Deep learning-enhanced diabetic retinopathy image classification.

Objective: Diabetic retinopathy (DR) can sometimes be treated and prevented from causing irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) model is employed to accurately identify all five stages of DR.

Methods: The suggested methodology presents two examples, one with and one without picture augmentation.

A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things.

In IoT networks, the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is vulnerable to various attacks. Routing attacks in RPL-based IoT are becoming critical with the increase in the number of IoT applications and devices globally. To address routing attacks in RPL-based IoT, several security solutions have been proposed in literature, such as machine learning techniques, intrusion detection systems, and trust-based approaches.

A Web-Based Model to Predict a Neurological Disorder Using ANN.

Dementia is a condition in which cognitive ability deteriorates beyond what can be anticipated with natural ageing. Characteristically it is recurring and deteriorates gradually with time affecting a person's ability to remember, think logically, to move about, to learn, and to speak just to name a few. A decline in a person's ability to control emotions or to be social can result in demotivation which can severely affect the brain's ability to perform optimally.

Earlier Detection of Brain Tumor by Pre-Processing Based on Histogram Equalization with Neural Network.

MRI is an influential diagnostic imaging technology specifically worn to detect pathological changes in tissues with organs early. It is also a non-invasive imaging method. Medical image segmentation is a complex and challenging process due to the intrinsic nature of images.

Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning.

An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide.

Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography.

Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilize for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- 1 year.

A benchmarking program to support software process improvement adaptation in a developing country, a Pakistan case.

Background: Standardization of software development processes using the software process improvement (SPI) frameworks like capability maturity model integration (CMMI) or the International Organization for Standardization (ISO) are vital for better performance and defect-free delivery of software engineering projects.

Problem: The studies from developing countries show that the organizations are unable to get the desired SPI benefits during early adaptation due to lack of benchmarking initiatives. These initiative needed bridging of the gap between major software engineering fields such as project performance, productivity, and SPI measurement metrics like effort, schedule, cost, productivity, quality, and customer satisfaction.

Detection of COVID-19 Based on Chest X-rays Using Deep Learning.

The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease's spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies.

A new spatial spherical pattern model into interactive cartography pattern: multi-dimensional data via geostrategic cluster.

A growing amount of research conducted in digital, cooperative with advances in Artificial Intelligence, Computer Vision including Machine learning, has managed to the advance of progressive techniques that aim to detect and process affective information contained in multi-modal evidences. This research intends to bring together for theoreticians and practitioners from academic fields, professionals and industries and extends to be visualizing cries such epidemic, votes, social Phenomena in spherical representation interactive model working in the broad range of topics relevant to multi - modal data processing and forensics tools developing. Furthermore, progress has been made in this research besides that in this research conducted progression of mapping claims in present epoch necessitate the capacities of virtual guide of any understandable Geo-Visualization of spatial features that talented to convert the quantities of spatial pattern into cartography.