Graphical User Interface-Based Detection of Kidney Stones Using Image Segmentation Techniques

2023-09-21T18:03:12+05:00

The exponential increase in detrimental surroundings and unhealthy nourishments is causing various health issues in humans. The most destructive effect of such lifestyles is on the kidneys, which cause many kidney diseases, and the most common among them are kidney stones. Kidney stones are a regular but life-threatening disease as they mostly remain unrecognized at the initial stages, leading to an increased threat of end-stage kidney failure. Due to the high recurring rate, Medical Imaging technologies are of paramount importance in detecting this serious public health threat worldwide. This research paper provides a Graphical User Interface for the detection of kidney stones that enables ease of understanding and point-and-click control of the algorithm in MATLAB. The proposed work uses CT scan images to explore image processing techniques due to their reliability and regularity. The algorithm enhances kidney stone screening by improving image quality and focusing on the region of interest. This study highlights the best solutions of imaging techniques to resolve problems like grainy pixels, low-resolution images, and the inaccurate detection of kidney stones due to size and resemblance with nearby parts. All this begins with examining the medical imaging slices from the body area, which later undergoes preprocessing, segmentation, and boundary detection techniques. To check the accuracy of the algorithm, 12 features were extracted using GLCM. Finally, the obtained features were classified using the Classification Designer App in MATLAB. An accuracy of 99.2% was acquired using Ensemble Classifiers. Also, for further progress in the detection of kidney stones in the future, an AI model can be trained so that it can deal with images having different thresholds for better management of the disease.

A Review of Real-Time Monitoring of Hybrid Energy Systems by Using Artificial Intelligence and IoT

2023-09-21T18:03:11+05:00

This research focuses on the invention of real-time monitoring of hybrid energy systems using artificial intelligence (AI) and the Internet of Things (IoT). The study aims to develop a monitoring system that provides real-time insights, anomaly detection, fault diagnosis, and energy optimization. The research methodology involves the integration of AI algorithms and IoT devices to collect, analyze, and visualize system data. The results demonstrate the effectiveness of the developed monitoring system in improving system performance, sustainability, and cost savings. The practical implementation and scalability of the system are also addressed, along with future research opportunities. This research contributes to the advancement of monitoring systems for hybrid energy applications, promoting efficiency and sustainability in energy management. This research provides significant contributions to the field of real-time monitoring of hybrid energy systems. The article focuses on addressing key problems related to the real-time monitoring of hybrid energy systems using AI and IoT technologies. The lack of real-time insights provided by conventional methods also limits timely decision-making and responsiveness to dynamic changes in the system.

Pakistan Journal of Engineering and Technology

Graphical User Interface-Based Detection of Kidney Stones Using Image Segmentation Techniques

A Review of Real-Time Monitoring of Hybrid Energy Systems by Using Artificial Intelligence and IoT