There are a lot of developing countries with inadequate meteorological stations to measure solar radiation. This has been a major drawback for solar power applications in these countries as the performance of the solar-powered system cannot be accurately forecasted. In this study, two novel hybrid neural networks namely; convolutional neural network/artificial neural network (CNN-ANN) and convolutional neural network/long short-term memory/artificial neural network (CNN-LSTM-ANN), have been developed for hourly global solar radiation prediction. ANN models are also developed and the performance of the hybrid neural network models is compared with it. This study contributes to the search for more accurate solar radiation estimation methods. The hybrid neural network models are trained/tested with data from ten different countries across Africa. Results from this study indicate that the performance of all the hybrid models developed in this study is superior to what has been presented in existing literature with their r values ranging from 0.9662 to 0.9930. CNN-ANN model is the best for solar radiation forecasting in Southern, Central, and West Africa. CNN-LSTM-ANN is better for East Africa while both CNN-ANN and CNN-LSTM-ANN are suitable for North Africa. CNN-ANN application for solar radiation prediction in Chad had the overall best performance with an r-value, MAE, RMSE, and MAPE of 0.9930, 15.70 W/m², 46.84 W/m², and 4.98% respectively. The integration of CNN and LSTM algorithms with an ANN model enhanced long-term computational dependency and reduce error terms for the model.

Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios. To accomplish such frontiers, we propose three artificial intelligence models through the application of deep learning algorithms to analyze and detect anomalies in human heartbeat signals. The three proposed models include an attention autoencoder that maps input data to a lower-dimensional latent representation with maximum feature retention, and a reconstruction decoder with minimum remodeling loss. The autoencoder has an embedded attention module at the bottleneck to learn the salient activations of the encoded distribution. Additionally, a variational autoencoder (VAE) and a long short-term memory (LSTM) network is designed to learn the Gaussian distribution of the generative reconstruction and time-series sequential data analysis. The three proposed models displayed outstanding ability to detect anomalies on the evaluated five thousand electrocardiogram (ECG5000) signals with 99% accuracy and 99.3% precision score in detecting healthy heartbeats from patients with severe congestive heart failure.

Secure localization of vehicles is gaining the attention of researchers from both academia and industry especially due to the emergence of internet of things (IoT). The modern vehicles are usually equipped with circuitries that gives connectivity with other vehicles and with cellular networks such as 4G/Fifth generation cellar network (5G). The challenge of secure localization and positioning is magnified further with the invention of technologies such as autonomous or driverless vehicles based on IoT, satellite, and 5G. Some satellite and IoT based localization techniques exploit machine learning, semantic segmentation, and access control mechanism. Access control provides access grant and secure information sharing mechanism to authorized users and restricts unauthorized users, which is necessary regarding security and privacy of government or military vehicles. Previously, static conflict of interest (COI) based access control was used for security proposes. However, static COI based access control creates excesses and administrative overload that creates latency in execution, which is the least tolerable factor in modern IoT or 5G control vehicles. Therefore, in this paper, a hybrid access control (HAC) model is proposed that implements the dynamic COI in the HAC model on the level of roles. The proposed model is enhanced by modifying the role-based access control (RBAC) model by inserting new attributes of the RBAC entities. The HAC model deals with COI on the level of roles in an efficient manner as compared to previously proposed models. Moreover, this model features significant improvement in terms of dynamic behavior, decreased administrative load, and security especially for vehicular localization. Furthermore, the mathematical modeling of the proposed model is implemented with an example scenario to validate the concept.

The scattering of atmospheric particles significantly alters images captured under hazy weather condition. Images appear distorted, blurry and low in contrast attenuation, which extensively affects computer vision systems. There has been development of several prior based methods to address this problem. However, these methods come at a high computational cost. We present a fast, single image dehazing method based on dark channel prior and Rayleigh scattering. Firstly, we present a simple but effective methodology for estimating the atmospheric light through the computation of average, minimum and maximum of the pixels in each of the three RGB colour channels. Then, using the theory of Rayleigh scattering, we model a scattering coefficient to estimate the initial transmission map. Also, a fast-guided filter is adopted to refine the initial transmission map due to inaccurate halo edges. Finally, we restore the haze-free image through the atmospheric scattering model. Extensive qualitative and computational experiments on hazy outdoor images demonstrate that the proposed method produces excellent results whiles achieving a faster processing time.

A major development in the field of access control is the dominant role-based access control (RBAC) scheme. The fascination of RBAC lies in its enhanced security along with the concept of roles. In addition, attribute-based access control (ABAC) is added to the access control models, which is famous for its dynamic behavior. Separation of duty (SOD) is used for enforcing least privilege concept in RBAC and ABAC. Moreover, SOD is a powerful tool that is used to protect an organization from internal security attacks and threats. Different problems have been found in the implementation of SOD at the role level. This paper discusses that the implementation of SOD on the level of roles is not a good option. Therefore, this paper proposes a hybrid access control model to implement SOD on the basis of permissions. The first part of the proposed model is based on the addition of attributes with dynamic characteristics in the RBAC model, whereas the second part of the model implements the permission-based SOD in dynamic RBAC model. Moreover, in comparison with previous models, performance and feature analysis are performed to show the strength of dynamic RBAC model. This model improves the performance of the RBAC model in terms of time, dynamicity, and automatic permissions and roles assignment. At the same time, this model also reduces the administrator’s load and provides a flexible, dynamic, and secure access control model.

Several deep learning techniques have been intensively reviewed for captioning tasks, enabling the possibility of textual understanding, and description of both simple and complex images. In advancing this knowledge, this paper proposes a multimodal end-to-end siamese difference captioning model (SDCM) to automatically generate a natural language description of differences in an image pair. The proposed supervised learning model combines several deep learning techniques in exploring the practicability of capturing, aligning, and computing the disparities between two image features, for the purpose of creating corresponding language model probability distribution. First, a deep siamese convolutional neural network is used to extract the feature vector discrepancies of an image pair, and then an attention mechanism enables the detection of salient regions of the feature vector which effectively allows a bidirectional long short-term memory decoder to generate a matching and semantically associated textual sequence. The evaluation of the model is tested on the spot-the-diff baseline dataset which consists of pairs of images and their equivalent captions. The results indicate that our proposed model demonstrates a highly competitive performance in comparison to the state of the art.