Gotowe bibliografie tematyczne / MachineLearning (ML)

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Gotowa bibliografia na temat „MachineLearning (ML)”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "MachineLearning (ML)"

. 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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Rozprawy doktorskie na temat "MachineLearning (ML)"

Narmack, Kirilll. "Dynamic Speed Adaptation for Curves using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233545.

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The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree

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Streszczenia konferencji na temat "MachineLearning (ML)"

Rafael de Oliveira Rodrigues, Bruno, and Fernando Silva Parreiras. "Predicting Bug-Fixing Time with Machine Learning - A Collaborative Filtering Approach." In Computer on the Beach. Universidade do Vale do Itajaí, 2022. http://dx.doi.org/10.14210/cotb.v13.p021-028.

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ABSTRACTPredicting bug-fixing time helps software managers and teams prioritizetasks, allocations and costs in software projects. In literature,machine learning (ML) models have been proposed to predict bugfixingtime. One of features highlighted by studies is the reporter(the person who open the bug) has positive influence in the timeto resolve a bug. In this way, this paper answers the following researchquestion: How does a collaborative filtering approach performin predicting bug-fixing time compared to the supervised machinelearning approaches? In order to answer this question we performeda

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Oni, Damilola, Satyam Mishra, Le Trung Thanh, Vu Minh Phuc, and Linh Nguyen. "PBC-ML: Predicting Breast Cancer in Humans using Machine Learning Approach." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003454.

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Cancer is a disease in which cells grow uncontrollably, potentially causing harm tosurrounding healthy tissue and organs. Breast cancer is a specific type of cancer that affectsthe breast and is the second most common cancer among women worldwide. Symptoms ofbreast cancer include a lump or tumour, swelling, nipple discharge, and swollen lymphnodes. Breast cancer is staged, with stage 0 being the earliest stage with minimal symptomsand stage 4 indicating the cancer has spread to other parts of the body. The future burdenof breast cancer is predicted to increase, with over 3 million new cases an

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