Computer Sciences Commons^™

Airbnb Price Prediction With Sentiment Classification, Peilu Liu

Master's Projects

Airbnb is an online platform that provides arrangements for short-term local home renting services. It is a challenging task for the house owner to price a rental home and attract customers. Customers also need to evaluate the price of the rental property based on the listing details. This paper demonstrates several existing Airbnb price prediction models using machine learning and external data to improve the prediction accuracy. It also discusses machine learning and neural network models that are commonly used for price prediction. The goal of this paper is to build a price prediction model using machine learning and sentiment …

Analysis Of Students’ Multi-Representation Ability In Augmented Reality-Assisted Learning, Sri Jumini, Edy Cahyono, Muhamad Miftakhul Falah

Library Philosophy and Practice (e-journal)

Not all learning sources can directly and cheaply be presented, so augmented reality media is needed to be applied to students with various talents and intelligence. This study aims to analyze students’ multi-representation ability through the use of augmented reality media. The research method was carried out through pre-experiment with one group posttest only design. Test question items were given to see the students’ multi-representation ability. Data analysis was carried out through the percentage of the number of students achieving test scores of more than or equal to 80 on a scale of 100. The results showed that 88% (28 …

Predicting Bus Travel Times In Washington, Dc Using Artificial Neural Networks (Anns), Stephen Arhin, Babin Manandhar, Hamdiat Baba Adam, Adam Gatiba

Mineta Transportation Institute

Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington …

Bibliometric Analysis Of Named Entity Recognition For Chemoinformatics And Biomedical Information Extraction Of Ovarian Cancer, Vijayshri Khedkar, Charlotte Fernandes, Devshi Desai, Mansi R, Gurunath Chavan Dr, Sonali Tidke Dr., M. Karthikeyan Dr.

Library Philosophy and Practice (e-journal)

With the massive amount of data that has been generated in the form of unstructured text documents, Biomedical Named Entity Recognition (BioNER) is becoming increasingly important in the field of biomedical research. Since currently there does not exist any automatic archiving of the obtained results, a lot of this information remains hidden in the textual details and is not easily accessible for further analysis. Hence, text mining methods and natural language processing techniques are used for the extraction of information from such publications.Named entity recognition, is a subtask that comes under information extraction that focuses on finding and categorizing specific …

Art 175 Website V1.0 Using Html5 And Css3, Gary Craig Hobbs

All Assignment Prompts

No abstract provided.

Technological Countermeasures Of Copyright Piracy Enforcement In Nigeria: An Alternative To Creative Works Owners’ Livelihood In A Monolithic Economy Post Covid-19, Leonard Ume

Library Philosophy and Practice (e-journal)

The Coronavirus pandemic is a global problem that came with its attendant economic shock that is higher than the one brought by World War 1 and 2 put together. The attendant consequences expectedly landed Nigeria economy to recession. Nigeria over the years has been grappling with the problem of diversification of her economy to forestall the ugly impact of monolithic economy. This paper investigated the effect of using technology in fighting intellectual copyright piracy which will encourage the citizens to harness their creative endowment to eke out a living with its resultant effect on the reduction of unemployment. The study …

Unsupervised Data Mining Technique For Clustering Library In Indonesia, Robbi Rahim, Joseph Teguh Santoso, Sri Jumini, Gita Widi Bhawika, Daniel Susilo, Danny Wibowo

Library Philosophy and Practice (e-journal)

Organizing school libraries not only keeps library materials, but helps students and teachers in completing tasks in the teaching process so that national development goals are in order to improve community welfare by producing quality and competitive human resources. The purpose of this study is to analyze the Unsupervised Learning technique in conducting cluster mapping of the number of libraries at education levels in Indonesia. The data source was obtained from the Ministry of Education and Culture which was processed by the Central Statistics Agency (abbreviated as BPS) with url: bps.go.id/. The data consisted of 34 records where the attribute …

Hybrid Cloud Workload Monitoring As A Service, Shreya Kundu

Master's Projects

Cloud computing and cloud-based hosting has become embedded in our daily lives. It is imperative for cloud providers to make sure all services used by both enterprises and consumers have high availability and elasticity to prevent any downtime, which impacts negatively for any business. To ensure cloud infrastructures are working reliably, cloud monitoring becomes an essential need for both businesses, the provider and the consumer. This thesis project reports on the need of efficient scalable monitoring, enumerating the necessary types of metrics of interest to be collected. Current understanding of various architectures designed to collect, store and process monitoring data …

Delineating Knowledge Domains In Scientific Domains In Scientific Literature Using Machine Learning (Ml), Abhay Maurya, Smarajit Paul Choudhury Mr., Kshitij Jaiswal Mr.

Library Philosophy and Practice (e-journal)

The recent years have witnessed an upsurge in the number of published documents. Organizations are showing an increased interest in text classification for effective use of the information. Manual procedures for text classification can be fruitful for a handful of documents, but the same lack in credibility when the number of documents increases besides being laborious and time-consuming. Text mining techniques facilitate assigning text strings to categories rendering the process of classification fast, accurate, and hence reliable. This paper classifies chemistry documents using machine learning and statistical methods. The procedure of text classification has been described in chronological order like …

Multimodal Detection Of Cyberbullying On Twitter, Jiabao Qiu

Master's Projects

Cyberbullying detection is one of the trending topics of research in recent years, due to the popularity of social media and the lack of limitations about using electronic communications. Detection of cyberbullying may prevent some bullying behaviors online. This paper introduced a Multimodal system that makes use of Convolutional Neural Network (CNN), Tensor Fusion Network, VGG-19 Network, and Multi-Layer Perceptron model, for the purpose of cyberbullying detection. This system can not only analyze the messages sent but also the extra information related to the messages (meta-information) and the images contained in the messages. The proposed system was trained and tested …

Predictive Maintenance Of Bearing Machinery Using Simulation- A Bibliometric Study, Karan Gulati Mr., Keshav Basandrai Mr., Shubham Tiwari Mr., Pooja Kamat Prof., Satish Kumar Dr.

Library Philosophy and Practice (e-journal)

Modelling is a way of constructing a virtual representation of software and hardware that involves a real-world device. We will discover the behaviour of the system if the software elements of this model are guided by mathematical relationships. For testing conditions that may be difficult to replicate with hardware prototypes alone, modelling and simulation are particularly useful, especially in the early phase of the design process when hardware might not be available. Model-based approach in MATLAB-Simulink can be useful for predictive maintenance of machines as it can reduce unplanned downtimes and maintenance costs when industrial equipment breaks. Through this bibliometric …

Cat Tracks – Tracking Wildlife Through Crowdsourcing Using Firebase, Tracy Ho

Master's Projects

Many mountain lions are killed in the state of California every year from roadkill. To reduce these numbers, it is important that a system be built to track where these mountain lions have been around. One such system could be built using the platform-as-a-service, Firebase. Firebase is a platform service that collects and manages data that comes in through a mobile application. For the development of cross-platform mobile applications, Flutter is used as a toolkit for developers for both iOS and Android. This entire system, Cat Tracks is proposed as a crowdsource platform to track wildlife, with the current focus …

A Neat Approach To Malware Classification, Jason Do

Master's Projects

Current malware detection software often relies on machine learning, which is seen as an improvement over signature-based techniques. Problems with a machine learning based approach can arise when malware writers modify their code with the intent to evade detection. This leads to a cat and mouse situation where new models must constantly be trained to detect new malware variants. In this research, we experiment with genetic algorithms as a means of evolving machine learning models to detect malware. Genetic algorithms, which simulate natural selection, provide a way for models to adapt to continuous changes in a malware families, and thereby …

End-To-End Learning Utilizing Temporal Information For Vision- Based Autonomous Driving, Dapeng Guo

Master's Projects

End-to-End learning models trained with conditional imitation learning (CIL) have demonstrated their capabilities in driving autonomously in dynamic environments. The performance of such models however is limited as most of them fail to utilize the temporal information, which resides in a sequence of observations. In this work, we explore the use of temporal information with a recurrent network to improve driving performance. We propose a model that combines a pre-trained, deeper convolutional neural network to better capture image features with a long short-term memory network to better explore temporal information. Experimental results indicate that the proposed model achieves performance gain …

Detecting Deepfakes With Deep Learning, Eric C. Tjon

Master's Projects

Advances in generative models and manipulation techniques have given rise to digitally altered videos known as deepfakes. These videos are difficult to identify for both humans and machines. Typical detection methods exploit various imperfections in deepfake videos, such as inconsistent posing and visual artifacts. In this paper, we propose a pipeline with two distinct pathways for examining individual frames and video clips. The image pathway contains a novel architecture called Eff-YNet capable of both segmenting and detecting frames from deepfake videos. It consists of a U-Net with a classification branch and an EfficientNet B4 encoder. The video pathway implements a …

Multi-Agent Deep Reinforcement Learning For Walkers, Inhee Park

Master's Projects

This project was motivated by seeking an AI method towards Artificial General Intelligence (AGI), that is, more similar to learning behavior of human-beings. As of today, Deep Reinforcement Learning (DRL) is the most closer to the AGI compared to other machine learning methods. To better understand the DRL, we compares and contrasts to other related methods: Deep Learning, Dynamic Programming and Game Theory.

We apply one of state-of-art DRL algorithms, called Proximal Policy Op- timization (PPO) to the robot walkers locomotion, as a simple yet challenging environment, inherently continuous and high-dimensional state/action space.

The end goal of this project is …

Lidar Object Detection Utilizing Existing Cnns For Smart Cities, Vinay Ponnaganti

Master's Projects

As governments and private companies alike race to achieve the vision of a smart city — where artificial intelligence (AI) technology is used to enable self-driving cars, cashier-less shopping experiences and connected home devices from thermostats to robot vacuum cleaners — advancements are being made in both software and hardware to enable increasingly real-time, accurate inference at the edge. One hardware solution adopted for this purpose is the LiDAR sensor, which utilizes infrared lasers to accurately detect and map its surroundings in 3D. On the software side, developers have turned to artificial neural networks to make predictions and recommendations with …

Image Spam Classification With Deep Neural Networks, Ajay Pal Singh, Katerina Potika

Faculty Publications, Computer Science

Image classification is a fundamental problem of computer vision and pattern recognition. We focus on images that contain spam. Spam is unwanted bulk content, and image spam is unwanted content embedded inside the images. Image spam potentially creates a threat to the credibility of any email-based communication system. While a lot of machine learning techniques are successful in detecting textual based spam, this is not the case for image spams, which can easily evade these textual-spam detection systems. In our work, we explore and evaluate four deep learning techniques that detect image spams. First, we train deep neural networks using …

Findfur: A Tool For Predicting Furin Cleavage Sites Of Viral Envelope Substrates, Christine Gu

Master's Projects

Most biologically active proteins of eukaryotic cells are initially synthesized in the secretory pathway as inactive precursors and require proteolytic processing to become functionally active. This process is performed by a specialized family of endogenous enzymes known as proproteases convertases (PCs). Within this family of proteases, the most notorious and well-research is furin. Found ubiquitously throughout the human body, typical furin substrates are cleaved at sites composed of paired basic amino acids, specifically at the consensus sequence, R-X-[K/R]-R↓. Furin is often exploited by many pathogens, such as enveloped viruses, for proteolytic processing and maturation of their proteins. Glycoproteins of enveloped …

Malware Classification With Gaussian Mixture Model-Hidden Markov Models, Jing Zhao

Master's Projects

Discrete hidden Markov models (HMM) are often applied to the malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture model-HMMs (GMM-HMM), are rarely considered in the field of cybersecurity. In this study, we apply GMM-HMMs to the malware classification problem and we compare our results to those obtained using discrete HMMs. As features, we consider opcode sequences and entropy-based sequences. For our opcode features, GMM-HMMs produce results that are comparable to those obtained using discrete HMMs, whereas for our entropy-based features, GMM-HMMs generally improve on the classification results that we can attain with …

The Use Of Evidential Reasoning Model With Biomarkers In Pancreatic Cancer Prediction, Qianhui Fan

Master's Projects

In this project, an evidential reasoning model is built to amalgamate factors that could be used in early detection of pancreatic cancer. Our machine learning model outputs a probability of a given patient having prostate cancer based on various input variables. These variables include health history factors, such as smoking and medical history, technical artifacts, such as biopsy sequencing technology, and genomic biomarkers such as mutational, transcriptional and methylomic profiles, cfDNA, and copy number variation. The dataset used in this project is a part of The Cancer Genome Atlas (TCGA) project and was collected from the National Cancer Institute (NIH) …

Visualization Of Large Networks Using Recursive Community Detection, Xinyuan Fan

Master's Projects

Networks show relationships between people or things. For instance, a person has a social network of friends, and websites are connected through a network of hyperlinks. Networks are most commonly represented as graphs, so graph drawing becomes significant for network visualization. An effective graph drawing can quickly reveal connections and patterns within a network that would be difficult to discern without visual aid. But graph drawing becomes a challenge for large networks. Am- biguous edge crossings are inevitable in large networks with numerous nodes and edges, and large graphs often become a complicated tangle of lines. These issues greatly reduce …

Bioinformatics Metadata Extraction For Machine Learning Analysis, Zachary Tom

Master's Projects

Next generation sequencing (NGS) has revolutionized the biological sciences. Today, entire genomes can be rapidly sequenced, enabling advancements in personalized medicine, genetic diseases, and more. The National Center for Biotechnology Information (NCBI) hosts the Sequence Read Archive (SRA) containing vast amounts of valuable NGS data. Recently, research has shown that sequencing errors in conventional NGS workflows are key confounding factors for detecting mutations. Various steps such as sample handling and library preparation can introduce artifacts that affect the accuracy of calling rare mutations. Thus, there is a need for more insight into the exact relationship between various steps of the …

Malware Classification Using Lstms, Dennis Dang

Master's Projects

Signature and anomaly based detection have long been quintessential techniques used in malware detection. However, these techniques have become increasingly ineffective as malware becomes more complex. Researchers have therefore turned to deep learning to construct better performing models. In this project, we create four different long-short term memory (LSTM) models and train each model to classify malware by family type. Our data consists of opcodes extracted from malware executables. We employ techniques used in natural language processing (NLP) such as word embedding and bidirection LSTMs (biLSTM). We also use convolutional neural networks (CNN). We found that our model consisting of …

Quantifying Deepfake Detection Accuracy For A Variety Of Natural Settings, Pratikkumar Prajapati

Master's Projects

Deep fakes are videos generated from a starting video of a person where that person's face has been swapped for someone else's. In this report, we describe our work to develop general, deep learning-based models to classify Deep Fake content. Our first experiments involved simple Convolution Neural Network (CNN)-based models where we varied how individual frames from the source video were passed to the CNN. These simple models tended to give low accuracy scores for discriminating fake versus non-fake videos of less than 60%. We then developed three more sophisticated models: one based on choosing test frames, one based on …

How Live Streaming And Twitch Have Changed The Gaming Industry, Krystal Ruiz

ART 108: Introduction to Games Studies

Live streaming in itself has become a booming industry in which its content consists of “streamers” who live broadcast numerous events and real-time interactions while simultaneously chatting with viewers drawing huge and increasing numbers (Adamovich). Twitch has especially excelled at garnering attention as one of the most popular live streaming platforms that focuses on broadcasting and viewing video game content (Adamovich). Twitch has grown rapidly within the last few years asserting its dominance as one of the major forces in the games industry and becoming a multi-billion-dollar industry (Adamovich). For example, according to Descrier, in 2016 there were approximately 292 …

The Impact Of Shigeru Miyamoto On The Game Design Industry, Luan Tran

ART 108: Introduction to Games Studies

Nintendo started as a small company in the 1970s that sold playing cards. Having seen the exemplary gift in his son, Miyamoto's father arranged for an interview with the president of Nintendo Hiroshi Yamauchi. Consequently, Miyamoto got a position in 1977 as an apprentice in the planning department after showing his toy creations to the president. He became the first Nintendo artist as he helped create the art for the first original coin-operated arcade game. The approach demonstrated his innate abilities that would help him become the ultimate guru in the industry. Through individual discovery, Miyamoto has managed to produce …

Factors Affecting Computer Science Research Productivity And Impact In Nigeria: A Bibliometric Evidence, Azubuike Ezenwoke

Library Philosophy and Practice (e-journal)

Computer science is a burgeoning research field and has the potential to accelerate the rate of industrialisation and subsequently, economic development. Using bibliometric data obtained from Scopus, this study employed a 15-year bibliometric analysis to highlight Nigeria’s productivity and impact trends in the computer science research landscape. Our findings are summarised as follows: First, Nigeria’s computer science research contribution and citations are meager in comparison to the global output. Secondly, international collaboration is generally weak as most collaborations are national in scope. Third, Nigeria’s computer science-related research is published in low-quality outlets, as Scopus has discontinued the indexing of most …

The History Of Nintendo: The Company, Consoles And Games, Laurie Takeda

ART 108: Introduction to Games Studies

From the start, Nintendo has evolved overall as a company; from a playing card manufacturer to developing a wide variety of video game consoles and video games that are played worldwide, they continue to research and expand upon what the company can offer.

Reliability And Validity Of Scales Assessing Anxiety Associated With Information Related Tasks: A Systematic Review, Muhammad Asif Naveed, Sajjad Ullah Jan, Mumtaz Ali Anwar

Library Philosophy and Practice (e-journal)

This research carried out a systematic review of the evidence of reliability and validity of scales available in studies reporting surveys of individuals to assess anxiety associated with information related tasks such as library anxiety, information seeking anxiety, and information anxiety. A systematic search using keywords ‘library anxiety’, ‘information anxiety’, 'information seeking anxiety', and 'information seeking' AND 'anxiety' was carried in Web of Science, Scopus, LISA, and LISTA to identify the relevant literature. This review included those studies reporting the use of any scale assessing information related anxiety, and published in the English language, and included all type of documents …