Building change detection (BCD) is a critical task in remote sensing, with applications in urban management and disaster assessment. However, achieving high accuracy in high-resolution BCD remains challenging due to the complexity of urban scenes. In this study, we propose ChangeCoTNet, a novel dual-branch deep learning model that integrates Contextual Transformer (CoT) blocks in the encoder and a Convolutional Neural Network (CNN) in the decoder. The CoT blocks enable the extraction of both static and dynamic contextual representations, while the Channel Attention Block (CAB) enhances discriminative feature extraction. The proposed model was implemented and evaluated on the LEVIR-CD and 2DCD datasets using a PyTorch backend. Experimental results demonstrate that ChangeCoTNet outperforms state-of-the-art methods, achieving F1-score improvements of 1.1% and 1.9% for the respective datasets. These results validate the effectiveness and efficiency of the proposed model in detecting changes with high precision and recall, making it a valuable tool for real-world applications.

In the realm of computer vision applied to cricket analysis, classifying batting shots poses a formidable challenge, demanding nuanced comprehension and categorization. The classification of cricket shots is crucial as it empowers the players to strategically assess, adapt, and execute their game plans effectively, shaping the outcome of matches. This article introduces the Cricket Batting Shots Image dataset (CBSId), a new benchmark dataset comprising 2160 meticulously annotated cricket shot images across seven distinct categories. The core objective of this research is to develop a robust system capable of effectively classifying cricket batting shots from images. To address this, we present a fine-tuned Vision Transformer-based model specifically adapted for cricket shot classification, termed Cricket Batting Shot Vision Transformer (Shot-ViT). Our proposed methodology demonstrates exceptional performance, achieving 92.58% validation accuracy on the CBSId. Shot-ViT notably outperforms established models such as VGG19, ResNet50, I-AlexNet, and ViT_B32 in cricket shot classification accuracy, showcasing the remarkable capabilities of Vision Transformers in surpassing existing deep learning architectures for complex visual tasks. Vision Transformers have the capacity to capture global context and long-range dependencies within images through self-attention mechanisms, enabling effective feature extraction and representation, which traditional models may struggle to achieve. The accurate classification of cricket batting shots holds profound implications for cricket coaching, player development, and match analysis. It has the potential to revolutionize training methodologies, providing players and coaches with precise insights into batting techniques and strategies and thereby contributing to the overall advancement of the sport.

Proper operations of Transformer have several issues such as; preventing of unscheduled removal’s, increasing of reliability and continues supply of consumer demand. This result would be obtained that load and harmonic orders of sensitivity Transformer at intervals appropriate for next hours and days is predicted to by selecting optimal utilization coefficient, reduction life of Transformer is prevented. A possible solution for load and harmonic orders forecasting is implementation of heuristically algorithm and method such as Artificial Neural Network (ANN). In this paper, firstly relationship between Transformer loss and life and effect of harmonic on its, is evaluated. Then by ANN method, load and harmonic orders of 400KVA distribution Transformer is predicted. Then by using of existing standards and programs written in MATLAB environment, Load ability or optimal utilization coefficient and life of Transformer is calculated.