The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.
Every newspaper publisher faced with the problem of determining the number of copies of newspaper and distributing them to the retail traders. Two aspects need to be balanced out in order to optimise the economical success which is the number of unsold copies should be minimal to reduce the cost of production, and the sell-out rate should be minimal to maximise the number of sold copies. Thus, a good sales rate prediction is necessary to optimise both antagonistic aspects. This paper utilised Artificial Neural Network to predict newspaper sales for one vendor in the area of Sungai Petani, Malaysia. The predicted sales value can help the company to optimise their sales. The main objective is to develop a prototype that apply artificial neural network so that it can predict the future trend as well as the future daily sale. The network will consist of three layer which is input layer, one hidden layer and output layer. The input layer will have six input node where this will be the factor that will affect the output which is the number of copies that sold. The network will be trained with history data of a one year records of data. The output produced has the error value as low as 1.24% while the correlation coefficient between prediction and actual value is 0.1197.
Artificial neural networks, sales prediction, newspaper
Malaysia is the largest exporter of Elaeis Guineensis (Palm oil) in the international market. Oil palm cultivation generates a significant amount of lignocellulosic biomass derived from empty fruit bunches (EFB) as waste product. This research focused on optimizing the mycelium growth in Pleurotus sp. cultivation by using EFB as a culture medium. The EFB was cut into the range of size of substrate (S) from 1.5 cm to 3.0 cm, soaked in water for overnight, applied steam treatment and incubated at the selected range of temperature (T) from 29 °C to 32 °C. The responses were mycelium extension rate (M) and nitrogen concentration in mycelium (N). The multi-objective optimisation of M and N requires the objective functions which represent both processes. For this type of problem, multi-objective genetic algorithm was chosen as the methodology, specifically using NSGA-II algorithm. Through the implementation of selected multi-objective genetic algorithm, it was able to produce the pareto front for optimising both nitrogen concentration and the extension rate of the mycelium. The highest nitrogen concentration and mycelium extension rate was from the result with crossover and mutation probability of 0.5 and 0.2. It produced 388.45 mg/L of nitrogen concentration and 0.370 cm/day of mycelium growth.
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