Browse Items (28 total)
- Collection: Vol 9 No 3 (2025)
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Enhancing Areca Nut Detection and Classification Using Faster R-CNN: Addressing Dataset Limitations with Haar-like Features, Integral Image, and Anchor Box Optimization
The classification and detection of areca nuts are essential for agriculture and food processing to ensure product quality and efficiency. The manual classification of areca nuts is time-consuming and prone to human error. For a more …
Enhancing Stroke Prediction with Logistic Regression and Support Vector MachineUsing Oversampling Techniques
Stroke is a significant health concern that can result in both death and disability, making the early identification of risk factors crucial. Previous studies on stroke prediction have been limited by inadequate handling of class …
Enhancing Tomato Leaf Disease DetectionviaOptimized VGG16and Transfer Learning Techniques
dentification of tomato leaf disease remains difficult because standard approaches are frequently incorrect in identifying distinct signs. Convolutional Neural Networks (CNNs) perform well in image classification and pattern…
Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms
In an era of collaboration, knowing someone's expertise is becoming increasingly necessary. Recognizing individuals' proficiency can be challenging because it requires considerable manual time. This study explores the expertise …
Forecasting Stock Returns Using Long Short-Term Memory (LSTM) Model Based on Inflation Data and Historical Stock Price Movements
The stock market is crucial for economic growth and development, offering profit opportunities that attract investors worldwide. However, its inherent volatility necessitates the inclusion of macroeconomic indicators like inflation,…
Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization
Health risk classification is important. However, health risk classification is challenging to address using conventional analytical techniques. The XGBoost algorithm offers many advantages over the traditional methods for …
Improving Frame-based Engagement Classification in E-Learning Using EfficientNet and Normalized Loss Weighting
Engagement can be defined as how individuals are involved in and interact with a task that requires attention and emotional conditions. Engagement is an affective state positively correlated with learning processes. Engagement along with…
Minangkabau Language Stemming: A New Approach with Modified Enhanced Confix Stripping
Stemming is an essential procedure in natural language processing (NLP), which involves reducing words to their root forms by eliminating affixes, including prefixes, infixes, and suffixes. The employed method assesses the efficacy of stemming, which…
Obesity Status Prediction Through Artificial Intelligence and Balanced Label Distribution Using SMOTE
Obesity, a global health challenge influenced by genetic and environmental factors, is characterized by excessive body fat that increases the risk of various diseases. With over two billion individuals affected worldwide, addressing this issue is…
Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction
Post-operative kyphosis represents a significant complication following pediatric spinal corrective surgery, necessitating sophisticated prediction methods to identify high-risk patients. This study developed and evaluated machine learning…