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Journal articles on the topic 'Cascade-forward neural network'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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1

Alrubaie, Shymaa Akram Hantoush. "Cascade-Forward Neural Network for Volterra Integral Equation Solution." Ibn AL- Haitham Journal For Pure and Applied Sciences 34, no. 3 (2021): 104–15. http://dx.doi.org/10.30526/34.3.2683.

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The method of solving volterra integral equation by using numerical solution is a simple operation but to require many memory space to compute and save the operation. The importance of this equation appeares new direction to solve the equation by using new methods to avoid obstacles. One of these methods employ neural network for obtaining the solution.
 This paper presents a proposed method by using cascade-forward neural network to simulate volterra integral equations solutions. This method depends on training cascade-forward neural network by inputs which represent the mean of volterra
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2

Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 892. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action r
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Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 892–99. https://doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action r
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Alkhasawneh, Mutasem Sh. "Hybrid Cascade Forward Neural Network with Elman Neural Network for Disease Prediction." Arabian Journal for Science and Engineering 44, no. 11 (2019): 9209–20. http://dx.doi.org/10.1007/s13369-019-03829-3.

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Warsito, Budi, Rukun Santoso, Suparti, and Hasbi Yasin. "Cascade Forward Neural Network for Time Series Prediction." Journal of Physics: Conference Series 1025 (May 2018): 012097. http://dx.doi.org/10.1088/1742-6596/1025/1/012097.

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Aribowo, Widi, Bambang Suprianto, I. Gusti Putu Asto Buditjahjanto, Mahendra Widyartono, and Miftahur Rohman. "An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator." ECTI Transactions on Electrical Engineering, Electronics, and Communications 19, no. 2 (2021): 136–44. http://dx.doi.org/10.37936/ecti-eec.2021192.241628.

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The parasitism – predation algorithm (PPA) is an optimization method that duplicates the interaction of mutualism between predators (cats), parasites (cuckoos), and hosts (crows). The study employs a combination of the PPA methods using the cascade-forward backpropagation neural network. This hybrid method employs an automatic voltage regulator (AVR) on a single machine system, with the performance measurement focusing on speed and the rotor angle. The performance of the proposed method is compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation ne
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Morinaga, Yuya, and Kazunori Yamaguchi. "Improvement of Neural Reverse Dictionary by Using Cascade Forward Neural Network." Journal of Information Processing 28 (2020): 715–23. http://dx.doi.org/10.2197/ipsjjip.28.715.

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Dhiaeddine, Mostefaoui Mohamed, and Benmouiza Khalil. "Optimal artificial neural network configurations for hourly solar irradiation estimation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 4878–85. https://doi.org/10.11591/ijece.v13i5.pp4878-4885.

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Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigat
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9

Kaveh, M., and R. A. Chayjan. "Mathematical and neural network modelling of terebinth fruit under fluidized bed drying." Research in Agricultural Engineering 61, No. 2 (2016): 55–65. http://dx.doi.org/10.17221/56/2013-rae.

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The paper presents an application which uses Feed Forward Neural Networks (FFNNs) to model the non-linear behaviour of the terebinth fruit drying. Mathematical models and Artificial Neural Networks (ANNs) were used for prediction of effective moisture diffusivity, specific energy consumption, shrinkage, drying rate and moisture ratio in terebinth fruit. Feed Forward Neural Network (FFBP) and Cascade Forward Neural Network (CFNN) as well as training algorithms of Levenberg-Marquardt (LM) and Bayesian regularization (BR) were used. Air temperature and velocity limits were 40–80&deg
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Wigati, Ekky Rosita Singgih, Budi Warsito, and Rita Rahmawati. "PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT." Jurnal Gaussian 7, no. 1 (2018): 64–72. http://dx.doi.org/10.14710/j.gauss.v7i1.26636.

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Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 uni
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Alkhasawneh, Mutasem Sh, and Lea Tien Tay. "A Hybrid Intelligent System Integrating the Cascade Forward Neural Network with Elman Neural Network." Arabian Journal for Science and Engineering 43, no. 12 (2017): 6737–49. http://dx.doi.org/10.1007/s13369-017-2833-3.

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Yan, Shaohong, Hailong Zhao, Liangxu Liu, Qiaozhi Sang, Peng Chen, and Jie Li. "Application Study of Sigmoid Regularization Method in Coke Quality Prediction." Complexity 2020 (July 20, 2020): 1–10. http://dx.doi.org/10.1155/2020/8785047.

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Coke is an indispensable and vital flue for blast furnace smelting, during which it plays a key role as a reducing agent, heat source, and support skeleton. Models of prediction of coke quality based on ANN are established to map the functional relationship between quality parameters Mt, Ad, Vdaf, St,d, and caking property (X, Y, and G) of mixed coal and quality parameters Ad, St,d, coke reactivity index (CRI), and coke strength after reaction (CSR) of coke. A regularized network training method based on Sigmoid function is designed considering that redundancy of network structure may lead to
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Golpour, Iman, Ana Cristina Ferrão, Fernando Gonçalves, Paula M. R. Correia, Ana M. Blanco-Marigorta, and Raquel P. F. Guiné. "Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)." Foods 10, no. 9 (2021): 2228. http://dx.doi.org/10.3390/foods10092228.

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This research study focuses on the evaluation of the total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberries according to different experimental extraction conditions by applying the Artificial Neural Networks (ANNs) technique. The experimental data were applied to train ANNs using feed- and cascade-forward backpropagation models with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANN inputs, whereas the three variables of total phenolic compounds, DP
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Kharat, J. P. "Comparative Study of Various Neural Network Architectures for MPEG-4 Video Traffic Prediction." International Journal of Advances in Applied Sciences 6, no. 4 (2017): 283. http://dx.doi.org/10.11591/ijaas.v6.i4.pp283-292.

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<p>Network traffic as it is VBR in nature exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper comments on the MPEG-4 video traffic predictions evaluated by different types of neural network architectures and compares the performance of the same in terms of mean square error for the same video frames. For that three types of neural architectures are used namely Feed forward, Cascaded Feed fo
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Dharmalingam, M., and G.D Praveenkumar. "Hierarchical Image Classification on Bayesian Cascade Neural Learning." Innovative Computing and Communication: An International Journal 1, no. 3 (2020): 1–6. https://doi.org/10.5281/zenodo.4743651.

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The performance of image classification on Bayesian cascade neural learning techniques using in coarse and fine layer in LSTM. Recurrent Neural Network (RNN) system experience from Vanishing Gradient (VG) issues. The Gradients needs to proliferate down through numerous layers of the Recurrent Neural Network (RNN).So we integrate the LSTM do not go through from vanishing gradient problem that forward layer. It support different number of layers in Convolutional Neural Network (CNN) is designed for image classification. The Long Short Term Memory (LSTM) processed with Bayesian Cascade Neural Lea
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Al-batah, Mohammad Subhi, Mutasem Sh Alkhasawneh, Lea Tien Tay, Umi Kalthum Ngah, Habibah Hj Lateh, and Nor Ashidi Mat Isa. "Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/512158.

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Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Sca
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17

Aribowo, Widi. "Slime Mould Algorithm Training Neural Network in Automatic Voltage Regulator." Trends in Sciences 19, no. 3 (2022): 2145. http://dx.doi.org/10.48048/tis.2022.2145.

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The research is proposed a new method of artificial intelligence (AI) to control automatic voltage regulators. A neural network has improved using a metaheuristic method, namely the slime mould algorithm (SMA). SMA has an algorithm based on the mode of slime mold in nature. SMA has characteristics that use adaptive weights to simulate the process to generate feedback from the movement of bio-oscillator-based slime molds in foraging, exploring, and exploiting areas. The performance of the proposed method is focused on speed and rotor angle. To know the competence and potency of the proposed met
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18

Dhiaeddine, Mostefaoui Mohamed, Benmouiza Khalil, and Oubbati Youcef. "Optimal artificial neural network configurations for hourly solar irradiation estimation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 4878. http://dx.doi.org/10.11591/ijece.v13i5.pp4878-4885.

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<span lang="EN-US">Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar i
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19

Mistry, Shivangi, and Falguni Parekh. "Flood Forecasting Using Artificial Neural Network." IOP Conference Series: Earth and Environmental Science 1086, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1755-1315/1086/1/012036.

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Abstract The process of assessing the timing, amount, and period of flood events based on observed features of a river basin is known as flood forecasting. Floods cause lots of damage to properties and create a risk to human life. Flood forecasting is critical for developing appropriate flood risk management strategies, reducing flood hazards, evacuating people from flood-prone areas. The main objective of this study is to apply artificial neural networks for forecasting of river flow in the Deo River, located in Gujarat. Rainfall and discharge are the parameters considered for model developme
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Dogan, Tamer. "A Comparison of the Use of Artificial Intelligence Methods in the Estimation of Thermoluminescence Glow Curves." Applied Sciences 13, no. 24 (2023): 13027. http://dx.doi.org/10.3390/app132413027.

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In this study, the thermoluminescence (TL) glow curve test results performed with eleven different dose values were used as training data, and its attempted to estimate the test results of the curves performed at four different doses using artificial intelligence methods. While the dose values of the data used for training were 10, 20, 50, 100, 150, 220, 400, 500, 600, 700, and 900 Gy, the selected dose values of the data for the testing were 40, 276, 320, and 800 Gy. The success of the experimental and artificial neural network results was determined according to the mean squared error (RMSE)
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Pwasong, Augustine, and Saratha Sathasivam. "A new hybrid quadratic regression and cascade forward backpropagation neural network." Neurocomputing 182 (March 2016): 197–209. http://dx.doi.org/10.1016/j.neucom.2015.12.034.

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22

Fatkhurokhman Fauzi, Dewi Ratnasari Wijaya, and Tiani Wahyu Utami. "Brent Crude Oil Price Forecasting using the Cascade Forward Neural Network." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 4 (2023): 964–69. http://dx.doi.org/10.29207/resti.v7i4.5052.

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Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the Gross Domestic Product in 2021. Excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The Cascade Forward Neural Network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crud
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Chheepa, Tarun Kumar, and Tanuj Manglani. "Power Quality Events Classification using ANN with Hilbert Transform." International Journal of Emerging Research in Management and Technology 6, no. 6 (2018): 227. http://dx.doi.org/10.23956/ijermt.v6i6.274.

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With the evolution of Smart Grid, Power Quality issues have become prominent. The urban development involves usage of computers, microprocessor controlled electronic loads and power electronic devices. These devices are the source of power quality disturbances. PQ problems are characterized by the variations in the magnitude and frequency in the system voltages and currents from their nominal values. To decide a control action, a proper classification mechanism is required to classify different PQ events. In this paper we propose a hybrid approach to perform this task. Different Neural topolog
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Aribowo, Widi, Supari Muslim, Fendi Achmad, and Aditya Chandra Hermawan. "Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor." Jurnal Elektronika dan Telekomunikasi 21, no. 1 (2021): 48. http://dx.doi.org/10.14203/jet.v21.48-54.

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This article presents a direct current (DC) motor control approach using a hybrid Seagull Optimization Algorithm (SOA) and Neural Network (NN) method. SOA method is a nature-inspired algorithm. DC motor speed control is very important to maintain the stability of motor operation. The SOA method is an algorithm that duplicates the life of the seagull in nature. Neural network algorithms will be improved using the SOA method. The neural network used in this study is a feed-forward neural network (FFNN). This research will focus on controlling DC motor speed. The efficacy of the proposed method i
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Thatoi, Dhirendranath, Punyaslok Guru, Prabir Kumar Jena, Sasanka Choudhury, and Harish Chandra Das. "Comparison of CFBP, FFBP, and RBF Networks in the Field of Crack Detection." Modelling and Simulation in Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/292175.

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The issue of crack detection and its diagnosis has gained a wide spread of industrial interest. The crack/damage affects the industrial economic growth. So early crack detection is an important aspect in the point of view of any industrial growth. In this paper a design tool ANSYS is used to monitor various changes in vibrational characteristics of thin transverse cracks on a cantilever beam for detecting the crack position and depth and was compared using artificial intelligence techniques. The usage of neural networks is the key point of development in this paper. The three neural networks u
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Aribowo, Widi, Reza Rahmadian, Mahendra Widyartono, Ayusta Wardani, and Aditya Prapanca. "Improved Feed-Forward Backpropagation Neural Network Based on Marine Predators Algorithm for Tuning Automatic Voltage Regulator." ECTI Transactions on Electrical Engineering, Electronics, and Communications 21, no. 2 (2023): 249830. http://dx.doi.org/10.37936/ecti-eec.2023212.249830.

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This research will discuss the application of an automatic voltage regulator based on the feed-forward back propagation neural network (FFBNN), which is enhanced by the marine predator algorithm (MPA). The marine predators algorithm is a method that adopts marine ecosystem life that is identified in the relationship between predators and prey. MPA is adopting a natural approach to arranging the best food search strategies and finding the latest strategy. The focus of the research is on the performance of speed and rotor angle. The performance of the proposed method will be tested using hidden
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Raheem Ajiboye, Adeleke. "Performance Evaluation of Classifiers Created using Elman Back-Propagation and Cascade Feed-forward Neural Networks." Journal of Applied Science, Information and Computing 2, no. 1 (2021): 1–6. http://dx.doi.org/10.59568/jasic-2021-2-1-01.

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Huge data being captured in our day-to-day activities are mostly imbalance. Such data therefore, calls for fast, accurate and robust techniques, through which they could be analyzed in order to fast-track early decision making. A Cascade Feed-forward Neural Networks and Elman Backpropagation are known techniques in neural network domain and their efficacies is therefore tested on separable data in this study. The objective of this study is to evaluate the performance of these techniques in solving a linear classification problem. The linear classification of data involves, splitting of separab
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Saeedi, Ehsan, Md Selim Hossain, and Yinan Kong. "Side-Channel Information Characterisation Based on Cascade-Forward Back-Propagation Neural Network." Journal of Electronic Testing 32, no. 3 (2016): 345–56. http://dx.doi.org/10.1007/s10836-016-5590-4.

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Hassoon, Israa Mohammed, and Shaymaa Akram Hantoosh. "CFNN for Identifying Poisonous Plants." Baghdad Science Journal 20, no. 3(Suppl.) (2023): 1122. http://dx.doi.org/10.21123/bsj.2023.7874.

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Identification of poisonous plants is a hard challenge for researchers because of the great similarity between poisonous and non- poisonous plants. Traditional methods to identify poisonous plant can be tiresome, therefore, automated poisonous plants identification system is needed. In this work, cascade forward neural network framework is proposed to identify poisonous plants based on their leaves. The proposed system was evaluated on both (poisonous leaves/non-poisonous leaves) which are collected using smart phone and internet. Combination of shape features and statistical features are extr
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Dada, Emmanuel Gbenga, Hurcha Joseph Yakubu, and David Opeoluwa Oyewola. "Artificial Neural Network Models for Rainfall Prediction." European Journal of Electrical Engineering and Computer Science 5, no. 2 (2021): 30–35. http://dx.doi.org/10.24018/ejece.2021.5.2.313.

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Rainfall prediction is an important meteorological problem that can greatly affect humanity in areas such as agriculture production, flooding, drought, and sustainable management of water resources. The dynamic and nonlinear nature of the climatic conditions have made it impossible for traditional techniques to yield satisfactory accuracy for rainfall prediction. As a result of the sophistication of climatic processes that produced rainfall, using quantitative techniques to predict rainfall is a very cumbersome task. The paper proposed four non-linear techniques such as Artificial Neural Netwo
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Sharma, Rohan S., and Serhat Hosder. "Mission-Driven Inverse Design of Blended Wing Body Aircraft with Machine Learning." Aerospace 11, no. 2 (2024): 137. http://dx.doi.org/10.3390/aerospace11020137.

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The intent of this work was to investigate the feasibility of developing machine learning models for calculating values of airplane configuration design variables when provided time-series, mission-informed performance data. Shallow artificial neural networks were developed, trained, and tested using data pertaining to the blended wing body (BWB) class of aerospace vehicles. Configuration design parameters were varied using a Latin-hypercube sampling scheme. These data were used by a parametric-based BWB configuration generator to create unique BWBs. Performance for each configuration was obta
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Al Khatib, Mohamed, and Samer Al Martini. "A Study on the Application of Artificial Neural Networks on Green Self Consolidating Concrete (SCC) under Hot Weather." Key Engineering Materials 677 (January 2016): 254–59. http://dx.doi.org/10.4028/www.scientific.net/kem.677.254.

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Self-consolidating concrete (SCC) has recently drawn attention to the construction industry in hot weather countries, due to its high fresh and mechanical properties. The slump flow is routinely used for quality control of SCC. Experiments were conducted by the current authors to investigate the effects of hot weather conditions on the slump flow of SCC. Self-consolidating concrete mixtures were prepared with different dosages of fly ash and superplasticizer and under different ambient temperatures. The results showed that the slump flow of SCC is sensitive to changes in ambient temperature, f
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Ituabhor, Odesanya, Joseph Isabona, Jangfa T. zhimwang, and Ikechi Risi. "Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets." International Journal of Computer Network and Information Security 14, no. 3 (2022): 63–74. http://dx.doi.org/10.5815/ijcnis.2022.03.05.

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In this work, adaptive learning of a monitored real-time stochastic phenomenon over an operational LTE broadband radio network interface is proposed using cascade forward neural network (CFNN) model. The optimal architecture of the model has been implemented computationally in the input and hidden units by means of incremental search process. Particularly, we have applied the proposed adaptive-based cascaded forward neural network model for realistic learning of practical signal data taken from an operational LTE cellular network. The performance of the adaptive learning model is compared with
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Hasoon, Safwan, and Fatima Younis. "Constructing Expert System to Automatic Translation for Software development." Al-Kitab Journal for Pure Sciences 2, no. 2 (2018): 231–47. http://dx.doi.org/10.32441/kjps.02.02.p16.

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the development in computer fields, especially in the software engineering, emerged the need to construct intelligence tool for automatic translation from design phase to coding phase, for producing the source code from the algorithm model represented in pseudo code, and execute it depending on the constructing expert system which reduces the cost, time and errors that may occur during the translation process, which has been built the knowledge base, inference engine, and the user interface. The knowledge bases consist of the facts and the rules for the automatic transition. The results are co
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Khatib, Tamer, Reziq Deria, and Asma Isead. "Assessment of Three Learning Machines for Long-Term Prediction of Wind Energy in Palestine." Mathematical Problems in Engineering 2020 (October 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/8303152.

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In this research, an approach for predicting wind energy in the long term has been developed. The aim of this prediction is to generate wind energy profiles for four cities in Palestine based on wind energy profile of another fifth city. Thus, wind energy data for four cities, namely, Nablus city, are used to develop the model; meanwhile, wind energy data for Hebron, Jenin, Ramallah, and Jericho cities are predicted based on that. Three machine learning algorithms are used in this research, namely, Cascade-forward neural network, random forests, and support vector machines. The developed model
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Laveglia, Vincenzo, and Edmondo Trentin. "Downward-Growing Neural Networks." Entropy 25, no. 5 (2023): 733. http://dx.doi.org/10.3390/e25050733.

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A major issue in the application of deep learning is the definition of a proper architecture for the learning machine at hand, in such a way that the model is neither excessively large (which results in overfitting the training data) nor too small (which limits the learning and modeling capabilities of the automatic learner). Facing this issue boosted the development of algorithms for automatically growing and pruning the architectures as part of the learning process. The paper introduces a novel approach to growing the architecture of deep neural networks, called downward-growing neural netwo
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Godara, Shivani. "Neural Networks for Iris Recognition: Comparisons between LVQ and Cascade Forward Back Propagation Neural network Models, Architectures and Algorithm." IOSR Journal of Engineering 3, no. 01 (2013): 07–10. http://dx.doi.org/10.9790/3021-03150710.

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Wang, Li Ying, and Wei Guo Zhao. "Pressure Fluctuation Based on Cascade Correlation Algorithm in Draft Tube." Advanced Materials Research 121-122 (June 2010): 38–42. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.38.

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The cascade-correlation(CC) is presented as a neural network growing technique which allows one to gradually build network architecture without the need to redefine the number of neurons to be used in a feed forward. In view of the actual situation that the corresponding space curved surface which expresses pressure fluctuation in draft tube is too complex to be analyzed, considering the pressure fluctuation in draft tube, the network model is established based on CC algorithm and it is applied to hydropower station. Comparing with BP neural network, the experimental results show the predictio
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Ansari, Abdul Ghani, Fareed Ahmed Jokhio, Muhammad Shehram Shah Syed, Hina Dharejo, and Fayaz Ahmed Memon. "Machine Learning-Driven Approach for DDOS Attacks Detection using Neural-Based Networks: A Proficiency Study." Mehran University Research Journal of Engineering and Technology 44, no. 3 (2025): 206–18. https://doi.org/10.22581/muet1982.3330.

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Cyber-attacks pose significant threats to the Internet and its connected resources, causing harm to institutions and governments. Advanced technologies like cloud computing, the Internet of Things (IoT), and Artificial Intelligence (AI) have made these attacks harder to detect. Botnets, controlled by malicious actors, are at the core of many Internet attacks, including Distributed Denial of Service (DDoS) attacks. Despite years of DDoS incidents, effective defense mechanisms are lacking. In our research, we use Resilient Backpropagation, Fletcher-Powell Conjugate Gradient (FPCG), and Scaled Co
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Janan Farag Yonan and Nagham Amjed Abdul Zahra. "Node Intrusion Tendency Recognition Using Network Level Features Based Deep Learning Approach." Babylonian Journal of Networking 2023 (January 10, 2023): 1–10. http://dx.doi.org/10.58496/bjn/2023/001.

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Adhoc network is highly susceptible for intrusion attacks due to the simplified accesscontrol and compacted network stack. Malicious node recognition in Mobile adhocnetwork (MANET) is challengeable due to nodes mobility and limited coverageof nodes. Thus, link may keep fluctuating throughout the communication period.In this paper, deep analytic model is made for extracting attacker node behaviorsfrom networking point of view. Attributed such as link durations, re-healing timeand number of received packets (by attacker) was the main features of this work.Later, deep learning paradigm is integra
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Badrudeen, Tayo Uthman, Nnamdi I. Nwulu, and Saheed Lekan Gbadamosi. "Neural Network Based Approach for Steady-State Stability Assessment of Power Systems." Sustainability 15, no. 2 (2023): 1667. http://dx.doi.org/10.3390/su15021667.

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The quest for an intelligence compliance system to solve power stability problems in real-time with high predictive accuracy, and efficiency has led to the discovery of deep learning (DL) techniques. This paper investigates the potency of several artificial neural network (ANN) techniques in assessing the steady-state stability of a power system. The new voltage stability pointer (NVSP) was employed to parameterize and reduce the input data to the neural network algorithms to predict the proximity of power systems to voltage instability. In this study, we consider five neural network algorithm
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Rahaju, Sri Mumpuni Ngesti, April Lia Hananto, Permana Andi Paristiawan, Abdullahi Tanko Mohammed, Anthony Chukwunonso Opia, and Muhammad Idris. "Comparison of Various Prediction Model for Biodiesel Cetane Number using Cascade-Forward Neural Network." Automotive Experiences 6, no. 1 (2023): 4–13. http://dx.doi.org/10.31603/ae.7050.

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Cetane number (CN) is one of the important fuel properties of diesel fuels. It is a measurement of the ignition quality of diesel fuel. Numerous studies have been published to predict the CN of biodiesels. More recently, the utilization of soft computing methods such as artificial neural networks (ANN) has received considerable attention as a prediction tool. However, most studies in the use of ANN for estimating the CN of biodiesels have only used one algorithm to train a small number of datasets. This study aims to predict the CN of 63 biodiesels based on the fatty acid methyl esters (FAME)
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Samantaray, Sandeep, and Abinash Sahoo. "Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 3 (2020): 243–51. http://dx.doi.org/10.3233/kes-200046.

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Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfe
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Hemmat Esfe, Mohammad, and Davood Toghraie. "Cascade forward Artificial Neural Network to estimate thermal conductivity of functionalized graphene-water nanofluids." Case Studies in Thermal Engineering 26 (August 2021): 101194. http://dx.doi.org/10.1016/j.csite.2021.101194.

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Roumpakias, Elias, and Tassos Stamatelos. "Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks." Sustainability 15, no. 10 (2023): 8326. http://dx.doi.org/10.3390/su15108326.

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The increasing installed volume of grid-connected PV systems in modern electricity networks induces variability and uncertainty factors which must be addressed from several different viewpoints, including systems’ protection and management. This study aims to estimate the actual performance and degradation of photovoltaic (PV) parks in Central Greece after several years of operation. Monitoring data over several years are analyzed and filtered, the performance ratio and normalized efficiency are computed, and five different ANNs are employed: (i) a feed-forward network (one hidden layer); (ii)
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Ansari, Shaheer, Afida Ayob, Molla Shahadat Hossain Lipu, Aini Hussain, and Mohamad Hanif Md Saad. "Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach." Sustainability 13, no. 23 (2021): 13333. http://dx.doi.org/10.3390/su132313333.

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Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent N
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Sankar Ganesh, S., Pachaiyappan Arulmozhivarman, and Rao Tatavarti. "Forecasting Air Quality Index Using an Ensemble of Artificial Neural Networks and Regression Models." Journal of Intelligent Systems 28, no. 5 (2017): 893–903. http://dx.doi.org/10.1515/jisys-2017-0277.

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Abstract Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multipl
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Luo, Yi, Chun Tian Li, and Hui Bin Xu. "Modeling of Resistance Spot Welding Process Using Nonlinear Regression Analysis and Neural Network Approach on Galvanized Steel Sheet." Advanced Materials Research 291-294 (July 2011): 823–28. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.823.

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Modeling of resistance spot welding process on galvanized steel sheet was investigated. Mathematical models developed by nonlinear multiple regression analysis and artificial neural network approach were employed in the prediction of welding quality factors, namely nugget diameter, penetration rate and tensile shear strength, under some welding conditions. According to the prediction models on quality, the prediction systems of welding process parameters were formulated respectively on the basis of Newton-Raphson iterative algorithm and cascade forward back propagation algorithm in order to ob
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Kote, A. S., and D. V. Wadkar. "Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks." Engineering, Technology & Applied Science Research 9, no. 3 (2019): 4176–81. http://dx.doi.org/10.48084/etasr.2725.

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Coagulation and chlorination are complex processes of a water treatment plant (WTP). Determination of coagulant and chlorine dose is time-consuming. Many times WTP operators in India determine the coagulant and chlorine dose approximately using their experience, which may lead to the use of excess or insufficient dose. Hence, there is a need to develop prediction models to determine optimum chlorine and coagulant doses. In this paper, artificial neural networks (ANN) are used for prediction due to their ability to learn and model non-linear and complex relationships. Separate ANN models for ch
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Kote, Alka S., and Dnyaneshwar V. Wadkar. "Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks." Engineering, Technology & Applied Science Research 9, no. 3 (2019): 4176–81. https://doi.org/10.5281/zenodo.3249101.

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Coagulation and chlorination are complex processes of a water treatment plant (WTP). Determination of coagulant and chlorine dose is time-consuming. Many times WTP operators in India determine the coagulant and chlorine dose approximately using their experience, which may lead to the use of excess or insufficient dose. Hence, there is a need to develop prediction models to determine optimum chlorine and coagulant doses. In this paper, artificial neural networks (ANN) are used for prediction due to their ability to learn and model non-linear and complex relationships. Separate ANN models for ch
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