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Journal articles on the topic 'Predictive Intelligence'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Alghamdi, Turki. "Prediction of Diabetes Complications Using Computational Intelligence Techniques." Applied Sciences 13, no. 5 (2023): 3030. http://dx.doi.org/10.3390/app13053030.

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Diabetes is a complex disease that can lead to serious health complications if left unmanaged. Early detection and treatment of diabetes is crucial, and data analysis and predictive techniques can play a significant role. Data mining techniques, such as classification and prediction models, can be used to analyse various aspects of data related to diabetes, and extract useful information for early detection and prediction of the disease. XGBoost classifier is a machine learning algorithm that effectively predicts diabetes with high accuracy. This algorithm uses a gradient-boosting framework an

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Şahin, Hüseyin. "Predictive UAV Battery Maintenance Planning with Artificial Intelligence." Journal of Aviation 9, no. 2 (2025): 260–69. https://doi.org/10.30518/jav.1546277.

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This research paper explores the use of artificial intelligence (AI) in the maintenance planning of electric batteries for unmanned aerial vehicles (UAVs). Traditional maintenance strategies are challenged by the impact on battery performance and the complexity of battery degradation, highlighting the importance of an AI-assisted predictive maintenance approach. The research predicts battery degradation using machine learning techniques, specifically Artificial Neural Networks (ANN) model, in combination with MATLAB's Remaining Useful Life (RUL) Prediction Toolbox. The AI model is designed to

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Zhang, Li, Song Wang, and Guo Jun Su. "Intelligence Predictive Control Study on Lime Rotary Kiln Temperature." Applied Mechanics and Materials 385-386 (August 2013): 848–51. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.848.

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For the non-linearity, large time lag characters of rotary kiln, we use intelligent predictive control method to control it. The prediction model, scrolling optimization and feedback adjustment are ultimate constituted the predictive control system each part. Gas flow measurement is used to realize rotary kiln`s temperature predictive control,and took NN-Model as prediction model to realize the intelligent forecast. The results of simulation show that this method has better stability and robustness than the traditional control method.

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Sugarwar, Kalyani S., and Santanu Sikdar. "Artificial Intelligence Applications in Predictive Healthcare Systems." Journal of Advances and Scholarly Researches in Allied Education 22, no. 01 (2025): 322–33. https://doi.org/10.29070/s2zg4656.

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Predictive systems made possible by artificial intelligence (AI) are revolutionising healthcare by allowing for more precise, rapid, and individualised medical procedures. Using data analytics, NLP, and machine learning algorithms, this article delves into the ways AI is being applied to predictive healthcare, specifically in the areas of illness risk prediction, treatment plan optimisation, and patient outcome improvement. Using massive datasets derived from genetic information, electronic health records, and real-time monitoring equipment, predictive algorithms seek out trends and outliers t

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Aleksić, Veljko, and Dionysios Politis. "Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in Serbian and Greek IT Students." International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE) 11, no. 2 (2023): 173–85. http://dx.doi.org/10.23947/2334-8496-2023-11-2-173-185.

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Even though research on predicting the academic achievement of IT students is not scarce, the inclusion of trait emotional intelligence and multiple intelligences as predictive factors is somewhat novel. The research examined associations between identified profiles of trait emotional intelligence and multiple intelligences, and academic success in the sample of 288 IT students, 208 from Serbia and 80 from Greece. The results show that trait emotional intelligence and multiple intelligences profile both proved to be important predictors of academic success. Another predictor of IT students’ ac

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Chornous, Galyna O., and Viktoriya L. Gura. "Integration of Information Systems for Predictive Workforce Analytics: Models, Synergy, Security of Entrepreneurship." European Journal of Sustainable Development 9, no. 1 (2020): 83. http://dx.doi.org/10.14207/ejsd.2020.v9n1p83.

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The era of information economy leads to redesigning not only business models of organizations but also to rethinking the human resources paradigm to harness the power of state-of-the-art technology for Human Capital Management (HCM) optimization. Predictive analytics and computational intelligence will bring transformative change to HCM. This paper deals with issues of HCM optimization based on the models of predictive workforce analytics (WFA) and Business Intelligence (BI). The main trends in the implementation of predictive WFA in the world and in Ukraine, as well as the need to protect bus

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Susanu, Carolina, Anamaria Hărăbor, Ingrid-Andrada Vasilache, Valeriu Harabor, and Alina-Mihaela Călin. "Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence." Medicina 60, no. 10 (2024): 1604. http://dx.doi.org/10.3390/medicina60101604.

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Background and Objectives: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of this study was to assess the predictive performance of four machine learning algorithms for the prediction of postpartum and intrapartum hemorrhage. Materials and Methods: A prospective multicenter study was conducted, involving 203 patients with or without intra/postpartum hemorrhage within the initial 24 h postpartum. The participants were categorized into two groups: those with intra/postpartum hemorrhage (PPH) and th

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Bi, Jinqiang, Hongen Cheng, Wenjia Zhang, Kexin Bao, and Peiren Wang. "Artificial Intelligence in Ship Trajectory Prediction." Journal of Marine Science and Engineering 12, no. 5 (2024): 769. http://dx.doi.org/10.3390/jmse12050769.

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Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techni

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Abdulrazzq, Raghdah Adnan, Nisreen Mustafa Sajid, and Marwan Sabah Hasan. "Artificial intelligence-driven predictive maintenance in IoT systems." South Florida Journal of Development 5, no. 12 (2024): e4781. https://doi.org/10.46932/sfjdv5n12-030.

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The study looks at the application of AI-driven predictive maintenance in IoT systems. Predictive device failure, efficient reduction in system downtime, reduced maintenance costs, and overall efficiency in connected devices will be enabled through machine learning and deep learning algorithms. The AI models developed within this research were able to provide a prediction accuracy of 92%, while the traditional methods of maintenance were far behind at 78%. It resulted in a 35% reduction in system downtime and a 28% decrease in maintenance costs while reducing the error rate to 8%. The above re

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Kamel, H. "Artificial intelligence for predictive maintenance." Journal of Physics: Conference Series 2299, no. 1 (2022): 012001. http://dx.doi.org/10.1088/1742-6596/2299/1/012001.

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Abstract Maintenance constitutes an important share of modern industrial activities. Reliable operations rely on the adequate application on maintenance. However, in the present competitive environment, maintenance processes must be optimized so that they will be performed only when needed, otherwise resources will be needlessly wasted. This is in contrast to the conventional approach where maintenance is scheduled according to a time plan regardless of it is needed or not. This paper presents the application of artificial intelligence to create a model that can successfully predict the condit

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Inukonda, Jaishankar. "Leveraging Artificial Intelligence for Predictive Insights from Healthcare Data." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 611–15. http://dx.doi.org/10.21275/sr241006040947.

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Kostenko, Roman, and Aleksey Ilyashenko. "The future of criminal justice: the role of artificial intelligence in predictive analytics." Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia 2024, no. 3 (2024): 200–206. http://dx.doi.org/10.35750/2071-8284-2024-3-200-206.

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Introduction. This article focuses on the importance and prospects for the use of artificial intelligence in predictive analytics in the criminal justice context. The research is motivated by the significant development of artificial intelligence and machine learning technologies, which are being used in a multitude of fields, including criminal justice. The authors detail the theoretical and practical aspects of predictive analytics, which makes it possible to predict future events based on statistical data and machine learning algorithms. Special attention is paid to the difference between a

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Noh, Mi Jin. "Artificial Intelligence Analysis for Crop Survival Prediction in Smart Agriculture." Korean Institute of Smart Media 14, no. 3 (2025): 19–26. https://doi.org/10.30693/smj.2025.14.3.19.

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Smart agriculture has been evolving by integrating ICT, IoT, and AI technologies to maximize agricultural productivity and optimize resource utilization. This study aims to predict crop survival in smart agriculture using various machine learning and deep learning models while analyzing and comparing their performance. To achieve this, Random Forest, XGBoost, LightGBM, LSTM, and GRU models were implemented, and their predictive performance was evaluated using accuracy, precision, recall, and F1-score. SHAP analysis was applied to enhance model interpretability and assess the impact of key vari

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Saleem, Mohammad, Abigail E. Watson, Aisha Anwaar, Ahmad Omar Jasser, and Nabiha Yusuf. "Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment." Biomolecules 15, no. 4 (2025): 589. https://doi.org/10.3390/biom15040589.

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Immune checkpoint inhibitors (ICIs) have transformed melanoma treatment; however, predicting patient responses remains a significant challenge. This study reviews the potential of artificial intelligence (AI) to optimize ICI therapy in melanoma by integrating various diagnostic tools. Through a comprehensive literature review, we analyzed studies on AI applications in melanoma immunotherapy, focusing on predictive modeling, biomarker identification, and treatment response prediction. Key findings highlight the efficacy of AI in improving ICI outcomes. Machine learning models successfully ident

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Pasupuleti, Murali Krishna. "AI-Enabled Agricultural World Models for Drought and Flood Prediction Using Multimodal Data." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 186–99. https://doi.org/10.62311/nesx/rp1425.

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Abstract: This research paper delves into the realm of AI-enabled agricultural world models for predicting droughts and floods by leveraging multimodal data. The rapid advancements in artificial intelligence (AI) offer promising solutions to address the challenges posed by unpredictable weather patterns and their effects on agriculture. By integrating diverse sources of data such as satellite imagery, weather patterns, soil moisture levels, and crop health indicators, the proposed models aim to provide accurate and timely predictions of droughts and floods. The utilization of AI algorithms, in

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Harrington, Linda. "Comparison of Generative Artificial Intelligence and Predictive Artificial Intelligence." AACN Advanced Critical Care 35, no. 2 (2024): 93–96. http://dx.doi.org/10.4037/aacnacc2024225.

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Karthi, Gopalaswamy. "SALESFORCE ARTIFICIAL INTELLIGENCE: TRANSFORMING CUSTOMER ENGAGEMENT THROUGH PREDICTIVE INTELLIGENCE." International Journal of Engineering Technology Research & Management (IJETRM) 08, no. 06 (2024): 238–46. https://doi.org/10.5281/zenodo.15541433.

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Businesses are spending less time on routine customer work because of the Einstein platform which helps thembuild better relationships with clients. It enables organisations to analyse extensive customer records, understandhow people behave, and accurately predict their future actions. Salesforce enables businesses to tailor theirmarketing efforts, enhance sales management, and improve service delivery.For example, AI ensures that top-selling leads are highlighted for teams and that personalized product ideas aredeveloped, building better relationships with customers and making them more conte

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Saurabh, Saurabh. "Comparative Study of Machine Learning Algorithms in Predicting Load-Induced Bridge Failures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48074.

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Abstract: Bridges are critical components of transportation infrastructure, and their failure can lead to severe economic losses and safety risks. Traditional methods of monitoring and predicting structural failures often rely on manual inspections and periodic maintenance, which may miss early warning signs of degradation. This research explores the application of Artificial Intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in predicting structural failures of bridges. By analyzing data from sensors embedded in bridge structures, such as strain gauges, acce

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Mohammad, Abdullah, Minhajul Amin Md, Yasmin Tisha Sakina, Samin Kafil Sabera, and Kownine Antor Tahsin. "Utilizing Artificial Intelligence for Predictive Project Management." International Journal of Novel Research in Engineering and Science 11, no. 2 (2024): 36–43. https://doi.org/10.5281/zenodo.14293301.

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<strong>Abstract:</strong> The integration of Artificial Intelligence (AI) into predictive project management has revolutionized the way projects are planned, monitored, and executed. This study explores the application of AI-driven models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF), to predict project timelines and costs accurately. The models were trained on real-world data, with results showing ANN as the most effective in reducing errors and improving reliability. Key insights reveal that AI enhances decision-making, minimizes deviatio

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Vaani Gupta and Aman Jatain. "Artificial Intelligence Based Predictive Analysis of Customer Churn." Formosa Journal of Computer and Information Science 2, no. 1 (2023): 95–110. http://dx.doi.org/10.55927/fjcis.v2i1.3926.

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Customer churn, also known as attrition, occurs when subscribers or customers stop doing business with an enterprise or organization by unsubscribing to a service, discontinuing membership or simply stopping payment. Churn is a critical metric because it is more cost-effective to retain existing customers than it is to acquire new ones. Since churning impedes growth, companies usually use a defined method for calculating customer churn in a given period. By monitoring churn rate and the various factors affecting it, organizations determine their customer retention success rates and identify st

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Margoum, Safae, Bekkay Hajji, Stefano Aneli, Giuseppe Marco Tina, and Antonio Gagliano. "Optimizing Nanofluid Hybrid Solar Collectors through Artificial Intelligence Models." Energies 17, no. 10 (2024): 2307. http://dx.doi.org/10.3390/en17102307.

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This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy is quantitatively evaluated, and their effectiveness measured. The results demonstrate that both XGB and ETR models consistently outperform KNN in accurately predicting both electrical and thermal efficiency. Specifically, the XGB model a

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Wu, Wenyuan, Huaizhi Su, Yanming Feng, et al. "A Novel Artificial Intelligence Prediction Process of Concrete Dam Deformation Based on a Stacking Model Fusion Method." Water 16, no. 13 (2024): 1868. http://dx.doi.org/10.3390/w16131868.

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Deformation effectively represents the structural integrity of concrete dams and acts as a clear indicator of their operational performance. Predicting deformation is critical for monitoring the safety of hydraulic structures. To this end, this paper proposes an artificial intelligence-based process for predicting concrete dam deformation. Initially, using the principles of feature engineering, the preprocessing of deformation safety monitoring data is conducted. Subsequently, employing a stacking model fusion method, a novel prediction process embedded with multiple artificial intelligence al

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ABBOD, M. F., J. W. F. CATTO, M. CHEN, D. A. LINKENS, and F. C. HAMDY. "ARTIFICIAL INTELLIGENCE FOR THE PREDICTION OF BLADDER CANCER." Biomedical Engineering: Applications, Basis and Communications 16, no. 02 (2004): 49–58. http://dx.doi.org/10.4015/s1016237204000098.

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New techniques for the prediction of tumour behaviour are needed as statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. We have previously shown that the predictive accuracies of neuro-fuzzy modelling (NFM) and artificial neural networks (ANN), two methods of AI, are superior to traditional statistical methods for the behaviour of bladder cancer (Catto et al, 2003). In this paper, we explain the AI techniques required to produce these predictive models. We used 9 parameters, which were a combination

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Adigüzel, Gökhan, Ümit Şentürk, and Kemal Polat. "Blood Glucose Level Estimation Using Photoplethysmography (PPG) Signals with Explainable Artificial Intelligence Techniques." Open Journal of Nano 9, no. 1 (2024): 45–62. http://dx.doi.org/10.56171/ojn.1473276.

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Estimating blood sugar levels is a critical task in effective diabetes management. This study focuses on leveraging the power of machine learning models such as CatBoost, XGBoost, and Extra Trees Regressor, along with explainable AI techniques like SHAP values and confusion matrices, to predict blood sugar levels using Photoplethysmography (PPG) signals. The dataset used in this research is carefully selected for glucose prediction from PPG signals and consists of data from 217 individuals. Information for each individual includes laboratory glucose measurements and approximately one minute of

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Muñoz-Izquierdo, Nora, María-del-Mar Camacho-Miñano, María-Jesús Segovia-Vargas, and David Pascual-Ezama. "Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence." International Journal of Financial Studies 7, no. 2 (2019): 20. http://dx.doi.org/10.3390/ijfs7020020.

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Despite the number of studies on bankruptcy prediction using financial ratios, very little is known about how external audit information can contribute to anticipating financial distress. A handful of papers have shown that a combination of ratios and audit data is significant for predictive purposes, but only one recent paper provided a predictive accuracy of 80% solely by using the disclosures contained in audit reports. This study was complemented by simplifying the analysis of audit reports for prediction purposes and the same predictive accuracy was achieved. By applying three artificial

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Naveed, Iqra, Muhammad Farhat Kaleem, Karim Keshavjee, and Aziz Guergachi. "Artificial intelligence with temporal features outperforms machine learning in predicting diabetes." PLOS Digital Health 2, no. 10 (2023): e0000354. http://dx.doi.org/10.1371/journal.pdig.0000354.

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Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learnin

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Nayira Azeema and Rafasya Nayira. "Artificial Intelligence in Predictive Maintenance for Industrial IoT." Proceeding of The International Conference of Inovation, Science, Technology, Education, Children, and Health 3, no. 2 (2025): 127–30. https://doi.org/10.62951/icistech.v3i2.125.

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The adoption of the Industrial Internet of Things (IIoT) has enabled real-time monitoring of machinery, leading to increased efficiency and reduced downtime. This paper presents an AI-driven predictive maintenance system that utilizes machine learning algorithms to detect potential failures before they occur. Using historical sensor data, the proposed model achieves high accuracy in predicting equipment malfunctions, allowing timely intervention. The study demonstrates the potential of AI in optimizing industrial processes and reducing operational costs.

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Yatmanov, Alexey N., Vasiliy Ya Apchel, Dmitrii V. Ovchinnikov, et al. "Use of value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment." Bulletin of the Russian Military Medical Academy 26, no. 4 (2024): 587–96. https://doi.org/10.17816/brmma635764.

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The paper demonstrates the potential for using value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment. A retrospective cohort study was conducted. For 2013–2021, 734 cadets of the Navy Military Training and Research Center “Soviet Union Fleet Admiral N.G. Kuznetsov Naval Academy” were examined, 48 of them were diagnosed with maladjustment. Neural networks were used for mathematical modeling of maladjustment prediction. The study included 8 cycles of neural network training and 7 cycles of neural network model testing. As the actual materi

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Lira Cortes, Ana Laura, and Carlos Fuentes Silva. "Artificial Intelligence Models for Crime Prediction in Urban Spaces." Machine Learning and Applications: An International Journal 8, no. 1 (2021): 1–13. http://dx.doi.org/10.5121/mlaij.2021.8101.

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This work presents research based on evidence with neural networks for the development of predictive crime models, finding the data sets used are focused on historical crime data, crime classification, types of theft at different scales of space and time, counting crime and conflict points in urban areas. Among some results, 81% precision is observed in the prediction of the Neural Network algorithm and ranges in the prediction of crime occurrence at a space-time point between 75% and 90% using LSTM (Long-ShortSpace-Time). It is also observed in this review, that in the field of justice, syste

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Kang, Hoseon, Jaewoong Cho, Hanseung Lee, Jeonggeun Hwang, and Hyejin Moon. "Development of an ANN-Based Urban Flood Alert Criteria Prediction Model and the Impact of Training Data Augmentation." Journal of the Korean Society of Hazard Mitigation 21, no. 6 (2021): 257–64. http://dx.doi.org/10.9798/kosham.2021.21.6.257.

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Urban flooding occurs during heavy rains of short duration, so quick and accurate warnings of the danger of inundation are required. Previous research proposed methods to estimate statistics-based urban flood alert criteria based on flood damage records and rainfall data, and developed a Neuro-Fuzzy model for predicting appropriate flood alert criteria. A variety of artificial intelligence algorithms have been applied to the prediction of the urban flood alert criteria, and their usage and predictive precision have been enhanced with the recent development of artificial intelligence. Therefore

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P, Suraj, Pranav H M, Prathiksha P Shetty, and Suhas B E. "Artificial Intelligence (AI) Enhanced Cognitive Mobile Computing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43702.

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The rise of mobile devices and enhanced by the development in Artificial Intelligence (AI) these power-ups have translated into humongous improvements on how people interact with applications. In this paper we explore the incorporation of cognitive computing approaches for creating mobile specific predictive interaction models. Powered by AI, these models seek to predict user activity, interests and demands for the most human like experience in mobile applications. Here, we present an all-inclusive methodology to collect data from mobile sensors design the model with machine learning algorithm

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S. Edwin Raja, M. Thiyagarajan, J. Jeno Jasmine, P. Raghavan, D. Dhinakaran,. "Optimizing Disease Prediction with Artificial Intelligence Driven Feature Selection and Attention Networks." Journal of Electrical Systems 20, no. 3s (2024): 12–27. http://dx.doi.org/10.52783/jes.1117.

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The rapid integration of machine learning methodologies in healthcare has ignited innovative strategies for disease prediction, particularly with the vast repositories of Electronic Health Records (EHR) data. This article delves into the realm of multi-disease prediction, presenting a comprehensive study that introduces a pioneering ensemble feature selection model. This model, designed to optimize learning systems, combines statistical, deep, and optimally selected features through the innovative Stabilized Energy Valley Optimization with Enhanced Bounds (SEV-EB) algorithm. The objective is t

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Kukkadapu, Sushma. "Quantum-Inspired Adaptive Intelligence Framework for Next-Generation Predictive Systems." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1068–72. https://doi.org/10.21275/sr25413081739.

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Soni, Umang, Saksham Gupta, Taranjeet Singh, Yash Vardhan, and Vipul Jain. "Predictive Model of Solar Irradiance Using Artificial Intelligence." International Journal of Information Retrieval Research 10, no. 2 (2020): 81–98. http://dx.doi.org/10.4018/ijirr.2020040105.

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Solar power in India is growing at a tremendous pace. India's solar power capacity is 20 GW and has grown 8-fold since 2014. Assessing the solar potential in India is thus the need of the hour. The objective of this study is to make an optimized prediction model of the monthly potential of solar irradiance of the Indian Subcontinent, by utilizing hour-wise unstructured voluminous (80 million line item) satellite-based data from 609 locations for 15 years. The variables chosen are temperature, pressure, relative humidity, month, year, latitude, longitude, altitude, DHI, DNI, and GHI. Combining

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Ebule, Amejuma Emmanuel. "Leveraging Artificial Intelligence in Business Intelligence Systems for Predictive Analytics." International Journal of Scientific Research and Management (IJSRM) 13, no. 01 (2025): 1862–79. https://doi.org/10.18535/ijsrm/v13i01.ec02.

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Artificial Intelligence (AI) and Business Intelligence (BI) are rapidly emerging as the next big things for organizations to analyze data and gain insights. As this article will go on to examine, the concept of using AI for BI is one that has significant implications about the possible integration of AI into various Business Intelligence systems examined in this article will focus on the application of AI for BI in the use of predicting analytics. When integrating Machine learning, natural language processing, and intelligent automation, these AI-Advanced BI systems assist organizations to go

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Gudelli, VENKATA RAMANA. "Artificial Intelligence for Predictive Maintenance in Cloud Services." INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT 6, no. 5 (2021): 1–10. https://doi.org/10.5281/zenodo.15097120.

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Abstract:The current digital economy requires cloud services to conduct business operations as well as handle data management and IT infrastructure requirements. The scalability and cost efficiency and flexibility which cloud computing provides require users to maintain constant service availability. The occasional service interruption creates business financial damage together with badreputation impact as well as dissatisfied customers especially in e-commerce and social networking sectors. A reliable cloud serviceoperation requires the implementation of powerful maintenance strateg

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Vovk, Vladimir, Jieli Shen, Valery Manokhin, and Min-ge Xie. "Nonparametric predictive distributions based on conformal prediction." Machine Learning 108, no. 3 (2018): 445–74. http://dx.doi.org/10.1007/s10994-018-5755-8.

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Tilimbe, Jiya. "Ethical Implications of Predictive Risk Intelligence." ORBIT Journal 2, no. 2 (2019): 1–28. http://dx.doi.org/10.29297/orbit.v2i2.112.

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Fan, Zeyu, Ziju He, Wenjun Miao, and Rongrong Huang. "Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic Review." Processes 11, no. 8 (2023): 2324. http://dx.doi.org/10.3390/pr11082324.

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The gastric cancer risk prediction model used for large-scale gastric cancer screening and individual risk stratification is an artificial intelligence tool that combines clinical diagnostic data with a classification algorithm. The ability to automatically make a quantitative assessment of complex clinical data contributes to increased accuracy for diagnosis with higher efficiency, significantly reducing the incidence of advanced gastric cancer. Previous studies have explored the predictive performance of gastric cancer risk prediction models, as well as the predictive factors and algorithms

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Gupta Lakkimsetty, N. V. Rama Sai Chalapathi. "Role of AI in Business Analytics: Predictive Insights for Future Trends." International Journal of Computer Science and Mobile Computing 14, no. 3 (2025): 1–10. https://doi.org/10.47760/ijcsmc.2025.v14i03.001.

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In the current corporate environment, combining Artificial Intelligence (AI) using Cloud corporate Intelligence (CBI) is a revolutionary way to improve data visualisation and predictive analytics. This article examines how cloud-based solutions and AI technologies work together, emphasising how both have an effect on decision-making. The ability to make decisions is significantly enhanced by AI-driven systems, which provide precise real-time insights and predictive analytics. In this article, we look at the many benefits of integrating AI with BI, including improved operational efficiency, per

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Hammadi, Jasim Faraj, Aliza Binti Abdul Latif, and Zaihisma Binti Che Cob. "Artificial intelligence approaches for cardiovascular disease prediction: a systematic review." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1208. https://doi.org/10.11591/ijeecs.v38.i2.pp1208-1218.

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Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis.

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Jasim, Faraj Hammadi Aliza Binti Abdul Latif Zaihisma Binti Che Cob. "Artificial intelligence approaches for cardiovascular disease prediction: a systematic review." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1208–18. https://doi.org/10.11591/ijeecs.v38.i2.pp1208-1218.

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Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis.

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43

Chen, Li. "Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence." Computational Intelligence and Neuroscience 2022 (October 4, 2022): 1–11. http://dx.doi.org/10.1155/2022/5465816.

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Through the research and development of the regression prediction function of support vector machine, this paper applies it to the prediction of drilling fluid performance parameters and the formulation design of drilling fluid. The research in this paper can reduce the experimental workload and improve the efficiency of drilling fluid formulation design. The apparent viscosity (AV), plastic viscosity (PV), API filter loss (FLAPI), and roll recovery (R) of the drilling fluid were selected as the inspection objects of the drilling fluid performance parameters, and the support vector machine was

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Ackerman, Phillip L., and Ruth Kanfer. "Cognitive Ability and Non-Ability Trait Predictors of Academic Achievement: A Four-Year Longitudinal Study." Journal of Intelligence 13, no. 7 (2025): 79. https://doi.org/10.3390/jintelligence13070079.

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Prediction of individual differences in academic achievement is one of the most prominent longstanding goals of differential psychology. Historically, the main source of prediction has been measures of intelligence and related cognitive abilities. Researchers have suggested that non-ability traits, such as personality, may also provide useful information in predicting academic achievement. Meta-analyses have indicated that there are significant correlations between such variables, but most of the existing studies have been conducted with cross-sectional designs, or with a limited inclusion of

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Rajitha, Akula, Lavish Kansal, Gowtham Raj, Ravi Kalra, Koushal Dhamija, and Dalael Saad Abdul-Zahra. "Biomaterials and Artificial Intelligence: Predictive Modeling and Design." E3S Web of Conferences 505 (2024): 01003. http://dx.doi.org/10.1051/e3sconf/202450501003.

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The emergence of artificial intelligence (AI) with synergistic integration is currently a paradigm-shifting strategy for the direction of biomaterials development and design. This paper analyzes the connection between AI and biomaterials, explaining the significant influence of predictive modelling on the path of the area. By carefully investigating state-of-the-art studies and unique applications, it illustrates how AI-driven predictive modelling redefined biomaterial design and entered a new era of unusual accuracy and productivity. This research covers a wide variety of AI technologies, fro

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Boyd, Alexander B., James P. Crutchfield, Mile Gu, and Felix C. Binder. "Thermodynamic overfitting and generalization: energetics of predictive intelligence." New Journal of Physics 27, no. 6 (2025): 063901. https://doi.org/10.1088/1367-2630/addf71.

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Abstract Overfitting is a crucial concern in machine learning, where an unnecessarily complex model closely captures the details of its training data but fails to generalize to new inputs. Regularization acts as a speed-bump to increasing complexity that ensures models only grow as necessary to fit the fundamental features of data. It mitigates overfitting and improves generalization by imposing a model complexity penalty. We show that both overfitting and regularization are rooted in physics, because they induce an energy penalty in thermodynamic engines that harvest work from data. Overfitti

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Wang, Xiaohui. "AI-Based Big Data Platform for Sports Training Construction and Application." International Journal of Information System Modeling and Design 16, no. 1 (2025): 1–23. https://doi.org/10.4018/ijismd.384305.

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To enhance the standardization, scientific accuracy, and intelligence of physical training, modern sports training should utilize information technology to manage big data in real-time and build an appropriate control platform. This paper examines big data management in track and field training, focusing on three key factors: data collection, analysis, and application. It also explores three artificial intelligence technologies—machine learning, deep learning, and predictive analytics—to evaluate their predictive accuracy. Results show that the multivariate regression method offers the highest

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Gusev, D. M., P. A. Melnikov, and V. A. Shashenko. "Using Artificial Intelligence as a Predictive Model of Atmospheric Air Pollutant Distribution." Ecology and Industry of Russia 29, no. 2 (2025): 56–59. https://doi.org/10.18412/1816-0395-2025-2-56-59.

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The experience of using artificial intelligence (AI) to create a predictive model of atmospheric air pollutant distribution in an urbanized area is presented. Various machine learning algorithms, their advantages and disadvantages in the context of air quality prediction are considered. The possibilities of using historical data accumulated from 2021 to June 2024 on atmospheric air pollution in Togliatti, meteorological conditions, topography and other factors affecting the distribution of pollutants for training of AI models are investigated. Simulation results demonstrating the effectiveness

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Rodilla, Enrique, Olast Arrizibita Iriarte, Blanca Miranda Serrano, et al. "PREDICTION OF CARDIOVASCULAR EVENTS IN HYPERTENSIVE SUBJECTS WITH ARTIFICIAL INTELLIGENCE TOOLS." Journal of Hypertension 42, Suppl 1 (2024): e2. http://dx.doi.org/10.1097/01.hjh.0001019340.58350.29.

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Objective: Cardiovascular diseases contribute substantially to morbi-mortality at the global level. To effectively address the increasing incidence of cardiovascular events, advanced preventive strategies are needed. Predictive modeling is a crucial tool to identify individuals at risk and facilitate proactive interventions to improve clinical outcomes. This study develops and evaluates a model for predicting cardiovascular events (ischemic heart disease and stroke in previously event-free hypertensive subjects) by integrating clinical data, biomarkers and established risk factors. Most import

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Lee, Joon. "Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?" Journal of Medical Internet Research 22, no. 8 (2020): e19918. http://dx.doi.org/10.2196/19918.

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In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the performance of machine learning–based patient outcome prediction models has rarely been compared with that of human clinicians in the literature. Human intuition and insight may be sources of underused predictive information that AI will not be able to identify in electronic data. Both human and AI predic

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