...Ahmad Yar; Muhammad Usman Purpose This study aims to systematically review the application of machine learning ( ML ) techniques in predicting two major categories of financial risk: consumer loan default risk ( CLDR ) and systemic risk ( SR ) arising from institutional interconnectedness...

... programming model that optimizes these decisions jointly to maximize campaign returns. Rather than assigning promotions to broad, manually defined customer segments, the framework applies machine learning to form data-driven micro-clusters based on customers’ estimated propensities for each promotion type...

... for traceability, machine learning ( ML ) for demand forecasting and circular economy principles for resource recovery. It is solved using three metaheuristic algorithms, NSGA-II, MOPSO and MOEA-D. Performance is assessed using criteria such as optimality gap, Pareto spread, time efficiency and solution...

... Accuracy Machine learning Learning time ANN = Artificial neural network; CA = Classification accuracy; CC = Corelation coefficient; CCGA = Integration of CC (first) and GA (next); Co-rel (Ai, Aj) = Corelation measure between attributes Ai and Aj; DBN...

.../no explanation, retention/LIME explanations, retention/SHAP explanations and synthesis retention. Four machine learning models were trained on both simulated and real alternative anonymized HR data (Logistic Regression, Random Forest, XGBoost and Neural Network). These measurable metrics (accuracy, confidence...

... supervised machine learning techniques (random forest, permutation importance and BORUTA ) with total interpretive structural modeling and Matrice d’Impacts Croisés Multiplication Appliquée à un Classement analysis. Expert evaluations from 100 professionals across the automotive and digital transformation...

... Publishing Limited Licensed re-use rights only Viable supply chain Machine learning Incomplete data imputation Decision support system Production fluctuation control Disruption propagation The concept of supply chain viability in operations management is a recent development...

... food services users. Data were analysed using partial least squares structural equation modelling to assess the relationships between e-satisfaction and e-recommendation behaviours. Additionally, machine learning techniques were used to test the robustness and predictive accuracy and uncover deeper...

...Ansari Saleh Ahmar; Agung Triutomo; Abdul Rahman Purpose This study aims to evaluate the efficacy of the novel BetaSutte forecasting methodology for enhancing export management decision-making in Indonesia, benchmarking its performance against established machine learning and statistical...

...Sandesh Singh; Dipasha Sharma; Aradhana Gandhi Purpose This study aims to investigate the potential of various machine-learning models to expedite the credit rating assignment process for non-banking finance companies (NBFCs) in India. It seeks to address the limitations of traditional credit...

...Tang Ting; Md Aslam Mia; Md Imran Hossain; Khaw Khai Wah Purpose Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance...

...) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic...

...Hassan Younis; Balan Sundarakani; Malek Alsharairi Purpose The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof...

...Muhammad Usman Tariq; Muhammad Babar; Marc Poulin; Akmal Saeed Khattak Purpose The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly. Design...

... market circumstances. Design/methodology/approach The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings...

... environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used...