The current outbreak of monkeypox (mpox) has become a major public health concern because of the quick spread of this disease across multiple countries. Early detection and diagnosis of mpox is crucial for effective treatment and management. Considering this, the purpose of this research was to detect and validate the best performing model for detecting mpox using deep learning approaches and classification models. To achieve this goal, we evaluated the performance of five common pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, and EfficientNetB3) and compared their accuracy levels when detecting mpox. The performance of the models was assessed with metrics (i.e., the accuracy, recall, precision, and F1-score). Our experimental results demonstrate that the MobileNetV2 model had the best classification performance with an accuracy level of 98.16%, a recall of 0.96, a precision of 0.99, and an F1-score of 0.98. Additionally, validation of the model with different datasets showed that the highest accuracy of 0.94% was achieved using the MobileNetV2 model. Our findings indicate that the MobileNetV2 method outperforms previous models described in the literature in mpox image classification. These results are promising, as they show that machine learning techniques could be used for the early detection of mpox. Our algorithm was able to achieve a high level of accuracy in classifying mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.

Due to recent developments in deep learning and artificial intelligence, the healthcare industry is currently going through a significant upheaval. Despite a considerable advance in medical imaging and diagnostics, the healthcare industry still has a lot of unresolved problems and unexplored applications. The transmission of a huge number of medical images in particular is a difficult and time-consuming problem. In addition, obtaining new medical images is too expensive. To tackle these issues, we propose deep pix2pix generative adversarial networks (GAN) for generating synthetic medical images. For the comparison, we implemented CycleGAN, Pix2Pix GAN and Deep Pix2Pix GAN. The result has shown that our proposed approach can generate a new synthetic medical image from a different image with more accuracy than that of the other models. To provide a robust model, we trained and evaluated our models on a widely used brain image dataset, the IXI Dataset.

Deep learning and the Internet of Things (IoT) are revolutionizing the healthcare industry. This study explores the potential commercial transformation resulting from IoT-enabled healthcare systems that use deep learning for patient monitoring and diagnosis. Wearables, smart sensors, and internet-connected medical devices allow doctors to monitor patients' vital signs, activities, and physiological traits in real time. However, these devices generate vast and complex data, making analysis and diagnosis challenging. Deep learning models are well-suited to analyze this growing volume of medical data. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks can automatically recognize complex patterns and relationships in sensor data, electronic health records, and patient-reported information. This capability aids clinical professionals in diagnosing illnesses, identifying warning signs, and tailoring treatments. This paper describes a Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -based IoT-enabled healthcare system that performs feature extraction, classification, prediction, and data preparation. Additionally, it addresses interpretability issues, privacy concerns, and resource limitations of deep learning models in real-time healthcare settings. The study demonstrates the effectiveness of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -powered IoT-based healthcare solutions, such as real-time patient monitoring, disease detection, risk prediction, and therapy optimization. These techniques can improve the quality, cost, and outcomes of healthcare. Combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) with IoT can significantly enhance healthcare by improving disease detection, personalized treatment, and patient monitoring through connected devices and powerful analytics.

At a time when deepfake technology is extensively employed, this study explores the serious problems produced by deepfake misinformation and recommends a comprehensive cyber security architecture to lessen its effects. Deepfakes are a serious threat to the authenticity of digital information because they use sophisticated artificial intelligence algorithms that have outlived their original novelty. This study examines the psychological effects of manipulated media, sheds light on the intricacies of creating deepfakes, and examines actual incidents that highlight the pervasive effects of deepfake misinformation. Our suggested cyber security plans include cutting-edge detection algorithms, blockchain technologies and extensive outreach programmes. Through promoting a shared dedication to openness and truth, symbolised by the figurative ‘code of silence’, this study seeks to strengthen the digital environment against the ubiquitous impact of misleading content, thereby adding to the larger conversation about preserving authenticity and trust in an increasingly digital age.