Mervat Abu-Elkheir

Ahmed Abolfadl, Marwa Mahmoud Abla, Mervat Abu-Elkheir et al.

Predicting emerging trends is vital for businesses, researchers, and policymakers; yet traditional approaches often lack scalability and adaptability. This paper presents a trend prediction framework based on Automated Machine Learning (AutoML), designed to extract insights from textual datasets with temporal attributes. The system ingests subject-specific textual entries accompanied by a date field. The pipeline begins with preprocessing and embedding, followed by AutoClustering, which uses meta-learning to select the optimal clustering algorithm. AutoTopicModeling then applies successive halving to identify the best topic modeling method: Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), BERTopic, or Non-negative Matrix Factorization (NMF) based on the coherence score for each cluster. For trend forecasting, AutoTrendAnalysis evaluates multiple models: Facebook Prophet, AutoRegressive Integrated Moving Average (ARIMA), Seasonal-Trend decomposition using Loess (STL), and Long Short-Term Memory (LSTM) selecting the most accurate based on Root Mean Square Error (RMSE), either through successive halving or exhaustive comparison. Topics are classified as strong signals, weak signals, or noise based on forecasting outcomes, enabling the identification of emerging trends. By automating clustering, topic modeling, and time series forecasting, this research enhances trend prediction accuracy while reducing manual effort. The proposed system offers a scalable and user-friendly solution suitable for real-time applications and stakeholders with limited machine learning expertise. Experimental results demonstrate that the proposed system's best trial achieves a final RMSE of 7.099, indicating high predictive accuracy.

Ahmed Abolfadl, Marwa Mahmoud, Basma Afifi et al.

The exponential increase in scientific publications has driven the emergence of new trends. Accurate forecasting of these developments is essential for researchers and professionals to stay updated with advancements in the field. This study presents an automated pipeline for trend prediction in the wireless networks and mobile computing domain by integrating clustering, topic modeling, and time series analysis. The process begins with the collection of 127,820 abstracts from high-impact journals and conferences, followed by extensive preprocessing and semantic embedding using the SPECTER model. AutoCluster applies meta-learning to select the most suitable clustering algorithm based on the dataset meta-features, ensuring semantically coherent groupings. AutoTopicModeling then employs a successive halving strategy to identify the best-performing topic model per cluster, followed by LLM-assisted topic labeling and optional label generalization. Finally, AutoTrendAnalysis transforms topic-labeled data into time series and applies forecasting models -ARIMA, STL, Prophet, or LSTM - to predict future topic popularity. Topics are classified as strong, weak, or noise signals based on forecast trajectories, offering interpretable insights into emerging and declining research themes. The framework is scalable, adaptive, and designed for robust trend analysis across scientific domains. Experimental results demonstrated high predictive accuracy, achieving a Root Mean Square Error (RMSE) of 36.76.

Passant Elchafei, Mayar Osama, Mohamed Rageh et al.

We present a graph-based approach enriched with lexicons to predict document-level readability in Arabic, developed as part of the Constrained Track of the BAREC Shared Task 2025. Our system models each document as a sentence-level graph, where nodes represent sentences and lemmas, and edges capture linguistic relationships such as lexical co-occurrence and class membership. Sentence nodes are enriched with features from the SAMER lexicon as well as contextual embeddings from the Arabic transformer model. The graph neural network (GNN) and transformer sentence encoder are trained as two independent branches, and their predictions are combined via late fusion at inference. For document-level prediction, sentence-level outputs are aggregated using max pooling to reflect the most difficult sentence. Experimental results show that this hybrid method outperforms standalone GNN or transformer branches across multiple readability metrics. Overall, the findings highlight that fusion offers advantages at the document level, but the GNN-only approach remains stronger for precise prediction of sentence-level readability.

Ghada Elkhawaga, Mervat Abuelkheir, Manfred Reichert

Predictive Process Monitoring (PPM) has been integrated into process mining tools as a value-adding task. PPM provides useful predictions on the further execution of the running business processes. To this end, machine learning-based techniques are widely employed in the context of PPM. In order to gain stakeholders trust and advocacy of PPM predictions, eXplainable Artificial Intelligence (XAI) methods are employed in order to compensate for the lack of transparency of most efficient predictive models. Even when employed under the same settings regarding data, preprocessing techniques, and ML models, explanations generated by multiple XAI methods differ profoundly. A comparison is missing to distinguish XAI characteristics or underlying conditions that are deterministic to an explanation. To address this gap, we provide a framework to enable studying the effect of different PPM-related settings and ML model-related choices on characteristics and expressiveness of resulting explanations. In addition, we compare how different explainability methods characteristics can shape resulting explanations and enable reflecting underlying model reasoning process

Ghada Elkhawaga, Mervat Abuelkheir, Manfred Reichert

Predictive business process monitoring (PPM) has been around for several years as a use case of process mining. PPM enables foreseeing the future of a business process through predicting relevant information about how a running process instance might end, related performance indicators, and other predictable aspects. A big share of PPM approaches adopts a Machine Learning (ML) technique to address a prediction task, especially non-process-aware PPM approaches. Consequently, PPM inherits the challenges faced by ML approaches. One of these challenges concerns the need to gain user trust in the predictions generated. The field of explainable artificial intelligence (XAI) addresses this issue. However, the choices made, and the techniques employed in a PPM task, in addition to ML model characteristics, influence resulting explanations. A comparison of the influence of different settings on the generated explanations is missing. To address this gap, we investigate the effect of different PPM settings on resulting data fed into an ML model and consequently to a XAI method. We study how differences in resulting explanations may indicate several issues in underlying data. We construct a framework for our experiments including different settings at each stage of PPM with XAI integrated as a fundamental part. Our experiments reveal several inconsistencies, as well as agreements, between data characteristics (and hence expectations about these data), important data used by the ML model as a result of querying it, and explanations of predictions of the investigated ML model.

Ghada Elkhawaga, Mervat Abuelkheir, Sherif I. Barakat et al.

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.