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Autoencoders implemented by artificial neural networks (ANNs) are utilized to learn the latent space representation of data in an unsupervised manner, and they have been widely used in recommender systems. For instance, several collaborative denoising autoencoder (CDAE) models have shown that their performance gains outperform that of the collaborative filtering based (CF-based) models. In this work, a near-optimal Top-K forecasting solution is proposed for our advanced autoencoder recommender systems. We propose a method which utilizes CDAE model in predicting the Top-K popular videos in an upcoming time period. In order to improve the prediction accuracy, we also propose an autoencoder based recommendation algorithm with the help of K-means clustering that upgrades the performance of the original autoencoder model. The experimental results show that our method increases significantly the Average Precision (AP) and Recall values by nearly 30%. We then further utilize our proposed autoencoder model with clustering in predicting Top-K popular videos. The applications of predicting Top-K popular videos can be used in the video delivery for the Mobile Edge Computing (MEC) environment to avoid bottleneck in the constricted capacity of backhaul link. Namely, the performance gain will be upgraded if our proposed method precisely predicts and caches the Top-K popular videos in advance with the help of a better forecasting model.

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Network anomaly detection (NAD) aims to capture potential abnormal behaviors by observing traffic data over a period of time. In this work, we propose a machine learning framework based on XGBoost and deep neural networks to classify normal traffic and anomalous traffic. Data-driven feature engineering and post-processing are further proposed to improve the performance of the models. The experiment results suggest the proposed model can achieve 94% for F1 measure in the macro average of five labels on real-world traffic data.