Introduction: Electrocardiograms (ECGs) play a crucial role in diagnosing heart diseases by capturing the electrical activity of the heart. With the rising need for real-time cardiac monitoring, portable solutions have gained significance for timely detection and intervention. This study presents a portable ECG monitoring system incorporating Convolutional Neural Networks (CNNs) for accurate classification of cardiac abnormalities, including arrhythmias.Methods: The proposed system consists of an Arduino Nano microcontroller interfaced with an AD8232 ECG sensor for real-time ECG signal acquisition. The collected ECG data undergoes preprocessing before being fed into CNN models trained on the MIT-BIH Arrhythmia dataset. The model is designed for both binary and multi-class classification, distinguishing normal and abnormal heart rhythms. Performance metrics, including accuracy, were evaluated against state-of-the-art approaches to assess classification effectiveness.Results: Experimental evaluations demonstrate the CNN model’s high classification accuracy, achieving 98.35% in binary classification and 99.3% in multi-class classification. These results surpass existing benchmarks, highlighting the efficiency of the proposed system. The system's low-cost hardware and real-time classification capabilities enhance its suitability for continuous cardiac monitoring.Discussion: The proposed ECG monitoring system presents a reliable and cost-effective solution for early arrhythmia detection. By leveraging CNNs, it ensures accurate classification of cardiac abnormalities, making it a promising tool for both clinical and remote healthcare settings. Its potential impact extends to real-time monitoring, early diagnosis, and personalized healthcare, contributing to improved cardiovascular health management.

The human skin serves as a critical indicator of underlying health conditions, often manifesting early signs of disorders affecting internal organs. Recognizing these signs is crucial for timely diagnosis and treatment. However, the significance of the body’s natural defence mechanism through the skin is sometimes overlooked. This study aims to develop a skin disease classification system using a multimodal approach, integrating ensemble methodology using DenseNet169 – Resnet50 ensemble Transfer learning models, and Natural Language Processing (NLP) within a Telegram chatbot interface. The primary aim is to enhance the chatbot’s ability to deliver customised skin-related diagnoses by using user-provided information, including skin type, chemical exposure, and previous treatments, ensuring more precise and personalised interactions. Additionally, the accuracy and generalization capability of the classification system is improved by analysing the data from both the chatbot and image analysis. The chatbot’s self-learning capabilities allow it to improve its comprehension over time in response to user input, which makes it more adept at personalising queries. Integrating DenseNet169 and ResNet50 strengthens the feature reuse by connecting each layer to every other layer in a dense manner for efficient gradient flow thus reducing the number of parameters. ResNet50, with its residual connections, helps mitigate vanishing gradient issues, allowing for deeper networks with stable training leading to improved feature extraction and representation. This hybrid approach captures fine-grained global features enhancing the feature learning attaining good performance in complex tasks. A total of 11,747 images were evaluated in the proposed study, with 7,930 images allocated for training and validation, and 3,817 images designated for testing. The model attained an accuracy of 77.07% and an AUC score of 96.72%. The NLP training model achieved an accuracy of 93.62% ensuring a comprehensive understanding of user data and accelerating engagement, leading to more precise and personalized predictions of skin disorders.

Multiple secret sharing algorithm using the YCH scheme, combined with DNA encoding is proposed, focusing at better security. Firstly, DNA encoding for multiple images is carried out; then the addition of these encoded components by DNA is performed. Secondly, the (t, n) scheme used the Lagrange interpolation polynomial to share these DNA scrambled matrices is performed. Thirdly, these shares are embedded using a modular operation. Finally, ‘t’ or more shares are pooled which reconstructs the scrambled matrices, and by decoding the DNA scrambled matrices multiple secrets are reconstructed without loss. The simulation results and the security analysis prove that this algorithm is perfect, and produces results with better PSNR value. The correlation co- efficient shows that this also has the ability of resisting various attacks

In renal disease classification, various pathologies such as kidney stones, renal cysts, and tumors pose significant health risks and diagnostic challenges. Annually, thousands of cases are diagnosed worldwide, impacting both pediatric and adult populations. To address this, Computer Assisted Diagnosis (CAD) systems have emerged, aiming to alleviate the burden on healthcare professionals, enhance diagnostic accuracy, and manage the vast amounts of medical data involved. These CAD systems traditionally leverage the expertise of nephrologists, specialized imaging features, and domain knowledge to aid in disease classification. The MSKd_Net architecture integrates Swin Transformer-based hierarchical learning, efficiently capturing both local and global features, along with a customized convolved model enhanced with a multi-head attention layer. Additionally, the custom convolved model enhanced with the multi-head attention layer efficiently captures long-range dependencies and relationships in the input sequence or set of features, resulting in fine-tuned features. The Swin Transformer features are fused additively with the convolved model enhanced with multi-head attention layer features and fed to linear layers to classify the input such as cyst, stone, tumor, and normal. The proposed method significantly enhanced the discrimination capability of the MSKd_Net model on the Riñones dataset, achieving optimal F1-scores of 0.97, 0.99, 0.98, and 0.9 for cyst, normal, stone, and tumor, respectively on testing data. The strategic fusion of these components effectively addresses classification challenges, enhancing crucial feature identification for accurate multi-class kidney disease image recognition, and seamlessly integrates within MSKd_Net to achieve superior performance.