David T. Smith

Blockchain is a distributed system where transactions are recorded on blocks. The blocks are linked with previous blocks creating a chain of blocks that ensures the integrity of the blockchain. Throughput operates as a pivotal performance indicator in blockchain contributing to its popularity among organizations and individual users. To achieve optimized use of blockchain for an increasing number of users, accurate prediction of performance at different timestamps is necessary. In this paper, two recurrent neural network (RNN)–based predictive models, i.e., long short‐term memory (LSTM) and gated recurrent unit (GRU) are proposed that predict the performance (throughput) of blockchain in different scenarios. Moreover, the study analyzes blockchain data to find optimal block size, impact of block size on performance, and uncover the relationship between performance and block size. The RNN models are evaluated using a dataset collected from Hyperledger Caliper on the HF framework 2.3. This study found that the performance of LSTM is approximately close to GRU regarding evaluation metrics MAE and RMSE. However, GRU is more suitable for practical applications and performs better overall. This study also shows a Spearman correlation value of 0.032, which is indicative of a weak association between the two factors.

Recent advancements in the Internet of Things and Machine Learning techniques have allowed the deployment of sensors on a large scale to monitor the environment and model and predict individual thermal comfort. The existing techniques have a greater focus on occupancy detection, estimations, and localization to balance energy usage and thermal comfort satisfaction. Different sensors, actuators, and analytic data methods are often non-invasively utilized to analyze data from occupant surroundings, identify occupant existence, estimate their numbers, and trigger the necessary action to complete a task. The efficiency of the non-invasive strategies documented in the literature, on the other hand, is rather poor due to the low quality of the datasets utilized in model training and the selection of machine learning technology. This study combines data from camera and environmental sensing using interactive learning and a rule-based classifier to improve the collection and quality of the datasets and data pre-processing. The study compiles a new comprehensive public set of training datasets for building occupancy profile prediction with over 40,000 records. To the best of our knowledge, it is the largest dataset to date, with the most realistic and challenging setting in building occupancy prediction. Furthermore, to the best of our knowledge, this is the first study that attained a robust occupancy count by considering a multimodal input to a single output regression model through the mining and mapping of feature importance, which has advantages over statistical approaches. The proposed solution is tested in a living room with a prototype system integrated with various sensors to obtain occupant-surrounding environmental datasets. The model’s prediction results indicate that the proposed solution can obtain data, and process and predict the occupants’ presence and their number with high accuracy values of 99.7% and 99.35%, respectively, using random forest.