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Juan M. Lujano-Rojas, Rodolfo Dufo-López, Jesús Sergio Artal-Sevil and Eduardo García-Paricio
Assessing the training process of artificial neural networks (ANNs) is vital for enhancing their performance and broadening their applicability. This paper employs the Monte Carlo simulation (MCS) technique, integrated with a stopping criterion, to const...
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Han Zhang, Yadong Wu, Weihan Zhang and Yuling Zhang
The precise ascertainment of stellar ages is pivotal for astrophysical research into stellar characteristics and galactic dynamics. To address the prevalent challenges of suboptimal accuracy in stellar age determination and limited proficiency in apprehe...
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Zhao Zhang, Feng Feng and Tingting Huang
The size of datasets is growing exponentially as information technology advances, and it is becoming more and more crucial to provide efficient learning algorithms for neural networks to handle massive amounts of data. Due to their potential for handling...
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Bailun Jiang, Boyang Li, Weifeng Zhou, Li-Yu Lo, Chih-Keng Chen and Chih-Yung Wen
A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging d...
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Yaju Rajbhandari, Anup Marahatta, Bishal Ghimire, Ashish Shrestha, Anand Gachhadar, Anup Thapa, Kamal Chapagain and Petr Korba
Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, an...
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Yuan-Jia Ma and Ming-Yue Zhai
Wind power generation output is highly uncertain, since it entirely depends on intermittent environmental factors. This has brought a serious problem to the power industry regarding the management of power grids containing a significant penetration of wi...
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Xu Huang, Yuan Zhang, Jiarun Liu, Honghao Zhong, Zhaolei Wang and Yue Peng
A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking control problem, the augmented system is defined by the derived...
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Damianos P. Sakas, Nikolaos T. Giannakopoulos, Marina C. Terzi and Nikos Kanellos
Deep learning has experienced an increased demand for its capabilities to categorize and optimize operations and provide higher-accuracy information. For this purpose, the implication of deep learning procedures has been described as a vital tool for the...
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Mohammad Asad Tariq, Vasanthi Sethu, Senthilkumar Arumugasamy, Anurita Selvarajoo
Pág. 1 - 14
In the present research, local rambutan seed extract was used as a bio-coagulant for the treatment of palm oil mill effluent (POME). Jar test experiments were conducted to find the optimal operating conditions for the removal of turbidity and total suspe...
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Muhammad Tayyab, Ijaz Ahmad, Na Sun, Jianzhong Zhou and Xiaohua Dong
Consistent streamflow forecasts play a fundamental part in flood risk mitigation. Population increase and water cycle intensification are extending not only globally but also among Pakistan’s water resources. The frequency of floods has increased i...
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Tushar Ganguli and Edwin K. P. Chong
We present a novel technique for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. Our technique is based on the number of times each neuron is activated during model...
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Soha Abd El-Moamen Mohamed, Marghany Hassan Mohamed and Mohammed F. Farghally
In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to so...
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A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed an...
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Georgios N. Kouziokas
Pág. 467 - 473
Public administration has adopted information and communication technology in order to construct new intelligent systems and design new risk prevention strategies in transportation management. The ultimate goal is to improve the quality of the transporta...
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Tameem Adel and Mark Levene
We investigate the utility of side information in the context of machine learning and, in particular, in supervised neural networks. Side information can be viewed as expert knowledge, additional to the input, that may come from a knowledge base. Unlike ...
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Yen-Cheng Chu, Yun-Jie Jhang, Tsung-Ming Tai and Wen-Jyi Hwang
The objective of this study is to present novel neural network (NN) algorithms and systems for sensor-based hand gesture recognition. The algorithms are able to classify accurately a sequence of hand gestures from the sensory data produced by acceleromet...
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Zahra Alizadeh, Jafar Yazdi, Joong Hoon Kim and Abobakr Khalil Al-Shamiri
Monthly flow predictions provide an essential basis for efficient decision-making regarding water resource allocation. In this paper, the performance of different popular data-driven models for monthly flow prediction is assessed to detect the appropriat...
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Anna Bakurova, Olesia Yuskiv, Dima Shyrokorad, Anton Riabenko, Elina Tereschenko
Pág. 14 - 22
The subject of the research is the methods of constructing and training neural networks as a nonlinear modeling apparatus for solving the problem of predicting the energy consumption of metallurgical enterprises. The purpose of this work is to develop a ...
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Arman Haghighi, Mostafa Safdari Shadloo, Akbar Maleki and Mohammad Yaghoub Abdollahzadeh Jamalabadi
Numerous studies have proposed to correlate experimental results, however there are still significant errors in those predictions. In this study, an artificial neural network (ANN) is considered for a two-phase flow pressure drop in microchannels incorpo...
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Salaheddin Hosseinzadeh, Mahmood Almoathen, Hadi Larijani and Krystyna Curtis
Among the many technologies competing for the Internet of Things (IoT), one of the most promising and fast-growing technologies in this landscape is the Low-Power Wide-Area Network (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has pr...
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