The intelligent management of power in electrical utilities depends on the high significance of load forecasting models. Since the industries are digitalized, power generation is supported by a variety of resources. Therefore, the forecasting accuracy of different models varies. The power utilities with different generation modalities (DGM) experience complexities and a noticeable amount of error in predicting future electrical consumption. To effectively manage the power flow with negligible power interruptions, a utility must utilize the forecasting tools to predict the future electricity demand with minimum error. Since the current literature supports individual and limited power sources involved in generation for load forecasting, thus the utilities with multiple power sources or DGM remain unexplored. Therefore, exploration of existing literature is required relating to analyzing the existing models which could be considered in load forecasting for DGM. This paper explores state-of-art methods recently utilized for electrical load forecasting highlighting the common practices, recent advances, and exposure of areas available for improvement. The review investigates the methods, parameters, and respective sectors considered for load forecasting. It performs in-depth analysis and discusses the strengths, weaknesses, and error percentages of models. It also highlights the peculiarities of methods used in residential, commercial, industrial, grid, and off-grid sectors aiming to help the researchers to appraise the common practices. Moreover, trends and research gaps are also discussed.

Education for sustainable development (ESD) is a growing research field, particularly over the last decade. Measuring the level of ESD that is currently embedded in curricula is useful for planning the further implementation of sustainability-related teaching. The Sustainable Development Goals (SDGs) are a useful benchmark for sustainability topics and so this paper follows a methodology in which a keyword scanning tool was created to quantify the level of SDG coverage within a list of learning outcomes for a module. The aim of the research is to further develop this methodology and compare the results from the keyword tool with results from a survey of the academic staff who deliver the modules. SDG-related keyword lists were collected from multiple sources for a meta-analysis, examining the performance of various lists. These lists were then compiled into one list of over 12,000 SDG keywords and a team of reviewers conducted a critical analysis on the relevancy of the context in which the keywords were found when scanned. This process reduced the list to 222 “crucial keywords” and gave the keywords a relevancy label based on the STARS definitions, the sustainability tracking assessment and rating system. Finally, ChatGPT was also investigated as a method of enriching the critically analysed list with contextually relevant synonyms. A survey was carried out within the College of Science and Engineering at the University of Galway. It asked staff to rate the level of SDG coverage within their own modules, in their own opinion. This gave results which could be compared with the keyword scanning tool. The findings show success in improving the accuracy of the SDG keywords. ChatGPT added synonyms to the crucial keywords identified and this list was the most accurate out of all keyword lists used in the study. Using these keywords and the modules that staff rated in the survey, a correlation was found in the SDG trend.

The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model’s classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis.

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.

Multispectral image classification has long been the domain of static learning with nonstationary input data assumption. The prevalence of Industrial Revolution 4.0 has led to the emergence to perform real-time analysis (classification) in an online learning scenario. Due to the complexities (spatial, spectral, dynamic data sources, and temporal inconsistencies) in online and time-series multispectral image analysis, there is a high occurrence probability in variations of spectral bands from an input stream, which deteriorates the classification performance (in terms of accuracy) or makes them ineffective. To highlight this critical issue, firstly, this study formulates the problem of new spectral band arrival as virtual concept drift. Secondly, an adaptive convolutional neural network (CNN) ensemble framework is proposed and evaluated for a new spectral band adaptation. The adaptive CNN ensemble framework consists of five (05) modules, including dynamic ensemble classifier (DEC) module. DEC uses the weighted voting ensemble approach using multiple optimized CNN instances. DEC module can increase dynamically after new spectral band arrival. The proposed ensemble approach in the DEC module (individual spectral band handling by the individual classifier of the ensemble) contributes the diversity to the ensemble system in the simple yet effective manner. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new spectral band during online image classification. Moreover, the extensive training dataset, proper regularization, optimized hyperparameters (model and training), and more appropriate CNN architecture significantly contributed to retaining the performance accuracy.

In the modern era of digitization, the analysis in the Internet of Things (IoT) environment demands a brisk amalgamation of domains such as high-dimension (images) data sensing technologies, robust internet connection (4 G or 5 G) and dynamic (adaptive) deep learning approaches. This is required for a broad range of indispensable intelligent applications, like intelligent healthcare systems. Dynamic image classification is one of the major areas of concern for researchers, which may take place during analysis under the IoT environment. Dynamic image classification is associated with several temporal data perturbations (such as novel class arrival and class evolution issue) which cause a massive classification deterioration in the deployed classification models and make them in-effective. Therefore, this study addresses such temporal inconsistencies (novel class arrival and class evolution issue) and proposes an adapted deep learning framework (ameliorated adaptive convolutional neural network (CNN) ensemble framework), which handles novel class arrival and class evaluation issue during dynamic image classification. The proposed framework is an improved version of previous adaptive CNN ensemble with an additional online training (OT) and online classifier update (OCU) modules. An OT module is a clustering-based approach which uses the Euclidean distance and silhouette method to determine the potential new classes, whereas, the OCU updates the weights of the existing instances of the ensemble with newly arrived samples. The proposed framework showed the desirable classification improvement under non-stationary scenarios for the benchmark (CIFAR10) and real (ISIC 2019: Skin disease) data streams. Also, the proposed framework outperformed against state-of-art shallow learning and deep learning models. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new concept changes during dynamic image classification. In future work, the authors of this study aim to develop an IoT-enabled adaptive intelligent dermoscopy device (for dermatologists). Therefore, further improvements in classification accuracy (for real dataset) is the future concern of this study.