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Titlebook: Nature Inspired Computing for Data Science; Minakhi Rout,Jitendra Kumar Rout,Himansu Das Book 2020 Springer Nature Switzerland AG 2020 Nat

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樓主: hearken
31#
發(fā)表于 2025-3-26 23:23:47 | 只看該作者
Lokeswari Venkataramana,Shomona Gracia Jacob,Saraswathi Shanmuganathan,Venkata Vara Prasad Dattulurickground.Clear organization.Ends with a interesting discussi.Quantization of physical systems requires a correct definition of quantum-mechanical observables, such as the Hamiltonian, momentum, etc., as self-adjoint operators in appropriate Hilbert spaces and their spectral analysis. ?Though a “na?v
32#
發(fā)表于 2025-3-27 01:11:44 | 只看該作者
33#
發(fā)表于 2025-3-27 06:15:37 | 只看該作者
34#
發(fā)表于 2025-3-27 10:21:08 | 只看該作者
An Efficient Classification of Tuberous Sclerosis Disease Using Nature Inspired PSO and ACO Based Oent machine learning approaches need to be discovered to analyze TSC1 and TSC2. This chapter concentrates on using convolutional neural network optimized with nature inspired approaches such as, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The main challenge of any machine le
35#
發(fā)表于 2025-3-27 17:24:18 | 只看該作者
Mid-Term Home Health Care Planning Problem with Flexible Departing Way for Caregivers, capable caregivers; patients must be served in their time window; each patient has specific preferences to caregivers for some personal reasons (e.g. gender); caregivers work in their contract working time with no more than daily maximum working time; a lunch break happens only if caregivers start
36#
發(fā)表于 2025-3-27 19:32:15 | 只看該作者
Optimization of Performance Parameter for Vehicular Ad-hoc NETwork (VANET) Using Swarm Intelligencey of vehicles, number of hops, etc. Different swarm intelligence techniques like Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), etc. can be used in VANET to optimize the parameters used in the routing protocol. In this chapter, Ant Colony Optimization (ACO) is used to establish mu
37#
發(fā)表于 2025-3-28 00:13:22 | 只看該作者
38#
發(fā)表于 2025-3-28 02:50:12 | 只看該作者
,Application of Genetic Algorithms for?Unit Commitment and Economic Dispatch Problems in Microgrids,mand uncertainty. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm is applied to an example scenario to illustrate its performance.
39#
發(fā)表于 2025-3-28 07:17:34 | 只看該作者
,Application of Genetic Algorithms for?Designing Micro-Hydro Power Plants in Rural Isolated Areas—A most efficient use of the resources. For this, a detailed model of the plant is first developed, followed by an optimization problem for the optimal design, which is formulated by considering the real terrain topographic data. The problem is presented in both single (to minimize the cost) and multi
40#
發(fā)表于 2025-3-28 12:17:17 | 只看該作者
Performance Evaluation of Different Machine Learning Methods and Deep-Learning Based Convolutional t decide classification and prediction of disease. In this chapter, we study and compare among different machine learning algorithms and deep neural networks for diabetes disease prediction, by measuring performance. The experiment results prove that convolution neural network based deep learning me
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