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    Green AI-Driven Concept for the Development of Cost-Effective and Energy-Efficient Deep Learning Method: Application in the Detection of Eimeria Parasites as a Case Study
    (Wiley, 2024) Acmali, Suheda Semih; Ortakci, Yasin; Seker, Huseyin
    Although large-scale pretrained convolutinal neural networks (CNN) models have shown impressive transfer learning capabilities, they come with drawbacks such as high energy consumption and computational cost due to their potential redundant parameters. This study presents an innovative weight-level pruning technique that mitigates the challenges of overparameterization, and subsequently minimizes the electricity usage of such large deep learning models. The method focuses on removing redundant parameters while upholding model accuracy. This methodology is applied to classify Eimeria species parasites from fowls and rabbits. By leveraging a set of 27 pretrained CNN models with a number of parameters between 3.0M and 118.5M, the framework has identified a 4.8M-parameter model with the highest accuracy for both animals. The model is then subjected to a systematic pruning process, resulting in an 8% reduction in parameters and a 421M reduction in floating point operations while maintaining the same classification accuracy for both fowls and rabbits. Furthermore, unlike the existing literature where two separate models are created for rabbits and fowls, this article presents a combined model with 17 classes. This approach has resulted in a CNN model with nearly 50% reduced parameter size while retaining the same accuracy of over 90%.
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    Optimising customer retention: An AI-driven personalised pricing approach
    (Pergamon-Elsevier Science Ltd, 2024) Ortakci, Yasin; Seker, Huseyin
    Customer churn has become one of the most important challenges that telecom companies have to deal with. Churn cases not only cause revenue losses but also impose extra costs of finding new customers. To overcome this issue, they develop various strategies to retain their customers. In this regard, this study presents an integrated artificial intelligence -based model that can meet the expectations of these companies not only to profile the customer churn, but also to predict a service fee that is likely to be accepted by the customers. The model first identifies the customers who are likely to churn and then offers the customers a personalised service fee that is likely to be acceptable. In this study, the K -nearest neighbours, Decision Tree, Random Forest, and Support Vector Machine methods are adapted as classifiers for churn prediction, and regression models of the same methods are utilised to predict the most optimum personalisedservice fee for potential customer churns. Additionally, to reduce the cost of data collection for companies, the feature selection method is used to determine the most optimal feature subset in churn analysis and service fee prediction. The results show that the Random Forest method is superior to other methods in both churn and price predictions and has resulted in as much as a predictive accuracy of 94% and AUC of 98%. The outcome of this comprehensive analysis using four artificial intelligence methods over three diverse telecom datasets, suggests that the proposed personalisedpricing model in the telecom sector could prevent the churn and increase the profitability by up to 36%. In addition, the model based on SVM suggests that it is possible to reduce the number of required data to be collected by as much as 20%. As the robustness and generalisation ability of the models has been demonstrated over three diverse data sets, it can be further adapted in different sectors.