Computer Sciences Commons^™

Discovering High-Profit Product Feature Groups By Mining High Utility Sequential Patterns From Feature-Based Opinions, Priyanka Motwani

Electronic Theses and Dissertations

Extracting a group of features together instead of a single feature from the mined opinions, such as “{battery, camera, design} of a smartphone,” may yield higher profit to the manufactures and higher customer satisfaction, and these can be called High Profit Feature Groups (HPFG). The accuracy of Opinion-Feature Extraction can be improved if more complex sequential patterns of customer reviews are learned and included in the user-behavior analysis to obtain relevant frequent feature groups. Existing Opinion-Feature Extraction systems that use Data Mining techniques with some sequences include those referred to in this thesis as Rashid13OFExt, Rana18OFExt, and HPFG19_HU. Rashid13OFExt …

An Enhancement To Cnn Approach With Synthesized Image Data For Disease Subtype Classification, Narider Pal Singh

Electronic Theses and Dissertations

The introduction of genetic testing has profoundly enhanced the prospects of early detection of diseases and techniques to suggest precision medicines. The subtyping of critical diseases has proven to be an essential part of the development of individualized therapies and has led to deeper insights into the heterogeneity of the disease. Studies suggest that variants in particular genes have significant effects on certain types of immune system cells and are also involved in the risk of certain critical illnesses like cancer. By analyzing the genetic sequence of a patient, disease types and subtypes can be predicted. Recent research work has …

Transmission Data Rate Control Based Mechanism For Congestion Control In Vehicular Ad Hoc Networks (Vanet), Srihari Jayachandran

Electronic Theses and Dissertations

Vehicular Ad Hoc Networks (VANET) supporting Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (v2I) communication can increase the efficiency and safety of the road transportation systems. VANET typically uses wireless communication technology and in scenarios with high vehicle densities, the communication channel faces congestion, negatively impacting the reliability of the safety applications. To prevent this, the European Telecommunication Standards Institute (ETSI) has proposed the Decentralized Congestion Control (DCC) methodology to effectively control the channel load, by controlling various message transmission parameters like message rate, data rate, and transmission power. Currently, most research works focus on the transmission power to control congestion, while the …

Approaches For Eye-Tracking While Reading, Xiaohao Sun

Electronic Theses and Dissertations

In this thesis, we developed an algorithm to detect the correct line being read by participants. The comparisons of the reading line classification algorithms are demonstrated using eye-tracking data collected from a realistic reading experiment in front of a low-cost desktop-mounted eye-tracker. With the development of eye-tracking techniques, research begins to aim at trying to understand information from the eyes. However, state of the art in eye-tracking applications is affected by a large amount of measurement noise. Even the expensive eye-trackers still suffer significant noise. In addition, the inherent characteristics of gaze movement increase the difficulty of obtaining valuable information …

Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satellite Using Enhanced Random Forest With Multidomain Features, Mofiyinoluwa Oluwatobi Folami

Electronic Theses and Dissertations

With the increasing number of satellite launches throughout the years, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex it becomes difficult to generate a high-fidelity model that accurately describes all the system components. With such constraints using data-driven approaches becomes a more feasible option. One of the most commonly used actuators in spacecraft is known as the reaction wheel. If these reaction wheels are not maintained or monitored, it could result in mission failure and unwarranted costs. That is why fault detection …

Cluster Hire In Social Networks Using Modified Weighted Structural Clustering Algorithm For Networks (Mwscan), Harshil Patel

Electronic Theses and Dissertations

The concept of effective collaboration within a group is immensely used in organizations as a viable means for improving team performance. Any organization or prominent institute, who works with multiple projects needs to hire a group of experts who can complete a set of projects. When hiring a group of experts, numerous considerations must be taken into account. In the Cluster Hire problem, we are given a set of experts, each having a set of skills. Also, we are given a set of projects, each requiring a set of skills. Upon completion of each project, a profit is generated for …

Hrotate: Hybrid Relational Rotation Embedding For Knowledge Graph, Akshay Mukundbhai Shah

Electronic Theses and Dissertations

Knowledge Graph (KG) represents the real world's information in the form of triplets (head, relation, and tail). However, most KGs are generated manually or semi-automatically, which resulted in an enormous number of missing information in a KG. The goal of a Knowledge-Graph Completion task is to predict missing links in a given Knowledge Graph. Various approaches exist to predict a missing link in a KG. However, the most prominent approaches are based on tensor factorization and Knowledge-Graph embeddings, such as RotatE and SimplE. The RotatE model depicts each relation as a rotation from the source entity (Head) to the target …

Meta-Heuristic Approach For Course Scheduling Problem, Amanta Sunny

Electronic Theses and Dissertations

Nowadays, much research is being carried out to find efficient algorithms for optimal automated university course timetable problems (UCTP). UCTP allocates the university's events like lectures, exams to the various resources, including instructors, students, lecture time and classrooms. Class scheduling is one of the biggest challenging problems of educational institutions. In this thesis, the aim is to improve the state-of-art for a class scheduling problem considering some hard and soft constraints. Hard constraints must be satisfied. Soft constraints need not be satisfied, but there is a penalty for each soft constraint violation. We also have a timing penalty for scheduling …

Active Community Opinion Network Mining And Maximization Through Social Networks Posts, Mayank Semwal

Electronic Theses and Dissertations

Existing OM systems like CONE take a partial historical rating of users on multiple products and perform opinion estimation to maximizes overall positive opinions using OM. However, CONE does not consider actual user opinions from social posts where users provide opinions through comments, likes and sharing about a product. OBIN mines users' low-frequency features from comments to create a community preference influence network utilizing user response on posts and relationships between them. However, OBIN only performs feature-level opinion mining and does not consider a joint approach that combines sentence-level and feature-level to remove subjective reviews and includes slang words and …

Experimental Study Of Evolving Communities In Online Social Networks, Pallavi Kaul

Electronic Theses and Dissertations

In the past few decades, the advancement in technology and the internet has leveraged the application of social networks in the world. Millions of people connect through social networks irrespective of their geographical boundaries. These users tend to form communities on common ground, such as similar hobbies, school, work, and much more. Deep level mining and analysis of these communities, connections within these communities, and the connections within the users of these communities divulge abundant data about the underlying features of these complex networks. This thesis focuses on detecting communities at specific times to track the change in memberships. Doing …

Mining Twitter Sequences Of Product Opinions With Multi-Word Aspect Terms, Vinay Kiran Manjunath

Electronic Theses and Dissertations

Social media platforms have opened doors to users' opinions and perceptions. The text remains the most popular means of contact on social media, despite different means of communication (audio/video and images). Twitter is one such microblogging platform that allows people to express their thoughts within 280 characters per message. The freedom of expression has made it difficult to understand the polarity (Positive, Negative, or Neutral) of the tweets/posts. Given a corpus of microblog texts (e.g., "the new iPhone battery life is good, but camera quality is bad"), mining aspects (e.g., battery life, camera quality) and opinions (e.g., good, bad) of …

A Network-Based Approach For Computational Drug Repurposing On Cancer Data, Ann Reba, Thomas Alexander

Electronic Theses and Dissertations

In this thesis, we are interested in finding the best drugs that can be repurposed for the disease and able to find the adverse effects such drugs that are FDA-Approved. Developing an effective drug can be a time-consuming and expensive crucible method. Network-based machine learning methods are used for predicting a given drug for A that can be used for B. It aims at finding new indications for already existing drugs and therefore increases the available therapeutic choices at a fraction of the cost of new drug development. The perturbation gene expression data corresponding to the MCF7 cell line was …

Comparative Study Of Reinforcement Learning Methods In Path Planning, Daniel Obawole

Electronic Theses and Dissertations

In order to perform a large variety of tasks and achieve human-level performance in complex real-world environments, an intelligent agent must be able to learn from its dynamically changing environment. Generally speaking, agents have limitations in obtaining an accurate description of the environment from what they perceive because they may not have all the information about the environment. The present research is focused on reinforcement learning algorithms that represent a defined category in the field of machine learning because of their unique approach based on a trial-error basis. Reinforcement learning is used to solve control problems based on received rewards. …

Internet Of Things Software And Hardware Architectures And Their Impacts On Forensic Investigations: Current Approaches And Challenges, Abel Alex Boozer, Arun John, Tathagata Mukherjee

Journal of Digital Forensics, Security and Law

The never-before-seen proliferation of interconnected low-power computing devices, patently dubbed the Internet of Things (IoT), is revolutionizing how people, organizations, and malicious actors interact with one another and the Internet. Many of these devices collect data in different forms, be it audio, location data, or user commands. In civil or criminal nature investigations, the data collected can act as evidence for the prosecution or the defense. This data can also be used as a component of cybersecurity efforts. When data is extracted from these devices, investigators are expected to do so using proven methods. Still, unfortunately, given the heterogeneity in …

The Survey On Cross-Border Collection Of Digital Evidence By Representatives From Polish Prosecutors’ Offices And Judicial Authorities, Paweł Olber Dr

Journal of Digital Forensics, Security and Law

Dynamic development of IT technology poses new challenges related to the cross-border collection of electronic evidence from the cloud. Many times investigators need to secure data stored on foreign servers directly and then look for solutions on how to turn the data into a legitimate source of evidence. To study the situation and propose solutions, I conducted a survey among Polish representatives of public prosecutors' offices and courts. This paper presents information from digital evidence collection practices across multiple jurisdictions. I stated that representatives from the prosecution and the judiciary in Poland are aware of the issues associated with cross-border …

Umaine Artificial Intelligence Webinar: Ai For Space And Aerospace Promotional Flyer, University Of Maine Artificial Intelligence, Institute Of Electrical And Electronics Engineers Maine Com/Cs Chapter, Vice President For Research And Dean Of The Graduate School

General University of Maine Publications

Promotional flyer for the first webinar in the UMaine Artificial Intelligence 2021-2022 webinar series.

The University of Maine Artificial Intelligence Initiative (UMaine AI) is a unique Maine-based venture that brings together university, industry, government, and community collaborators from Maine and beyond to advance the field of artificial intelligence, and through development of innovative technologies and applications find transformative solutions to enhance human life and societal well-being in Maine and beyond.

(Linked) Data Quality Assessment: An Ontological Approach, Aparna Nayak, Bojan Bozic, Luca Longo

Conference papers

The effective functioning of data-intensive applications usually requires that the dataset should be of high quality. The quality depends on the task they will be used for. However, it is possible to identify task-independent data quality dimensions which are solely related to data themselves and can be extracted with the help of rule mining/pattern mining. In order to assess and improve data quality, we propose an ontological approach to report data quality violated triples. Our goal is to provide data stakeholders with a set of methods and techniques to guide them in assessing and improving data quality

What Is The Relationship Between Language And Thought?: Linguistic Relativity And Its Implications For Copyright, Christopher S. Yoo

All Faculty Scholarship

To date, copyright scholarship has almost completely overlooked the linguistics and cognitive psychology literature exploring the connection between language and thought. An exploration of the two major strains of this literature, known as universal grammar (associated with Noam Chomsky) and linguistic relativity (centered around the Sapir-Whorf hypothesis), offers insights into the copyrightability of constructed languages and of the type of software packages at issue in Google v. Oracle recently decided by the Supreme Court. It turns to modularity theory as the key idea unifying the analysis of both languages and software in ways that suggest that the information filtering associated …

Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard

James Madison Undergraduate Research Journal (JMURJ)

The dissemination of deep fakes for nefarious purposes poses significant national security risks to the United States, requiring an urgent development of technologies to detect their use and strategies to mitigate their effects. Deep fakes are images and videos created by or with the assistance of AI algorithms in which a person’s likeness, actions, or words have been replaced by someone else’s to deceive an audience. Often created with the help of generative adversarial networks, deep fakes can be used to blackmail, harass, exploit, and intimidate individuals and businesses; in large-scale disinformation campaigns, they can incite political tensions around the …

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an …