AI/ML-Based Medical Image Processing and Analysis.

The medical field is experiencing remarkable advancements, notably with the latest technologies-artificial intelligence (AI), big data, high-performance computing (HPC), and high-throughput computing (HTC)-that are in place to offer groundbreaking solutions to support medical professionals in the diagnostic process [...

DeepFruit: A dataset of fruit images for fruit classification and calories calculation.

A dataset of fully labeled images of 20 different kinds of fruits is developed for research purposes in the area of detection, recognition, and classification of fruits. Applications can range from fruit recognition to calorie estimation, and other innovative applications. Using this dataset, researchers are given the opportunity to research and develop automatic systems for the detection and recognition of fruit images using deep learning algorithms, computer vision, and machine learning algorithms.

Multiclass tumor identification using combined texture and statistical features.

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape.

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model.

Rice is considered one the most important plants globally because it is a source of food for over half the world's population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20-40% crop loss production.

Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features.

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not prevent the disease but rather reduce the severity of the symptoms. Until a vaccine is developed that can prevent COVID-19 infection, the testing of individuals will be a continuous process. Medical personnel monitor and treat all health conditions; hence, the time-consuming process to monitor and test all individuals for COVID-19 becomes an impossible task, especially as COVID-19 shares similar symptoms with the common cold and pneumonia.

Glioma Tumors' Classification Using Deep-Neural-Network-Based Features with SVM Classifier.

The complexity of brain tissue requires skillful technicians and expert medical doctors to manually analyze and diagnose Glioma brain tumors using multiple Magnetic Resonance (MR) images with multiple modalities. Unfortunately, manual diagnosis suffers from its lengthy process, as well as elevated cost. With this type of cancerous disease, early detection will increase the chances of suitable medical procedures leading to either a full recovery or the prolongation of the patient's life.

COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques.

It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs.

Recent Advancements in Fuzzy C-means Based Techniques for Brain MRI Segmentation.

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques.

Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors.

Brain MR Image Classification for Glioma Tumor detection using Deep Convolutional Neural Network Features.

Background: Detection of brain tumor is a complicated task, which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. Different objects within an MR image have similar size, shape, and density, which makes the tumor classification and segmentation even more complex.

Intelligent Security and Privacy of Electronic Health Records Using Biometric Images.

Background: In the presence of Cloud Environment and the migration of Electronic Health Systems and records to the Cloud, patient privacy has become an emergent problem for healthcare institutions. Government bylaws, electronic health documentation, and innovative internet health services generate numerous security issues for healthcare conformity and information security groups. To deal with these issues, healthcare institutes must protect essential IT infrastructure from unauthorized use by insiders and hackers.

ArASL: Arabic Alphabets Sign Language Dataset.

A fully-labelled dataset of Arabic Sign Language (ArSL) images is developed for research related to sign language recognition. The dataset will provide researcher the opportunity to investigate and develop automated systems for the deaf and hard of hearing people using machine learning, computer vision and deep learning algorithms. The contribution is a large fully-labelled dataset for Arabic Sign Language (ArSL) which is made publically available and free for all researchers.

Potential of electronic waste recycling in Gulf Cooperation Council states: an environmental and economic analysis.

This paper for the first time aims to valorize the environmental and economic values of electronic waste recycling for member states of the Gulf Cooperation Council (GCC) from the year 2018 up to 2040. GCC countries have a unique situation due to the significant economic growth with the resulting urbanization and population growth accompanied by high standards of living that in turn increase all types of waste. A direct link among the living standards and quantity of electronic waste production is observed in the GCC states.

Optimized FPGA Implementation of the Thyroid Hormone Secretion Mechanism Using CAD Tools.

The goal of this paper is to implement the secretion mechanism of the Thyroid Hormone (TH) based on bio-mathematical differential eqs. (DE) on an FPGA chip. Hardware Descriptive Language (HDL) is used to develop a behavioral model of the mechanism derived from the DE.