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All Journal International Journal of Advances in Intelligent Informatics International Journal Of Science, Technology & Management (IJSTM)
Kibuku, Rachael
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering 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 Mechanical Engineering Transportation

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Cybersecurity with Quantum Cryptography: An Analysis of Current Techniques and Future Trends Omol, Edwin; Kibuku, Rachael; Abuonji, Paul
International Journal of Science, Technology & Management Vol. 6 No. 2 (2025): March 2025
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v6i2.1269

Abstract

Using a systematic literature review methodology comprising the following stages: Identification of Relevant Literature, Screening and Selection, Data Extraction and Synthesis, Qualitative Analysis, and SWOT Analysis, this paper explores the role of quantum cryptography in enhancing cybersecurity. The analysis begins with an introduction to the vulnerabilities of classical cryptography in the context of quantum computing advancements. It delves into Quantum Key Distribution (QKD) protocols such as BB84 and E91, Quantum Random Number Generators (QRNGs), and post-quantum cryptography algorithms in detail. Real-world case studies are presented to illustrate the practical applications and advancements in quantum cryptographic techniques. Additionally, the paper addresses the challenges associated with implementing quantum cryptography and proposes strategies for its integration with existing cybersecurity frameworks. The discussion on future trends highlights anticipated technological advancements, potential applications in quantum internet and blockchain security, suggested research directions, and policy implications. The significance of quantum cryptography in securing sensitive data and establishing trust in critical sectors is thoroughly emphasized.
Edge optimized multimodal cross fusion model with statistical validation for multi crop disease detection Njoroge, Thomas Kinyanjui Njoroge; Mugoye, Kelvin Sindu; Kibuku, Rachael
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.2051

Abstract

Accurate and timely crop disease detection is crucial for mitigating agricultural losses and ensuring food security, particularly in resource-limited settings. Traditional diagnostic methods are inefficient and prone to errors, while existing deep learning models, such as ResNet50 and Inception V3, struggle with generalizability and computational efficiency. This study proposes a Dynamic Edge-Optimized Multimodal Fusion (DEMF) model, integrating EfficientNetV2 and MobileNetV2 to enhance feature learning and scalability. The model was trained on a 76-class dataset comprising PlantVillage and locally collected images of crop diseases, ensuring robustness across diverse conditions. Feature fusion via concatenation, combined with compound scaling and transfer learning, enabled the model to capture fine-grained patterns of disease. Extensive experiments, including ablation studies and comparative evaluations against DenseNet-121, DenseNet-50, AlexNet, and ResNet-152, validated the model’s superiority. The proposed model achieved 99.2% accuracy, a Kappa of 0.9919, and an AUC of 0.9999, outperforming benchmarks. Statistical validation confirmed significant improvements (p<0.05) and stability. To enhance accessibility, an AI-powered mobile application was deployed on the Google Play Store, enabling real-time disease detection and actionable recommendations. To enhance accessibility, an AI-powered mobile application was deployed on the Google Play Store, enabling real-time disease detection and actionable recommendations. This research advances transfer learning, feature fusion, and statistical validation for robust, scalable crop disease detection in low-resource environments.
Co-Authors Abuonji, Paul Mugoye, Kelvin Sindu Njoroge, Thomas Kinyanjui Njoroge Omol, Edwin