## 5 Experimental Work and Results
### Accuracy Results  
Accuracy was generated for the study's developed model ITM-OSELM, and, for the two benchmarks, FA-OSELM and OSELM, in addition to the accuracy results. Figures 6-9 represent the detailed accuracy with respect to chunks, and the overall accuracy in each cycle for the three datasets, respectively. Each point in the curve indicates the accuracy of one chunk in the sequential data. The chunks are coming in sequential manner because the data represents a time series data. Analyzing the curves, for the initial cycle, the ITM-OSELM, FA-OSELM, and OSELM had similar performances because the models did not have previous knowledge to remember. In the second and third cycles, the ITM-OSELM was superior to the others, which was attributed to the comparison among their knowledge preservation aspects. FA-OSELM had transfer learning capability. However, transfer learning is a Markov type, which means it only remembers a previous state and brings its values to the current. ITM-OSELM, however, can restore older knowledge whenever necessary. On the other side, FA-OSELM and OSELM had similar performance regardless of repeating the cycle. Figure 8: Accuracy change with respect to data chunks for three cycles using the KDD99 dataset. Figure 9: Overall accuracy for the three models with respect to cycles. (**a**) TampereU; (**b**) UJIndoorLoc; and (**c**) KDD99 datasets. For further elaboration, we present Table 2, which provides the numerical values of the accuracies of each cycle for each of the three models. We observe that ITM-OSELM has achieved the highest accuracies for the three datasets in the second and third cycles. The best achieved accuracy for ITM-OSELM has been achieved in UJIndoorLoc where the overall accuracy in the third cycle was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively, for the third cycle. This emphasizes the superiority of ITM-OSELM over FA-OSELM and OSELM. On the other side, we observe the increase of the learning performance in ITM-OSELM when the accuracy has been increased from 16.48% in the first cycle to 88.36% in the second and 91.69% in the third cycle. Table 3 was generated, where ITM-OSELM's learning capability performance was quantified and compared to that of FA-OSELM and ITM-OSELM, from one cycle to another. This comparison included the learning improvement percentage during all cycles. The highest learning percentage from one cycle to another was achieved for ITM-OSELM in all three datasets; thus, ITM-OSELM was the best in terms of gaining knowledge from one cycle to another. The other two models showed negative learning rates; hence, they were not capable of carrying knowledge from one cycle to another. Moreover, the ITM-OSELM achieved better learning improvement in Cycles 2 to 1 of 182.52% compared to Cycles 3 to 1 of 0.54%, yet this improvement was not due to less capability but performance saturation as accuracy reached \(\sim\)100% in the Cycle 3. In order to validate our hypothesis of superiority of ITM-OSELM over OSELM and FA-OSELM, we adopted a \(t\)-test using a confidence level of 0.05% for rejection of \(H_{0}\) of non-statistical difference in performance. In Table 4, we see in all cells of cycle 2 and cycle 3 that the values of the \(t\)-test were lower than 0.05, which means that ITM-OSELM outperforms the two baseline approaches OSELM and FA-OSELM. This proves that the reason of the superiority in the model is the transfer learning