Data Analytics Perspective Research Articles

The Use of Data Analytics in Public Administration for Corruption Prevention During Hybrid Warfare

The relevance of the selected theme is stipulated by the destructive effect of corruption on the social structure of society and the weakening of the rule of law. Corruption schemes as an instrument of geopolitics are used as a part of the foreign policy arsenal of authoritarian states such as Russia. Under such circumstances, data analytics provides researchers and public society with a large volume of analytical instruments, enabling comprehensive monitoring of governments. Such monitoring enhances the efficiency of corruption prevention in public administration. The article aims to determine the data analytics method for corruption prevention in public administration during hybrid warfare. Using the methods of content analysis, historical, abstract-logical methods, we found how data analytics can contribute to effective corruption prevention in public administration. Perspective and appropriate methods of data analytics used for corruption prevention in public administration were found. Based on the results of the conducted study, recommendations for improvement of the use of data analytics in public administration for corruption prevention during hybrid warfare were offered. The perspective of further studies includes research on introducing data analytics in the everyday work of particular state institutions.

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

Solving the imbalanced data issue: automatic urgency detection for instructor assistance in MOOC discussion forums

In MOOCs, identifying urgent comments on discussion forums is an ongoing challenge. Whilst urgent comments require immediate reactions from instructors, to improve interaction with their learners, and potentially reducing drop-out rates—the task is difficult, as truly urgent comments are rare. From a data analytics perspective, this represents a highly unbalanced (sparse) dataset. Here, we aim to automate the urgent comments identification process, based on fine-grained learner modelling—to be used for automatic recommendations to instructors. To showcase and compare these models, we apply them to the first gold standard dataset for Urgent iNstructor InTErvention (UNITE), which we created by labelling FutureLearn MOOC data. We implement both benchmark shallow classifiers and deep learning. Importantly, we not only compare, for the first time for the unbalanced problem, several data balancing techniques, comprising text augmentation, text augmentation with undersampling, and undersampling, but also propose several new pipelines for combining different augmenters for text augmentation. Results show that models with undersampling can predict most urgent cases; and 3X augmentation + undersampling usually attains the best performance. We additionally validate the best models via a generic benchmark dataset (Stanford). As a case study, we showcase how the naïve Bayes with count vector can adaptively support instructors in answering learner questions/comments, potentially saving time or increasing efficiency in supporting learners. Finally, we show that the errors from the classifier mirrors the disagreements between annotators. Thus, our proposed algorithms perform at least as well as a ‘super-diligent’ human instructor (with the time to consider all comments).

Implicit Bias of Deep Learning in the Large Learning Rate Phase: A Data Separability Perspective

Previous literature on deep learning theory has focused on implicit bias with small learning rates. In this work, we explore the impact of data separability on the implicit bias of deep learning algorithms under the large learning rate. Using deep linear networks for binary classification with the logistic loss under the large learning rate regime, we characterize the implicit bias effect with data separability on training dynamics. From a data analytics perspective, we claim that depending on the separation conditions of data, the gradient descent iterates will converge to a flatter minimum in the large learning rate phase, which results in improved generalization. Our theory is rigorously proven under the assumption of degenerate data by overcoming the difficulty of the non-constant Hessian of logistic loss and confirmed by experiments on both experimental and non-degenerated datasets. Our results highlight the importance of data separability in training dynamics and the benefits of learning rate annealing schemes using an initial large learning rate.

On-Device Deep Learning for Mobile and Wearable Sensing Applications: A Review

Although running deep-learning (DL) algorithms is challenging due to resource constraints on mobile and wearable devices, they provide performance improvements compared to lightweight or shallow architectures. The widespread application areas for on-device DL include computer vision, image processing, natural language processing, and audio classification. However, mobile and wearable sensing applications are also gaining attention. They can benefit from on-device DL, given that these devices are integrated with various sensors and produce large amounts of data. This article reviews state-of-the-art studies on on-device DL for mobile and wearable devices, particularly from the sensor data analytics perspective. We first discuss the general optimization techniques of DL algorithms to meet the resource limitations of the devices. Then, we elaborate on model update and personalization techniques and review the studies by classifying them according to several aspects, including application areas, sensors, types of devices, utilized DL algorithms, mode of implementation, methods for optimizing DL algorithms for the target devices, training method, implementation toolkit/platform, performance metrics, and resource consumption analysis. Finally, we discuss the open issues and future research directions about on-device DL for mobile and wearable sensing applications.

Implementation of Data Analytics and Machine Learning in Thailand Education Sector

Since the global epidemic of the coronavirus disease 2019 (COVID-19) over the past few years, Thailand education sector has been affected by the requisites for a digitization system and distance education. This sudden change has affected the quality of learning and statistical evaluation in the long term. Consequently, data analysis and categorization in learning quality assessment are critical for predicting the number of future students and learning performance after the COVID-19 outbreak. However, vast data analytics might be applied to the education sector in many aspects. In addition, machine learning can influence the categorization of students that are useful for analyzing the performance of different educational systems. Therefore, this study reviews the perspective and usability of data analytics and machine learning that influences current situations in Thailand's education sector.

Toward the internet of things forensics: A data analytics perspective

AbstractThe widespread use of networked, intelligent, and adaptable devices in various domains, such as smart cities and home automation, climate control, manufacturing and logistics, healthcare, education, and agriculture, has been hastened by recent developments in hardware and software technologies. In all these application domains, the concept of the Internet of Things helps to achieve process automation and decrease labor costs. While IoT has been an established domain for quite a while, it has seen a lot of advances and challenges in different subdomains over the years. One such subdomain is IoT Forensics which involves digital forensics concerning IoT devices, networks, or clouds. In this process of obtaining substantial evidence from the devices, networks, or cloud, a large amount of data and operations on said data are involved. Hence, looking through IoT Forensics through the methodology dealing with data, known as data analytics, is essential. This paper presents an interpretation of IoT Forensics from the standpoint of data analytics. To explain the same in detail, the paper focuses on IoT Forensics, its methodologies, and how they relate to data analytics stages. Toward the end, the paper discusses current developments in IoT Forensics from the data analytics perspective, limitations observed in the existing technologies, adoption challenges, and possible future advancements.

Toward Interpretability in Fault Diagnosis for Autonomous Vehicles: Interpretation of Sensor Data Anomalies

To guarantee the safety and reliability of autonomous driving applications, it is indispensable to construct a proper fault diagnosis framework tailored to autonomous vehicles. Fault diagnosis aims to provide essential information about the system operational status and its interpretation facilitates decision-making and mitigates the potential operation risks. In the present work, interpretability issue in fault diagnosis for autonomous vehicles is discussed from the sensor data analytics perspective. Environmental impact is first evaluated using the noise energy as a measure to interpret the impact on sensor data caused by the environment. A signal quality indicator is proposed and Savitzky–Golay filters are applied for online denoising, acting as a countermeasure to mitigate the impact and to enhance the data quality. Then, the adversarial learned denoising shrinkage autoencoder (ALDSAE), an adversarial learning neural network, is constructed for sensor data anomaly detection. It considers an adversarial training technique to improve the performance of the anomaly detector. A residual explainer specific to the ALDSAE model is employed to calculate the contribution of each input feature to the anomaly score in order to interpret the anomaly detection results. Several experiments with the collected data from an autonomous vehicle in a real test field are implemented to validate the effectiveness of the proposed approaches. Results show that the area under the ROC curve (AUC_ROC) of the proposed ALDSAE is over 20% higher in average than several traditional anomaly detectors, and the mean explanation accuracy of the residual explainer achieves similar performance with the widely employed kernel Shapley additive explanation (SHAP) with more than 99% reduction in mean response time.

Brain Tumor Radiogenomic Classification of O6-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning.

Artificial Intelligence (AI) is the subject of a challenge and attention in the field of oncology and raises many promises for preventive diagnosis, but also fears, some of which are based on highly speculative visions for the classification and detection of tumors. A brain tumor that is malignant is a life-threatening disorder. Glioblastoma is the most prevalent kind of adult brain cancer and the 1 with the poorest prognosis, with a median survival time of less than a year. The presence of O6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation, a particular genetic sequence seen in tumors, has been proven to be a positive prognostic indicator and a significant predictor of recurrence.This strong revival of interest in AI is modeled in particular to major technological advances which have significantly increased the performance of the predicted model for medical decision support. Establishing reliable forecasts remains a significant challenge for electronic health records (EHRs). By enhancing clinical practice, precision medicine promises to improve healthcare delivery. The goal is to produce improved prognosis, diagnosis, and therapy through evidence-based sub stratification of patients, transforming established clinical pathways to optimize care for each patient's individual requirements. The abundance of today's healthcare data, dubbed "big data," provides great resources for new knowledge discovery, potentially advancing precision treatment. The latter necessitates multidisciplinary initiatives that will use the knowledge, skills, and medical data of newly established organizations with diverse backgrounds and expertise.The aim of this paper is to use magnetic resonance imaging (MRI) images to train and evaluate your model to detect the presence of MGMT promoter methylation in this competition to predict the genetic subtype of glioblastoma based transfer learning. Our objective is to emphasize the basic problems in the developing disciplines of radiomics and radiogenomics, as well as to illustrate the computational challenges from the perspective of big data analytics.

Exploiting HBM on FPGAs for Data Processing

Field Programmable Gate Arrays (FPGAs) are increasingly being used in data centers and the cloud due to their potential to accelerate certain workloads as well as for their architectural flexibility, since they can be used as accelerators, smart-NICs, or stand-alone processors. To meet the challenges posed by these new use cases, FPGAs are quickly evolving in terms of their capabilities and organization. The utilization of High Bandwidth Memory (HBM) in FPGA devices is one recent example of such a trend. In this article, we study the potential of FPGAs equipped with HBM from a data analytics perspective. We consider three workloads common in analytics-oriented databases and implement them on an FPGA showing in which cases they benefit from HBM: range selection, hash join, and stochastic gradient descent for linear model training. We integrate our designs into a columnar database (MonetDB) and show the trade-offs arising from the integration related to data movement and partitioning. We consider two possible configurations of the HBM, using a single and a dual clock version design. With the right design, FPGA+HBM-based solutions are able to surpass the highest performance provided by either a two-socket POWER9 1 system or a 14-core Xeon 2 E5 by up to 5.9× (range selection), 18.3× (hash join), and 6.1× (SGD).

On Paradigm of Industrial Big Data Analytics: From Evolution to Revolution

The arrival of the intelligent manufacturing and industrial internet era brings more and more opportunities and challenges to modern industry. Specifically, the revolution of the production mode of traditional manufacturing is undergoing thanks to the techniques including but not limited to digits, network, intelligence, and industrial automation fields. As the core link between intelligent manufacturing and industrial internet platform, industrial Big Data analytics has been paid more and more attention by academia and industry. The efficient mining of the high-value information covered under industrial Big Data and the utilization of the real-life industrial process are among the hottest topics at present. Meanwhile, with the advanced development of industrial automation toward knowledge automation, the learning paradigm of industrial Big Data analytics is also evolving accordingly. Therefore, starting from the perspective of industrial Big Data analytics and aiming at the corresponding industrial scenarios, this article actively explores the revolution of the learning paradigm under the background of industrial Big Data: 1) The evolution of the industry Big Data analytics paradigm is analyzed, that is, from isolated learning to lifelong learning, and their relationships are further summarized; 2) Mainstream directions of lifelong learning are listed, and their applications in industrial scenarios are discussed in detail; 3) Prospects and future directions are given.

Data analytics in quality 4.0: literature review and future research directions

ABSTRACT The quality level in manufacturing processes increasingly concerns manufacturing firms, as they respond to pressures such as increasing complexity and variety of products, more complex value chains and shortened time-to-market. Quality management is becoming increasingly challenging as model variety, and highly complex products harbour the danger of distributing defective products in the market. Data analytics has started gathering the interest of quality researchers and practitioners, who investigate approaches, algorithms, and methods for supporting the manufacturing quality procedures in the context of Industry 4.0. This trend is facilitated by the wide expansion of sensory technology and the accelerated adoption of information systems by the manufacturing firms. Since quality and process control has been identified as one of the major challenges with a high potential of big data analytics, in this paper we investigated the manufacturing quality research field from a data analytics perspective. Specifically, we examined the existing literature, we provided clarity to the Quality 4.0 research field, we synthesized the literature review outcomes, and we identified the research gaps and challenges. On top of them, we proposed directions for future research.

Detecting ransomware attacks using intelligent algorithms: recent development and next direction from deep learning and big data perspectives

Recently, cybercriminals have infiltrated different sectors of the human venture to launch ransomware attacks against information technology infrastructure. They demand ransom from individuals and industries, thereby inflicting significant loss of data. The use of intelligent algorithms for ransomware attack detection began to gain popularity in recent times and proved feasible. However, no comprehensive dedicated literature review on the applications of intelligent machine learning algorithms to detect ransomware attacks on information technology infrastructure. Unlike the previous reviews on ransomware attacks, this paper aims to conduct a comprehensive survey on the detection of ransomware attacks using intelligent machine learning algorithms. The study analysed literature from different perspectives focusing on intelligent algorithms detection of ransomware. The survey shows that there is a growing interest in recent times (2016—date) on the application of intelligent algorithms for ransomware detection. Deep learning algorithms are gaining tremendous attention because of their ability to handle large scale datasets, prominence in the research community, and ability to solve problems better than the conventional intelligent algorithms. To date, the potentials of big data analytics are yet to be fully exploited for the smart detection of ransomware attacks. Future research opportunities from the perspective of deep learning and big data analytics to solve the challenges identified from the survey are outlined to give the research community a new direction in dealing with ransomware attacks.

Big Data analytics in Agile software development: A systematic mapping study

Over the last decade, Agile methods have changed the software development process in an unparalleled way and with the increasing popularity of Big Data, optimizing development cycles through data analytics is becoming a commodity. Although a myriad of research exists on software analytics as well as on Agile software development (ASD) practice on itself, there exists no systematic overview of the research done on ASD from a data analytics perspective. Therefore, the objective of this work is to make progress by linking ASD with Big Data analytics (BDA). As the primary method to find relevant literature on the topic, we performed manual search and snowballing on papers published between 2011 and 2019. In total, 88 primary studies were selected and analyzed. Our results show that BDA is employed throughout the whole ASD lifecycle. The results reveal that data-driven software development is focused on the following areas: code repository analytics, defects/bug fixing, testing, project management analytics, and application usage analytics. As BDA and ASD are fast-developing areas, improving the productivity of software development teams is one of the most important objectives BDA is facing in the industry. This study provides scholars with information about the state of software analytics research and the current trends as well as applications in the business environment. Whereas, thanks to this literature review, practitioners should be able to understand better how to obtain actionable insights from their software artifacts and on which aspects of data analytics to focus when investing in such initiatives.

Role of Analytics for Operational Risk Management in the Era of Big Data

ABSTRACTOperational risk management (ORM) is critical for any organization, and in the big data era, analytical tools for operational risk management are evolving faster than ever. This paper examines recent developments in academic ORM literature from the data analytics perspective. We focus on identifying present trends in ORM related to various types of natural and man‐made disasters that have been challenging all aspects of life. Although we examine the broader operations management (OM) literature, we keep the focus on the articles published in the well‐regarded OM journals, including both empirical and analytical outlets. We highlight how the use of data analytics tools and methods have facilitated ORM. We discuss the need for data monitoring and the integration of various analytical tools into decision making processes by classifying the literature on application fields, analytics techniques, and the strategies used for implementation. We summarize our findings and propose a process to implement data‐driven ORM with future research directions.

Big Data: Analytical Solutions, Research Challenges and Trends

The term Big Data refers to an extensive collections of digital data generating every second. Produced datasets come in structured, semi-structured, and unstructured formats throughout the world, which is difficult for the traditional database management systems to analyze. Recently, big data analytics emerges as an essential research area due to the popularity of the Internet and the advent of new Web technologies. This growing area of research represents a multi-disciplinary that attracts researchers from various research fields. Interested researchers are invited to design, develop, and implement several tools, technologies, architecture, and platforms for analyzing these large volumes of data. This paper begins with a brief introduction to big data and related concepts, including the main characteristics of big data, followed by discussions of the most significant open research challenges and emerging trends. Next, reviewing a study of big data analytics, the advantages of using big data solutions, and the preliminary assessments required before migrating from traditional solutions. Finally, presenting a review of the recent main applications to obtain a broad perspective of big data analytics.

APIUaaS: a reference architecture for facilitating API usage from a data analytics perspective

Source code examples are key resources for software developers to learn application programming interfaces (APIs) and to understand corresponding usage patterns. Developers usually have to utilise, evaluate and understand code examples from multiple sources, which involve heavy manually processing efforts. To reduce such efforts, there has been growing interest in developing source code mining and recommendation systems. This study proposes API usage as a service (APIUaaS), a reference architecture for facilitating API usage, which allows infrastructures to be built for recommending proper API code examples based on semi-automatic data analytics. This reference architecture contains five logical layers and six global-level architectural concerns. API queries are accepted from programmers, and corresponding code example candidates are extracted from the data sources layer. The detailed structural links between API elements and source codes are captured and stored in the data model & code assets layer. During the recommendation phase, API usages mining, clustering and ranking algorithms are enabled in the knowledge discover & intelligent model layer. Services such as code assist and bug detection are assembled in the API usage services layer. Finally, the authors evaluate APIUaaS from three perspectives: rationality, feasibility, and usability.

Optimal decisions for operations management of BDAR: A military industrial logistics data analytics perspective

Industrial logistics plays a key role in industrial engineering. Since battle damage assessment and repair (BDAR) process is essentially military industrial logistics process, optimal decisions for operations management of BDAR are crucial to military industrial logistics planning. The purpose of this paper is to present a systematic procedure of optimal decisions for operations management of BDAR and form a framework of military industrial logistics data analytics. Based on war statistics from the collected historical real-world combat data, by classifying the damaged equipments into emergency group and non-emergency group, this study establishes three mathematical models on decision-making for operations management of BDAR, and presents resolutions to problems of optimized deployment, task allocation and selection of formation schemes for the resources in military industrial logistics system. Subsequently, an agent-based simulation model for military industrial logistics system is developed to demonstrate and verify the method. The results show that the method is feasible and effective.

A Data Analytics Perspective of Power Grid Analysis-Part 2: Teaching Old Power Systems New Tricks [Lecture Notes

In Part 1 of this "Lecture Notes" article [1], we introduced a modern perspective on the standard tools for power system analysis-the Clarke and related transforms-through the lens of data analytics. We also indicated their limitations when dealing with unbalanced power system conditions.

Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergencies-oriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.