Research Article
Performance Assessment of Quantum CNN vs RNN for Medicinal Leaf Classification with UI Sustenance
Issue:
Volume 14, Issue 2, April 2026
Pages:
66-78
Received:
19 February 2026
Accepted:
2 March 2026
Published:
17 March 2026
Abstract: Accurate as well as automated medicinal leaf categorization is a critical chore in medicinal plant species identification. However, manual categorization is time compelling, error prone and mostly reliant on expert skills due to high inter as well as intra class variability among medicinal plant categories. Recent advancement in image processing and artificial intelligence has enabled automated plant species identification, providing reliable as well as scalable alternative to traditional procedures. This research represents a comparative analysis of Recurrent Neural Network (RNN) and Quantum Convolutional Neural Network (QCNN) for automated medicinal leaf categorization in terms of performance evaluation including accuracy, precision, recall and F1 score. Experimental outcome shows that QCNN significantly outperforms the RNN. RNN exhibits 68% accuracy, macro avg. precision 67%, recall 67%, f1 score 66% and weighted avg. precision 70%, recall 68%, f1 score 68% which is less as compared to QCNN which shows 96% accuracy, macro avg. precision 96%, recall 96%, f1 score 96% and weighted avg. precision 96%, recall 96%, f1 score 96%. Owing to its superior performance QCNN further integrated into a user interface framework to enable real time medicinal leaf categorization. The developed interface offers a user friendly, efficient and scalable platform for medicinal leaf identification application. The suggested system establishes the effectiveness of quantum motivated deep learning model in medicinal leaf image categorization as well as its usage and highlights the potential of QCNN trusted systems for intelligent medical applications.
Abstract: Accurate as well as automated medicinal leaf categorization is a critical chore in medicinal plant species identification. However, manual categorization is time compelling, error prone and mostly reliant on expert skills due to high inter as well as intra class variability among medicinal plant categories. Recent advancement in image processing an...
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Research Article
A Smart Helmet for Accident Detection and Response for Emergency Communication
Issue:
Volume 14, Issue 2, April 2026
Pages:
79-90
Received:
29 November 2025
Accepted:
11 March 2026
Published:
26 March 2026
DOI:
10.11648/j.jeee.20261402.12
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Views:
Abstract: Road Traffic Accidents (RTA) have been a major cause of death and life-threatening injuries globally. The delay in Emergency Response Services (ERS) has heightened the number of death casualties in the event of motorcycle accidents. To address these life-threatening issues, the Smart Helmet for Accident Detection and Res communication device (SHADR) was developed to automatically detect accident, notify the registered emergency contact about the incident and disclose the location of the incident. This concept is designed for rural communities where motocycling activities are predominant. The design was actualized by deploying an accelerometer to detect accident, a load sensor to define when the helmet is being worn, GPS module to ascertain the exact location of the incident, GSM module for call activation and delivering short message service (SMS) of the emergency immediately after the incident has occurred. It leverages the user the opportunity to register the emergency contact by sending information as a coded text to the SHADR. This therefore eliminates the need for interface components and reduces the power consumption level of the device. The outcome of the design implementation demonstrated an efficient operation, with a fast response time for the GPS and GSM communication. Its major contribution stems from the fact that the response time was adequate since it was not affected by network delays and failures associated with communication systems in rural communities. It is a cost effective device which operates with minimum power consumption, the SMS delivery time was adequate and the call functionality was good, at minimum network connectivity. The implementation of SHADR on motorcyclists will greatly reduce casualties from road traffic accidents, provide more data for road traffic studies and give more confidence to road users. Future improvements will require the implementation of this device using 5G technology to improve communication speed and reduce latency for emergency services in urban communities.
Abstract: Road Traffic Accidents (RTA) have been a major cause of death and life-threatening injuries globally. The delay in Emergency Response Services (ERS) has heightened the number of death casualties in the event of motorcycle accidents. To address these life-threatening issues, the Smart Helmet for Accident Detection and Res communication device (SHADR...
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,
Neeraj Marwaha
