The increasing demand for continual learning in sequential data processing has led to progressively complex training methodologies and larger recurrent network architectures. Consequently, this has widened the knowledge gap between continual learning with recurrent neural networks (RNNs) and their ability to operate on devices with limited memory and compute. To address this challenge, we investigate the effectiveness of simplifying RNN architectures, particularly gated recurrent unit (GRU), and its impact on both single-task and multitask sequential learning. We propose a new variant of GRU, namely the minion recurrent unit (MiRU). MiRU replaces conventional gating mechanisms with scaling coefficients to regulate dynamic updates of hidden

The study seeks to analyze the perceptions of audience in UAE towards the performance of Emirates Satellite TV Channels. It analyzed the exposure motivations of audience to satellite TV channels, its positive and negative aspects and to what extent they abide by media ethics. A survey is conducted with a sample of four hundred. The study shows significant differences between male and female towards the characteristics of TV channels, its positive and negative aspects and its commitments to media ethics.

            The study also shows that the expectancy value model and third person effect model are applicable in studying the perceptions of audience and media people in UAE t

The study aims to identify the impact of electronic games on increasing the dropout rate among students in the basic stage in Jordan from their teachers’ point of view. The study adopted a descriptive survey method. Its community consisted of all fe(male) teachers of the basic stage in public and private schools, (First Amman, Irbid, and special education in Zarqa and Amman). The electronic questionnaire was used as a tool for the study. The results have shown that the effect of electronic games on increasing the dropout rate among students in the basic stage in Jordan was high. Besides, there are statistically significant differences due to the gender variable for males. There are statistically significant differences due to the varia

The study aimed to explore the relationship between future anxiety and life orientation of male and female nurses, working in government hospitals of Gaza Strip governorates. The study sample consisted of 228 nurses (131 male nurses and97 female nurses. To achieve the study objectives , the researcher used the future anxiety scale, prepared by the researcher, and life orientation scale prepared by Scheier and Craver (1985 ) and translated into Arabic by Bader Al-Ansari . The results indicated that the level of future anxiety among nurses working at government hospitals was (64.85%), a high percentage, whereas life orientation was (65.96%), a low percentage. Additionally , the results showed that the Pearson correlation coefficient betwee

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach