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Entropy performance of nonlinear mathematical hydrocarbon based model using ternary alloys (AA7072/AA7075/TiAIV) under influential solar radiations and convective effects.
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Department of Physics, Laghman University, Mehtarlam City, Laghman, 2701, Afghanistan.
Aluminum alloys have promising characteristics which make them more useful in industrial applications for thermal management and entropy of the fluidic system. Hence, the current research deals with the analysis of entropy and thermal performance of (CHO-HO)/50:50% saturated by (AA7072/AA7076/TiAIV) alloys. Traditional problem modified using enhanced characteristics of ternary alloys and hydrocarbon 50:50% base fluid.
View Article and Find Full Text PDFContinual deep reinforcement learning with task-agnostic policy distillation.
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Department of Informatics, University of Hamburg, Hamburg, Germany.
Central to the development of universal learning systems is the ability to solve multiple tasks without retraining from scratch when new data arrives. This is crucial because each task requires significant training time. Addressing the problem of continual learning necessitates various methods due to the complexity of the problem space.
View Article and Find Full Text PDFA Greedy Tabu Dual Heuristic algorithm for the cyclic pickup and delivery problem with 3D loading constraints.
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Shandong University of Science and Technology, College of Transportation, Qingdao, 266590, China.
The optimization of auto parts supply chain logistics plays a decisive role in the development of the automotive industry. To reduce logistics costs and improve transportation efficiency, this paper addresses the joint optimization problem of multi-vehicle pickup and delivery transportation paths under time window constraints, coupled with the three-dimensional loading of goods. The model considers mixed time windows, three-dimensional loading constraints, cyclic pickup and delivery paths, varying vehicle loads and volumes, flow balance, and time window constraints.
View Article and Find Full Text PDFOptimizing VGG16 deep learning model with enhanced hunger games search for logo classification.
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Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
View Article and Find Full Text PDFA stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification.
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Department of Computer Science and Digital Technologies, University of East London, London, UK.
Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.
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