<p>The process of product development in the health sector, especially pharmaceuticals, goes through a series of precise procedures due to its directrelevance to human life. The opinion of patients or users of a particular drugcan be relied upon in this development process, as the patients convey their experience with the drugs through their opinion. The social media field provides many datasets related to drugs through knowing the user's ratingand opinion on a drug after using it. In this work, a dataset is used that includes the user’s rating and review on the drug, for the purpose of classifying the user’s opinions (reviews) whether they are positive ornegative. The proposed method in this article includes two phases. The first phase is to use the Global vectors for word representation model for converting texts into the vector of embedded words. As for the second stage, the deep neural network (Bidirectional longshort-termmemory) is employedin the classification of reviews. The user's rating is used as a ground truth inevaluating the classification results. The proposed method present sencouraging results, as the classification results are evaluated through threecriteria, namely Precision, Recall and F-score, whose obtained values equal(0.9543, 0.9597and0.9558), respectively. The classification results of theproposed method are compared to a number of classifiers, and it was noticed that the results of the proposed method exceed those of the alternative classifiers.</p>

Johnson's rule is a scheduling method for the sequence of jobs. Its primary goal is to find the perfect sequence of functions to reduce the amount of idle time, and it also reduces the total time required to complete all functions. It is a suitable method for scheduling the purposes of two functions in a specific time-dependent sequence for both functions and where the time factor is the only parameter used in this way. Therefore, it is not suitable for scheduling work for computers network, where there are many factors affecting the completion time such as CPU speed, memory, bandwidth, and size of data. In this research, Johnson's method will adopt by adding many factors that affect the completion time of the work so that it becomes suitable for the site’s job scheduling purposes to reduce the waiting and idle time for a group of jobs.

Load balancing is a critical aspect of managing server resources efficiently and ensuring optimal performance in distributed systems. The Weighted Round Robin (WRR) algorithm is commonly used to allocate incoming requests among servers based on their assigned weights. However, static weights may not reflect the changing demands of servers, leading to imbalanced workloads. To address this issue, this study proposes a dynamic mechanism for assigning weights to servers in the WRR algorithm based on the data rate and incorporates the Least Connection approach for the best result. The dynamic mechanism takes into account the real-time data rate of each server, representing its current load. Servers with higher data rates are assigned higher weights to attract a larger share of incoming requests, while those with lower data rates receive lower weights to manage their loads effectively. This dynamic weight assignment allows the algorithm to adapt to varying workloads and achieve better load balancing across servers. To further refine the distribution of requests, the Least Connection approach is employed to handle tie-breaking situations and for more fairness in distributing the loads. The proposed algorithm is a hybrid of data rate and the Least Connection, it is evaluated through simulations and real-world experiments. The results demonstrate its superiority in achieving improved load balance compared to traditional static-weight WRR algorithms. By dynamically adjusting weights based on data rate and employing the Least Connection approach, the algorithm optimizes server resource usage, minimizes response times, and enhances overall system performance in distributed environments.

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Incoming Information Technology (IT) services appear with cloud computing perspectives that provide users access to IT resources anytime, anywhere. These services should be good enough for the user with some advantages for the cloud service provider. To achieve this goal, you must face many challenges, load balancing is one of these challenges. The most convenient option for some functions does not mean that option is always a good choice to achieve the entire work all the time. Resource overload and bad traffic that can lead to time exhaustion should be avoided, this can be obtained through appropriate load balancing mechanisms. This paper offers a simple solution for choosing the preferred server to distribute functions based on minimum bandwidth consumption.