On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently. Currently, the most widely used method is Reverse Transcription Polymerase Chain Reaction (RT-PCR), a clinical technique for infection identification. However, RT-PCR is expensive, has limited sensitivity, and requires specialized medical expertise. One of the major challenges in the rapid diagnosis of COVID-19 is the need for reliable imaging, particularly X-ray imaging. This work takes advantage of artificial intelligence (AI) techniques to enhance diagnostic accuracy by automating the detection of COVID-19 infections from chest X-ray (CXR) images. We obtained and analyzed CXR images from the Kaggle public database (4035 images in total), including cases of COVID-19, viral pneumonia, pulmonary opacity, and healthy controls. By integrating advanced techniques with transfer learning from pre-trained convolutional neural networks (CNNs), specifically InceptionV3, ResNet50, and Xception, we achieved an accuracy of 95%, significantly higher than the 85.5% achieved with ResNet50 alone. Additionally, our proposed method, CXR-DNNs, can accurately distinguish between three different types of chest X-ray images for the first time. This computer-assisted diagnostic tool has the potential to significantly enhance the speed and accuracy of COVID-19 diagnoses.

Information and Communication Technologies are regularly used in medical services and associations around the world. An increasing number of devices running Android as Smartphones, smart TV and tablets, are comprehensively utilized for numerous purposes. For these devices, there are distinctive types of health and medical applications that provide an easy access to patients and their caretakers to save time [1]. In this work, we develop an Android application named Help Pro with an important goal: save lives by a single tap. In case of emergency, Help Pro users could have an ambulance available within 100 m of the user’s real-time location. Through the Dijkstra algorithm, this application employs Google Maps API (Application Programming Interface) to trace the nearest way in order to an available ambulance arrives on time. Consequently, not only time is efficiently saved but also precious lives.

Information and Communication Technologies are regularly used in medicinal services and associations around the world. The Android working framework-based electronic contraptions, for example, Smartphones, smart TV and tablets are comprehensively utilized for numerous purposes. Within android devices, there are distinctive types of health and medical applications that provide ease to patients and their caretakers to lessen time [1]. This undertaking includes a thought that is proposed, to save lives by a single tap. It is very advantageous for users in case of emergency: through this application, an ambulance could be available within 100 meters of the user’s real-time location. The Dijkstra algorithm is used in this application by Google Maps API (Application Programming Interface) and this way nearest ambulance arrives on time. Consequently, not only time is efficiently saved but also precious lives.

Augmented reality is extremely imperative in envisioning real models or thoughts. It can be used to create erudition applications that can encourage the teaching and learning process. Augmented reality technology is easy and can be understood by the students. The innovation of computer equipment empowers 3D modelling to perform effectively and comparatively easy manner. Chemical reactions were in chemistry usually uninteresting by students for learning. Through augmented reality, an application will help the students to visualize and understand the 3D prototypical of each chemical component and can get the simulation of integration of two or more elements of it and other types Furthermore, several chemical reactions can be combined based on different markers or tracker detected from the camera. The elements from the periodic table we are using H, N, C, Cl, O has been tested with this application. Similarly, different combining elements like HCl, H2O, CO2, HCN, and NH3 etc. have been tested by augmented reality applications. The results of the present study indicated the learning benefits of this application as tested on different students.

Early and precise identification is essential for improving patient prognosis because melanoma is still one of the most aggressive and deadly dermatological cancers. This paper suggests an improved deep learning framework that uses a refined Convolutional Neural Network (CNN) based on InceptionV3 to automatically classify skin lesions. The model tackles the intrinsic complexity of dermoscopic images by combining transfer learning with sophisticated data augmentation and tailored training hyperparameters. Over 100 training epochs, the method was assessed using a binary dataset of benign and malignant tumors. Metrics including accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) were used to thoroughly evaluate performance. According to experimental data, the suggested model obtains a high classification accuracy of around 94%, with sensitivity and F1-scores of 93% respectively. Additionally, even in the presence of class imbalance, the model's resilience and discriminative capacity are confirmed by ROC and Precision-Recall assessments. When compared to baseline designs and modern state-of-the-art CNN models like ResNet50, VGG16, and EfficientNet, the proposed approach demonstrates competitive performance with improved sensitivity and specificity balance while maintaining reduced false positive and false negative predictions. By merging an enhanced InceptionV3 architecture with effective transfer learning, adaptive hyperparameter tuning, and enhanced augmentation strategies, the proposed framework provides a more balanced and dependable melanoma classification performance than conventional CNN-based methods. These results highlight the potential of the improved InceptionV3 model as a trustworthy computer-aided diagnostic (CAD) tool, giving dermatologists vital decision help in the early identification of melanoma.