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Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women of reproductive age, characterized by hormonal imbalance, irregular menstrual cycles, and ovarian cysts. Traditional diagnostic approaches, which include clinical evaluations, radiological studies, and surgical interventions, are often time-consuming, costly, and not always reliable. To improve the accuracy and efficiency of PCOS diagnosis, this research introduces the Feature Fusion Context Attention U-Net (FCAU-Net) model, leveraging deep learning (DL) techniques. This study makes two key contributions. First, it enhances dataset preparation through Fuzzy Contrast Enhanced (FCE) imaging. Second, it integrates a Feature Fusion Context (FFC) module into the Attention U-Net model, optimizing the extraction of context and position weights from feature maps for better classification performance. An openly available PCOS Ultrasound Image Dataset with 3,800 images was partitioned with 80: 20 to ensure that only original images were used for testing, while augmented samples were exclusively utilized for training to enhance model generalization and robustness. The remaining 3040 images was augmented to form 45,600 images and split into training and validation sets in an 80:20 ratio. The augmented images were processed and tested with several DL models, including DenseNet, AlexNet, VGG19, ResNet, U-Net, and Attention U-Net. Among these, the Attention U-Net initially achieved over 80% accuracy in detecting PCOS. The proposed FCAU-Net, which incorporates the FFC module, demonstrated superior performance, achieving a detection accuracy of 99.89%, significantly outperforming existing DL models. This research highlights the potential of FCAU-Net in providing a more accurate and efficient tool for the diagnosis of PCOS.

Deep learning-based nut classification has emerged as a viable way to automate the detection and categorization of different nut varieties in the food processing and agriculture sectors. Conventional techniques for classifying nuts mostly rely on manually created characteristics like texture, color, shape, or edges. These characteristics frequently fall short of capturing the image’s complete complexity, particularly when nuts show tiny visual variances. This research proposes Deep Atrous Context Convolution Generative Adversarial Network (DAC-GAN) model that categorize the 8 classes of nuts like brazil nuts, cashew, peanut, pecan nut, pistachio, chest nut, macadamia and Walnut. This research uses Common Nut KAGGLE dataset with 4,000 nuts images of 8 nuts classes. The DAC-GAN approach overcomes the difficulties of having limited labelled data for nut classification tasks by employing DCGANs’ ability to produce high-quality, synthetic nut images to supplement the dataset. The DCGAN comprises of a discriminator and a generator block. The discriminator block develops the ability to differentiate between synthetic and real images, while the generator block generates realistic nut images from random noise. The real images along with the DCGAN generated images are processed with feature filtering methods to extract the Corner Key Points Featured (CKPF) nuts images. To further enhance the feature selection, the CKPF edges are extracted from the image that provides unique, geometrically distinctive critical corners to further process for representative learning. To proceed with the effective feature extraction and model learning, the CKPF nuts images are processed with atrous convolution that capture the intricate details by expanding the receptive field without losing resolution. The novelty of this work exists by appending the filtration and atrous convolution that acquire the spatial data features from the nut’s images at various resolutions. Atrous convolution was refined by appending the pre-context and post-context block that add the image level information to the features. The effectiveness of the DAC-GAN model was validated with the traditional augmented dataset with all existing filtering images and CNN models. Implementation outcome shows that DAC-GAN found to exhibit high accuracy of 99.83% towards the nuts type classification. The superiority of the DAC-GAN method over traditional approaches is demonstrated by extensive experiments on augmented and DCGAN generated datasets, which achieve higher classification accuracy and generalization across a variety of nut type categorization. The outcome demonstrates that the DCGAN together with atrous convolution have the potential to be an effective tool for automating nut sorting in food industry.

Recognition is an extremely high-level computer vision evaluating task that primarily involves categorizing objects by identifying and evaluating their key distinguishing characteristics. Categorization is important in botany because it makes comprehending the relationships between various flower species easier to organize. Since there is a great deal of variability among flower species and some flower species may resemble one another, classifying flowers may become difficult. An appropriate technique for classification that uses deep learning technology is vital to categorize flower species effectively. This leads to the design of proposed Sobel Restricted Boltzmann VGG19 (SRB-VGG19), which is highly effective at classifying flower species and is inspired by VGG19 model. This research primarily contributes in three ways. The first contribution deals with the dataset preparation by means of feature extraction through the use of the Sobel filter and the Restricted Boltzmann Machine (RBM) neural network approach through unsupervised learning. The second contribution focuses on improving the VGG19 and DenseNet model for supervised learning, which is used to classify species of flowers into five groups. The third contribution overcomes the issue of data poisoning attack through Fast Gradient Sign Method (FGSM) to the input data samples. The FGSM attack was addressed by forming the Adversarial Noise Layer in the dense block. The Flowers Recognition KAGGLE dataset preprocessing was done to extract only the important features using the Sobel filter that computes the image intensity gradient at every pixel in the image. The Sobel filtered image was then applied to RBM to generate RBM Component Vectorized Flower images (RBMCV) which was divided into 3400 training and 850 testing images. To determine the best CNN, the training pictures are fitted with the existing CNN models. According to experiment results, VGG19 and DenseNet can classify floral species with an accuracy of above 80%. So, VGG19 and DenseNet were fine tuned to design the proposed SRB-VGG19 model. The Novelty of this research was explored by designing two sub models SRB-VGG FCL model, SRB-VGG Dense model and validating the security countermeasure of the model through FGSM attack. The proposed SRB-VGG19 initially begins by forming the RBMCV input images that only includes the essential flower edges. The RBMCV Flower images are trained with SRB-VGG FCL model, SRB-VGG Dense model and the performance analysis was done. When compared to the current deep learning models, the implementation results show that the proposed SRB-VGG19 Dense Model classifies the flower species with a high accuracy of 98.65%.

Concrete bridge maintenance is crucial for infrastructure sustainability and public safety. Accurate identification of cracks in bridge components is a critical task, yet traditional inspection methods often fall short due to their limitations in accuracy and efficiency. This paper introduces the Gradient Transformer Attention U-Net (GTAU-Net) model, a novel deep learning approach that significantly advances crack detection in bridge components. Unlike conventional attention-based U-Nets, GTAU-Net introduces a Quantum Fused Filter (QFF) to pre-process images by integrating multiple edge and gradient patterns through a quantum-inspired hybrid filtering strategy. It further computes Gradient Saliency Scores (GSS) to dynamically guide the self-attention mechanism, enabling more precise localization and feature extraction. Through this dual enhancement, GTAU-Net effectively handles varying crack sizes, shapes, orientations, and environmental conditions. Experimental results demonstrate that GTAU-Net achieves an impressive 99.42% accuracy significantly outperforming existing models and measures crack lengths, highlighting sustainability. This research contributes to the advancement of automated crack detection technology, offering a promising solution for enhancing infrastructure safety and durability. To promote transparency and reproducibility, the code and dataset used in this study are publicly available at Zenodo: https://doi.org/10.5281/zenodo.15617661 .

Kidney stone classification is a critical yet complex task in medical imaging, traditionally performed using computed tomography (CT) and ultrasound scans. Manual interpretation of these images is time‐consuming and prone to variability, highlighting the need for automated diagnostic solutions. This study proposes Contour‐Detected Normalized Residual VGG19 (CDR‐VGG19), a deep learning model inspired by VGG19 and enhanced with residual connections to improve classification accuracy. The model leverages contour detection for unsupervised feature extraction, followed by supervised learning using a hybrid of VGG19 and ResNet architectures. Using the Kidney Stone KAGGLE dataset of 2602 images, the model applies data augmentation, preprocessing, and feature filtering. Images are split into training, validation, and testing sets (80:10:10), and multiple CNNs are evaluated. Results show that the proposed CDR‐VGG19 achieves a high classification accuracy of 99.61%, demonstrating its effectiveness in detecting kidney stones from contour‐enhanced images.

Skin cancer is the uncontrolled growth of irregular cancer cells in the human-skin's outer layer. Skin cells commonly grow in an uneven pattern on exposed skin surfaces. The majority of melanomas, aside from this variety, form in areas that are rarely exposed to sunlight. Harmful sunlight, which results in a mutation in the DNA and irreparable DNA damage, is the primary cause of skin cancer. This demonstrates a close connection between skin cancer and molecular biology and genetics. Males and females both experience the same incidence rate. Avoiding revelation to ultraviolet (UV) emissions can lower the risk rate. This needed to be known about in order to be prevented from happening. To identify skin cancer, an improved image analysis technique was put forth in this work. The skin alterations are routinely monitored by this proposed skin cancer categorization approach. Therefore, early detection of suspicious skin changes can aid in the early discovery of skin cancer, increasing the likelihood of a favourable outcome. Due to the blessing of diagnostic technology and recent advancements in cancer treatment, the survival rate of patients with skin cancer has grown. The strategy for detecting skin cancer using image processing technologies is presented in this paper. The system receives the image of the skin lesion as an input and analyses it using cutting-edge image processing methods to determine whether skin cancer is present. The Lesion Image Analysis Tools use texture, size, and shape assessment for image segmentation and feature phases to check for various cancer criteria including asymmetries, borders, pigment, and diameter. The image is classified as Normal skin and a lesion caused by skin cancer using the derived feature parameters.

Sentiment analysis (SA) has been an important focus of study in the fields of computational linguistics and data analysis for a decade. Recently, promising results have been achieved when applying DNN models to sentiment analysis tasks. Long short-term memory (LSTM) models, as well as its derivatives like gated recurrent unit (GRU), are becoming increasingly popular in neural architecture used for sentiment analysis. Using these models in the feature extraction layer of a DNN results in a high dimensional feature space, despite the fact that the models can handle sequences of arbitrary length. Another problem with these models is that they weight each feature equally. Natural language processing (NLP) makes use of word embeddings created with word2vec. For many NLP jobs, deep neural networks have become the method of choice. Traditional deep networks are not dependable in storing contextual information, so dealing with sequential data like text and sound was a nightmare for such networks. This research proposes multichannel word embedding and employing stack of neural networks with lexicon-based padding and attention mechanism (MCSNNLA) method for SA. Using convolution neural network (CNN), Bi-LSTM, and the attention process in mind, this approach to sentiment analysis is described. One embedding layer, two convolution layers with max-pooling, one LSTM layer, and two fully connected (FC) layers make up the proposed technique, which is tailored for sentence-level SA. To address the shortcomings of prior SA models for product reviews, the MCSNNLA model integrates the aforementioned sentiment lexicon with deep learning technologies. The MCSNNLA model combines the strengths of emotion lexicons with those of deep learning. To begin, the reviews are processed with the sentiment lexicon in order to enhance the sentiment features. The experimental findings show that the model has the potential to greatly improve text SA performance.

With the tremendous technological growth, the world is shifted to adapt the different food and life style by the people that results in the improper working of the body organs. The change in the food habits leads to a major problems that we face in the current scenario is the presence of hypothyroid in the body. The likelihood of hypothyroid still ruins as a challenging issue due to the uncertainty of proper symptoms. With this background, the machine learning can be used towards health care scenarios for the prediction of disease based on the patients past history. This paper focus on predicting the existence of hypothyroid with respect to the patients' medical parameters. The hypothyroid patient dataset is taken from the UCI Metadata repository with 24 columns and 3163 unique patient's records is used for the experimentation of hypothyroid with the following contributions. Firstly, the hypothyroid dataset from UCI machine repository is subjected with the data processing and exploratory analysis of the dataset. Secondly, the unrefined data set is fixed with different classifier algorithm to find the presence of hypothyroid and to examine the efficiency metrics before and after feature scaling. Thirdly, the data is processed to PCA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Fourth, the data is processed to LDA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Experimental results show that the Kernel Support Vector Machine classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced PCA dataset. Similarly, the Logistic Regression, Kernel Support Vector Machine and Gaussian Naïve Bayes classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced LDA dataset.

Facial Expression Recognition is the critical part in the human emotional detection in the field of image processing. The application tends to soft or hard real time application based on the power of expression detection of idle image or videos. The Social communication with any object can be done by verbal or non-verbal format. Expression and Emotion detection completely rely on the non-verbal communication and facial expression. Machine Learning play an important role in the recognition of facial expression. An attempt is made in this paper to analyse the performance of Convolutional neural network models with diverse activation layer for the performance evaluation.Firstly, the facial expression dataset is extracted from the website http://www.consortium.ri.cmu.edu/ckagree/, http://app.visgraf.impa.br/database/faces/ is subjected with the data processing. Secondly, the data analysis is done for the distribution of expression image in the training and testing dataset. Thirdly, the facial expression images are detected with HAAR cascade and then the images are cropped with (350, 350). Fourth, the facial expression images is applied with normalized and the bottleneck features are created for training and testing data. Fifth, the training dataset is fitted with convolutional sequential neural network models with various activation layers like Sigmoid, Elu, Relu, Selu, Tanh, Softsign and Softplus. Sixth, the performance analysis is done with loss and accuracy for all the epoch of all CNN models for all the activation layers. Experimental results show that CNN sequential model with Relu activation layer is found to have the accuracy of 100%.

Early identification of root diseases is vital for safeguarding crop health and maximizing agricultural yield. Traditional diagnostic approaches are often constrained by their manual nature, slow response time, limited scalability, and inconsistent accuracy. To overcome these limitations, this study proposes a novel hybrid RIFATA Attention-PSPNet (RA-PSPNet) framework designed for automated and precise root disease identification. The RA-PSPNet replaces the CNN to Convolutional block attention module (CBAM) in the Pyramid Scene Parsing Network (PSPNet). Further, RA-PSPNet is optimized with a metaheuristic optimization technique called the Remora Improved Feedback Artificial Tree Algorithm (RIFATA), which enhances model performance through dynamic hyperparameter tuning. The proposed system includes a multi-stage pipeline: root images are first pre-processed using Rot Sensitive Gaussian (RSG) filtering, followed by processing with CBAM to provide multiscale attention feature map and finally segmented PSPNet. Both the segmentation and classification components of proposed RA-PSPNet are optimized using RIFATA. RIFATA combines the strengths of the Remora Optimization Algorithm (ROA) and Improved Feedback Artificial Tree Algorithm (IFATA), which incorporates elements from Improved Invasive Weed Optimization (IIWO) and Feedback Artificial Tree (FAT) models for efficient hyperparameter tuning. Experimental evaluations conducted on multiple datasets from Maize root, Root cowpea, Wheat root, Rice root gellan, Alfalfa root, Chrono root, Root crown images of soybean and wheat datasets in the ratio of 80:10:10. Implementation reveals that proposed RA-PSPNet outperforms with the accuracy of 99.63%, high sensitivity (99.13%), specificity (99.23%), precision (99.03%), recall (99.13%), and F1-score (99.08%) towards healthy and rot root classification. These results demonstrate the effectiveness and scalability of RA-PSPNet for sustainable precision agriculture and root disease diagnosis. The implementation are documented in our publicly accessible repository on Zenodo to enhance the transparency and reproducibility. ( https://doi.org/10.5281/zenodo.16825955 ).