Medical Image Classifications for 6G IoT-Enabled Smart Health Systems

. 2023 Feb 22;13(5):834. doi: 10.3390/diagnostics13050834

Algorithm 1 Pseudocode of the developed AOAHG algorithm

1:
Initialize the parameters.
2:
Split the dataset into training and testing sets after extracting the features.
3:
Initialize the number of solutions (N).
4:
repeat
5:
Determine the value of the fitness function.
6:
Find the best solution.
7:
Update the $M O A$ value using Equation (1).
8:
Update the $M_{O P}$ value using Equation (3).
9:
Calculate the hunger weight of each position using Equations (9) and (10).
10:
Enhance $H i$ using Equation (12).
11:
for $i = 1$ to N do
12:
for $j = 1$ to $P o s i t i o n s$ do
13:
Generate a random values in [0, 1] ( $P_{i}$ , $P_{A}$ , and $P_{H}$ ).
14:
if $P_{i}$ > $M O A$ then
15:
Position limitations can be adjusted for new seeds.
16:
if $P_{A}$ > 0.5 then
17:
Update ith solutions’ positions by the first rule in Equation (2).
18:
else
19:
Update ith solutions’ positions by the second rule in Equation (2).
20:
else
21:
if $P_{H}$ > 0.5 then
22:
Update ith solutions’ positions by the first rule in Equation (22).
23:
else
24:
Update ith solutions’ positions by the second rule in Equation (22).
25:
until The iteration (t) criterion has been met.
26:
Return the best solution.