Election Algorithm for Random k Satisfiability in the Hopfield Neural Network

Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm, inspired by the presidential election model conducted globally. In this research, we will investigate the effect of Bipolar EA in enhancing the learning processes of a Hopfield Neural Network (HNN) to generate global solutions for Random k Satisfiability (RANkSAT) logical representation. Specifically, this paper utilizes a bipolar EA incorporated with the HNN in optimizing RANkSAT representation. The main goal of the learning processes in our study is to ensure the cost function of RANkSAT converges to zero, indicating the logic function is satisfied. The effective learning phase will affect the final states of RANkSAT and determine whether the final energy is a global minimum or local minimum. The comparison will be made by adopting the same network and logical rule with the conventional learning algorithm, namely, exhaustive search (ES) and genetic algorithm (GA), respectively. Performance evaluation analysis is conducted on our proposed hybrid model and the existing models based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), and Mean Absolute Error (MAPE). The result demonstrates the capability of EA in terms of accuracy and effectiveness as the learning algorithm in HNN for RANkSAT with a different number of neurons compared to ES and GA.

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Modified election algorithm in hopfield neural network for optimal random k satisfiability representation

International Journal for Simulation and Multidisciplinary Design Optimization ◽

10.1051/smdo/2020008 ◽

2020 ◽

Vol 11 ◽

pp. 16

Author(s):

Hamza Abubakar ◽

Shamsul Rijal Muhammad Sabri ◽

Sagir Abdu Masanawa ◽

Surajo Yusuf

Keyword(s):

Neural Network ◽

Mean Absolute Error ◽

Search Algorithm ◽

Optimal Solution ◽

Information Criterion ◽

Hopfield Neural Network ◽

Absolute Error ◽

Current Standard ◽

Election Algorithm ◽

Logical Representation

Election algorithm (EA) is a novel metaheuristics optimization model motivated by phenomena of the socio-political mechanism of presidential election conducted in many countries. The capability and robustness EA in finding an optimal solution to optimization has been proven by various researchers. In this paper, modified version of EA has been utilized in accelerating the searching capacity of Hopfield neural network (HNN) learning phase for optimal random-kSAT logical representation (HNN-R2SATEA). The utility of the proposed approach has been contrasted with the current standard exhaustive search algorithm (HNN-R2SATES) and the newly developed algorithm HNN-R2SATICA. From the analysis obtained, it has been clearly shown that the proposed hybrid computational model HNN-R2SATEA outperformed other existing model in terms of global minima ratio (Zm), mean absolute error (MAE), Bayesian information criterion (BIC) and execution time (ET). The finding portrays that the MEA algorithm surpassed the other two algorithms for optimal random-kSAT logical representation.

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Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

10.20944/preprints202001.0365.v1 ◽

2020 ◽

Author(s):

Hamza Abubakar ◽

Saratha Sathasivam ◽

Mohd. Asyraf Mansor ◽

Mohd Shareduwan Mohd Kasihmuddin

Keyword(s):

Neural Network ◽

Bayesian Information Criterion ◽

Mean Absolute Error ◽

Logic Program ◽

Information Criterion ◽

Hopfield Neural Network ◽

Absolute Error ◽

Global Minima ◽

Expected Outcome ◽

Election Algorithm

Election Algorithm (EA) is a powerful metaheuristics model motivated by phenomena of the socio-political mechanism of the presidential election conducted in many countries. EA is selected as a topic of discussion due to its capability and robustness to carry out complex problems in the random-2SAT logic program. This paper utilizes a hybridized EA assimilated with the Hopfield neural network (HNN) in carrying out random logic program (HNN-R2SATEA). The efficiency of the proposed method was compared with the existing traditional exhaustive search (HNN-R2SATES) model and the recently introduced HNN-R2SATICA model. From the result obtained, clearly proven that based on our proposed hybrid model outperformed other existing model based on the Global Minima Ratio (ZM), Mean Absolute Error (MAE), Bayesian Information Criterion (BIC) and Execution Time (ET). The expected outcome portrays that the EA algorithm outperformed the other two algorithms in doing random-kSAT logic program. The results proved the robustness, effectiveness, and compatibility of the HNN-R2SATEA model.

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Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽

10.3390/vibration4020022 ◽

2021 ◽

Vol 4 (2) ◽

pp. 341-356

Author(s):

Jessada Sresakoolchai ◽

Sakdirat Kaewunruen

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Mean Absolute Error ◽

Absolute Error ◽

Machine Learning Techniques ◽

Rolling Stock ◽

Raw Data ◽

Learning Techniques ◽

Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

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Novel Hopfield Neural Network Model with Election Algorithm for Random 3 Satisfiability

Processes ◽

10.3390/pr9081292 ◽

2021 ◽

Vol 9 (8) ◽

pp. 1292

Author(s):

Muna Mohammed Bazuhair ◽

Siti Zulaikha Mohd Jamaludin ◽

Nur Ezlin Zamri ◽

Mohd Shareduwan Mohd Kasihmuddin ◽

Mohd. Asyraf Mansor ◽

...

Keyword(s):

Neural Network ◽

Performance Metrics ◽

Hopfield Neural Network ◽

Higher Order ◽

Research Field ◽

Learning Phase ◽

Logical Rule ◽

Election Algorithm ◽

Logical Rules ◽

Retrieval Phase

One of the influential models in the artificial neural network (ANN) research field for addressing the issue of knowledge in the non-systematic logical rule is Random k Satisfiability. In this context, knowledge structure representation is also the potential application of Random k Satisfiability. Despite many attempts to represent logical rules in a non-systematic structure, previous studies have failed to consider higher-order logical rules. As the amount of information in the logical rule increases, the proposed network is unable to proceed to the retrieval phase, where the behavior of the Random Satisfiability can be observed. This study approaches these issues by proposing higher-order Random k Satisfiability for k ≤ 3 in the Hopfield Neural Network (HNN). In this regard, introducing the 3 Satisfiability logical rule to the existing network increases the synaptic weight dimensions in Lyapunov’s energy function and local field. In this study, we proposed an Election Algorithm (EA) to optimize the learning phase of HNN to compensate for the high computational complexity during the learning phase. This research extensively evaluates the proposed model using various performance metrics. The main findings of this research indicated the compatibility and performance of Random 3 Satisfiability logical representation during the learning and retrieval phase via EA with HNN in terms of error evaluations, energy analysis, similarity indices, and variability measures. The results also emphasized that the proposed Random 3 Satisfiability representation incorporates with EA in HNN is capable to optimize the learning and retrieval phase as compared to the conventional model, which deployed Exhaustive Search (ES).

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Hybrid Imperialistic Competitive Algorithm Incorporated with Hopfield Neural Network for Robust 3 Satisfiability Logic Programming

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v8.i2.pp144-155 ◽

2019 ◽

Vol 8 (2) ◽

pp. 144 ◽

Cited By ~ 1

Author(s):

Vigneshwer Kathirvel ◽

Mohd. Asyraf Mansor ◽

Mohd Shareduwan Mohd Kasihmuddin ◽

Saratha Sathasivam

Keyword(s):

Neural Network ◽

Logic Programming ◽

Computation Time ◽

Imperialist Competitive Algorithm ◽

Hopfield Neural Network ◽

Absolute Error ◽

Training Algorithm ◽

Competitive Algorithm ◽

Bayesian Criterion ◽

Expected Outcome

Imperialist Competitive algorithm (ICA) is a robust training algorithm inspired by the socio-politically motivated strategy. This paper focuses on utilizing a hybridized ICA with Hopfield Neural Network on a 3-Satisfiability (3-SAT) logic programming. Eventually the performance of the proposed algorithm will be compared to other 2 algorithms, which are HNN-3SATES (ES) and HNN-3SATGA (GA). The performance shall be evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squares Error (SSE), Schwarz Bayesian Criterion (SBC), Global Minima Ratio and Computation Time (CPU time). The expected outcome will portray that the IC algorithm will outperform the other two algorithms in doing 3-SAT logic programming.

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A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram

Journal of Healthcare Engineering ◽

10.1155/2018/7804243 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 15

Author(s):

Ludi Wang ◽

Wei Zhou ◽

Ying Xing ◽

Xiaoguang Zhou

Keyword(s):

Neural Network ◽

Blood Pressure ◽

Mean Absolute Error ◽

Wearable Sensors ◽

Absolute Error ◽

Noninvasive Estimation ◽

Treatment Of Hypertension ◽

The Mean ◽

Artificial Neural Network Ann ◽

Blood Pressure Estimation

The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

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On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression

IEEE Signal Processing Letters ◽

10.1109/lsp.2020.3016837 ◽

2020 ◽

Vol 27 ◽

pp. 1485-1489 ◽

Cited By ~ 1

Author(s):

Jun Qi ◽

Jun Du ◽

Sabato Marco Siniscalchi ◽

Xiaoli Ma ◽

Chin-Hui Lee

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Mean Absolute Error ◽

Absolute Error

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Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach

Entropy ◽

10.3390/e22060596 ◽

2020 ◽

Vol 22 (6) ◽

pp. 596 ◽

Cited By ~ 3

Author(s):

Nur Ezlin Zamri ◽

Mohd. Asyraf Mansor ◽

Mohd Shareduwan Mohd Kasihmuddin ◽

Alyaa Alway ◽

Siti Zulaikha Mohd Jamaludin ◽

...

Keyword(s):

Neural Network ◽

Data Science ◽

Clonal Selection ◽

Data Extraction ◽

Hopfield Neural Network ◽

Clonal Selection Algorithm ◽

Selection Algorithm ◽

Proposed Model ◽

Artificial Neural Network Ann ◽

Logical Representation

Amazon.com Inc. seeks alternative ways to improve manual transactions system of granting employees resources access in the field of data science. The work constructs a modified Artificial Neural Network (ANN) by incorporating a Discrete Hopfield Neural Network (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic to initiate an Artificial Intelligence (AI) model that executes optimization tasks for industrial data. The selection of 3-SAT logic is vital in data mining to represent entries of Amazon Employees Resources Access (AERA) via information theory. The proposed model employs CSA to improve the learning phase of DHNN by capitalizing features of CSA such as hypermutation and cloning process. This resulting the formation of the proposed model, as an alternative machine learning model to identify factors that should be prioritized in the approval of employees resources applications. Subsequently, reverse analysis method (SATRA) is integrated into our proposed model to extract the relationship of AERA entries based on logical representation. The study will be presented by implementing simulated, benchmark and AERA data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperformed the other existing methods in AERA data extraction.

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Use of artificial intelligence for estimating cost of integral bridges

Journal of the Croatian Association of Civil Engineers ◽

10.14256/jce.2831.2019 ◽

2021 ◽

Vol 73 (03) ◽

pp. 265-273

Author(s):

Stjepan Lakusic

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Cost Estimation ◽

Construction Projects ◽

Mean Absolute Error ◽

Absolute Error ◽

Estimation Model ◽

Construction Costs ◽

The Mean ◽

Early Phases

Estimation of costs is important in every phase of realisation of construction projects. However, the influence of cost estimation is the highest in early phases as it is then that the decision about accepting the job or withdrawing from the project is made. The quantity of data available in initial phases of the project is smaller compared to subsequent phases, which affects accuracy of cost estimation in such early phases. A research making use of artificial intelligence to estimate construction costs of integral road bridges is presented in the paper. The estimation model is prepared by means of neural networks. The best neural network model has proven to be highly accurate in the estimation of costs based on the mean absolute error, which amounts to 13.40 %.

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Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China

10.1101/2020.03.23.20041608 ◽

2020 ◽

Cited By ~ 13

Author(s):

Chiou-Jye Huang ◽

Yung-Hsiang Chen ◽

Yuxuan Ma ◽

Ping-Huan Kuo

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Deep Neural Network ◽

Mean Absolute Error ◽

Absolute Error ◽

Forecasting Model ◽

Cumulative Number ◽

Mean Square ◽

Multiple Input ◽

Network Method

AbstractCOVID-19 is spreading all across the globe. Up until March 23, 2020, the confirmed cases in 173 countries and regions of the globe had surpassed 346,000, and more than 14,700 deaths had resulted. The confirmed cases outside of China had also reached over 81,000, with over 3,200 deaths. In this study, a Convolutional Neural Network (CNN) was proposed to analyze and predict the number of confirmed cases. Several cities with the most confirmed cases in China were the focus of this study, and a COVID-19 forecasting model, based on the CNN deep neural network method, was proposed. To compare the overall efficacies of different algorithms, the indicators of mean absolute error and root mean square error were applied in the experiment of this study. The experiment results indicated that compared with other deep learning methods, the CNN model proposed in this study has the greatest prediction efficacy. The feasibility and practicality of the model in predicting the cumulative number of COVID-19 confirmed cases were also verified in this study.

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