A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models حسب Osama Bassam J. Rabie (21323741)

"…For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. …"

Neural Networks as a Convex Problem حسب Khalil, Baha

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PERF solutions for distributed query optimization. (c1999) حسب Fany, Roula

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Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection حسب Alok Singh Chauhan (8264985)

Minimizing Deadline Misses of Mobile IoT Requests in a Hybrid Fog- Cloud Computing Environment حسب Omer, Dalia Fatahelrahman

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Using artificial bee colony to optimize software quality estimation models. (c2015) حسب Abou Assi, Tatiana Antoine

"…We compare our models to others constructed using other well established techniques such as C4.5, Genetic Algorithms, Simulated Annealing, Tabu Search, multi-layer perceptron with back-propagation, multi-layer perceptron hybridized with ABC and the majority classifier. …"
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A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks حسب Umesh Kumar Lilhore (17727684)

"…The proposed model also utilized an enhanced back propagation neural network for data fusion operation, which is based on multi-hop system and also operates a highly optimized momentum technique, which helps to choose only optimum energy nodes and avoid duplicate selections that help to improve the overall energy and further reduce the quantity of data transmission. …"

Design of A Theoretical Framework For A Real-Time Fire Evacuation Guidance System حسب Abougharib, Arwa

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Random vector functional link network: Recent developments, applications, and future directions حسب A.K. Malik (16003193)

"…<p>Neural networks have been successfully employed in various domains such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. …"