Prediction of Dental Implants Using Machine Learning Algorithms.

It has been claimed that artificial intelligence (AI) has transformative potential for the healthcare sector by enabling increased productivity and creative methods of delivering healthcare services. Recently, there has been a major shift to artificial intelligence by businesses, government, and private sectors in general and the health sector in particular. Many studies have proven that artificial intelligence is contributing greatly to the health sector by discovering diseases and determining the best treatments for patients. Dentistry requires new innovative methods that serve both the patient and the service provider in obtaining the best and appropriate medical services. Artificial intelligence has the ability to develop the field of dentistry through early diagnosis and prediction of dental implant cases. This research develops a set of four machine learning algorithms to predict when a patient might need dental implants. These models are the Bayesian network, random forest, AdaBoost algorithm, and improved AdaBoost algorithm. This work shows that the developed algorithms can predict when a patient needs dental implants. Also, we believe that this proposal will advise managers and decision-makers in targeting patients with particular diagnoses. Analysis of the obtained results indicates good performance of the developed machine learning. As a result of this research, we note that the proposed improved AdaBoost algorithm increases the level of prediction accuracy and gives significantly higher performance than the other studied methods with the accuracy for the improved AdaBoost algorithm reaching 91.7%.

Smart-parking management algorithms in smart city.

Recently, various advanced technologies have been employed to build smart cities. Smart cities aim at improving the quality of life through the delivery of better services. One of the current services that are essential for any smart city, is the availability of enough parking spaces to ensure smooth and easy traffic flow. This research proposes a new framework for solving the problem of parking lot allocation, which emphasizes the equitable allocation of people based on the overall count of people in each parking space. The allocation process is performed while considering the available parking lots in each parking space. To accomplish the desired goal, this research will develop a set of seven algorithms to reduce the gap in the number of people between parking spaces. Many experiments carried out on 2430 different cases to cover several aspects such as the execution time and the gap calculations, were used to explore the performance of the developed algorithm. Analyzing the obtained results indicates a good performance behavior of the developed algorithms. Also, it shows that the developed algorithms can solve the studied problem in terms of gap and time calculations. The MR algorithm gained excellent performance results compared to one of the best algorithms in the literature. The MR algorithm has a percentage of 96.1 %, an average gap of 0.02, and a good execution time of 0.007 s.

A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments.

BACKGROUND: This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. METHODOLOGY: SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015-2021) that belongs to SI algorithms are reviewed and summarized. RESULTS: It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. CONCLUSIONS: The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

Cancer in Saudi Arabia (CSA): Web-Based Application to Study Cancer Data Among Saudis Using Waterfall Model.

BACKGROUND: Information technology (IT) has emerged as a promising enabler to address the issue of big data in health care. Despite the urgent need for an IT-based tool to tackle this issue, one is not available to specifically study the massive data related to cancer among Saudis. OBJECTIVE: To develop a web-based application, which we named "Cancer in Saudi Arabia (CSA)" to provide an interactive, quick, and easy method to reach, extract, compare, and visualize cancer data from Saudi Cancer Incidence Reports (SCIRs). METHODS: We used waterfall model to develop CSA. Next, we used CSA to study the data of non-Hodgkin lymphoma (NHL) in Saudis reported in the SCIRs (1999-2015). RESULTS: CSA-based analysis showed that NHL incidence rate increased with age and the disease was more common among males compared with females. In addition, NHL was most predominant in the regions of Riyadh and Eastern, while it was the least prevalent in Jazan Region. Interestingly, the largest proportion of NHL patients was diagnosed in the late stage, and malignant lymphoma, large B-cell diffuse, OS (DLBCL) were the most frequent subtypes of NHL. CONCLUSION: As a user-friendly application, we believe that CSA will be a useful tool for studying cancer data in Saudis and will make the data published in SCIRs more reachable and usable. Our findings of NHL provided an almost comprehensive view of the epidemiology of the disease in Saudis for 17 years.

Algorithms for Investment Project Distribution on Regions.

This paper proposes an optimization system for solving an NP-hard problem by using several new algorithms and application programs. This study aims to identify a suitable distribution of investment projects across several developed industrial regions. It is assumed that all industrial regions involved have the same economic and strategic characteristics. The problem involves a set of projects that are to be assigned across regions. Each project creates an estimated number of new jobs, and the distribution of projects can be guided by minimizing the maximum total number of newly created jobs. The problem is NP-hard one, and it is difficult to determine the most appropriate distribution. We apply scheduling algorithms in order to solve the analyzed problem. Severalheuristics are developedto obtain the appropriate distribution of newly created jobs across all regions. A branch-and-bound method is employed in order to obtain the exact solution. The performance of the algorithm is demonstrated by the experimental results for a total number of 1850 instances.