1,786 research outputs found
SCANN: Synthesis of Compact and Accurate Neural Networks
Deep neural networks (DNNs) have become the driving force behind recent
artificial intelligence (AI) research. An important problem with implementing a
neural network is the design of its architecture. Typically, such an
architecture is obtained manually by exploring its hyperparameter space and
kept fixed during training. This approach is time-consuming and inefficient.
Another issue is that modern neural networks often contain millions of
parameters, whereas many applications and devices require small inference
models. However, efforts to migrate DNNs to such devices typically entail a
significant loss of classification accuracy. To address these challenges, we
propose a two-step neural network synthesis methodology, called DR+SCANN, that
combines two complementary approaches to design compact and accurate DNNs. At
the core of our framework is the SCANN methodology that uses three basic
architecture-changing operations, namely connection growth, neuron growth, and
connection pruning, to synthesize feed-forward architectures with arbitrary
structure. SCANN encapsulates three synthesis methodologies that apply a
repeated grow-and-prune paradigm to three architectural starting points.
DR+SCANN combines the SCANN methodology with dataset dimensionality reduction
to alleviate the curse of dimensionality. We demonstrate the efficacy of SCANN
and DR+SCANN on various image and non-image datasets. We evaluate SCANN on
MNIST and ImageNet benchmarks. In addition, we also evaluate the efficacy of
using dimensionality reduction alongside SCANN (DR+SCANN) on nine small to
medium-size datasets. We also show that our synthesis methodology yields neural
networks that are much better at navigating the accuracy vs. energy efficiency
space. This would enable neural network-based inference even on
Internet-of-Things sensors.Comment: 13 pages, 8 figure
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization
Nowadays, online social media is online
discourse where people contribute to create content, share
it, bookmark it, and network at an impressive rate. The
faster message and ease of use in social media today is
Twitter. The messages on Twitter include reviews and
opinions on certain topics such as movie, book, product,
politic, and so on. Based on this condition, this research
attempts to use the messages of twitter to review a movie by
using opinion mining or sentiment analysis. Opinion mining
refers to the application of natural language processing,
computational linguistics, and text mining to identify or
classify whether the movie is good or not based on message
opinion. Support Vector Machine (SVM) is supervised
learning methods that analyze data and recognize the
patterns that are used for classification. This research
concerns on binary classification which is classified into two
classes. Those classes are positive and negative. The positive
class shows good message opinion; otherwise the negative
class shows the bad message opinion of certain movies. This
justification is based on the accuracy level of SVM with the
validation process uses 10-Fold cross validation and
confusion matrix. The hybrid Partical Swarm Optimization
(PSO) is used to improve the election of best parameter in
order to solve the dual optimization problem. The result
shows the improvement of accuracy level from 71.87% to
77%
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