In this study, multiple lung diseases are diagnosed with the help of the Neural Network algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia, Pneumothorax, Atelectasis, Edema, Effusion, Hernia, Cardiomegaly, Pulmonary Fibrosis, Nodule, and Consolidation, are studied from the ChestX-ray14 dataset. A proposed fine-tuned MobileLungNetV2 model is employed for analysis. Initially, pre-processing is done on the X-ray images from the dataset using CLAHE to increase image contrast. Additionally, a Gaussian Filter, to denoise images, and data augmentation methods are used. The pre-processed images are fed into several transfer learning models; such as InceptionV3, AlexNet, DenseNet121, VGG19, and MobileNetV2. Among these models, MobileNetV2 performed with the highest accuracy of 91.6% in overall classifying lesions on Chest X-ray Images. This model is then fine-tuned to optimise the MobileLungNetV2 model. On the pre-processed data, the fine-tuned model, MobileLungNetV2, achieves an extraordinary classification accuracy of 96.97%. Using a confusion matrix for all the classes, it is determined that the model has an overall high precision, recall, and specificity scores of 96.71%, 96.83% and 99.78% respectively. The study employs the Grad-cam output to determine the heatmap of disease detection. The proposed model shows promising results in classifying multiple lesions on Chest X-ray images.

Human skin disease, the most infectious dermatological ailment globally, is initially diagnosed by sight. Some clinical screening and dermoscopic analysis of skin biopsies and scrapings for accurate classification are medically compulsory. Classification of skin diseases using medical images is more challenging because of the complex formation and variant colors of the disease and data security concerns. Both the Convolution Neural Network (CNN) for classification and a federated learning approach for data privacy preservation show significant performance in the realm of medical imaging fields. In this paper, a custom image dataset was prepared with four classes of skin disease, a CNN model was suggested and compared with several benchmark CNN algorithms, and an experiment was carried out to ensure data privacy using a federated learning approach. An image augmentation strategy was followed to enlarge the dataset and make the model more general. The proposed model achieved a precision of 86%, 43%, and 60%, and a recall of 67%, 60%, and 60% for acne, eczema, and psoriasis. In the federated learning approach, after distributing the dataset among 1000, 1500, 2000, and 2500 clients, the model showed an average accuracy of 81.21%, 86.57%, 91.15%, and 94.15%. The CNN-based skin disease classification merged with the federated learning approach is a breathtaking concept to classify human skin diseases while ensuring data security.

We have investigated graphene-based three various refractive index sensors (split ring resonator (SRR), split ring resonator with thin wire (SRRTW), and thin wire (TW) refractive index sensors) for the encoding and sensing-based applications. The sensors are designed to detect the presence of hemoglobin biomolecules with high sensitivity. The results are analyzed in the form of transmittance, and electric field and detailed sensitivity analysis is also carried out for the proposed graphene-based refractive index sensors for four various concentrations of hemoglobin biomolecules. We have also investigated the sensor’s performance in terms of quality factor, Q, and figure of merit (FOM). The encoding of ‘0’ and ‘1’ is attained by varying the graphene chemical potential fulfilling the one-digit coding. An array of these sensors can then be used for encoding-based applications. The detailed analysis of reported sensors is also carried out by checking the effect of varying physical parameters such as substrate thickness, split ring gap, and thin wire width on tunability. These sensors can be applied in biomedical or encoding-based applications. Experiments are performed using XGBoost regressor to determine, whether simulation time and resources can be reduced by using regression analysis to predict the transmittance values of intermediate frequency or not. Experimental results prove that regression analysis using XGBoost Regressor can reduce the simulation time and resources by at least 70 percent.

Almost 17.9 million people are losing their lives due to cardiovascular disease, which is 32% of total death throughout the world. It is a global concern nowadays. However, it is a matter of joy that the mortality rate due to heart disease can be reduced by early treatment, for which early‐stage detection is a crucial issue. This study is aimed at building a potential machine learning model to predict heart disease in early stage employing several feature selection techniques to identify significant features. Three different approaches were applied for feature selection such as chi‐square, ANOVA, and mutual information, and the selected feature subsets were denoted as SF1, SF2, and SF3, respectively. Then, six different machine learning models such as logistic regression (C1), support vector machine (C2), K‐nearest neighbor (C3), random forest (C4), Naive Bayes (C5), and decision tree (C6) were applied to find the most optimistic model along with the best‐fit feature subset. Finally, we found that random forest provided the most optimistic performance for SF3 feature subsets with 94.51% accuracy, 94.87% sensitivity, 94.23% specificity, 94.95 area under ROC curve (AURC), and 0.31 log loss. The performance of the applied model along with selected features indicates that the proposed model is highly potential for clinical use to predict heart disease in the early stages with low cost and less time.