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The shortest path problem is a classic problem in mathematics and computer science with applications in Economics (sequential decision making, analysis of social networks, etc.). The presented work is an example of realizing and applying of Dijkstra's algorithm to find the shortest path between two vertices in a connected, undirected graph, which i...
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... Click the calendar icon on the DataGridView control to freely select the check-in date. After entering the complete information, click the check-in button to add the customer information and the room information to the database, and the usage status of the room in the database will also be refreshed to the occupied status in real time [3]. In the input information page, there is also a reset button. ...
... The Dijkstra's algorithm serves as the basis for many other pathfinding algorithms, which improves the performance of the pathfinding by adding one or more criteria to determine the most efficient path. Some of the improvements made for the Dijkstra's algorithm are visualizing the path generated by the algorithm [7], adding the ability to handle parameters given in neutrosophic numbers [8], applying the algorithm on a curved surface [9], combining it with other algorithms such as the Floyd-Warshall algorithm [10], and one of the well-known extensions of the Dijkstra's algorithm is the A-Star algorithm [11]. Adding a heuristic function to the pathfinding algorithm improves the performance of the A-Star algorithm compared to the Dijkstra's algorithm. ...
Theta Star is an efficient algorithm that can be used to find an optimal path in a map with better performance compared to the A-Star algorithm. Combining the Theta Star with Hierarchical Pathfinding further enhances its performance by abstracting a large map into several clusters. What this combination lacks are the capability to handle a dynamic element in the map. Without that capability, the agent could potentially collide with elements in the map that is undesirable in certain conditions, while adding that capability might reduce the pathfinding algorithm's performance. The proposed algorithm aims to provide the capability to handle dynamic elements without severe negative impact on the performance of the algorithm. The effectiveness of the proposed algorithm is verified in terms of execution time, number of nodes explored, final path length, and the number of collisions that occurred.
... Higher education may be facing a crisis, but it leads to a greater opportunity for universities to demonstrate why they must remain a cornerstone of a society fueled by technology [6]. This article emphasizes the role of technology integrated into the classroom enhancing student achievement and prepare students [7], [8] to participate in the 21st-century economy. ...
The article provides a systematic approach for designing laboratories for various cyber-security disciplines. The approach respects the basic principle of linking education with industry. Offers vision
and practice for accessing the achievements of high-tech companies. It is distinguished by the ability for students to gain knowledge of existing elite cyber-security technologies, have real working contact
with live products from leading cyber-security companies, cyber investigations, and cyber resilience.
To this end, we share experiences with available free online resources that are offered by the companies for study by interested customers and to get acquainted with the experts who support
product acquisition decisions. The products that can be used for training in various problem areas in the field of cyber-security have been systematized. The personal empirical experience of applying this approach in practice allows us to share analyzes and comparisons made. A list of relevant resources available online from various cyber-security vendors has been created. The effectiveness of the approach is motivated. The article shares the experience of implementing affordable online cybersecurity products in student education.
Keywords: cyber, security, discipline, exercises, experience.
... A* algorithm is the result of Dijkstra's algorithm with an empirical search, leading A* algorithm to path planning quicker than Dijkstra's algorithm. To determine the shortest path for an Indoor Positioning System, a study has tested the algorithms of A* and Dijkstra's [15][16]. The study found that due to Dijkstra's frequent tries to improve an initial approximation (cost) of each node, A* algorithm is the fastest. ...
In the single-source shortest path (SSSP) problem, the shortest paths from a source vertex v to all other vertices in a graph should be executed in the best way. A common algorithm to solve the (SSSP) is the A* and Ant colony optimization (ACO). However, the traditional A* is fast but not accurate because it does not calculate all node’s distance of the graph. Moreover, it is slow in path computation. In this paper, we propose a new technique that consists of a hybridizing of A* algorithm and ant colony optimization (ACO). This solution depends on applying the optimization on the best path. For justification, the proposed algorithm has been applied to the parking system as a case study to validate the proposed algorithm performance. First, A*algorithm generates the shortest path in faster time complexity. ACO will optimize this path and output the best path. The result
showed that the proposed solution provides an average decreasing time complexity e is 13.5%.
... A* algorithm is the result of Dijkstra's algorithm with an empirical search, leading A* algorithm to path planning quicker than Dijkstra's algorithm. To determine the shortest path for an Indoor Positioning System, a study has tested the algorithms of A* and Dijkstra's [15][16]. The study found that due to Dijkstra's frequent tries to improve an initial approximation (cost) of each node, A* algorithm is the fastest. ...
span lang="EN-US">In the single-source shortest path (SSSP) problem, the shortest paths from a source vertex v to all other vertices in a graph should be executed in the best way. A common algorithm to solve the (SSSP) is the A* and Ant colony optimization (ACO). However, the traditional A* is fast but not accurate because it doesn’t calculate all node's distance of the graph. Moreover, it is slow in path computation. In this paper, we propose a new technique that consists of a hybridizing of A* algorithm and ant colony optimization (ACO). This solution depends on applying the optimization on the best path. For justification, the proposed algorithm has been applied to the parking system as a case study to validate the proposed algorithm performance. First, A*algorithm generates the shortest path in fast time processing. ACO will optimize this path and output the best path. The result showed that the proposed solution provides an average decreasing time performance is 13.5%.</span
A comprehensive study on algorithm visualization reveals insights into content distribution, creator demographics, and visualization quality. The associated wiki catalogues over 350 algorithm visualizations and includes an annotated bibliology on algorithm visualization literature. Unfortunately, the majority of visualizations are deemed low-quality, with a bias towards simpler topics. The report proposes the development of an e-learning tool focusing on specific visualizations like Pathfinder, Prime Numbers, Sorting Algorithms, N Queen, Convex Hull, and Binary Search Game. The absence of effective repositories for algorithm visualizations is recognized as a significant gap. Emphasizing the need for improved dissemination, the report suggests initiatives to inform developers about existing gaps and requirements within the field. It also underscores the importance of propagating established best practices for creating high-quality visualizations. In conclusion, the report highlights the urgency of addressing these deficiencies to cultivate a more robust and accessible ecosystem for algorithm visualization resources. Despite the challenges, there is a clear call to action to enhance the standard of algorithm visualizations and foster a more inclusive and informed community. Key Words: Algorithm, Visualization, Pathfinder, Sorting, Search Game.
Computer Science is a field of not just generating solutions to day-to-day activities but also to enhance it to the fullest. Algorithms are the basic practical unit for improving any activity that offer work with specific productivity, and to obtain and increase the productivity of a task. Sorting is the key component of Data structures and Algorithms. However, it is complicated and we weren't able to recognize the whole algorithm and code for the first time. So the use of this application has many superior understanding of education. The key element of this project is to help beginners to be able to visualize the sorting algorithms so our brain can understand faster and remember better. And also get a better understanding of underlying operations.
