Intelligence Analytics Research Articles

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

The evolution of knowledge acquisition and representation in manufacturing technologies is vital for translating complex manufacturing data into actionable insights and advancing a comprehensive knowledge framework. This framework is pivotal in driving innovation and efficiency in intelligent manufacturing. This review aggregates recent research on knowledge acquisition and representation within the manufacturing process, addressing existing challenges and mapping potential future developments. It includes an analysis of 123 papers that focus on harnessing advanced intelligent analytics to extract operationally relevant knowledge from the extensive datasets typical in manufacturing environments. The narrative then examines the methodologies for constructing models of knowledge in manufacturing processes and explores their applications in manufacturing principles, design, management, and decision-making. This paper highlights the limitations of current technologies and projects emerging research avenues in the acquisition and representation of process knowledge within intelligent manufacturing systems, with the objective of informing future technological breakthroughs.

Data-Driven Decision-Making Under Uncertainty: Investigating OLAP's Mediating Role to Leverage Business Intelligence Analytics for Entrepreneurship

Data-Driven Decision-Making Under Uncertainty: Investigating OLAP's Mediating Role to Leverage Business Intelligence Analytics for Entrepreneurship

ALGEBRAIC MODELS FOR DATA AND KNOWLEDGE REPRESENTATION IN MODERN DATABASE MANAGEMENT SYSTEMS

The article discusses algebraic data and knowledge representation models in modern database management systems. It is shown that despite the effectiveness of the relational model in storing large volumes of structured information, its capabilities are limited for expressing machine learning algorithms. In this regard, new approaches are proposed based on advanced algebraic models that allow formalizing the architecture and operations of neural networks in SQL. Methods of hybridization of SQL and GPU computations, application of specialized operators, combining data processing and analysis stages are considered. The results confirm the high efficiency of the developed solutions for intelligent analytics.

The Regulatory Affairs Automation tools used in the Pharmaceutical Industry: An overview

Automation is becoming increasingly prevalent in various industries, including healthcare and pharmaceuticals. The pharmaceutical business is influenced by a variety of worldwide trends, with one of the most significant being the use of automation technologies, which will have a transformative effect on the research and development of new pharmaceutical products as well as the speed and efficiency with which products reach patients in need. Regulatory automation is enabled by a variety of technology tools, such as Electronic Document Management Systems, Regulatory Information Management (RIM) Systems, Artificial Intelligence (AI) Analytics Tools, Natural Language Processing (NLP) Tools, and Submission Publishing Tools. Automation tools can be used to automate regulatory activities such as administrative work, dossier completion, data extraction, auditing, regulatory implementation as well as quality management. Automation tools establish process links and minimize complexity, resulting in a more efficient management system. Human-AI interaction creates new prospects in regulatory concerns. This article investigates the potential use of automation techniques in pharmaceutical regulatory concerns.

Evaluation of an Optimized Workflow for the Radiofrequency Catheter Ablation of Paroxysmal Atrial Fibrillation.

BACKGROUND A significant number of atrial fibrillation (AF) recurrences occur after initial ablation, often due to pulmonary vein reconnections or triggers from non-pulmonary veins. MATERIAL AND METHODS Patients with paroxysmal AF who underwent radiofrequency catheter ablation for the first time were enrolled. Base on propensity score matching (1: 1 matching), 118 patients were selected for an optimized workflow for the radiofrequency catheter ablation of paroxysmal AF (OWCA) group and a conventional group. Comparative analysis of the acute and 12-month clinical outcomes was conducted. Moreover, an artificial intelligence analytics platform was used to evaluate the quality of pulmonary vein isolation (PVI) circles. RESULTS PVI was successfully achieved in all patients. Incidence of first-pass isolation of bilateral PVI circles was higher (P=0.009) and acute pulmonary vein reconnections was lower (P=0.027) in the OWCA group than conventional group. The OWCA group displayed a significant reduction in the number of fractured points (P<0.001), stacked points (P=0.003), and a greater proportion of cases in which the radiofrequency index achieved the target value (P=0.003). Additionally, the contact force consistently met the force over time criteria (P<0.001) for bilateral PVI circles in the OWCA group, accompanied by a shorter operation time (P=0.017). During the 12-month follow-up period, the OWCA group exhibited a higher atrial arrhythmia-free survival rate following the initial ablation procedure than did the conventional group. CONCLUSIONS The optimized workflow for radiofrequency catheter ablation of paroxysmal AF could play a crucial role in creating higher quality PVI circles. This improvement is reflected in a significantly elevated 12-month atrial arrhythmia-free survival rate.

Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial.

We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2). A mixed-method analysis was conducted at 2 pilot sites. Interviews were conducted with 12 of 27 enrolled patients and with 13 participating clinicians. iPARIHS constructs were used for interview construction to identify workflow, communication patterns, and clinician's beliefs. Interviews were transcribed and analyzed using inductive coding protocols to identify key themes. Behavioral response data from the AI-generated notifications were collected. Clinicians responded to notifications within 24 hours in 95% of instances, with 26.7% resulting in clinical action. Four implementation themes emerged: (1) High anticipatory expectations for reliable patient communications, reduced patient burden, and less proactive provider monitoring. (2) The AI notifications required a differential and tailored balance of trust and action advice related to role. (3) Clinic experience with other home-based programs influenced utilization. (4) Responding to notifications involved significant effort, including electronic health record (EHR) review, patient contact, and consultation with other clinicians. Clinician's use of AI data is a function of beliefs regarding the trustworthiness and usefulness of the data, the degree of autonomy in professional roles, and the cognitive effort involved. The implementation planning analysis guided development of strategies that addressed communication technology, patient education, and EHR integration to reduce clinician and patient burden in the subsequent main randomized phase of the trial. Our results provide important insights into the unique implications of implementing AI analytics into clinical workflow.

Business Intelligence Analytics Job Postings: Understanding and Predicting Salaries

Business Intelligence Analytics Job Postings: Understanding and Predicting Salaries

Advancing E-Commerce Analytics The Development of Intelligent Analytics Software for Enhanced Customer Experience

In the current era of rapid growth in electronic commerce, it is crucial for organizations to possess an in-depth understanding of client habits in order to effectively optimize their plans. This position paper presents a proposal that centers on the creation of Intelligent Analytics Software designed specifically for Casper, a well-known e-commerce platform that specializes in the selling of electronic devices. The main purpose of the software is to facilitate the real-time monitoring and analysis of users' browsing patterns, utilizing historical navigation data as a basis. The effort arises from the growing necessity to utilize data-driven insights in order to improve client experiences and achieve commercial advantages. The statement above highlights the need for secure and accurate information collecting, rapidly adapting to user behaviors in real-time, and delivering personalized benefits, as well as effectively addressing platform-related challenges. Our research and development efforts involve conducting comprehensive analyses of user navigation, ensuring secure processing of data, and effectively managing behavioral data. The project is notable for its implementation of innovative analytical software that enables real-time tracking of consumer activity, generation of analytical reports, identification of consecutive actions using Sankey diagrams, segmentation analysis, and production of heatmaps. Moreover, the program enhances cross-selling capabilities by monitoring often co-purchased items, so providing a useful asset to the e-commerce platform's repertoire. In addition to expanding the company's understanding of client habits and broadening its range of products, the project also serves to advance national expertise and pursue international academic contributions via research papers. The success criteria include strong benchmarks for clustering and association analysis techniques, emphasizing the dedication to providing fast analytical solutions. The paper offers a comprehensive overview of the project's significance, objectives, methodology, and the urgent need it answers within the field of e-commerce analytics.

A bibliometric and systematic review analysis of adopting decision intelligence analytics for rational decision-making

Purpose This paper aims to study and discover the unsearched area in behavioral finance in the new era of technology enhancement. The study has been done with two significant methodologies of reviews. This study also covers the whole structure of the investment decision scenario. Design/methodology/approach A systematic and bibliometric analysis has been done to make this study conceptual. Data collection sources are highly indexed journals, Scopus, Web of Science and Google Scholar. The “R” package has been used to do bibliometric analysis. Start with data cleaning and import the data in biblioshiny to get and interpret the result. A total of 642 data has been finalized from 1973 to 2022. Findings Various noticeable results have been found to accomplish the objectives and fill the gap in the study. There is a need to research both technological and psychological factors to determine the relation of these two variables with the investment decision-making of investors. Originality/value This study has done a systematic literature review and a bibliometric analysis that shows the importance of technology enhancement for further research, which has been searchable throughout this study.

Innovative AI Regional Economic Development Driven by Information System Innovation: Opportunities and Challenges - A Boosting Factor from Digitalization, Business Intelligence Analytics

This investigation looks at the intricate relationships between business intelligence (BI) analytics, digitalization, cutting-edge artificial intelligence (AI), and information system innovation, as well as how they affect local economic growth. The study emphasizes the transformative potential of AI as a powerful accelerator for regional growth by stimulating innovation, enhancing efficiency, and expediting decision-making processes. It does this by applying map analysis and spatial econometric model regression to a dataset covering Chinese regions from 2010 to 2022. The study highlights the critical need for the development of sophisticated digitalization frameworks and information systems to adapt to the ever-evolving dynamics of regional economies and advocates for the collabourative efforts of researchers and local governments in tailoring AI-driven solutions that cater to the unique demands of individual localities. Although the research highlights the initial benefits provided by BI analytics, it also strongly emphasizes the need for ongoing innovation to maintain long-term success, encouraging academics to improve and streamline current BI analytics programmes. The paper also highlights the crucial importance of computing sustainability, effective administration of massive datasets, seamless human-AI collabouration, geographic computing, and the ethical considerations connected with AI adoption. The study emphasizes the value of utilizing AI's revolutionary potential while proactively tackling the issues it presents by offering a series of doable recommendations for policymakers, practitioners, and researchers. In the end, this study makes a significant contribution to the body of knowledge already available about the role of AI in regional economic development, providing priceless insights for those involved in directing the course of regional economies.

Cloud-based Data Analytics for Business Intelligence

Abstract: In the era of data-driven decision-making, organizations are turning to cloud-based data analytics for business intelligence to overcome the limitations of traditional on-premises systems. This paradigm shift offers the promise of scalable, agile, and advanced analytics capabilities. This paper explores the landscape of cloud- based data analytics for business intelligence by investigating existing systems, challenges, and opportunities. The study first examines leading cloud platforms such as Amazon Web Services (AWS) Redshift, Microsoft Azure Synapse Analytics, Google BigQuery, and Snowflake[11]. It evaluates their features, scalability, and integration options to assess their suitability for modern BI needs. Moreover, the research identifies critical challenges in transitioning to cloud-based analytics, including data integration complexities, security concerns, and cost management. The integration of advanced analytics techniques, such as machine learning and AI, into cloud- based environments is also explored. The study delves into the benefits and challenges of predictive analytics, anomaly detection, and other emerging capabilities that empower organizations to extract deeper insights from data. Furthermore, hybrid cloud architectures, which combine on-premises infrastructure with cloud resources, are investigated. Strategies for seamless data integration and workload distribution are discussed, enabling organizations to strike a balance between performance and data governance.

Digital Twins for Healthcare 4.0—Recent Advances, Architecture, and Open Challenges

Recent trends have shown a widespread increase in the landscape of digital healthcare (i.e., Healthcare 4.0) services, such as personalized healthcare, intelligent rehabilitation, telemedicine, and smart diet management, among others. These healthcare services are based on a variety of diverse requirements. Fulfilling these requirements require proactive intelligent analytics and self-sustainability of networks. Self-sustainability enables the operation of a network with minimum possible interaction from the end-users/network operators, whereas proactive intelligent analytics enables efficient management of resources in response to users' requests. To enable healthcare 4.0 with proactive online analytics and self-sustainability, one can leverage digital twins. In this article, we present an overview and recent advances of digital twins for healthcare 4.0. An architecture of digital twins for healthcare is also proposed. Furthermore, we present several use cases of digital twins. Finally, we present open research challenges with possible solutions.

Security Mechanisms of Distributed Denial-Of-Service (DDOS) Attack in Everything-as-a-Service (XaaS) - Survey

— This paper illustrates research on DDoS attack which overload targeted servers with traffic to render hosted resources inaccessible and unavailable. The attacks are increasingly being launched from multiple locations, gradually spreading over larger networks, consequently to obscure the attack origin. The precipitous cloud computing as the new ‘anywhere anytime’ paradigm, in the form of Everything-as-a-Service (XaaS), transforms these attack mechanisms to become progressively destructive, affecting Quality Of Service (QoS) performance. The attack vectors examination ranges from volumetric attack that flood network links, to application layer attack that target specific services, to protocol attack that exhaust network's resources, with evolving consequences. DDoS attacks mitigation in XaaS environments poses unique challenges. Current literature explores the limitations of traditional on-premise and XaaS-based mitigation techniques to instantaneously detect and mitigate malicious traffic. The role of intelligent analytics in distinguishing legitimate and malicious traffic are also being investigated by application of machine learning algorithms to safeguard against prospective interruptions to XaaS-based services’ availability and reliability

Intelligent Decision Support for Energy Management: A Methodology for Tailored Explainability of Artificial Intelligence Analytics

This paper presents a novel development methodology for artificial intelligence (AI) analytics in energy management that focuses on tailored explainability to overcome the “black box” issue associated with AI analytics. Our approach addresses the fact that any given analytic service is to be used by different stakeholders, with different backgrounds, preferences, abilities, skills, and goals. Our methodology is aligned with the explainable artificial intelligence (XAI) paradigm and aims to enhance the interpretability of AI-empowered decision support systems (DSSs). Specifically, a clustering-based approach is adopted to customize the depth of explainability based on the specific needs of different user groups. This approach improves the accuracy and effectiveness of energy management analytics while promoting transparency and trust in the decision-making process. The methodology is structured around an iterative development lifecycle for an intelligent decision support system and includes several steps, such as stakeholder identification, an empirical study on usability and explainability, user clustering analysis, and the implementation of an XAI framework. The XAI framework comprises XAI clusters and local and global XAI, which facilitate higher adoption rates of the AI system and ensure responsible and safe deployment. The methodology is tested on a stacked neural network for an analytics service, which estimates energy savings from renovations, and aims to increase adoption rates and benefit the circular economy.

Predictive Analytics for Hydropower Fleet Intelligence

A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&amp;M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

Cloud-Based Artificial Intelligence Analytics to Assess Combined Sewer Overflow Performance

Cloud-Based Artificial Intelligence Analytics to Assess Combined Sewer Overflow Performance

A Generic Framework for Deploying Video Analytic Services on the Edge

This paper introduces a novel distributed model for handling in real-time, edge-based Artificial Intelligence analytics, such as the ones required for smart video surveillance. The novelty of the model relies on decoupling and distributing the services into several decomposed functions which are linked together, creating virtual function chains ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$VFC$</tex-math></inline-formula> model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$VFC$</tex-math></inline-formula> model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$VFC$</tex-math></inline-formula> model have shown that it can reduce the total edge cost, compared with a Monolithic and a Simple Frame Distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Spark ©) and an edge-deployement framework (Kubernetes©), have been deployed. A cloud service has also been considered. Experiments have shown that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$VFC$</tex-math></inline-formula> can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a caching and a QoS monitoring service based on Long-Term-Short-Term models are introduced and evaluated.

РОЗРОБКА ІНТЕЛЕКТУАЛЬНОЇ СИСТЕМИ НАВІГАЦІЇ ТА ПРОСТОРОВОЇ ОРІЄНТАЦІЇ ДЛЯ ЛЮДЕЙ З ВАДАМИ ЗОРУ

A prototype of an information and communication navigation and spatial orientation system for people with visual impairments has been developed based on a mobile device. The prototype has several advantages, including portability, convenience, and ease of use. This is achieved through a harmonious combination of optimized software modules that provide data collection, navigation, intelligent analytics, and image processing within a single smartphone. To implement the prototype, stages of development of the information and communication system were previously conducted, subject area analysis was carried out, software and hardware options were researched, and an architecture was designed based on an algorithm for asynchronous processing of a large stream of data. The result of the work is a verified basic prototype of the system, ready for use in testing conditions and data collection for further research.

The Role of Artificial Intelligence in Deciphering Diet-Disease Relationships: Case Studies.

Modernization of society from a rural, hunter-gatherer setting into an urban and industrial one, with the associated dietary changes, has led to an increased prevalence of cardiometabolic and additional noncommunicable diseases, such as cancer, inflammatory bowel disease, and neurodegenerative and autoimmune disorders. However, while dietary sciences have been rapidly evolving to meet these challenges, validation and translation of experimental results into clinical practice remain limited for multiple reasons, including inherent ethnic, gender, and cultural interindividual variability, among other methodological, dietary reporting-related, and analytical issues. Recently, large clinical cohorts with artificial intelligence analytics have introduced new precision and personalized nutrition concepts that enable one to successfully bridge these gaps in a real-life setting. In this review, we highlight selected examples of case studies at the intersection between diet-disease research and artificial intelligence. We discuss their potential and challenges and offer an outlook toward the transformation of dietary sciences into individualized clinical translation. Expected final online publication date for the Annual Review of Nutrition, Volume 43 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Intelligent analytics algorithms in breach detection systems for securing VANETs and data for smart transportation management

Due to the increasing prevalence of sensor-centric communication and computing devices installed within a vehicle for a wide range of functions, including vehicle monitoring, physical wiring minimization, and driving efficiency, in-vehicle communication has emerged as an integral part of the modern driving environment. Nevertheless, there are presently no certain remedies for in-vehicle cyber dangers provided by the relevant literature on cyber security for in-vehicle communication techniques. Existing solutions do not provide a complete security framework for in-vehicle communication since they depend heavily on protocol-specific security techniques. The purpose of this research is to provide a method for smart vehicle management in VANET breach detection that makes use of efficient data transfer and clever machine learning. In this research, we use a novel approach to intrusion detection based on an ensemble of adversarial Boltzmann CNNs. And then we utilize the secure short hop opportunistic local routing protocol to get the information where it needs to go. Experimentation analysis for a wide range of security-based datasets includes throughput, QoS, training accuracy, validation accuracy, and network security analysis. Throughput of 88%, Quality of Service of 77%, Training Accuracy of 93%, Validation Accuracy of 96%, Network Security Analyses of 63%, Scalability of 75% were all achieved using the suggested method.