed. A method has been developed for dividing retail outlets into strata based on features describing the position of the retail outlets, competitive environment, transport accessibility, and a typical consumer. A modification of the K-means clustering method is proposed. Conclusion . The paper proposes an approach to solving the task of promoting a wide range of products by the manufacturer through existing distribution networks. A method for quantitative assessment of the potential of outlets is proposed. The proposed method is based on the analog method of comparing outlets and uses the methods of clustering outlets across a wide range of indicators. The results of approbation of the approach on the data for 33 regions of the Russian Federation are given. The results of the work are planned to be used in the future to solve the problem of building a matrix of consumer preferences for outlets.

Delineating conditions and subtypes in chronic pain using neuroimaging.

Differentiating subtypes of chronic pain still remains a challenge—both from a subjective and objective point of view. Personalized medicine is the current goal of modern medical care and is limited by the subjective nature of patient self-reporting of symptoms and behavioral evaluation. Physiology-focused techniques such as genome and epigenetic analyses inform the delineation of pain groups; however, except under rare circumstances, they have diluted effects that again, share a common reliance on behavioral evaluation. The application of structural neuroimaging towards distinguishing pain subtypes is a growing field and may inform pain-group classification through the analysis of brain regions showing hypertrophic and atrophic changes in the presence of pain. Analytical techniques such as machine-learning classifiers have the capacity to process large volumes of data and delineate diagnostically relevant information from neuroimaging analysis. The issue of defining a “brain type” is an emerging field aimed at interpreting observed brain changes and delineating their clinical identity/significance. In this review, 2 chronic pain conditions (migraine and irritable bowel syndrome) with similar clinical phenotypes are compared in terms of their structural neuroimaging findings. Independent investigations are compared with findings from application of machine-learning algorithms. Findings are discussed in terms of differentiating patient subgroups using neuroimaging data in patients with chronic pain and how they may be applied towards defining a personalized pain signature that helps segregate patient subgroups (eg, migraine with and without aura, with or without nausea; irritable bowel syndrome vs other functional gastrointestinal disorders).