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Journal articles on the topic 'Artificial intelligence AI-Readiness Forest industry'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Orajaka, Amaka V., and Chukwunonso V. Orajaka. "The Role of Artificial Intelligence in Enhancing Supply Chain Performance within Nigeria’s Oil and Gas Sector." International Journal of Research and Innovation in Applied Science X, no. V (2025): 1236–52. https://doi.org/10.51584/ijrias.2025.1005000110.

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This study examines the application of artificial intelligence (AI) in enhancing supply chain performance within Nigeria’s oil and gas sector, with a focus on efficiency, agility, and resilience. As global energy demands and operational complexities rise, the need for digital transformation in supply chain management has become increasingly vital. However, in many developing economies, AI adoption remains limited due to organizational, cultural, and infrastructural barriers. A quantitative research design was employed, using survey data collected from professionals within the Nigerian oil and

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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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Ooi, Jun Jie, Yit Hong Choo, Andi Prademon Yunus, Wei Hong Lim, and Sui Yang Khoo. "Review on Advancements in Artificial Intelligence and its Applications in Sports." International Journal on Robotics, Automation and Sciences 7, no. 1 (2025): 58–63. https://doi.org/10.33093/ijoras.2025.7.1.7.

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The sport industry is being transformed by Artificial Intelligence (AI) in many ways. This paper seeks to discuss how AI has improved sports science, particularly in boosting the athletes’ performance and avoiding injuries, through various machine learning models like Extreme Gradient Boosting, Support Vector Machines, and Random Forest Regression. These AI tools are more effective than the traditional methods, as they predict the athletes’ performance results more accurately and managing their injuries more proactively. This paper also discusses the challenges of using AI in the sport industr

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Espinel, Ramón, Gricelda Herrera-Franco, José Luis Rivadeneira García, and Paulo Escandón-Panchana. "Artificial Intelligence in Agricultural Mapping: A Review." Agriculture 14, no. 7 (2024): 1071. http://dx.doi.org/10.3390/agriculture14071071.

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Artificial intelligence (AI) plays an essential role in agricultural mapping. It reduces costs and time and increases efficiency in agricultural management activities, which improves the food industry. Agricultural mapping is necessary for resource management and requires technologies for farming challenges. The mapping in agricultural AI applications gives efficiency in mapping and its subsequent use in decision-making. This study analyses AI’s current state in agricultural mapping through bibliometric indicators and a literature review to identify methods, agricultural resources, geomatic to

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Meenakshi Devi Mudunuri, Nagaraju Yalampati, Akshay Kumar Voosala, et al. "Biotechnology and artificial intelligence integration: A concise review of advanced application, advantages and challenges in healthcare." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 2380–88. https://doi.org/10.30574/wjarr.2024.24.3.3761.

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Everyone is talking about artificial intelligence (AI) these days. Unprecedented new potential solutions are made possible when biotechnology and artificial intelligence breakthroughs are coupled. This can support significant Sustainable Development Goals and assist with a number of global issues. Food security, health and well-being sustainable energy, conscientious production and consumption, climate action, and life below water, safeguarding, restoring, and promoting the environmentally friendly forest management and the sustainable utilization of terrestrial ecosystems, preventing desertif

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Meenakshi, Devi Mudunuri, Yalampati Nagaraju, Kumar Voosala Akshay, et al. "Biotechnology and artificial intelligence integration: A concise review of advanced application, advantages and challenges in healthcare." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 2380–88. https://doi.org/10.5281/zenodo.15229477.

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Everyone is talking about artificial intelligence (AI) these days. Unprecedented new potential solutions are made possible when biotechnology and artificial intelligence breakthroughs are coupled. This can support significant Sustainable Development Goals and assist with a number of global issues. Food security, health and well-being sustainable energy, conscientious production and consumption, climate action, and life below water, safeguarding, restoring, and promoting the environmentally friendly forest management and the sustainable utilization of terrestrial ecosystems, preventing desertif

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Li, Jinhui. "The Impact of AI Industry Growth on U.S. AI Sector Stocks: A Machine Learning Analysis." Advances in Economics, Management and Political Sciences 94, no. 1 (2024): 175–86. http://dx.doi.org/10.54254/2754-1169/94/2024ox0203.

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The rapid development of artificial intelligence (AI) since 2020 has significantly impacted the U.S. stock market, necessitating a deeper understanding of its influence on AI-related stocks. This study aims to analyze and predict the returns of the Global X Robotics & Artificial Intelligence ETF (BOTZ) as a proxy for AI industry performance. Employing Random Forest and XGBoost machine learning models, we trained on over a thousand data points to forecast BOTZ ETF returns. Our research reveals that AI-focused stocks and ETFs have outperformed the broader market since 2020, driven by increas

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Suman Kumar Swarnkar. "Integrating Artificial Intelligence and Data Analytics for Supply Chain Optimization in the Pharmaceutical Industry." Journal of Electrical Systems 20, no. 3s (2024): 682–90. http://dx.doi.org/10.52783/jes.1358.

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This inquire about examines the integration of Artificial Intelligence (AI) and information analytics to optimize supply chain forms within the pharmaceutical industry. Through tests and writing audits, the ponder investigates the adequacy of AI calculations counting Linear Regression, Random Forest Regression, K-Means Clustering, and Deep Learning Neural Systems over request estimating, stock optimization, generation planning, and coordination optimization. Results appear that Random Forest Relapse beats Direct Relapse in request determining with RMSE of 80.20, MAE of 60.75, R² of 0.90, and M

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Venu Gopal Avula. "Predictive Intelligence in retail operations: AI-powered forecasting models for demand planning, customer behavior analysis, and supply chain optimization." World Journal of Advanced Engineering Technology and Sciences 4, no. 1 (2021): 106–14. https://doi.org/10.30574/wjaets.2021.4.1.0074.

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The retail industry has undergone significant transformation with the integration of artificial intelligence (AI) and machine learning (ML) technologies. This study presents a comprehensive analysis of AI-powered forecasting models for retail operations, focusing on demand planning, customer behavior analysis, and supply chain optimization. Through the implementation of advanced predictive algorithms including Long Short-Term Memory (LSTM) networks, Random Forest, and XG Boost models, we demonstrate significant improvements in forecasting accuracy. Our findings reveal that AI-driven approaches

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Grace, Camille Curtom, Madhure Nagaraju Sangeetha, Mendonca Eder, Abu-Samaha Mamoun, and Hee Kim Jeong. "Creating an Artificial Intelligence (AI) Model for Healthcare Diagnostics." European Journal of Advances in Engineering and Technology 9, no. 3 (2022): 1–6. https://doi.org/10.5281/zenodo.10643762.

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<strong>ABSTRACT</strong> The US healthcare system is a cottage industry and as it moves toward electronic healthcare data, there is no standard for healthcare interoperability, there is no proper integration among the different clinics and services leading to a lack of continuity and coordination of care with issues concerning data privacy and security. This leads to patients seeking medical services from several clinics to get the most accurate healthcare diagnosis. To solve such problems, we used artificial intelligence (AI) technology more specifically using AI modeling to obtain better an

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Arshad, Jehangir, Adan Qaisar, Atta-Ur Rehman, et al. "Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence." Applied Sciences 12, no. 21 (2022): 10813. http://dx.doi.org/10.3390/app122110813.

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The combination of signal processing and Artificial Intelligence (AI) is revolutionizing the robotics and automation industry by the deployment of intelligent systems and reducing human intervention. Reading human brain signal through electroencephalography (EEG) has provided a new direction of research that automate machines through the human brain and computer interface or Brain–Computer Interface (BCI). The study is also inspired by the same concept of intelligently controlling a robotic arm using BCI and AI to help physically disabled individuals. The proposed system is non-invasive, unlik

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He, Chenggang, and Chris H. Q. Ding. "Predicting Partner’s Digital Transformation Based on Artificial Intelligence." Applied Sciences 12, no. 1 (2021): 91. http://dx.doi.org/10.3390/app12010091.

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Partner’s digital transformation is one of the most important metrics for businesses, particularly for businesses in the subscription world. Hence, how to predict partner transformation is a consistent focus in the industry. In this paper, we use an AI (Artificial Intelligence) relevant algorithm to analyze partner’s digital transformation issues and propose a novel method, named the hybrid VKR (VAE, K-means, and random forest) algorithm, to predict partner transformation. We apply our algorithm to partner transformation issues. First, we show the prediction of about 5980 partners from 25,689

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To, Wai Ming, and Billy T. W. Yu. "Artificial Intelligence Research in Tourism and Hospitality Journals: Trends, Emerging Themes, and the Rise of Generative AI." Tourism and Hospitality 6, no. 2 (2025): 63. https://doi.org/10.3390/tourhosp6020063.

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This study examined the trends and key themes of artificial intelligence in the field of tourism and hospitality research. On 5 March 2025, a search was performed using “artificial intelligence” and related terms in the “Title, Abstract, and Keywords”, focusing on tourism and hospitality journals indexed in Scopus. The identified documents were subjected to performance analysis and science mapping techniques. The search yielded 921 documents, comprising 882 articles and 39 reviews. The number of documents increased from 3 in 1987 to 277 in 2024. R. Law from the University of Macau was the most

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Muntean, Emanuel, Monica Leba, and Andreea Cristina Ionica. "AI-Driven Arm Movement Estimation for Sustainable Wearable Systems in Industry 4.0." Sustainability 17, no. 14 (2025): 6372. https://doi.org/10.3390/su17146372.

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In an era defined by rapid technological advancements, the intersection of artificial intelligence and industrial innovation has garnered significant attention from both academic and industry stakeholders. The emergence of Industry 4.0, characterized by the integration of cyber–physical systems, the Internet of Things, and smart manufacturing, demands the evolution of operational methodologies to ensure processes’ sustainability. One area of focus is the development of wearable systems that utilize artificial intelligence for the estimation of arm movements, which can enhance the ergonomics an

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Massaro, Alessandro. "Artificial Intelligence Signal Control in Electronic Optocoupler Circuits Addressed on Industry 5.0 Digital Twin." Electronics 13, no. 22 (2024): 4543. http://dx.doi.org/10.3390/electronics13224543.

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The paper is focused on the modeling of a digital twin (DT) through a circuit simulation and artificial intelligence (AI) analysis to determine the effects of disturbances and noise in optocoupler devices integrated into programmable logic controller (PLC) systems. Specifically, the DT analyzes the parametric and the predicted simulations about the sensitivity of the optocouplers versus noise and interference to provide possible corrective actions, compensating for the distortion of the output signal. The model is structured into two main data processing steps: the first is based on the circui

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Alexiou, Kiriakos, Efthimios G. Pariotis, Theodoros C. Zannis, and Helen C. Leligou. "Prediction of a Ship’s Operational Parameters Using Artificial Intelligence Techniques." Journal of Marine Science and Engineering 9, no. 6 (2021): 681. http://dx.doi.org/10.3390/jmse9060681.

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The maritime industry is one of the most competitive industries today. However, there is a tendency for the profit margins of shipping companies to reduce due to an increase in operational costs, and it does not seem that this trend will change in the near future. The most important reason for the increase in operating costs relates to the increase in fuel prices. To compensate for the increase in operating costs, shipping companies can either renew their fleet or try to make use of new technologies to optimize the performance of their existing one. The software structure in the maritime indus

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Nunes, Leonel J. R. "The Role of Artificial Intelligence (AI) in the Future of Forestry Sector Logistics." Future Transportation 5, no. 2 (2025): 63. https://doi.org/10.3390/futuretransp5020063.

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Background: The forestry industry plays an important role in the economy and environmental sustainability, facing significant logistical challenges such as the geographical dispersion of plantations, the variability of raw materials, and high transportation costs. Artificial Intelligence (AI) emerges as a promising tool to optimize logistics processes, contributing to the reduction in costs, waste, and environmental impacts. Methods: This study combines a literature review and case analysis to assess the impact of AI on forestry logistics. Machine Learning algorithms, optimization systems, and

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Vu, Van Vien, Phuoc Tai Le, Thi Mai Thom Do, et al. "An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation." JOIV : International Journal on Informatics Visualization 8, no. 1 (2024): 158. http://dx.doi.org/10.62527/joiv.8.1.2641.

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This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest

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Anil Tiwari. "Integrating Computer Science with Management Education: A Framework for Enhancing Decision-Making Skills in the Digital Age." Journal of Information Systems Engineering and Management 10, no. 21s (2025): 339–51. https://doi.org/10.52783/jisem.v10i21s.3340.

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Management education cannot afford to remain bereft of computer science in the age of the digital. Integration of computer science in management education is important so as to improve decision making skills. Referred in this research: A framework using artificial intelligence (AI), machine learning, gamification and data driven analytics to enhance strategic thinking in management studies. The study examines four main algorithms i.e. Decision Trees, Random Forest, Support Vector Machine (SVM) and Artificial Neural Network (ANN) to figure out which one best suits in predictive decision making.

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Fan, Lingling. "Review of theoretical advancements in AI/ML classification models for credit risk assessment." Journal of Risk Management in Financial Institutions 18, no. 2 (2025): 171. https://doi.org/10.69554/nkjl6812.

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In the realm of credit risk assessment, the utilisation of artificial intelligence (AI) and machine learning (ML) classification models has become increasingly prevalent. This paper thoroughly investigates latest advancements in AI/ML classification models for credit risk assessment, which are crucial for assessing the creditworthiness of individuals and businesses. Key findings reveal that modern AI/ML techniques, particularly Random Forest and XGBoost, outperform traditional logistic regression methods. Additionally, interpretability techniques, including Shapley Additive exPlanations (SHAP)

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Ram, Malakit L., Jorton A. Tagud, Jose C. Agoylo Jr, et al. "Assessing the Impact of Artificial Intelligence and Machine Learning Tools on Software Development Efficiency in Agile Frameworks: A Structured Evaluation Using Machine Learning Models." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 17, no. 1 (2025): 35–39. https://doi.org/10.54554/jtec.2025.17.01.005.

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Adopting artificial intelligence (AI) and machine learning (ML) in software development processes presents an opportunity to systematically assess improvements in efficiency, accuracy, and project management. However, evaluating these technologies requires structured assessment models rather than generalized claims. This study utilizes a Kaggle dataset and applies linear regression, random forest classifiers, and K-means clustering to examine the impact of AI tools within Agile frameworks. The analysis reveals that AI tools enhance decision-making, productivity, and resource allocation in Agil

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Islam, Musfikul, Gospel, and Gabriel Obahor. "Leveraging Artificial Intelligence and Data Science for Enhancing Occupational Safety: A Multidisciplinary Approach to Risk Prediction and Hazard Mitigation in the Workplace." Indonesian Journal of Science, Technology and Humanities 3, no. 1 (2025): 21–31. https://doi.org/10.60076/ijstech.v3i1.1297.

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The issue of occupational risks in the mining industry in Kogi State is a serious concern due to the lack of proactive safety systems. As a practical application of Artificial Intelligence (AI) and machine learning, this study aimed to forecast hazards in the workplace and use the information to develop data-driven interventions. Researchers applied the supervised models Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Decision Tree to analyze the data of 1,200 mining workers, real-time environmental sensors (PM2.5, CO, noise, temperature, vibration), and five y

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Ali, Zainab Hasan, Abbas M. Burhan, Murizah Kassim, and Zainab Al-Khafaji. "Developing an Integrative Data Intelligence Model for Construction Cost Estimation." Complexity 2022 (September 29, 2022): 1–18. http://dx.doi.org/10.1155/2022/4285328.

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Construction cost estimation is one of the essential processes in construction management. Project cost is a complex engineering problem due to various factors affecting the construction industry. Accurate cost estimation is important in construction management and significantly impacts project performance. Artificial intelligence (AI) models have been effectively implemented in construction management studies in recent years owing to their capability to deal with complex problems. In this research, extreme gradient boosting is developed as an advanced input selector algorithm and coupled with

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D. Sumathi, T. Poongodi, P. Suresh, S. Karthikeyan, and N. Sree Chand1. "Visualisation and prediction of Covid-19 data using random forest regression." Journal of Current Science and Technology 13, no. 2 (2023): 221–36. http://dx.doi.org/10.59796/jcst.v13n2.2023.1738.

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The outbreak of COVID-19 has spread among several parts of the world. The data pool increases tremendously, which needs excellent attention by researchers of various domains to analyze and determine the measures to handle it. Hence, researchers worldwide are looking into Artificial Intelligence (AI) to resolve the challenges due to this COVID-19. It could be stated that AI can examine huge data mounds so that several new findings can be determined. AI could be deployed in various fields, such as the pharmaceutical industry, the analysis and development of vaccines and antibodies, and drug desi

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Shi, Yunye, Diego Mauricio Yepes Maya, Electo Silva Lora, and Albert Ratner. "Hydrogen Enhancement in Syngas Through Biomass Steam Gasification: Assessment with Machine Learning Models." Energies 18, no. 5 (2025): 1200. https://doi.org/10.3390/en18051200.

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Artificial intelligence (AI), particularly supervised machine learning, has revolutionized the biofuel industry by enhancing feedstock selection, predicting fluid compositions, optimizing operations, and streamlining decision-making. These algorithms outperform traditional models by accurately handling complex, high-dimensional data more efficiently and cost-effectively. This study assesses the effectiveness of various machine learning algorithms in engineering, focusing on a comparative analysis of artificial neural networks (ANNs), support vector machines (SVMs), tree-based models, and regul

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Abdullahi, Mujaheed, Yahia Baashar, Hitham Alhussian, et al. "Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review." Electronics 11, no. 2 (2022): 198. http://dx.doi.org/10.3390/electronics11020198.

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In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categoriz

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GhoshRoy, Debasmita, Parvez Ahmad Alvi, and KC Santosh. "Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP." Healthcare 11, no. 7 (2023): 929. http://dx.doi.org/10.3390/healthcare11070929.

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Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in lifestyle and environmental factors are the prime reasons for the declining rate of male fertility. Artificial intelligence (AI)/machine learning (ML) models have become an effective solution for early fertility detection. Seven industry-standard ML models are used: support vector machine, random forest (RF), decision tree, logistic regression, na

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Saurabh, Kumar, Deep Suman, and Kalra Pourush. "Enhancing Customer Service in Banking with AI: Intent Classification Using Distilbert." International Journal of Current Science Research and Review 07, no. 05 (2024): 2706–13. https://doi.org/10.5281/zenodo.11183414.

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Abstract : With the increasing demand for efficient and responsive customer service in the banking sector, artificial intelligence offers a promising solution. This paper presents a comparative analysis of artificial intelligence methodologies applied to intent classification within the banking sector customer service domain. Utilizing a comprehensive dataset of banking service inquiries, we evaluate several machine learning approaches, including Naive Bayes, Logistic Regression, Support Vector Machine with Linear Kernel, Random Forest, XGBoost, and the transformer-based DistilBERT model. The

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Pérez-Briceño, Citlaly, Pedro Ponce, Qipei Mei, and Aminah Robinson Fayek. "A Type-2 Fuzzy Logic Expert System for AI Selection in Solar Photovoltaic Applications Based on Data and Literature-Driven Decision Framework." Processes 13, no. 5 (2025): 1524. https://doi.org/10.3390/pr13051524.

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Artificial intelligence (AI) has emerged as a transformative tool for optimizing photovoltaic (PV) systems, enhancing energy efficiency, predictive maintenance, and fault detection. This study presents a systematic literature review and bibliometric analysis to identify the most commonly used AI techniques and their applications in PV systems. The review provides details on the advantages, limitations, and optimal use cases of various review techniques, such as Artificial Neural Networks, Fuzzy Logic, Convolutional Neural Networks, Long-Short Term Memory, Support Vector Machines, Decision Tree

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Nanyonga, Aziida, Hassan Wasswa, Keith Joiner, Ugur Turhan, and Graham Wild. "Explainable Supervised Learning Models for Aviation Predictions in Australia." Aerospace 12, no. 3 (2025): 223. https://doi.org/10.3390/aerospace12030223.

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Artificial intelligence (AI) has demonstrated success across various industries; however, its adoption in aviation remains limited due to concerns regarding the interpretability of AI models, which often function as black box systems with opaque decision-making processes. Given the safety-critical nature of aviation, the lack of transparency in AI-generated predictions poses significant challenges for industry stakeholders. This study investigates the classification performance of multiple supervised machine learning models and employs SHapley Additive exPlanations (SHAPs) to provide global mo

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Ogunmolu, Akinde Michael, Oluwaseun Oladeji Olaniyi, Anuoluwapo Deborah Popoola, Anthony Obulor Olisa, and Oluwatobi Bamigbade. "Autonomous Artificial Intelligence Agents for Fault Detection and Self-Healing in Smart Manufacturing Systems." Journal of Energy Research and Reviews 17, no. 8 (2025): 20–37. https://doi.org/10.9734/jenrr/2025/v17i8445.

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This research develops and validates autonomous artificial intelligence agents for fault detection and self-healing in smart manufacturing systems within the context of Industry 4.0, addressing critical challenges in operational efficiency and reliability. Motivated by the need to reduce unplanned downtime and enhance production resilience, the study pursues three objectives: designing a robust AI architecture for fault detection, implementing effective self-healing mechanisms, and evaluating performance through rigorous testing. The literature review highlights gaps in temporal-spatial data i

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Walacik, Marek, and Aneta Chmielewska. "Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation." Sustainability 16, no. 3 (2024): 1079. http://dx.doi.org/10.3390/su16031079.

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As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets r

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Francesc Wilhelmi, David G�ez, Paola Soto, et al. "Machine learning for performance prediction of channel bonding in next-generation IEEE 802.11 WLANS." ITU Journal on Future and Evolving Technologies 2, no. 4 (2021): 67–79. http://dx.doi.org/10.52953/nbgs1213.

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With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the First AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement 13 (P

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D N, Varshitha, Ranjan Kumar H S, Dharini K R, Kavyashree N, Shreya R, and Soundarya M. "Enhancing Parkinson's Disease Detection using AI Techniques." Salud, Ciencia y Tecnología 5 (March 28, 2025): 1523. https://doi.org/10.56294/saludcyt20251523.

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Abstract: One of the severe illnesses that causes uncontrollable and unexpected outcomes is Parkinson's disease(PD). People over 50 years of age are typically the ones who contract this illness. The patients' symptoms progressively become worse leading to a variety of abnormalities such as body part rigidity and abnormalities in speech and gait. In addition, the patients have sadness, sleep deprivation, memory problems, mental illness, and numerous other health problems. Parkinson's disease is caused by damage or death of neurons in the brain's basal ganglia, but scientists and doctors are una

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Alkhodair, Majid, and Hanadi Alkhudhayr. "Harnessing Industry 4.0 for SMEs: Advancing Smart Manufacturing and Logistics for Sustainable Supply Chains." Sustainability 17, no. 3 (2025): 813. https://doi.org/10.3390/su17030813.

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The complex integration of Industry 4.0 technologies into SMEs necessitates robust frameworks to address adoption barriers and enhance sustainability. The present study investigates the impact of artificial intelligence (AI), the Internet of Things (IoT), and blockchain on smart manufacturing, logistics, and sustainability in SMEs. Using a cross-sectional design, data were collected from 300 SMEs across manufacturing, logistics, and retail sectors through purposive sampling, focusing on technology adoption, and sustainability performance from 2018 to 2022. Data were analyzed using advanced mac

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Rohan, Pai, and Wadhwa Anubhav. "Artificial Intelligence based Modern Approaches to Diagnose Alzheimer's." Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 2, no. 2 (2022): 1–14. https://doi.org/10.54105/ijainn.B1045.022222.

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<strong>Abstract:</strong> In current times the level at which Alzheimer’s disease is rising is at an alarming rate. This rise points to the need for much more accurate and faster modes of diagnosis that the country wants. Artificial intelligence can resolve this issue as it uses extensive human surveys and real-time machine medicine monitors. The use of biomarkers that work on detecting unusual changes in the brain and the spectrochemical analysis of blood that works on the principle of vibrational spectroscopy Inclusive of Raman spectroscopy and FTIR cannot be used at a large scale. Th

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Negi, Deepti. "Automating Fraud Detection in Financial Services: An AI-based Approach." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1315–25. http://dx.doi.org/10.17762/msea.v70i2.2323.

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One of the most common reasons why financial fraud occurs is due to credit card fraud. Unfortunately, traditional methods of detecting this issue have not been able to effectively prevent it. This has prompted the need for more sophisticated and efficient fraud detection techniques. Artificial intelligence has emerged as a promising tool for this problem. The paper looks into the use of AI methods to detect credit card fraud in the financial services industry. We analyze the performance of different algorithms, such as the Random Forest, the Neural Network, and the Naive Bayes. We also perform

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Eljaji, Halah, and Remudin Reshid Mekuria. "MATHEMATICAL MODELS AND ALGORITHMS IN LOGISTICS: REAL- WORLD APPLICATIONS AND AI IMPACT." Alatoo Academic Studies 24, no. 2 (2024): 412–21. http://dx.doi.org/10.17015/aas.2024.242.33.

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Have you ever pondered the importance of mathematics in the field of logistics? Have you also reflected on whether a solid understanding of mathematical principles is crucial for achieving success in a logistics profession? This article explores the intersection of mathematics and logistics, shedding light on the indispensable role mathematics plays within the industry. By exploring practical applications, it provides insights into the mathematical concepts integral to the daily activities of logistics professionals. Whether aspiring to enter the field or carrying out further studies in logist

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Sternik, S. G., E. B. Tyutyukina, and A. A. Pomulev. "The risk assessment of public-private partnership projects using artificial intelligence algorithms." MIR (Modernization. Innovation. Research) 15, no. 3 (2024): 421–38. http://dx.doi.org/10.18184/2079-4665.2024.15.3.421-438.

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Purpose: is to develop an innovative approach to risk management in public-private partnership (PPP) projects using advanced artificial intelligence technologies that allow creating the risk assessment model that takes into account non-linear relationships between various risk factors.Methods: in addition to traditional methods of scientific knowledge, interdisciplinary approaches of risk management and established practice of machine learning were used in the work. The methodological basis of the study was formed by works on the risk assessment and the application of AI algorithms in this are

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Wang, John, Jeffrey Hsu, and Zhaoqiong Qin. "Exploring Nvidia's evolution, innovations, and future stock trends." Journal of Management and Engineering Integration 17, no. 1 (2024): 21–33. http://dx.doi.org/10.62704/10057/28082.

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This paper undertakes a thorough examination of Nvidia's stock market performance, intertwining historical analysis with forward-looking projections to illuminate the dynamic trajectory of this semiconductor industry giant. Commencing with a retrospective review, the authors delve into pivotal milestones, technological innovations, and strategic maneuvers that have shaped Nvidia's stock evolution. Utilizing advanced machine learning algorithms, including Random Forest and Support Vector Regression (SVR), alongside traditional statistical forecasting methods, we forecast future patterns. Throug

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Pai, Rohan, and Anubhav Wadhwa. "Artificial Intelligence based Modern Approaches to Diagnose Alzheimer s." Indian Journal of Artificial Intelligence and Neural Networking 2, no. 2 (2022): 1–14. http://dx.doi.org/10.54105/ijainn.b1045.022222.

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In current times the level at which Alzheimer’s disease is rising is at an alarming rate. This rise points to the need for much more accurate and faster modes of diagnosis that the country wants. Artificial intelligence can resolve this issue as it uses extensive human surveys and real-time machine medicine monitors. The use of biomarkers that work on detecting unusual changes in the brain and the spectrochemical analysis of blood that works on the principle of vibrational spectroscopy Inclusive of Raman spectroscopy and FTIR cannot be used at a large scale. The underemployment of these method

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Tormos, Bernardo, Benjamín Pla, Ramón Sánchez-Márquez, and Jose Luis Carballo. "Explainable AI Using On-Board Diagnostics Data for Urban Buses Maintenance Management: A Study Case." Information 16, no. 2 (2025): 74. https://doi.org/10.3390/info16020074.

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Industry 4.0, leveraging tools like AI and the massive generation of data, is driving a paradigm shift in maintenance management. Specifically, in the realm of Artificial Intelligence (AI), traditionally “black box” models are now being unveiled through explainable AI techniques, which provide insights into model decision-making processes. This study addresses the underutilization of these techniques alongside On-Board Diagnostics data by maintenance management teams in urban bus fleets for addressing key issues affecting vehicle reliability and maintenance needs. In the context of urban bus f

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Bratić, Diana, Petar Miljković, Denis Jurečić, and Tvrtko Grabarić. "AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality." Applied Sciences 15, no. 5 (2025): 2266. https://doi.org/10.3390/app15052266.

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The Six Sigma methodology for quality improvement enabled a high degree of process compliance and enhanced process capability. This research develops a new model for optimizing the offset printing process based on the Six Sigma approach, with the aim of reducing process variability and achieving stable, predictable production outcomes. Special focus was placed on defining Critical Product Characteristics (CPCs) and Critical to Quality (CTQs) points and analysing their impact on process output quality, defined by the sigma level. Based on the research, variability limits of production parameter

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Chang, Victor, Karl Hall, Qianwen Xu, Folakemi Amao, Meghana Ganatra, and Vladlena Benson. "Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models." Algorithms 17, no. 6 (2024): 231. http://dx.doi.org/10.3390/a17060231.

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Customer churn is a significant concern, and the telecommunications industry has the largest annual churn rate of any major industry at over 30%. This study examines the use of ensemble learning models to analyze and forecast customer churn in the telecommunications business. Accurate churn forecasting is essential for successful client retention initiatives to combat regular customer churn. We used innovative and improved machine learning methods, including Decision Trees, Boosted Trees, and Random Forests, to enhance model interpretability and prediction accuracy. The models were trained and

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Serugga, Joas. "AI-Assisted Game Theory Approaches to Bid Pricing Under Uncertainty in Construction." AppliedMath 5, no. 2 (2025): 39. https://doi.org/10.3390/appliedmath5020039.

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The construction industry is inherently marked by high uncertainty levels driven by its complex processes. These relate to the bidding environment, resource availability, and complex project requirements. Accurate bid pricing under such uncertainty remains a critical challenge for contractors seeking a competitive advantage while managing risk exposure. This exploratory study integrates artificial intelligence (AI) into game theory models in an AI-assisted framework for bid pricing in construction. The proposed model addresses uncertainties from external market factors and adversarial behaviou

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Kanavheti, Roy, Wellington Makondo, and Wellington Manjoro. "LEVERAGING AI AND BLOCKCHAIN FOR DECENTRALIZED CREDENTIAL VERIFICATION: A CASE STUDY IN ZIMBABWE’S EDUCATION SECTOR." International Journal of Computer Science and Mobile Computing 14, no. 7 (2025): 1–10. https://doi.org/10.47760/ijcsmc.2025.v14i07.001.

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Academic qualification forgery poses a major concern for higher learning institutions, employers, and regulatory authorities throughout the world. In Zimbabwe, the increase in the level of fake degrees has greatly eroded trust in the education industry. Conventional verification processes are time-consuming, manual, and highly vulnerable to tampering. This paper introduces a hybrid blockchain-based and AI-enabled academic qualification verification platform to fight the problems. A prototype was implemented integrating various artificial intelligence algorithms including Convolutional Neural N

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Olutimehin, Abayomi Titilola, Oluwaseun Oladeji Olaniyi, Anuoluwapo Deborah Popoola, Akinde Michael Ogunmolu, and Faith Hauwa Oluwapamilerin Kolo. "AI and IoT Integration for Predictive Maintenance and Risk Management in Smart Manufacturing." Asian Journal of Research in Computer Science 18, no. 7 (2025): 120–42. https://doi.org/10.9734/ajrcos/2025/v18i7724.

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Predictive maintenance has emerged as a cornerstone of Industry 4.0, enabling manufacturers to proactively identify and address equipment failures, minimize unplanned downtime, and optimize operational costs. However, realizing effective predictive maintenance in smart manufacturing environments requires overcoming persistent challenges related to real-time data communication, cybersecurity vulnerabilities, and system scalability. This study addresses these gaps by investigating the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies for predictive maintenance

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Sahoo, Barsha Baishali, and Snehasis Dey. "SVM and RF Based Performance Enhancement in Organization: A Study on KGI, Odisha and its Organizational Growth." Journal of Engineering Research and Reports 26, no. 6 (2024): 190–97. http://dx.doi.org/10.9734/jerr/2024/v26i61173.

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Organization and organizational growth are two important aspects of a society and its relevant advancement. Now along with organization and its growth, the society advancement becomes most priority for the new age. Technologies that are becoming essential for these growths must have to be inculcated in new age society for complete advancement. Artificial intelligence, machine learning and deep learning are taking industry in a different horizon for complete globalization and adequate smarter than ever before. This paper dives into the organizational growth in particular having studied differen

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Atay, Mehmet Tarik, and Munevver Turanli. "ANALYSIS OF CUSTOMER CHURN PREDICTION USING LOGISTIC REGRESSION, -NEAREST NEIGHBORS, DECISION TREE AND RANDOM FOREST ALGORITHMS." Advances and Applications in Statistics 92, no. 2 (2024): 147–69. https://doi.org/10.17654/0972361725008.

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Customer churn predictions (CCPs) and their comprehensive analysis have become prevalent in the global telecom industry over the last five years, driven by advancements in machine learning (ML) technologies. In addition, AI (artificial intelligence) and ML-based predictive methods are currently employed for CCP applications to enhance customer retention. This predictive CCP methodology streamlines customer management processes and ensures sustainable profit growth. The machine learning models focus on identifying features derived from data that is rich in various types of information. This stu

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AIENLOSHAN, OMOIKHEFE. "On-demand AI-driven Predictive Analysis: Bridging the Gap for Small and Medium Enterprises (SMEs)." Journal of Energy Research and Reviews 17, no. 5 (2025): 9–25. https://doi.org/10.9734/jenrr/2025/v17i5409.

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Real-time predictive analytics driven by Artificial Intelligence provides benefits to all major sectors while small and medium enterprises (SMEs) need additional study regarding its effects. Active AI predictive analytics technology serves as the focus of this research since it optimizes real estate energy consumption and improves market predictive models. Small business operations apply big data analytics with machine learning to enhance efficiency in energy usage and waste management and create precise market projection data. The research article utilizes Random Forest (RF), XGBoost together

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