14 research outputs found
Predicting the Effects of News Sentiments on the Stock Market
Stock market forecasting is very important in the planning of business
activities. Stock price prediction has attracted many researchers in multiple
disciplines including computer science, statistics, economics, finance, and
operations research. Recent studies have shown that the vast amount of online
information in the public domain such as Wikipedia usage pattern, news stories
from the mainstream media, and social media discussions can have an observable
effect on investors opinions towards financial markets. The reliability of the
computational models on stock market prediction is important as it is very
sensitive to the economy and can directly lead to financial loss. In this
paper, we retrieved, extracted, and analyzed the effects of news sentiments on
the stock market. Our main contributions include the development of a sentiment
analysis dictionary for the financial sector, the development of a
dictionary-based sentiment analysis model, and the evaluation of the model for
gauging the effects of news sentiments on stocks for the pharmaceutical market.
Using only news sentiments, we achieved a directional accuracy of 70.59% in
predicting the trends in short-term stock price movement.Comment: 4 page
CoBEVFusion: Cooperative Perception with LiDAR-Camera Bird's-Eye View Fusion
Autonomous Vehicles (AVs) use multiple sensors to gather information about
their surroundings. By sharing sensor data between Connected Autonomous
Vehicles (CAVs), the safety and reliability of these vehicles can be improved
through a concept known as cooperative perception. However, recent approaches
in cooperative perception only share single sensor information such as cameras
or LiDAR. In this research, we explore the fusion of multiple sensor data
sources and present a framework, called CoBEVFusion, that fuses LiDAR and
camera data to create a Bird's-Eye View (BEV) representation. The CAVs process
the multi-modal data locally and utilize a Dual Window-based Cross-Attention
(DWCA) module to fuse the LiDAR and camera features into a unified BEV
representation. The fused BEV feature maps are shared among the CAVs, and a 3D
Convolutional Neural Network is applied to aggregate the features from the
CAVs. Our CoBEVFusion framework was evaluated on the cooperative perception
dataset OPV2V for two perception tasks: BEV semantic segmentation and 3D object
detection. The results show that our DWCA LiDAR-camera fusion model outperforms
perception models with single-modal data and state-of-the-art BEV fusion
models. Our overall cooperative perception architecture, CoBEVFusion, also
achieves comparable performance with other cooperative perception models
Detecting Irregular Patterns in IoT Streaming Data for Fall Detection
Detecting patterns in real time streaming data has been an interesting and
challenging data analytics problem. With the proliferation of a variety of
sensor devices, real-time analytics of data from the Internet of Things (IoT)
to learn regular and irregular patterns has become an important machine
learning problem to enable predictive analytics for automated notification and
decision support. In this work, we address the problem of learning an irregular
human activity pattern, fall, from streaming IoT data from wearable sensors. We
present a deep neural network model for detecting fall based on accelerometer
data giving 98.75 percent accuracy using an online physical activity monitoring
dataset called "MobiAct", which was published by Vavoulas et al. The initial
model was developed using IBM Watson studio and then later transferred and
deployed on IBM Cloud with the streaming analytics service supported by IBM
Streams for monitoring real-time IoT data. We also present the systems
architecture of the real-time fall detection framework that we intend to use
with mbientlabs wearable health monitoring sensors for real time patient
monitoring at retirement homes or rehabilitation clinics.Comment: 7 page
A Voice Controlled E-Commerce Web Application
Automatic voice-controlled systems have changed the way humans interact with
a computer. Voice or speech recognition systems allow a user to make a
hands-free request to the computer, which in turn processes the request and
serves the user with appropriate responses. After years of research and
developments in machine learning and artificial intelligence, today
voice-controlled technologies have become more efficient and are widely applied
in many domains to enable and improve human-to-human and human-to-computer
interactions. The state-of-the-art e-commerce applications with the help of web
technologies offer interactive and user-friendly interfaces. However, there are
some instances where people, especially with visual disabilities, are not able
to fully experience the serviceability of such applications. A voice-controlled
system embedded in a web application can enhance user experience and can
provide voice as a means to control the functionality of e-commerce websites.
In this paper, we propose a taxonomy of speech recognition systems (SRS) and
present a voice-controlled commodity purchase e-commerce application using IBM
Watson speech-to-text to demonstrate its usability. The prototype can be
extended to other application scenarios such as government service kiosks and
enable analytics of the converted text data for scenarios such as medical
diagnosis at the clinics.Comment: 7 page
Xu: An Automated Query Expansion and Optimization Tool
The exponential growth of information on the Internet is a big challenge for
information retrieval systems towards generating relevant results. Novel
approaches are required to reformat or expand user queries to generate a
satisfactory response and increase recall and precision. Query expansion (QE)
is a technique to broaden users' queries by introducing additional tokens or
phrases based on some semantic similarity metrics. The tradeoff is the added
computational complexity to find semantically similar words and a possible
increase in noise in information retrieval. Despite several research efforts on
this topic, QE has not yet been explored enough and more work is needed on
similarity matching and composition of query terms with an objective to
retrieve a small set of most appropriate responses. QE should be scalable,
fast, and robust in handling complex queries with a good response time and
noise ceiling. In this paper, we propose Xu, an automated QE technique, using
high dimensional clustering of word vectors and Datamuse API, an open source
query engine to find semantically similar words. We implemented Xu as a command
line tool and evaluated its performances using datasets containing news
articles and human-generated QEs. The evaluation results show that Xu was
better than Datamuse by achieving about 88% accuracy with reference to the
human-generated QE.Comment: Accepted to IEEE COMPSAC 201