Nadella Sandhya

Object detection systems often struggle with challenges such as noise, illumination variation, and partial occlusion, which degrade both localization accuracy and recognition performance. To address these issues, this study introduces a novel Fractional You Only Look Once Efficient Network (Frac-YOLOEffNet), which integrates Fractional Concept (FC) with the YOLOv8 and EfficientNet architectures to improve feature sensitivity and robustness. The initial step involves applying an Adaptive Median Filter to the input image for noise reduction. Moreover, extraction of features is performed, where the significant features, such as Shape Local Binary Texture (SLBT), Gray-Level Co-occurrence Matrix (GLCM), Speeded Up Robust Features (SURF), Oriented Fast and Rotated BRIEF (ORB), and Scale-Invariant Feature Transform (SIFT), are extracted. Object recognition is later achieved with a Deep Convolutional Neural Network (DCNN) with weights and bias trained via the Spider-Tailed Horned Viper Optimization (STHVO). The developed Frac-YOLOEffNet model achieved an F1-score of 96.168%, a recall of 97.877%, and an accuracy of 96.998%, respectively.

This paper presents a novel approach for the early detection of cattle diseases. We present a uniquely integrated image classification-based project for real-time cattle disease diagnosis that combines image classification models to identify diseases accurately; a seamless, user-friendly dashboard for real-time monitoring with data visualization and instant predictions; and a mobile application that acts as a data source. The mobile application enables real-time collection of farmer and cattle-related data, including age, number of cattle, vaccination cycles, cattle images, and location metadata. Our AI-based cattle health monitoring project enables the early, efficient, scalable, and timely detection of Lumpy Skin Disease (LSD) and Foot and Mouth Disease (FMD) in cattle with high accuracy. A dataset of approximately 1600 LSD/non-LSD images and 840 FMD images was used to train multiple classification networks such as EfficientNetB0, ResNet50, VGG16, EfficientNetV2B0, and EfficientNetV2S, along with a soft-voting ensemble at inference. The proposed framework achieved a maximum testing accuracy of 98.36% for LSD classification and 99.84% for FMD classification under internal validation. These results indicate strong disease recognition capability, with ensemble-based prediction improving robustness, particularly for FMD classification. The proposed system enables practical, early, efficient, and scalable applications of AI research to improve livestock health monitoring and support the early prevention of widespread disease outbreaks.

Parkinson’s disease (PD) is a progressive neurodegenerative disease with multiple motor and non-motor characteristics. PD patients commonly face vocal impairments during the early stages of the disease. Therefore, diagnosis systems based on vocal disorders are at the forefront of recent PD detection studies. Our study proposes two frameworks based on Convolutional Neural Networks to classify Parkinson’s disease (PD). In recent years, Convolutional Neural Networks (CNNs) have proven highly effective in various medical applications, particularly disease classification. However, standard CNN designs have significant limitations because they require extensive manual calibration and supervision, which can result in biases and poor performance in practical applications. This paper proposes the Self-Operating Convolutional Neural Network (SOCNN) in conjunction with Convergent Cross-Mapping (CCM) to address these issues. The SOCNN architecture is intended to modify its internal parameters automatically, eliminating the need for manual intervention during training and increasing the model’s adaptability to unknown data. Adopting CCM principles, we construct a seamless connection between the input and output domains, allowing for rapid information transfer and preservation, which are crucial for accurate disease classification. To this end, we construct causal networks, extract network features, and perform deep learning analysis to distinguish Parkinson’s disease patients (PD) from age and gender-matched healthy controls (HC). Using a large dataset of Parkinson’s Disease (PD) patients and healthy controls, the effectiveness of the proposed SOCNN with CCM is evaluated. Specifically, we use the SOCNN-CCM to compute the centrality of the network nodes, which act as features for the classification models. Extensive experiments are conducted to compare the SOCNN to conventional CNN models and innovative techniques. The results demonstrate that the SOCNN-CMM outperforms state-of-the-art in terms of accuracy, sensitivity, and specificity when classifying Parkinson’s patients, confirming its diagnostic potential.

The advancement of modern agriculture is heavily dependent on accurate weed detection, which contributes to efficient resource utilization and increased crop yield. Traditional methods, however, often need more accuracy and efficiency. This paper presents WeedFocusNet, an innovative approach that leverages attention-driven ResNet152V2 transfer learning addresses these challenges. This approach enhances model generalization and focuses on critical features for weed identification, thereby overcoming the limitations of existing methods. The objective is to develop a model that enhances weed detection accuracy and optimizes computational efficiency. WeedFocusNet, a novel deep-learning model, performs weed detection better by employing attention-driven transfer learning based on the ResNet152V2 architecture. The model integrates an attention module, concentrating its predictions on the most significant image features. Evaluated on a dataset of weed and crop images, WeedFocusNet achieved an accuracy of 99.28%, significantly outperforming previous methods and models, such as MobileNetV2, ResNet50, and custom CNN models, in terms of accuracy, time complexity, and memory usage, despite its larger memory footprint. These results emphasize the transformative potential of WeedFocusNet as a powerful approach for automating weed detection in agricultural fields.