Involving machine learning techniques in heart disease diagnosis: a performance analysis

Artificial intelligence is a science that is growing at a tremendous speed every day and has become an essential part of many domains, including the medical domain. Therefore, countless artificial intelligence applications can be seen in the medical domain at various levels, which are employed to enhance early diagnosis and prediction and reduce the risks associated with many diseases, including heart diseases. In this article, machine learning techniques (logistic regression, random forest, artificial neural network, support vector machines, and k-nearest neighbors) are utilized to diagnose heart disease from the Cleveland Clinic dataset got from the University of California Irvine machine learning (UCL) repository and Kaggle platform then create a comparison between the performance of these techniques. In addition, some literature related to machine learning and deep learning techniques that aim to provide reasonable solutions in monitoring, detecting, diagnosing, and predicting heart disease and how these technologies assist in making health decisions are reviewed. Ten studies are selected and summarized by the authors published between 2017 and 2022 are illustrated. After executing a series of tests, it is seen that the most profitable performance in diagnosing heart disease is the support vector machines, with a diagnostic accuracy of 96%. This article has concluded that these techniques play a significant and influential role in assisting physicians and health care workers in analyzing heart patients' data, making health decisions, and saving patients' lives

Flight-schedule using Dijkstra's algorithm with comparison of routes findings

The Dijkstra algorithm, also termed the shortest-route algorithm, is a model that is categorized within the search algorithms. Its purpose is to discover the shortest-route, from the beginning node (origin node) to any node on the tracks, and is applied to both directional and undirected graphs. However, all edges must have non-negative values. The problem of organizing inter-city flights is one of the most important challenges facing airplanes and how to transport passengers and commercial goods between large cities in less time and at a lower cost. In this paper, the authors implement the Dijkstra algorithm to solve this complex problem and also to update it to see the shortest-route from the origin node (city) to the destination node (other cities) in less time and cost for flights using simulation environment. Such as, when graph nodes describe cities and edge route costs represent driving distances between cities that are linked with the direct road. The experimental results show the ability of the simulation to locate the most cost-effective route in the shortest possible time (seconds), as the test achieved 95% to find the suitable route for flights in the shortest possible time and whatever the number of cities on the tracks application

Beyond the Pandemic: The Interplay and Biological Effects of COVID-19 on Cancer Patients -A Mini Review

This article delves into the relationship between COVID-19 and cancer. The challenges and effects of the COVID-19 pandemic on cancer patients are highlighted, along with an explanation of the most crucial strategies that must be adhered to avoid this virus. Explaining the importance of healthcare systems in providing services to patients and assisting them to improve their health condition. This article concentrates on recent studies and clinical observations as it allows for an accurate and comprehensive understanding of the effects of this pandemic on cancer patients. The main issues will be focused on the impact of viral infections on cancerous tumours while clarifying the long-term consequences on patients’ lives. The main goal of this article is to inform healthcare workers, physicians, and researchers about the impact and seriousness of COVID-19 on cancer patients

Survey on the Significance of Artificial Neural Network

The word "neural networks" has a strong connotation. It alludes to devices that resemble minds and may be laden with the Frankenstein mythos' science fantasy meanings. One of the top aims of this report is to deconstruct neural networks and demonstrate how they function. Although they do have much to do with minds, their research crosses over into other scientific disciplines, such as technology and math. While some numerical terminology is needed for quantified defining such laws, processes, and frameworks, the goal is to do this in a non-technical manner

Novel Energy Optimized LDPC Codes for Next-Generation MIMO OFDM Systems

With the increasing prevalence of internet services in our daily lives, there is a growing demand for spectrum, resulting in a shortage of available frequencies. This is primarily due to the recent surge in subscribers seeking faster and more dependable data rates. Researchers globally have suggested various solutions to combat the spectrum shortage, such as technology, modulation schemes, beamforming, intelligent reflective surfaces, and channel coding schemes. With respect to channel coding, multiple codes have been proposed to overcome challenging and uncertain channel conditions. The paper introduces a DWT-incorporated LDPC STBC system as a solution to meet the rigorous demands of 5G deployment and use cases. A comparative analysis of the proposed codes is conducted to assess their suitability for next-generation communication networks, considering SNR and BER. Results indicate that the suggested code performs optimally, achieving a BER of 10-4 at 7dB SNR for Rayleigh Faded wireless channel. Therefore, the suggested code is deemed the most suitable option for the deployment of 5G and beyond systems.

The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review

People in the modern era spend most of their lives in virtual environments that offer a range of public and private services and social platforms. Therefore, these environments need to be protected from cyber attackers that can steal data or disrupt systems. Cybersecurity refers to a collection of technical, organizational, and executive means for preventing the unauthorized use or misuse of electronic information and communication systems to ensure the continuity of their work, guarantee the confidentiality and privacy of personal data, and protect consumers from threats and intrusions. Accordingly, this article explores the cybersecurity practices that protect computer systems from attacks, hacking, and data thefts and investigates the role of artificial intelligence in this domain. This article also summarizes the most significant literature that explore the roles and effects of machine learning and deep learning techniques in cybersecurity. Results show that machine learning and deep learning techniques play significant roles in protecting computer systems from unauthorized entry and in controlling system penetration by predicting and understanding the behaviour and traffic of malicious software

Mobile-base Registration System for Blood Donation (MBRS-BD)

In the healthcare management domain, blood donation receives a particular interest due to its crucial and vital importance in saving people’s lives. In Iraq, the blood donation procedure usually consumes a lot of time for donors as it is carried out through a non-automated and paper-based process, which is done only in hospitals/ medical centers for those who are willing to donate. Patients who are in a need for blood donation may have to wait until they receive the service, and this may results in dramatic or undesired consequences. At the same, the blood donation procedure negatively affects people who are willing or wish to donate blood and mostly leads to ignore this matter by a lot of them unless there is a critical situation concerning one of their family members.  This paper propose a Mobile-Base Registration System for Blood Donation (MBRS_BD) using Firebase Cloud Messaging (FCM) to manage the process of donor’s registration automatically using a smartphone to simulate, ease, and minimize the time required for that.  Donor can register in any available Iraqi hospitals/ medical center using MBRS-BD and go in the exact time to complete his/her donation process

Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning

In the modern era, many terms related to artificial intelligence, machine learning, and deep learning are widely used in domains such as business, healthcare, industries, and military. In these fields, the accurate prediction and analysis of data are crucial, regardless of how large the data are. However, using big data is confusing due to the rapid growth and massive development in public life, which requires a tremendous human effort in order to deal with such type of data and extract worthy information from it. Thus, the role of artificial intelligence begins in analyzing big data based on scientific techniques, especially in machine learning, whereby it can identify patterns of decision-making and reduce human intervention. In this regard, the significance role of artificial intelligence, machine learning and deep learning is growing rapidly. In this article, the authors decide to highlight these sciences by discussing how to develop and apply them in many decision-making domains. In addition, the influence of artificial intelligence in healthcare and the gains this science provides in the face of the COVID-19 pandemic are highlighted. This article concludes that these sciences have a significant impact, especially in healthcare, as well as the ability to grow and improve their methodology in decision-making. Additionally, artificial intelligence is a vital science, especially in the face of COVID-19