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Alghamdi, Mohammed I. "A Hybrid Model for Intrusion Detection in IoT Applications." Wireless Communications and Mobile Computing 2022 (May 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/4553502.
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Internet of Things (IoT) networks has recently become an important component of smart cities, smart buildings, health care, and other applications. It finds it beneficial due to the inherent characteristics of low cost, compact, and low-powered IoT devices. At the same time, security remains a challenging issue in the design of IoT networks. Intrusion detection systems (IDS) can be used to identify the occurrence of intrusions in the network, i.e., abnormal activities in the network. The latest advances in machine learning (ML) and metaheuristics can be employed to design effective IDS models for IoT networks. This article develops a novel political optimizer with cascade forward neural network (PO-CFNN-)-based IDS in the IoT environment. The major intention of the PO-CFNN technique is to determine the occurrence of intrusions from the IoT environment. The PO-CFNN technique follows three major processes, namely, preprocessing, classification, and parameter optimization. Initially, the networking data is preprocessed to transform it into a useful format. Following that, the CFNN technique is employed for the identification and classification of intrusions in the IoT environment. In the final stage, the PO algorithm is applied for the optimal adjustment of the parameters involved in the CFNN model. The experimental validation of the PO-CFNN technique on a benchmark dataset stated the better outcomes of the PO-CFNN technique over recent approaches.
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Lin, Cheng-Jian, Chun-Hui Lin, and Frank Lin. "Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network." Applied Sciences 13, no. 5 (2023): 3337. http://dx.doi.org/10.3390/app13053337.
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The spindle of a machine tool plays a key role in machining because the wear of a spindle might result in inaccurate production and decreased productivity. To understand the condition of a machine tool, a vector-based convolutional fuzzy neural network (vector-CFNN) was developed in this study to diagnose faults from signals. The developed vector-CFNN mainly comprises a feature extraction part and a classification part. The feature extraction phase encompasses the use of convolutional layers and pooling layers, while the classification phase is facilitated through the deployment of a fuzzy neural network. The fusion layer plays an important role by being placed between the feature extraction and classification parts. It combines the characteristics and then passes the feature information to the classification part to improve the model’s performance. The developed vector-CFNN was experimentally evaluated against existing fusion methods; vector-CFNN required fewer parameters and achieved the highest average accuracy (99.84%) in fault diagnosis relative to conventional neural networks, fuzzy neural networks, and convolutional neural networks. Moreover, vector-CFNN achieved superior fault diagnosis using spindle vibration signals and required fewer parameters relative to its counterparts, indicating its feasibility for online spindle vibration monitoring.
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Lin, Cheng-Jian, Min-Su Huang, and Chin-Ling Lee. "Malware Classification Using Convolutional Fuzzy Neural Networks Based on Feature Fusion and the Taguchi Method." Applied Sciences 12, no. 24 (2022): 12937. http://dx.doi.org/10.3390/app122412937.
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The applications of computer networks are increasingly extensive, and networks can be remotely controlled and monitored. Cyber hackers can exploit vulnerabilities and steal crucial data or conduct remote surveillance through malicious programs. The frequency of malware attacks is increasing, and malicious programs are constantly being updated. Therefore, more effective malware detection techniques are being developed. In this paper, a convolutional fuzzy neural network (CFNN) based on feature fusion and the Taguchi method is proposed for malware image classification; this network is referred to as FT-CFNN. Four fusion methods are proposed for the FT-CFNN, namely global max pooling fusion, global average pooling fusion, channel global max pooling fusion, and channel global average pooling fusion. Data are fed into this network architecture and then passed through two convolutional layers and two max pooling layers. The feature fusion layer is used to reduce the feature size and integrate the network information. Finally, a fuzzy neural network is used for classification. In addition, the Taguchi method is used to determine optimal parameter combinations to improve classification accuracy. This study used the Malimg dataset to evaluate the accuracy of the proposed classification method. The accuracy values exhibited by the proposed FT-CFNN, proposed CFNN, and original LeNet model in malware family classification were 98.61%, 98.13%, and 96.68%, respectively.
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Tan, Kuang-Hsiung, Faa-Jeng Lin, Chao-Yang Tsai, and Yung-Ruei Chang. "A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation." Energies 11, no. 8 (2018): 1996. http://dx.doi.org/10.3390/en11081996.
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A distribution static compensator (DSTATCOM) is proposed in this study to improve the power quality, which includes the total harmonic distortion (THD) of the grid current and power factor (PF), of a mini grid with nonlinear and linear inductive loads. Moreover, the DC-link voltage regulation control of the DSTATCOM is essential especially under load variation conditions. Therefore, to improve the power quality and keep the DC-link voltage of the DSTATCOM constant under the variation of nonlinear and linear loads effectively, the traditional proportional-integral (PI) controller is substituted with a new online trained compensatory fuzzy neural network with an asymmetric membership function (CFNN-AMF) controller. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. Furthermore, the dimensions of the Gaussian membership functions are directly extended to AMFs for the optimization of the fuzzy rules and the upgrade of learning ability of the networks. In addition, the network structure and online learning algorithm of the proposed CFNN-AMF are introduced in detail. Finally, the effectiveness and feasibility of the DSTATCOM using the proposed CFNN-AMF controller to improve the power quality and maintain the constant DC-link voltage under the change of nonlinear and linear inductive loads have been demonstrated by some experimental results.
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Ebadzadeh, Mohammad Mehdi, and Armin Salimi-Badr. "CFNN: Correlated fuzzy neural network." Neurocomputing 148 (January 2015): 430–44. http://dx.doi.org/10.1016/j.neucom.2014.07.021.
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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 extracted from leaf then fed to cascade-forward neural network which used TRAINLM function for training. 500 samples of leaf images are used, 250 samples are poisonous, the remaining 250 samples are non-poisonous.300 samples used in training, 200 samples for testing. Our system is achieved an accuracy value of 99.5%.
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A. S., Rajesh, M. S. Prabhuswamy, and Ishwarya Komalnu Raghavan. "Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625." Advances in Materials Science and Engineering 2022 (October 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/7520962.
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In this paper, the influence of process components on surface roughness in turning of Inconel 625 using cubic boron nitride (CBN) is studied. A predictive model is developed to forecast the surface roughness using the cascade forward neural network (CFNN). The experiments are designed based on Taguchi. L27 orthogonal array (OA) is used to perform the experimental trails by considering speed, feed, and depth of cut as input factors. Out of 27 experimental trails, 18 experiments are used for training and 9 experimental trails are used for testing. The developed predictive model by the CFNN is compared with regression model values. The average prediction error for surface roughness is 2.94% with R2 = 99.99% by the CFNN. The CFNN is known to be superior to predict the response with minimum of percentage error. The minimum and maximum roughness observed at trail 8 and trail 20 is noted, respectively, and the increases in roughness at experimental trail 8 is equal to 3.384 times higher than the roughness observed at experimental trail.20. The feed rate dominates effectively on the roughness rather than other factors. The consequences of process factors on surface roughness are studied with the help of ANOVA. This experimental study and developed model would be used for aero parts manufacturing to forecast the roughness accurately before to the actual experiment so that actual machining and material cost could be avoided.
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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°C and 0.81–4.35 m/s, respectively. The best outcome for the use of ANN for the effective moisture diffusivity appertained to CFNN network with BR training algorithm, topology of 2-3-1 and threshold function of TANSIG. Similarly, the best outcome for the use of ANN for drying rate and moisture ratio also appertained to CFNN network with LM training algorithm, topology of 3-2-4-2 and threshold function of TANSIG.
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Alipour Bonab, Shahin, Wenjuan Song, and Mohammad Yazdani-Asrami. "A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines." Applied Sciences 13, no. 20 (2023): 11180. http://dx.doi.org/10.3390/app132011180.
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The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to estimate EMF with extremely high accuracy in a significantly short time. In this paper, two models based on the Artificial Neural Network (ANN) have been developed for estimating electric and magnetic fields, i.e., feed-forward neural network (FFNN) and cascade-forward neural network (CFNN). By performing the sensitivity analysis on controlling/hyper-parameters of these two ANN models, the best setup resulting in the highest possible accuracy considering their response time has been chosen. Overall, the CFNN achieved a significant 56% reduction in Root Mean Squared Error (RMSE) for the electric field and a 5% reduction for the magnetic field, compared to the FFNN. This indicates that the CFNN model provided more accurate predictions, particularly for the electric field than the proposed methods in other recent works, making it a promising choice for this application. When the model is trained, it will be tested by a different dataset. Then, the accuracy and response time of the model for new data points of that layout will be evaluated through this process. The model can predict the fields with an accuracy near 99.999% of the actual values in times under 10 ms. Also, the results of sensitivity analysis indicated that the CFNN models with triple and double hidden layers are the best options for the electric and magnetic field estimation, respectively.
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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 a benchmark feedforward neural network model (FFNN) using a number of measured stochastic SINR datasets obtained over a period of three months at two indoors and outdoors locations of the LTE network. The results showed that proposed CFNN model provided the best adaptive learning performance (0.9310 RMSE; 0.8669 MSE; 0.5210 MAE; 0.9311 R), compared to the benchmark FFNN model (1.0566 RMSE; 1.1164 MSE; 0.5568 MAE; 0.9131 R) in the first studied outdoor location. Similar robust performances were attained for the proposed CFNN model in other locations, thus indicating that it is superior to FFNN model for adaptive learning of real-time stochastic phenomenon.
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Khatib, Tamer, Azah Mohamed, K. Sopian, and M. Mahmoud. "Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction." International Journal of Photoenergy 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/946890.
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This paper presents an assessment for the artificial neural network (ANN) based approach for hourly solar radiation prediction. The Four ANNs topologies were used including a generalized (GRNN), a feed-forward backpropagation (FFNN), a cascade-forward backpropagation (CFNN), and an Elman backpropagation (ELMNN). The three statistical values used to evaluate the efficacy of the neural networks were mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Prediction results show that the GRNN exceeds the other proposed methods. The average values of the MAPE, MBE and RMSE using GRNN were 4.9%, 0.29% and 5.75%, respectively. FFNN and CFNN efficacies were acceptable in general, but their predictive value was degraded in poor solar radiation conditions. The average values of the MAPE, MBE and RMSE using the FFNN were 23%, −.09% and 21.9%, respectively, while the average values of the MAPE, MBE and RMSE using CFNN were 22.5%, −19.15% and 21.9%, respectively. ELMNN fared the worst among the proposed methods in predicting hourly solar radiation with average MABE, MBE and RMSE values of 34.5%, −11.1% and 34.35%. The use of the GRNN to predict solar radiation in all climate conditions yielded results that were highly accurate and efficient.
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Liu, Mei Rong, Yi Gang He, and Xiang Xin Li. "Fault Diagnosis of Analog Circuits Based on CFNN." Advanced Engineering Forum 6-7 (September 2012): 1045–50. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.1045.
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An analog circuits fault diagnosis method based on chaotic fuzzy neural network (CFNN) is presented. The method uses the advantage of the global movement characteristic inherent in chaos to overcome the shortcomings that BPNN is usually trapped to a local optimum and it has a low speed of convergence weights. The chaotic mapping was added into BPNN algorithm, and the initial value of the network was selected. The algorithm can effectively and reliably be used in analog circuit fault diagnosis by comparing the two methods and analyzing the results of the example.
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Chiang, Y. M., L. C. Chang, M. J. Tsai, Y. F. Wang, and F. J. Chang. "Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks." Hydrology and Earth System Sciences Discussions 7, no. 5 (2010): 6725–56. http://dx.doi.org/10.5194/hessd-7-6725-2010.
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Abstract. Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagatiom fuzzy neural network (CFNN) for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.
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Tabassum, Zahira, and B. S. Chandrasekar Shastry. "Short Term Load Forecasting of Residential and Commercial Consumers of Karnataka Electricity Board using CFNN." International Journal of Electrical and Electronics Research 10, no. 2 (2022): 347–52. http://dx.doi.org/10.37391/ijeer.100247.
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Electricity use and its access are correlated in the economic development of any country. Economically, electricity cannot be stored, and for stability of an electrical network a balance between generation and consumption is necessary. Electricity demand depends on various factors like temperature, everyday activities, time of day, days of the week days/Holidays. These parameters have led to price volatility and huge spikes in electricity prices. The research work proposes a short term Load prediction Model for LT2 (residential consumers), LT3 (Commercial Consumers) of Karnataka State Electricity Board using Cascaded Feed Forward Neural Network (CFNN). MATLAB software is utilized to design and test the forecasting model for predicting the power consumption. Furthermore, a shallow feed forward neural network-based prediction model is constructed and evaluated for performance comparison. The Performance metrics include Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The suggested STLF CFNN prediction model outperformed shallow feed forward networks on both performance metrics with prediction errors of less than 1%.
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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 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE
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Manikandan and Dr.Wang hong ling. "Machine Learning-based Book Recommendation Systems: A Comparative Study of CFNN and KNN Algorithms." Journal of Modern Applied Statistical Methods 23 (December 19, 2024): 63–80. https://doi.org/10.56801/jmasm.v23.i2.8.
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Among recommendation systems, collaborative filtering is a widely used method that leverages user preferences and collective actions to provide accurate book recommendations. With so many books available today, it can be harder and harder for readers to find books that suit their interests. As a result, recommender systems have become a vital tool for addressing this problem head-on, attempting to provide users with personalized book recommendations based on their unique interests and preferences. The studies have employed diverse datasets and machine learning technique KNN with Sparse Matrix, and Deep learning algorithm collaborative filtering Neural Network . Preprocessing carried out by Exploratory Data Analysis. These algorithms have demonstrated a significant improvement in recommendation accuracy. The KNN achieved accuracy levels of 81%, 85%, and 93% for different neighbour values 4, 5, 6 while CFNN achieved the accuracy of 95%. The studies have also delved into understanding the impact of various factors on book recommendations, including user preferences and collaborative patterns among readers and it recommends CFNN is suitable method for recommendation system.
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Turan, Nurdan Gamze, and Okan Ozgonenel. "Study of Montmorillonite Clay for the Removal of Copper (II) by Adsorption: Full Factorial Design Approach and Cascade Forward Neural Network." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/342628.
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An intensive study has been made of the removal efficiency of Cu(II) from industrial leachate by biosorption of montmorillonite. A 24factorial design and cascade forward neural network (CFNN) were used to display the significant levels of the analyzed factors on the removal efficiency. The obtained model based on 24factorial design was statistically tested using the well-known methods. The statistical analysis proves that the main effects of analyzed parameters were significant by an obtained linear model within a 95% confidence interval. The proposed CFNN model requires less experimental data and minimum calculations. Moreover, it is found to be cost-effective due to inherent advantages of its network structure. Optimization of the levels of the analyzed factors was achieved by minimizing adsorbent dosage and contact time, which were costly, and maximizing Cu(II) removal efficiency. The suggested optimum conditions are initial pH at 6, adsorbent dosage at 10 mg/L, and contact time at 10 min using raw montmorillonite with the Cu(II) removal of 80.7%. At the optimum values, removal efficiency was increased to 88.91% if the modified montmorillonite was used.
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Li, Qiang, Shaoyu Wang, and Xing Huang. "Evaluation Model of Landslide Lake Risk Disposal Based on CFNN." Journal of Applied Sciences 13, no. 10 (2013): 1746–52. http://dx.doi.org/10.3923/jas.2013.1746.1752.
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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 crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the Mean Absolute Percentage Error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting results for Brent crude oil for the next 12 months tend to experience a downward trend until December 2023.
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Lin, Faa-Jeng, Kuang-Hsiung Tan, Yu-Kai Lai, and Wen-Chou Luo. "Intelligent PV Power System With Unbalanced Current Compensation Using CFNN-AMF." IEEE Transactions on Power Electronics 34, no. 9 (2019): 8588–98. http://dx.doi.org/10.1109/tpel.2018.2888732.
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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, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.
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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 algorithms viz. feedforward neural network (FFNN), cascade-forward neural network (CFNN), layer recurrent neural network (LRNN), linear layer neural network (LLNN), and Elman neural network (ENN). The evaluation is based on the predictability and accuracy of these techniques for dynamic stability in power systems. The neural network algorithms were trained to mimic the NVSP dataset using a Levenberg-Marquardt (LM) model. Similarly, the performance analyses of the neural network techniques were deduced from the regression learner algorithm (RLA) using a root-mean-squared error (rmse) and response plot graph. The effectiveness of these NN algorithms was demonstrated on the IEEE 30-bus system and the Nigerian power system. The simulation results show that the FFNN and the CFNN possess a relatively better performance in terms of accuracy and efficiency for the considered power networks.
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Khadidos, Adil O., Zenah Mahmoud AlKubaisy, Alaa O. Khadidos, Khaled H. Alyoubi, Abdulrhman M. Alshareef, and Mahmoud Ragab. "Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment." Sensors 23, no. 16 (2023): 7207. http://dx.doi.org/10.3390/s23167207.
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Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter–prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures.
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Ghosh, Tamal, and Kristian Martinsen. "CFNN-PSO: An Iterative Predictive Model for Generic Parametric Design of Machining Processes." Applied Artificial Intelligence 33, no. 11 (2019): 951–78. http://dx.doi.org/10.1080/08839514.2019.1661110.
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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 chlorine and coagulant doses are explored with radial basis neural network (RBFNN), feed-forward neural network (FFNN), cascade feed forward neural network (CFNN) and generalized regression neural network (GRNN). For modeling, daily water quality data of the last four years are collected from the plant laboratory of WTP in Maharashtra (India). In order to improve performance, these models are established by varying input variables, hidden nodes, training functions, spread factor, and epochs. The best models are selected based on the comparison of performance measures. It is observed that the best performing chlorine dose model using defined statistics is found to be RBFNN with R=0.999. Similarly, the CFNN coagulant dose model with Bayesian regularization (BR) training function provided excellent estimates with network architecture (2-40-1) and R=0.947. Based on the above models, two graphical user interfaces (GUIs) were developed for real-time prediction of chlorine and coagulant dose, which will be useful for plant operators and decision makers.
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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 chlorine and coagulant doses are explored with radial basis neural network (RBFNN), feed-forward neural network (FFNN), cascade feed forward neural network (CFNN) and generalized regression neural network (GRNN). For modeling, daily water quality data of the last four years are collected from the plant laboratory of WTP in Maharashtra (India). In order to improve performance, these models are established by varying input variables, hidden nodes, training functions, spread factor, and epochs. The best models are selected based on the comparison of performance measures. It is observed that the best performing chlorine dose model using defined statistics is found to be RBFNN with R=0.999. Similarly, the CFNN coagulant dose model with Bayesian regularization (BR) training function provided excellent estimates with network architecture (2-40-1) and R=0.947. Based on the above models, two graphical user interfaces (GUIs) were developed for real-time prediction of chlorine and coagulant dose, which will be useful for plant operators and decision makers.
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Man, Chun Tao, Tian Feng Wang, Xiao Bo Sun, Xin Xin Yang, and Jia Cui. "A Compensatory Fuzzy Neural Network Modeling Method Based on Particle Swarm Clustering." Applied Mechanics and Materials 48-49 (February 2011): 5–8. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.5.
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According to modeling problem for complex systems, a compensatory fuzzy neural network (CFNN) modeling method based on particle swarm clustering is proposed: the particle swarm clustering is used to automatically separate the space of input-output data, obtain the numbers of inference rules of fuzzy model and find fuzzy rules. Based on the rules, we modified fuzzy reasoning process and established initial structure of compensatory fuzzy neural network. Then using adaptive rate algorithm optimized initial network parameters, which can obtain a faster training speed and more precision. Simulation results show that the proposed network has successfully modeled the oxidation decomposition reaction process.
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Çamur, Hüseyin, and Ahmed Muayad Rashid Al-Ani. "Prediction of Oxidation Stability of Biodiesel Derived from Waste and Refined Vegetable Oils by Statistical Approaches." Energies 15, no. 2 (2022): 407. http://dx.doi.org/10.3390/en15020407.
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The oxidation stability (OX) of the biodiesel is an essential parameter mainly during storage, which reduces the quality of the biodiesel, thus affecting the engine performance. Moreover, many factors affect oxidation stability. Therefore, determining the most significant parameter is essential for achieving accurate predictions. In this paper, an empirical equation (Poisson Regression Model (PRM)), machine learning models (Multilayer Feed-Forward Neural Network (MFFNN), Cascade Feed-forward Neural Network (CFNN), Radial Basis Neural Network (RBFNN), and Elman neural network (ENN)) with various combinations of input parameters are utilized and employed to identify the most relevant parameters for prediction of the oxidation stability of biodiesel. This study measured the physicochemical properties of 39 samples of waste frying methyl ester and their blends with various percentages of palm biodiesel and refined canola biodiesel. To this aim, 14 parameters including concentration amount of WFME (X1), PME (X2), and RCME (X3) in the mixture, kinematic viscosity (KV) at 40 °C, density at 15 °C (D), cloud point (CP), pour point (PP), the estimation value of the sum of the saturated (∑SFAMs), monounsaturated (∑MUFAMs), polyunsaturated (∑PUFAMs), degree of unsaturation (DU), long-chain saturated factor (LCSF), very-long-chain fatty acid (VLCFA), and ratio (∑MUFAMs+∑PUFAMs∑SFAMs) fatty acid composition were considered. The results demonstrated that the RBFNN model with the combination of X1, X2, X3, ∑SFAMs, ∑MUFAMs, ∑PUFAMs. VLCFA, DU, LCSF, ∑MUFAMs+∑PUFAMs∑SFAMs, KV, and D has the lowest value of root mean squared error and mean absolute error. In the end, the results demonstrated that the RBFNN model performed well and presented high accuracy in estimating the value of OX for the biodiesel samples compared to PRM, MFFNN, CFNN, and ENN.
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Sharkawy, Abdel-Nasser, Asmaa Ameen, Shuaiby Mohamed, Gamal Abdel-Jaber, and I. Hamdan. "Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms." Automation 5, no. 2 (2024): 190–212. http://dx.doi.org/10.3390/automation5020012.
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The use of renewable energy, especially wind power, is the most practical way to mitigate the environmental effects that various countries around the world are suffering from. To meet the growing need for electricity, wind energy is, nevertheless, being used more and more. Researchers have come to understand that a near-perfect output power estimate must be sacrificed. Variations in the weather influence wind energy, including wind speed, surface temperature, and pressure. In this study, the wind turbine output power was estimated using three approaches of artificial neural networks (ANNs). The multilayer feed-forward neural network (MLFFNN), cascaded forward neural network (CFNN), and recurrent neural network (RNN) were employed for estimating the entire output power of wind turbine farms in Egypt. Therefore, each built NN made use of wind speed, surface temperature, and pressure as inputs, while the wind turbine’s output power served as its output. The data of 62 days were gathered from wind turbine farm for the training and efficiency examination techniques of every implemented ANN. The first 50 days’ worth of data were utilized to train the three created NNs, and the last 12 days’ worth of data were employed to assess the efficiency and generalization capacity of the trained NNs. The outcomes showed that the trained NNs were operating successfully and effectively estimated power. When analyzed alongside the other NNs, the RNN produced the best main square error (MSE) of 0.00012638, while the CFNN had the worst MSE of 0.00050805. A comparison between the other relevant research studies and our suggested approach was created. This comparison led us to the conclusion that the recommended method was simpler and had a lower MSE than the others. Additionally, the generalization ability was assessed and validated using the approved methodology.
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Mellah, hacene, Kamel Eddoine Hemsas, Rachid Taleb, and carlo CECATI. "Estimation of speed, armature temperature and resistance in brushed DC machines using a CFNN based on BFGS BP." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 26, no. 6 (2018): 3181–91. https://doi.org/10.3906/elk-1711-330.
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In this paper, a sensorless speed and armature resistance and temperature estimator for Brushed (B) DC machines is proposed, based on a Cascade-Forward Neural Network (CFNN) and Quasi-Newton BFGS backpropagation (BP). Since we wish to avoid the use of a thermal sensor, a thermal model is needed to estimate the temperature of the BDC machine. Previous studies propose either non-intelligent estimators which depend on the model, such as the Extended Kalman Filter (EKF) and Luenberger's observer, or estimators which do not estimate the speed, temperature and resistance simultaneously. The proposed method has been verified both by simulation and by comparison with the simulation results available in the literature
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Sugandhi, Yunita Pipiet, Budi Warsito, and Arief Rachman Hakim. "Prediksi Harga Saham Harian Menggunakan Cascade Forward Neural Network (CFNN) Dengan Particle Swarm Optimization (PSO)." STATISTIKA Journal of Theoretical Statistics and Its Applications 19, no. 2 (2019): 71–82. http://dx.doi.org/10.29313/jstat.v19i2.4878.
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Ellahi, Manzoor, Muhammad Rehan Usman, Waqas Arif, et al. "Forecasting of Wind Speed and Power through FFNN and CFNN Using HPSOBA and MHPSO-BAACs Techniques." Electronics 11, no. 24 (2022): 4193. http://dx.doi.org/10.3390/electronics11244193.
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Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year’s dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results.
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MELLAH, Hacene, Kamel Eddine HEMSAS, Rachid TALEB, and Carlo CECATI. "Estimation of speed, armature temperature, and resistance in brushed DC machines using a CFNN based on BFGS BP." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 26, no. 6 (2018): 3182–92. http://dx.doi.org/10.3906/elk-1711-330.
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Chiang, Y. M., L. C. Chang, M. J. Tsai, Y. F. Wang, and F. J. Chang. "Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks." Hydrology and Earth System Sciences 15, no. 1 (2011): 185–96. http://dx.doi.org/10.5194/hess-15-185-2011.
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Abstract. Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.
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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 recognition. Histogram of oriented gradients (HOG) and histogram of optical flow (HOF) are used as features for representing the actions. HOG represents the spatial features of the video while HOF gives motion features of the video. The performance of two neural network architectures is compared based on recognition accuracy. Well-known publically available datasets for action and interaction detection are used for testing. It is seen that, for human action recognition applications, feed forward neural network gives better results in terms of higher recognition accuracy than Cascade forward neural network.
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Science and Engineering, Advances in Materials. "Retracted: Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625." Advances in Materials Science and Engineering 2023 (December 29, 2023): 1. http://dx.doi.org/10.1155/2023/9834301.
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Thakkar, Unnati, and Hicham Chaoui. "Remaining Useful Life Prediction of an Aircraft Turbofan Engine Using Deep Layer Recurrent Neural Networks." Actuators 11, no. 3 (2022): 67. http://dx.doi.org/10.3390/act11030067.
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The turbofan engine is a pivotal component of the aircraft. Engine components are susceptible to degradation over the life of their operation, which affects the reliability and performance of an engine. In order to direct the necessary maintenance behavior, remaining useful life prediction is the key. This research uses machine learning to provide a prediction framework for an aircraft’s remaining useful life (RUL) based on the entire life cycle data and deterioration parameter data (ML). For the engine’s lifetime assessment, a Deep Layer Recurrent Neural Network (DL-RNN) model is presented. The suggested method is compared to Multilayer Perceptron (MLP), Nonlinear Auto Regressive Network with Exogenous Inputs (NARX), and Cascade Forward Neural Network (CFNN), as well as the Prognostics and Health Management (PHM) conference Challenge dataset and NASA’s C-MAPSS dataset. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are calculated for both the datasets, and the values are in the range of 0.15% to 0.203% for DL-RNN, whereas for the other three topologies, they are in the range of 0.2% to 4.8%. Comparative results show a better predictive accuracy with respect to other ML algorithms.
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Ding, Xiaohua, Mehdi Jamei, Mahdi Hasanipanah, Rini Asnida Abdullah, and Binh Nguyen Le. "Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines." Sustainability 15, no. 10 (2023): 8424. http://dx.doi.org/10.3390/su15108424.
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Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce the potential risk of damage. In other words, the minimization of flyrock can lead to sustainability of surroundings environment in blasting sites. To this aim, the present study develops several new hybrid models for predicting flyrock. The proposed models were based on a cascaded forward neural network (CFNN) trained by the Levenberg–Marquardt algorithm (LMA), and also the combination of least squares support vector machine (LSSVM) and three optimization algorithms, i.e., gravitational search algorithm (GSA), whale optimization algorithm (WOA), and artificial bee colony (ABC). To construct the models, a database collected from three granite quarry sites, located in Malaysia, was applied. The prediction values were then checked and evaluated using some statistical criteria. The results revealed that all proposed models were acceptable in predicting the flyrock. Among them, the LSSVM-WOA was a more robust model than the others and predicted the flyrock values with a high degree of accuracy.
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Bharathi, S. Lakshmi Kanthan, V. Thanigaivelan, S. Shenbagaraman, and M. Ramesh Babu. "Hybrid EVO-CFNN approach for improve voltage stability of distribution system performance using superconducting magnetic energy storages inter linked with wind turbine." Journal of Energy Storage 112 (March 2025): 115465. https://doi.org/10.1016/j.est.2025.115465.
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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 Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.
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Althobaiti, Maha M., Ahmed Almulihi, Amal Adnan Ashour, Romany F. Mansour, and Deepak Gupta. "Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans." Journal of Healthcare Engineering 2022 (January 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/2872461.
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Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur’s Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.
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Le, Tuan-Ho, Li Dai, Hyeonae Jang, and Sangmun Shin. "Robust Process Parameter Design Methodology: A New Estimation Approach by Using Feed-Forward Neural Network Structures and Machine Learning Algorithms." Applied Sciences 12, no. 6 (2022): 2904. http://dx.doi.org/10.3390/app12062904.
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In robust design (RD) modeling, the response surface methodology (RSM) based on the least-squares method (LSM) is a useful statistical tool for estimating functional relationships between input factors and their associated output responses. Neural network (NN)-based models provide an alternative means of executing input-output functions without the assumptions necessary with LSM-based RSM. However, current NN-based estimation methods do not always provide suitable response functions. Thus, there is room for improvement in the realm of RD modeling. In this study, a new NN-based RD modeling procedure is proposed to obtain the process mean and standard deviation response functions. Second, RD modeling methods based on the feed-forward back-propagation neural network (FFNN), cascade-forward back-propagation neural network (CFNN), and radial basis function network (RBFN) are proposed. Third, two simulation studies are conducted using a given true function to verify the proposed three methods. Fourth, a case study is examined to illustrate the potential of the proposed approach. In conclusion, a comparative analysis of the three feed-forward NN structure-based modeling methods and conventional LSM-based RSM proposed in this study showed that the proposed methods were significantly lower in the expected quality loss (EQL) and various variability indicators.
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Peterson, Kyle, Vasit Sagan, Paheding Sidike, Amanda Cox, and Megan Martinez. "Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine." Remote Sensing 10, no. 10 (2018): 1503. http://dx.doi.org/10.3390/rs10101503.
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Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.
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Robinson, Farrel R., Andreas Straube, and Albert F. Fuchs. "Participation of Caudal Fastigial Nucleus in Smooth Pursuit Eye Movements. II. Effects of Muscimol Inactivation." Journal of Neurophysiology 78, no. 2 (1997): 848–59. http://dx.doi.org/10.1152/jn.1997.78.2.848.
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Robinson, Farrel, R., Andreas Straube, and Albert F. Fuchs. Participation of the caudal fastigial nucleus in smooth pursuit eye movements. II. Effects of muscimol inactivation. J. Neurophysiol. 78: 848–859, 1997. We studied the effect of temporarily inactivating the caudal fastigial nucleus (CFN) in three rhesus macaques trained to make smooth pursuit eye movements. We injected the γ-aminobutyric acid A agonist muscimol into one or both CFNs where we had recorded pursuit-related neurons a few minutes earlier. Inactivating the CFN on one side impaired pursuit in one monkey so severely that it could not follow step-ramp targets moving at 20°/s, the target velocity that we used to test the other two monkeys. We tested this monkey with targets moving at 10°/s. In all three monkeys, unilateral CFN inactivation either increased the acceleration of ipsilateral step-ramp pursuit (in 2 monkeys, to 144 and 220% of normal) or decreased the acceleration of contralateral pursuit (in 1 monkey, to 71% of normal). Muscimol injected into both CFNs in two of the monkeys left both ipsilateral and contralateral acceleration nearly normal in both monkeys (101% of normal). Unilateral CFN inactivation also impaired the velocity of maintained pursuit as the monkeys tracked a target moving at a constant velocity or oscillating sinusoidally. Averaged across both types of movements in all three monkeys, gains for ipsilateral, contralateral, upward, and downward pursuit were 94, 67, 84, and 73% of normal, respectively. Unilateral CFN inactivation also impaired the monkeys' ability to suppress their vestibuloocular reflex (VOR). Averaged across the two monkeys VOR gain during suppression increased from 0.06 to 0.25 during yaw rotation and from 0.21 to 0.59 during pitch rotation. Bilateral CFN inactivation reduced pursuit gains in all directions more than unilateral injection did. In the two monkeys tested, ipsilateral, contralateral, upward, and downward gains went from 94, 86, 85, and 74% of normal, respectively, after we inactivated one CFN to 88, 73, 80, and 64% of normal after we also inactivated the second CFN. We can explain many, but not all, of the effects of CFN activation on smooth pursuit with the behavior of CFN neurons, and the assumption that the activity of each CFN neuron helps drive pursuit movements in the direction that best activates that neuron. We conclude that the CFN, like the flocculus-ventral paraflocculus, is a cerebellar region involved in control of smooth pursuit.
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Vartio, T., L. Laitinen, O. Narvanen, et al. "Differential expression of the ED sequence-containing form of cellular fibronectin in embryonic and adult human tissues." Journal of Cell Science 88, no. 4 (1987): 419–30. http://dx.doi.org/10.1242/jcs.88.4.419.
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Monoclonal mouse hybridoma antibodies were obtained for secreted cellular fibronectin (cFn) from A8387 fibrosarcoma cells. One of them, 52-DH1 (DH), reacted exclusively with cFns but not with plasma Fns (pFns) in immunoblotting and solid-phase EIA. The DH antibody also recognized thermolysin cFn fragments and beta-galactosidase-Fn fusion protein which contained the ED sequence specific to at least some forms of cFns. On the other hand, the DH antibody failed to recognize a fusion protein that was otherwise identical but lacked the ED sequence. Thus, the antigenic determinant for the DH antibody was located to the ED sequence. The DH antibody was then used to study the expression of ED sequence containing cFn (EcFn). For comparisons, another monoclonal antibody, 52BF12 (BF), recognizing equally well both pFns and cFns, was used. Immunoblotting of pFn fragments indicated that this antibody had the antigenic determinant at or close to the cell-binding site of Fn. EcFn was revealed by the DH antibody in embryonic and adult fibroblasts and in a variety of other cultured normal and malignant human cells. In embryonic tissues EcFn was abundant in developing basement membranes, as shown in foetal kidney and muscle, while in adult tissues it was confined only to endothelia of larger blood vessels. Furthermore, in embryonic tissues the capillaries showed bright EcFn-positivity not found any more in adult tissues. Human plasma contained a small quantity of EcFn, which may hence have an endothelial origin. EcFn was also prominent in the stroma of malignant tumours as well as in reactive benign conditions, such as granulation tissue and decidual cells. The results suggest that EcFn is a form of the protein which may have a particular role in developing and reactive tissues in embryos and adults.
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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 Networks (ANN) for rainfall prediction. ANN has the capacity to map different input and output patterns. The Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), Recurrent Neural Network (RNN), and Elman Neural Network (ENN) were used to predict rainfall. The dataset used for this work contains some meteorological variables such as temperature, wind speed, humidity, rainfall, visibility, and others for the year 2015-2019. Simulation results indicated that of all the proposed Neural Network (NN) models, the Elman NN model produced the best performance. We also found out that Elman NN has the best performance for the year 2018 (having the lowest RMSE, MSE, and MAE of 6.360, 40.45, and 0.54 respectively). The results indicated that NN algorithms are robust, dependable, and reliable algorithms that can be used for daily, monthly, or yearly rainfall prediction.
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Nawab, Faisal, Ag Sufiyan Abd Hamid, Ali Alwaeli, Muhammad Arif, Mohd Faizal Fauzan, and Adnan Ibrahim. "Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan." Sustainability 14, no. 13 (2022): 7945. http://dx.doi.org/10.3390/su14137945.
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Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring solar irradiation makes it impractical in every location. This study’s primary objective was to develop an artificial neural network (ANN) model for global horizontal irradiation (GHI) prediction using satellite data inputs. Three types of ANN, namely, the feed forward neural network (FFNN), cascaded forward neural network (CFNN), and Elman neural network (EMNN), were tested. The findings revealed that altitude, relative humidity, and satellite GHI are the most effective parameters, as they are present in all the best-performing models. The best model for daily GHI prediction was FFNN, which decreased daily MAPE, RMSE, and MBE by 25.4%, 0.11 kWh/m2/d, and 0.01 kWh/m2/d. The FFNN daily MAPE, RMSE, and MBE values were 7.83%, 0.49 kWh/m2/d, and 0.01 kWh/m2/d. The EMNN performed best for monthly and annual prediction, reducing monthly MAPE, RMSE, and MBE by 50.62%, 0.13 kWh/m2/d, and 0.13 kWh/m2/d, while the reduction for yearly was 91.6%, 0.11 kWh/m2/d, 0.2 kWh/m2/d. The EMNN monthly MAPE, RMSE, and MBE values were 3.36%, 0.16 kWh/m2/d, and 0.16 kWh/m2/d, while the yearly values were 0.47%, 0.18 kWh/m2/d, and 0.004 kWh/m2/d.
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Ahmed, Ahmed, Necati .., and Sandy Montajab Hazzouri. "Farmland Fertility Optimization with Deep Learning based COVID-19 Detection for Healthcare Decision Making." International Journal of Advances in Applied Computational Intelligence 5, no. 1 (2024): 29–39. http://dx.doi.org/10.54216/ijaaci.050103.
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Machine Learning (ML) and Artificial Intelligence (AI) are being employed in the fight against COVID19 by supporting the analysis of medical images, like X-rays and CT scans, to find characteristic paradigms linked with the virus. AI methods can evaluate huge volumes of data, which includes imaging data and patient medical records, for enriching the speed and precision of COVID19 diagnosis. Also, the use of ML and AI in medical imaging can aid in detecting new variants of viruses and forecasting their spread. The integration of ML and AI in COVID19 healthcare has greater potential to enhance the efficiency and accuracy of diagnoses along with that informing public health decision-making. Thus, the study proposes a Farmland Fertility Optimization Algorithm with Deep Learning based Healthcare Decision Making (FFOADL-HDM) approach for the detection of COVID19. The presented FFOADL-HDM approach emphasises the identification and classification of COVID19 using a CT scan. To achieve this, the FFOADL-HDM method exploits a modified SqueezeNet model for the generation of feature vector. Also, the hyperparameters of the modified SqueezeNet model can be selected by the use of FFOA. At last, the COVID-19 detection procedure is executed by the use of Adamax optimizer with (CFNN). The stimulation analysis of the FFOADL-HDM algorithm is studied on the SARS-CoV-2 CT image dataset from the Kaggle repository. The results highlighted the improved detection rate of the FFOADL-HDM technique over recent state of art approaches
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Wiernicka, Anna, Karolina Piwczynska, Paulina Mika-Stepkowska, Dorota Kazimierska, Piotr Socha, and Anna Rybak. "Impact of the Gut-Brain Hormonal Axis and Enteric Peptides in the Development of Food Neophobia in Children with Genetically Determined Hypersensitivity to the Bitter Taste." Gastrointestinal Disorders 4, no. 4 (2022): 237–48. http://dx.doi.org/10.3390/gidisord4040023.
Full textAbstract:
Objective: The aim of this prospective study was to determine the role of the gut-brain hormonal axis and the effect of the enteric peptides, as well as the role of genetically determined sensitivity to the bitter taste, on the development of child food neophobia (CFN). Methods: 114 children were enrolled in the study: 43 in food neophobia group (FNG), 21 In the control group (CG) and 50 in prospective group (PG). All patients were assessed with the child food neophobia scale (CFNS), underwent an oral 6-propylthiouracil (6-PROP) test, buccal swab for bitter-taste genotyping, anthropometric measurements, and were tested for serum levels of leptin, adiponectin, insulin-like growth factor-1(IGF-1), ghrelin, and neuropeptide Y (NPY), and complete blood count (CBC); measurements were taken from a blood sample after 4 h fasting. Results: Subjects from FNG were more often hypersensitive to bitter taste (6-PROP) than CG (p = 0.001). There was no correlation between the result of genetic analysis and CFNS (p = 0.197), nor the body mass index (BMI) at the age of 18–36 months (p = 0.946) found. Correlation between 6-PRO perception and genotype have not been confirmed (p = 0.064). The score of CFNS was positively related to the serum level of NPY (p = 0.03). BMI percentile was negatively related to serum level of NPY (p = 0.03), but positively related to leptin serum level (p = 0.027). Conclusions: Bitter taste sensitivity to 6-PROP plays an important role in the development of the CFN, but correlation between 6-PROP perception and genotype have not been confirmed. Children with food neophobia due to elevated serum NPY level should be constantly monitored in order to control the nutritional status at a later age.
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Sharma, Anamika Shukla, and Dr H. S. Hota. "ECG Analysis-Based Cardiac Disease Prediction Using Signal Feature Selection with Extraction Based on AI Techniques." International Journal of Communication Networks and Information Security (IJCNIS) 14, no. 3 (2022): 73–85. http://dx.doi.org/10.17762/ijcnis.v14i3.5573.
Full textAbstract:
ECG (Electrocardiogram) performs classification using a machine learning model for processing different features in the ECG signal. The electrical activity of the heart is computed with the ECG signal with machine learning library. The key issue in the handling of ECG signals is an estimation of irregularities to evaluate the health status of patients. The ECG signal evaluate the impulse waveform for the specialized tissues in the cardiac heart diseases. However, the ECG signal comprises of the different difficulties associated with waveform estimation to derive certain features. Through machine learning (ML) model the input features are computed with input ECG signals. In this paper, proposed a Noise QRS Feature to evaluate the features in the ECG signals for the effective classification. The Noise QRS Feature model computes the ECG signal features of the waveform sequences. Initially, the signal is pre-processed with the Finite Impulse response (FIR) filter for the analysis of ECG signal. The features in the ECG signal are processed and computed with the QRS signal responses in the ECG signal. The Noise QRS Feature evaluate the ECG signal with the kNN for the estimation and classification of features in the ECG signals. The performance of the proposed Noise QRS Feature features are comparatively examined with the Discrete Wavelet Transform (DWT), Dual-Tree Complex Wavelet Transforms (DTCWT) and Discrete Orthonormal Stockwell Transform (DOST) and the machine learning model Cascade Feed Forward Neural Network (CFNN), Feed Forward Neural Network (FFNN). Simulation analysis expressed that the proposed Noise QRS Feature exhibits a higher classification accuracy of 99% which is ~6 – 7% higher than the conventional classifier model.