Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematically studied by exploring available studies of different metaheuristic algorithms used for FS to improve TC. This paper will contribute to the body of existing knowledge by answering four research questions (RQs): 1) What are the different approaches of FS that apply metaheuristic algorithms to improve TC? 2) Does applying metaheuristic algorithms for TC lead to better accuracy than the typical FS methods? 3) How effective are the modified, hybridized metaheuristic algorithms for text FS problems?, and 4) What are the gaps in the current studies and their future directions? These RQs led to a study of recent works on metaheuristic-based FS methods, their contributions, and limitations. Hence, a final list of thirty-seven (37) related articles was extracted and investigated to align with our RQs to generate new knowledge in the domain of study. Most of the conducted papers focused on addressing the TC in tandem with metaheuristic algorithms based on the wrapper and hybrid FS approaches. Future research should focus on using a hybrid-based FS approach as it intuitively handles complex optimization problems and potentiality provide new research opportunities in this rapidly developing field.
The spread of novel coronavirus disease (COVID-19) has resulted in chaos around the globe. The infected cases are still increasing, with many countries still showing a trend of growing daily cases. To forecast the trend of active cases, a mathematical model, namely the SIR model was used, to visualize the spread of COVID-19. For this article, the forecast of the spread of the virus in Malaysia has been made, assuming that all Malaysian will eventually be susceptible. With no vaccine and antiviral drug currently developed, the visualization of how the peak of infection (namely flattening the curve) can be reduced to minimize the effect of COVID-19 disease. For Malaysians, let’s ensure to follow the rules and obey the SOP to lower the
Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education, and entertainment. However, when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified in
... Show MoreThe life insurance companies need a sound system to use it in selecting Insurable risks so they can avoid or reduce possible losses that may be insured to a minimum levels , But the application within IRAQI INSURANCE COMPANY reflects that it still depends on a traditional ways in the procedures used to select that risks .
This research represents an attempt to put acceptable suggestions about developing a system for selecting insurable risks which used now by iraqi insurance company by recognizing the risks of life insurance , determining kinds of risks which can difined as normal and upnormal risks , rectification of the
... Show MoreThis study focusses on the effect of using ICA transform on the classification accuracy of satellite images using the maximum likelihood classifier. The study area represents an agricultural area north of the capital Baghdad - Iraq, as it was captured by the Landsat 8 satellite on 12 January 2021, where the bands of the OLI sensor were used. A field visit was made to a variety of classes that represent the landcover of the study area and the geographical location of these classes was recorded. Gaussian, Kurtosis, and LogCosh kernels were used to perform the ICA transform of the OLI Landsat 8 image. Different training sets were made for each of the ICA and Landsat 8 images separately that used in the classification phase, and used to calcula
... Show MoreCare and attention to the structure in the sixties of the last century replaced the mark, and if the structure of Ms. pampered in research and studies, it has become the mark is also a spoiled lady .. But the relationship between the structure and the mark was not a break and break, but the relationship of integration, His themes are structural analysis, and these are intellectual themes that can not be surpassed in contemporary research, especially since semiotics have emerged from the linguistic inflection.
We have tried to distinguish between text and speech, which is a daunting task, as it seems that whenever the difference between them is clear and clear, we come back to wonder whether the text is the same discourse, and is
... Show MoreThe Dagum Regression Model, introduced to address limitations in traditional econometric models, provides enhanced flexibility for analyzing data characterized by heavy tails and asymmetry, which is common in income and wealth distributions. This paper develops and applies the Dagum model, demonstrating its advantages over other distributions such as the Log-Normal and Gamma distributions. The model's parameters are estimated using Maximum Likelihood Estimation (MLE) and the Method of Moments (MoM). A simulation study evaluates both methods' performance across various sample sizes, showing that MoM tends to offer more robust and precise estimates, particularly in small samples. These findings provide valuable insights into the ana
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