Other Computer Sciences Commons^™

Deep Learning-Based Framework For Autism Functional Mri Image Classification, Xin Yang, Saman Sarraf, Ning Zhang

Journal of the Arkansas Academy of Science

The purpose of this paper is to introduce deep learning-based framework LeNet-5 architecture and implement the experiments for functional MRI image classification of Autism spectrum disorder. We implement our experiments under the NVIDIA deep learning GPU Training Systems (DIGITS). By using the Convolutional Neural Network (CNN) LeNet-5 architecture, we successfully classified functional MRI image of Autism spectrum disorder from normal controls. The results show that we obtained satisfactory results for both sensitivity and specificity.

Novel Support Vector Machines For Diverse Learning Paradigms, Gabriella A. Melki

Theses and Dissertations

This dissertation introduces novel support vector machines (SVM) for the following traditional and non-traditional learning paradigms: Online classification, Multi-Target Regression, Multiple-Instance classification, and Data Stream classification.

Three multi-target support vector regression (SVR) models are first presented. The first involves building independent, single-target SVR models for each target. The second builds an ensemble of randomly chained models using the first single-target method as a base model. The third calculates the targets' correlations and forms a maximum correlation chain, which is used to build a single chained SVR model, improving the model's prediction performance, while reducing computational complexity.

Under the multi-instance paradigm, …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

College of Graduate Studies: Theses & Dissertations

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …

Sports Analytics With Computer Vision, Colby T. Jeffries

Senior Independent Study Theses

Computer vision in sports analytics is a relatively new development. With multi-million dollar systems like STATS’s SportVu, professional basketball teams are able to collect extremely fine-detailed data better than ever before. This concept can be scaled down to provide similar statistics collection to college and high school basketball teams. Here we investigate the creation of such a system using open-source technologies and less expensive hardware. In addition, using a similar technology, we examine basketball free throws to see whether a shooter’s form has a specific relationship to a shot’s outcome. A system that learns this relationship could be used to …

Comparing And Improving Facial Recognition Method, Brandon Luis Sierra

Electronic Theses, Projects, and Dissertations

Facial recognition is the process in which a sample face can be correctly identified by a machine amongst a group of different faces. With the never-ending need for improvement in the fields of security, surveillance, and identification, facial recognition is becoming increasingly important. Considering this importance, it is imperative that the correct faces are recognized and the error rate is as minimal as possible. Despite the wide variety of current methods for facial recognition, there is no clear cut best method. This project reviews and examines three different methods for facial recognition: Eigenfaces, Fisherfaces, and Local Binary Patterns to determine …

Rating By Ranking: An Improved Scale For Judgement-Based Labels, Jack O'Neill, Sarah Jane Delany, Brian Mac Namee

Conference papers

Labels representing value judgements are commonly elicited using an interval scale of absolute values. Data collected in such a manner is not always reliable. Psychologists have long recognized a number of biases to which many human raters are prone, and which result in disagreement among raters as to the true gold standard rating of any particular object. We hypothesize that the issues arising from rater bias may be mitigated by treating the data received as an ordered set of preferences rather than a collection of absolute values. We experiment on real-world and artificially generated data, finding that treating label ratings …

Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang

Department of Computer Science Faculty Scholarship and Creative Works

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be …

Accurate And Justifiable : New Algorithms For Explainable Recommendations., Behnoush Abdollahi

Electronic Theses and Dissertations

Websites and online services thrive with large amounts of online information, products, and choices, that are available but exceedingly difficult to find and discover. This has prompted two major paradigms to help sift through information: information retrieval and recommender systems. The broad family of information retrieval techniques has given rise to the modern search engines which return relevant results, following a user's explicit query. The broad family of recommender systems, on the other hand, works in a more subtle manner, and do not require an explicit query to provide relevant results. Collaborative Filtering (CF) recommender systems are based on algorithms …

An Analysis Of The Application Of Simplified Silhouette To The Evaluation Of K-Means Clustering Validity, Fei Wang, Hector-Hugo Franco-Penya, John D. Kelleher, John Pugh, Robert J. Ross

Conference papers

Silhouette is one of the most popular and effective internal measures for the evaluation of clustering validity. Simplified Silhouette is a computationally simplified version of Silhouette. However, to date Simplified Silhouette has not been systematically analysed in a specific clustering algorithm. This paper analyses the application of Simplified Silhouette to the evaluation of k-means clustering validity and compares it with the k-means Cost Function and the original Silhouette from both theoretical and empirical perspectives. The theoretical analysis shows that Simplified Silhouette has a mathematical relationship with both the k-means Cost Function and the original Silhouette, while empirically, we show that …

Travel Mode Identification With Smartphone Sensors, Xing Su

Dissertations, Theses, and Capstone Projects

Personal trips in a modern urban society typically involve multiple travel modes. Recognizing a traveller's transportation mode is not only critical to personal context-awareness in related applications, but also essential to urban traffic operations, transportation planning, and facility design. While the state of the art in travel mode recognition mainly relies on large-scale infrastructure-based fixed sensors or on individuals' GPS devices, the emergence of the smartphone provides a promising alternative with its ever-growing computing, networking, and sensing powers. In this thesis, we propose new algorithms for travel mode identification using smartphone sensors. The prototype system is built upon the latest …

Predicting Pancreatic Cancer Using Support Vector Machine, Akshay Bodkhe

Master's Projects

This report presents an approach to predict pancreatic cancer using Support Vector Machine Classification algorithm. The research objective of this project it to predict pancreatic cancer on just genomic, just clinical and combination of genomic and clinical data. We have used real genomic data having 22,763 samples and 154 features per sample. We have also created Synthetic Clinical data having 400 samples and 7 features per sample in order to predict accuracy of just clinical data. To validate the hypothesis, we have combined synthetic clinical data with subset of features from real genomic data. In our results, we observed that …

Path-Finding Methodology For Visually-Impaired Patients Based On Image-Processing, Abhilash Goyal

Master's Projects

The objective of this project is to propose and develop the path-finding methodology for the visually impaired patients. The proposed novel methodology is based on image-processing and it is targeted for the patients who are not completely blind. The major problem faced by visually impaired patients is to walk independently. It is mainly because these patients can not see obstacles in front of them due to the degradation in their eye sight. Degradation in the eye-sight is mainly because either the light doesn't focus on the retina properly or due to the malfunction of the photoreceptor cells on the retina, …

Computational Analysis Of Cryptic Splice Sites, Remya Mohanan

Master's Projects

DNA in the nucleus of all eukaryotes is transcribed into mRNA where it is then translated into proteins. The DNA which is transcribed into mRNA is composed of coding and non-coding regions called exons and introns, respectively. It undergoes a post-trancriptional process called splicing where the introns or the non-coding regions are removed from the pre-mRNA to give the mature mRNA. Splicing of pre-mRNAs at 5 ́ and 3ˊ ends is a crucial step in the gene expression pathway. The mis-splicing by the spliceosome at different sites known as cryptic splice sites is caused by mutations which will affect the …

Comparing Authentic And Cryptic 5’ Splice Sites Using Hidden Markov Models And Decision Trees, Pratikshya Mishra

Master's Projects

Splicing is the editing of the precursor mRNA produced during transcription. The mRNA contains a large number of nucleotides in the introns and exons which are spliced to remove the introns and bind the exons to produce the mature mRNA which is translated to generate proteins. Hence accurate splicing at 5’ and 3’ splice sites (authentic splice sites (AuthSS)) is of foremost importance. The 5’ and 3’ splice sites are characterized by consensus sequences. Eukaryotic genome also contains splice sites known as Cryptic Splice Sites (CSS) that match the consensus. But the CSS are activated only when there is a …

Application Of Computational Methods To Study The Selection Of Authentic And Cryptic Splice Sites, Tapomay Dey

Master's Projects

Proteins are building blocks of the bodies of eukaryotes, and the process of synthesizing proteins from DNA is crucial for the good health of an organism [13]. However, some mutations in the DNA may disrupt the selection of 5’ or 3’ splice sites by a spliceosome. An important research question is whether the disruptions have a stochastic relation to the position of nucleotides in the vicinity of the known authentic and cryptic splice sites. This can be achieved by proving that the authentic and cryptic splice sites are intrinsically different. However, the behavior of the spliceosome is not accurately known. …

Investigating Trust And Trust Recovery In Human-Robot Interactions, Abigail L. Thomson

Celebration of Learning

As artificial intelligence and robotics continue to advance and be used in increasingly different functions and situations, it is important to look at how these new technologies will be used. An important factor in how a new resource will be used is how much it is trusted. This experiment was conducted to examine people’s trust in a robotic assistant when completing a task, how mistakes affect this trust, and if the levels of trust exhibited with a robot assistant were significantly different than if the assistant were human. The task was to watch a computer simulation of the three-cup monte …

Image Segmentation Using De-Textured Images, Yaswanth Kodavali

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Image segmentation is one of the fundamental problems in computer vision. The outputs of segmentation are used to extract regions of interest and carry out identification or classification tasks. For these tasks to be reliable, segmentation has to be made more reliable. Although there are exceptionally well-built algorithms available today, they perform poorly in many instances by producing over-merged (combining many unrelated objects) or under-merged (one object appeared as many) results. This leads to far fewer or more segments than expected. Such problems primarily arise due to varying textures within a single object and/or common textures near borders of adjacent …

An Introduction To The Theory And Applications Of Bayesian Networks, Anant Jaitha

CMC Senior Theses

Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating a graphical system to model the data. It then develops probability distributions over these variables. It explores variables in the problem space and examines the probability distributions related to those variables. It conducts statistical inference over those probability distributions to draw meaning from them. They are good means to explore a large set of data efficiently to make inferences. There are a number of real world applications that already exist and are being actively researched. This paper discusses the theory and applications of …

An Alternative Approach To Training Sequence-To-Sequence Model For Machine Translation, Vivek Sah

Honors Theses

Machine translation is a widely researched topic in the field of Natural Language Processing and most recently, neural network models have been shown to be very effective at this task. The model, called sequence-to-sequence model, learns to map an input sequence in one language to a vector of fixed dimensionality and then map that vector to an output sequence in another language without any human intervention provided that there is enough training data. Focusing on English-French translation, in this paper, I present a way to simplify the learning process by replacing English input sentences by word-by-word translation of those sentences. …

Dna Analysis Using Grammatical Inference, Cory Cook

Master's Projects

An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA.

An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm.

Testing shows that the accuracy of inferred languages for …

Categorizing Blog Spam, Brandon Bevans

Master's Theses

The internet has matured into the focal point of our era. Its ecosystem is vast, complex, and in many regards unaccounted for. One of the most prevalent aspects of the internet is spam. Similar to the rest of the internet, spam has evolved from simply meaning ‘unwanted emails’ to a blanket term that encompasses any unsolicited or illegitimate content that appears in the wide range of media that exists on the internet.

Many forms of spam permeate the internet, and spam architects continue to develop tools and methods to avoid detection. On the other side, cyber security engineers continue to …

Multiple Sequence Alignment With Pro Le Hidden Markov Models, Shubhangi Rakhonde

Master's Projects

The human genome consists of various patterns and sequences that are of biolog- ical signi cance. Capturing these patterns can help us in resolving various mysteries related to the genome, like how genomes evolve, how diseases occur due to genetic mutation, how viruses mutate to cause new disease and what is the cure for these diseases. All these applications are covered in the study of bioinformatics.

One of the very common tasks in bioinformatics involves simultaneous alignment of a number of biological sequences. In bioinformatics, this is widely known as Mul- tiple Sequence Alignment. Multiple sequence alignments help in grouping …

Improving Electroencephalography-Based Imagined Speech Recognition With A Simultaneous Video Data Stream, Sarah J. Stolze

Computer Science and Computer Engineering Undergraduate Honors Theses

Electroencephalography (EEG) devices offer a non-invasive mechanism for implementing imagined speech recognition, the process of estimating words or commands that a person expresses only in thought. However, existing methods can only achieve limited predictive accuracy with very small vocabularies; and therefore are not yet sufficient to enable fluid communication between humans and machines. This project proposes a new method for improving the ability of a classifying algorithm to recognize imagined speech recognition, by collecting and analyzing a large dataset of simultaneous EEG and video data streams. The results from this project suggest confirmation that complementing high-dimensional EEG data with similarly …

A Study Of Android Malware Detection Techniques And Machine Learning, Balaji Baskaran, Anca Ralescu

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware …

Extended Pixel Representation For Image Segmentation, Deeptha Girish, Vineeta Singh, Anca Ralescu

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

We explore the use of extended pixel representation for color based image segmentation using the K-means clustering algorithm. Various extended pixel representations have been implemented in this paper and their results have been compared. By extending the representation of pixels an image is mapped to a higher dimensional space. Unlike other approaches, where data is mapped into an implicit features space of higher dimension (kernel methods), in the approach considered here, the higher dimensions are defined explicitly. Preliminary experimental results which illustrate the proposed approach are promising.

An Autonomic Computing System Based On A Rule-Based Policy Engine And Artificial Immune Systems, Rahmira Rufus, William Nick, Joseph Shelton, Albert Esterline

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

Autonomic computing systems arose from the notion that complex computing systems should have properties like those of the autonomic nervous system, which coordinates bodily functions and allows attention to be directed to more pressing needs. An autonomic system allows the system administrator to specify high-level policies, which the system maintains without administrator assistance. Policy enforcement can be done with a rule based system such as Jess (a java expert system shell). An autonomic system must be able to monitor itself, and this is often a limiting factor. We are developing an automatic system that has a policy engine and uses …

Towards The Development Of A Cyber Analysis & Advisement Tool (Caat) For Mitigating De-Anonymization Attacks, Siobahn Day, Henry Williams, Joseph Shelton, Gerry Dozier

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

We are seeing a rise in the number of Anonymous Social Networks (ASN) that claim to provide a sense of user anonymity. However, what many users of ASNs do not know that a person can be identified by their writing style.

In this paper, we provide an overview of a number of author concealment techniques, their impact on the semantic meaning of an author's original text, and introduce AuthorCAAT, an application for mitigating de-anonymization attacks. Our results show that iterative paraphrasing performs the best in terms of author concealment and performs well with respect to Latent Semantic Analysis.

Situations And Evidence For Identity Using Dempster-Shafer Theory, William Nick, Yenny Dominguez, Albert Esterline

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

We present a computational framework for identity based on Barwise and Devlin’s situation theory. We present an example with constellations of situations identifying an individual to create what we call id-situations, where id-actions are performed, along with supporting situations. We use Semantic Web standards to represent and reason about the situations in our example. We show how to represent the strength of the evidence, within the situations, as a measure of the support for judgments reached in the id-situation. To measure evidence of an identity from the supporting situations, we use the Dempster-Shafer theory of evidence. We enhance Dempster- Shafer …

Student Understanding And Engagement In A Class Employing Comps Computer Mediated Problem Solving: A First Look, Jung Hee Kim, Michael Glass, Taehee Kim, Kelvin Bryant, Angelica Willis, Ebonie Mcneil, Zachery Thomas

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

COMPS computer-mediated group discussion exercises are being added to a second-semester computer programming class. The class is a gateway for computer science and computer engineering students, where many students have difficulty succeeding well enough to proceed in their major. This paper reports on first results of surveys on student experience with the exercises. It also reports on the affective states observed in the discussions that are candidates for analysis of group functioning. As a step toward computer monitoring of the discussions, an experiment in using dialogue features to identify the gender of the participants is described.

A Tool For Staging Mixed-Initiative Dialogs, Joshua W. Buck, Saverio Perugini

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

We discuss and demonstrate a tool for prototyping dialog-based systems that, given a high-level specification of a human-computer dialog, stages the dialog for interactive use. The tool enables a dialog designer to evaluate a variety of dialogs without having to program each individual dialog, and serves as a proof-of-concept for our approach to mixed-initiative dialog modeling and implementation from a programming language-based perspective.