Generalized Net model of asymptomatic osteoporosis diagnosing

Ribagin, Simeon; Roeva, Olympia; Pencheva, Tania

doi:10.1109/is.2016.7737488

Cited by 16 publications

(6 citation statements)

References 4 publications

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“…GNs without some of their components form special classes called reduced GNs [2]. The presented GN-model shows similar features with previous models for medical diagnosing [3][4][5] but this is the first one highlighting the diagnostic algorithm for patients with muscle pain.…”

Section: Introductionsupporting

confidence: 60%

Generalized Net Model of Muscle Pain Diagnosing

Ribagin

Chountas

Pencheva

2017

Flexible Query Answering Systems

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Abstract. Pain is the most common symptom of the many musculoskeletal pathologies. Musculoskeletal pain affects the muscles, ligaments, tendons, nerves and bones and might be caused by diverse factors. Musculoskeletal pain ranges from mild to severe. It can be local or diffuse, and acute or chronic. Due to the wide range of conditions that may cause such a symptom, diagnosing process is challenging and a systematic approach is necessary. In this investigation we present a successful example of generalized nets application in medical diagnosing and propose a novel approach leading to the appropriate diagnostic considerations. The method proposed in this investigation accurately identifies the various steps during the muscle pain diagnosing process and significantly improves the health care level. Obtained so far results could be used to assist in the decision making in the diagnostic processes.

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Section: Introductionsupporting

confidence: 60%

Generalized Net Model of Muscle Pain Diagnosing

Ribagin

Chountas

Pencheva

2017

Flexible Query Answering Systems

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Section: Introductionmentioning

confidence: 56%

Application of Topological Operators over Data from InterCriteria Analysis

Roeva

Vassilev

Chountas

2017

Flexible Query Answering Systems

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Pain is the most common symptom of the many musculoskeletal pathologies. Musculoskeletal pain affects the muscles, ligaments, tendons, nerves and bones and might be caused by diverse factors. Musculoskeletal pain ranges from mild to severe. It can be local or diffuse, and acute or chronic. Due to the wide range of conditions that may cause such a symptom, diagnosing process is challenging and a systematic approach is necessary. In this investigation we present a successful example of generalized nets application in medical diagnosing and propose a novel approach leading to the appropriate diagnostic considerations. The method proposed in this investigation accurately identifies the various steps during the muscle pain diagnosing process and significantly improves the health care level. Obtained so far results could be used to assist in the decision making in the diagnostic processes.

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“…Selected methods are: Random Search (Weise, 2009), Hill Climbing (Volna, 2012) (Majer, 2003), Tabu Search (Monticelli, Romero, & Asada, 2007), (Weise, 2009) (Volna, 2012), Local Search (Majer, 2003), Downhill Simplex (Clerc, 1999) (Eberhart & Shi, Comparing inertia weights and constriction factors in particle swarm optimization, 2000) (Ma, Zhang, & Xu, 2015) (Tvrdik, Stochastic Algorithms for Global Optimization (in Czech language: Stochastické algoritmy pro globální optimalizaci), 2010), Simulated Annealing (Weise, 2009) (Tvrdik, Evolutionary algorithms -Study Texts (in Czech language Evoluční algoritmy -učební texty), 2004), Differential Evolution (Volna, 2012) (Tvrdik, Stochastic Algorithms for Global Optimization (in Czech language: Stochastické algoritmy pro globální optimalizaci), 2010), Evolution Strategy (Volna, 2012) (Tvrdik, Stochastic Algorithms for Global Optimization (in Czech language: Stochastické algoritmy pro globální optimalizaci), 2010) (Marik, Stepankova, & Lazansky, 2001), Particle Swarm Optimization (Clerc, 2010) and Genetic algorithm (A.J. & P.D., 2015), (Kumar & Gopal, 2013), (Ribagin, Roeva, & Pencheva, 2016), (Ahmed, 2010). We tested different types of optimization strategies of the optimization methods e.g.…”

Section: ̌= Argmin ∈̃ ( ) = {̌∈̃: (̌) ≤ ( )∀ ∈̃}mentioning

confidence: 99%

Methodology for evaluating optimization experiments

Raška

Ulrych²

2020

Proceedings of the 32nd European Modeling &Amp; Simulation Symposium (EMSS 2020)

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The paper presents a methodology for evaluating optimization experiments performed on simulation models (discrete event simulation models and Bin Packing Problem) which focus on various problems in industrial engineering. We created an evaluation application to validate our methodology. The application evaluates the simulation experiments generated by a simulation optimizer. The first goal is to evaluate the behaviour of the tested optimization methods -evaluate a series of optimization experiments (replicated simulation optimization experiments) with different settings of the optimization algorithms according to different characteristics of the optimization (e.g. efficiency of finding a global optimum, quality of a found solution, speed of finding an optimum, etc.). The next goal is to evaluate the difficulty of the simulation optimization -to determine relative frequencies of the number of simulation experiments until the optimum of the objective function is found for different intervals. The objective function represents the quality of a possible solution in the n-dimensional search space for the tested simulation model.

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Generalized Net model of asymptomatic osteoporosis diagnosing

Cited by 16 publications

References 4 publications

Generalized Net Model of Muscle Pain Diagnosing

Generalized Net Model of Muscle Pain Diagnosing

Application of Topological Operators over Data from InterCriteria Analysis

Methodology for evaluating optimization experiments

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