<p class="0abstract">Various mobile applications such as Mobile Health (mHealth) have been developed and spread across the world which has played an important role in mitigating the Coronavirus pandemic (COVID-19). As the COVID-19 pandemic spreads, several people have drawn parallels to influenza. While both viruses cause respiratory infections, they propagate in very different ways. This has a major impact on the public health measures that can be used to fight each virus. These viruses are pandemic-causing in the same way. That is, they both cause respiratory disease, and can present themselves in several ways, ranging from asymptomatic to severe and deadly. A proposal is presented in this paper that uses two algorithms to define and classify these pandemics, they are: The Back Propagation (BP) classification algorithm and the Fuzzy C-Mean (FCM) clustering algorithm. Two stages are implemented in the proposed system: in the first step, the FCM algorithm is used to find out the type of virus, and this algorithm is capable of handling ambiguous features of viruses. In the second step, a BP neural network is used as a classifier to detect the pandemic class. The proposed system was trained and tested using a well-known dataset (covid-19 vs influenza). Information Gain (IG) is used to optimize the related features that affect the classification process to improve speed and accuracy.  The proposed mobile application is developed to support users easily detecting the COVID-19 infection by inputting the medical tests as significant features to the proposed system. The proposed system's accuracy is up to (89%), the framework was created using the Matlab programming environment and an Android Studio for Mobil application designing.</p>

In this age of information load, it becomes a herculean task for user to get the relevant things from vast number of information. This huge number of data demand specially designed Recommender system that can plays an important role in suggesting relevant information preferred by the users. From this point, this paper presents a modest approach to enhance prediction in MovieLens dataset with high scalability by applying user-based collaborative filtering methods on clustered data. The proposal consists of three consequence phases: preprocessing phase, similarity phase, prediction phase. The experimental results obtained conducting K-means clustering and correlation coefficient similarity measures against MovieLens datasets lead to an increase in the scalability of recommender system.

Recommender systems are used to find user's interested things among a huge amount of digital information. Collaborative filtering is used to generate recommendations. However, the data sparsity problem leads to generate unreasonable recommendations for those users who provide no ratings. From this point, this paper presents a modest approach to enhance prediction in movielens dataset with high sparsity by applying collaborative filtering methods. The proposal consists of three consequence phases: preprocessing phase, similarity phase, prediction phase. The experimental results obtained conducting similarity measures against movielens user rating datasets show that the result of prediction is enhanced about 10% to15% with the non-sparse rating matrix.

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Production sites suffer from idle in marketing of their products because of the lack in the efficient systems that analyze and track the evaluation of customers to products; therefore some products remain untargeted despite their good quality. This research aims to build a modest model intended to take two aspects into considerations. The first aspect is diagnosing dependable users on the site depending on the number of products evaluated and the user's positive impact on rating. The second aspect is diagnosing products with low weights (unknown) to be generated and recommended to users depending on logarithm equation and the number of corated users. Collaborative filtering is one of the most knowledge discovery techniques used positively in recommendation system. Similarity measures are the core operations in collaborative filtering; however, there is a certain deviance through using traditional similarity measures, which decreases the recommendation accuracy. Thus, the proposed model consists of a combination of measures: constraint Pearson correlation, jaccard distance measure and inverse user frequency (IUF).The experimental results implemented on movielens data set using MATLAB show a comparison between the results of the proposed model and some of the traditional similarity measures. The outcome results of the comparison show that the proposed model can be used as a parameter in the prediction process to achieve accurate prediction results during recommendation process.

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In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor methods. After experimental results, it was determined that out of 71 tested Iraqi tourism companies, 28% from these companies have very good assessment, 26% from these companies have good assessment, 31% from these companies have medium assessment, 4% from these companies have acceptance assessment and 11% from these companies have bad assessment. These results helped the companies to improve their work and programs responding sufficiently and quickly to customer demands.