Time series forecasting remains challenging in the presence of nonstationarity, regime changes, and observation noise. Many existing machine learning approaches rely on complex architectures that often lead to unstable training and limited robustness. To address these limitations, we propose QAAR-SIREN, a compact forecasting framework that improves stability through residual learning and complementary feature representations. Instead of predicting absolute values, the model forecasts temporal increments, mitigating nonstationarity effects. It integrates three information sources: raw temporal lags, attention-based contextual summarization, and lightweight nonlinear features extracted from a shallow variational quantum circuit applied to the most recent observation. The quantum component functions as a compact nonlinear feature extractor that enriches the input representation without increasing architectural complexity. Experiments on synthetic signals with regime transitions and heterogeneous noise, as well as real-world datasets from climate, energy demand, finance, and transportation, demonstrate that QAAR-SIREN achieves strong and stable predictive performance. The model attains coefficients of determination up to approximately 0.985 with low mean squared error. Ablation studies confirm that observed gains arise from the complementary effects of residual learning, attention-based context aggregation, and quantum feature extraction.

Artificial Intelligence (AI) and Software Defined Radio (SDR) are transforming the field of signal intelligence. However, the full extent of the capabilities is unknown. This poster presents a paper in development that introduces a cloud-based platform leveraging artificial intelligence to detect and apply 11 modulation schemes (8 digital and 3 analog) to complex or quadrature radio signals. The SNR values analysed range from 0.0 to 40.0, with moderate drift, slight fading, and labelled increments. A comprehensive synthetic database developed by DeepSig is used to train four AI models. These will be integrated with the Google Cloud AI platform to enhance flexibility and processing power. The system will undergo testing with an SDR platform in GNU Radio, showcasing its potential for real-world signal processing applications. Cloud-based platforms offer the adaptability and computational power needed to replace traditional computers for AI-driven signal processing. Initial results indicate successful identification and accurate modulation type detection, with convenient access to the system through internet-connected devices.

Electricity demand prediction is crucial to ensure the operational safety and cost-efficient operation of the power system. Electricity demand has predominantly been predicted deterministically, while uncertainty analysis has been usually overlooked. To address this research gap, an integrated Neural Facebook Prophet (NFBP) model and Gaussian Kernel Density Estimation (KDE) model is proposed in this paper, as a way to obtain point and interval predictions of electricity demand, quantifying this way the uncertainty in the predictions. First, historical lagged data, created by utilizing the Partial Auto-correlation Function and Mutual Information Test, is applied to train a prediction model based on NFBP, Deep Learning (DL) as well as Statistical Models. Second, the model Prediction Errors (PE) are derived from the difference between actual and predicted values. A splitting strategy based on the mean and standard deviation of PE is proposed. Finally, electricity demand prediction intervals are obtained by applying Gaussian KDE on split PE. To verify the effectiveness of the proposed model, simulation studies are carried out for three prediction horizons on freely available datasets for the Bulimba sub-station in Southeast Queensland, Australia. Compared with DL models (Long-Short Term Memory Network and Deep Neural Network), the Root Mean Square Error of the NFBP model was reduced by 6.1% and 11.3% for 0.5-hr ahead, 22.7% and 26.3% for 6-hr ahead, and 31.8% and 29.9% for daily prediction. In addition, the Prediction Interval normalized Interval width is smaller in magnitude for the proposed NFBP-KDE model compared to other DL and Statistical models

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, the CNN algorithm is used to extract local patterns as well as common features that occur recurrently in time series data at different intervals. Then, the SVR is subsequently adopted to replace the fully connected CNN layers to predict the daily GSR time series data at six solar farms in Queensland, Australia. To develop the hybrid CSVR model, we adopt the most pertinent meteorological variables from Global Climate Model and Scientific Information for Landowners database. From a pool of Global Climate Models variables and ground-based observations, the optimal features are selected through a metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters of the proposed CSVR are optimized by mean of the HyperOpt method, and the overall performance of the objective algorithm is benchmarked against eight alternative DL methods, and some of the other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML and M5TREE) methods. The results obtained shows that the proposed CSVR model can offer several predictive advantages over the alternative DL models, as well as the conventional ML models. Specifically, we note that the CSVR model recorded a root mean square error/mean absolute error ranging between ≈ 2.172–3.305 MJ m2/1.624–2.370 MJ m2 over the six tested solar farms compared to ≈ 2.514–3.879 MJ m2/1.939–2.866 MJ m2 from alternative ML and DL algorithms. Consistent with this predicted error, the correlation between the measured and the predicted GSR, including the Willmott’s, Nash-Sutcliffe’s coefficient and Legates & McCabe’s Index was relatively higher for the proposed CSVR model compared to other DL and Machine Learning methods for all of the study sites. Accordingly, this study advocates the merits of CSVR model to provide a viable alternative to accurately predict GSR for renewable energy exploitation, energy demand or other forecasting-based applications.

This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, three-stream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce image-classified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89 %, with a receiver operating characteristic of 93 %. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients' pain level accurately.

• Correlation graphs are used to identify EEG sleep stages. • Different modularity algorithms are tested and investigated. • An ensemble machine learning is designed to classify graphs attributes. • MCDM is used to select classifiers to design an ensemble machine learning. Sleep plays an essential role in repairing and healing human mental and physical health. Developing an efficient method for scoring electroencephalogram (EEG) sleep stages is expected to help medical specialists in the early diagnosis of sleep disorders. In this paper, a novel technique is proposed for classifying sleep stages EEG signals using correlation graphs. First, each 30 s EEG segment is divided into a set of sub-segments. The dimensionality of each sub-segment is reduced by using a statistical model. Second, each EEG segment is transferred into a graph considering each sub-segment as a node in a graph, and a link between each pair of nodes is calculated based on their correlation coefficient. Graph's modularity is used as input features into an ensemble classifier. Different community detection algorithm based correlation graph are investigated to discern the most effective features to reveal the differences between EEG sleep stages. A combination of various classification techniques: a least square vector machine (LS-SVM), k-means, Naïve Bayes, Fuzzy C-means, k-nearest, and logistic regression are tested using multi criteria decision making (MCDM) to design an ensemble classifier. Based on the results of the MCDM, the best four: LS-SVM, Naïve Bayes, logistic regression and k-nearest are integrated, to finally utilise as an ensemble classifier to categorise the graph's characteristics. The results obtained from the ensemble classifier are compared with those from the individual classifiers. The performance of the proposed method is compared with state of the art of sleep stages classification. The experimental results showed that the EEG sleep classification based on correlation graphs are able to achieve better recognition results than the existing state of the art techniques.

Accurate and reliable forecasting models for electricity demand (G) are critical in engineering applications. They assist renewable and conventional energy engineers, electricity providers, end-users, and government entities in addressing energy sustainability challenges for the National Electricity Market (NEM) in Australia, including the expansion of distribution networks, energy pricing, and policy development. In this study, data-driven techniques for forecasting short-term (24-h) G-data are adopted using 0.5 h, 1.0 h, and 24 h forecasting horizons. These techniques are based on the Multivariate Adaptive Regression Spline (MARS), Support Vector Regression (SVR), and Autoregressive Integrated Moving Average (ARIMA) models. This study is focused in Queensland, Australia’s second largest state, where end-user demand for energy continues to increase. To determine the MARS and SVR model inputs, the partial autocorrelation function is applied to historical (area aggregated) G data in the training period to discriminate the significant (lagged) inputs. On the other hand, single input G data is used to develop the univariate ARIMA model. The predictors are based on statistically significant lagged inputs and partitioned into training (80%) and testing (20%) subsets to construct the forecasting models. The accuracy of the G forecasts, with respect to the measured G data, is assessed using statistical metrics such as the Pearson Product-Moment Correlation coefficient (r), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Normalized model assessment metrics based on RMSE and MAE relative to observed means (RMSEG¯andMAEG¯), Willmott’s Index (WI), Legates and McCabe Index (ELM), and Nash–Sutcliffe coefficients (ENS) are also utilised to assess the models’ preciseness. For the 0.5 h and 1.0 h short-term forecasting horizons, the MARS model outperforms the SVR and ARIMA models displaying the largest WI (0.993 and 0.990) and lowest MAE (45.363 and 86.502 MW), respectively. In contrast, the SVR model is superior to the MARS and ARIMA models for the daily (24 h) forecasting horizon demonstrating a greater WI (0.890) and MAE (162.363 MW). Therefore, the MARS and SVR models can be considered more suitable for short-term G forecasting in Queensland, Australia, when compared to the ARIMA model. Accordingly, they are useful scientific tools for further exploration of real-time electricity demand data forecasting.

The prediction of drought is of major importance in climate-related studies, hydrologic engineering, wildlife or agricultural studies. This study explores the ability of two machine learning methods to predict 1, 3, 6 and 12 months standardized precipitation and evapotranspiration index (SPEI) for the Wilsons Promontory station in Eastern Australia. The two methods are multiple linear regression (MLR) and artificial neural networks (ANN). The data-driven models were based on combinations of the input variables: mean precipitations, mean, maximum and minimum temperatures and evapotranspiration, for data between 1915 and 2012. Two performance metrics were used to compare the performance of the optimum MLR and ANN models: the coefficient of determination (R2) and the root mean square error (RMSE). It was found that ANN provided greater accuracy than MLR in forecasting the 1, 3, 6 and 12 months SPEI.