Muhammad Usman Khan2025-10-022025-10-022020https://escholar.umt.edu.pk/handle/123456789/7765Prescient cycle checking targets determining the conduct, execution, and results of business measures at runtime. It recognizes issues before they happen and re-apportion assets before they are squandered. Albeit Direct learning (DL) has yielded discoveries, most existing methodologies expand on classical machine learning (ML) procedures, especially with regards to result arranged prescient cycle checking. This situation mirrors an absence of comprehension about which occasion log properties encourage the utilization of DL methods. To address this hole, the creators thought about the exhibition of DL (i.e., straightforward feedforward profound neural organizations and long transient memory organizations) and ML strategies (i.e., arbitrary backwoods and backing vector machines) in view of five freely accessible occasion logs. It could be seen that DL by and large beats traditional ML strategies. Besides, three explicit suggestions could be induced from further perceptions: First, the outperformance of DL procedures is especially solid for logs with a high variation to-case proportion (i.e., numerous non-standard cases).enThesis