Multi-attention graph neural networks for city-wide bus travel time estimation using limited data

Multi-attention graph neural networks for city-wide bus travel time estimation using limited dat

P2OP—Plant Pathology on Palms: A deep learning-based mobile solution for in-field plant disease detection

Plant diseases are one of the dominant factors that threaten sustainable agriculture, leading to economic losses. Developing an accurate mobile-based plant disease detection methodology is important for enabling rapid identification of emerging diseases directly from the farms. The deep learning methods have limited usage in mobile-based applications as they require larger memory and processing power to operate directly on smartphones or internet connectivity when used with a client–server computing model. To address this challenge, we propose a mobile-based lightweight deep learning-based model, which requires only a small footprint and processing power while maintaining higher detection accuracy. With around 0.088 billion multiply–accumulation operations, 0.26 million parameters, and 1 MB storage space, this framework achieved 97%, 97.1% and 96.4% accuracies on apple, citrus and tomato leaves datasets, respectively. One of our tiny models achieved 93.33% accuracy on a custom sourced in-the-wild apple leaves images dataset, which affirms the in-field applicability of the proposed framework. The superiority of the proposed model is further demonstrated through a comparative study with equivalent lightweight models

Error Spectrum Analysis of Solar Power Prediction for Deakin Microgrid Digital Twin

In 2021, the AUD 23 million Deakin University Renewable Energy Microgrid commenced operation. To date, it has the largest solar farm at an Australian University-one that is capable of producing 7.25-megawatt of power. In the year following that, a web-based digital twin was developed with the aims of providing operators with intelligence and insights through AI-driven capabilities. A key component of these AI-driven capabilities is a fast algorithm that predicts power generation from weather data for rapid decision-making and frequent health assessment. In particular, the accuracy of the prediction algorithm needs to be high and consistent, regardless of the magnitude of the power generated. To this end, we tested a number of commonly-used ML algorithms. From our experiments, Extreme Gradient Boost (XGBoost) and Random Forest (RF) are capable of training 7500 samples well within a few seconds with reasonable accuracy. With this observation, we further tuned the two methods and compared them for accuracy and consistency using data from different sections of the solar farm, mainly the three main blocks (each with over 6000 solar panels) and the four research arrays (each with 144 solar panels). Given a similar (or much less) computation time, RF outperformed XGBoost in terms of prediction accuracy using the new accuracy profile metrics we have introduced: namely the x-percentile Closeness Scores and the x-percentile Absolute Errors. These new metrics measure the performance by reporting the whole error spectrum instead of just a single value

Double-layer data-hiding mechanism for ECG signals

AbstractDue to the advancement in biomedical technologies, to diagnose problems in people, a number of psychological signals are extracted from patients. We should be able to ensure that psychological signals are not altered by adversaries and it should be possible to relate a patient to his/her corresponding psychological signal. As far as our awareness extends, none of the existing methods possess the capability to both identify and verify the authenticity of the ECG signals. Consequently, this paper introduces an innovative dual-layer data-embedding approach for electrocardiogram (ECG) signals, aiming to achieve both signal identification and authenticity verification. Since file name-based signal identification is vulnerable to modifications, we propose a robust watermarking method which will embed patient-related details such as patient identification number, into the medically less-significant portion of the ECG signals. The proposed robust watermarking algorithm adds data into ECG signals such that the patient information hidden in an ECG signal can resist the filtering attack (such as high-pass filtering) and noise addition. This is achieved via the use of error buffers in the embedding algorithm. Further, modification-sensitive fragile watermarks are added to ECG signals. By extracting and checking the fragile watermark bits, we can determine whether an ECG signal is modified or not. To ensure the security of the proposed mechanism, two secret keys are used. Our evaluation demonstrates the usefulness of the proposed system

Deakin microgrid digital twin and analysis of AI models for power generation prediction

To achieve carbon neutral by 2025, Deakin University launched a AUD 23 million Renewable Energy Microgrid in 2020 with a 7-megawatt solar farm, the largest at an Australian University. A web-based digital twin (DT) is developed to provide operators with intelligence and insights through several AI-driven capabilities. Accurate and computationally efficient power generation prediction is one of the critical elements in this DT. To this end, we researched the literature and identified the commonly used Machine Learning-based prediction models and compared them computationally using power generation and weather sensor data obtained from the solar farm. From the computational experiments, we find that, overall, Artificial Neural Network (ANN) has achieved the highest R2-score (0.944) and the lowest RMSE (14.848). To obtain further insights, we compared the methods using our two novel metrics, the x-percentile Closeness scores and the x-percentile Absolute error scores. The new metrics provide us with a spectrum to measure the consistency and robustness of the prediction methods instead of just a single value. Further, power generation can fluctuate substantially, and a prediction model should be accurate regardless of the magnitude of the output, hence measuring the relative error has its merits. By our two new metrics, using the data from our Deakin Microgrid, Random Forrest (RF) outperformed the other methods tested, with the smallest absolute relative error across the whole spectrum (from 0.011 to 0.457). RF is also the fastest in model training time at 4.894 s and XGBoost came second at 5.115 s–a big contrast to ANN at 144.102 s. All prediction times are under 1 s. RF is therefore used as a power prediction algorithm in our Deakin Microgrid Digital Twin

EvAnGCN: Evolving Graph Deep Neural Network Based Anomaly Detection in Blockchain

Detecting anomalous behaviors in the blockchain is important for maintaining its integrity. An imminent challenge is to capture the evolving model of transactions in the network. Representing the network with a dynamic graph helps model the system’s time-evolving nature. However, as the graph evolves, real-world scenarios further stimulate the development of Graph Neural Networks (GNNs) to handle dynamic graph structures. In this paper, we propose a novel dynamic Graph Convolutional Network framework, namely EvAnGCN (Evolving Anomaly detection GCN), that helps detect anomalous behaviors in the blockchain. EvAnGCN exploits the time-based neighborhood feature aggregation of transactional features and the dynamic structure of the transaction network to detect anomalous nodes within the network. We conducted experiments on the Ethereum blockchain transaction dataset. Our experimental results demonstrate that EvAnGCH outperformed the baseline models

Navigating Uncertainty: Gold Price Forecasting with Kolmogorov-Arnold Networks in Volatile Markets

Navigating Uncertainty: Gold Price Forecasting with Kolmogorov-Arnold Networks in Volatile Market

Exploiting Redundancy in Network Flow Information for Efficient Security Attack Detection

Securing communication networks has become increasingly important due to the growth in cybersecurity attacks, such as ransomware and denial of service attacks. In order to better observe, detect and track attacks in large networks, accurate and efficient anomaly detection algorithms are needed. In this paper, we address how the redundancy of the normal and attack traffic information available from network flow data can be exploited to develop a computationally efficient method for security attack detection. In this work, several sampling strategies are integrated with two graph neural network frameworks that have been employed to detect network attacks with reduced computational overhead, while achieving high detection accuracy. Using network flow data from several types of networks, such as Internet of Things data, the trade-off between model accuracy and computational efficiency for different attacks has been evaluated

It's PageRank All The Way Down: Simplifying Deep Graph Networks

First developed to rank website relevance, PageRank has become ubiquitous in many areas of graph machine learning including deep learning. We demonstrate that a number of recently published deep graph neural networks are qualitatively equivalent to shallow networks utilizing Personalized PageRank (PPR), and that their performance improvements over existing PPR implementations can be fully explained by hyperparameter choices. We also show that PPR with these hyperparameters outperform more recently published sophisticated variations of PPR-based graph neural networks, and present efficient implementations that reduce training times and memory requirements while improving scalability

A Scalable Framework for Trajectory Prediction

Trajectory prediction (TP) is of great importance for a wide range of location-based applications in intelligent transport systems such as location-based advertising, route planning, traffic management, and early warning systems. In the last few years, the widespread use of GPS navigation systems and wireless communication technology enabled vehicles has resulted in huge volumes of trajectory data. The task of utilizing this data employing spatio-temporal techniques for trajectory prediction in an efficient and accurate manner is an ongoing research problem. Existing TP approaches are limited to short-term predictions. Moreover, they cannot handle a large volume of trajectory data for long-term prediction. To address these limitations, we propose a scalable clustering and Markov chain based hybrid framework, called Traj-clusiVAT-based TP, for both short-term and long-term trajectory prediction, which can handle a large number of overlapping trajectories in a dense road network. In addition, Traj-clusiVAT can also determine the number of clusters, which represent different movement behaviours in input trajectory data. In our experiments, we compare our proposed approach with a mixed Markov model (MMM)-based scheme, and a trajectory clustering, NETSCAN-based TP method for both short- and long-term trajectory predictions. We performed our experiments on two real, vehicle trajectory datasets, including a large-scale trajectory dataset consisting of 3.28 million trajectories obtained from 15,061 taxis in Singapore over a period of one month. Experimental results on two real trajectory datasets show that our proposed approach outperforms the existing approaches in terms of both short- and long-term prediction performances, based on prediction accuracy and distance error (in km)