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Redundancy estimation and adaptive density control inwireless sensor networks
(Old City Publishing, 2010)
While dense random deployment satisfies coverage and sensing requirements, constructing dense networks of sensor nodes poses the problems of obtaining node location information.We provide an analytical framework for ...
Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods
(TÜBİTAK, 2012)
The fast changes and developments in the world's economy have substantially increased energy consumption. Consequently, energy planning has become more critical and important. Forecasting is one of the main tools utilized ...
Symbolic knowledge extraction for explainable nutritional recommenders
(Elsevier, 2023-06)
Background and objective: This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A ...
Comparative study of credit risk evaluation for unbalanced datasets using deep learning classifiers
(IEEE, 2023)
Credit risk assessment deals with calculating the risk of a loan not being repaid. For this reason, a lot of research effort is directed at credit risk analysis. In this study, machine learning models such as Light ...
Generalization to unseen viewpoint images of objects via alleviated pose attentive capsule agreement
(Springer, 2023-02)
Despite their achievements in object recognition, Convolutional Neural Networks (CNNs) particularly fail to generalize to unseen viewpoints of a learned object even with substantial samples. On the other hand, recently ...
Neural network estimators for optimal tour lengths of traveling salesperson problem instances with arbitrary node distributions
(Informs, 2024)
It is essential to solve complex routing problems to achieve operational efficiency in logistics. However, because of their complexity, these problems are often tackled sequentially using cluster-first, route-second ...
Comparison of computational intelligence models on forecasting automated teller machine cash demands
(Old City Publishing, 2020)
We take up the problem of forecasting the amount of money to be withdrawn from automated teller machines (ATM). We compare the performances of eleven different algorithms from four different research areas on two different ...
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