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Journal articles 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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1

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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Dintén, Ricardo, and Marta Zorrilla. "Design, Building and Deployment of Smart Applications for Anomaly Detection and Failure Prediction in Industrial Use Cases." Information 15, no. 9 (2024): 557. http://dx.doi.org/10.3390/info15090557.

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This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidat

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Journal, IJSREM. "Robust Credit Card Fraud Detection: A Model Comparison Study." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27343.

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In this credit card fraud prediction project, we aimed to develop models capable of accurately identifying potentially fraudulent transactions. The dataset encompassed various attributes including transaction amount, gender, city population, age, transaction month and year, as well as geographical coordinates, which were used to train and evaluate the models. Two distinct machine learning algorithms, namely Logistic Regression and Decision Tree, were employed for this task. The models were fine-tuned using techniques such as feature selection and hyperparameter tuning to enhance their predicti

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Christian Chukwuemeka Ike, Adebimpe Bolatito Ige, Sunday Adeola Oladosu, Peter Adeyemo Adepoju, and Adeoye Idowu Afolabi. "Advancing real-time decision-making frameworks using interactive dashboards for crisis and emergency management." International Journal of Management & Entrepreneurship Research 6, no. 12 (2024): 3915–50. https://doi.org/10.51594/ijmer.v6i12.1762.

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Effective crisis and emergency management heavily relies on timely and accurate decision-making to mitigate impacts and ensure public safety. Traditional decision-making processes often struggle with the rapid flow of information and the need for real-time responses. This paper explores the potential of interactive dashboards in advancing real-time decision-making frameworks, emphasizing their role in crisis and emergency management. Interactive dashboards, powered by advanced data analytics, provide a centralized platform for visualizing complex data streams, enabling decision-makers to quick

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Oluwasola, Emmanuel Adesemoye, C. Chukwuma-Eke Ezinne, Iyabode Lawal Comfort, Joan Isibor Ngozi, Oyeronke Akintobi Abiola, and Sophia Ezeh Florence. "Comprehensive Review of Predictive Modeling and Risk Management Techniques in Financial Services." Engineering and Technology Journal 10, no. 04 (2025): 4626–48. https://doi.org/10.5281/zenodo.15314770.

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The financial services industry faces increasing complexities and uncertainties, making effective risk management and predictive modeling critical for ensuring stability and profitability. This comprehensive review explores the role of predictive modeling and risk management techniques in the financial sector, highlighting their applications in forecasting potential risks and optimizing decision-making processes. Predictive modeling, powered by advanced statistical methods and machine learning algorithms, enables financial institutions to forecast trends, detect anomalies, and predict the like

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Alonge, Enoch Oluwabusayo, Nsisong Louis Eyo-Udo, Bright Chibunna Ubanadu, Andrew Ifesinachi Daraojimba, Emmanuel Damilare Balogun, and Kolade Olusola Ogunsola. "Developing an Advanced Machine Learning Decision-Making Model for Banking: Balancing Risk, Speed, and Precision in Credit Assessments." International Journal of Multidisciplinary Research and Growth Evaluation. 5, no. 1 (2024): 1567–81. https://doi.org/10.54660/.ijmrge.2024.5.1.1567-1581.

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The banking industry faces significant challenges in balancing risk, speed, and precision when assessing creditworthiness. Traditional credit assessment methods often rely on rigid scoring systems that fail to adapt to dynamic market conditions, resulting in inefficiencies and heightened risks. This study focuses on developing an advanced machine learning (ML) decision-making model tailored for credit assessments in banking. By leveraging ML algorithms, the proposed model aims to enhance predictive accuracy, optimize decision-making speed, and mitigate risks associated with loan approvals and

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Jennifer Muhindo, Kevin Mukasa, Doreen Kitakufe, and Jimmy Kato. "Advancing credit risk assessment and financial decision-making: Integrating modern techniques and insights." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2019–27. http://dx.doi.org/10.30574/wjarr.2024.23.2.2565.

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Credit risk assessment and fraud detection are critical functions in the financial industry, necessary for ensuring the stability and integrity of financial institutions. Traditional approaches often struggle to accurately assess risk and detect fraudulent activities in a timely manner. However, the rise of machine learning has introduced powerful tools that leverage large datasets and advanced algorithms to improve these processes. This research paper investigates the application of machine learning techniques in credit risk assessment and fraud detection within financial transactions. The pa

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Jennifer, Muhindo, Mukasa Kevin, Kitakufe Doreen, and Kato Jimmy. "Advancing credit risk assessment and financial decision-making: Integrating modern techniques and insights." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2019–27. https://doi.org/10.5281/zenodo.14869154.

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Credit risk assessment and fraud detection are critical functions in the financial industry, necessary for ensuring the stability and integrity of financial institutions. Traditional approaches often struggle to accurately assess risk and detect fraudulent activities in a timely manner. However, the rise of machine learning has introduced powerful tools that leverage large datasets and advanced algorithms to improve these processes. This research paper investigates the application of machine learning techniques in credit risk assessment and fraud detection within financial transactions. The pa

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Al-Tawil, Arar, Worood Al-Muhtaseb, Laiali Almazaydeh, and Hanaa Fathi. "Enhancing Alzheimer’s disease diagnosis through metaheuristic feature selection and advanced classification techniques." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 3 (2025): 3382. https://doi.org/10.11591/ijece.v15i3.pp3382-3395.

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A diverse array of diagnostic and detection methods has been developed as a result of the advent of Alzheimer’s disease (AD) as a significant global health issue. This study employs bio-inspired algorithms, such as the parrot optimization algorithm (POA), grey wolf optimizer (GWO), and differential evolution (DE), to identify the most effective feature selection techniques for AD diagnosis. The predictive accuracy of these algorithms was improved by the simple keywords: Alzheimer’s disease optimization classification machine learning metaheuristic mentation of the Alzheimer’s disease Dataset.

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Omoniyi Babatunde Johnson, Yodit Wondaferew Weldegeorgise, Emmanuel Cadet, Olajide Soji Osundare, and Harrison Oke Ekpobimi. "Developing advanced predictive modeling techniques for optimizing business operations and reducing costs." Computer Science & IT Research Journal 5, no. 12 (2024): 2627–44. https://doi.org/10.51594/csitrj.v5i12.1757.

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In today's competitive business landscape, organizations are increasingly turning to predictive modeling techniques to enhance operational efficiency and reduce costs. By leveraging data analytics, machine learning, and statistical methods, predictive models enable businesses to anticipate market trends, optimize resource allocation, and make data-driven decisions. This review explores the development of advanced predictive modeling techniques to optimize various business processes, from inventory management and supply chain optimization to customer relationship management and financial foreca

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Solodkov, Denis, and Natalia Hryshko. "AN INTEGRATION OF ARTIFICIAL INTELLIGENCE AND BUSINESS ANALYTICS FOR A MANAGERIAL DECISION-MAKING SUPPORT IN THE CONDITIONS OF RESOURCE CONSTRAINT." Economic scope, no. 198 (March 10, 2025): 115–22. https://doi.org/10.30838/ep.198.115-122.

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Artificial Intelligence (AI) and Business Intelligence (BI) are critical tools for optimizing decision-making and enhancing business process efficiency in today's digital economy. This article explores the integration of AI and BI in business practices, focusing on their potential to overcome key challenges, such as technical complexity, limited financial and human resources, and insufficient managerial expertise. A comprehensive analysis is provided, highlighting modern solutions that enable automation, improve decision accuracy, and reduce costs. The study emphasizes the importance of combin

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Yadav, Pramod Kumar, Sanjeev Kumar Verma, Aiswareya G, and Deepika Rajendra Singh Bais. "Impact of technology on orthodontic practice." Journal of Dental Specialities 12, no. 1 (2024): 25–31. http://dx.doi.org/10.18231/j.jds.2024.006.

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This paper explores the pivotal role of Artificial Intelligence (AI) in transforming orthodontic practice, focusing on its profound impact on diagnosis, treatment planning, and patient care. AI-powered algorithms, coupled with machine learning techniques, have revolutionized orthodontic workflows, enhancing efficiency, precision, and patient outcomes. By analysing vast datasets, AI facilitates predictive modelling for treatment outcomes, aiding orthodontists in devising personalized treatment plans tailored to individual patient needs. Moreover, AI-driven image analysis techniques enable autom

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Koppichetti, Ravi Kiran. "Real-Time Anomaly Detection in Biopharmaceutical Manufacturing: A Machine Learning Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 08 (2023): 1–9. https://doi.org/10.55041/ijsrem25181.

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Integrating the Internet of Things (IoT) and Machine Learning (ML) within smart manufacturing facilities has significantly transformed anomaly detection processes, thereby ensuring predictive maintenance, optimizing processes, and enhancing operational efficiency. This paper reviews existing research regarding real-time anomaly detection in IoT-enabled manufacturing environments, specifically emphasizing biopharmaceutical production. A variety of Machine Learning (ML) techniques, including convolutional neural networks (CNNs), hidden Markov models (HMMs), and statistical methodologies, are exa

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Akshat Khemka and Dr. Neeraj Saxena. "Robotic Process Automation (RPA) In Legacy System Migrations: Reducing Operational Inefficiencies In Digital Transformation." Universal Research Reports 12, no. 1 (2025): 436–46. https://doi.org/10.36676/urr.v12.i1.1503.

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Generative Artificial Intelligence (AI) is revolutionizing the utilization of large-scale data lakes, significantly enhancing business decision-making processes. As enterprises increasingly depend on vast volumes of data for strategic insights, the challenge remains in efficiently analyzing and extracting actionable intelligence from these expansive repositories. This review explores recent advancements in applying generative AI to manage and interpret data lakes, emphasizing their role in improving data quality, optimizing data governance, and enabling predictive and prescriptive analytics. B

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Rahman, Anisur. "AI AND MACHINE LEARNING IN BUSINESS PROCESS AUTOMATION: INNOVATING WAYS AI CAN ENHANCE OPERATIONAL EFFICIENCIES OR CUSTOMER EXPERIENCES IN U.S. ENTERPRISES." Non human journal 1, no. 01 (2024): 41–62. http://dx.doi.org/10.70008/jmldeds.v1i01.41.

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This study presents a comprehensive review of the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing business processes across various industries. By examining a total of 75 peer-reviewed articles, the review highlights key areas where AI and ML have demonstrated significant impact, including operational efficiency, customer engagement, and strategic decision-making. Findings indicate that AI-driven process optimizations, particularly through predictive maintenance and resource management, have led to substantial cost savings and improved productivity by

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Kirovich, Andriy. "INTEGRATION OF DIGITAL TOOLS INTO SERVICE SECTOR RISK MANAGEMENT." Economics and Management, no. 1 (2025): 161–67. https://doi.org/10.32782/2312-7872.1.2025.23.

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The article explores the integration of digital tools into risk management in the service sector within the context of digital transformation. It examines key technologies such as artificial intelligence, machine learning, blockchain, IoT, and cloud computing, which enhance risk identification, assessment, forecasting, and monitoring. The challenges of implementing digital technologies are analyzed, including cybersecurity threats, high costs of adapting business processes, and the incompatibility between traditional and digital solutions. Emphasis is placed on the importance of strategic plan

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Kothandapani, Hariharan Pappil. "Machine Learning for Enhancing Mortgage Origination Processes: Streamlining and Improving Efficiency." International Journal of Scientific Research and Management (IJSRM) 10, no. 02 (2024): 752–73. http://dx.doi.org/10.18535/ijsrm/v10i2.ec02.

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The mortgage industry, historically characterized by manual processes, paperwork, and complex decision-making, is on the brink of a digital revolution driven by machine learning (ML). For decades, mortgage lenders have relied on human judgment, traditional data analysis, and legacy systems to process applications, assess risk, and prevent fraud. These methods, while effective to a point, have created a bottleneck in terms of speed, efficiency, and accuracy. As the volume of mortgage applications continues to grow and the expectations of borrowers evolve toward faster and more transparent proce

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Nwaozomudoh, Mark Osemedua, Princess Eloho Odio, Eseoghene Kokogho, Taiwo Akindele Olorunfemi, Ilerioluwase Emmanuel Adeniji, and Adedamola Sobowale. "Developing a Conceptual Framework for Enhancing Interbank Currency Operation Accuracy in Nigeria's Banking Sector." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 1 (2021): 481–94. https://doi.org/10.54660/.ijmrge.2021.2.1.481-494.

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The banking sector in Nigeria plays a pivotal role in facilitating economic growth, with interbank currency operations being a cornerstone for efficient financial transactions. However, the accuracy of interbank operations has been hampered by systemic inefficiencies, errors in currency reconciliation, and inadequate technological integration, leading to financial losses and customer dissatisfaction. This study seeks to develop a conceptual framework aimed at enhancing the accuracy of interbank currency operations within Nigeria’s banking sector. The proposed framework integrates advanced digi

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Nur, Siti. "The Role of Digital Health Technologies and Sensors in Revolutionizing Wearable Health Monitoring Systems." International Journal of Innovative Research in Computer Science and Technology 12, no. 6 (2024): 69–80. http://dx.doi.org/10.55524/ijircst.2024.12.6.10.

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The rapid advancement of digital health technologies and sensor innovations has transformed wearable health monitoring systems, enabling unprecedented levels of personalized care, real-time health tracking, and early disease detection. This paper explores the pivotal role of these technologies in revolutionizing the healthcare landscape. We examine the integration of cutting-edge sensors, including biosensors, motion sensors, and environmental sensors, within wearable devices, which allow for continuous monitoring of physiological parameters such as heart rate, blood pressure, glucose levels,

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Adetunji Adejumo Paul and Chinonso Ogburie. "The Role of AI in preventing financial fraud and enhancing compliance." GSC Advanced Research and Reviews 22, no. 3 (2025): 269–82. https://doi.org/10.30574/gscarr.2025.22.3.0086.

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The rapid evolution of financial technology has led to an increase in financial fraud, making the role of artificial intelligence (AI) crucial in detecting, preventing, and mitigating fraudulent activities. AI-driven systems leverage machine learning (ML), natural language processing (NLP), and predictive analytics to identify suspicious patterns, anomalies, and fraudulent transactions in real time. These technologies enhance traditional rule-based fraud detection mechanisms by continuously learning from new data and improving accuracy. AI plays a vital role in regulatory compliance by automat

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Hussain, Nurudeen Yemi, Faith Ibukun Babalola, Eseoghene Kokogho, and Princess Eloho Odio. "A Robust Model for Integrating Artificial Intelligence into Financial Risk Management: Addressing Compliance, Accuracy, and Scalability Issues." International Journal of Research and Innovation in Social Science IX, no. II (2025): 3651–68. https://doi.org/10.47772/ijriss.2025.9020283.

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The integration of Artificial Intelligence (AI) into financial risk management has transformed the industry by enabling real-time analysis, enhanced decision-making, and predictive insights. However, challenges related to compliance with regulatory frameworks, the accuracy of AI models, and the scalability of these solutions persist. This study proposes a robust model that systematically integrates AI into financial risk management while addressing these critical issues. The model combines machine learning (ML) algorithms, natural language processing (NLP), and explainable AI (XAI) techniques

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Hasan, Mahade, Farhana Yasmin, Md Mehedi Hassan, et al. "Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques." PLOS ONE 20, no. 1 (2025): e0312914. https://doi.org/10.1371/journal.pone.0312914.

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Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosti

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Moon, Jihoon, Muazzam Maqsood, Dayeong So, Sung Wook Baik, Seungmin Rho, and Yunyoung Nam. "Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence." PLOS ONE 19, no. 11 (2024): e0307654. http://dx.doi.org/10.1371/journal.pone.0307654.

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Accurate electricity consumption forecasting in residential buildings has a direct impact on energy efficiency and cost management, making it a critical component of sustainable energy practices. Decision tree-based ensemble learning techniques are particularly effective for this task due to their ability to process complex datasets with high accuracy. Furthermore, incorporating explainable artificial intelligence into these predictions provides clarity and interpretability, allowing energy managers and homeowners to make informed decisions that optimize usage and reduce costs. This study comp

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Senthil, G. A., R. Prabha, R. M. Asha, S. U. Suganthi, and S. Sridevi. "Integrating Bioengineering and Machine Learning: A Multi-Algorithm Approach to Enhance Agricultural Sustainability and Resource Efficiency." BIO Web of Conferences 172 (2025): 02001. https://doi.org/10.1051/bioconf/202517202001.

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The novel research incorporates high-level machine learning algorithms for optimizing agricultural performance regarding sustainability and resource efficiencies. By using random forests and SVMs, this work successfully achieved 92% prediction accuracy for crop yields and an 89% classification accuracy of agricultural regions, thereby highly enhancing the decision-making power of farmers and policymakers. With over 10,000 historical records, the random forest model established a hypothesis that maize yields could be increased by almost 25% in ideal conditions. At the same time, the SVM identif

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Bernard Owusu Antwi, Beatrice Oyinkansola Adelakun, and Augustine Obinna Eziefule. "Transforming Financial Reporting with AI: Enhancing Accuracy and Timeliness." International Journal of Advanced Economics 6, no. 6 (2024): 205–23. http://dx.doi.org/10.51594/ijae.v6i6.1229.

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The landscape of financial reporting is undergoing a profound transformation fueled by advancements in Artificial Intelligence (AI) technologies. This review explores the revolutionary impact of AI on financial reporting, with a specific focus on enhancing accuracy and timeliness. AI-driven technologies such as machine learning, natural language processing, and predictive analytics are reshaping traditional financial reporting processes. These technologies enable organizations to automate routine tasks, analyze vast volumes of financial data, and extract valuable insights with unprecedented sp

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Sandhyarani Ganipaneni, Ravi Kiran Pagidi, Aravind Ayyagiri, Prof.(Dr) Punit Goel, Prof.(Dr.) Arpit Jain, and Dr Satendra Pal Singh. "Machine Learning for SAP Data Processing and Workflow Automation." Darpan International Research Analysis 12, no. 3 (2024): 744–75. http://dx.doi.org/10.36676/dira.v12.i3.131.

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In the rapidly evolving landscape of enterprise resource planning, the integration of Machine Learning (ML) into SAP data processing and workflow automation presents significant opportunities for enhancing operational efficiency and decision-making. This paper explores the methodologies and applications of ML algorithms in optimizing SAP environments, focusing on data processing, predictive analytics, and automation workflows. Firstly, we examine the role of ML in automating data extraction, transformation, and loading processes, which traditionally require substantial manual intervention. By

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Vivek, Prasanna Prabu. "Leveraging Edge Computing for Real-Time Warehouse Operations in Retail." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 7, no. 6 (2021): 1–11. https://doi.org/10.5281/zenodo.15155683.

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Edge computing presents a transformative approach to warehouse operations within the retail industry by significantly enhancing real-time data processing capabilities. The shift from traditional centralized cloud models to distributed edge architectures provides substantial improvements in latency reduction, operational efficiency, and decision-making speed. This white paper explores edge computing's pivotal role in enabling real-time inventory management, automated picking processes, predictive maintenance, and enhancing security and compliance standards within retail warehouse settings. By i

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Maithili, Kamble Dr. Shivappa Nagoba* Avinash Swami Nivrutti Kotsulwar Mayur Upade Amrapali Rajput. "Review On Artificial Intelligence Revolutionizing the Pharmaceutical Industry." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 2028–43. https://doi.org/10.5281/zenodo.15393564.

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Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the pharmaceutical industry, driving advancements across drug discovery, formulation, manufacturing, and clinical trials. AI tools such as molecular visualization software, predictive algorithms like Random Forest, and Principal Component Analysis (PCA) are pivotal in assessing drug stability and designing stable drug-polymer systems, which are essential for developing solid dispersions. Artificial Neural Networks (ANNs) have demonstrated superior accuracy in predicting the crystalline and amorphous content of drugs com

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Joshi, Saurabh Anant, and Vidyut K. Deshpande. "Role of Robotics and AI in Manufacturing Industry." InSight Bulletin: A Multidisciplinary Interlink International Research Journal 2, no. 3 (2025): 124–28. https://doi.org/10.5281/zenodo.15294633.

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<strong><em>Abstract </em></strong> <em>The integration of robots and artificial intelligence (AI) has revolutionized the industrial sector by significantly enhancing productivity, accuracy, and efficiency. AI-driven algorithms enable robotics to automate complex tasks, minimizing human intervention and reducing errors. By leveraging predictive analytics and machine learning models, AI optimizes quality control, refines manufacturing processes, and strengthens decision-making capabilities. Additionally, robots and AI improve workplace safety by undertaking hazardous tasks, thereby reducing ris

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Joshi, Saurabh Anant, and Vidyut K. Deshpande. "Role of Robotics and AI in Manufacturing Industry." InSight Bulletin: A Multidisciplinary Interlink International Research Journal 2, no. 3 (2025): 124–28. https://doi.org/10.5281/zenodo.15423462.

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<strong><em>Abstract </em></strong> <em>The integration of robots and artificial intelligence (AI) has revolutionized the industrial sector by significantly enhancing productivity, accuracy, and efficiency. AI-driven algorithms enable robotics to automate complex tasks, minimizing human intervention and reducing errors. By leveraging predictive analytics and machine learning models, AI optimizes quality control, refines manufacturing processes, and strengthens decision-making capabilities. Additionally, robots and AI improve workplace safety by undertaking hazardous tasks, thereby reducing ris

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Vinay, Singh. "AI and ERP Integration for Adaptive Dynamic Costing Based on Consumer Demand Fluctuations in Manufacturing." European Journal of Advances in Engineering and Technology 12, no. 3 (2025): 1–7. https://doi.org/10.5281/zenodo.15165976.

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This research aims to automate product costing based on consumer demand fluctuation using artificial intelligence (AI) integration with the Oracle ERP system. From raw material procurement and factory scheduling to real-time product cost estimates and financial forecasts, AI can simplify and improve many activities as manufacturing processes get more complicated. Automating product costing based on consumer demand allows artificial intelligence to help producers reach higher accuracy, efficiency, and cost optimization, thus enhancing organizations profitability. The paper examines how artifici

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Klymenko, Mykhailo, and Pavlo Fedorka. "Adjustment of the analytic hierarchy process indicators using AI tools." Information Technology and Computer Engineering 22, no. 1 (2025): 103–14. https://doi.org/10.63341/vitce/1.2025.103.

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This study aimed to enhance the analytic hierarchy process (AHP) by integrating artificial intelligence (AI) algorithms for the automatic adjustment of its indicators, thereby improving the method’s accuracy, consistency, and adaptability. A conceptual analysis of both the traditional and AI-oriented approaches was conducted. The research methodology included a systematic literature review, identification of the key limitations of the classical method, and testing of AI capabilities to improve the consistency and precision of weighting coefficients. The findings demonstrate that the integratio

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Rezk, Nermeen Gamal, Samah Alshathri, Amged Sayed, and Ezz El-Din Hemdan. "Explainable AI for Chronic Kidney Disease Prediction in Medical IoT: Integrating GANs and Few-Shot Learning." Bioengineering 12, no. 4 (2025): 356. https://doi.org/10.3390/bioengineering12040356.

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According to recent global public health studies, chronic kidney disease (CKD) is becoming more and more recognized as a serious health risk as many people are suffering from this disease. Machine learning techniques have demonstrated high efficiency in identifying CKD, but their opaque decision-making processes limit their adoption in clinical settings. To address this, this study employs a generative adversarial network (GAN) to handle missing values in CKD datasets and utilizes few-shot learning techniques, such as prototypical networks and model-agnostic meta-learning (MAML), combined with

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Kokogho, Eseoghene, Ilerioluwase Emmanuel Adeniji, Taiwo Akindele Olorunfemi, Mark Osemedua Nwaozomudoh, Princess Eloho Odio, and Adedamola Sobowale. "Conceptualizing Improved Cash Forecasting Accuracy for Effective Currency Reserve Management in Nigerian Banks." International Journal of Management and Organizational Research 3, no. 6 (2024): 120–30. https://doi.org/10.54660/ijmor.2024.3.6.120-130.

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Effective currency reserve management is critical for maintaining financial stability and operational efficiency in Nigerian banks. Cash forecasting accuracy plays a vital role in ensuring the optimal allocation of currency reserves, minimizing liquidity risks, and meeting customer demands. However, Nigerian banks face persistent challenges in cash forecasting, including inaccuracies due to inadequate data integration, manual processes, and limited adoption of advanced technologies. This study aims to conceptualize an improved cash forecasting framework to enhance currency reserve management i

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Aghaalizadeh Darandashi, Amir, Gholamreza Zomorodian, Fatemeh Samadi, and Hossein Badiei. "Developing a Qualitative Model of Artificial Intelligence Based on Accounting Procedures." Business, Marketing, and Finance Open 2, no. 4 (2025): 1–11. https://doi.org/10.61838/bmfopen.2.4.14.

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In today's rapidly evolving world of modern technologies, artificial intelligence has emerged as a key factor in improving processes and enhancing efficiency across various domains. This study aims to develop a model for leveraging artificial intelligence in accounting procedures. The primary objective of this research is to identify and integrate the factors influencing the automation and optimization of accounting processes using machine learning algorithms and artificial intelligence. Employing a mixed-methods research approach and utilizing qualitative content analysis, this study presents

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Silva, Anabela Costa, José Machado, and Paulo Sampaio. "Predictive quality model for customer defects." TQM Journal 36, no. 9 (2024): 155–74. http://dx.doi.org/10.1108/tqm-09-2023-0302.

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PurposeIn the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribu

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Gideon Oluseyi Daramola, Boma Sonimiteim Jacks, Olakunle Abayomi Ajala, and Abiodun Emmanuel Akinoso. "ENHANCING OIL AND GAS EXPLORATION EFFICIENCY THROUGH AI-DRIVEN SEISMIC IMAGING AND DATA ANALYSIS." Engineering Science & Technology Journal 5, no. 4 (2024): 1473–86. http://dx.doi.org/10.51594/estj.v5i4.1077.

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This paper delves into the advancements in AI-driven seismic imaging and data analysis techniques aimed at augmenting the efficiency of oil and gas exploration. We explore various AI algorithms and machine learning models that have been deployed to interpret seismic data, predict subsurface structures, and identify potential hydrocarbon reservoirs with unprecedented precision. Furthermore, we discuss the integration of big data analytics and high-performance computing in handling vast volumes of seismic data, thereby facilitating rapid decision-making in exploration projects. Through case stud

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Joseph Kuba Nembe, Joy Ojonoka Atadoga, Noluthando Zamanjomane Mhlongo, et al. "THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING TAX COMPLIANCE AND FINANCIAL REGULATION." Finance & Accounting Research Journal 6, no. 2 (2024): 241–51. http://dx.doi.org/10.51594/farj.v6i2.822.

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Artificial Intelligence (AI) has emerged as a transformative force in various domains, including tax compliance and financial regulation. This review explores the pivotal role of AI in enhancing these critical aspects of governance and economic stability. In the realm of tax compliance, AI-driven solutions offer unprecedented opportunities for governments to streamline tax administration processes, detect non-compliance, and mitigate tax evasion. Machine learning algorithms can analyze vast volumes of financial data with remarkable speed and accuracy, identifying patterns indicative of tax fra

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Thompson, Odion Igunma, Kehinde Adeleke Adeniyi, and Sikhakhane Nwokediegwu Zamathula. "Developing networked metrology systems to optimize smart manufacturing applications through real-time data integration." International Journal of Trends in Emerging Research and Development 1, no. 1 (2023): 340–56. https://doi.org/10.5281/zenodo.14874576.

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Smart manufacturing relies on real-time data integration to enhance efficiency, precision, and productivity in industrial operations. Networked metrology systems (NMS) play a crucial role in achieving this by ensuring accurate and reliable measurements across interconnected manufacturing processes. This paper explores the development of NMS to optimize smart manufacturing applications through real-time data integration. By leveraging advanced sensor networks, artificial intelligence (AI), and the Industrial Internet of Things (IIoT), NMS facilitate seamless data collection, analysis, and feedb

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VORA, Dr K. B. "The Role of Machine Learning in Water Quality Assessment: Current Applications and Future Scope." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41578.

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Water quality is vital for human health, ecosystems, industries, and agriculture. However, increasing contamination and pollution over recent decades have posed significant challenges to maintaining clean water sources. Effective monitoring is essential for safeguarding public health, protecting the environment, and ensuring sustainable water management. Artificial Intelligence (AI), particularly machine learning (ML), offers powerful tools for water quality assessment, classification, and prediction. With the rapid expansion of aquatic environmental data, ML has become indispensable for analy

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Bokaba, Tebogo, Wesley Doorsamy, and Babu Sena Paul. "A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion." Applied Sciences 12, no. 3 (2022): 1337. http://dx.doi.org/10.3390/app12031337.

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Increased road traffic congestion is due to different factors, such as population and economic growth, in different cities globally. On the other hand, many households afford personal vehicles, contributing to the high volume of cars. The primary purpose of this study is to perform a comparative analysis of ensemble methods using road traffic congestion data. Ensemble methods are capable of enhancing the performance of weak classifiers. The comparative analysis was conducted using a real-world dataset and bagging, boosting, stacking and random forest ensemble models to compare the predictive p

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