The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation and outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition and classification tasks. While previous studies have focused on fruits, land sliding, and medical images, there is a need for further research on the detection of natural images, particularly glaciers and mountains. To address the limitations of traditional CNNs, such as vanishing gradients and the need for many layers, the proposed work introduces a novel model called DenseHillNet. The model utilizes a DenseHillNet architecture, a type of CNN with densely connected layers, to accurately classify images as glaciers or mountains. The model contributes to the development of automation technologies in transportation and outdoor activities. The dataset used in this study comprises 3,096 images of each of the "glacier" and "mountain" categories. Rigorous methodology was employed for dataset preparation and model training, ensuring the validity of the results. A comparison with a previous work revealed that the proposed DenseHillNet model, trained on both glacier and mountain images, achieved higher accuracy (86%) compared to a CNN model that only utilized glacier images (72%). Researchers and graduate students are the audience of our article.
The integration of hydrogen in the primary energy mix requires a major technological shift in virtually every energy-related application. This study has attempted to investigate the techno-economic solar photovoltaic (PV) integrated water electrolysis and waste incineration system. Three different strategies, i.e., (i) PV + Battery(Hybrid mode with required batteries); (ii) auto-ignition (Direct coupling); and (iii) PV + Secondary-Electrolyzer(Direct coupling assisted with secondary electrolyzer), have been envisioned. The 'PV + Battery' consume 42.42 % and 15.07 % less energy than the auto-ignition and 'PV + Secondary-Electrolyzer' methods. However, the capital cost of 'PV + Battery' has been calculated to be 15.4 % and 11.8 % more than auto-ignition and 'PV + Secondary-Electrolyzer, respectively.The energy consumption relative to waste input, the 'PV + Battery' method used 80 % less energy, while auto-ignition and 'PV + Secondary-Electrolyzer' showed 70.5 % and 77.5 % less energy, respectively. Furthermore, these approaches showed a vast difference in cost-benefit for the longer run. 'PV + Battery' was forecasted to be 73.3 % and 23.3 % more expensive than auto-ignition and 'PV + Secondary-Electrolyzer' methods, respectively, for 30 years. Overall, this study can benefit from using either of these methods depending on the application, usage scale, and climatic conditions.