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Archive of Advanced Engineering Science — Volume 58 (2026), Issue 2

Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-05-05-2026-911
Title : A Comprehensive Review of Image Forensics Using Blockchain Enabled System
Koushik R, , Vikas S,
Abstract : With the surge in digital imagery across law, healthcare, and media, ensuring authenticity and tamper-proof evidence has become a critical concern. The current survey offers an in-depth overview of how blockchain technology can strengthen digital image forensics (DIF), which exploit weaknesses involving unauthentic changes, metadata tampering, and ineffective chain of custody. The paper starts by a brief description of the issues of the traditional DIF systems, especially in distributed systems
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-05-05-2026-912
Title : Real Time Gesture Recognition and Alert System Using Edge Cloud Architecture for Emergency Response
Chandrakala G Raju, , Roopa R, , Rakhee Patil, , Rajeshwari J, , Mohit Jain, , Om Patil, , Prakruthi Madhav,
Abstract : In critical situations such as medical emergencies, domestic violence, or fire incidents, people, particularly those with speech or physical impairments, may not be able to seek help by conventional means. This work presents a gesture-based real-time emergency alert system that uses AI-driven technologies integrated within an edge-cloud architecture. The system runs on a lightweight edge device, such as a Raspberry Pi, and captures live video to detect predefined emergency gestures using a compu
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-05-05-2026-913
Title : Reducing Hallucinations in Large Language Models through an Optimized Retrieval-Augmented Generation Pipeline
Rajbharath R, , Chandiran S, , Praveen Kumar D, , Raviraghav S, , Balaji R, , Jeganathan I, , Mohamad Siddiq S,
Abstract : While developing Generative and Training Language Models, there is a problem called "hallucinations", which refers to generative language models producing responses that seem reasonable given a context but are actually factually incorrect. In this paper, we present a new Optimised Retrieval Augmented Generation Pipeline that seeks to eliminate the hallucination problem, by dynamically grounding responses to external, retrievable knowledge. We have developed a system that leverages the following:
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Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-05-05-2026-914
Title : Transformer-Based Cotton Plant Disease Detecting: Comparative Study to Deep Learning and Machine Learning
Hina Shafi, , Ali Ghulam, , Mir. Sajjad Hussain Talpur, , Rahu Sikander,
Abstract : Cotton is one of the most important cash crops in the world, and many plant diseases have a big effect on how much it grows. To protect food and economic security, it is important to find and treat cotton plant diseases as soon as possible. Machine learning (ML) and deep learning (DL) methods have been widely used in the last few years to automatically find plant diseases using pictures of leaves. Nonetheless, current studies present findings derived from varying experimental conditions, dataset
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