Search Results - (( developing allocation tree algorithm ) OR ( java implication based algorithm ))

Enhancing a cluster-based TDMA MAC protocol for vehicle-to-vehicle communications / Abubakar Bello Tambawal by Abubakar Bello , Tambawal

“…A dynamic time slot allocation strategy using a binary tree algorithm has been proposed to prevent vehicles from the adjacent clusters to reserve the same time slot. …”
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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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Analyzing UiTMCTKKT vehicle utilization and travel pattern using predictive analytics by Syed Mohamad, Sharifah Masyitah

“…The Experiment 1 focused on classifying vehicle types based on utilization and Experiment 2 involved predicting no of trips per day using classifiers such as Random Forest, Decision Tree, and Support Vector Machine (SVM). Data from 2023 to 2024 was cleaned and analyzed, and visualizations were developed through an interactive dashboard using Power BI. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method by Ashraf Saleh Mohammad Alomoush

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

“…Employee working productivity prediction is vital for effective resource allocation, increased productivity, and upholding a high-performance culture in organizations. …”
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Artificial Neural Network‑Based Prediction of Nipa Sugar Production in Sarawak, Malaysia by Muzamil, Ayoub, Ana Sakura, Zainal Abidin, Kasumawati, Lias, Imtiyaz Akbar, Najar, Rasli, Muslimen

“…The FFANN model incorporates key environmental variables, including temperature, humidity, wind speed, atmospheric pressure and sap yield, and is trained using the resilient backpropagation (RPROP+) algorithm. The model’s performance was compared to classical time series models (ARIMA and seasonal naïve) and a decision tree regression model. …”
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Predicting factors of library traffic for UiTMCTKKT Cendekiawan Library using predictive analytics / Azzatul Husna Abdul Aziz by Abdul Aziz, Azzatul Husna

“…The CRISP-DM methodology was followed to apply machine learning algorithms, namely Random Forest, Decision Tree, and Naive Bayes, to the data gathered in the library which is traffic, book rentals, and questionnaires. …”
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