Journal of Quantum Technologies and Informatics Research

The Future Place of Quantum Technologies in the Intelligence Plane

2024-10-28T00:07:35+03:00

This study examines the potential impacts of Quantum Technologies in the fields of intelligence and national security. This technology, referred to as disruptive innovation, presents both opportunities and threats in the intelligence discipline. Efforts such as Quantum Computer and Quantum Internet in particular can bring revolutionary changes in data processing, storage and transmission processes. The superposition feature of quantum technologies, which can have both 0 and 1 values at the same time, increases processing speed and capacity far beyond classical computers. This technology has great potential, especially in the fields of cybersecurity and intelligence. While investigating the impacts of QUT on intelligence activities and processes, the study offers the use of structured analysis techniques such as “Alternative Futures Analysis” and “Red Team Analysis”. It is evaluated that these analysis techniques will be useful in evaluating the uncertain future of QUT and predicting how it can be used in intelligence activities.

Overview of Optimization Algorithms: Classical and Modern Methods

2024-10-28T00:10:02+03:00

This study focuses on the fundamental principles, methods, and application areas of optimization and search algorithms. The basic concepts of optimization, such as the definition, decision variables, objective function, and constraints, are discussed, and the characteristics of analytical, heuristic, and meta-heuristic methods are detailed. It emphasizes that nature-inspired meta-heuristic methods provide quick and effective solutions for complex problems. The Dijkstra, Bellman-Ford, A*, Firefly, Ant Colony, and Wolf Colony algorithms are examined, and their effectiveness and usage examples in various application areas (such as engineering, artificial intelligence, data analytics, and robotics) are discussed. The study highlights the importance of optimization techniques in developing solutions for complex problems.

Feature Selection in Music Data with Meta-Heuristic Methods

2024-10-28T00:01:55+03:00

In today's world, the rapid increase in multimedia content production has made accessing valuable information more challenging. Data mining has become critical to facilitate access to meaningful data, and an important step in this process is reducing the size of the data. Feature selection reduces the data size by eliminating irrelevant, noisy, or missing data from the dataset, allowing the methods used in data analysis to operate faster and more efficiently. In this study, feature selection was performed using nature-inspired metaheuristic algorithms. The selected features were analyzed by classifying them using machine learning algorithms and artificial neural networks. Improvements made on the music dataset increased track popularity classification performance by 3,2%, achieving an accuracy of 88%. The methods used were presented comparatively, and the findings were evaluated.