Cascade Correlation Research Articles

Cascade correlation neural network with deterministic weight modification for predicting stock market price

A new weight updating approach is proposed for cascade correlation neural network (CCNN) in this paper. The deterministic weight modification (DWM) algorithm is used to adjust the connection weights of CCNN. The introduced new method can improve the global convergence capability of the conventional CCNN and optimally reduced the system error. The proposed DWM+CCNN prediction algorithm is applied well-known stock market dataset in order to evaluate the robustness and efficiency. The experimental results are confirmed that the proposed DWM+CCNN algorithm is achieves higher performance in terms of convergence rate and the capability of global converges.

A Subset Feature Selection Based DDoS Detection Using Cascade Correlation Optimal Neural Network for Improving Network Resources in Virtualized Cloud Environment

Cloud computing offers a technological revolution to the end-users need less infrastructure costs with virtualizes resources, and storage remains the insecure to delivers the scalability. The most common type of Distributed Denial of Service DDoS attack, (denial of service), is a serious damage measure that affects virtual cloud users and Internet Service Providers (ISPs) are predominantly affects ongoing service attacks. I'm the recipient. These legacy of machine learning approach used to detect vulnerabilities to the attacker's leading network traffic intervention opening the door. By concentrating feature selection and classification approach with optimized neural network model to detect the DDoS type monitoring. This presents a deep neural network based DDoS detection system using Subset Feature Selection based Cascade Correlation Optimal Neural Network (SFS-C2ONN). The proposed approach is based on assumptions based on flow rate which is collected as dataset previously extracted from a model for network traffic. The test results shows that the sensitivity and specify based calcification approach which is suitable for the detection of neural network architecture and hyper parameters, and the optimizer DDoS attack. The results are obtained by calculating the accuracy of the attack detection.

Competitive and complementary influence maximization in social network: A follower’s perspective

The problem of influence maximization is to select a small set of seed users in a social network to maximize the spread of influence. Recently, this problem has attracted considerable attention due to its applications in both commercial and social fields, such as product promotion, contagion prevention and public opinion forecasting. Most of prior work focuses on the diffusion model of single propagating entity, purely-complementary entities or purely-competitive entities. However, in reality, the influence diffusion in the social network is certainly more general, involving multiple propagating entities, which are competitive or complementary rather than single entity, purely-complementary entities or purely-competitive entities.In this paper, we consider the problem that a company (follower) intends to promote a new product into the market by maximizing the influence of a social network, where multiple competitive and complementary products have been spreading. We propose a Competitive and Complementary Independent Cascade (CCIC) diffusion model, and propose a novel optimization problem, follower-based influence maximization that aims to select top-K influential nodes as seed nodes, which can maximize the influence of a social network where multiple competitive and complementary products have already been propagated. To solve follower-based influence maximization problem, we propose a Deep Recursive Hybrid model (DRH) and an approximation algorithm (DRHGA). The DRH model dynamically tracks entity correlations, cascade correlations, causalities between ratings and next-period adoption through a deep recursive network and computes influence probabilities between nodes on target product. Then, with the influence probabilities predicted through DRH model, the DRHGA algorithm can efficiently find the seed node set for the target product under the CCIC diffusion model. Experimental results conducted on several public datasets show that our method outperforms the state-of-the-art methods on prediction accuracy and efficiency.

Targeting pivotal inflammatory pathways in COVID-19: A mechanistic review

As an emerging global health crisis, coronavirus disease 2019 (COVID-19) has been labeled a worldwide pandemic. Growing evidence is revealing further pathophysiological mechanisms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Amongst these dysregulated pathways inflammation seems to play a more critical role toward COVID-19 complications. In the present study, precise inflammatory pathways triggered by SARS-CoV-2, along with potential therapeutic candidates have been discussed.Prevailing evidence has indicated a close correlation of inflammatory cascades with severity, pathological progression, and organ damages in COVID-19 patients. From the mechanistic point of view, interleukin-6, interleukin-1β receptor, interferon-gamma, tumor necrosis factor-alpha receptor, toll-like receptor, receptor tyrosine kinases, growth factor receptor, Janus kinase/signal transducers and transcription pathway, mammalian target of rapamycin, cytokine storm and macrophage activation have shown to play critical roles in COVID-19 complications. So, there is an urgent need to provide novel mechanistic-based anti-inflammatory agents. This review highlights inflammatory signaling pathways of SARS-CoV-2. Several therapeutic targets and treatment strategies have also been provided in an attempt to tackle COVID-19 complications.

Cascade neural network algorithm with analytical connection weights determination for modelling operations and energy applications

The performance and learning speed of the Cascade Correlation neural network (CasCor) may not be optimal because of redundant hidden units’ in the cascade architecture and the tuning of connection weights. This study explores the limitations of CasCor and its variants and proposes a novel constructive neural network (CNN). The basic idea is to compute the input connection weights by generating linearly independent hidden units from the orthogonal linear transformation, and the output connection weights by connecting hidden units in a linear relationship to the output units. The work is unique in that few attempts have been made to analytically determine the connection weights on both sides of the network. Experimental work on real energy application problems such as predicting powerplant electrical energy, predicting seismic hazards to prevent fatal accidents and reducing energy consumption by predicting building occupancy detection shows that analytically calculating the connection weights and generating non-redundant hidden units improves the convergence of the network. The proposed CNN is compared with that of the state-of-the-art machine learning algorithms. The work demonstrates that proposed CNN predicts a wide range of applications better than other methods.

Аналіз існуючих варіантів класифікації хворих на серцево-судинними захворюваннями за допомогою нейронними мережами

Робота присвячена аналізу інформаційних технологій хронобіологічного моніторингу кардіосистем, розробці систему підтримки прийняття рішень для лікаря-дослідника на базі методів класифікації з використанням нейронних мереж таких як імовірностна неронна мережа PNN (Probabilistic Neural Networks), багатошаровий персептрон MLP NN (Multi-Layer Perceptron), каскадно-кореляційна мережа CasCor (Cascade Correlation). У результаті отримано наступне: найкращим класифікатором є нейромережа каскадної кореляції з 85-88% точністю класифікації. Найгіршим класифікатором стала ймовірнісна нейронна мережа, оскільки точність цього алгоритму залежить від розміру набору даних.

Recurrent neural variational model for follower-based influence maximization

Influence Maximization, aiming at selecting a small set of seed users in a social network to maximize the spread of influence, has attracted considerable attention recently. Most of the existing influence maximization algorithms focus on the diffusion model of one single-entity, which assumes that only one entity is propagated by users in social network. However, the diffusion situations in real world social networks often involve multiple entities, competitive or complementary, spreading through the whole network, and are more complex than the situations of single independent entity.In this paper, we propose a novel optimization problem, namely, the follower-based influence maximization, which aims to promote a new product into the market by maximizing the influence of a social network where other competitive and complementary products have already been propagating. We tackle this problem by proposing a Recurrent Neural Variational model (RNV) and a follower-based greedy algorithm (RNVGA). The RNV model dynamically tracks entity correlations and cascade correlations through a deep generative model and recurrent neural variational inference, while the RNVGA algorithm applies the greedy approach for submodular maximization and efficiently computes the seed node set for the target product. Extensive experiments have been conducted to evaluate effectiveness and efficiency of our method, and the results show the superiority of our method compared with the state-of-the-art methods.

Comparative Assessment of Reference Evapotranspiration Estimation Using Conventional Method and Machine Learning Algorithms in Four Climatic Regions

Reference evapotranspiration (ETo) is considered as an essential component in hydrological and agro meteorological processes. Its accurate estimation becomes an imperative in the planning and management of irrigation practices. ETo estimation also plays a vital role in improving the irrigation efficiency, water reuse and irrigation scheduling. The conventional physical model of Penmen Montieth (PM56) developed by Food and agriculture organization (FAO) has been recommended worldwide for ETo estimation. This model was firstly used in this study to determine ETo by using required meteorological data and obtained results used as referenced values. Afterward, five data machine learning algorithms/data driven models, support vector machine (SVM), multilayer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN) and cascade correlation neural network (CCNN), were applied to estimate ETo values. The climatic data of maximum and minimum temperatures, wind speed, average relative humidity and sunshine hours of six stations from Pakistan was used to train and test data driven model. Data driven models were also applied on other climatic stations without training data which lie in China, New Zealand and USA to further validate and investigate their performance. Comparison results indicated that model efficiency (ME) and correlation coefficient (r) of SVM were obtained (ranges: ME = 95–99%; r = 0.96–1) maximum for all the selected stations. Alternatively, model errors (RMSE = 0.016, MSE = 0.0001 & MAE = 0.08) for SVM were found minimum in comparison to GMDH, MLP, CCNN and GRNN. In addition, all data driven models show enough divergence from hyper arid to high humid climate except SVM which shows almost identical results for all the climatic zones in comparison to standard FAO-PM56 method. Finally, it can be concluded that SVM could be considered as a reliable alternative method for ETo estimation among data driven models.

Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia

Accurately predicting river flows over daily timescales is considered as an important task for sustainable management of freshwater ecosystems, agricultural applications, and water resources management. In this research paper, artificial intelligence (AI) techniques, namely the cascade correlation neural networks (CCNN) and the random forest (RF) models, were employed in daily river stage and river flow prediction for two river systems (i.e., Dulhunty River and Herbert River) in Australia. To develop the CCNN and RF models, a significant 3-day antecedent river stage and river flow time series were used. 80% of the whole data were used for model training and the remaining 20% for model testing. A total of ten different model structures with different input combinations were used to evaluate the optimal model in the training phase, and the results were analyzed using statistical metrics including the root mean square error (RMSE), Nash–Sutcliffe coefficient (NS), Willmott’s index of agreement (WI), and Legate and McCabe’s index (ELM) in the testing phase. The inter-comparison of CCNN and RF models for both river systems showed that the CCNN model was able to generate a more accurate prediction of the river stage and river flow compared to the RF model. Due to hydro-geographic differences leading to a different underlying historical data characteristics, the optimal CCNN’s performance for the Dulhunty River was found to be most accurate, in terms of ELM = 0.779, WI = 0.964, and ENS = 0.862 versus 0.775, 0.968, and 0.885 for the Herbert River. Following the performance accuracies, the authors ascertained that the CCNN model can be taken as a preferred data intelligent tool for river stage and river flow prediction.

Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

ABSTRACT Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation – the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM) – were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R 2 = .92), and with all variables as inputs at Station II (R 2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.

Pruned Cascade Neural Network Image Classification

In this paper we propose a new model of deep neural network to build in deeper network. The convoluational neural network is one of the leading Image classification problem. The vanishing gradient problem requires us to use small learning rate with gradient descent which needs many small steps to converge and its take long time to proceed . By using GPU we can process more than one dataset (CIFAR-100) in a particular session. To overcome vanishing gradient problem by using the prune cascade correlation neural network learning algorithm compared to the deep cascade learning in CNN architecture. We improve the filter size, to reduce to the problem by training algorithm that trains in the network from bottom to top approach and its performing attain the task for better image classification in Google Net. We reduce the time complexity (training time ), storage capacity can be used pre training algorithm. Index Terms: Deep Learning, Vanishing gradient, GoogleNet, CNN, Image classification.

Can Decomposition Approaches Always Enhance Soft Computing Models? Predicting the Dissolved Oxygen Concentration in the St. Johns River, Florida

This study evaluates standalone and hybrid soft computing models for predicting dissolved oxygen (DO) concentration by utilizing different water quality parameters. In the first stage, two standalone soft computing models, including multilayer perceptron (MLP) neural network and cascade correlation neural network (CCNN), were proposed for estimating the DO concentration in the St. Johns River, Florida, USA. The DO concentration and water quality parameters (e.g., chloride (Cl), nitrogen oxides (NOx), total dissolved solid (TDS), potential of hydrogen (pH), and water temperature (WT)) were used for developing the standalone models by defining six combinations of input parameters. Results were evaluated using five performance criteria metrics. Overall results revealed that the CCNN model with input combination III (CCNN-III) provided the most accurate predictions of DO concentration values (root mean square error (RMSE) = 1.261 mg/L, Nash-Sutcliffe coefficient (NSE) = 0.736, Willmott’s index of agreement (WI) = 0.919, R2 = 0.801, and mean absolute error (MAE) = 0.989 mg/L) for the standalone model category. In the second stage, two decomposition approaches, including discrete wavelet transform (DWT) and variational mode decomposition (VMD), were employed to improve the accuracy of DO concentration using the MLP and CCNN models with input combination III (e.g., DWT-MLP-III, DWT-CCNN-III, VMD-MLP-III, and VMD-CCNN-III). From the results, the DWT-MLP-III and VMD-MLP-III models provided better accuracy than the standalone models (e.g., MLP-III and CCNN-III). Comparison of the best hybrid soft computing models showed that the VMD-MLP-III model with 4 intrinsic mode functions (IMFs) and 10 quadratic penalty factor (VMD-MLP-III (K = 4 and α = 10)) model yielded slightly better performance than the DWT-MLP-III with Daubechies-6 (D6) and Symmlet-6 (S6) (DWT-MLP-III (D6 and S6)) models. Unfortunately, the DWT-CCNN-III and VMD-CCNN-III models did not improve the performance of the CCNN-III model. It was found that the CCNN-III model cannot be used to apply the hybrid soft computing modeling for prediction of the DO concentration. Graphical comparisons (e.g., Taylor diagram and violin plot) were also utilized to examine the similarity between the observed and predicted DO concentration values. The DWT-MLP-III and VMD-MLP-III models can be an alternative tool for accurate prediction of the DO concentration values.

Estimation of a ground motion model for induced events by Fahlman's Cascade Correlation Neural Network

In this paper, a ground motion model (GMM) estimation by Fahlman's Cascade Correlation Neural Network is proposed. We start with a linearised regression estimator and improve the GMM for the Legnica-Głogów Copper District in Poland by adding levels with nonlinear neurons. This gradually leads to the creation of a deep Artificial Neural Network GMM. The paper focuses on the selection of criteria for stopping the development of the network, including minimising the prediction risk, which is defined as the estimation of the quality of the GMM in future observations. Therefore, the solution presented here only applies to the simplest case, which considers only magnitude and distance.

Prediction of the Stock Price Using Fuzzy Cascade Correlation Neural Networks

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Beyond feature integration: a coarse-to-fine framework for cascade correlation tracking

Discriminative correlation filters (DCF) have achieved enormous popularity in the tracking community. Recently, the performance advancement in DCF-based trackers is predominantly driven by the use of convolutional features. In pursuit of extreme tracking performance, state-of-the-art trackers (e.g., cascade correlation tracking [1] and HCF [2]) equip DCF with hierarchical convolutional features to capture both semantics and spatial details of the target appearance. While such methods have been shown to work well, multiple feature integration results in high model complexity which significantly increases the over-fitting risk and computational burden. In this paper, we present a coarse-to-fine framework for cascade correlation tracking (CCT). Instead of integrating hierarchical features, this framework decomposes a complicated tracker into two low-complexity modules, a coarse tracker $${\mathcal {C}}$$ and a refined tracker $${\mathcal {R}}$$ , working in a coarse-to-fine manner. The coarse tracker $${\mathcal {C}}$$ employs low-resolution semantic convolutional features extracted from a large search area to cope with large target displacement and appearance change between adjacent frames. By contrast, the refined tracker $${\mathcal {R}}$$ employs high-resolution handcraft features extracted from a small search area to further refine the coarse location of $${\mathcal {C}}$$ . Our CCT tracker enjoys the strong discriminative power of $${\mathcal {C}}$$ and the high efficiency of $${\mathcal {R}}$$ . Experiments on the OTB2013 and TC128 benchmarks show that CCT performs favorably against state-of-the-art trackers.

Hybrid Framework using Data Mining Techniques for Early Detection and Prevention of Oral Cancer

This paper presents the usage of classification and association data mining techniques for early detection and prevention of oral cancer. The indigenous dataset of 1,025 patients who visited a tertiary care centre during 2004 to 2009 was used for the research. Ten classification data mining models are designed using varied types of data mining techniques like regression analysis, classification trees and neural networks. Regression analysis models are linear regression model and logistic regression model; classification tree models are decision tree model, decision tree forest model and TreeBoost model and artificial neural networks are multilayer perceptron model, radial basis function model, group method of data handling model, cascade correlation model and probabilistic - general regression neural network model. Association rules are generated using apriori algorithm. The classification models and association rules are evaluated using various estimation parameters. Finally, a hybrid oral cancer management system is designed using the classification model and the association rules.

AN AUTOMATED COMPUTER-AIDED DETECTION (CADe) AND DIAGNOSIS (CADx) SYSTEM FOR BREAST MICROCALCIFICATIONS IN MAMMOGRAMS

ABSTRACT An automated computer aided diagnosis system has been proposed for detection of microcalcification (MC) clusters in mammograms. The proposed system is a whole system including suspicious regions identification, MCs detection, false positive reduction and benign/malign classification. For classification of suspicious microcalcification regions, a multilayer perceptron (MLP) neural network was used with grey level co-occurrence matrix (GLCM) and statistical features. Then to decrease the false positive classification ratio, we used cascade correlation neural network (CCNN) with grey level run length matrix (GLRLM) features. In the last step, hybrid form of discriminant analysis and support vector machine (SVM) methods were used with GLRLM features for benign/malign classification of detected MC clusters. The open access Mammographic Image Analysis Society (MIAS) database was used for the study. Experimental results show that the proposed algorithm obtained 86% sensitivity, 98.3% specificity and 1.163 FPpI rates for detection an for diagnosis of breast cancer, the obtained sensitivity and specificity values are 100% and 100% respectively. Despite the vision difficulty of MC clusters, the novel system provides very satisfactory results. Furthermore, the developed system is fully automatic whole system which gives outputs as percentages and transformed assessment categories. Keywords: Mammograms, Breast cancer, Computer aided diagnosis, Cascade correlation neural network (CCNN), Grey level co-occurrence matrix ( GLCM), Grey level run length matrix (GLRLM).

Deep Cascade Learning.

In this paper, we propose a novel approach for efficient training of deep neural networks in a bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep cascade learning, is motivated by the cascade correlation approach of Fahlman and Lebiere, who introduced it in the context of perceptrons. We demonstrate our algorithm on networks of convolutional layers, though its applicability is more general. Such training of deep networks in a cascade directly circumvents the well-known vanishing gradient problem by ensuring that the output is always adjacent to the layer being trained. We present empirical evaluations comparing our deep cascade training with standard end-end training using back propagation of two convolutional neural network architectures on benchmark image classification tasks (CIFAR-10 and CIFAR-100). We then investigate the features learned by the approach and find that better, domain-specific, representations are learned in early layers when compared to what is learned in end-end training. This is partially attributable to the vanishing gradient problem that inhibits early layer filters to change significantly from their initial settings. While both networks perform similarly overall, recognition accuracy increases progressively with each added layer, with discriminative features learned in every stage of the network, whereas in end-end training, no such systematic feature representation was observed. We also show that such cascade training has significant computational and memory advantages over end-end training, and can be used as a pretraining algorithm to obtain a better performance.

Real Time Determination of Rechargeable Batteries’ Type and the State of Charge via Cascade Correlation Neural Network

Batteries are used to store electrical energy as chemical energy. They have a wide using area from portable equipment to electric vehicles. It is important to know the state of charge of a battery to use it efficiently. In this study, a graphical user interface is developed using a visual programming language to monitor the electrical situations of batteries. Cascade neural network, which is one of the most chosen artificial neural networks, is used to determine the type and state of charge of batteries. The software is able to identify type and state of charge of batteries online. Lead acid, Lithium Ion, Lithium polymer, Nickel Cadmium, Nickel Metal Hydride rechargeable batteries are used in experiments. The experimental results indicate that accurate estimation results can be obtained by the proposed method.DOI: http://dx.doi.org/10.5755/j01.eie.24.1.20150

Reliability Worth Analysis of Distribution Systems Using Cascade Correlation Neural Networks

Reliability worth analysis is of great importance in the area of distribution network planning and operation. The reliability worth's precision can be affected greatly by the customer interruption cost model used. The choice of the cost models can change system and load point reliability indices. In this study, a cascade correlation neural network is adopted to further develop two cost models comprising a probabilistic distribution model and an average or aggregate model. A contingency-based analytical technique is adopted to conduct the reliability worth analysis. Furthermore, the possible effects of adding distributed generation units into the network are evaluated. The proposed approach has been tested on a radial distribution test network evaluating the reliability worth. The results show that the probabilistic distribution model provides a more realistic model for the reliability analysis.