scholarly journals Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes

2021 ◽  
Vol 12 (2) ◽  
pp. 32-50
Author(s):  
Anand Kumar Srivastava ◽  
Yugal Kumar ◽  
Pradeep Kumar Singh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Artificial Bee Colony ◽  
Diagnostic Tools ◽  
Diagnostic Model ◽  
Learning Approaches ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Diagnosis

A large number of machine learning approaches are implemented in healthcare field for effective diagnosis and prediction of different diseases. The aim of these machine learning approaches is to build automated diagnostic tool for helping the physician as well as monitor the health status of patients. These diagnostic tools are widely adopted in intensive care unit for life expectancy of patients. In this study, an effort is made to design an automated diagnostic model for the diagnosis and prediction of diabetes patients. The proposed diagnostic model is designed using artificial bee colony (ABC) algorithm and deep neural network (DNN) technique, called ABC-DNN-based diagnostic model. The ABC algorithm is applied to determine the relevant features for diabetes prediction and diagnosis while DNN technique is adopted for the prediction and diagnosis of diabetes affected patients. The performance of proposed diagnostic model is tested over Pima Indian Diabetes dataset and evaluated using accuracy, sensitivity, specificity, F-measure, Kappa, and area under curve (AUC) parameters. Further, 10-fold and 50-50% training-testing method are considered to assess the performance of proposed diagnostic model. The experimental results of proposed ABC-DNN model is compared with DNN technique and several existing diabetes studies. It is observed that proposed ABC-DNN model achieves 94.74% accuracy rate using 10-fold method.

scholarly journals Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data

2021 ◽  
Vol 12 (5) ◽  
pp. 67-83
Author(s):  
Ajay Dev ◽  
Sanjay Kumar Malik
Keyword(s):  
Neural Network ◽  
Medical Imaging ◽  
Deep Neural Network ◽  
Artificial Bee Colony ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Diagnostic Tools ◽  
Healthcare Data ◽  
Bee Colony ◽  
Learning Techniques

The healthcare domain gets wide attention among the research community due to incremental data growth, advanced diagnostic tools, medical imaging processes, and many more. Enormous healthcare data is generated through diagnostic tool and medical imaging process, but handling of these data is a tough task due to its nature. A large number of machine learning techniques are presented for handling the healthcare data and right diagnosis of disease. However, the accuracy is one of primary concerns regarding the disease diagnosis. Hence, this study explores the applicability of deep neural network (DNN) technique for handling the imbalance of healthcare data. An artificial bee colony technique is adopted to determine the relevant features of stroke disease called ABC-FS-optimized DNN. The performance of proposed ABC-FS-optimized DNN model is evaluated using accuracy, precision, and recall parameters and compared with state of art existing techniques. The simulation results showed that proposed model obtains 87.09%, 84.28%, and 85.72% accuracy, precision, and recall rates, respectively.

Functional Link Neural Network with Modified Artificial Bee Colony for Data Classification

2017 ◽  
Vol 13 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Tutut Herawan ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Artificial Bee Colony ◽  
Classification Problem ◽  
Functional Link ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Learning Scheme ◽  
Feed Forward Network ◽  
Functional Link Neural Network

Functional Link Neural Network (FLNN) has emerged as an important tool for solving non-linear classification problem and has been successfully applied in many engineering and scientific problems. The FLNN structure is much more modest than ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights for training. However, the standard Backpropagation (BP) learning uses for FLNN training prone to get trap in local minima which affect the FLNN classification performance. To recover the BP-learning drawback, this paper proposes an Artificial Bee Colony (ABC) optimization with modification on bee foraging behaviour (mABC) as an alternative learning scheme for FLNN. This is motivated by good exploration and exploitation capabilities of searching optimal weight parameters exhibit by ABC algorithm. The result of the classification accuracy made by FLNN with mABC (FLNN-mABC) is compared with the original FLNN architecture with standard Backpropagation (BP) (FLNN-BP) and standard ABC algorithm (FLNN-ABC). The FLNN-mABC algorithm provides better learning scheme for the FLNN network with average overall improvement of 4.29% as compared to FLNN-BP and FLNN-ABC.

scholarly journals Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian’qiang He ◽  
Naian Liu ◽  
Mei’lin Han ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Abc Algorithm ◽  
Bee Colony

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

Methodology for synthesis of neural network diagnostic models of complex technical objects

2020 ◽  
Vol 6 (4) ◽  
pp. 466-483
Author(s):  
V. V. Grachev ◽  
◽  
A. V. Grishchenko ◽  
V. A. Kruchek ◽  
F. Yu. Bazilevsky ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Test Sample ◽  
Learning Problems ◽  
Current Status ◽  
Design Stage ◽  
Diagnostic Tools ◽  
Diagnostic Model ◽  
Technical Diagnostics ◽  
Diagnostic Models

Despite the vast experience of using the neural networks for solving various machine learning problems, the numerous attempts to use them in technical diagnostics have not yet led to complete solutions so far (with rare exceptions). The reason is the specific nature of technical diagnostics that distinguishes such tasks from traditional machine learning problems. Having analyzed these specific features, the authors propose an approach to diagnosing complex technical objects that is focused on the use in built-in diagnostics systems and is based on the neural network reference diagnostic models of functionally isolated nodes and assemblies. The article describes the methodology for the synthesis of such models, their training on the data obtained by monitoring the object being tested using built-in diagnostic tools, determining the permissible response errors, and adapting to the current status of the object. The fuzzification of the diagnostic model results using the test sample proposed in the article makes it possible to standardize the approach to diagnosing complex technical objects designed for various purposes. The use of D. Trigg’s tracking control signal proposed by the authors to monitor regression residuals during the learning increases the training quality and generalization ability of models. The value of this signal determined by the model run on a test sample is an additional informative diagnostic parameter that increases the accuracy of classifying the status of the object under test. The proposed methodology applied at the complex technical object design stage allows optimizing the monitored parameters’ array and multiplying the efficiency of the diagnostic information recorded by the built-in diagnostic and monitoring tools.

scholarly journals Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Bai Li
Keyword(s):  
Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Gold Price ◽  
Selection Strategy ◽  
Price Forecasting ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Previous Cycle ◽  
Subsequent Cycle

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.

Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

2022 ◽  
pp. 728-748
Author(s):  
Gummadi Srinivasa Rao ◽  
Y. P. Obulesh ◽  
B. Venkateswara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Load Flow ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.

Enrichment of Distribution System Stability Through Artificial Bee Colony Algorithm and Artificial Neural Network

2019 ◽  
pp. 35-55
Author(s):  
Gummadi Srinivasa Rao ◽  
Y. P. Obulesh ◽  
B. Venkateswara Rao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Load Flow ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this chapter, an amalgamation of artificial bee colony (ABC) algorithm and artificial neural network (ANN) approach is recommended for optimizing the location and capacity of distribution generations (DGs) in distribution network. The best doable place in the network has been approximated using ABC algorithm by means of the voltage deviation, power loss, and real power deviation of load buses and the DG capacity is approximated by using ANN. In this, single DG and two DGs have been considered for calculation of doable place in the network and capacity of the DGs to progress the voltage stability and reduce the power loss of the system. The power flow of the system is analyzed using iterative method (The Newton-Raphson load flow study) from which the bus voltages, active power, reactive power, power loss, and voltage deviations of the system have been achieved. The proposed method is tested in MATLAB, and the results are compared with particle swarm optimization (PSO) algorithm, ANN, and hybrid PSO and ANN methods for effectiveness of the proposed system.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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