Enhanced Hopfield Network for Pattern Satisfiability Optimization

Mansor, Mohd. Asyraf; Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha

doi:10.5815/ijisa.2016.11.04

Cited by 22 publications

(10 citation statements)

References 23 publications

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“…Utilizing previously published works, ANN researchers conducted research on SAT integrated with ANN. Interestingly, Mansor et al [13] extended a systematic SAT logical function expressed in Conjunctive Normal Form (CNF) which is known as 3 Satisfiability (3SAT) with DHNN. Note that the 3SAT logical rule contains strictly three literals in each independent clause.…”

Section: Introductionmentioning

confidence: 99%

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

et al. 2021

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A conventional systematic satisfiability logic suffers from a nonflexible logical structure that leads to a lack of interpretation. To resolve this problem, the advantage of introducing nonsystematic satisfiability logic is important to improve the flexibility of the logical structure. This paper proposes Random 3 Satisfiability (RAN3SAT) with three types of logical combinations (k =1, 3, k =2, 3, and k =1, 2, 3) to report the behaviors of multiple logical structures. The different types of RAN3SAT enforced with Discrete Hopfield Neural Network (DHNN) are included with benchmark searching techniques, such as Exhaustive Search algorithm. Additionally, to strengthen and certify the behavior of the proposed model, we extensively conducted several performance evaluation metrics with a specific number of neurons. In particular, the experimental results revealed that RAN3SAT was able to be implemented in DHNN, and each logical combination has its characteristics. Nonetheless, RAN3SAT provides more neuron variations in the whole solution space. The proposed model can also be applied in real-world applications such as the logic mining approach since RAN3SAT consists of various logic combinations that behave as input language to transform raw data into informative output.

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Section: Introductionmentioning

confidence: 99%

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

et al. 2021

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“…ANN has several comprehensive architectures of feed-forward or feedback networks. Artificial Intelligence (AI) practitioners utilized ANN as a platform in applications such as entity classification problems [ 6 ], conducting analysis [ 7 , 8 ], pattern recognition [ 9 , 10 ], clustering problems [ 11 , 12 ] and circuits [ 13 , 14 ]. Nonetheless, another popular network of feedback ANN is the Hopfield Neural Network (HNN), which was formulated by [ 15 ] to solve optimization tasks.…”

Section: Introductionmentioning

confidence: 99%

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

Zamri

Mansor

Kasihmuddin

et al. 2020

Entropy

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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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“…Recurrent Neural Network [4,24,25] is used for learning the sequence of data like series of video frame, text, music etc. The main difference of RNN over feed forward ANN is that we add another weight matrix, that matrix comes from previous hidden state.…”

Section: B Recurrent Neural Network (Rnn)mentioning

confidence: 99%

A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network

Jha¹,

Dey²,

Kumar³

et al. 2019

IJIMAI

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Visual Question Answering (VQA) is a stimulating process in the field of Natural Language Processing (NLP) and Computer Vision (CV). In this process machine can find an answer to a natural language question which is related to an image. Question can be open-ended or multiple choice. Datasets of VQA contain mainly three components; questions, images and answers. Researchers overcome the VQA problem with deep learning based architecture that jointly combines both of two networks i.e. Convolution Neural Network (CNN) for visual (image) representation and Recurrent Neural Network (RNN) with Long Short Time Memory (LSTM) for textual (question) representation and trained the combined network end to end to generate the answer. Those models are able to answer the common and simple questions that are directly related to the image's content. But different types of questions need different level of understanding to produce correct answers. To solve this problem, we use faster Region based-CNN (R-CNN) for extracting image features with an extra fully connected layer whose weights are dynamically obtained by LSTMs cell according to the question. We claim in this paper that a single R-CNN architecture can solve the problems related to VQA by modifying weights in the parameter prediction layer. Authors trained the network end to end by Stochastic Gradient Descent (SGD) using pretrained faster R-CNN and LSTM and tested it on benchmark datasets of VQA.

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Enhanced Hopfield Network for Pattern Satisfiability Optimization

Cited by 22 publications

References 23 publications

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

Random Satisfiability: A Higher-Order Logical Approach in Discrete Hopfield Neural Network

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

A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network

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