Search results for “entropy

About 3 results in articles

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3 articles
Human Psychology Open Access

On Tesla-inspired Extended Quantum-Holographic Framework for Reprogramming Macro-Quantum Correlations of Individual and Collective Consciousness

Sep 2024 DOI 10.14302/issn.2644-1101.jhp-24-5247
Rakovic DejanCorresponding author

Nikola Tesla's visions and inventions realized in his controlled altered states of consciousness were considered previously as manifestations of his meditative insights within an extended framework of quantum-holographic macroquantum correlations of individual & collective consciousness. This Tesla-inspired paper now focuses on related psychosomatic aspects of quantum entropy & entanglement, and consequences on the usually observable biological arrow of time & spiritual-informational time reversal. Finally we discuss spiritual-informational implications for free will and individual and collective consciousness reprogramming.

Study of The ID3 and C4.5 Learning Algorithms

Apr 2020 DOI 10.14302/issn.2641-5526.jmid-20-3302
Y.FakirCorresponding author Laboratory of Information Processing and Decision Support, Faculty of Sciences and Technics, Sultane Moulay Slimane University, Beni Mellal, Morocco

Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms such as ID3, C4.5 and CART etc. The present study considers ID3 and C4.5 algorithms to build a decision tree by using the “entropy” and “information gain” measures that are the basics components behind the construction of a classifier model 

Evolutionary Science Open Access

Structuredness as a Measure of the Complexity of the Structure and the Role of Post-Dissipative Structures and Ratchet Processes in Evolution

Jan 2020 DOI 10.14302/issn.2689-4602.jes-19-3155
Mikhailovsky GeorgeCorresponding author Global Mind Share, 878 W Ocean View Ave., Norfolk, VA, 23503, USA 

As shown earlier, the algorithmic complexity, like Shannon information and Boltzmann entropy, tends to increase in accordance with the general law of complification. However, the algorithmic complexity of most material systems does not reach its maximum, i.e. chaotic state, due to the various laws of nature that create certain structures. The complexity of such structures is very different from the algorithmic complexity, and we intuitively feel that its maximal value should be somewhere between order and chaos. I propose a formula for calculation such structural complexity, which can be called - structuredness. The structuredness of any material system is determined by structures of three main types: stable, dissipative, and post-dissipative. The latter are defined as stable structures created by dissipative ones, directly or indirectly. Post-dissipative structures, as well as stable, can exist for an unlimited time, but at the micro level only, without energy influx. The appearance of such structures leads to the “ratchet” process, which determines the structure genesis in non-living and, especially, in living systems. This process allows systems with post-dissipative structures to develop in the direction of maximum structuring due to the gradual accumulation of these structures, even when such structuring contradicts the general law of complification. 

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