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

Table 2

ELM-IP-Sig.

1	0.150817	0.079697	0.177631	0.313474
2	0.497792	-0.61757	0.639849	0.171902
3	0.485416	0.676318	-0.87409	0.892018
4	0.487825	-0.32242	-0.08277	0.106813
5	0.188734	-0.30055	-0.54529	0.159318
6	0.546301	0.065917	-0.79026	0.584198
7	0.013209	-0.77928	0.869907	0.851785
8	0.459677	-0.51982	-0.12869	0.833567
9	0.613977	0.761094	-0.90338	0.111132
10	0.544568	0.966314	-0.4342	0.513367
11	0.643131	0.763693	-0.49786	0.134723
12	-0.73284	0.947836	0.958558	0.603095
13	0.761244	0.444111	0.016878	0.874886
14	0.36752	0.670529	0.904865	0.143051
15	0.550825	-0.77383	-0.38174	0.360877
16	-0.76514	-0.25939	-0.1984	0.192199
17	-0.75756	-0.50261	0.207484	0.86659
18	-0.29544	-0.3991	-0.1797	0.116135
19	-0.13844	0.580539	0.717628	0.775766
20	0.387409	-0.47336	0.297244	0.194758
21	0.353658	-0.41446	-0.01801	0.328061
22	-0.97888	0.060107	-0.80834	0.66384
23	-0.34688	0.296413	0.95046	0.501888
24	0.658427	0.539294	0.04276	0.048745
25	-0.88241	-0.53608	0.005874	0.464105
26	-0.01034	0.088041	-0.75189	0.832069
27	-0.69636	-0.5922	-0.09102	0.6647
28	-0.82263	-0.11932	-0.35983	0.966932
29	-0.4391	-0.04195	-0.55205	0.710365
30	-0.70348	-0.37291	0.717197	0.754428
31	-0.52181	-0.03268	-0.84714	0.40837