الصفحة 1
الصفحة 1
Motivation and career satisfaction at higher education in Syria: A sample from private university
This research paper examines the effect of motivational factors , extrinsic and intrinsic factors which effect on staff satisfaction at work at universities in Syria. The research methodology employs a quantitative design of questionnaire instrument. The model predicts that if employees develop high levels of motivation in their work and organizations, this will stimulate a good quality in their productivity and develop satisfaction at work. Motivation in general , extrinsic factors, intrinsic factors and job satisfaction are based on prior research measures. Sampling strategy employed non-probability sampling. The size of the sample is 35. The results of the research designate that intrinsic and extrinsic motivation factors are positively associated with employee job satisfaction
Introduction to Machine Learning with Applications in Information Security
Provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn't prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec.
