Engineering the Energy Gap of Cupric Oxide Nanomaterial Using Extreme Learning Machine and Stepwise Regression Algorithms

Table 1

ELM-IP-Sin.

1	-0.0132	-0.54762	-0.99652	0.723074	-3973821
2	-0.26673	-0.75439	0.830837	0.500469	-2477024
3	-0.5032	0.228787	0.635176	0.353879	40239.99
4	-0.58463	0.149019	-0.69563	0.900182	30932667
5	-0.11529	0.34942	-0.92196	0.431383	-6494008
6	0.454564	0.711009	0.811431	0.243646	4505010
7	0.749548	-0.27287	-0.96442	0.436853	-5097280
8	-0.38063	-0.48935	0.967636	0.746954	10739917
9	-0.85371	0.461603	-0.04926	0.26297	53865484
10	-0.05877	0.530997	-0.89718	0.37412	47276900
11	-0.13489	0.989218	0.702337	0.040661	-8708592
12	0.235108	-0.19435	-0.27745	0.348728
13	0.540066	0.737888	0.48948	0.397303	9792359
14	0.722984	-0.0377	0.221749	0.883563
15	-0.25701	-0.43914	-0.66814	0.250623
16	0.374358	-0.39904	0.493294	0.784239
17	0.689445	0.295375	-0.57035	0.592349	10009577
18	0.749265	-0.57982	-0.85605	0.589163	4000648
19	-0.90476	0.347213	-0.8149	0.467506	12603569
20	0.371268	-0.93299	0.890024	0.062538	12637194
21	-0.94848	0.622763	0.999422	0.017784	-2744478
22	-0.41958	0.592968	-0.29751	0.267533	21542001
23	-0.92682	0.046075	0.250731	0.574447	-6691960
24	0.818398	-0.49557	0.96772	0.768608	6447039
25	-0.04291	0.084512	0.574165	0.579762
26	-0.57615	0.691391	-0.25016	0.878811	-2432681
27	0.627888	0.258804	-0.21725	0.516842
28	-0.70548	0.012369	-0.82704	0.117576
29	-0.70855	-0.07151	-0.30963	0.577031
30	0.948044	-0.82533	-0.02062	0.539359	15620369