Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly in knowledge discovery tasks. In this paper, we a propose non-satisfiability logical rule that combines two sub-logical rules, namely Maximum 2 Satisfiability and Random 2 Satisfiability, that play a vital role in creating explainable artificial intelligence. Interestingly, the combination will result in the negative logical outcome where the cost function of the proposed logic is always more than zero. The proposed logical rule is implemented into Discrete Hopfield Neural Network by computing the cost function associated with each variable in Random 2 Satisfiability. Since the proposed logical rule is difficult to be optimized during training phase of DHNN, Election Algorithm is implemented to find consistent interpretation that minimizes the cost function of the proposed logical rule. Election Algorithm has become the most popular optimization metaheuristic technique for resolving constraint optimization problems. The fundamental concepts of Election Algorithm are taken from socio-political phenomena which use new and efficient processes to produce the best outcome. The behavior of Random Maximum 2 Satisfiability in Discrete Hopfield Neural Network is investigated based on several performance metrics. The performance is compared between existing conventional methods with Genetic Algorithm and Election Algorithm. The results demonstrate that the proposed Random Maximum 2 Satisfiability can become the symbolic instruction in Discrete Hopfield Neural Network where Election Algorithm has performed as an effective training process of Discrete Hopfield Neural Network compared to Genetic Algorithm and Exhaustive Search.

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Understand the extreme volatility in the market is important for the investor to make a correct prediction. This paper evaluated the performance of generalized lambda distribution (GLD) by comparing with the popular probability distribution namely generalized extreme value (GEV), Generalized logistic (GLO), generalized Pareto (GPA), and Pearson (PE3) using Kuala Lumpur composite index stock return data. The parameter for each distribution estimated using the L-moment method. Based on k-sample Anderson darling goodness of fit test, GLD performs well in weekly maximum and minimum period. Evidence for preferring GLD as an alternative to extreme value theory distribution also described.

GPS monitoring systems and the development of driverless vehicles are almost inseparable from camera images. The images taken by traffic cameras often contain certain sky areas and noise, the traditional dark channel prior (DCP) algorithm easily produces color distortion and halo effect, when processing the hazy traffic images with sky and high brightness areas. An optimized Retinex model and dark channel prior algorithm (ORDCP) is proposed in this paper. Firstly by adjusting the calculation method of dark channel image, the proportion of dark channel is improved; Then, the transmittance image is corrected and smoothed by guided filtering and mean filtering. Finally, the Retinex model is fused to save the details.ORDCP corrects the inaccurate calculation of scene transmittance value in DCP algorithm,and modifies some dehazing problems, such as the loss of details, halo effect, contrast and color distortion,etc. Using information entropy (IE) as the objective evaluation index, combined with the subjective evaluation, it is concluded that the algorithm proposed in this paper can effectively retain the detailed information of the image, and eliminate the halo effect. Meanwhile, it meets the visual characteristics of human eyes better, and has some practicality and applicability in traffic control and intelligent detection.

This study evaluated the performance of probability distribution in various financial periods by investigating the effect of economic cycle on extreme stock return activity. Malaysian stock price KLCI data from 1994–2008 were split into three economy periods correspond to the growth, financial crisis, and the recovery. Four prevalent distributions specifically generalized lambda distribution (GLD), generalized extreme value (GEV), generalized logistic (GLO), and generalized pareto (GPA) were employed to model weekly and monthly maximum and minimum Kuala Lumpur Composite Index (KLCI) share returns. The L-moment approach was used to estimate the parameter while k-sample Anderson darling (k-ad) test was applied to measure the goodness of fit estimation. In conclusion, GLD is the most appropriate distribution representing a weekly maximum minimum return for overall three economic scenarios in Malaysia.