Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient’s chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.

Recent advances in artificial intelligence (AI), notably deep learning, have sparked widespread curiosity with bioinformatics, particularly the challenges presented by medical imaging. It’s been really helpful in enabling the Computer Aided Diagnosis CAD system to provide precise outcomes. Nonetheless, it is still a difficult task to identify breast cancer in mammography images. The purpose of this effort is to lower False Positive Rate FPR and False Negative Rate FNR and increase Matthews’s correlation coefficient MCC value. Two highly tailored object detection models, YOLOv5 and Mask R-CNN, are utilized to get the job done. YOLOv5 is able to detect the mass and determine whether it is benign or malignant. However, YOLOV5’s limited real estate necessitates certain tweaks to the original model in order to get the desired effects. Tumor borders and size are both identified by Mask RCNN as it traverses breast parenchyma in search of malignancies. Stages of cancer are based on the magnitude of the patients’ tumours. This model employs YOLOv5+Mask RCNN and is trained on the INbreast, CBIS-DDSM, and BNS dataset. The proposed model is compared against the baseline version of YOLOv5 to determine how well it performs. The proposed method improves performance, with an FPR of 0.049%, a FNR of 0.029%, and a high MCC value of 92.02%. Based on the results of the studies, combining YOLOv5 with Mask RCNN improves accuracy by 0.06 percentage points compared to using either method alone. Furthermore, this effort may aid in determining the patient’s prognosis and allowing clinicians to be more accurate and predictable in the diagnosing process at an early stage.

Oral cancer poses a formidable global health threat, demanding urgent attention to combat its devastating impact. Timely detection of oral squamous cell carcinoma (OSCC) is pivotal for successful treatment and improved survival rates. However, manual histopathological analysis, reliant on the expertise of medical practitioners, can be time-consuming and vulnerable to subjective discrepancies. To surmount these challenges and elevate diagnostic outcomes, this research explores the transformative potential of artificial intelligence (AI) in OSCC diagnosis. Three distinct methodologies, namely Gabor + CatBoost, ResNet50 + CatBoost, and Gabor+ ResNet50 + CatBoost, were implemented to leverage the power of AI. By extracting 32 low-level features from the Gabor Filter and 100,532 high-level features from the ResNet50 model, the study adopts principal component analysis (PCA) to mitigate overfitting, retaining the top 4096 components. The extracted features underwent individual classification using CatBoost, followed by concatenation and image classification. Remarkably, the third strategy, which synergized Gabor filtering with ResNet50 feature extraction, along with CatBoost classification, demonstrated the most exceptional performance. Achieving an impressive accuracy of 94.92%, 95.51% precision, 84.30% sensitivity, 95.54% specificity, 94.90% F1 score, and 94.9% AUC, these AI-based approaches herald a new era of accurate and efficient OSCC diagnosis.

The intricate and multifaceted nature of diabetes disrupts the body’s crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew’s correlation coefficient, receiver operating characteristic area, and precision–recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain.

Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. The model was trained on a composite dataset consisting of local pediatric PCG signals and publicly accessible dataset. Preprocessing methods, such as low- and high-pass filtering (60–650 Hz), resampling, and noise reduction, were utilized to enhance signal quality. Data augmentation techniques, including chunking, padding, and pitch-shifting, were employed to rectify dataset imbalances and improve model efficacy. Experimental results indicate substantial enhancements, attaining an accuracy of 98.56 %, precision of 98.56 %, F1 score of 98.55 %, sensitivity of 0.98, and specificity of 0.99. The comparative analysis demonstrates the proposed approach's superiority over current methods regarding accuracy and reliability. The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. Future endeavors involve extending to multiclass categorization and assessing performance across a wider range of medical problems. This study represents a significant advancement in accessible, automated CHD diagnoses, enhancing clinical competence to elevate pediatric treatment.

This research work examines the reliability of a wireless communication system that uses a hybrid radio frequency (RF) and underwater optical wireless communication (UOWC) in a dual-hop system. The proposed system consists of a single source (S) node that sends information to a destination (D) node through an amplify-and-forward (AF) relay (R) node using a multiple-input multiple-output (MIMO)-RF and UOWC links. The channel between the (S) node is subject to Nakagami-m fading using a transmit antenna selection (TAS) technique and maximal ratio combining (MRC) at (R) node, and exponential generalized gamma distribution (EGG) at (D) node to evaluate the overall statistical analysis of the proposed end-to-end(E2E) system. The proposed system has been extensively tested and validated under various conditions using a novel cumulative distribution function(CDF) and probability density function(pdf), and the paper provides closed-form expressions for the outage probability (OP) and average bit error rate (BER) for each channel model.