65,963 research outputs found
Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series
Smart grids and smart homes are getting people\u27s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with day as covariates remained better than the 1, 2, 3, and 4-week scenarios
The Implications of Diverse Applications and Scalable Data Sets in Benchmarking Big Data Systems
Now we live in an era of big data, and big data applications are becoming
more and more pervasive. How to benchmark data center computer systems running
big data applications (in short big data systems) is a hot topic. In this
paper, we focus on measuring the performance impacts of diverse applications
and scalable volumes of data sets on big data systems. For four typical data
analysis applications---an important class of big data applications, we find
two major results through experiments: first, the data scale has a significant
impact on the performance of big data systems, so we must provide scalable
volumes of data sets in big data benchmarks. Second, for the four applications,
even all of them use the simple algorithms, the performance trends are
different with increasing data scales, and hence we must consider not only
variety of data sets but also variety of applications in benchmarking big data
systems.Comment: 16 pages, 3 figure
Spartan Daily, November 28, 2017
Volume 149, Issue 39https://scholarworks.sjsu.edu/spartan_daily_2017/1080/thumbnail.jp
Big Data solutions for law enforcement
Big Data, the data too large and complex for most current information infrastructure to store and analyze, has changed every sector in government and industry.
Today’s sensors and devices produce an overwhelming amount of information that is often unstructured, and solutions developed to handle Big Data now allowing us to track more information and run more complex analytics to gain a level of insight once thought impossible.
The dominant Big Data solution is the Apache Hadoop ecosystem which provides an open source platform for reliable, scalable, distributed computing on commodity hardware. Hadoop has exploded in the private sector and is the back end to many of the leading Web 2.0 companies and services. Hadoop also has a growing footprint in government, with numerous Hadoop clusters run by the Departments of Defense and Energy, as well as smaller deployments by other agencies.
One sector currently exploring Hadoop is law enforcement. Big Data analysis has already been highly effective in law enforcement and can make police departments more effective, accountable, efficient, and proactive. As Hadoop continues to spread through law enforcement agencies, it has the potential to permanently change the way policing is practiced and administered
Heartbeat Anomaly Detection using Adversarial Oversampling
Cardiovascular diseases are one of the most common causes of death in the
world. Prevention, knowledge of previous cases in the family, and early
detection is the best strategy to reduce this fact. Different machine learning
approaches to automatic diagnostic are being proposed to this task. As in most
health problems, the imbalance between examples and classes is predominant in
this problem and affects the performance of the automated solution. In this
paper, we address the classification of heartbeats images in different
cardiovascular diseases. We propose a two-dimensional Convolutional Neural
Network for classification after using a InfoGAN architecture for generating
synthetic images to unbalanced classes. We call this proposal Adversarial
Oversampling and compare it with the classical oversampling methods as SMOTE,
ADASYN, and RandomOversampling. The results show that the proposed approach
improves the classifier performance for the minority classes without harming
the performance in the balanced classes
Are Condorcet and minimax voting systems the best?
For decades, the minimax voting system was well known to experts on voting
systems, but was not widely considered to be one of the best systems. But in
recent years, two important experts, Nicolaus Tideman and Andrew Myers, have
both recognized minimax as one of the best systems. I agree with that. This
paper presents my own reasons for preferring minimax. The paper explicitly
discusses about 20 systems, though over 50 are known to exist.Comment: 41 pages, no figures. The Introduction has been changed. Also fixed
some version 6 errors in referencing subsection numbers in section
- …