1. Conference paper- "Transforming Plant Health: Leveraging Advanced Transformer Model for Plant Disease Detection and Classification" ICICCT-JNU, New Delhi 🔗
This research paper presents an innovative approach to agricultural technology by applying advanced transformer models for automated plant disease detection and classification. The study addresses the critical challenge of early disease identification in crops, which is essential for maintaining agricultural productivity and food security. The paper demonstrates how state-of-the-art deep learning architectures, specifically transformer models originally developed for natural language processing, can be effectively adapted for computer vision tasks in agriculture. The research likely covers methodology for training these models on plant disease datasets, performance evaluation metrics, and comparative analysis with traditional image classification approaches. This work contributes to the growing field of precision agriculture and AI-driven farming solutions, offering farmers and agricultural professionals a potentially powerful tool for proactive crop management and disease prevention.
2. SCI journal- "Metaverse For Connected World: Key Enablers, Challenges and Opportunities" accepted for publication in Journal of Circuits, Systems, and Computers 🔗
Strengthening research on the competitiveness of urban tourism is crucial for assessing the current situation and potential of urban tourism, for maintaining high-quality tourism development and for supporting regional macro-decisions. Based on the conceptual model of tourism destination competitiveness, this paper develops an evaluation system for the competitiveness of urban tourism in Sichuan Province by collecting cross-sectional data for 2021. Based on 15 cities at and above the prefecture level in Sichuan Province in the Chengdu–Chongqing Economic Region, the entropy weight method was used to calculate the corresponding weight of each index. Then, the linear weighting method and the spatial analysis function of ArcGIS were used to analyze the characteristics and rules of spatial differentiation of urban tourism competitiveness in Sichuan. The results show that there are significant spatial differences and disparities in the tourism competitiveness of the 15 cities and that the tourism industry in Sichuan Province is still in the resource-oriented stage with Chengdu emerging as a leading city due to its superior performance in multiple dimensions of tourism competitiveness. This paper provides a guide for the development of tourism in some typical areas.