Relevant bibliographies by topics / Enhancing predictive accuracy and decision-making processes. Furthermore

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Academic literature on the topic 'Enhancing predictive accuracy and decision-making processes. Furthermore'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Enhancing predictive accuracy and decision-making processes. Furthermore"

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MD ROKIOBUL HASAN. "Addressing Seasonality and Trend Detection in Predictive Sales Forecasting: A Machine Learning Perspective." Journal of Business and Management Studies 6, no. 2 (2024): 100–109. http://dx.doi.org/10.32996/jbms.2024.6.2.10.

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Sales prediction plays a paramount role in the decision-making process for organizations across various industries. Nonetheless, accurately predicting sales is challenging because of trends and seasonality in sales data. The prime objective of this research paper was to explore machine learning methodologies and techniques that can efficiently address seasonality and trend detection in predictive sales forecasting. The research focused on pinpointing suitable features based on correlation coefficients, which were then adopted to train the three different models: random forests, linear regressi
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Syahra, Yohanni, Yuni Franciska Br. Tarigan, Karina Andriani, Hevlie Winda Nazry S, and Roziyani Setik. "Decision Trees in Predicting Loan Default Risk in Customer Relationships within the Financial Sector." Sinkron 9, no. 2 (2025): 734–45. https://doi.org/10.33395/sinkron.v9i2.14672.

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Loan default prediction is an important aspect of risk management in financial institutions. Accurate prediction models enable banks and lending organizations to mitigate risks, allocate resources effectively, and optimize decision-making processes. This study investigates the application of decision tree algorithms in predicting loan default risk in the financial sector. Decision trees are renowned for their interpretability, adaptability to non-linear data, and ability to handle missing values, making them a valuable tool in credit risk analysis. Using a dataset consisting of borrower profil
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Saleh, Haifa Hadi, Azzam Khalid Chyad, Maha Barakat, and Ghazwan Salim Naamo. "Enhancing Business Operations Efficiency thorough Predictive Analytics." Journal of Ecohumanism 3, no. 5 (2024): 700–714. http://dx.doi.org/10.62754/joe.v3i5.3932.

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Background: In today's competitive world, organizations constantly seek innovative ways to improve operational efficiency and maintain a competitive advantage. Introducing big data and advanced analytics techniques has created new opportunities for optimizing corporate processes. Objective: The article aims to investigate the potential of predictive analytics in improving business operations efficiency, emphasizing cost savings, process optimization, and better decision-making. Methods: We used a mixed-methods research design, integrating quantitative analysis of operational data from 30 organ
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Anthony, Waiswa Micheal. "Bayesian Decision Making in Public Health Interventions in Uganda." IAA JOURNAL OF ART AND HUMANITIES 11, no. 2 (2024): 46–48. http://dx.doi.org/10.59298/iaajah/2024/11.4648.33.

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This article explores the application of Bayesian statistics in enhancing decision-making processes for public health interventions in Uganda. Bayesian methods offer a flexible framework that integrates prior knowledge, expert opinions, and real-time data to inform evidence-based strategies under uncertainty. The paper discusses the role of Bayesian statistics in disease modeling, highlighting its ability to improve predictive accuracy by incorporating historical data and epidemiological trends. It also examines how Bayesian decision-making optimizes resource allocation in Uganda's healthcare
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Balicka, Honorata, and Jerzy Balicki. "QUANTUM ARTIFICIAL INTELLIGENCE IN MANAGEMENT OF SELECTED BUSINESS PROCESSES." Scientific Papers of Silesian University of Technology Organization and Management Series 2024, no. 208 (2024): 9–26. https://doi.org/10.29119/1641-3466.2024.208.1.

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Purpose: The aim of the article is to present potential applications of Quantum Artificial Intelligence (QAI) in enhancing Business Process Management (BPM), with a particular focus on predictive analytics. Design/methodology/approach: The primary research methods include a critical analysis of the literature. Deep neural network testing was also conducted to identify efficient predictors and detectors for BPM systems. In addition, intensive computational experiments were carried out to analyze the quality of solutions defined by the proposed quantum-inspired algorithms. Findings: The results
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Kosaraju, Deekshitha. "Enhancing Cardiac Imaging with Deep Learning: New Frontiers in Diagnosis." Galore International Journal of Applied Sciences and Humanities 8, no. 1 (2024): 39–45. http://dx.doi.org/10.52403/gijash.20240106.

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The incorporation of Deep Learning (DL) in imaging represents a significant leap forward in medical diagnostics transforming the approach to identifying, diagnosing, and treating cardiovascular diseases. By utilizing algorithms and extensive datasets DL greatly enhances the precision, speed, and predictive capabilities of diagnostic methods like echocardiography, magnetic resonance imaging (MRI) and computed tomography (CT). This piece explores the influence of DL on cardiac imaging by illustrating how these technologies not only enhance image clarity and accuracy but also streamline and impro
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Azzahra, Yasmin Aulia, and Yuma Akbar. "Komparasi Penerapan Algoritma C4.5 dan Naïve Bayes untuk Ketepatan Waktu Pengiriman Barang Pada PT. Rtrans Logistik Artamandiri." Jurnal Indonesia : Manajemen Informatika dan Komunikasi 5, no. 3 (2024): 2768–80. http://dx.doi.org/10.35870/jimik.v5i3.1003.

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The logistics industry faces significant challenges in maintaining the punctual delivery of goods, which is a critical factor in enhancing customer satisfaction and reducing operational costs. This research aims to compare the effectiveness of the C4.5 and Naïve Bayes algorithms in analyzing the factors that influence delivery punctuality at PT. Rtrans Logistics Artamandiri. A dataset comprising 1,000 shipping records and 13 relevant attributes was utilized to assess each algorithm’s predictive performance in supporting decision-making processes related to delivery efficiency. The findings rev
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Rahman, Md Atikur, and Md Shah Alam. "HOW INTERACTIVE DASHBOARDS IMPROVE MANAGERIAL DECISION-MAKING IN OPERATIONS MANAGEMENT." American Journal of Advanced Technology and Engineering Solutions 1, no. 01 (2025): 122–46. https://doi.org/10.63125/cqm5jk84.

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In today’s data-driven business environment, interactive dashboards play a crucial role in enhancing managerial decision-making by providing real-time analytics, performance tracking, and predictive insights. However, the complexity, usability, and adoption challenges associated with dashboards often affect their effectiveness in organizational decision-making processes. This study investigates the impact of dashboard complexity, user skepticism, training interventions, and governance frameworks on managerial decision-making by conducting an in-depth case study analysis across six industries:
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Oluwatosin Abdul-Azeez, Alexsandra Ogadimma Ihechere, and Courage Idemudia. "Enhancing business performance: The role of data-driven analytics in strategic decision-making." International Journal of Management & Entrepreneurship Research 6, no. 7 (2024): 2066–81. http://dx.doi.org/10.51594/ijmer.v6i7.1257.

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In today’s highly competitive business landscape, organizations are increasingly turning to data-driven analytics to enhance performance and inform strategic decision-making. This approach leverages vast amounts of data, transforming it into actionable insights that drive efficiency, innovation, and growth. The role of data-driven analytics is multifaceted, encompassing predictive, prescriptive, and descriptive analytics, each contributing uniquely to the decision-making process. Predictive analytics forecasts future trends and behaviors, enabling proactive strategies. Prescriptive analytics p
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Adesemoye, Oluwasola Emmanuel, Ezinne C. Chukwuma-Ek, Comfort Iyabode Lawal, Ngozi Joan Isibor, Abiola Oyeronke Akintobi, and Florence Sophia Ezeh. "A Conceptual Framework for Integrating Data Visualization into Financial Decision-Making for Lending Institutions." International Journal of Management and Organizational Research 1, no. 1 (2022): 171–83. https://doi.org/10.54660/ijmor.2022.1.1.171-183.

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In today's rapidly evolving financial landscape, lending institutions face increasing challenges in making accurate, timely, and informed decisions. Traditional decision-making processes, often based on static reports and raw data, can be slow and prone to errors, especially when analyzing vast and complex datasets. The integration of data visualization into financial decision-making offers a transformative solution by converting complex data into intuitive visual representations, making it easier for decision-makers to interpret and act upon. This presents a conceptual framework for integrati
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Book chapters on the topic "Enhancing predictive accuracy and decision-making processes. Furthermore"

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Rane, Nitin Liladhar, Pravin Desai, Jayesh Rane, and Mallikarjuna Paramesha. "Artificial intelligence, machine learning, and deep learning for sustainable and resilient supply chain and logistics management." In Trustworthy Artificial Intelligence in Industry and Society. Deep Science Publishing, 2024. http://dx.doi.org/10.70593/978-81-981367-4-9_5.

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Integrating artificial intelligence (AI) and machine learning (ML) into logistics and supply chain management is crucial for enhancing resilience and efficiency in today's unpredictable global market. This research explores the latest advancements and applications of AI and ML technologies that are transforming logistics and supply chain operations. AI-driven predictive analytics and real-time data processing have enabled companies to anticipate disruptions, optimize routes, and improve demand forecasting accuracy. Machine learning algorithms are essential in identifying patterns and anomalies
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Chhikara, Harshdeep, Sumit Chhikara, and Lovelesh Gupta. "Predictive Analytics in Finance." In Advances in Finance, Accounting, and Economics. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-8507-4.ch017.

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This chapter examines the transformative role of artificial intelligence (AI) across various sectors, focusing on its impact on financial decision-making, business analytics, and risk management. It highlights how AI technologies, such as machine learning and predictive modeling, are enhancing decision-making accuracy and operational efficiency in financial sectors and supply chain management. The study also explores AI's influence in emerging economies, driving economic growth and innovation despite challenges. Additionally, it addresses ethical considerations, including data privacy and the
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Krishna Pasupuleti, Murali. "Artificial Intelligence in Legal Services: Enhancing Case Analysis and Streamlining Legal Processes." In AI in Legal Research: Tools for Streamlining Case Analysis and Decision Making. National Education Services, 2024. http://dx.doi.org/10.62311/nesx/932120.

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Abstract This chapter explores the transformative impact of Artificial Intelligence (AI) on legal services, focusing on how AI technologies are enhancing case analysis and streamlining legal processes. It examines the role of AI-driven tools, including machine learning, natural language processing (NLP), and predictive analytics, in improving the efficiency, accuracy, and speed of legal research, document review, and litigation strategies. The chapter also addresses the ethical and regulatory challenges posed by AI adoption in the legal industry, emphasizing the need for legal professionals to
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Karthikeyan, C. "Artificial Intelligence (AI) Influences on Strategic Decision-Making." In Advances in Business Strategy and Competitive Advantage. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-8442-8.ch007.

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As we advance into an era marked by rapid technological evolution, artificial intelligence (AI) is poised to fundamentally transform strategic decision-making across industries. By 2030, AI is anticipated to usher in an unprecedented phase of hyper-personalization and autonomous decision-making, redefining how organizations approach strategic planning and operational management. AI-driven predictive models will enable businesses to achieve unparalleled precision in forecasting market trends, customer behaviours, and potential disruptions, thereby enhancing strategic agility and decision-making
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Mohitkar, Chhavi, and D. Lakshmi. "Explainable AI for Transparent Cyber-Risk Assessment and Decision-Making." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-7540-2.ch010.

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The research paper “Explainable AI for Transparent Cyber-Risk Assessment and Decision-Making” explores the integration of explainable artificial intelligence (XAI) in enhancing the transparency and effectiveness of cyber-risk assessments. It highlights the necessity for AI systems to provide interpretable outputs, enabling stakeholders to understand the rationale behind risk evaluations and decisions. The study emphasizes that traditional models often lack transparency, leading to challenges in trust and accountability in cybersecurity contexts. By employing XAI techniques, the paper demonstra
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Kaur, Bhupinder, Avneet Kaur, Shivansh Rai, Disket Angmo, and Poonam Kukana. "AI POWERED INCIDENT RESPONSE." In Artificial Intelligence and the Cybersecurity Revolution: Innovations and Implications. Iterative International Publishers (IIP), Selfypage Developers Pvt Ltd., 2025. https://doi.org/10.58532/nbennuraicr6.

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This chapter discusses the application of AIpowered incident response systems in modern emergency management, highlighting the integration of predictive analytics, automated decision-making, and real-time coordination between agencies. It reviews existing solutions, such as emergency response platforms and cybersecurity systems, pointing out their limitations, including fragmentation, delayed responses, and lack of predictive capabilities. The proposed AI-driven systems are designed to overcome these challenges by offering predictive models, contextual analysis, and improved coordination. Thro
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Senthil Kumar, N. K., Shobana D, and M. Nithyanandan. "Advanced Data Preprocessing and Feature Engineering Techniques for Infrastructure Risk Analysis." In Deep Neural Networks for Predictive Analytics and Proactive Decision-Making in Securing Critical Infrastructure. RADemics Research Institute, 2025. https://doi.org/10.71443/9788197933684-02.

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Infrastructure risk analysis involves the integration and interpretation of diverse datasets to predict failures, assess vulnerabilities, and optimize system performance. These datasets often exhibit challenges such as temporal misalignment, heterogeneous formats, missing values, noise, and imbalanced distributions, which hinder the accuracy and reliability of risk predictions. This chapter provides an in-depth exploration of advanced data preprocessing and feature engineering techniques tailored to address these issues. It examines methods for synchronizing temporally misaligned data, harmoni
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Abu Radia, Mohamed. "Next-Generation Monitoring." In Practice, Progress, and Proficiency in Sustainability. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-7117-6.ch010.

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With the increasing demand for smart and effective solutions to achieve sustainable development, the integration of the Internet of Things (IoT) and Artificial Intelligence (AI) into wireless data monitoring systems reshapes the approach to managing and processing data. IoT enables the seamless connection and communication between devices, allowing for real-time data collection and monitoring across various environments. When integrated with AI, these systems gain the ability to analyze vast amounts of data, generate predictive insights, and automate decision-making processes, enhancing effici
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Alsubeia, Fahad Saeed, Muhammed Bin Yusof, and Amer Abdulwahab Mahyoub Murshed. "Exploring the Role of Artificial Intelligence in Enhancing Values-Based Leadership and Its Impact on Workplace Engagement." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-5550-4.ch009.

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Abstract: Artificial Intelligence (AI) has increasingly been integrated into leadership and workplace engagement, transforming decision-making processes, employee interactions, and organizational efficiency. This study systematically reviews the role of AI in leadership and workplace engagement over the last five years, utilizing bibliometric analysis and the PRISMA framework to identify key research trends, methodologies, and future research directions. The findings indicate that AI-driven applications, such as large language models (LLMs), generative AI, and machine learning (ML) systems, ha
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Kumari, Priyanka. ""HARNESSING ARTIFICIAL INTELLIGENCE (AI) AND AUTOMATION FOR ENHANCED DECISION-MAKING IN MANAGEMENT"." In Futuristic Trends in Management Volume 3 Book 19. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bhma19p2ch2.

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The rapidly evolving landscape of technology has paved the way for groundbreaking advancements in the field of artificial intelligence (AI) and automation. These technologies hold immense potential to revolutionize management practices across various industries. This study aims to explore the integration of AI and automation in management decision-making processes and the potential benefits and challenges it presents. Research Objectives: • To investigate the current state of AI and automation in management and identify the emerging trends and applications. • To understand the impact of AI-dri
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Conference papers on the topic "Enhancing predictive accuracy and decision-making processes. Furthermore"

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Horchuk, Yurii, Mariia Yukhimchuk, and Volodymyr Dubovoy. "ENHANCING DECISION-MAKING IN BUSINESS PROCESS MANAGEMENT WITH PREDICTIVE ANALYTICS BASED ON ARTIFICIAL INTELLIGENCE." In 17th IC Measurement and Control in Complex Systems. VNTU, 2024. https://doi.org/10.31649/mccs2024.2-07.

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This thesis examines the role of artificial intelligence (AI), specifically AI-based predictive analytics, in enhancing decision-making within the framework of Business Process Management (BPM). As organizations strive for increased efficiency and adaptability in their processes, predictive analytics has emerged as a key tool that empowers businesses to make more informed decisions. By leveraging AI models such as ChatGPT, Gemini AI, and others, companies can analyze vast amounts of historical and real-time data to forecast trends, optimize resource allocation, and mitigate risks in their oper
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Li, Yunchao, Shangfei Song, Qi Kang, et al. "Enhancing Multiphase Flow Computational Performance: A Robust Approach Incorporating Phase Transition Modeling and Dynamic Simulation." In 2024 15th International Pipeline Conference. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/ipc2024-133598.

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Abstract In this study, we enhance the stability of flow prediction in multiphase pipelines by integrating modified source term calculations into the widely used SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm. We address the challenges faced by traditional two-fluid models, particularly in accurately representing physical processes during operational disruptions such as terrain changes. By incorporating corrective measures into the two-fluid equations, including the continuity equation and momentum conservation equation, we refine the accuracy of flow predictions and mit
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Sanchez Cortes, Gustavo, Dimitrios Ziakkas, and Debra Henneberry. "The Implementation of AI in Aviation Accidents Investigations." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006499.

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Accident investigation is fundamental to aviation safety, serving to identify causal factors and prevent the recurrence of accidents. Traditionally, such investigations have depended on systematic methodologies like the SHELL model and Fault Tree Analysis, drawing on data from flight data recorders, cockpit voice recorders, and eyewitness accounts. However, the rapid integration of digital technologies, the increasing complexity of modern systems, and the challenges posed by globalized operations have created an urgent need for more sophisticated investigative tools. Artificial intelligence of
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Wollweber, Michael. "AI-DRIVEN DECISION-MAKING: A REVIEW ABOUT TRANSFORMING PROJECT MANAGEMENT THROUGH ADVANCED TECHNOLOGIES." In INTERNATIONAL Conference on Business, Management, and Economics Engineering Future-BME. Faculty of Technical Sciences, Novi Sad, 2025. https://doi.org/10.24867/future-bme-2024-092.

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Artificial Intelligence (AI) has become a transformative tool in project management, enhancing decision-making processes through advanced data analysis, pattern recognition, and automation. This paper explores the foundations of AI-driven decision-making focusing on its potential in project management. Therefore, established applications for decision-making in various industries, including business, healthcare, and agriculture are highlighted. Within project management, AI's integration is primarily supportive, aiding in resource allocation, risk management, scheduling, and budget forecasting.
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Jamil, Neha, Mohammad Rasheed Khan, Syed Hassan Ali Shah, and Zubair -. "Enhancing Drilling Efficiency Through Automated Data Management and Artificial Intelligence for Real-Time Monitoring Services." In SPE Conference at Oman Petroleum & Energy Show. SPE, 2025. https://doi.org/10.2118/224910-ms.

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Abstract The oil and gas industry is undergoing a significant transformation as advanced technologies are integrated to enhance efficiency, reduce costs, and minimize downtime in drilling operations. As real-time monitoring and data analytics become central to decision-making, the need for automated, data-driven workflows is increasingly critical. The growing complexity of drilling operations demands a shift from traditional manual intervention tracking to a more structured, data-driven approach that ensures timely and informed decision-making. This paper presents an integrated approach to int
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Almarhabi, Mohammed. "The role of electronic transactions in enhancing Government performance in the Municipality of Makkah." In Proceedings of International Multilingual Academic Journal. شبكة المؤتمرات العربية, 2025. https://doi.org/10.24897/acn.64.68.20255001.

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Using electronic transactions within the context of government services can unveil new horizons for enhancing government services, improving governmental performance, and increasing citizen participation in the decision-making process. Technologies, data analysis, and artificial intelligence applications can contribute to shaping government policies and better analyzing community needs. Additionally, electronic applications can play a pivotal role in achieving the goals of Egovernment by working to improve service quality, accelerate business processes, and ensure accuracy in the context of Eg
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Oliveira, L. H. L., T. Nóbrega, M. V. G. Jacinto, et al. "Enhancing Predictive Models Through Cuttings Reinterpretation: An Ecosystem to Leverage AI Applications for Well Drilling Technologies." In Offshore Technology Conference Brasil. OTC, 2023. http://dx.doi.org/10.4043/32738-ms.

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Abstract This paper discusses a methodology to enhance machine learning (ML) models developed to predict lithology from real-time mud logging data. Inaccuracies of geological data obtained in the field can lead to inconsistencies in model predictions. To address this, drilling cuttings collected from the field were transported, re-described, and re-evaluated by a team of geologists in a laboratory environment with more favorable conditions. The analysis process carried out was composed of consistent and robust analysis methods and processes, such as microscopic examination (for rock type ident
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Rascanu, G., R. Dzhusupova, and Z. Guo. "Enhancing Engineering Document Analysis Through Structured Data Mapping." In ADIPEC. SPE, 2024. http://dx.doi.org/10.2118/221950-ms.

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Abstract This paper presents an innovative approach for mapping engineering documents, particularly unstructured PDF files, to knowledge graphs, offering significant benefits for both engineering processes (i.e. such as ‘review-check-approve’ process) and AI-powered development. The method aims at improving efficiency, enhancing decision-making capabilities, reducing document turn-around duration, and thus decreasing costs while increasing productivity. Specific advantages include: Quick overview for reviewers to prioritize tasks - more weight can be given to identified main topics with less t
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Rinosha Banu, K. "The Future of Work – Data Driven Performance Management." In International Conference on Artificial Intelligence in Commerce and Management. Shanlax Publications, 2025. https://doi.org/10.34293/icaicm-25.ch026.

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This report on the Future of Work Data driven offers a structural and best-practices prescriptive overview of digitally-enabled, data-driven management and decision-making. It reframes the core objectives of management performance in the era of technological disruption and provides an agile operational framework for adopting data-driven management, adaptable to businesses of any size or industry and scalable across organizations. The report also includes a wealth of curated data and insights from the field of digital transformation, identifying common obstacles to success, outlining key strate
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Jin, Congjun, and Rui Liu. "An Evaluation of Capabilities, Benefits, and Challenges of Developing Digital Twin Models for Sustainable Development." In 2024 AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005732.

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Since the recent AECO industry has increasingly focused on sustainable development, with an emphasis on achieving long-term goals like enhancing eco-sustainability and durability, the demand for applying digital and revolutionary technologies has increased. Digital twin technology, enabling a digital model to represent a physical entity in real-time dynamically, has gained wide attention in manufacturing, aerospace, and healthcare. Although digital twin technology, which integrates with various digital technical tools, has been explored by some researchers, The overall understanding of how dig
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