Analysis Capabilities Research Articles

A Systematic Review of Intelligent Systems and Analytic Applications in Credit Card Fraud Detection

The use of credit cards plays a crucial role in cash management and in meeting the needs for individual and commercial customers due to the spread of risks to the future by making monthly instalments instead of cash transactions. The use of credit cards therefore provides benefits not only to the customers but also to the banks as it enables and sustains a long-term relationship in between them. Despite the increase in the use of credit cards, there is also a significant increase in fraud transactions. To detect and prevent possible fraud operations, banks generally use rule-based techniques or analytical models. In this respect, analytical models have an important place due to their effectiveness, performance, and fast response. The main aim of this paper is therefore to enhance the theoretical and practical understanding of credit card fraud operations, review basic approaches, and propose a more comprehensive approach utilizing the agents. Note that in this study, static analytic modelling (existing approaches) and dynamic analytic modelling (emerging approaches) techniques are compared in terms of methodology, performance, and respective approaches. Since fraud methods and transactions are constantly changing over time, it is thought that there will be an increase in the use of agent-based models with dynamic analytical capabilities. Additionally, in this paper, a proposed model and empiric study are presented for an agent-based intelligent credit card fraud detection system.

A frustratingly easy way of extracting political networks from text.

This study demonstrates the use of GPT-4 and variants, advanced language models readily accessible to many social scientists, in extracting political networks from text. This approach showcases the novel integration of GPT-4's capabilities in entity recognition, relation extraction, entity linking, and sentiment analysis into a single cohesive process. Based on a corpus of 1009 Chilean political news articles, the study validates the graph extraction method using 'legislative agreement', i.e., the proportion of times two politicians vote the same way. It finds that sentiments identified by GPT-4 align with how frequently parliamentarians vote together in roll calls. Comprising two parts, the first involves a linear regression analysis indicating that negative relationships predicted by GPT-4 correspond with reduced legislative agreement between two parliamentarians. The second part employs node embeddings to analyze the impact of network distance, considering both with and without sentiment, on legislative agreements. This analysis reveals a notably stronger predictive power when sentiments are included. The findings underscore GPT-4's versatility in political network analysis.

AI-driven Test Automation for Salesforce and System Integration

Integrating artificial intelligence into test automation frameworks has transformed quality assurance practices in Salesforce environments and system integrations. AI-driven solutions have revolutionized testing approaches through smart test selection, risk-based analysis, and dynamic element identification capabilities. These advancements enable organizations to detect defects earlier, reduce false positives, and significantly decrease test maintenance efforts. Self-healing locators and context-aware selection mechanisms have enhanced test stability across dynamic web applications, while pattern recognition and anomaly detection capabilities proactively identify potential issues. Real-world implementations demonstrate substantial improvements in testing efficiency, reliability, and cost-effectiveness. Despite the challenges of data requirements and implementation complexity, AI-powered testing solutions have proven particularly effective in handling complex Salesforce configurations and multi-system integrations. The continuous evolution of these technologies promises enhanced predictive capabilities, improved integration support, and more sophisticated automated testing approaches, marking a significant shift in how organizations approach quality assurance in modern software development.

Broadband and High‐Resolution Mid‐Infrared Spectroscopy Enabled by a Single Phase Change Metasurface

AbstractThe mid‐infrared (MIR) spectral region is crucial for various applications due to its unique properties, but traditional spectrometers are often bulky. Miniaturized spectrometers face a trade‐off between spectral and spatial resolution. Here, a novel approach to MIR spectroscopy is numerically demonstrated by employing an electrically controlled phase‐change metasurface. This method fully exploits the high optical contrast and the quasi‐continuous phase change characteristics of chalcogenide phase change materials, enabling the construction of a set of spectral responses that provide broad spectral coverage with low correlation, utilizing a single metasurface pixel. With this innovative strategy, a broadband and high‐resolution spectral reconstruction is numerically demonstrated with a full width at half maximum (FWHM) resolution of 20 nm and a dual‐peak resolution of 160 nm within a 2400 nm bandwidth. Furthermore, the potential of the spectral detection scheme is underscored by the successful numerical reconstruction of the absorption peaks of methane and carbon dioxide, highlighting its capability for gas analysis and molecular identification. The integration of the spectral detection method into the field of spectral imaging is anticipated to have significant implications, suggesting substantial improvements in chemical process monitoring, and rapid diagnostic techniques in combustion environments.

The Application of Ceramic Materials in Tourism Product Design for Craft Arts Majors

In the realm of tourism product design, the integration of traditional arts and crafts with modern technological advancements presents a compelling opportunity for innovation. This study focuses on the utilization of ceramic materials as a cultural anchor for tourism product development. By proposing an optimized model based on improved Convolutional Neural Networks (CNN), the research explores the enhanced analysis and artistic expression capabilities of ceramic tourism products. The model's effectiveness is demonstrated through achieving over 95% accuracy in identifying and localizing ceramic product appearance features. Emphasizing the intersection of three-dimensional parametric technology and computer aesthetics, the study highlights advancements in digital modeling that elevate the design efficiency and cultural resonance of ceramic art within the tourism industry. This work contributes to advancing arts and crafts education and underscores the transformative impact of digital technologies on traditional craftsmanship and cultural heritage preservation.

Implementation of Business Intelligence Technologies of Course Learning Outcomes based on Multidimensional Model Approaches

Business Intelligence (BI) technologies offer intuitive insights and advanced analytical capabilities by transforming raw data from integrated sources into actionable knowledge. In the context of higher education, where institutions face heightened competition, dwindling public funding, and increased accountability, BI serves as a pivotal tool for data-driven decision-making. This paper addresses the problem of optimizing Course Learning Outcomes (CLOs) by implementing BI technologies. Our objectives are to integrate CLO data into a multidimensional model, apply BI tools for CLO analysis, and evaluate how BI can enhance CLO performance in higher education institutions (HEIs). We propose the Intelligence Course Learning Outcomes Platform (ICLOP) as a solution, utilizing BI technologies to generate efficient student data and performance metrics. The case study involving students from Universiti Sultan Zainal Abidin (UniSZA), Malaysia, demonstrates that BI can significantly improve enrolment management, resource allocation, and student success. The findings conclude that BI tools provide an effective means to enhance CLOs, offering a robust framework for improving educational outcomes and institutional efficiency, highlighting the transformative potential of BI in the academic sector.

Comparing the Effectiveness of Continuous Glucose Monitoring Versus Traditional Blood Glucose Monitoring in Reducing HbA1c Levels among Adults with Type 2 Diabetes Over Six Months

Continuous glucose monitoring (CGM) and traditional blood glucose monitoring (BGM) are pivotal tools in managing Type 2 diabetes mellitus (T2DM), with significant implications for glycemic control. This review compared the effectiveness of CGM versus BGM in reducing hemoglobin A1c (HbA1c) levels among adults with T2DM over six months. Utilizing a narrative synthesis approach based on existing literature, this article evaluated the mechanisms, impacts, behavioral implications, and challenges associated with each monitoring method. CGM, characterized by its real-time data and trend analysis capabilities, offers superior HbA1c reduction outcomes, especially for individuals with poor baseline control. By providing continuous feedback, CGM facilitates immediate adjustments in lifestyle and medication, empowering proactive diabetes management. Conversely, BGM, reliant on episodic fingerstick testing, is effective but often less impactful due to its intermittent nature and dependence on user adherence. Nevertheless, it remains a cost-effective and accessible option for many. Challenges such as high costs for CGM and adherence barriers for BGM were explored, along with the potential of emerging innovations, including integrated automated insulin delivery systems for CGM and enhanced digital tools for BGM. This review underscores the importance of aligning monitoring strategies with individual patient needs to optimize HbA1c reduction and improve quality of life for adults with T2DM. Keywords: Continuous Glucose Monitoring (CGM), Traditional Blood Glucose Monitoring (BGM), HbA1c Reduction, Type 2 Diabetes Mellitus (T2DM), Glycemic Control Strategies.

An analytical model of label-free nanoscale chemical imaging reveals avenues toward improved spatial resolution and sensitivity

Atomic force microscopy-infrared spectroscopy (AFM-IR) is a photothermal scanning probe technique that combines nanoscale spatial resolution with the chemical analysis capability of mid-infrared spectroscopy. Using this hybrid technique, chemical identification down to the single molecule level has been demonstrated. However, the mechanism at the heart of AFM-IR, the transduction of local photothermal heating to cantilever deflection, is still not fully understood. Existing physical models only describe this process in few special cases but not in many of the types of sample geometries encountered in the practical use of AFM-IR. In this work, an analytical expression for modeling the temperature and photothermal expansion process is introduced, verified with finite element simulations, and validated with AFM-IR experiments. This method describes AFM-IR signal amplitudes in vertically and laterally heterogeneous samples and allows studying the effect of position and size of an absorber, pump laser repetition rate and pulse width on AFM-IR signal amplitudes and spatial resolution. The analytical model can be used to identify optimal AFM-IR experimental settings in conventional and advanced AFM-IR modes (e.g., tapping mode, surface-sensitive mode). The model also paves the way for signal inversion based superresolution AFM-IR.

Benchmarking of proficiency tests for the fingerprint field.

Since 2015, European fingerprint service providers carrying out laboratory activities must be EN ISO/IEC 17025 accredited. Consequently, relevant and reliable proficiency tests are required by those accredited forensic laboratories to regularly assess and certify their analytical capabilities. In this context, Work package #10 of the ENFSI-EU funded project named "Competency, Education, Research, Testing, Accreditation, and Innovation in Forensic Science" (Grant #101051099) aimed at proposing guidance to forensic laboratories for the selection of proficiency tests in the fields of fingermark visualization, imaging, and comparison/identification. In this paper, the methodology that has been set and followed is described, and the resulting guidance document presented. Additionally, a critical overview of the current market of commercial proficiency tests dedicated to the fingerprint field is proposed.

О заблуждениях в современной логике

The article discusses the inconsistency of three "indisputable" principles in modern logic: the inconsistency of the concept of "set," the absolute necessity of axioms in logic, and the infallibility of syllogistic reasoning. To address the first misconception, the authors propose incorporating the algebra of sets into the foundations of logic, following the approach outlined in R. Courant and G. Robbins' book What is Mathematics? The second misconception is addressed by deriving known laws of algebra of sets, corresponding to classical logic, through the enumeration method. The third misconception is resolved by developing a mathematical model of polysyllogistic reasoning based on the laws of algebra of sets. The novelty of this proposed reasoning model lies in introducing restrictions alongside premises, with any violation of these restrictions signaling errors in reasoning. This model enhances the analytical capabilities of logical analysis, enabling the detection of errors in traditional syllogistic reasoning, including instances where some correct inferences are classified as "incorrect" modes. Furthermore, new laws of algebra of sets are formulated and justified: the law of paradox, the condition of non-empty intersection, and the law of existence.

A Clinical Drug as the Three-Photon Fluorescence Probe forInVivo Microscopic Imaging of Mouse Kidney.

Three-photon fluorescence (3PF) microscopy encounters significant challenges in biological research and clinical applications, primarily due to the limited availability of high-performance probes. We took a shortcut by exploring the excellent 3PF property of berberine hydrochloride (BH), a clinically utilized drug derived from the traditional Chinese medicine, Coptis. Capitalizing on its renal metabolism characteristics, we employed BH for invivo 3PF microscopic imaging of the mouse kidney. This approach enabled high-resolution structural observation of renal tubules and facilitated the diagnosis of drug-induced acute kidney injury (AKI) based on renal tubule morphology. The analytical capabilities of 3PF microscopy for renal physiology and pathological diagnosis suggest its potential for future clinical applications.

Can Artificial Intelligence Help Orthopaedic Surgeons in the Conservative Management of Knee Osteoarthritis? A Consensus Analysis

Background: Knee osteoarthritis is a prevalent condition that significantly impacts patients’ quality of life. Effective management typically involves a combination of pharmacological and non-pharmacological treatments. However, establishing a consensus on the optimal treatment strategy is crucial for standardizing care. The present study is the result of a rigorous process that combines artificial intelligence with human expertise to improve the reliability of medical recommendations. Methods: A new software platform (Butterfly Decisions, 2021, Italy) was employed to leverage AI-assisted decision-making, facilitating the digitalization of the entire consensus process. The process started with data collection through an online survey including simulated clinical cases of knee osteoarthritis collected by 30 orthopedic surgeons; artificial intelligence (AI) analyzed the collected clinical data and identified the key concepts and relevant patterns. Subsequently, AI generated detailed statements summarizing key concepts extracted from the data and proposed a reformulation of the statements to be discussed during the discussion session of the advisory board. The advisory board, composed of four qualified, experienced specialists of knee osteoarthritis, evaluated statements, providing their agreement levels, confidence, and supporting evidence. The AI tools calculated the degree of certainty and contradiction for each statement based on these evaluations. The literature was critically evaluated to ensure that there was an evidence-based evaluation of the proposed treatment statements. Finally, revised versions were proposed to address the feedback, evidence was collected to refine the scientific report, and the board members evaluated the AI performance too. Results: The consensus analysis revealed a high level of agreement in the need for a multimodal approach to treating knee osteoarthritis. The feedback highlighted the importance of integrating physical therapy and weight management, non-pharmacological methods, with Symptomatic Slow-Acting Drug for Osteoarthritis (SYSADOAs) and pharmacological treatments, such as anti-inflammatory drugs and intra-articular knee injections. The board members found that AI was easy to use and understand and each statement was structured clearly and concisely. Conclusions: The expert consensus about knee osteoarthritis conservative management being facilitated with AI met with unanimous agreement. AI-assisted decision-making was shown to have excellent analytical capabilities, but algorithms needs to be trained by orthopaedic experts with the correct inputs. Future additional efforts are still required to evaluate the incorporation of AI in clinical workflows.

Enhanced Digital Capabilities for Building Resilient Supply Chains: Exploring Visibility, Collaboration, and Resilience in China’s Electric Vehicle Industry

This study investigates the impact of digital capabilities (DC) on building resilient supply chains in China’s electric vehicle (EV) industry. As the complexity of the EV sector continues to grow, improving supply chain resilience (SCR) is essential for sustaining long-term growth and maintaining competitiveness. This research focuses on how visibility and collaboration, supported by DC, contribute to the development of SCR. Using structural equation modeling (SEM), data from 399 Chinese EV supply chain enterprises were analyzed to examine the moderating effects of DC and their sub-dimensions—digital infrastructure capability, digital analytics capability, and strategic support capability—on the relationships between visibility, collaboration, and resilience. The results reveal that both visibility and collaboration significantly and positively influence resilience, with visibility having the strongest impact. Furthermore, digital analytics capability enhances the positive effect of collaboration on resilience, while overall DC and other dimensions, such as digital infrastructure and strategic support capabilities, show limited impact. The findings also underscore that digital infrastructure capability plays a vital role in amplifying the impact of visibility on resilience. Consequently, EV supply chain enterprises are encouraged to invest continuously in digital infrastructure and analytics capabilities to strengthen their SCR.

Quantitative Analysis of Phosphorothioate Isomers in CRISPR sgRNA at Single-Residue Resolution Using Endonuclease Digestion Coupled with Liquid Chromatography Cyclic Ion Mobility Mass Spectrometry (LC/cIMS).

Phosphorothioate (PS) modifications in single-guided RNA (sgRNA) are crucial for genome editing applications using the CRISPR/Cas9 system. These modifications may enhance sgRNA stability, pharmacokinetics, and binding to targets, thereby facilitating the desired genetic alterations. Incorporating multiple PS groups at varying positions may introduce chiral centers into the sgRNA backbone, resulting in a complex mixture of constitutional- and stereoisomers that challenges current analytical capabilities for reliable identification and quantification. In this study, we developed an innovative methodology that combines endonuclease digestion of sgRNA with ion pairing reversed-phase liquid chromatography coupled with cyclic ion mobility mass spectrometry (IPRP-LC/cIMS) to fully distinguish PS-induced isomers in a complex mixture. The relative abundance of each isomer was quantified using a two-step method, wherein the ion abundance was sequentially extracted from an LC/MS ion chromatogram and LC/cIMS two-dimensional ion mobiligram. This quantification method was thoroughly evaluated, demonstrating excellent sensitivity, precision, dynamic range, repeatability, and accuracy. In addition, this method enables the investigation of the kinetics of forming PS to phosphodiester (PO) impurities in sgRNA under oxidative stress conditions, offering unprecedented insights into PS stability at a single-residue resolution. In this context, this method highlights the in-depth characterization of PS, demonstrating its capability to support biomedical research, development, and production of sgRNA products.

Orchestrating the digital symphony: the impact of data-driven orientation, organizational culture and digital maturity on big data analytics capabilities

PurposeThis study aims to explore the factors that influence the development of big data analytics capabilities (BDAC) in organizations, an area that has received limited attention in the academic literature.Design/methodology/approachEmploying partial least squares structural equation modeling, this research scrutinizes the interconnections between various antecedents and BDAC. Notably, it examines the mediating roles of organizational culture (OC) and digital maturity (DM) in the nexus between managerial data orientation and BDAC.FindingsAnalysis indicates that OC and DM play crucial roles in enhancing the efficacy of managerial data orientation on BDAC. The sequential mediation by these factors underscores the importance of nurturing an appropriate OC and advancing DM to optimize the benefits of managerial data orientation towards BDAC.Practical implicationsThe findings bear significant implications for organizational practice. They underscore the necessity of enhancing managerial analytical skills and commitment to digital transformation. Furthermore, the study highlights the critical need for aligning OC with strategic objectives and the digital context. The formulation of a cultural strategy that advocates for a data-driven mindset and champions digital initiatives is essential for fostering BDAC development, thereby bolstering organizational performance and competitiveness in the big data era.Originality/valueThis study enriches the body of literature by illuminating the overlooked antecedents of BDAC. It extends the discourse on the human-centric aspects of digital transformation, offering insights into how managerial data orientation can be effectively translated into improved BDAC. This innovative angle deepens our comprehension of the strategies through which organizations can leverage big data technology for value creation and informed decision-making, emphasizing the pivotal role of OC and the requisite digital competencies and resources.

Study on Financial Risk Prevention of NIO in the Context of Big Data

With the continuous advancement of global informatization, the way of enterprise financial management under the big data environment is undergoing profound changes. This paper takes NIO as the research object to explore how to prevent financial risks in the context of big data. Firstly, the definition of big data and its specific application in financial management are studied to reveal the supportive role of big data technology in the collection, analysis and decision-making of enterprise financial information. Second, this paper provides a detailed definition and categorization of financial risks to help identify potential financial problems. Through in-depth analysis of NIO's financial data, possible financial risk points are identified, and an effective risk early warning model is established by combining the methods of big data analysis, aiming to realize the early identification and prediction of financial risks. Finally, based on the results of the analysis, relevant financial risk prevention measures are proposed, in order to provide reference and reference for NIO and other similar high-tech enterprises in financial risk management. Financial risk management in the context of big data not only requires enterprises to have strong data analysis capabilities, but also needs to eliminate potential risk factors in the system design to enhance the enterprise risk prevention capabilities. The research in this paper is of great practical significance to promote the intelligence and scientization of enterprise financial management.

The Evolution and Architecture of Multimodal AI Systems

This technical article explores the evolution, architecture, and implementation challenges of multimodal AI systems, which represent a significant advancement in artificial intelligence. The article explores how these systems integrate multiple input modalities to achieve comprehensive understanding and analysis capabilities, mirroring human cognitive processes. Through detailed analysis of system architectures, performance metrics, and implementation strategies, we investigate the current state of multimodal AI across various applications, from virtual assistants to healthcare analytics. The article covers core technical components, data synchronization challenges, resource optimization techniques, and future directions in the field, providing insights into both theoretical frameworks and practical implementations.

Data-Driven Manufacturing: Leveraging Analytics for Operational Excellence

This comprehensive article explores the transformative impact of data-driven analytics on modern manufacturing operations, emphasizing its role in enhancing operational excellence and innovation. The article examines five key areas: production intelligence, supply chain optimization, sustainable manufacturing analytics, data-driven innovation, and implementation frameworks. Through detailed analysis, the article demonstrates how advanced analytics capabilities are revolutionizing manufacturing processes by enabling predictive maintenance, optimizing supply chains, promoting sustainable practices, and accelerating innovation cycles. The article reveals how manufacturing organizations leverage real-time monitoring, machine learning algorithms, and artificial intelligence to achieve improved operational efficiency, reduced maintenance costs, enhanced product quality, and increased market responsiveness. The article also addresses organizational readiness and technical infrastructure requirements for successfully implementing data-driven manufacturing solutions. By examining technical and organizational dimensions, this study provides valuable insights into how manufacturers can effectively transition to data-driven operations while maintaining competitive advantage in an increasingly dynamic market environment.

The Impact of Artificial Intelligence on Digital Forensics

This paper explores artificial intelligence (AI) ‘s profound impact on digital forensics. As cybercrime grows in complexity and scale, traditional forensic methods are increasingly insufficient and must improve upon challenges. AI technologies are fundamentally revolutionizing digital forensics by significantly enhancing data analysis capabilities, automating repetitive and time-consuming tasks, and dramatically improving digital investigations’ efficiency and effectiveness. This comprehensive paper discusses the wide-ranging applications of AI in digital forensics, the challenges that arise from its implementation, and the prospects for this rapidly evolving field.

Sinergi MNC University, SHARP Class, dan SMK Boedi Luhur dalam Pengembangan UMKM

The development of Micro, Small and Medium Enterprises (MSMEs) is one of the strategic efforts to encourage local and national economic growth. However, low technological literacy and data analytical capabilities are the main obstacles for MSMEs in facing competition in the digital era. To answer these challenges, MNC University, in collaboration with the SHARP Class Corporate Social Responsibility (CSR) program from PT SHARP Electronics Indonesia, as well as Boedi Luhur Bekasi Vocational School, is implementing a technology-based community service program. This program focuses on providing data analytics skills to vocational school students as young people who have the potential to support MSMEs in their surrounding environment. This activity includes intensive training on data processing using computer science-based tools and methods, designed to improve students' ability to understand, analyze and apply data in making business decisions. A collaborative approach involving academics, industry practitioners and vocational educators ensures that the material presented is relevant to the needs of industry and MSMEs. The results of this program show a significant increase in participants' understanding and skills in data analytics, as well as the real contribution of Boedi Luhur Vocational School students in supporting the development of local MSMEs. This study provides empirical evidence that synergy between educational institutions, industry and vocational schools can be a model of sustainability in developing technology-based human resource capacity.