Results 161 to 170 of about 12,025 (251)

Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches. [PDF]

Eur J Med Res
Sadr H, Nazari M, Khodaverdian Z, Farzan R, Yousefzadeh-Chabok S, Ashoobi MT, Hemmati H, Hendi A, Ashraf A, Pedram MM, Hasannejad-Bibalan M, Yamaghani MR.   +11 more
europepmc   +1 more source

Circular RNA profiling identifies circ_0001522, circ_0001278, and circ_0001801 as predictors of unfavorable prognosis and drivers of triple-negative breast cancer hallmarks. [PDF]

Cell Death Discov
Awata D, Radhakrishnan V, Shaath H, Elango R, Rashid S, Akhtar M, Azis TKA, Ahmed I, Ouararhni K, Al-Shabeeb Akil AS, Alajez NM.   +10 more
europepmc   +1 more source

Explainable Thyroid Cancer Diagnosis Through Two-Level Machine Learning Optimization with an Improved Naked Mole-Rat Algorithm. [PDF]

Cancers (Basel)
Książek W.
europepmc   +1 more source

The schema deceptiveness and deceptive problems of genetic algorithms

Science in China Series F Information Sciences, 2001
Genetic algorithms (GA) are a new type of global optimization methodology based on nature selection and heredity, and its power comes from the evolution process of the population of feasible solutions by using simple genetic operators. The past two decades saw a lot of successful industrial cases of GA application, and also revealed the urgency of ...
Minqiang Li, Jisong Kou
  +5 more sources

Schema processing, proportional selection, and the misallocation of trials in genetic algorithms

Information Sciences, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
David B. Fogel, Adam Ghozeil
openalex   +4 more sources

Problem-Independent Schema Synthesis for Genetic Algorithms

, 2003
As a preprocessing for genetic algorithms, static reordering helps genetic algorithms effectively create and preserve high-quality schemata, and consequently improves the performance of genetic algorithms. In this paper, we propose a static reordering method independent of problem-specific knowledge.
Yong-Hyuk Kim, Yung‐Keun Kwon, Byung-Ro Moon   +2 more
openalex   +3 more sources

Schema theorem of real-coded nonlinear genetic algorithm

Proceedings. International Conference on Machine Learning and Cybernetics, 2003
Through the mechanism analysis of simple genetic algorithm (SGA), every genetic operator can be considered as a linear function. So some disadvantages of SGA may be solved if the genetic operators are modified to a nonlinear function. According to the above method, a nonlinear genetic algorithm is introduced.
Zhihua Cui, Jianchao Zeng
openalex   +3 more sources