Intelligent Data Analysis Research Articles

Artificial Intelligence Studies and Data Analysis in Chronic Lymphocytic Leukemia: A Current Review

Artificial Intelligence Studies and Data Analysis in Chronic Lymphocytic Leukemia: A Current Review

STRATEGIC APPROACHES TO LEAN MANUFACTURING IN INDUSTRY 4.0: A COMPREHENSIVE REVIEW STUDY

This systematic review, based on the analysis of 130 peer-reviewed articles, explores the strategic integration of lean manufacturing with Industry 4.0 technologies, focusing on the role of the Internet of Things (IoT), artificial intelligence (AI), and big data analytics in enhancing lean practices. Using the PRISMA guidelines, the study identified key synergies between lean and Industry 4.0, demonstrating how these technologies facilitate real-time data collection, predictive maintenance, and process optimization, all of which align with lean’s core principles of waste reduction and continuous improvement. The review also highlights significant challenges to this integration, including high implementation costs, workforce skill gaps, and resistance to organizational change, particularly within small and medium-sized enterprises. Additionally, emerging trends suggest that the future of lean-Industry 4.0 integration will increasingly focus on sustainability, with companies leveraging digital tools to enhance energy efficiency and reduce environmental waste. The findings underscore the need for further research to address these challenges and explore scalable solutions that can drive the broader adoption of lean-Industry 4.0 integration in diverse industrial contexts.

Healthcare Data Analytics and Predictive Modelling: Enhancing Outcomes in Resource Allocation, Disease Prevalence and High-Risk Populations

Purpose: This study aims to explore the role of healthcare data analytics and predictive modeling in enhancing healthcare outcomes, specifically in resource allocation, disease forecasting, and identifying high-risk populations. Methodology: The research employs a comprehensive approach, utilizing various sources of healthcare data such as electronic health records (EHRs) and public health databases. Advanced analytical techniques, including machine learning, artificial intelligence, and big data analytics, are applied to derive actionable insights. Findings: The study reveals that predictive modeling significantly enhances resource optimization, enables accurate disease prevalence forecasting, and improves the identification of high-risk populations. Case studies demonstrate how these technologies lead to more efficient healthcare delivery, cost reduction, and better patient care outcomes. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theoretical understanding of healthcare data analytics by integrating advanced predictive modeling techniques with real-world healthcare applications. It offers valuable insights for policymakers on the importance of investing in data infrastructure and promoting data-driven decision-making. Practically, the study provides healthcare organizations with actionable strategies to implement predictive analytics for improved resource allocation and patient care.

Real-time solar PV generation in a building using LSTM-based time series forecasting

The increasing demand for renewable energy has forced the researchers to lay importance on studying the behavior of these sources by monitoring the operational data and performing data analysis. The application of data analytics to renewable energy is one of the most powerful methods to maintain the system's reliability and stability. As the power system becomes more complex the necessity to perform data analytics also increases. When data analytic techniques are applied to solar energy generations through Photovoltaic (PV) dataset, the possible behavior of PV generation performance which is affected by changes in environmental conditions can be predicted and further analytical approaches allow us to detect possible PV panel and inverter failures. This paper is an attempt towards applying the intelligent data analytics approaches to solar PV generation of a real-time photovoltaic plant. The main purpose of the data analytics platform is to analyze the energy yield, assess the PV system performance, and forecast solar PV generation. In this article, Long Short-Term Memory (LSTM) machine learning model is developed to assess and interpret the available information from the gathered data of the PV plant. The proposed model is a sequence-to-sequence regression-based forecasting model which performs time series forecasting in a real-time 100 kW PV plant in the University building of SRM Institute of Science and Technology, Kattankulathur, Tamilnadu.

Augmenting supply chain resilience through AI and big data

PurposeNatural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).Design/methodology/approachThis research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.FindingsA major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.Originality/valueFew studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

Artificial Intelligence Model for Detecting Tax Evasion Involving Complex Network Schemes

Tax evasion through complex network schemes poses a significant challenge to tax authorities, leading to substantial revenue losses. This paper aims to develop and evaluate an artificial intelligence model designed to detect tax evasion within complex corporate networks, providing a comprehensive overview and prediction of tax avoidance behaviors. Employing a systematic literature review and document analysis of applicable tax regulations, the study utilizes Social Network Analysis (SNA) as a primary technique for mapping and analyzing taxpayer networks. The process involves matching taxable identities, constructing taxpayer graphs, extracting features, and developing a machine learning model. The proposed architectures and processes demonstrate the potential for tax authorities to enhance their capabilities in detecting tax evasion involving complex networks, with the machine learning model effectively identifying features related to both individual and network characteristics of taxpayers. The findings suggest that the integration of artificial intelligence and big data analytics can significantly improve the detection of tax evasion in complex corporate structures, offering valuable tools for tax authorities to better enforce tax compliance.

Formation of Bachelor`s Skills in the Field of Machine Learning and Intelligent Data Analysis

The article presents the content of the educational disciplines “Workshop on Python Programming” and “Fundamentals of Artificial Intelligence”, included in the module “Data Analysis. Machine learning. Artificial Intelligence”, implemented in Ural universities. Modern teachers note that when preparing the next generation of teachers, the priority should be their acquisition of experience in using artificial intelligence tools in teaching. The objectives of mastering the module are developing students’ skills in the field of neural network mathematical modeling, developing knowledge about basic data analysis algorithms, development of skills in visualization and interpretation of data to solve applied problems using object-oriented programming technologies and artificial neural network technologies. As part of project activities, students gain experience in data mining by completing research projects (for example, a project on comparative assessment of the effectiveness of multilayer perceptron learning algorithms) and applied projects (for example, on the development of a neural network system for predicting the success of schoolchildren’s research work; on the development of a neural network system for predicting student attendance training sessions). At USPU, software systems have been developed in Python that implement the presented neural network prognostic systems. Educational and methodological materials for the disciplines of the module include video lectures and presentations for them, laboratory work (including those based on GoogleColab notebooks), datasets, test sets, theory questions and practical tasks for exams and tests. The effectiveness of the developed teaching methodology is confirmed by the results of a pedagogical experiment. Keywords: neural network technologies, data mining, predictive systems, machine learning, content of the academic discipline

A Study on the Role of Digital Transformation in Enhancing Bank Performance

This study investigates the role of digital transformation in enhancing the performance of banks, focusing on how technological advancements have reshaped the banking sector. The research explores various dimensions of digital transformation, including the adoption of digital banking platforms, artificial intelligence (AI), blockchain technology, and big data analytics. By analyzing secondary data from financial reports, industry studies, and case studies of banks that have undergone digital transformation, the study aims to assess the impact of these technologies on operational efficiency, customer experience, and financial performance. Key performance indicators such as cost- to-income ratios, transaction processing times, customer satisfaction levels, and risk management effectiveness are evaluated to understand the overall benefits of digital transformation. The findings reveal that digital transformation significantly enhances bank performance by improving operational efficiency, increasing customer engagement, and driving revenue growth. Specifically, the adoption of digital platforms has streamlined processes, reduced costs, and improved the accuracy of transactions. AI and machine learning have enabled more personalized customer service and effective risk management, while blockchain technology has enhanced transaction security and transparency. The research concludes with recommendations for banks to optimize their digital strategies and overcome these challenges, and suggests areas for further research to explore emerging trends and technologies in the banking sector. Keywords: Digital Transformation, Bank Performance, Financial Technology (Fintech) Artificial Intelligence (AI), Financial Performance Metrics, Technology Adoption

On the Problem of Explaining the Results of Intelligent Data Analysis

On the Problem of Explaining the Results of Intelligent Data Analysis

AKUNTANSI MANAJEMEN STRATEGIS DALAM PERSPEKTIF REVOLUSI INDUSTRI 5.0

The Industrial Revolution has undergone several notable transformations, transitioning from the introduction of mechanization and steam power to the advent of digitalization, referred to as the Industrial Revolution 4.0. With the emergence of Industrial Revolution 5.0, or Society 5.0, emphasis has shifted towards the utilization of contemporary technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data analysis, with the aim of effectively fulfilling human needs and enhancing social welfare. The overarching objective of Society 5.0 is to achieve harmonious equilibrium between economic progress and the resolution of social issues through the establishment of seamless interconnectivity between virtual and tangible realms. This study employed a qualitative methodology, specifically a scoping approach. It examines the impact of the Society 5.0 concept on the accounting perspective in strategic management, with the aim of facilitating more effective decision-making and enhancing the predictive capacity of an organization's future performance. The field of strategic management accounting, which supports the strategic management of corporations, has adapted to technological advancements. The application of digital technology in accounting enables more accurate and quality analyses, and improves the efficiency and effectiveness of the decision-making process. However, digital transformation also faces various challenges, including employee engagement, management support, and skill gaps. This research is expected to provide practical implications for industrial companies on how the Society 5.0 era changes the perspective of accounting in strategic management

Finite predicate-driven logic networks method for enhanced education data analysis

The subject matter of the study is intelligent data analysis in the field of academic information. The goal of the study is to create a mathematical model for analyzing students' academic information using the predicate-driven logic networks method, which allows taking into account both logical dependencies and probabilistic transitions between states. To achieve this goal, the following tasks were defined: analysis of the theoretical foundations of the logic networks method and predicate logic, integration of these approaches into a single mathematical model, development of approaches for its application in academic data analysis problems. The research used the methods of mathematical modeling, complex logical analysis, and method for constructing logic networks. The following results were obtained: a theoretical model was developed that integrates the principles of logic networks and predicate logic for analyzing student academic performance; the model accounts for both probabilistic transitions between states and logical dependencies among student parameters; the mathematical model also incorporates logical rules to enhance the accuracy of logical analysis within the academic context. The model was tested on a dataset of student performance, demonstrating its effectiveness in accurately predicting academic outcomes and confirming the validity of the integrated approach. Conclusions. The scientific novelty of the results obtained is as follows: 1) a theoretical model for analyzing student academic data was developed by integrating logic networks and predicate logic, allowing for the simultaneous consideration of probabilistic transitions and logical dependencies among student parameters; 2) the approach enhances the analysis process by incorporating logical rules into the probabilistic framework, providing a more nuanced and accurate tool for analyzing academic data; 3) this combined model offers a novel method for addressing complex logical analysis tasks in educational settings, paving the way for further research and practical applications in intelligent data analysis. The successful testing of the model on actual student data further underscores its potential as a powerful tool in educational data analysis.

Construction of integrated network order system of main distribution network based on power grid operation control platform

This study presents a major advance in grid management: the development and deployment of an integrated network command system for the main distribution network. The system integrates cutting-edge information technology, including modules such as command issuance, intelligent routing, security assurance and in-depth data analysis, opening a new era of refined and intelligent power grid management. The research focuses on the application of core technologies such as information communication technology, distributed control system, artificial intelligence and big data analysis, and strengthens the system operation foundation. The chapter on system architecture details the innovative integration of DDQN algorithm and attention mechanism, and carefully constructs intelligent scheduling engine and status monitoring and early warning system, which significantly improves real-time response, decision optimization and active security defense capabilities. Simulation experiments and actual case analysis verify the effectiveness of the system, specifically, the response time is reduced by 75.7%(from 2.1 s to 0.51 s in the traditional system), the data processing speed is still maintained at a high level under high load (100,000 data processing rate is 300/s), and the system stability is as high as 99.97%. The new system also achieved a high degree of automation, reducing annual operation and maintenance costs by 20%, and increasing user satisfaction to 90%, an increase of 28.6% over the previous period. These improvements not only optimize power quality and grid efficiency, but also further confirm that the fault response time is reduced by 30% and the user outage time is reduced by 25%. Therefore, this study not only highlights the innovation of the proposed system, but also demonstrates its significant contribution to accelerating the modernization of power grid management and ensuring safe operation with empirical data.

Digitalization and Technology in the Fight Against Crimes, Related to Pharmacy Drug Addiction

In modern society, digitalization and technology play a key role in the fight against crimes related to drug addiction. This study examines various aspects of the use of digital technologies to combat prescription drug abuse and drug trafficking. The introduction of electronic prescriptions, centralized systems for monitoring drug sales and digital patient databases makes it possible to effectively monitor and control the process of prescribing and receiving drugs. The use of artificial intelligence and big data analytics helps identify patterns associated with drug addiction and early detection of abnormal activities. Online platforms for public information and education, as well as mobile applications for self-control and support, have a significant impact on preventing the development of addiction. As a result, digital technologies provide significant support in the fight against pharmacy addiction by increasing transparency, accountability and safety in the pharmaceutical field.

DECENTRALIZED ELECTRONIC VOTING WITH ETHEREUM BLOCKCHAIN IN DEMOCRATIC AND POLITICAL ELECTIONS

The Ethereum blockchain-based electronic voting (e-voting) systems can emerge as a viable strategy in this era of contemporary democracies to revolutionize political elections and augment the efficacy of the electoral process. There are myriad advantages that the Ethereum blockchain has to offer, from fairness to increased voting rates. Unlike traditional voting protocols, the Ethereum blockchain can assure substantial cost savings and eliminate the necessity for electoral intermediaries. The use of the Ethereum blockchain in political contexts also ensures that elections are held with integrity while preserving the voters’ privacy. Due to its popularity, provision of smart contracts logic, and various promising advantages, this systematic review aims to examine the potential deployment of decentralized e-voting systems integrated with Ethereum blockchain technology for democratic political elections. A systematic literature review (SLR) and the PICO approach, which stands for population, intervention, control, and outcomes, were adopted in this study to systematically analyze the existing literature. Key technological approaches identified in the voting system include the hybrid blockchain and privacy-preserving score voting. Among the noteworthy findings are the following: while adoption and complexity remain challenges across numerous e-voting frameworks, scalability, end-to-end security, enhanced efficiency, and effectiveness are key benefits. An exploration into the prospective future innovations, such as the integration of artificial intelligence and big data analytics into the Ethereum blockchain, was also included to further improve the reliability of the e-voting systems. It is believed that the Ethereum blockchain has a promising transformative impact on electoral politics and democratic processes, presenting a ray of hope for future elections.

Connecting the green to the digital: Integrating Eco Cities and Smart Regions

<p>This article explores the integration of Eco Cities and Smart Regions to create sustainable and innovative urban environments. Eco Cities focus on environmental sustainability through renewable energy, waste management, and green infrastructure, aiming to reduce ecological footprints and promote healthy lifestyles. Smart Regions, on the other hand, utilize advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics to enhance public service efficiency, optimize resource use, and foster economic development. The study highlights how combining these two approaches can lead to significant benefits, including improved operational efficiency, better quality of life, environmental resilience, and active citizen participation. Case studies of cities like Copenhagen and Singapore demonstrate successful integration, showcasing achievements in sustainability and technological innovation. The article also identifies the main challenges in this integration, including technical compatibility, cybersecurity risks, high initial investments, digital inequality, and the need for robust governance frameworks. Overcoming these challenges requires collaborative efforts among governments, private sectors, and civil society, as well as innovative financing strategies and capacity-building programs. Future considerations emphasize the need for a holistic approach to urban planning that incorporates emerging technologies, flexible and adaptive strategies, and inclusive citizen engagement. By addressing these factors, cities and regions can become more resilient, sustainable, and inclusive, meeting the needs of both current and future generations. This integrated approach promises to transform urban environments into hubs of sustainability and innovation, driving long-term positive outcomes for society and the environment.<strong></strong></p>

Solving supply chain management issues with AI and Big Data analytics for future operational efficiency

This review paper examines the use of artificial intelligence (AI) and big data analytics in solving supply chain management (SCM) issues and enhancing future operational efficiency. The primary objective is to synthesize existing research and provide a comprehensive overview of how these technologies are revolutionizing SCM. The paper systematically reviews recent literature on AI and big data applications in SCM, focusing on key areas such as demand forecasting, inventory management, and logistics optimization. By analyzing various studies and case examples, it highlights the transformative effects of these technologies on supply chain processes. The review covers a range of AI techniques including machine learning, deep learning, and predictive analytics, as well as the role of big data in capturing and processing large volumes of supply chain-related information. Key findings from the literature indicate significant improvements in supply chain visibility, decision-making accuracy, and operational efficiency. AI and big data analytics enable more precise demand forecasting, better inventory control, and optimized logistics, leading to cost reductions and enhanced responsiveness to market fluctuations. The review also discusses the challenges and considerations for implementing these technologies, such as data quality, integration complexity, and the need for specialized skills. The paper emphasizes the critical role of AI and big data analytics in addressing contemporary SCM issues and fostering future-ready supply chains. It underscores the necessity for organizations to adopt these technologies to stay competitive and achieve long-term operational excellence. The insights provided serve as a valuable resource for researchers and practitioners aiming to leverage AI and big data for supply chain innovation. Keywords: Artificial Intelligence (AI), Big Data Analytics (BDA), Supply Chain Management (SCM), Predictive Analytics, Internet of Things (IoT), Blockchain Technology, Automation, Machine Learning, Inventory Management, Logistics Optimization, Supply Chain Visibility, Real-time Monitoring, Data Quality, System Integration, Risk Management, Operational Efficiency, Strategic Decision-making, Sustainability, Supply Chain Resilience, Emerging Technologies.

Accelerating Fintech optimization and cybersecurity: The role of segment routing and MPLS in service provider networks

This review paper explores the profound impact of advanced financial modeling techniques on strategic business planning and performance. The paper begins with an overview of the evolution of financial modeling, highlighting the importance of advanced techniques such as Monte Carlo simulations, real options analysis, and machine learning models. It delves into the role of technology, including artificial intelligence and big data analytics, in enhancing financial modeling capabilities. The discussion then shifts to how these advanced models support strategic business planning by providing deeper insights, improving risk management, and facilitating better decision-making. The paper also examines the challenges of implementing advanced financial modeling, including data quality issues and skill gaps, and offers practical business recommendations. Future trends in financial modeling, such as increased AI integration and the incorporation of ESG factors, are also explored. The review concludes by emphasizing the significant benefits of advanced financial modeling in driving strategic business decisions and improving overall business performance. Keywords: Advanced Financial Modeling, Strategic Business Planning, Monte Carlo Simulations, Machine Learning Models, Risk Management.

Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial.

To assess the value of deep learning in selecting the optimal embryo for in vitro fertilization, a multicenter, randomized, double-blind, noninferiority parallel-group trial was conducted across 14 in vitro fertilization clinics in Australia and Europe. Women under 42 years of age with at least two early-stage blastocysts on day 5 were randomized to either the control arm, using standard morphological assessment, or the study arm, employing a deep learning algorithm, intelligent Data Analysis Score (iDAScore), for embryo selection. The primary endpoint was a clinical pregnancy rate with a noninferiority margin of 5%. The trial included 1,066 patients (533 in the iDAScore group and 533 in the morphology group). The iDAScore group exhibited a clinical pregnancy rate of 46.5% (248 of 533 patients), compared to 48.2% (257 of 533 patients) in the morphology arm (risk difference -1.7%; 95% confidence interval -7.7, 4.3; P = 0.62). This study was not able to demonstrate noninferiority of deep learning for clinical pregnancy rate when compared to standard morphology and a predefined prioritization scheme. Australian New Zealand Clinical Trials Registry (ANZCTR) registration: 379161 .

A Review of Traditional and Modern Approaches in Pharmacovigilance for Hypertension and Kidney Disease Treatments

Pharmacovigilance plays a crucial role in monitoring the safety of herbal, homeopathic, and allopathic drugs, particularly for treatments of hypertension and kidney diseases. The global increase in complementary and alternative medicine use alongside conventional therapies necessitates a thorough understanding of potential interactions and side effects. Current pharmacovigilance systems for herbal and homeopathic remedies face unique challenges compared to established allopathic drug monitoring processes. Recent research on allergic reactions to these diverse therapeutic approaches reveals varying prevalence, mechanisms, and clinical implications. Regulatory frameworks governing these treatment modalities differ across countries, highlighting the need for harmonized international standards. Emerging technologies, including artificial intelligence and big data analytics, show promise in enhancing adverse drug reaction (ADR) detection and prevention. Integration of findings from multiple disciplines provides healthcare professionals, researchers, and policymakers with insights into ensuring safe use of herbal, homeopathic, and allopathic drugs in managing hypertension and kidney diseases. This review summarizes current knowledge, identifies research gaps, and proposes future directions for improving pharmacovigilance across these diverse therapeutic approaches

The Transformative Impact of Financial Technology (FinTech) on Regulatory Compliance in the Banking Sector

Financial Technology (FinTech) has emerged as a disruptive force in the banking sector, revolutionizing the way financial services are delivered and consumed. This review explores the transformative impact of FinTech on regulatory compliance within the banking industry. The integration of advanced technologies such as artificial intelligence, blockchain, and big data analytics has enabled financial institutions to enhance operational efficiency, improve customer experience, and expand market reach. However, these innovations have also posed unprecedented challenges to traditional regulatory frameworks designed to safeguard financial stability and consumer protection. This review examines how FinTech innovations have necessitated regulatory adaptation and evolution. It highlights the complexities introduced by novel financial products, digital payment systems, and decentralized finance (DeFi) platforms, which often operate beyond conventional regulatory boundaries. Regulatory compliance in areas such as anti-money laundering (AML), know your customer (KYC) requirements, and data privacy has become more intricate as FinTech solutions blur geographical and jurisdictional lines. Moreover, the strategies employed by regulatory bodies and financial institutions to address these challenges effectively. These include leveraging regulatory technology (RegTech) solutions for enhanced monitoring and compliance automation, fostering collaboration between regulators and industry stakeholders, and advocating for agile regulatory frameworks capable of accommodating rapid technological advancements. Looking ahead, the review anticipates ongoing shifts in regulatory paradigms to accommodate the transformative impact of FinTech. It emphasizes the importance of proactive regulatory approaches that balance innovation with risk management, ensuring the integrity and resilience of the banking sector amidst a rapidly evolving digital landscape. This provides a comprehensive overview of how FinTech is reshaping regulatory compliance in banking. It underscores the need for adaptive regulatory strategies and collaborative efforts to harness the full potential of FinTech while safeguarding financial stability and consumer trust.