Meeting the energy needs of a growing population is of paramount importance in today’s society. The use of renewable energy sources, especially solar energy, can help reduce greenhouse gas emissions from traditional sources. Solar irradiance, which depends on several factors such as the availability of sunlight, cloud cover index, latitude, orientation and tilt of solar panels, and technical factors, plays a crucial role in the use of solar energy. Predicting solar irradiance is therefore essential for enhancing the reliability and efficiency of solar energy systems. Artificial intelligence (AI) and machine learning (ML) models are increasingly being used to predict solar irradiance forecasting. These models can learn from historical weather data and identify complex patterns to predict future solar irradiance with high accuracy. Techniques such as regression, neural networks and ensemble methods are frequently used and enable more efficient planning and operation of solar energy systems. This study provides a comprehensive analysis of the existing state-of-the-art models for solar irradiance forecasting. The study evaluates a variety of forecasting models, including machine learning, numerical weather prediction and hybrid approaches for assessing their accuracy, strengths and weaknesses. As an outcome of this study, potential future improvements in the prediction of solar irradiance are highlighted and the importance of interdisciplinary collaboration and emerging technologies is emphasized. To ensure a sustainable and resilient energy future, it is crucial to continue efforts to better integrate solar energy into mainstream electricity systems. This work can serve as a fundamental analysis for future researchers to identify the most appropriate approaches for medium and long-term solar irradiance forecasts.

Improving software quality by proactively detecting potential defects during development is a major goal of software engineering. Software defect prediction plays a central role in achieving this goal. The power of data analytics and machine learning allows us to focus our efforts where they are needed most. A key factor in the success of software fault prediction is selecting relevant features and reducing data dimensionality. Feature selection methods contribute by filtering out the most critical attributes from a plethora of potential features. These methods have the potential to significantly improve the accuracy and efficiency of fault prediction models. However, the field of feature selection in the context of software fault prediction is vast and constantly evolving, with a variety of techniques and tools available. Based on these considerations, our systematic literature review conducts a comprehensive investigation of feature selection methods used in the context of software fault prediction. The research uses a refined search strategy involving four reputable digital libraries, including IEEE Explore, Science Direct, ACM Digital Library, and Springer Link, to provide a comprehensive and exhaustive review through a rigorous analysis of 49 selected primary studies from 2014. The results highlight several important issues. First, there is a prevalence of filtering and hybrid feature selection methods. Second, single classifiers such as Naïve Bayes, Support Vector Machine, and Decision Tree, as well as ensemble classifiers such as Random Forest, Bagging, and AdaBoost are commonly used. Third, evaluation metrics such as area under the curve, accuracy, and F-measure are commonly used for performance evaluation. Finally, there is a clear preference for tools such as WEKA, MATLAB, and Python. By providing insights into current trends and practices in the field, this study offers valuable guidance to researchers and practitioners to make informed decisions to improve software fault prediction models and contribute to the overall improvement of software quality.

In the fourth industrial revolution, the scale of execution for interactive applications increased substantially. These interactive and animated applications are human-centric, and the representation of human motion is unavoidable, making the representation of human motions ubiquitous. Animators strive to computationally process human motion in a way that the motions appear realistic in animated applications. Motion style transfer is an attractive technique that is widely used to create realistic motions in near real-time. motion style transfer approach employs existing captured motion data to generate realistic samples automatically and updates the motion data accordingly. This approach eliminates the need for handcrafted motions from scratch for every frame. The popularity of deep learning (DL) algorithms reshapes motion style transfer approaches, as such algorithms can predict subsequent motion styles. The majority of motion style transfer approaches use different variants of deep neural networks (DNNs) to accomplish motion style transfer approaches. This paper provides a comprehensive comparative analysis of existing state-of-the-art DL-based motion style transfer approaches. The enabling technologies that facilitate motion style transfer approaches are briefly presented in this paper. When employing DL-based methods for motion style transfer, the selection of the training dataset plays a key role in the performance. By anticipating this vital aspect, this paper provides a detailed summary of existing well-known motion datasets. As an outcome of the extensive overview of the domain, this paper highlights the contemporary challenges faced by motion style transfer approaches.

In the public health sector and the field of medicine, the popularity of data mining and its usage in knowledge discovery and databases (KDD) are rising. The growing popularity of data mining has discovered innovative healthcare links to support decision making. For this reason, there is a great possibility to better diagnose patient’s diseases and maintain the quality of healthcare services in hospitals. So, there is an urgent need to make disease diagnosis possible by discovering the hidden patterns from the patients’ history information in developing countries. This work is a step towards how to use the extracted knowledge to enhance the quality of healthcare facilities. In this paper, we have proposed a web-centered hospital information management system (HIMS) that identifies frequent patterns from the data with eye disorder patients using the association rule-based Apriori data mining technique. The proposed framework has the capability to overcome all the key issues and problems in the current hospital information management system regarding data analysis and reporting services. For this purpose, data were collected from more than 1000 university students (China citizens) both online and manually (printed questionnaire). After applying the Apriori algorithm on the collected data, we revealed that almost 140 individuals out of 1035 had myopia (near-sighted disorder), at current age of 22 years, and that there were no male patients found with myopia. We concluded that their clinical relevance and utility can generate favorable results from prospective clinical studies by mapping out the habits or lifestyles that potentially lead to fatal diseases. In the future, we plan to extend this work to fully automate HIMS to help practitioners to diagnose the reasons of various diseases by extracting patient lifestyle patterns.

Lightweight security provisioning is a recent topic in resource-constrained software-defined networking (SDN). Integration between SDN, internet of things (IoT), network function virtualization (NFV) provides massive application services for 5G/B5G communications. Current state-of-the-art security solutions were caused by higher resource consumption, weaker key distribution, and forged credentials. In this paper, we proposed a blockchain-based lightweight security architecture (Bloc-Sec) in SDN/NFV that enabled cloud of IoT networks with 5G/B5G communication. Firstly, we authenticate all IoT devices to the blockchain server using multiple factors Blake-256 hashing algorithm. Secondly, we select the optimal virtual network function (VNF) using the cuttlefish optimization algorithm. Thirdly, blockchain is invoked to keep store the hashed flow rules that are deployed in VNF. Fourthly, the controller is involved in the packet classification by proposing the packet header inspection and packet content inspection using spiking dual fuzzy neural networks. For testing, NS3.26 is implemented and the performance evaluated.