Algorithm parameters can manifest multiple instances of thought threads
and function as a mirror for hypothesis processes. The multi-process algorithm
can save costs, maximize profits, and deliver a competitive advantage for
algorithm designers.
Multi-process algorithms have a wide variety of features and parameter
implementation based on system activities and desired intentions or goals of
profit maximization.
This study constraints algorithm features and describes two model-based
approaches with pattern recognition. Multi-process algorithms with parallel and
consecutive-based perspectives can focus on structural patterns, general
scenarios, and embedded transaction processing on the system platform.
Modeling a multi-process algorithm with a parallel approach modifies the
properties of individual processes and entities on internal and external system
environments through Global Variables concurrently. This approach can contain
two distinct phases. A modified parameter
property must be established in a consolidation process roadmap in the first
phase. In the second phase, a roadmap of
the consolidation process is supposed to align with a target's instance
threads. Parameter alignment set into array unique key to tackle
target property. The outcome of multi-process algorithm planning is hidden
profits similar to (Fig 1) in the next section.
A multi-process algorithm with a consecutive approach modifies a property
of individual processes and entities sequentially on internal and external
system environments. Modified parameter property tackles target property. This
approach may contain a range of multiple targets. The existence of various
targets requires extra operation (Entanglement cycle) before the outcome of
algorithm tackling relating to (Fig 2) in the next section.
The features of multi-process algorithms and complex task force
operations are conditions for keeping the integrity of system visions and
hidden benefits within various tactical operations.
A possible disadvantage of using a multi-process algorithm is opponents'
interception of transparent algorithm parameters (external forces).
Opponents can detect multi-process algorithm parameter implementation. A
competitor may observe multi-process algorithm parameters and then identify an
instance of thought threads and absolute values on Global Variables.
Evaluations of modified parameters on internal and external system environments
illustrate desired intentions or goals driven by algorithm designers.
Observation:
Multi-process algorithm parameters can
generate Invisible Entities on the system platform.
Observation:
The stimulus-response approach method can conduct the reliability of
observation for the entire process by opponents.
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