Physical Sciences and Mathematics Commons^™

Context Aware Music Recommendation And Playlist Generation, Elias Mann

SMU Journal of Undergraduate Research

There are many reasons people listen to music, and the type of music is largely determined by what the listener may be doing while they listen. For example, one may listen to one type of music while commuting, another while exercising, and yet another while relaxing. Without access to the physiological state of the user, current music recommendation methods rely on collaborative filtering - recommending music based on what other similar users listen to - and content based filtering - recommending songs based on their similarities to songs the user already prefers. With the rise in popularity of smart devices …

Semantic Lung Segmentation From Chest X-Ray Images Using Seg-Net Deep Cnn Model, Dathar Abas Hasan, Umed Hayder Jader

Polytechnic Journal

Implementing an accurate image segmentation to extract the lung shape from X-ray images is a vital step in designing a CAD system that diagnoses various types of chest diseases. Lung segmentation is a complex process due to the blurred regions that separate the lung area and the rest of the image. The conventional image segmentation techniques do not meet the ambitions to achieve precise lung segmentation. In this paper, we utilized the Seg-Net semantic segmentation model as a practical approach to distinguish the lung region pixels in X-ray images. The model involves an encoder network that extracts the data from …

Deep Learning-Based Cad System For Predicting The Covid-19 X-Ray Images, Aqeel R. Talib, Hana’ M. Ali

Karbala International Journal of Modern Science

According to World Health Organization data, Coronavirus (COVID-19) has infected about 660, 378, 145 patients around the world. It is nonetheless difficult for physicians to detect COVID-19 infections out of CT or X-ray radiographs. Thus, several computer-aided diagnosis (CAD) systems based on deep learning and radiographs were developed to detect COVID-19 infections. However, the majority of approaches considered small datasets, which is ineligible to provide diverse COVID-19 radiographs. This work utilizes a massive number of X-ray radiographs, and compared standard CNN, DenseNet-121, and GoogLeNet for isolating COVID-19 infections out from normal and other pneumonia radiographs. The dataset in this work …

Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel

SMU Data Science Review

Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …

Skin Lesion Segmentation Based On U-Shaped Network, Muna Khalaf, Ban N. Dhannoon

Karbala International Journal of Modern Science

Skin lesion segmentation is an essential step toward accurate skin lesion diagnosis. The need to automate Skin lesion segmentation on the one hand, and the challenges it faces, on the other hand, have made it a growing area of research and focus. Automation of skin lesion segmentation helps reduce the effort and time needed for diagnosis and treatment and helps make better utilization of available data and shared experiences. The challenges faced by the automation of skin lesion segmentation can be broadly defined by (but not limited to); variations in texture, shape, and size for skin lesions and the low …

Textual Emotion Detection Approaches: A Survey, Mahinda Mahmoud Samy Zidan, Ibrahim Elhenawy, Ahmed R. Abas, Mahmoud Othman

Future Computing and Informatics Journal

Over the past decades, social media attracted individuals to express their feelings on any topic or item, resulting in an incremental growth in the size of created data. These feelings and unstructured data paved the path for business organizations to gather information and build statistical analysis. Various machine learning and natural language processing-based approaches are used for sentiment and emotion analysis. Moreover, deep learning-based approaches recently gained popularity due to their remarkable performance in text analysis. This paper provides a comprehensive overview of the prominent machine learning models applied in emotion analysis. It explores various emotion analysis taxonomies, in addition …

A Comparative Study Of Combining Deep Learning And Homomorphic Encryption Techniques, Emad M. Alsaedi, Alaa Kadhim Farhan

Al-Qadisiyah Journal of Pure Science

Deep learning simulation necessitates a considerable amount of internal computational resources and fast training for large amounts of data. The cloud has been delivering software to help with this transition in recent years, posing additional security risks to data breaches. Modern encryption schemes maintain personal secrecy and are the best method for protecting data stored on a server and data sent from an unauthorized third party. However, when data must be stored or analyzed, decryption is needed, and homomorphic encryption was the first symptom of data security issues found with Strong Encryption.It enables an untrustworthy cloud resource to process encrypted …

A New Distributed Anomaly Detection Approach For Log Ids Management Based Ondeep Learning, Murat Koca, Muhammed Ali̇ Aydin, Ahmet Sertbaş, Abdül Hali̇m Zai̇m

Turkish Journal of Electrical Engineering and Computer Sciences

Today, with the rapid increase of data, the security of big data has become more important than ever for managers. However, traditional infrastructure systems cannot cope with increasingly big data that is created like an avalanche. In addition, as the existing database systems increase licensing costs per transaction, organizations using information technologies are shifting to free and open source solutions. For this reason, we propose an anomaly attack detection model on Apache Hadoop distributed file system (HDFS), which stands out in open source big data analytics, and Apache Spark, which stands out with its speed performance in analysis to reduce …

Pristine Sentence Translation: A New Approach To A Timeless Problem, Meenu Ahluwalia, Brian Coari, Ben Brock

SMU Data Science Review

Abstract.

Pristine Sentence Translation (PST) is a new approach to language translation based upon sentence-level granularity. Traditional translation approaches, including those utilizing advanced machine learning or neural network-based approaches, translate on a word-by-word or phrase-by-phrase basis; thereby, potentially missing the context or meaning of the complete sentence. Instead of these piecewise translations, PST utilizes deep learning and predictive modeling techniques to translate complete sentences from their source language into their target language. With these approaches we were able to translate sentences that closely conveyed the meaning of the original sentences. Our results demonstrated that PST’s method of translating an entire …

Self-Driving Cars: Evaluation Of Deep Learning Techniques For Object Detection In Different Driving Conditions, Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, Robert Slater

SMU Data Science Review

Deep Learning has revolutionized Computer Vision, and it is the core technology behind capabilities of a self-driving car. Convolutional Neural Networks (CNNs) are at the heart of this deep learning revolution for improving the task of object detection. A number of successful object detection systems have been proposed in recent years that are based on CNNs. In this paper, an empirical evaluation of three recent meta-architectures: SSD (Single Shot multi-box Detector), R-CNN (Region-based CNN) and R-FCN (Region-based Fully Convolutional Networks) was conducted to measure how fast and accurate they are in identifying objects on the road, such as vehicles, pedestrians, …

Leveraging Natural Language Processing Applications And Microblogging Platform For Increased Transparency In Crisis Areas, Ernesto Carrera-Ruvalcaba, Johnson Ekedum, Austin Hancock, Ben Brock

SMU Data Science Review

Through microblogging applications, such as Twitter, people actively document their lives even in times of natural disasters such as hurricanes and earthquakes. While first responders and crisis-teams are able to help people who call 911, or arrive at a designated shelter, there are vast amounts of information being exchanged online via Twitter that provide real-time, location-based alerts that are going unnoticed. To effectively use this information, the Tweets must be verified for authenticity and categorized to ensure that the proper authorities can be alerted. In this paper, we create a Crisis Message Corpus from geotagged Tweets occurring during 7 hurricanes …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Intelligent Technology Of Command And Control System In The Rts Perspective, Wenfeng Wu, Zhang Yu, Rong Ming

Journal of System Simulation

Abstract: Real-Time Strategy (RTS) games have important reference value for studying the intelligent technology of command and control systems. The similarities between RTS games and the strategic battle level command and control systems are described according to the decision process. The challenges brought by the problems of planning, learning, uncertainty and space-time reasoning in the intelligent technology of RTS games are analyzed. The key technologies and latest research progress of action sequence planning, plan recognition, state assessment, multi-agent collaboration and multi-scale AI are studied. The trend of intelligent technology development of strategic and operational level command and control systems is …

Fake News Detection: A Deep Learning Approach, Aswini Thota, Priyanka Tilak, Simrat Ahluwalia, Nibrat Lohia

SMU Data Science Review

Fake news is defined as a made-up story with an intention to deceive or to mislead. In this paper we present the solution to the task of fake news detection by using Deep Learning architectures. Gartner research [1] predicts that “By 2022, most people in mature economies will consume more false information than true information”. The exponential increase in production and distribution of inaccurate news presents an immediate need for automatically tagging and detecting such twisted news articles. However, automated detection of fake news is a hard task to accomplish as it requires the model to understand nuances in natural …

Walknet: A Deep Learning Approach To Improving Sidewalk Quality And Accessibility, Andrew Abbott, Alex Deshowitz, Dennis Murray, Eric C. Larson

SMU Data Science Review

This paper proposes a framework for optimizing allocation of infrastructure spending on sidewalk improvement and allowing planners to focus their budgets on the areas in the most need. In this research, we identify curb ramps from Google Street View images using traditional machine learning and deep learning methods. Our convolutional neural network approach achieved an 83% accuracy and high level of precision when classifying curb cuts. We found that as the model received more data, the accuracy increased, which with the continued collection of crowdsourced labeling of curb cuts will increase the model’s classification power. We further investigated a model …