Mohd Sakib

Personalised services, inventory optimisation, and fraud detection have all been made possible by the incorporation of machine learning (ML) into e-commerce. But it also makes platforms vulnerable to adversarial attacks, in which criminals alter data inputs in order to take advantage of flaws in the model. System dependability, data security, and customer trust are all compromised by these threats, which range from phishing emails to tampered reviews and transaction data. Critical defence techniques provided by Natural Language Processing (NLP) include anomaly detection, ensemble modelling, adversarial training, and context-aware analysis with models such as BERT and GPT. Dynamic feature selection is one technique that reduces computational load while increasing performance. Unusual patterns in user behaviour and content can be found with the aid of behavior-based techniques and NLP-driven analytics. When combined, these tactics strengthen resistance to changing threats. For cybersecurity in sustainable e-commerce, continuous research, real-time

Ross River virus (RRV) disease is one of the most epidemiological mosquito-borne diseases in Australia. Its major consequences on public health require building a precise and accurate model for predicting any forthcoming outbreaks. Several models have been developed by machine learning (ML) researchers, and many studies have been published as a result. Later, deep learning models have been introduced and shown tremendous success in forecasting, mainly the long short-term memory (LSTM), which performs significantly better than the traditional machine learning approaches. There are four common problems that previously developed models need to solve. They are exploding gradient, vanishing gradient, uncertainty and parameter bias. LSTM has already solved the first two problems, i.e. exploding and vanishing gradient problems, and the remaining two are overcome by [ ]-LSTM. However, developing a prediction model for the RRV disease is a challenging task because it presents a wide range of symptoms, and there needs to be more accurate information available on the disease. To address these challenges, we propose a data-driven ensemble deep learning model using multi-networks of LSTM neural network for RRV disease forecasting in Australia. Data is collected between 1993 and 2020 from the Health Department of the Government of Australia. Data from 1993 to 2016 is taken to train the model, while the data of 2016–2020 is used as a test dataset. Previous research has demonstrated the efficacy of both ARIMA and exponential smoothing techniques in the field of time-series forecasting. As a result, our study sought to evaluate the performance of our proposed model in comparison to these established parametric methods, including ARIMA and ARMA, as well as the more recent deep learning approaches such as encoder–decoder and attention mechanism models. The results show that [ ]-LSTM achieves higher accuracy and has a less mean-square error. We have also discussed the comparison of the models in detail. Such forecasting gives an insight into being well prepared and handling the situation of the outbreak.