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Journal articles on the topic 'MachineLearning (ML)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

. Narsimhulu, B. "Predicting Chronic Kidney Disease using MachineLearning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41262.

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In today's busy world, health is often neglected until symptoms appear. Chronic Kidney Disease (CKD) is particularly challenging as it often shows no symptoms, making early detection difficult and increasing the risk of severe complications. Machine learning (ML) provides a solution with its strong predictive capabilities.This study evaluated nine ML models, including KNN, Decision Tree, Random Forest, XGBoost, Stochastic Gradient Boosting, Gradient Boosting Classifier, CatBoost, Ada Boost and Extra Tree Classifier proving its effectiveness in CKD prediction. Keywords—Kidney disease, Machine L

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ROJA,, PAINATI. "Performance of Software Quality Prediction with Machinelearning Methods." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42572.

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The prediction of software quality through machine learning (ML) is an expanding area that focuses on applying different ML algorithms to anticipate the quality of software systems. Software quality assessment is a crucial task required at different phases of software development. It can be utilized for organizing the quality assurance practices of the project and for comparison purposes. In prior studies, two approaches (Multiple Criteria Linear Programming and Multiple Criteria Quadratic Programming) were employed to assess software quality. Additionally, C5.0, SVM, and neural networks were

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Ganla Sneha, G Manogna, and T C Swetha Priya. "Harnessing Machine Learning for AdvancedAttacker Behavior Analysis in Cybersecurity." international journal of engineering technology and management sciences 9, Special Issue 1 (2025): 18–26. https://doi.org/10.46647/ijetms.2025.v09si01.003.

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With the increasing complexity of cyber threats, many traditional security methods have becomeineffective in keeping up with the ever-evolving tactics of cybercriminals. Since attackers constantlyadapt and change their strategies, there is a clear need for proactive defense mechanisms. MachineLearning and Artificial Intelligence have become key forces in transforming cybersecurity, enablingreal-time insights into attack behavior, predictive threat assessments, and automated responsemechanisms. This paper explores how AI-based tools utilize supervised and unsupervised learning,anomaly detection

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Muthusamy, Rajendiran, Charulatha Kannan, Jayarathna Mani, Rathinasabapathi Govindharajan, and Karthikeyan Ayyasamy. "Artificial intelligence-powered intelligent reflecting surface systems countering adversarial attacks in machine learning." International Journal of Reconfigurable and Embedded Systems (IJRES) 13, no. 2 (2024): 414. http://dx.doi.org/10.11591/ijres.v13.i2.pp414-423.

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With the increase in the computation power of devices wireless communication has started adopting machine learning (ML) techniques. Intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic wave propagation by changing the electric and magnetic values of its surface. State-of-the-art ML especially on deep learning (DL)-based IRS-enhanced communication is an emerging topic. Yet while integrating IRS with other emerging technologies possibilities of adversarial data creaping is high. Threats to security, their mitigation, and complexes for AI-power

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Raut, Ms Mayuri, Ms Divya Shende, Mr Pranay Girdhari, Ms Bhagyashri Nimgade, and Prof Anuja Ghasad. "Helmet Detection on Two-Wheeler Riders using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1450–53. http://dx.doi.org/10.22214/ijraset.2023.50375.

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Abstract: In daily life, the role of a helmet is vital for motorists. The human brain is an important organ, which is protected by the skull. So the head is to be protected by a helmet in case of an accident. From our literature survey we found that in india, the majority of motorists do not wear a helmet. This negligence causes fatal injuries. We want to minimize this risk. Our project uses ML and OPENCV tools for Helmet Detection. In this project we use a camera module to detect the face of the person. The preprocessed input is fed to the Machine Learning model. This model processes it and t

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Bindu, Ms A. Hima. "Airfare Forecasting using Machine Learning to Predict Prices." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1481–86. http://dx.doi.org/10.22214/ijraset.2024.60024.

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Abstract: The issue of predicting ticket 111 costs is the focus of this essay. With the assumption that these characteristics have an impact on the cost of an airline ticket, a set of features typical of a normal flight is determined for this purpose. Eight cutting edge machine learning (ML) models using the characteristics are trained to forecast the cost of airline tickets, and the models' output is contrasted with one another. This work examines how the feature set used to represent an airline affects accuracy as well as the prediction accuracy of each model. To train each machinelearning m

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Onah, Emeka Harrison, N. L. Lethole, and P. Mukumba. "Optoelectronic Devices Analytics: MachineLearning-Driven Models for Predicting the Performance of a Dye-Sensitized Solar Cell." Electronics 14, no. 10 (2025): 1948. https://doi.org/10.3390/electronics14101948.

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Optoelectronic devices, which combine optics and electronics, are vital for converting light energy into electrical energy. Various solar cell technologies, such as dye-sensitized solar cells (DSSCs), silicon solar cells, and perovskite solar cells, among others, belong to this category. DSSCs have gained significant attention due to their affordability, flexibility, and ability to function under low light conditions. The current research incorporates machine learning (ML) models to predict the performance of a modified Eu3+-doped Y2WO6/TiO2 photo-electrode DSSC. Experimental data were collect

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Sachin, M. Elgandelwar, Bairagi Vinayak, S. Vasekar Shridevi, Nanthaamornphong Aziz, and Tupe-Waghmare Priyanka. "Analyzing electroencephalograph signals for early Alzheimer's disease detection: deep learning vs. traditional machine learning approaches." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 2602–15. https://doi.org/10.11591/ijece.v14i3.pp2602-2615.

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Alzheimer’s disease (AD) stands as a progressive neurodegenerative disorder with a significant global public health impact. It is imperative to establish early and accurate diagnoses of AD to facilitate effective interventions and treatments. Recent years have witnessed the emergence of machine learning (ML) and deep learning (DL) techniques, displaying promise in various medical domains, including AD diagnosis. This study undertakes a comprehensive contrast between conventional machinelearning methods and advanced deep learning strategies for early AD 

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O, Apoorva G., and Spoorthi M. "Raita Mitra for Crop and Pesticide Recommendation along with Disease Prediction using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 1253–62. http://dx.doi.org/10.22214/ijraset.2023.49243.

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Abstract: Agriculture seems to be a key part of both a country's food security and its economic growth. Choosing which crops to grow is one of the most important parts of planning agriculture. The suggested system helps farmers choose crops that will do well in their area. For agriculture to grow, it's important to be able to make accurate predictions about which crops to grow. We've given you a machine-learning method called "Random Forests" that can predict how crop choices will change based on the current climate and biophysical changes. We have gathered a lot of information about crop sele

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Murugan, R., Flaize Sara Thomas, G. Geetha Shree, S. Glory, and A. Shilpa. "Linear Regression Approach to Predict Crop Yield." Asian Journal of Computer Science and Technology 9, no. 1 (2020): 40–44. http://dx.doi.org/10.51983/ajcst-2020.9.1.2152.

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The agriculture plays a very big and important role for the country’s growth. The agriculture science system facing lots of problems from the environmental change. Machinelearning (ML) is the best approach to overcome the problems by building the good and effective solutions. Crop yield prediction include prediction of yield for the crop by analyzing the existing data by considering several parameters like weather, soil, water and temperature etc. This project addresses and defines the predicting yield of the crop based on the previous year’s data using Linear Regression algorithm. The approac

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Imankulova, Assemay, Arailym Serikbay, and Meraryslan Meraliyev. "A COMPREHENSIVE REVIEW OF APPROACHES, CHALLENGESIN CAREER RECOMMENDATION SYSTEMS." Suleyman Demirel University Bulletin Natural and Technical Sciences 64, no. 1 (2024): 5–34. https://doi.org/10.47344/sdubnts.v64i1.1148.

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This research presents an extensive investigation intorecommendation systems pertinent to career guidance, encompassing jobmatching, education, and skill development applications. The study rigorouslyexamines methodologies, algorithms, and data sources integral to these systems,evaluating their strengths and limitations. It thoroughly explores evaluationmetrics, real-world case studies, and emerging trends, emphasizing challengeslike data sparsity, scalability, and fairness.Furthermore, the paper provides a comprehensive analysis of machinelearning (ML), deep learning (DL), and reinforcement l

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Rico, Kurniawan, Utomo Budi, N. Siregar Kemal, et al. "Hypertension prediction using machine learning algorithm among Indonesian adults." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 776–84. https://doi.org/10.11591/ijai.v12.i2.pp776-784.

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Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machinelearning algorithms such as

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ABABNEH, Mustafa, Aayat ALJARRAH, and Damla KARAGOZLU. "The Role of Big Data and Machine Learning in COVID-19." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 11, no. 2 Sup.1 (2020): 01–20. https://doi.org/10.18662/brain/11.2Sup1/89.

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 The big rise in the existence of digital data contributed tocreating many good chances, especially related to corporations, institutionsand firms. Also, it gives the capability to scrimp data regarding its majoror area, where the countries have benefited from the analysis of big data(BD) greatly in the face of epidemics and diseases, especially COVID-19since BD is now available everywhere around us, from official reports andscientific studies related to virology and epidemiology. The general aim ofthis study is to clarify how the conjunction among both BD and machinelearning (ML) created

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S.PRATHAP, A.RAJITHA, B.RAJESHWARI, and G.PALLAVI. "MACHINE LEARNING AND END-TO-END DEEP LEARNING FOR THE DETECTION OF CHRONIC HEART FAILURE FROM HEART SOUNDS." Journal of Engineering Sciences 15, no. 10 (2024): 157–64. http://dx.doi.org/10.36893/jes.2024.v15i10.019.

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Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is increasing by 2% annually. Despite the significant burden that CHF poses and despite the ubiquity of sensors in our lives, methods for automatically detecting CHF are surprisingly scarce, even in the research community. We present a method for CHF detection based on heart sounds. The method combines classic MachineLearning (ML) and end-to-end Deep Learning (DL). The classic ML learns from expert features, and the DL learns from a spectro-temporal representation of the signal. The method was evaluated

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M. Mathumathi and A. Rama. "Automatic Segmentation of Flood Region in Otsu’s/Kapur’s Threshold Enhanced Images using Deep-Learning Scheme." Advances in Artificial Intelligence and Machine Learning 05, no. 02 (2025): 3755–67. https://doi.org/10.54364/aaiml.2025.52213.

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Artificial Intelligence (AI) supported data analytics is adopted in variety of domains to process the data with a guaranteed accuracy. The application of the AI-schemes, like MachineLearning (ML) and Deep-Learning (DL) are commonly considered when a faster and accurate image examination is necessary. Hence, AI techniques are frequently utilized to process gray/RGB images. This research aims to propose a DL-supported segmentation tool to examine the Flood Monitoring Image (FMI) data. The developed system encompasses the following phases: (i) image collection and resizing, (ii) image pre-process

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Shahwan, Younis Ali, and Maseeh Hajar. "AI-Powered Database Management: Predictive Analytics for Performance Tuning." Engineering and Technology Journal 10, no. 05 (2025): 5100–5112. https://doi.org/10.5281/zenodo.15472012.

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As data volumes and query complexities grow in modern applications, ensuring optimal database performance has become increasingly challenging. Traditional manual tuning approaches are reactive, time-consuming, and often lack adaptability to dynamic workloads. This paper explores the integration of Artificial Intelligence (AI) and predictive analytics into database management systems (DBMS) for proactive performance tuning. By leveraging machine learning models, such as regression analysis and anomaly detection, AI-powered systems can forecast performance degradation, recommend tuning actions,

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Masino, Nicolas Martín, and Antonio Quintero-Rincon. "Diagnóstico de cáncer de mama usando el tamaño del efecto d de Cohen como selector de características." Inteligencia Artificial 28, no. 75 (2025): 260–80. https://doi.org/10.4114/intartif.vol28iss75pp260-280.

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Breast cancer is a tumor that begins to grow in the milk ducts or lobules and can become lethal iftreatment is not administered in time. According to the World Health Organization (WHO), there were approximately2.3 million cases of breast cancer in 2020. Furthermore, breast cancer can affect anyone, particularlywomen over 50 years old. Therefore, it is crucial to have early diagnostic techniques. We propose a novel methodbased on Cohen’s d for feature selection in this context. Cohen’s d is a statistical concept that quantifies thestrength of the relationship between two populations on a numer

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Mr., Dhavalkumar Upendrabhai Patel. "A Review, Synthesizing Frameworks, and Future Research Agenda: Use of AI & ML Models in Cardiovascular Diseases Diagnosis." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 12, no. 11 (2023): 12–19. https://doi.org/10.35940/ijitee.K9733.10121123.

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<strong>Abstract: </strong>Cardiovascular diseases (CVDs) continue to be a leading cause of morbidity and mortality worldwide. Early detection and accurate diagnosis of the initial phases of CVDs are crucial for effective intervention and improved patient outcomes. In recent years, advances in intelligent automation and machine learning (ML) techniques have shown promise in enhancing the accuracy and efficiency of CVD detection. This systematic review aims to comprehensively analyze and synthesize the existing literature on the application of intelligent automation and ML adaptive classifier m

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Yosra, Ali Hassan, and R. M. Zeebaree Subhi. "Big Data Cloud Computing and AI-Driven Digital Marketing in Enterprise Systems." Engineering and Technology Journal 10, no. 04 (2025): 4597–615. https://doi.org/10.5281/zenodo.15303155.

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The integration of Big Data, Cloud Computing, and Artificial Intelligence (AI) has significantly transformed digital marketing and modern enterprise systems. These technologies enable advanced data analytics, predictive modeling, and real-time customer engagement, fostering more personalized marketing strategies and improving overall business efficiency. AI-powered tools, including machine learning algorithms, automated Customer Relationship Management (CRM) systems, and sentiment analysis platforms, facilitate the delivery of targeted content and enhance customer satisfaction. Additionally, c

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Islam, Md mafiqul. "Applications of MachineLearning(ML): The real situation of the Nigeria Fintech Market." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 1, no. 1 (2024). http://dx.doi.org/10.60087/jaigs.v1i1.34.

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In the world of technology, machine learning, or ML, is a well recognized word. It is concerning, therefore, when ML models are used in financial institutions. Actually, in order to provide their clients with the greatest experience possible, the Industry 4.0 has pushed them to grow their digital system. The definition and uses of machine learning as well as the current state of the finetech market in Nigeria will be covered in this publication. As a result, we will forecast how financial institutions will develop in the future and whether or not to employ machine learning.

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Shravani, Shete Kshama Singh Kaustubh Thakare Ishika Patel Minal Lopes. "Wine Quality Prediction System." July 18, 2024. https://doi.org/10.5281/zenodo.12770356.

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This project investigates the utilization of machinelearning (ML) techniques, namely XGBoost, Artificial NeuralNetworks (ANN), and Random Forest, to predict wine qualitybased on physiochemical data from red and white wines. Thesealgorithms were selected for their robust learning capabilities andsuitability for complex pattern recognition tasks. By leveragingthese methods, the study aims to analyze the intricate relationships between intrinsic properties of wine and its quality rating.Through comparative analysis, the project seeks to identify themost effective ML approach for precise wine qual

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Aktar, Mst Shuly. "Analyzing the Use of Artificial Intelligence and MachineLearning in Customer Support Systems." PMIS Review 2, no. 1 (2023). http://dx.doi.org/10.56567/pmis.v2i1.12.

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In order to identify areas that need further research, this study will analyze how machine learning (ML) and artificial intelligence (AI) technologies are applied in various customer services. This is done by using a systematic review of literature procedure to examine publications on the application of AI and ML in customer service that have been posted on various academic websites. Numerous AI and ML approaches may be applied by businesses to improve customer support and assistance. The use of self-service technologies, integrated product-service offers, service excellence, and word-of-mouth

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Debasis, Mondal, Adhikary Shreya, Sinha Roy anmay, Babul Akhtar Sk, and Suvro Sannyashi Tomal. "Advances in Prostate Cancer Detection: A Comprehensive Review of Machine Learning Techniques and Their Clinical Applications." June 7, 2024. https://doi.org/10.5281/zenodo.14377413.

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Prostate cancer remains one of the most prevalent cancers among men globally,necessitating the development of advanced diagnostic methods. Recent advancements in machinelearning (ML) have shown promising results in enhancing the accuracy and efficiency of prostatecancer detection. This review provides a comprehensive overview of ML techniques applied toprostate cancer detection, including supervised and unsupervised learning, deep learning, andhybrid methods. We discuss the key methodologies, their clinical applications, performancemetrics, and future directions for integrating ML into routine

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NAVEEN S, MURUGESH, and VENKATACHALAM NR. "Sales Forecasting for Future Trends :As a Machine Learning Approach." International Journal For Multidisciplinary Research 7, no. 2 (2025). https://doi.org/10.36948/ijfmr.2025.v07i02.39954.

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For companies to create well-informeddecisions about resource allocation,marketing tactics, Accurate salesforecasting is crucial for inventory control.The intricacy and unpredictability presentin contemporary markets are frequentlyoverlooked by conventional forecastingtechniques. With its sophisticatedalgorithms that can analyse big datasets,identify non-linear trends, and adapt toshifting market conditions, machinelearning (ML) has become a potent tool forovercoming these constraints. This studyexamines the use of machine learning (ML)in sales forecasting, offering a thoroughexamination of ap

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Chesmore, Grace, Alexandre Adler, Nicholas Cothard, et al. "The Simons Observatory: HoloSim-ML: machinelearning applied to the efficient analysis of radioholography measurements of complex optical systems." Applied Optics, September 14, 2021. http://dx.doi.org/10.1364/ao.435007.

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Veernapu, Kiran. "Predicting Employee Attrition in Healthcare Using AI Models, Deep Learning and Natural Language Processing." Predicting Employee Attrition in Healthcare Using AI Models, Deep Learning and Natural Language Processing 6, no. 6 (2022). https://doi.org/10.47363/PMS/2022(6)E158.

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Employee attrition, or turnover, is a significant concern in many industries, particularly in healthcare,where retaining skilled professionals is critical for maintaining high-quality patient care. Healthcareorganizations face unique challenges, including high stress, burnout, and work-life balance issues,which can contribute to higher employee turnover rates. Predicting employee attrition can helporganizations implement targeted interventions to retain employees, reduce turnover costs, andensure a stable workforce. This paper explores the use of Artificial Intelligence (AI) and MachineLearnin

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MAHMUD, KHALED, Rohayanti Hassan, Muhammad Edzuan Zainodin, Shahreen Kasim, and Johanna Ahmad. "A Novel Framework for Malware Detection UsingEntropy-Based Statistical Features and MachineLearning Models Across File Types." Engineering Research Express, May 8, 2025. https://doi.org/10.1088/2631-8695/add645.

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Abstract As cyber threats continue to evolve, the accurate detection of malicious files has&#xD;become increasingly crucial. Traditional approaches often fall short due to limited&#xD;adaptability to diverse file types and a high incidence of false predictions. This study&#xD;addresses these gaps by systematically evaluating entropy-based features in conjunction&#xD;with machine learning (ML) models for malicious file detection. Using diverse file&#xD;types—documents, images, and compressed files—we employed byte-level analysis of&#xD;each file’s raw stream—without any

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Oğuz, Suzan, and Deniz Yalçıntaş. "A REVIEW ON THE USE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGIES IN THE LOGISTICS SECTOR." Trends in Business and Economics, July 19, 2024. http://dx.doi.org/10.16951/trendbusecon.1494826.

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In recent years, developments in Artificial Intelligence (AI) and MachineLearning (ML) technologies have had profound effects on all sectors. The logistics industry has also become a sector where these technologies are being used to a significant extent. The emergence of intelligent logistics systems offers several opportunities for the advancement of this sector by facilitating digital transformation in supply chain and logistics. The aim of this study is to provide a comprehensive review of recent studies examining the use of AI and ML systems in the logistics industry. In this study, which

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Gurrapu, Sai, Nazmul Sikder, Pei Wang, Nitish Gorentala, Madison Williams, and Feras A. Batarseh. "Applications of Machine Learning For Precision Agriculture and Smart Farming." International FLAIRS Conference Proceedings 34, no. 1 (2021). http://dx.doi.org/10.32473/flairs.v34i1.128497.

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Recent deglobalization movements have had a transformativeimpact and an increase in uncertainty on manyindustries. The advent of technology, Big Data, and MachineLearning (ML) further accelerated this disposition.Many quantitative metrics that measure the globaleconomy’s equilibrium have strong and interdependentrelationships with the agricultural supply chain and internationaltrade flows. Our research employs econometricsusing ML techniques to determine relationshipsbetween commonplace financial indices (such asthe DowJones), and the production, consumption, andpricing of global agricultural

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Bouziani Idrissi, Mohammed, Idriss Moumen, Sara Taghzouti, et al. "Harnessing Machine Learning for QSPR Modeling of Corrosion Inhibitors in HCl for Mild Steel Protection." Current Analytical Chemistry 20 (September 4, 2024). http://dx.doi.org/10.2174/0115734110312696240822101941.

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Background: The corrosion of Mild Steel (MS) in harsh acidic environments, such as Hydrochloric acid (HCl), is a significant industrial issue with environmental consequences. Corrosion inhibitors, particularly those containing heteroatoms and aromatic rings, are a proven method for mitigating corrosion. Traditional methods for studying corrosion inhibitors often require resource- intensive experiments. Methods: This study explores the use of Quantitative Structure-Property Relationship (QSPR) modeling, a Machine Learning (ML) technique, to predict the inhibition efficiency of organic corrosion

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Head, Tim. "Learning computationally expensive functions." May 18, 2015. https://doi.org/10.5281/zenodo.17720.

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MOSOPE, WILLIAMS, F. YUSSUF MOSHOOD, and OLUWAROMIKA OLUKOYA AYOMIDE. "MACHINE LEARNING FOR PROACTIVE CYBERSECURITY RISK ANALYSIS AND FRAUD PREVENTION IN DIGITAL FINANCE ECOSYSTEMS." International Journal of Engineering Technology Research & Management (ijetrm) 05, no. 12 (2021). https://doi.org/10.5281/zenodo.14735561.

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The rapid expansion of digital financial systems has introduced both unprecedented opportunities and complexchallenges, particularly in the realms of cybersecurity and fraud management. Cyberattacks and fraudulentschemes have grown increasingly advanced, rendering traditional defense mechanisms insufficient. Machinelearning (ML) has emerged as a groundbreaking solution, enabling organizations to conduct proactive riskassessments and prevent fraudulent activities. By harnessing sophisticated algorithms, ML facilitates theidentification of threats, anomaly detection, and timely responses, ensuri

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Mrs., Katuri Lalitha, Mishra Ayush, Abhilash Goud A., and Sai Kishan K. "DATA-DRIVEN EARLY DIAGNOSIS OF CHRONIC KIDNEY DISEASE: DEVELOPMENT AND EVALUATION OF AN EXPLAINABLE AI MODEL." International Journal of Engineering Technology Research & Management (IJETRM) 09, no. 04 (2025). https://doi.org/10.5281/zenodo.15278680.

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Chronic Kidney Disease (CKD) is becoming an increasingly common health concern and, if not identified early, canresult in severe complications and premature death. The integration of Artificial Intelligence (AI) and MachineLearning (ML) can assist in detecting CKD at earlier stages, potentially minimizing kidney deterioration. However,the lack of interpretability in AI models often deters clinical adoption. To address this, explainable AI (XAI)methodologies are employed to enhance the transparency of CKD prediction models. This project focuses oncreating a data-centric and interpretable model

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"Presentation held at ISC 2024: PREDICTING COMPATIBILITY OF SEALING MATERIALWITH BIO-HYBRID FUELS: DEVELOPMENT ANDCOMPARISON OF MACHINE LEARNING METHODS." O+P Fluidtechnik 68, no. 11-12 (2024): 28–33. http://dx.doi.org/10.51202/2747-8009-2024-11-12-28.

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Bio-hybrid fuels, derived from sustainable raw materials and green energies, offer apromising alternative to conventional fuels made of mineral oil. Within the cluster ofexcellence “The Fuel Science Center (FSC)” at RWTH Aachen, bio-hybrid fuels areinvestigated on a holistic level, including an examination of their compatibility withsealing materials. Previous time-consuming experiments revealed that many biohybrid fuels show poor material compatibility with elastomer sealing materials(e.g. NBR & FKM) leading to issues such as volume expansion, hardness alteration, orchemical reactions upo

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Dr., Mahesh Kumar Porwal, and Nishant Porwal Mr. "IoT and Machine Learning Integrated Smart Pavement Repair System." June 7, 2023. https://doi.org/10.5281/zenodo.14604094.

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—Potholes are a major challenge on the road,leading to vehicle damage, vehicle downtime andincreased safety concerns. Traditional methods forlocating and repairing potholes are laborious, timeconsuming and costly. This project presents a uniquepothole detection and filling robot that uses Internet ofThings (IoT) and machine learning (ML) technologiesto automate the process of identifying and repairingpotholes.The system includes ultrasonic sensors to detect roadirregularities and cameras equipped with machinelearning algorithms to identify and classify potholes.The robot wirelessly trans

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Veernapu, Kiran. "AI-Driven Diagnostics for Retinal Disorders and Eye Diseases: Transforming Ophthalmology." AI-Driven Diagnostics for Retinal Disorders and Eye Diseases: Transforming Ophthalmology 2, no. 4 (2024). https://doi.org/10.51219/JAIMLD/kiran-veernapu/451.

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Artificial Intelligence (AI) has become a transformative force in changing the healthcare system in many ways. Machinelearning (ML) and deep learning (DL) are used to analyze medical images like CT scans, MRIs, ultrasounds, and X-rays. UsingDL algorithms systems can analyze and detect anomalies such as tumors, lesions other abnormalities at a high accuracyand assist the medical professionals in a better way. AI can assist in automating the diagnosis process by analyzing the medicalimages and to identify potential issues for review. This paper examines the role of AI in diagnosing retinal disor

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Pappula, Ashok, Mallikarjuna Reddy D., and Saheb Shaik Ameen. "An Analysis and Comparative Study with Machine Learning Algorithms for Cryptocurrency Price Prediction." Organization Development Journal 42, no. 4 (2025). https://doi.org/10.5281/zenodo.14603993.

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Because of its decentralized nature and huge financial benefits, Bitcoin is a popular investment throughout theworld. The unpredictable nature and variability of the cryptocurrency market make it difficult for investors to forecastprice variations and make profitable purchases. To solve this issue, this study looked at the accuracy of three MachineLearning (ML) algorithms, Gradient Boosting (GB), Random Forest (RF), and Bagging, in forecasting the daily closingvalues of Binance, Bitcoin, and Ethereum. This research uses price data from January 1, 2020 to June 30, 2024. Foralgorithm evaluation,

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Himanshu, Sinha. "Predicting Bitcoin Prices Using Machine Learning Techniques With Historical Data." August 7, 2024. https://doi.org/10.5281/zenodo.14264144.

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—Cryptocurrency is becoming morepopular. The first cryptocurrency to be created wascalled Bitcoin. One of a most popularcryptocurrencies is bitcoin, and more study is beingdone on price prediction. Establishing a moreplentiful index system and prediction model with animproved prediction effect is required to moreaccurately and efficiently forecast variations in theprice of Bitcoin. Because of this, there is asignificant risk for investors investing in Bitcoinbecause its price fluctuates every second. Interest intrading that is aided by artificial intelligence andmachine learning has grow

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Vourlioti, Paraskevi, Stylianos Kotsopoulos, Theano Mamouka, et al. "Maximizing the potential of numerical weather prediction models: lessons learned from combining high-performance computing and cloud computing." March 20, 2023. https://doi.org/10.5194/asr-20-1-2023.

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To promote cloud and HPC computing, GRAPEVINE* project objectives include using these toolsalong with open data sources to provide a reusable IT service. In this service a predictive model based on Machinelearning (ML) techniques is created with the aim of preventing and controlling grape vine diseases in thewine cultivation sector. Aside from the predictive ML, meteorological forecasts are crucial input to train theML models and on a second step to be used as input for the operational prediction of grapevine diseases. To thisend, the Weather and Research Forecasting model (WRF) has been deplo

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Soma Mitra and Dr. Saikat Basu. "Remote Sensing Based Land Cover Classification Using Machine Learning and Deep Learning: A Comprehensive Survey." International Journal of Next-Generation Computing, March 31, 2023. http://dx.doi.org/10.47164/ijngc.v14i2.1137.

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Since the 1990s, remote sensing images have been used for land cover classification combined with MachineLearning algorithms. The traditional land surveying method only works well in places that are hard to get to, likehigh mountain regions, arid and semi-arid land, and densely forested areas. As the satellites and airborne sensorspass over a specific point of land surface periodically, it is possible to assess the change in land cover over a longtime. With the advent of ML methods, automated land cover classification has been at the center of researchfor the last few decades. From 2015 forwar

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Mr., Rahul Ranjan, Ahmad Khan Faiz, Ahmad Altamash, Baker Aftab Khan Abu, Siddiqui Aman, and Faiz Mohd. "Stock Market Prediction with High Accuracy using Machine Learning Techniques." June 7, 2025. https://doi.org/10.5281/zenodo.15334061.

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Stock market trading is a major and predominant activity when one talks about the financial markets.With the inevitable uncertainty and volatility in the prices of the stocks, an investor keeps looking forways to predict the future trends in order to dodge the losses and make the maximum possible profits.However, it cannot be denied that as of yet there is no such technique to predict the upcoming trendsin the markets with complete accuracy, while multiple methods are being explored to improve the predictive performance of models to an extent as large as possible. With the advancement in Machi

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Gopala, Krishna Indraganti. "AI MACHINE LEARNING IN MANUFACTURING INDUSTRY." International Journal of Engineering Technology Research & Management (ijetrm) 08, no. 02 (2024). https://doi.org/10.5281/zenodo.14841195.

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The fundamental economic sector undergoes modern technological transformations due to its delivery of essentialeconomic systems throughout international markets. AI and ML provide the primary basis for transforming industrialproduction due to their technological advancement. High-tech systems and prompt analysis capabilities now emergefrom manufacturing process integration thanks to the implementation of AI and ML technology. By implementingthese technologies, manufacturers gain operation system improvements that lead to cost reductions and enhancedquality standards, enabling better market suc

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Dr., C. .Poongodi, M. J. Manjula, V. Nisha, and P. Shirinithee. "SHIELDHUB – VIRTUAL MENTAL HEALTH SUPPORT FOR WOMEN AND CHILDREN." June 7, 2025. https://doi.org/10.5281/zenodo.15254569.

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Abstract:  ShieldHub is an innovative online platform thatprovides a safe space for women's and children's mental healthand safety. It offers virtual counseling, gamified activities, legaleducation, and essential resources. An AI-powered chatbotconducts mental health assessments using NLP-basedprocessing and survey-based interactions, analyzing responsesto predict stress levels and offering personalized support. Theplatform includes interactive tools, relaxation exercises, andemotional resilience activities, as well as a legal awarenessmodule that educates users on their rights, safety me

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Moses, Oseghale Ikeakhe. "THE ROLE OF AI IN FINTECH, BANKING, AND DATA SCIENCE: TRANSFORMING FINANCIAL SERVICES THROUGH MACHINE LEARNING AND AUTOMATION." International Journal of Engineering Technology Research & Management (ijetrm) 08, no. 10 (2024). https://doi.org/10.5281/zenodo.14837136.

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Artificial Intelligence (AI) and Data Science have revolutionized the FinTech and banking sectors by enhancing frauddetection, improving customer experiences, automating credit risk assessments, and strengthening cybersecurity.Machine Learning (ML) models, natural language processing (NLP), and blockchain integration have enabledfinancial institutions to operate efficiently, securely, and accurately. This research investigates AI-driven solutions forbanking automation, explores the role of predictive analytics in credit risk evaluation, and assesses AI-powered fraudprevention frameworks. Throu

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