Creating optimal logic mining is strongly dependent on how the learning data are structured. Without structure, intelligence systems integrated into mining, such as an artificial neural network, tend to converge suboptimal solution. This paper proposed a novel that integrates supervised via association analysis identify most arrangement with respect given logical rule. By utilizing Hopfield network associative memory store information of rule, rule from correlation will be learned and...

Mining the best logical rule from data is a challenging task because not all attribute of dataset will contribute towards optimal representation. Even if correct attributes were selected, wrong connection in formula lead to suboptimal representation datasets. These two factors must be carefully considered creating more robust logic mining method. In this paper, we proposed novel by introducing log-linear analysis select which formulate that embedded into energy-based ANN named Discrete...

Amazon.com Inc. seeks alternative ways to improve manual transactions system of granting employees resources access in the field data science. The work constructs a modified Artificial Neural Network (ANN) by incorporating Discrete Hopfield (DHNN) and Clonal Selection Algorithm (CSA) with 3-Satisfiability (3-SAT) logic initiate an Intelligence (AI) model that executes optimization tasks for industrial data. selection 3-SAT is vital mining represent entries Amazon Employees Resources Access...

The k Satisfiability logic representation (kSAT) contains valuable information that can be represented in terms of variables.This paper investigates the use a particular non-systematic logical rule namely Random (RANkSAT).RANkSAT series satisfiable clauses but structure formula is determined randomly by user.In present study, RANkSAT successfully implemented Hopfield Neural Network (HNN) obtaining optimal synaptic weights.We focus on different regimes for ≤ 2 taking advantage non-redundant...

An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on relationship between factors contributing systematic recruitment, artificial neural network logic mining approach can be adopted as a data extraction model. this work, energy based k satisfiability reverse analysis incorporating Hopfield is proposed extract electronic (E) set. The attributes E set are represented form logical representation. We...

One of the influential models in artificial neural network (ANN) research field for addressing issue knowledge non-systematic logical rule is Random k Satisfiability. In this context, structure representation also potential application Despite many attempts to represent rules a structure, previous studies have failed consider higher-order rules. As amount information increases, proposed unable proceed retrieval phase, where behavior Satisfiability can be observed. This study approaches these...

Hybridized algorithms are commonly employed to improve the performance of any existing method. However, an optimal learning algorithm composed evolutionary and swarm intelligence can radically quality final neuron states has not received creative attention yet. Considering this issue, paper presents a novel metaheuristics combined with several objectives—introduced as Hybrid Election Algorithm (HEA)—with great results in solving optimization combinatorial problems over binary search space....

Radial Basis Function Neural Network (RBFNN) is a class of Artificial (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered from significant computational error and difficulties in approximating the optimal neuron, especially when dealing Boolean Satisfiability logical rule. In this paper, we present comprehensive investigation potential effect systematic programming as rule, namely 2...

Real life logical rule is not always satisfiable in nature due to the redundant variable that represents formulation. Thus, intelligence system must be optimally governed ensure can behave according non-satisfiable structure finds practical applications particularly knowledge discovery tasks. In this paper, we a propose non-satisfiability combines two sub-logical rules, namely Maximum 2 Satisfiability and Random Satisfiability, play vital role creating explainable artificial intelligence....

The primary objective in building predictive analytics models is to achieve optimal accuracy with real datasets. limitations of existing lie their storage capacity, which hinders the progress generating high accuracy. When a model capacity limited, it may struggle process large datasets and encounter underfitting issues, preventing from capturing complexities data. Hence, this paper addresses these challenges by introducing novel approach analytics, focusing on expanding Discrete Hopfield...

<p>Evaluating behavioral patterns through logic mining within a given dataset has become primary focus in current research. Unfortunately, there are several weaknesses the research regarding models, including an uncertainty of attribute selected model, random distribution negative literals logical structure, non-optimal computation best logic, and generation overfitting solutions. Motivated by these limitations, novel model incorporating mechanism to control literal systematic...

The effectiveness of the logic mining approach is strongly correlated to quality induced logical representation that represent behaviour data. Specifically, optimum indicates capability in generalizing real datasets different variants and dimensions. main issues with extracted by standard techniques are lack interpretability weakness terms structural arrangement 2 Satisfiability causing lower accuracy. To address issues, permutation serves as an alternative mechanism can enhance probability...

During the SARS-CoV-2 (Covid-19) pandemic, credit applications skyrocketed unimaginably. Thus, creditors or financial entities were burdened with information overload to ensure they provided proper right person. The existing methods employed by prone overfitting and did not provide any regarding behavior of creditor. However, outcome consider attribute creditor that led default outcome. In this paper, a swarm intelligence-based algorithm named Artificial Bee Colony has been implemented...

Ovarian cancer among women is known as “The Silent Killer”. It caused by the malignant ovarian cyst, which can spread to other organs if it not treated at an early stage. Some are benign cyst be through medical procedures such laparoscopic and laparotomy. The type of procedure that patients have undergo depends on size cyst. A few risk factors cause age, pregnancy, menopause menstrual cycle. Apart from that, there a symptoms fever, nausea abdominal pain, distension, dysmenorrhea...

The dynamical behaviors of logic mining in real datasets are strongly dependent by its logical structure. In this case, rule that has been embedded to neural network long suffered from a lack interpretability and accuracy. This severely limited the practical usability mining. Logical permutation is definitive finite arrangement attributes makes 2SAT became true. It was believed effect will increase accuracy system. paper, we presented (2SATRA) integrated with recurrent Hopfield Neural...

Online shopping is a multi-billion-dollar industry worldwide. However, several challenges related to purchase intention can impact the sales of e-commerce. For example, e-commerce platforms are unable identify which factors contribute high product. Besides, online sellers have difficulty finding products that align with customers’ preferences. Therefore, this work will utilize an artificial neural network provide knowledge extraction for or might improve their and services. There limited...

The tripartite industry classification, which divides all economic activities into three parts, is a classification method to reflect the dynamic process of development and historical trend change resource allocation structure.The fact shows that proportion each has become an important symbol level national development. compositional data,which kind complex multidimensional data used in many fields. All components are non-negative carry only relative information. In practice, there could be...

The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity effectiveness. Based on the advantages, study this paper delves deeper into key parameters that influence performance of algorithm, implemented modifications such as integrating reverse learning adding elite opposition solution create modified Sine Cosine Algorithm (the SCA). Furthermore, by combining fuzzy k-nearest neighbor with SCA, simulates numeric datasets two or...

Logic mining has been widely used in many fields as an aid to extract logical rule that are significance the data set. However, a previous study Discrete Hopfield Neural Network (DHNN) formulated random attributes for logic implemented mining. Hence, this introduced statistical analysis which is log-linear will be finding best gives important effect outcome. This embed 2 Satisfiability based Reverse Analysis method with approach of attribute selection (2SATRA) and simulated by using several...

The efficiency of learning algorithm can be improved by introducing propositional satisfiability in Discrete Hopfield Neural Network and modeling the neuron structure neural network. To improve flexibility logic to meet requirements all combinatorial problems, a special is introduced Network. In this paper, Exhaustive Search was harnessed as search fitness, constant trial conducted for sake implementation. order study mechanism on logic, we focused influence different trials evaluated...

The satisfiability problem (SAT) of propositional logic formulas is great significance in computer science and artificial intelligence. It the primary NP-complete that has been proved broadly introduced into intelligent systems. Many significant achievements have made direction integrating SAT Hopfield network to solve optimal problems. However, it remains unexplored for multiple aspects, such as knowledge representation, reasoning model construction, there still much space research....

In a binary Artificial Bee Colony, the exploration mechanism occurs in nectar equation where it improvises search space to obtain an optimal solution rapidly. However, there is much uncertainty about which parameter operates well optimizing discrete problems. this study, focus modification of nectars by replacing NAND logic gate ABC called nand-ABC. The proposed model will be integrated into stage generate Weighted Random k Satisfiability for k=1, 2 that takes account higher ratio negative...