Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

Purpose – The Cloud computing (CC) and its services have enabled the information centers of organizations to adapt their informatic and technological infrastructure and making it more appropriate to develop flexible information systems in the light of responding to the informational and knowledge needs of their users. In this context, cloud-data governance has become more complex and dynamic, requiring an in-depth understanding of the data management strategy at these centers in terms of: organizational structure and regulations, people, technology, process, roles and responsibilities. Therefore, our paper discusses these dimensions as challenges that facing information centers in according to their data governance and the impa

In data transmission a change in single bit in the received data may lead to miss understanding or a disaster. Each bit in the sent information has high priority especially with information such as the address of the receiver. The importance of error detection with each single change is a key issue in data transmission field.
The ordinary single parity detection method can detect odd number of errors efficiently, but fails with even number of errors. Other detection methods such as two-dimensional and checksum showed better results and failed to cope with the increasing number of errors.
Two novel methods were suggested to detect the binary bit change errors when transmitting data in a noisy media.Those methods were: 2D-Checksum me

Cloud-based Electronic Health Records (EHRs) have seen a substantial increase in usage in recent years, especially for remote patient monitoring. Researchers are interested in investigating the use of Healthcare 4.0 in smart cities. This involves using Internet of Things (IoT) devices and cloud computing to remotely access medical processes. Healthcare 4.0 focuses on the systematic gathering, merging, transmission, sharing, and retention of medical information at regular intervals. Protecting the confidential and private information of patients presents several challenges in terms of thwarting illegal intrusion by hackers. Therefore, it is essential to prioritize the protection of patient medical data that is stored, accessed, and shared on

The antagonism of the rhizospheric bacteria toward pathogenic fungi Macrophomina phaseolina was investigated. Ten soil samples were collected from the rhizospheric zone around Cowpea root (Vignaunguiculata L.). These samples were used as the source of Arbuscular Mycorrhizal fungi (AMF) and rhizobacterial isolates. Twenty-five bacteria were isolated and evaluated as an antagonistic agent against pathogenic fungi. M. phaseolina was isolated from infected roots of Cowpea and used as a pathogen. Twenty-five bacteria were isolated and evaluated as an antagonistic agent against pathogenic fungi. M. phaseolina was isolated from infected roots of Cowpea and used as a pathogen. The synergistic effect between A. siccitolerans and (AMF) Glomusmosseae,