Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches.

The study discusses the development of a reliable strategy for detecting and segmenting kidney stones from CT scans. Kidney stones are solid materials that can form in the kidneys due to excessive amounts of specific minerals in the urine. Severe back, side, lower abdominal, or groin discomfort, or blood in urine can indicate kidney stone presence. The current method for detection using CT scans is widely used, but it requires more precise and efficient technology due to the laborious and time-consuming manual process. The objective of this report is to propose a robust approach for kidney stone detection and segmentation, employing threshold segmentation and the watershed algorithm along with pre-and post-image processing for noise reduction. Additionally, the method estimates the size of the stone, aiding in selecting the appropriate medical procedure for stone removal. The entire process is implemented on a dataset comprising numerous kidney stone images to test and validate its accuracy and reproducibility. The system's image quality enhancement is evaluated by calculating several parameters such as SSIM, PSNR, FSIM, NCC, and NAE on the output images. To further validate the suggested method, a comparison is made with two other existing algorithms based on the mentioned evaluation criteria. The proposed strategy promises to improve the accuracy and reliability of kidney stone detection and segmentation, providing a more efficient and effective solution for medical diagnosis and treatment

The traditional industries have been transformed by the rapid expansion of the Internet of Things (IoT), leading to a need for real-time data processing and minimal latency. Meeting these demands has been a challenge for centralized cloud computing, resulting in the emergence of edge computing as a viable solution. In this evaluation, we delve into how edge computing improves data processing efficiency, reduces latency, and tackles the obstacles in deploying intelligent IoT applications. We analyze its structure, primary advantages, difficulties, and real-world applications, all supported by tables, graphs, diagrams, and pertinent data.