Information technology for creating intelligent computer programs for training in algorithmic tasks. Part 1: Mathematical foundations

Kulik, Anatoliy; Chukhray, Andrey; Havrylenko, Olena

doi:10.20535/srit.2308-8893.2021.4.02

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…Experimental studies of the proposed method [4] were performed using m-ary trees for the metrics of K. Tai, K. Zhang, D. Shasha -the minimum editing distance between m-ary trees. The performance of the developed NearestHash method was compared with the speed of the "naive" method and the method of Bustos, Navarro, Chavez (BNC).…”

Section: Experimental Studiesmentioning

confidence: 99%

“…Fig. 1 shows a comparison of the performance of the NearestHash method with the performance of the BNC method (basic and modified) by input conditions [4]. In the experiment for 10 and 100 trees, the first 10 trees from the original list of trees were searched, and in the experiment with 1000 -the first 50.…”

Section: Experimental Studiesmentioning

confidence: 99%

“…The third subtask, which is the set 3 SSim [4] formation, is solved using a method developed in collaboration with A.Yu. Zavgorodny [5].…”

Section: Experimental Studiesmentioning

confidence: 99%

“…The goal of the presented work was to develop ITSs for different domains on the basis of system approach [2; 3], models, methods and tools, which were described in part 1 of the paper [4] and to carry out their experimental studies.…”

Section: Introductionmentioning

confidence: 99%

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Information technology for creating intelligent computer programs for training in algorithmic tasks. Part 2: Research and implementation

Kulik

Chukhray

Havrylenko

2023

SRIT

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Information technologies, particularly artificial intelligence methods, involve more and more deeply into all spheres of human activity: science, technology, art, and education. Ukraine also has sufficient potential and needs to develop educational support, which is the subject of this paper. The work aims to demonstrate the components of information technology for the creation of Intelligent Tutoring Systems (ITS), which are involved in studying various engineering disciplines. The work includes methods of system analysis, mathematical and simulation modeling, technical diagnostics, and artificial intelligence. The proposed models and methods are implemented in ITS prototypes for teaching mathematics, programming, and the automatic control theory. The Intelligent Tutoring Systems were implemented in the educational process of KhAI University and other institutions in Ukraine, Great Britain, Austria, and China. Experimental studies have shown increased student learning success rates using ITS compared to traditional methods. Improved and adapted for computer training methods of technical diagnostics, Bayesian networks, and developed models of algorithmic tasks, the learning process and the learner are valuable from a scientific point of view. In a practical sense, the obtained results can be used to create new specialized ITSs and build an expandable common learning platform integrating the basic disciplines of a specific educational field.

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Section: Experimental Studiesmentioning

confidence: 99%

Section: Experimental Studiesmentioning

confidence: 99%

“…The third subtask, which is the set 3 SSim [4] formation, is solved using a method developed in collaboration with A.Yu. Zavgorodny [5].…”

Section: Experimental Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Information technology for creating intelligent computer programs for training in algorithmic tasks. Part 2: Research and implementation

Kulik

Chukhray

Havrylenko

2023

SRIT

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“…On the basis of physical location and distribution, various deployment models are available now. Task scheduling is critical for maximizing the use of cloud resources as well as providing end users with quality of service (QoS) [3].…”

Section: Introductionmentioning

confidence: 99%

Cost effective hybrid genetic algorithm for workflow scheduling in cloud

Bothra

Singhal²,

Goyal³

2022

SRIT

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Cloud computing plays a significant role in everyone’s lifestyle by snugly linking communities, information, and trades across the globe. Due to its NP-hard nature, recognizing the optimal solution for workflow scheduling in the cloud is a challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced approach to schedule workflow in a heterogeneous environment. Our model is based on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize makespan. Instead of assigning the task randomly to a virtual machine, we apply a greedy strategy that assigns the task to the lowest-loaded virtual machine. After completing the mutation operation, we verify the dependency constraint instead of each crossover operation, which yields a better outcome. The proposed model incorporates the virtual machine’s performance variance as well as acquisition delay, which concedes the minimum makespan and computing cost. One of the most astounding aspects of our cost-effective hybrid genetic algorithm (CHGA) is its capacity to anticipate by creating an optimistic cost table (OCT) while maintaining quadratic time complexity. Based on the results of our meticulous experiments on some real-world workflow benchmarks and comprehensive analysis of some recently successful scheduling algorithms, we concluded that the performance of our CHGA is melodious. CHGA is 14.58188%, 11.40224%, 11.75306%, and 9.78841% cheaper than standard Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Cost Effective Genetic Algorithm(CEGA), and Cost-Effective Load-balanced Genetic Algorithm (CLGA), respectively.

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