The development of automated diagnostic tools is essential for efficiently analyzing medical data, especially for conditions like diabetic retinopathy, a leading cause of Vision impairment and blindness in adults. The APTOS 2019 blindness detection dataset, with its comprehensive retinal images, is critical for developing these tools. This study leverages the Pyramid Vision Transformer (PVT) to enhance accuracy and efficiency in detecting diabetic retinopathy. Unlike the Vision Transformer (ViT), which incurs high computational costs and yields low-resolution outputs due to its single-scale structure, PVT’s pyramid architecture enables efficient multi-scale feature representation. This allows for effective management of large feature maps and improved resolution, both essential for precise image-based diagnoses. By implementing PVT, our approach demonstrates improved accuracy and resource efficiency, outperforming traditional CNN methods. Extensive experiments demonstrate that PVT significantly improves detection and classification accuracy, making it a valuable tool for clinical applications. The model achieved 92.38% accuracy and an AUC of 99.58%. Future research will focus on optimizing the model and exploring clinical integration.

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.

The early and accurate detection of plant diseases is vital for ensuring food security and enhancing agricultural productivity. Tomato plants, being one of the most widely cultivated crops, are particularly susceptible to several prevalent leaf conditions. These conditions can lead to significant crop losses and adversely affect both yield and quality, posing a substantial challenge to farmers and the agricultural industry. To identify tomato leaf conditions, traditional methods such as machine learning (ML) and modern approaches like various deep learning (DL) architectures have been developed and studied by researchers. This paper presents a novel approach for the detection of ten classes of tomato leaf conditions, encompassing both healthy and diseased leaves. The proposed method leverages a new Quantum Vision Transformer (QViT) architecture, integrating variational quantum circuits within both the attention mechanism and the multi-layer perceptron. In our study, we conducted extensive experiments comparing the performance of QViT with the Vision Transformer (ViT). The experimental results demonstrate that the QViT model achieves an Area Under the Curve (AUC) of 0.928 and an accuracy of 66.85%, while the ViT model reaches an AUC of 0.95 and an accuracy of 72.15%. This highlights the effectiveness and robustness of both models in accurately detecting tomato leaf conditions. The research findings suggest that the QViT architecture can serve as a powerful tool for early detection in agricultural applications using quantum computers, contributing to more efficient and sustainable farming practices.

In this paper, a new high step-up current-fed LLC resonant DC-DC converter with a center-tapped Transformer is proposed. By selecting the switching frequency to be lower than, but near to, the series resonant frequency of the LLC resonant tank, soft-switching operation of all semiconductors, i.e., zero voltage switching (ZVS) turn-on of power MOSFETs and zero current switching (ZCS) turn-off of diodes is achieved. This leads to lower electromagnetic interference (EMI) and lower switching losses and improves the converter efficiency. An interleaved structure is used at the primary side. Thus, input current ripple is smaller, and its frequency is twice the switching frequency. Consequently, a smaller input filter is necessary in practice. The converter with 1.2 kW output power and 760 V regulated output voltage with 80-200 V input voltage variations is simulated. The output voltage is regulated by using asymmetric pulse width modulation (APWM) at 200 kHz switching frequency. Finally, a 700-W prototype has been implemented and experimental results are also presented to verify the simulation results.