# Actionable Pattern-Driven Analytics and Prediction
## 2 Contributions
The obtained values in the Bi-LSTM module is passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experimental results indicate that the designed framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of the IHEPC dataset in real-time, short-term, medium-term, and long-term timespans. The sixth paper [6] proposes a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a generative-adversarial network (GAN)-structured automated labeled data augmentation method (GALDM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDM can be used to greatly reduce labor costs because data labeling is the most labor-intensive work in this type of solution. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80%. The seventh paper [7] proposes a model to forecast the foreign exchange rate. It incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, these sensitive factors for exchange rate forecasting is then considered. The study also shows the importance of incorporating the investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with the statistical techniques normally employed for prediction. It also presents evidence that the exchange rate of all three countries is more exposed to events happening in the US. The eighth paper [8] proposes a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. The proposed UAS technique predicts a partial video skip due to long-time viewing by the user. This prediction prevents unnecessary network usage and guarantees maximum continuity by minimizing the video interruption and delay. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms. The ninth paper [9] proposes a big data analytics framework (named the multiple item support frequent patterns (MISFP)-growth algorithm) that uses Hadoop-based parallel computing to achieve high-efficiency mining of itemsets with multiple item supports (MIS). To facilitate decision makers in setting MIS, the authors also proposed the concept of classification of item (COI), which classifies items of higher homogeneity into the same class, by which the items inherit class support as their item support. The experimental results show an approximately 38% reduction in the execution time on parallel architectures. Furthermore, according to the experimental results, the enhanced performance of the proposed algorithm indicates that it could have big data analytics applications. The tenth paper [10] proposes a method that can rapidly mine frequent patterns under varying network bandwidths. This fast distributed mining in changing network bandwidth (FDCNB)-mining method is based on the effective structure for distributed computing environments. Additionally, the authors added a monitor node to observe variable network bandwidths in cloud or distributed computing environments, which is beneficial for the kernel node to select the optimal computing node.