Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Journal of Information Systems and Informatics
Leonard, Stanley
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Hand LightWeightNet: an optimized hand pose estimation for interactive mobile interfaces Banzi, Jamal Firmat; Leonard, Stanley
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2076-2087

Abstract

In this paper, a hand pose estimation method is introduced that combines MobileNetV3 and CrossInfoNet into a single pipeline. The proposed approach is tailored for mobile phone processors through optimizations, modifications, and enhancements made to both architectures, resulting in a lightweight solution. MobileNetV3 provides the bottleneck for feature extraction and refinements while CrossInfoNet benefits the proposed system through a multitask information sharing mechanism. In the feature extraction stage, we utilized an inverted residual block that achieves a balance between accuracy and efficiency in limited parameters. Additionally, in the feature refinement stage, we incorporated a new best-performing activation function called “activate or not” ACON, which demonstrated stability and superior performance in learning linearly and non-linearly gates of the whole activation area of the network by setting hyperparameters to switch between active and inactive states. As a result, our network operated with 65% reduced parameters, but improved speed by 39% which is suitable for running in a mobile device processor. During experiment, we conducted test evaluation on three hand pose datasets to assess the generalization capacity of our system. On all the tested datasets, the proposed approach demonstrates consistently higher performance while using significantly fewer parameters than existing methods. This indicates that the proposed system has the potential to enable new hand pose estimation applications such as virtual reality, augmented reality and sign language recognition on mobile devices.
Enhancing Coffee Leaf Rust Detection with DenseNet201Plus and Transfer Learning Karia, Adrian Jackob; Ally, Juma S; Leonard, Stanley
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1191

Abstract

Coffee leaf rust (CLR) is a disease of coffee leaves caused by the fungus Hemileia Vastatrix, posing a major threat to global coffee production. Early and accurate detection is crucial for sustainable farming practices and disease management. This study proposes a novel deep learning approach that integrates DenseNet201Plus, an enhanced version of DenseNet201, with transfer learning to improve the accuracy and efficiency of CLR detection. DenseNet201Plus incorporates fine-tuned layers and optimized hyperparameters designed for plant disease classification, while transfer learning utilizes pre-trained weights from large-scale image datasets, enabling the model to adapt the characteristics of CLR images with limited training data. The model was evaluated on two datasets: the newly collected, high-quality Mbozi CLR dataset and the publicly available ImageNet CLR dataset, using accuracy, precision, recall, and F1-score. Results demonstrate that DenseNet201Plus achieved an accuracy of 99.0% on the Mbozi dataset, surpassing 97.78% obtained by the ImageNet Public dataset, with corresponding gains across all performance metrics. Results confirm that integration of DenseNet201Plus with transfer learning on the high-quality dataset significantly enhances CLR detection. The method outperformed several other baseline methods. The proposed approach offers a scalable, real-time detection solution for field deployment, supporting precision agriculture, enabling timely and targeted interventions.
Co-Authors Ally, Juma S Banzi, Jamal Firmat Karia, Adrian Jackob