Digital Commons Network^™

Towards Automated Domain-Oriented Lexicon Construction And Dimension Reduction For Arabic Sentiment Analysis, Hasan A. Alshahrani

Dissertations

Sentiment analysis is a type of text mining that uses Natural Language Processing (NLP) tools to identify and label opinionated text. There are two main approaches of sentiment analysis: lexicon-based, and statistical approach. In our research, we use the lexicon-based approach because the lexicon contains sentiment words and phrases which are the main linguistic units to express sentiments. More specifically, we work with domain-oriented lexicons as they are more efficient than general ones because the polarity is heavily driven by domains.

Arabic language has a degree of uniqueness that makes it hard to be processed with the available cross-language tools …

Protecting Privacy Of Data In The Internet Of Things With Policy Enforcement Fog Module, Abduljaleel Al-Hasnawi

Dissertations

The growth of IoT applications has resulted in generating massive volumes of data about people and their surroundings. Significant portions of these data are sensitive since they reflect peoples' behaviors, interests, lifestyles, etc. Protecting sensitive IoT data from privacy violations is a challenge since these data need to be handled by public networks, servers and clouds, most of which are untrusted parties for data owners. In this study, a solution called Policy Enforcement Fog Module (PEFM) is proposed for protecting sensitive IoT data. The primary task of the PEFM solution is mandatory enforcement of privacy polices for sensitive IoT data-whenever …

Efficacy Of Deep Learning In Support Of Smart Services, Basheer Mohammed Basheer Qolomany

Dissertations

The massive amount of streaming data generated and captured by smart service appliances, sensors and devices needs to be analyzed by algorithms, transformed into information, and minted to extract knowledge to facilitate timely actions and better decision making. This can lead to new products and services that can dramatically transform our lives. Machine learning and data analytics will undoubtedly play a critical role in enabling the delivery of smart services. Within the machine-learning domain, Deep Learning (DL) is emerging as a superior new approach that is much more effective than any rule or formula used by traditional machine learning. Furthermore, …

Exploring The Impact Of Pretrained Bidirectional Language Models On Protein Secondary Structure Prediction, Dillon G. Daudert

Masters Theses

Protein secondary structure prediction (PSSP) involves determining the local conformations of the peptide backbone in a folded protein, and is often the first step in resolving a protein's global folded structure. Accurate structure prediction has important implications for understanding protein function and de novo protein design, with progress in recent years being driven by the application of deep learning methods such as convolutional and recurrent neural networks. Language models pretrained on large text corpora have been shown to learn useful representations for feature extraction and transfer learning across problem domains in natural language processing, most notably in instances where the …

Exploring The Role Of Semi-Supervised Deep Reinforcement Learning And Ensemble Methods In Support Of The Internet Of Things, Mehdi Mohammadi

Dissertations

Smart services are an important element of the Internet of Things (IoT) ecosystem where insights are drawn from raw data through the use of machine learning techniques. However, the pathway to develop IoT smart services is complicated as IoT data presents several challenges for machine learning, including handling big data, shortage of labeled data, and the need to benefit from the spatio-temporal relations hidden in the training data.

In this dissertation, after reviewing the state-of-the-art deep learning (DL) and deep reinforcement learning (DRL) techniques and their use in support of IoT applications, this study proposes to extend DRL to semi-supervised …

Lee Honors College Mobile Application, James Ward

Honors Theses

In the spring of 2018 three Computer Science students Benjamin Campbell, James Ward, and Peter Shutt created a mobile application. This app was developed over the span of two semesters for their senior design project; a capstone to their degrees.

Their client, The Lee Honors College at Western Michigan University --referred to as LHC and WMU respectively hereafter-- has a plethora of academic and social information, and a large demand for access to it. This information includes building hours, contact information, health resources, a LHC specific course catalog, social media posts, event descriptions, and much more. The volume of information, …

Similarity Based Classification Of Adhd Using Singular Value Decomposition, Taban Eslami, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for …

Using Agent-Based Implementation Of Active Data Bundles For Protecting Privacy In Healthcare Information Systems, Raed M. Salih

Dissertations

Sharing healthcare information—including electronic health/medical records (EHRs/EMRs)—among healthcare information systems is necessary for improving the quality of healthcare. However, facilitating data exchange increases privacy threats—due to easier copying and dissemination of healthcare information.

We propose a solution that provides privacy protection for patients’ EHRs/EMRs disseminated among different authorized healthcare information systems. Our solution builds upon the existing construct named an Active Data Bundle (ADB). In the proposed solution, ADBs keep EHRs/EMRs as sensitive data; include metadata describing sensitive data and prescribing their use; and encompass a policy enforcement engine (called a virtual machine or VM), which …

Support Assurance-Based Software Development For Mission Critical Domains Using The Model Driven Architecture, Chung-Ling Lin

Dissertations

In the past decades, software development for mission critical applications has drawn great attention not only in various mission critical communities but also software engineering communities. One of the important reasons is that the failure of these systems can lead to some serious consequences such as huge financial loss and even loss of life. Therefore, software certification has become an important activity for mission critical applications in that software assurance for such a system should be certified. With the increasing complexity of a software system in mission critical sectors, certifiers have found hard time to understand how a software system …

Exploring The Use Of Hierarchal Statistical Analysis And Deep Neural Networks To Detect And Mitigate Covert Timing Channels, Omar Darwish

Dissertations

Covert timing channels provide a mechanism to transmit unauthorized information across different processes. It utilizes the inter-arrival times between the transmitted packets to hide the communicated data. It can be exploited in a variety of malevolent scenarios such as leaking military secrets, trade secrets, and other forms of Intellectual Property (IP). They can be also used as a vehicle to attack existing computing systems to disseminate software viruses or worms while bypassing firewalls, intrusion detection and protection systems, and application filters. Therefore, the detection and mitigation of covert channels is a key issue in modern Information Technology (IT) infrastructure. Many …

A Holistic Computational Approach To Boosting The Performance Of Protein Search Engines, Majdi Ahmad Mosa Maabreh

Dissertations

Despite availability of several proteins search engines, due to the increasing amounts of MS/MS data and database sizes, more efficient data analysis and reduction methods are important. Improving accuracy and performance of protein identification is a main goal in the community of proteomic research. In this research, a holistic solution for improvement in search performance is developed.

Most current search engines apply the SEQUEST style of searching protein databases to define MS/MS spectra. SEQUEST involves three main phases: (i) Indexing the protein databases, (ii) Matching and Ranking the MS/MS spectra and (iii) Filtering the matches and reporting the final proteins. …

A Survey Of Security And Privacy In Mobile Cloud Computing, Bhuvaneswari Rayapuri

Masters Theses

Cloud Computing is an emerging technology that provides shared processing resources and data to computers and other devices on demand. On the other hand, Mobile Computing allows transmission of data, voice and video. From these two there emerges a new concept Mobile Cloud Computing which not only overcomes the problems of Mobile Computing but also integrates Cloud Computing into Mobile Environments to overcome obstacles related to Performance, Security and Environment. This paper also provides a decent description on Security and Privacy, its related problems, threats and challenges. This paper first provides details on survey of Mobile Cloud Computing, then it …