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The rising mean sea level is a significant concern globally, particularly in coastal regions such as Malaysia. Accurate prediction and forecasting of sea level variations are crucial for effective coastal management and adaptation strategies. This study aims to investigate the predictive capabilities of the Seasonal Autoregressive Integrated Moving-Average (SARIMA) model for Malaysia mean sea level. The research utilizes historical sea level data obtained from Permanent Service for Mean Sea Level (PSMSL) at three selected stations in Malaysia, namely Pulau Pinang, Pelabuhan Kelang, and Lumut. The SARIMA model is implemented to capture the seasonal patterns, autoregressive behavior, and short-term dependencies in the sea level time series. The predictive performance of the SARIMA model is assessed using appropriate evaluation metrics, including root mean squared error (RMSE) and mean absolute error (MAE). Additionally, the forecasted sea level values are compared with observed data to validate the models accuracy. The findings of this study contribute to a better understanding of the application of Seasonal Autoregressive Integrated Moving Average (ARIMA) in forecasting Malaysia mean sea level dynamics and providing valuable insights for coastal hazard management. The research outcomes emphasize the potential of seasonal ARIMA modeling as a reliable tool for predicting and forecasting mean sea level in Malaysia. This aids in decision-making processes and supports climate change mitigation efforts. [ABSTRACT FROM AUTHOR]

Accurate prediction of rainfall is crucial for anticipating and preparing for potential disasters such as floods or droughts in specific areas. This study introduces the Autocorrelation Function (ACF) method as a potent tool for modeling these dependencies within Pahang rainfall data. By examining the temporal relationships between current and past values, the ACF method yields valuable insights. This study leverages ACF to forecast rainfall at specific time intervals, capturing crucial temporal patterns within the daily, weekly maximum and monthly maximum interval. Our findings highlight the remarkable accuracy of the ACF method in enhancing Support Vector Machine (SVM) model performance, especially when there is a strong relationship between the current and past values of the rainfall series. Moreover, the forecasting analysis reveals that incorporating the ACF method into the SVM model surpasses conventional techniques, as evidenced by the lowest root mean square error (RMSE) values. In conclusion, this study advances our comprehension of rainfall dynamics in Pahang, strengthening the capabilities for improved forecasting and risk mitigation strategies. [ABSTRACT FROM AUTHOR]

In the present, the study on Alzheimer disease is exceptionally important since the number of cases are increasing by almost 10 million every year. According to World Health Organization, Alzheimer disease is one of the major effects of disability and dependency among older people and is currently the seventh leading cause of death among other diseases. There are many experiments conducted by scholars focusing on Alzheimer disease but most of them give little attention to raise awareness on the early detection of Alzheimer disease. Early detection of Alzheimer symptoms can be beneficial to help prevent the development of Alzheimer or slow the progression in people who have symptoms. One of the perspectives that integrate Alzheimer disease in Artificial Intelligence is by using a logic mining model with based Reverse Analysis method that operates in Discrete Hopfield Neural Network. In this paper, a method named Permutation 2 Satisfiability based Reverse Analysis method is implemented to obtain the logical relationship among the factors that contribute towards Alzheimer Disease. The proposed method essentially produces a pattern from analyzing the data and provides an induced logic that represents the possible outcome of the dataset with an average of 95% accuracy. The dataset used in this paper was retrieved from a reputable site of the Alzheimer Disease Neuroimaging Initiative which consists of researchers in the field of dementia science. The investigation shows that several medical factors such as the severity of gross findings in cerebral cortex atrophy, lobar atrophy, and hypopigmentation are highly contributed to the neuropathology in Alzheimer disease classification. [ABSTRACT FROM AUTHOR]

This finding presents a logic mining technique to model student’s performance in terms of specialized attributes. In this study, k Satisfiability based reverse analysis method (kSATRA) will be proposed to extract the logical relationship among key student’s attribute and will be used to classify or project the outcome of the current and future performance trend. The performance of the studied trends was narrowed down to three critical components: difficulties towards assigned subject, the ability to achieve the threshold marks and the decision to continue their study to higher level education. kSATRA is a method that utilized the beneficial feature of Hopfield Neural Network and k Satisfiability representation. The dataset used in this study includes data from several schools which contain insightful features. The robustness of kSATRA in extracting logical rule in student’s performance will be evaluated based on the root mean square error (RMSE), mean absolute error (MAE) and mean percentage error (MAPE). The result obtained from the computer simulation demonstrates the effectiveness of kSATRA in representing the performance of the students. [ABSTRACT FROM AUTHOR]

This finding presents the limitation of traditional Hopfield Neural Network (HNN) in doing 2 Satisfiability problem (2SAT). More precisely, both traditional exhaustive search method (ES) and ant colony optimization (ACO) were proposed in doing 2SAT problem. Both learning method will reduce logical inconsistencies of 2SAT in HNN. Since both learning method will eventually complete the learning phase, the efficiency of both method is difficult to describe. In this study, both learning method will undergo restricted learning environment during learning phase of HNN. The robustness of ACO and ES in doing 2SAT will be evaluated based on root mean square error (RMSE), mean absolute error (MAE) and mean percentage error (MAPE). The results obtained from the computer simulation demonstrates the effectiveness of ACO in doing 2SAT in HNN. [ABSTRACT FROM AUTHOR]

Logic programming is a superior language because it operates on a higher level of mathematical or logical reasoning. Logic programming is well-suited in building the artificial intelligence systems. In this paper, we reviewed the performance of the logic programming in Hopfield Neural Network (HNN) and Radial Basis Function Neural Network (RBFNN). Logic programming by using the Embedding method will improve the performance of RBFNN. In HNN, the logic programming can be implemented by finding the optimal synaptic weight via Wan Abdullah method. RBFNN is expected to do logic programming optimally compared to HNN. This study gives an overview of HNN and RBFNN regarding architectures, learning processing, and their application in 2 Satisfiability (2SAT) logic programming. Both networks will be assessed based on accuracy, sensitivity, and robustness. Pursuing that, RBFNN is expected to outperform HNN in doing 2 Satisfiability logic programming. [ABSTRACT FROM AUTHOR]

The training of a radial basis function neural network (RBFNN) involves finding the optimal number of hidden neurons in the hidden layer and finding the RBFNN parameters such as center, width, and the output weight. 2 Satisfiability logic programming will be embedded in RBFNN during the training phase. Two training techniques, notraining, and half-training are proposed in this paper. The experiment of both techniques has been examined by using Microsoft Visual Studio 2008 C# Express software. The detailed comparison of the performance of two different techniques in performing 2SAT is discussed in term of root mean square error (RMSE), the number of the hidden neurons and CPU time. The results obtained from the computer simulation have shown that RBFNN-2SAT in halftraining technique outperforms than RBFNN-2SAT in no-training technique due to the terms of RMSE, the number of hidden neurons and CPU time are typically much less than the number of data points, and the centers are not restricted to be data points. [ABSTRACT FROM AUTHOR]

Logic programming is extensively used to describe relations and have both a declarative and an operational meaning. A 3-Satisfiability (3-SAT) program is a class of Boolean Satisfiability problem, where strictly 3 literals per clause are involved. There are various algorithms utilized to solve the 3-Satisfiability logic program. Exhaustive Search (ES) is the standard method, where the algorithm enumerates for every possible iteration to search for a solution. Imperialist Competitive (IC) algorithm, on the other hand, an evolutionary optimization method that is inspired by the imperialistic competition model. Applying the Imperialist Competitive algorithm to 3-Satisfiability logic programming shows its ability in dealing with different types of optimization problems. In this paper, the objective was to compare IC algorithm and ES algorithm in doing 3-SAT logic programming with Hopfield Neural Network (HNN). The performance of both models were evaluated by performance evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Global Minima Ratio and CPU Time. The expected outcome of the experiment is that the IC algorithm outperformed ES in 3-SAT logic programming. [ABSTRACT FROM AUTHOR]

In recent years, there have been significant improvements in algorithm for Maximum 2- Satisfiability Problem (MAX-2SAT). This paper studies the limitation of traditional Hopfield neural network in doing MAX-2SAT problem. More precisely, both traditional exhaustive search method (ES) and artificial bee colony (ABC) were proposed in doing MAX-2SAT problem. Both learning method will optimize the learning phase of Hopfield neural network that act as an intelligent system. Since both learning method will eventually complete the learning phase, the efficiency of both methods is difficult to imagine. In this study, both learning method will undergo restricted learning environment during learning phase of Hopfield neural network. The simulation incorporated with ABC and ES will be examined by using Microsoft Visual 2015 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluated based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Schwarz Bayesian Criterion (SBC), Global minima ratio (Zm) and CPU time. The result obtained from the computer simulation demonstrates the effectiveness, acceleration features of artificial bee colony in doing MAX-2SAT in Hopfield neural network. [ABSTRACT FROM AUTHOR]

Accurate diabetes diagnosis is vital to ensure patient with certain symptom can be treated appropriately. Medical diagnosis is often challenging because not all patient's symptoms will arrive to the same diagnosis. Medical practitioner or specialist is not able to diagnose the disease accurately when the number of attribute that contribute to the disease increase. Logic mining is a platform for data scientist to induce the logical rule from large data set. In this study, 2 Satisfiability based reverse analysis method (2SATRA) will be proposed to extract the logical rule from the data set. The extracted logical rule from the data set will be use to classify the outcome of the diagnosis. 2SATRA is a method that utilized the beneficial feature of Hopfield neural network and 2 Satisfiability problems or HNN-2SAT. The simulation will be examined by using Microsoft Visual 2015 C++ Express software. The robustness of 2SATRA in extracting logical rule in medical data set will be evaluated based on root mean square error (RMSE), mean absolute error (MAE) and CPU time. The result obtained from the computer simulation demonstrates the effectiveness of 2SATRA in doing diabetis diagnosis. [ABSTRACT FROM AUTHOR]

Social media plays a predominant role in this new era. Hence, the performance metrics of social media are usually evaluated by using statistical methods and machine learning approach. The core impetus of this research is to formulate a robust classification paradigm by integrating metaheuristic and neural network as a single paradigm. In this paper, artificial immune system algorithm incorporated with Hopfield neural network (HFB-3SATAIS) is proposed to evaluate the Facebook performance metrics data set. The proposed approach outperforms the standard Hopfield neural network with exhaustive search (HFB-3SATES) in terms of root mean square error (RMSE), mean absolute error (MAE), sum of squared error (SSE), symmetric mean absolute percentage error (SMAPE), Bayesian information criterion (BIC) and the CPU time. Thus, the accuracy, stability and robustness of the proposed network demonstrated a solid performance compared to the standard model. In addition, the work can be expanded by evaluating different class of benchmark data set such as time series data set. [ABSTRACT FROM AUTHOR]

Pattern satisfiability (Pattern-SAT) is another variant of the satisfiability problem that inspires researchers to develop model verification based on Boolean algebra. Since Boolean algebra is a solid representation of a classification problem, it was seen to work outstandingly in representing the pattern or image. In this research, enhanced artificial bee colony system (ABC) and modified genetic algorithm (GA) were deployed with the logic programming as a learning tool for pattern satisfiability problem. The aim of this study is to investigate the efficiency and performance of enhanced ABC and GA in doing Pattern-SAT. The comparison of ABC and GA is examined by using Microsoft Visual C++ 2015 and Microsoft Visual Basic C Sharp. The performance of the ABC and GA in doing Pattern-SAT will be appraised in terms of global Pattern-SAT, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and computation time. Hence, the result obtained from computer simulation indicates the good features of ABC compared to GA in doing Pattern-SAT. [ABSTRACT FROM AUTHOR]

3-Satisfiability logic programming is a brand-new approach in the data mining field. In recent years, the conventional data mining technique only emphasizes on the standalone neural network paradigm. To frame the novelty, the 3-Satisfiability logic programming is incorporated with the discrete Hopfield neural network as a single data mining tool. Hence, the proposed approach is applied in evaluating numerous cardiovascular diseases data sets. Pursuing that, the results obtained can assist the medical practitioners in cardiovascular disease early diagnosis. Dev C++ 5.11 was used as a platform for training, testing and validating the performances of the proposed approach. The performance of the proposed approach is evaluated by performance metrics such as root mean square error (RMSE), mean bias error (MBE), sum of squared error (SSE), symmetric mean absolute percentage error (SMAPE) and CPU time. The error evaluations and accuracy of the proposed method have demonstrated a promising result when applied in heart disease data set (HNN- 3SATHDD) and statlog heart data (HNN-3SATSD). Therefore, the results had provided the concrete evidence of the effectiveness of the proposed approach in the data mining. [ABSTRACT FROM AUTHOR]

Swarm intelligence is a research area that models the population of the swarm based on natural computation. Artificial bee colony (ABC) algorithm is a swarm based metaheuristic algorithm introduced by Karaboga to optimize numerical problem. Pattern-SAT is a pattern reconstruction paradigm that utilized 2SAT logical rule in representing the behavior of the desired pattern. The information of the desired pattern in terms of 2SAT logic is embedded to Hopfield neural network (HNN-P2SAT) and the desired pattern is reconstructed during the retrieval phase. Since the performance of HNN-P2SAT in Pattern-SAT deteriorates when the number of 2SAT clause increased, newly improved ABC is used to reduce the computation burden during the learning phase of HNN-P2SAT (HNN-P2SATABC). The aim of this study is to investigate the performance of Pattern-SAT produced by ABC incorporated with HNN-P2SAT and compare it with conventional standalone HNN. The comparison is examined by using Microsoft Visual Basic C++ 2013 software. The detailed comparison in doing Pattern-SAT is discussed based on global Pattern-SAT, ratio of activated clauses and computation time. The result obtained from computer simulation indicates the beneficial features of HNN-P2SATABC in doing Pattern-SAT. This finding is expected to result in a significant implication on the choice of searching method used to do Pattern-SAT. [ABSTRACT FROM AUTHOR]

Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network. [ABSTRACT FROM AUTHOR]

Maximum 3-Satisfiability (MAX-3SAT) is a counterpart of the Boolean satisfiability problem that can be treated as a constraint optimization problem. It deals with a conundrum of searching the maximum number of satisfied clauses in a particular 3-SAT formula. This paper presents the implementation of enhanced Hopfield network in hastening the Maximum 3-Satisfiability (MAX-3SAT) logic programming. Four post optimization techniques are investigated, including the Elliot symmetric activation function, Gaussian activation function, Wavelet activation function and Hyperbolic tangent activation function. The performances of these post optimization techniques in accelerating MAX-3SAT logic programming will be discussed in terms of the ratio of maximum satisfied clauses, Hamming distance and the computation time. Dev-C++ was used as the platform for training, testing and validating our proposed techniques. The results depict the Hyperbolic tangent activation function and Elliot symmetric activation function can be used in doing MAX-3SAT logic programming. [ABSTRACT FROM AUTHOR]

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design. [ABSTRACT FROM AUTHOR]

Activation function is a dynamic transfer function that translates input signal to output signals. Architecture with incorporated activation functions is used to do logic programming in Hopfield neural network. The proposed activation function is based on Unipolar activation function. The aim of this study is to investigate the features of Unipolar activation function and compare it with McCulloch-Pitts activation function which is proposed by Wan Abdullah. The comparisons of Unipolar activation function and McCulloch-Pitts activation function doing logic programming in Hopfield neural network will be examined by using Microsoft Visual C++ 2010 Express software. The detailed comparison on the performance of Unipolar activation function and McCulloch-Pitts activation function doing logic programming will be discussed based on global minima ratio, running time and hamming distance. Studies showed that the output produced by Unipolar activation function has a higher global minima ratio, lower running time and lower hamming distance. The result obtained from the computer simulation indicates the beneficial features of Unipolar activation function compared to McCulloch-Pitts Activation function. This finding leads to significant implication on the choice of activation function used in order for user to produce optimum and desired output. [ABSTRACT FROM AUTHOR]