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Jing, Qi
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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A Lightweight Deep Learning Model for Crop Disease Detection on Mobile Devices Jing, Qi
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1535

Abstract

Early detection of crop disease is an important part of modern agriculture since early detection would help in reducing crop loss and improving food security. The purpose of this study is to develop and evaluate lightweight deep-learning models for disease detection using simulation-based data where the output device would be a mobile device. Training and testing three types of machine learning models, Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) on simulated agricultural data of soil health, weather conditions and plant health is a part of the research methodology. To evaluate the models, the accuracy, F1-score and inference time were used. And results indicate that RF and SVM both performed with 100% accuracy (F1 score equal 1.0) whereas the CNN model has 87.5 % accuracy and loss = 0.2279. The CNN model, although it has slightly lower performance, is promising for deployment on mobile as it offers better results. The study concludes that there is room for light-weight CNN models for real-time disease detection on mobile devices. The future study will analyze how CNN architecture can be optimized using real-world data. This study has practical implications for mobile-based solutions for crop disease management in resource-constrained environments. A major weakness is that the data used is simulated data and may not account for the realities of agricultural conditions.
Blockchain-Enabled Secure Data Sharing Framework for Healthcare IoT Devices Jing, Qi
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1520

Abstract

Medical data security challenges have increased dramatically because healthcare institutions continue to integrate more Internet of Things devices to deliver data-driven clinical services. Access control systems based on RBAC, ABAC and MAC do not meet the requirements of flexible protection and scalable and context-aware security which are needed for dynamic healthcare environments. The research objective focuses on creating a resilient decentralized access control solution which delivers secure time-sensitive access permissions in healthcare IoT systems. A blockchain-based hybrid access control framework with RBAC and ABAC provides the solution to meet this requirement. A dual mechanism of smart contracts and IPFS storage runs the model while variables and user-facing elements shift based on environmental characteristics and individual circumstances. Results from experimental evaluation show that this proposed framework delivers 96.5% access precision together with policy evaluation times below 3.2 ms and 120 ms response times while handling 74 transactions per second while remaining affordable at $2.1 and demanding 45 to 52 MB from critical system memory. The obtained results demonstrate better scalability together with enhanced performance and adaptability when compared to using ABAC, RBAC and MAC singularly. Healthcare IoT systems should implement a blockchain-based hybrid access control system as an optimal method to secure data sharing in real-time resource-constrained scenarios.
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