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# br The first three algorithms

The first three algorithms have been detailed in Section 1.5 and in [26,27], while the others are discussed below and applied to the SPA-PAL2v computational structure.

The general form of how SPA-PAL2v is applied in Lipo3000 cancer di-agnosis is shown in the flowchart of Fig. 3.

As can be seen in Fig. 3, in the first step, the paraconsistent Raman patterns are generated, and in the second step, inquiries are made for the validation of the SPA-PAL2v. The validation is done by verifying the diagnoses correctness through randomly selected samples.

Fig. 3. Flowchart of the set of paraconsistent algorithms (SPA-PAL2v) used to discriminate the skin spectra in one of the groups.

3.2. Algorithm for extracting the degrees of Raman intensity evidence

The algorithm that extracts the degrees of evidence [5] Raman In-tensity (μ) has the action of transforming the values of the spectra of ex vivo skin tissue samples obtained by Raman spectroscopy in evidence degrees ranging from 0 to 1 and belonging to the set of real numbers [4,25]. Initially, the minimum and maximum values of the Raman In-tensity in each line of the wavelength of the diverse samples are es-tablished. Subsequently, the normalization of the Raman intensity va-lues surveyed in the Raman spectroscopy database of each type is characterized by the equation:

1
If Xvalue > Maxvalue

Xvalue − Minvalue
If Xvalue ∈ [Min value , Maxvalue]

μPP =
Max value − Minvalue

If Xvalue < Minvalue

Through the linear variation equation, the normalized evidence degree in an interest interval (or discourse universe) can be obtained.

3.3. Acquisition of paraconsistent patterns for each histopathological group and arrangement

At this stage of data preparation, the histopathological group under study (NO and [BCC + SCC] and AK) goes through the algorithm ex-tractor of eﬀects of contradiction (Section 1.5), whose details for other applications can be seen in [5].

In this process, the degree of real evidence that characterizes the paraconsistent pattern value (PPμE) for each measure of the Raman wavelength is obtained. In this data treatment, in addition to the actual degree of real evidence, the maximum and minimum values of each measurement of the Raman wavelength are also stored in the database table. This way, the necessary measures are established by the SPA-PAL2v for comparisons, which will improve the future study of the samples and their recognition to promote a diagnosis and thus char-acterize the paraconsistent pattern values for NO (PPμENO), BCC + SCC (PPμE(BCC + SCC)), and AK (PPμEAK).

3.4. Algorithms to randomly select and extract the sample evidence degree

Through this algorithm, a sample is randomly selected from the Raman database.

The extractor degree of evidence algorithm [5] is applied to the selected sample (Avalue).

The normalization process of this step is based on the Maxvalue and Minvalue established in the study of the pattern of each type (PPμE) to create the interest interval through the following equation:

1
If Avalue > Maxvalue

Avalue − Minvalue
If Avalue ∈ [Min value , Maxvalue]

μA =
Max value − Minvalue

If Avalue < Minvalue

The algorithm works by randomly selecting and generating a sample and compares Chromosome walking with the range of interest in the studied histopatho-logical group. By applying the above equation, the sample’s evidence degree values for the NO group (μA_NO), the BCC + SCC group (μA_BCC + SCC), and the AK group (μA_AK) are obtained.

3.5. Algorithm to detect the number of occurrences of similarities

To initiate this algorithm, the SPA-PAL2v initially groups the ma-trices μA with the histopathological group pattern (PPμE) and then makes line-by-line comparisons with μA to detect similarities.
The verification is performed considering two conditions: first, if the sample is within the maximum and minimum Raman intensity limits, therefore it is within the interest interval defined in the previous