Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence

Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted in regression and classification tasks. However, deep learning has its limitations such as the assumption of equally spaced and ordered data, and the lack of ability to incorporate graph structure in terms of time-series prediction. Graphical neural network (GNN) has the ability to overcome these challenges and capture the temporal dependencies in time-series data. In this study, we propose a novel approach for predicting time-series data using GNN and monitoring with Reinforcement Learning (RL). GNNs are able to explicitly incorporate the graph structure of the data into the model, allowing them to capture temporal dependencies in a more natural way. This approach allows for more accurate predictions in complex temporal structures, such as those found in healthcare, traffic and weather forecasting. We also fine-tune our GraphRL model using a Bayesian optimisation technique to further improve performance. The proposed framework outperforms the baseline models in time-series forecasting and monitoring. The contributions of this study include the introduction of a novel GraphRL framework for time-series prediction and the demonstration of the effectiveness of GNNs in comparison to traditional deep learning models such as RNNs and LSTMs. Overall, this study demonstrates the potential of GraphRL in providing accurate and efficient predictions in dynamic RL environments.

FedStack: Personalized activity monitoring using stacked federated learning

Recent advances in remote patient monitoring (RPM) systems can recognize various human activities to measure vital signs, including subtle motions from superficial vessels. There is a growing interest in applying artificial intelligence (AI) to this area of healthcare by addressing known limitations and challenges such as predicting and classifying vital signs and physical movements, which are considered crucial tasks. Federated learning is a relatively new AI technique designed to enhance data privacy by decentralizing traditional machine learning modeling. However, traditional federated learning requires identical architectural models to be trained across the local clients and global servers. This limits global model architecture due to the lack of local models heterogeneity. To overcome this, a novel federated learning architecture, FedStack, which supports ensembling heterogeneous architectural client models was proposed in this study. This work offers a protected privacy system for hospitalized in-patients in a decentralized approach and identifies optimum sensor placement. The proposed architecture was applied to a mobile health sensor benchmark dataset from 10 different subjects to classify 12 routine activities. Three AI models, ANN, CNN, and Bi-LSTM were trained on individual subject data. The federated learning architecture was applied to these models to build local and global models capable of state of the art performances. The local CNN model outperformed ANN and Bi-LSTM models on each subject data. Our proposed work has demonstrated better performance for heterogeneous stacking of the local models compared to homogeneous stacking. This work sets the stage to build an enhanced RPM system that incorporates client privacy to assist with clinical observations for patients in an acute mental health facility and ultimately help to prevent unexpected death.

A Preference Random Walk Algorithm for Link Prediction through Mutual Influence Nodes in Complex Networks

Predicting links in complex networks has been one of the essential topics within the realm of data mining and science discovery over the past few years. This problem remains an attempt to identify future, deleted, and redundant links using the existing links in a graph. Local random walk is considered to be one of the most well-known algorithms in the category of quasi-local methods. It traverses the network using the traditional random walk with a limited number of steps, randomly selecting one adjacent node in each step among the nodes which have equal importance. Then this method uses the transition probability between node pairs to calculate the similarity between them. However, in most datasets, this method is not able to perform accurately in scoring remarkably similar nodes. In the present article, an efficient method is proposed for improving local random walk by encouraging random walk to move, in every step, towards the node which has a stronger influence. Therefore, the next node is selected according to the influence of the source node. To do so, using mutual information, the concept of the asymmetric mutual influence of nodes is presented. A comparison between the proposed method and other similarity-based methods (local, quasi-local, and global) has been performed, and results have been reported for 11 real-world networks. It had a higher prediction accuracy compared with other link prediction approaches.

Aspect-Based Opinion Extraction from Customer reviews

Text is the main method of communicating information in the digital age. Messages, blogs, news articles, reviews, and opinionated information abound on the Internet. People commonly purchase products online and post their opinions about purchased items. This feedback is displayed publicly to assist others with their purchasing decisions, creating the need for a mechanism with which to extract and summarize useful information for enhancing the decision-making process. Our contribution is to improve the accuracy of extraction by combining different techniques from three major areas, named Data Mining, Natural Language Processing techniques and Ontologies. The proposed framework sequentially mines products aspects and users opinions, groups representative aspects by similarity, and generates an output summary. This paper focuses on the task of extracting product aspects and users opinions by extracting all possible aspects and opinions from reviews using natural language, ontology, and frequent (tag) sets. The proposed framework, when compared with an existing baseline model, yielded promising results.