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In December 2019, the outbreak of Covid-19 does cause a lot of serious issues, fatality, economic crisis, the rise of the unemployment rate, air pollution, and so on. This paper focuses on the impact of Covid-19 that relates to the long-lasting issue of air quality. The scope of this study is focused on the changing of air quality that occurred within the Movement Control Order (MCO) which was launched by the Malaysian government against Covid-19 spreading in Malaysia. Multiple reports suggest during the MCO period, the overall air quality in Malaysia was improved. However, when we just look into the history of air quality index (AQI) value without doing any further analysis, we can see there is no significant difference between the AQI value since the MCO was executed. Therefore, we need to understand how AQI value is obtained and compare available data before MCO is launched to the first three phases of MCO. This paper estimates the impact of MCO towards reduction of moving vehicles which can give impact to air quality in terms of 24-hours PM2.5 concentrations. Air quality index data have been analyzed by developed code using MATLAB. From the investigating and analyzing the data conducted, we found out the impact of MCO on the air quality in Malaysia is positive. [ABSTRACT FROM AUTHOR]

2 Satisfiability (2SAT) logic programming has been a prominent logical rule that defines the structure of Radial Basis Function Neural Network. Training Radial Basis Function Neural Network with logic 2 Satisfiability is an optimization task since it is desired to find the optimal output weights during the training process. In this paper, artificial immune system (AIS) algorithm will be introduced to facilitate the training of RBFNN-2SAT. AIS is used for updating the output weights during training RBFNN-2SAT. In this study, the effectiveness of our hybrid computing paradigm, namely RBFNN-2SATAIS can be estimated by evaluating its testing data result using the root mean square error (RMSE) and computation time (CT). The obtained findings show that the proposed method was effective for achieving acceptable results for 2SAT logic rule. [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 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]

A linear Kernel is a machine computationally efficient, also it has the ability to work by the analysis-data in the high-dimensional feature space with complex structure random. The purpose for the study in this paper is to introduce a new vision into the linear kernel machines by integrated with Hopfield Neural Network for doing the logical programming by using horn clause logic to become new network called Linear Kernel Hopfield Neural Network (LKHNN). The benefit by integrating kernel machine with Hopfield network to reduce the arithmetic burden by intelligently determining the pattern of memory embedded in high-dimensional space feature. The new network (LKHNN) able to formulation estimates for the state of neurons. The LKHNN and HNN simulation are performed using Dev C ++ program. The outcomes from simulator show the effectiveness of LKHNN in doing logic program by optimization horn clauses problem and improve efficiency to find the global solution. The robustness of LKHNN and HNN in doing logic programming by using horn clause are evaluated based on global minima ratio(zM), Hamming distance(HD) and computational time(CT). [ABSTRACT FROM AUTHOR]

This paper presents a new framework for the development of generalized composite kernels machines for discrete Hopfield neural network and to upgrading the performance of logic programming in Hopfield network by applying kernels machines in the system. We put up a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the analyze data that make use of kernels. The present an analysis based on kernels machines (Linear kernel Hopfield neural network, Polynomial kernel Hopfield neural network, and Gaussian kernel Hopfield neural network) that measure how the capacity of Hopfield network to use for solving the combinatorial problem that always occurs in Hopfield network and guaranty that the solution is optimal. This work is merely focusing on the ways to upgrade the performance of logic programming in Hopfield network. We carried out computer simulations to demonstrate the ability of kernels Hopfield neural network in enhancing the performance of the system. By applying kernels Hopfield neural network in the system, it does not only produce better quality solutions but it also can handle the network better even though the complexity increased. Besides that, the system also makes the solutions converge faster. Thus, the presence of this kernels Hopfield neural network in the system will produce solutions with better quality [ABSTRACT FROM AUTHOR]