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84 articles for “AI Threat Detection”
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Implementing Blockchain to Enhance Security in the Pharmaceutical Industry and Combat Drug Counterfeiting
Abstract: Drug counterfeiting has emerged as a critical threat to public health, as it has enabled inferior and counterfeit drugs to flood many markets around the world thereby eroding trust in healthcare systems and patient safety. This paper seeks to address the glaring need for adequate security safeguards to curb the circulation of counterfeit drugs by proposing a blockchain model designed specifically for the pharmaceutical industries. In general, the idea of …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 1, 2025 · pp. 33–46 Read article
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Deep Learning based Solution for Leaf disease Detection in Crops and Fertilizer Recommendation
Abstract: The field of agriculture faces significant threats, including diseases that attack plant leaves. To address this issue, our system assists farmers in promptly detecting plant diseases using advanced technology. The user, typically a farmer, only needs to capture an image of the affected leaf and input it into our system. Our system then analyzes the uploaded image to accurately identify the specific disease afflicting the leaf. This analytical process is …
Published in Current Trends in Signal Processing · Vol. 14, Issue 3, 2024 · pp. 31–40 Read article
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A Study to Assess the Risk Factors and Symptoms of Dialysis Disequilibrium Syndrome among Patients Undergoing Hemodialysis
Abstract: Dialysis Disequilibrium Syndrome (DDS) is a rare but potentially life-threatening neurological complication associated with hemodialysis, particularly during initiation or in high-risk patients. It is characterized by a spectrum of neurological manifestations ranging from mild symptoms such as headache and nausea to severe outcomes including seizures, coma, and death. Early identification of risk factors and prompt recognition of symptoms are crucial to prevent morbidity and mortality. The present study aimed to …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 16, Issue 1, 2026 Read article
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Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network
Abstract: The detection of malicious network traffic is a critical aspect of cybersecurity, aiming to protect sensitive data and maintain the integrity of network systems. This study introduces a novel approach that combines hybrid feature selection with ensemble neural networks to enhance the accuracy and efficiency of malicious network traffic detection. The dataset used in this study was obtained from Kaggle and offers a wide-ranging and varied collection of network traffic …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 27, Issue 3, 2025 Read article