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Cloud computing technology is a new computing paradigm phenomenon that has recently received a significant attention by several research studies. However, the previous works have concentrated on the adoption of this technology and limited studies focused on the factors influencing the intention to use it. Therefore, the proposed study developed a model to figure out these factors. This study used an online questionnaire to collect data. A total of 712 responses were received. Structural equation modelling was employed by using SmartPLS 3 software to analyse the collected data. The findings of this study indicate that awareness, user readiness, and satisfaction are important factors related to the use of cloud computing, while privacy seems to have no significant influence on the use of this technology. Thus, this study recommends users to attend courses and workshops to garner knowledge and understanding of cloud computing and hence become appropriately qualified to use it. Moreover, such courses and workshops will provide users with methods and techniques to protect their privacy, which should be given priority attention.

Continuous data stream analysis primarily focuses on the unanticipated changes in the transmission of data distribution over time. Conceptual change is defined as the signal distribution changes over the transmission of continuous data streams. A drift detection scenario is set forth to develop methods and strategies for detecting, interpreting, and adapting to conceptual changes over data streams. Machine learning approaches can produce poor learning outcomes in the conceptual change environment if the sudden change is not addressed. Furthermore, due to developments in concept drift, learning methodologies have been significantly systematic in recent years. The research introduces a novel approach using the fully connected committee machine (FCM) and different activation functions to address conceptual changes in continuous data streams. It explores scenarios of continual learning and investigates the effects of over-learning and weight decay on concept drift. The findings demonstrate the effectiveness of the FCM framework and provide insights into improving machine learning approaches for continuous data stream analysis. We used a layered neural network framework to experiment with different scenarios of continual learning on continuous data streams in the presence of change in the data distribution using a fully connected committee machine (FCM). In this research, we conduct experiments in various scenarios using a layered neural network framework, specifically the fully connected committee machine (FCM), to address conceptual changes in continuous data streams for continual learning under a conceptual change in the data distribution. Sigmoidal and ReLU (Rectified Linear Unit) activation functions are considered for learning regression in layered neural networks. When the layered framework is trained from the input data stream, the regression scheme changes consciously in all scenarios. A fully connected committee machine (FCM) is trained to perform the tasks described in continual learning with M hidden units on dynamically generated inputs. In this method, we run Monte Carlo simulations with the same number of units on both sides, K and M, to define the advancement of intersections between several hidden units and the calculation of generalization error. This is applied to over-learnability as a method of over-forgetting, integrating weight decay, and examining its effects when a concept drift is presented.

Radio spectrum awareness, including understanding radio signal activities, is crucial for improving spectrum utilization, detecting security vulnerabilities, and supporting adaptive transmissions. Related tasks include spectrum sensing, identifying systems and terminals, and understanding various protocol layers. In this paper, we investigate various identification and classification tasks related to fading channel parameters, signal distortions, Medium Access Control (MAC) protocols, radio signal types, and cellular systems. Specifically, we utilize deep learning methods in those identification and classification tasks. Performance evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.

A method based on a constellation diagram is proposed to identify QAM modulation of different orders in static, slow, and frequency selective fading channels. Although constellation diagrams have been studied and classified in literature, most of the work focused on noise. Little has been done to study the effect of multipath fading channels. We develop a highly accurate modulation classification method by exploiting deep learning with the constellation diagram. Based on the experimental results, our CNN model achieves a classification accuracy of 100% at −10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel.

Wireless communications systems design and its performance depend on the wireless fading channels, which are often characterized using a Rician probability function. A Rician K-factor is used to describe the fading severity in a Rician fading channel and is used in the system design and performance evaluation. Therefore, the estimation of the Rician K-factor is important in wireless communications research and development. Traditionally, a Rician K-factor equation, the statistics of the instantaneous frequency of the received signal with a lookup table, or the James-Stein estimator with the maximum likelihood estimation is used for the K-factor estimation. In this paper, we explore the use of deep learning for K-factor estimation. Specifically, we use the convolutional neural network (CNN) to estimate the Rician K-factor from a waveform signal in a Rician channel. Numerical results demonstrate its good performance in estimating the K-factor of the Rician channel.

Spectrum awareness allows the understanding of the wireless systems environment and it gives engineers and designers better control in systems design and analysis. Phase noise is one of the characteristics of the channel distortion or device distortion, which causes transmission errors. In this paper, a deep learning network is utilized to study and identify different phase noise levels for quadrature phase shift keying (QPSK) signals. Our experiment results show that the deep learning neural network is capable of classifying a wide range of phase noise levels.

Spectrum awareness, including identifying different types of signals, is very important in a cellular system environment. In this paper, a neural network is utilized to identify 5G signals among different cellular communications signals, including Long-Term Evolution (LTE) and Universal Mobile Telecommunication Service (UMTS). We explore the use of deep learning in wireless communications systems. We consider the effects of training dataset size, features extracted, and channel fading in our study. Experiment results demonstrate the effectiveness of deep learning neural networks in identifying cellular system signals, including UMTS, LTE, and 5G.