Search Results - (( exploring practices tree algorithm ) OR ( java implication based algorithm ))

A safe overtaking control scheme for autonomous vehicles using rapid-exploration random tree by Yincong Ma, Kit Guan Lim, Min Keng Tan, Helen Sin Ee Chuo, Lorita Angeline, Kenneth Tze Kin Teo

“…In order to enhance the commuting ability of autonomous vehicles on the road and ensure the comfort and safety of passengers, the Rapid-exploration Random Tree (RRT) algorithm is applied to the research of safe overtaking control of autonomous vehicles. …”
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Robotic path planning using rapidly-exploring random trees by Sherwani, Fahad

“…This study concerns the implementation of Rapidly-Exploring Random Trees (RRTs) algorithm for an autonomous robot path planning. …”
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Exploring students' performance in mathematics in Portugal using data analytics techniques: a data science use-case by Hassan, Raini, Fadzleey, Nur Zulfah Insyirah, Ab Hamid, Annesa Maisarah, Abd Aziz, Rabiatul Adawiyah, Jamalullain, Afiefah, Syaiful 'Adli, Fatin Syafiqah

“…The purpose is to identify the key factors influencing academic performance, providing insights for targeted interventions and support systems. Machine learning algorithms, specifically Random Forest Regression and Decision Trees, are utilized to analyze the dataset and determine the most significant factor impacting student performance. …”
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The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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Forecast of Muslimah fashion trends in Caca's company / Muhammad Saifullah Mohd Taip by Mohd Taip, Muhammad Saifullah

“…The results showed that the decision tree algorithm had a higher accuracy of 100% for category prediction, 47% for colour prediction, and 65% for size prediction, while the random forest algorithm had a higher accuracy of 100% for category prediction, 85% for colour prediction, and 91% for size prediction. …”
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Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…Machine learning model selection is an iterative process of exploring, evaluating, and improving algorithms. Selecting an optimal model for a particular domain is rigid, challenging, and complicated. …”
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Analyzing visitor trends to optimize data-driven strategies in Pusat Sains & Kreativiti Terengganu by Zainol Abidin, Nur Sarah

“…To overcome these issues, historical visitor data from January 2022 to December 2024 was collected and analyzed using the CRISP-DM methodology, which guided the project through business understanding, data preparation, modelling, and deployment. Three predictive algorithms including Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) were tested to classify visitor levels into low, medium, and high categories. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

Machine learning models for predicting the compressive strength of concrete with shredded pet bottles and m sand as fine aggregate by Nadimalla, Altamashuddinkhan, Masjuki, Siti Aliyyah, Gubbi, Abdullah, Khan, Anjum, Mokashi, Imran

“…The study highlights the potential of DT models in sustainable construction practices, emphasizing the importance of comprehensive datasets and further exploration of alternative algorithms. …”
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Exploring employee working productivity: initial insights from machine learning predictive analytics and visualization by Razali, Mohd Norhisham, Ibrahim, Norizuandi, Hanapi, Rozita, Mohd Zamri, Norfarahzila, Abdul Manaf, Syaifulnizam

“…Traditional human resource management practices often lack data-driven insights, resulting in poor resource allocation and productivity enhancement strategies. …”
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