Ants Colony Optimization Algorithm in the Hopfield Neural Network for Agricultural Soil Fertility Reverse Analysis

2022 ◽  
pp. 32-42
Author(s):  
Hamza Abubakar ◽  
Abdullahi Muhammad ◽  
Smaiala Bello
Keyword(s):  
Neural Network ◽  
Soil Fertility ◽  
Agricultural Soil ◽  
Random Search ◽  
Hopfield Neural Network ◽  
Decision Problems ◽  
Experimental Simulation ◽  
Boolean Formula ◽  
Data Set ◽  
Reverse Analysis

The Boolean Satisfiability Problem (BSAT) is one of the most important decision problems in mathematical logic and computational sciences for determining whether or not a solution to a Boolean formula.. Hopfield neural network (HNN) is one of the major type artificial neural network (NN) popularly known for it used in solving various optimization and decision problems based on its energy minimization machinism. The existing models that incorporate standalone network projected non-versatile framework as fundamental Hopfield type of neural network (HNN) employs random search in its training stages and sometimes get trapped at local optimal solution. In this study, Ants Colony Optimzation Algorithm (ACO) as a novel variant of probabilistic metaheuristic algorithm (MA) inspired by the behavior of real Ants, has been incorporated in the training phase of Hopfield types of the neural network (HNN) to accelerate the training process for Random Boolean kSatisfiability reverse analysis (RANkSATRA) based for logic mining. The proposed hybrid model has been evaluated according to robustness and accuracy of the induced logic obtained based on the agricultural soil fertility data set (ASFDS). Based on the experimental simulation results, it reveals that the ACO can effectively work with the Hopfield type of neural network (HNN) for Random 3 Satisfiability Reverse Analysis with 87.5 % classification accuracy

scholarly journals Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 40
Author(s):  
Siti Zulaikha Mohd Jamaludin ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Ahmad Izani Md Ismail ◽  
Mohd. Asyraf Mansor ◽  
Md Faisal Md Basir
Keyword(s):  
Neural Network ◽  
Data Extraction ◽  
Hopfield Neural Network ◽  
Data Set ◽  
Reverse Analysis ◽  
Insurance Agency ◽  
Logical Representation ◽  
Relationship Of ◽  
Extraction Model ◽  
The Relationship

An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.

scholarly journals 3-satisfiability reverse analysis method with Hopfield neural network for medical data set

2020 ◽  
Author(s):  
Samaila Abdullahi ◽  
Mohd. Asyraf Mansor ◽  
Saratha Sathasivam ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Nur Ezlin Binti Zamri
Keyword(s):  
Neural Network ◽  
Hopfield Neural Network ◽  
Medical Data ◽  
Analysis Method ◽  
Data Set ◽  
Reverse Analysis

A Boltzmann machine for high-resolution prestack seismic inversion

2019 ◽  
Vol 7 (3) ◽  
pp. SE215-SE224 ◽  
Author(s):  
Son Dang Thai Phan ◽  
Mrinal K. Sen
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Learning Algorithm ◽  
Hopfield Neural Network ◽  
Seismic Inversion ◽  
Good Prospect ◽  
Boltzmann Machine ◽  
Inversion Algorithm ◽  
Data Set ◽  
Prestack Seismic Inversion

Seismic inversion is one popular approach that aims at predicting some indicative properties to support the geologic interpretation process. Existing inversion techniques indicate weaknesses when dealing with complex geologic area, where the uncertainties arise from the guiding model, which are provided by the interpreters. We have developed a prestack seismic inversion algorithm using a machine-learning algorithm called the Boltzmann machine. Unlike common inversion approaches, this stochastic neural network does not require a starting model at the beginning of the process to guide the solution; however, low-frequency models are required to convert the inversion-derived reflectivity terms to the absolute elastic P- and S-impedance as well as density. Our algorithm incorporates a single-layer Hopfield neural network whose neurons can be treated as the desired reflectivity terms. The optimization process seeks the global minimum solution by combining the network with a stochastic model update from the mean-field annealing algorithm. Also, we use a Z-shaped sample sorting scheme and the first-order Tikhonov regularization to improve the lateral continuity of the results and to stabilize the inversion process. The algorithm is applied to a field 2D data set to invert for high-resolution indicative P- and S-impedance sections to better capture some features away from the reservoir zone. The resulting models are strongly supported by the well results and reveal some realistic features that are not clearly displayed in the model-based deterministic inversion result. In combination with well-log analyses, the new features appear to be a good prospect for hydrocarbon.

scholarly journals Neuro-Symbolic Integration of Hopfield Neural Network for Optimal Maximum Random kSatisfiability (Maxrksat) Representation

2020 ◽  
Author(s):  
Hamza Abubakar ◽  
Sagir Abdu Masanawa ◽  
Surajo Yusuf 
Keyword(s):  
Neural Network ◽  
Hamming Distance ◽  
Computation Time ◽  
Hopfield Neural Network ◽  
Absolute Error ◽  
Boolean Formula ◽  
Search Problems ◽  
Neural Network Learning ◽  
Symbolic Integration ◽  
Lyapunov Energy Function

Boolean satisfiability logical representation is a programming paradigm that has its foundations in mathematical logic. It has been classified as an NP-complete problem that difficult practical combinatorial optimization and search problems can be easily converted into it. Random Maximum kSatisfiability (MAX-RkSAT) composed of the most consistent mapping in a Boolean formula that generates a maximum number of random satisfied clauses. Many optimization and search problems can be easily expressed by mapping the problem into a Hopfield neural network (HNN) to minimize the optimal configuration of the corresponding Lyapunov energy function. In this paper, a hybrid computational model hs been proposed that incorporates the Random Maximum kSatisfiability (MAX-RkSAT) into the Hopfield neural network (HNN) for optimal Random Maximum kSatisfiability representation (HNN-MAX-RkSAT). Hopfield neural network learning will be integrated with the random maximum satisfiability to enhance the correct neural state of the network model representation. The computer simulation using C+++⁣+ has been used to demonstrate the ability of MAX-RkSAT to be embedded optimally in Hopfield neural network to serve as Neuro-symbolic integration. The performance of the proposed hybrid HNN-MAXRkSAT model has been explored and compared with the existing model. The proposed HNN-MAXRkSAT demonstrates good agreement with the existing models measured in terms of Global minimum Ratio (Gm), Hamming Distance (HD), Mean Absolute Error (MAE) and network computation Time CPU time). The proposed framework explored that MAX-RkSAT can be optimally represented in HNN and subsequently provides an additional platform for neural-symbolic integration, representing the various types of satisfiability logic.

scholarly journals Logic mining in football matches

2020 ◽  
Vol 17 (2) ◽  
pp. 1074 ◽  
Author(s):  
Liew Ching Kho ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Mohd. Asyraf Mansor ◽  
Saratha Sathasivam
Keyword(s):  
Optimization Problems ◽  
Hopfield Neural Network ◽  
Absolute Error ◽  
Population Based ◽  
Percentage Error ◽  
Data Set ◽  
Combinatorial Optimization Problems ◽  
Logical Rule ◽  
Nature Inspired Algorithm ◽  
Reverse Analysis

Sports results forecast has became increasingly popular among the fans nowadays. It made predicting the outcome of a sport’s match, a new and interesting challenge. This paper presented a logic mining technique to model the results (Win Draw / Lose) of the football matches played in English Premier League, Spanish La Liga and France Ligue 1. In this research, a method namely <em>k</em> satisfiability based reverse analysis method (<em>k</em>SATRA) hybridized with Ant Colony Optimization (ACO) was brought forward to obtain the logical relationship among the clubs in these leagues. The logical rule obtained from the football matches was used to categorize the results of future matches. ACO is a population-based and nature-inspired algorithm to decipher several combinatorial optimization problems. <em>k</em>SATRA made use of the advantages of Hopfield Neural Network and k Satisfiability representation. The data set used in this study included the data of 6 clubs from each league, which composed of all league matches from year 2014 to 2018. The effectiveness of <em>k</em>SATRA with ACO in obtaining logical rule in football matches was tested based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and CPU time. Results acquired from the computer simulation showed the robustness of <em>k</em>SATRA in exhibiting the performance of the clubs.

Students’ performance via satisfiability reverse analysis method with Hopfield Neural Network

2019 ◽  
Author(s):  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Mohd. Asyraf Mansor ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Hopfield Neural Network ◽  
Analysis Method ◽  
Reverse Analysis

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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