Digital Commons Network^™

A Systematic Approach For Evaluating And Selecting Healthcare Waste Treatment Devices Using Owcm-Codas And Triangular Neutrosophic Sets, Asmaa Elsayed, Bilal Arain

Neutrosophic Systems with Applications

Healthcare is a fundamental aspect of human life, impacting individuals, communities, and societies. Investing in healthcare infrastructure, services, and education is essential for fostering a healthy, thriving population. Healthcare waste management is a critical aspect of public health, and it requires a concerted effort from healthcare facilities, governments, and communities to ensure that waste is managed in a way that minimizes risks to human health and the environment. This paper proposes a hybrid methodology combining the Opinion Weight Criteria Method (OWCM) and the Combinative Distance-Based Assessment (CODAS) within the framework of Triangular Neutrosophic Sets (TNS) to evaluate and select the …

Leveraging An Uncertainty Methodology To Appraise Risk Factors Threatening Sustainability Of Food Supply Chain, Rehab Mohamed, Mahmoud M. Ismail

Neutrosophic Systems with Applications

By diminishing the risk factors associated with the food supply chain (FSC), we have recourse to strengthen the food supply chain's resilience, decrease food waste, and increase its sustainability. Prioritizing and identifying the risk factors impacting the sustainability of the food supply chain is essential for managing uncertainty and averting unfavorable consequences. This study attempts to identify and rank the most significant risks affecting the sustainability of the food supply chain under an uncertain environment. We use the α-Discounting multi-criteria decision-making (α-D MCDM) method for the main three risk factors: the risks of supply, the risks of demand, and the …

Single-Valued Neutrosophic Mcdm Approaches Integrated With Merec And Ram For The Selection Of Uavs In Forest Fire Detection And Management, Mai Mohamed, Amira Salam, Jun Ye, Rui Yong

Neutrosophic Systems with Applications

In recent times, the world has experienced a rise in the frequency of forest fires. These fires cause severe economic damage and pose a significant threat to human lives. Therefore, it is essential to search for solutions that can help combat fires and detect them early. Once a fire reaches a certain level, it becomes challenging to control it. Various systems have been proposed to collect data and detect forest fires, such as satellites and other traditional methods. However, these solutions have been ineffective in terms of cost, coverage of large areas, accuracy, and the safety of human lives. To …

Harnessing Social Media For Disaster Response: Intelligent Identification Of Reliable Rescue Requests During Hurricanes, Wael Khallouli

Engineering Management & Systems Engineering Theses & Dissertations

Hurricanes pose a significant threat to both human lives and infrastructure. Decision-makers face substantial challenges during such events, as they must act quickly to address victims’ needs. Social media platforms provide a valuable source for quick and real-time information. Recent hurricane events have shown that people turn to social media to call for help when official communication channels, such as 911, are overwhelmed. However, extracting actionable information from the massive number of messages posted on social media is challenging. Furthermore, verifying social media messages posted by the public is a critical concern for disaster response practitioners, making them hesitant to …

Exploring The Application Of Digital Twin Technology In The Energy Sector Using Merec And Mairca Methods, Asmaa Elsayed, Bilal Arain, Karam M. Sallam

Neutrosophic Systems with Applications

Smart city sustainability initiatives prioritize creating environmentally, economically, and socially sustainable urban environments. Digital Twin (DT) technology creates precise digital replicas of physical assets, systems, or processes. These digital twins play a crucial role in advancing the goals of smart city sustainability. This paper explores the development and application of DT technology for integrated regional energy systems in smart cities, emphasizing its potential to optimize energy consumption, reduce costs, and enhance overall system performance. The CloudIEPS platform, an energy internet planning platform based on digital twin technology, is a great example of how digital twin technology can be applied in …

Structural And Parametric Identification Of Recycling Systems, Khusan Igamberdiev, Madina Mirxalilovma Fozilova

Chemical Technology, Control and Management

The issue of identifying recirculation systems with a specific control loop, in which information and control information about various technological processes circulate, is being considered. A methodology for identifying recirculation systems has been developed, based on algorithms for structural identification of the model based on the results of assessing the sensitivity of the optimum of control parameters and a fuzzy inference algorithm based on production rules that contribute to solving the problem of parametric identification.

Synthesis Of An Adaptive Neuro-Fuzzy Control System For Steam Generator Temperature, Isomiddin Xakimovich Siddikov, Dilnoza Maxamadjanovna Umurzakova

Chemical Technology, Control and Management

This paper is devoted to the modeling and synthesis of an adaptive neuro-fuzzy control system for steam generator temperature. Temperature control systems play an important role in industry because accurate and stable control is a prerequisite for the efficient operation of steam systems. Traditional control methods based on mathematical models and fixed parameter controllers may have limitations in providing optimal performance and adapting to changing operating conditions. The paper proposes a synthesized system combining fuzzy logic and adaptation methods to achieve more accurate and stable temperature control. A detailed structural diagram of the system, modeling, and tuning methods are presented. …

A Robust Data-Driven Framework For Artificial Intelligent Systems, Quoc H. Nguyen

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) systems have demonstrated remarkable performance across various domains. However, their robustness remains a critical concern, particularly in terms of data and model reliability. This dissertation aims to address the challenges associated with building robust AI systems by focusing on two key aspects: data robustness and model robustness. Data robustness poses significant challenges, including data shift, concept shifting, limited and imbalanced datasets, and interoperability issues in IoT systems for data collection. Existing methods fall short in handling dynamic business objectives and evolving data landscapes effectively. To bridge these gaps, we propose an IoT framework that ensures interoperability, seamless …

Advancing Adversarial Audio: Human-In-The-Loop Black-Box Attacks, Rui Duan

USF Tampa Graduate Theses and Dissertations

Adversarial audio attacks pose significant security challenges to real-world audio applications. Attackers may manipulate speech to impersonate a speaker, gaining access to smart devices like Amazon Echo. In audio applications, there are two key areas: music and speech. In music, most attackers create a small noise-like perturbation on the original signal to evade copyright detection. However, this method degrades music's perceived quality for human listeners. In the speech, creating an adversarial example often requires many queries to the target model, a process too cumbersome for practical use in real-world scenarios, like interacting with smart devices numerous times.

In this dissertation, …

Enhancing Contractor Selection Through Fuzzy Topsis And Fuzzy Saw Techniques, Mohammadsoroush Tafazzoli, Ayoub Hazrati, Kishor Shrestha, Krishna Kisi

Civil Engineering & Construction: Faculty Publications

Contractors play an integral role in construction projects, and their qualifications directly impact various aspects of a project’s success. The unbiased selection of contractors is a challenge in the construction industry worldwide, particularly in public projects where impartiality in the final selection is essential. Numerous factors must be considered when evaluating contractors, making the selection process challenging for the human brain. This paper introduces and compares two methods for assessing contractor prequalification by applying the fuzzy theory. The idea is to facilitate using human judgments in a mathematical system for decision-making with regard to selecting contractors. The method is based …

Aligning Language Models With The Human World, Ruibo Liu

Dartmouth College Ph.D Dissertations

The field of Natural Language Processing (NLP) has undergone a significant transformation with the emergence of large language models (LMs). These models have enabled the development of human-like conversational assistants (e.g., OpenAI's ChatGPT), and expert-level AI software engineering agents (e.g., Devin from Cognition Lab). However, these models face a fundamental challenge related to their training methodology. Predominantly trained on vast datasets scraped from the web, their self-supervised learning objective---predict missing tokens---unintentionally perpetuates the biases, inaccuracies, and sensitive information inherent in their training data. These issues lead to what is termed misaligned}behaviors and pose a significant hurdle in the development of …

Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco

Theses and Dissertations

Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …

Visionarium, June 2024, Full Issue

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

No abstract provided.

Absent Presence: The Human Influence In Ai-Generated Content In The Age Of Technoculture, Kathi Vosevich, James Hutson

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

In recent years, the field of artificial intelligence (AI), particularly under the banner of generative AI (GAI), has made unprecedented advancements, pushing the boundaries of what was traditionally considered within the realm of human capability. AI systems have not only matched but also surpassed human proficiency in various tasks, sparking widespread discussions about their significant impact across multiple fields, including the arts and humanities. This paper considers the profound implications of AI's rapid progress and explores the concept of "Absent Presence" within the context of Derrida and Technoculture, particularly as illustrated in the arts and humanities. Drawing parallels with renowned …

Ranking Cloud Service Providers Using Swara-Marcos In Type-2 Neutrosophic Number Set Environment, Mai Mohamed, Shaimaa Ayman, Rui Yong, Jun Ye

Neutrosophic Systems with Applications

Cloud computing is a model for allowing suitable, on-demand network access to a shared store of resources such as servers, networks, storage, apps, and services, modified according to specific needs or requirements. The main goal of cloud technology development is to increase the use of resources that work together to achieve reliability at the lowest cost. Cloud service providers (CSPs) have gained popularity in recent years due to their accessibility and availability, as well as the growing quantity of cloud service providers (CSPs) that appear. Choosing (CSPs) has grown to be a challenging decision for many companies. The paper aims …

A Comparative Study Of The Npm, Pypi, Maven, And Rubygems Open-Source Communities, Saurav Gupta

Master's Theses

Open-source software (OSS) ecosystems, defined as environments composed of package managers and programming languages (e.g., NPM for JavaScript), are essential for software development and foster collaboration and innovation. Although their significance is acknowledged, understanding what makes OSS communities healthy and sustainable requires further exploration. This thesis quantitatively assesses the health of OSS projects and communities within the NPM, PyPI, Maven, and RubyGems ecosystems. We explore five research questions addressing project standards, community responsiveness, contribution distribution, contributor retention, and newcomer integration strategies. Our analysis shows varied documentation practices, insider engagement levels, and contribution patterns. Our findings highlight both strengths and different …

Hatt-Flow: Hierarchical Attention-Flow Mechanism For Group-Activity Scene Graph Generation In Videos, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Thi Hoang Ngan Le, Page Daniel Dobbs, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Group-activity scene graph (GASG) generation is a challenging task in computer vision, aiming to anticipate and describe relationships between subjects and objects in video sequences. Traditional video scene graph generation (VidSGG) methods focus on retrospective analysis, limiting their predictive capabilities. To enrich the scene-understanding capabilities, we introduced a GASG dataset extending the JRDB dataset with nuanced annotations involving appearance, interaction, position, relationship, and situation attributes. This work also introduces an innovative approach, a Hierarchical Attention–Flow (HAtt-Flow) mechanism, rooted in flow network theory to enhance GASG performance. Flow–attention incorporates flow conservation principles, fostering competition …

Accessible Real-Time Eye-Gaze Tracking For Neurocognitive Health Assessments, A Multimodal Web-Based Approach, Daniel C. Tisdale

Master's Theses

We introduce a novel integration of real-time, predictive eye-gaze tracking models into a multimodal dialogue system tailored for remote health assessments. This system is designed to be highly accessible requiring only a conventional webcam for video input along with minimal cursor interaction and utilizes engaging gaze-based tasks that can be performed directly in a web browser. We have crafted dynamic subsystems that capture high-quality data efficiently and maintain quality through instances of user attrition and incomplete calls. Additionally, these subsystems are designed with the foresight to allow for future re-analysis using improved predictive models, as well as enable the creation …

Product Design For Repairability: Identifying Failure Modes With Topic Modeling And Designing For Electronic Waste Reuse, Claire J. Franz

Master's Theses

Design for repairability is imperative to making products that last long enough to justify the resources they consume and the pollution they generate. While design for repairability has been gaining steady momentum, especially with recent advances in Right to Repair legislation, there is still work to be done. There are gaps in both the tools available for repair-conscious designers and the products coming onto store shelves. This thesis work aims to help set sails in the right direction on both fronts.

This research explores the use of topic modeling (a natural language processing technique) to extract repairability design insights from …

Naturify 2300, Yarina Yiwei Dai

Masters Theses

In my art practice, I explore the interplay between human desires to manipulate and anthropomorphize nature, as seen in the technological augmentation of plants and living entities. This investigation delves into how this intersection, alongside empathy towards these creations, contributes to fears of uncontrollability and the risks of addiction and excessive dependence on technology.

Bioengineering and genetic modification have cultivated unprecedented developments, allowing humans to manipulate the fundamental building blocks of life. My research speculates on this technology further, modifying the genetic code of organisms and creating bioengineered wearable entities with enhanced traits or entirely new functionalities. The primary objective …

Creating Synthetic Energy Meter Data Using Conditional Diffusion And Building Metadata, Chun Fu, Hussain Kazmi, Matias Quintana, Clayton Miller

Research Collection College of Integrative Studies

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational …

Rough Fermatean Neutrosophic Sets And Its Applications In Medical Diagnosis, P. Dhanalakshmi

Neutrosophic Systems with Applications

This paper introduces the concept of rough fermatean neutrosophic sets and investigates their properties. Additionally, a cosine similarity measure between these sets is proposed. By applying this measure to a medical diagnosis example, the paper illustrates how the method can be used in practical situations, highlighting its effectiveness in complex decision-making scenarios. This innovation holds promise for improving decision-making processes, especially in critical areas like medical diagnosis, where making accurate assessments amidst uncertainty is crucial.

Mobile Robot Path Planning Optimization Problem Using Multi- Objective Genetic Algorithm, Suresh Ks

Theses and Dissertations

Mobile Robot Path Planning problem (MRPPP) is the most prominent research area employed in different real-time environments. The research domain of robotics offers abundant opportunities for researchers in various engineering fields with different dimensions. The automation of physical movements of the robots with intelligence to make dynamic decisions for interacting with the environment opens up a lot of challenges to the research community.

A variety of approaches are employed to solve the Mobile Robot Path Planning Problem (MRPP), which is to derive a feasible collision-free path to reach the destination from the given starting point by avoiding obstacles. The solution …

Text-To-Sql: A Methodical Review Of Challenges And Models, Ali Buğra Kanburoğlu, Faik Boray Tek

Turkish Journal of Electrical Engineering and Computer Sciences

This survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English …

Dense Video Description Method Based On Multi-Modal Fusion In Transformer Network, Xiang Li, Haifeng Sang

Journal of System Simulation

Abstract: In order to solve the problems that most of the current dense video description models use twostage methods, which have low efficiency, ignore audio and semantic information, and have incomplete description results, a multi-modal and semantic information fusion dense video description method was proposed. An adaptive R(2+1)D network was proposed to extract visual features, a semantic detector was designed to generate semantic information, audio features were added to supplement it, a multi-scale deformable attention module was established, and a parallel prediction head was applied to accelerate the convergence rate and improve the accuracy of the model. The experimental results …

An Innovative Approach On Yao’S Three-Way Decision Model Using Intuitionistic Fuzzy Sets For Medical Diagnosis, Wajid Ali, Tanzeela Shaheen, Iftikhar Ul Haq, Florentin Smarandache, Hamza Ghazanfar Toor, Faiza Asif

Neutrosophic Systems with Applications

In the realm of medical diagnosis, intuitionistic fuzzy data serves as a valuable tool for representing information that is uncertain and imprecise. Nevertheless, decision-making based on this kind of knowledge can be quite challenging due to the inherent vagueness of the data. To address this issue, we employ power aggregation operators, which prove effective in combining several sources of data, such as expert thoughts and patient information. This allows for a more correct diagnosis; a particularly crucial aspect of medical practice where precise and timely diagnoses can significantly impact medication policy and patient results. In our research, we introduce a …

A Robust Decision-Making Model For Medical Supplies Via Selecting Appropriate Unmanned Aerial Vehicle, Amira Salam, Mai Mohamed, Rui Yong, Jun Ye

Neutrosophic Systems with Applications

Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In …

Development Of Hybrid Multi Criteria Decision Making Techniques For Efficient Cloud Service Selection, Obulaporam Gireesha

Theses and Dissertations

During the past few decades, cloud computing became a primary driver for the next generation of digital technology due to the increase in organizational performance and profitability based on a ‘pay-as-you-use’ fashion at anytime and anywhere across the globe. Cloud computing enables various enterprises to access pooled resources (like storage, network bandwidth, software applications, processing power, etc.) over the Internet with minimal Information Technology (IT) infrastructure and capital expenditure.

Indeed, the enormous popularity of cloud computing in both academia & industry over the decade has resulted in a wide range of similar cloud services offered by numerous service providers. Even …

Generalized Model To Enable Zero-Shot Imitation Learning For Versatile Robots, Yongshuai Wu

Master's Theses

The rapid advancement in Deep Learning (DL), especially in Reinforcement Learning (RL) and Imitation Learning (IL), has positioned it as a promising approach for a multitude of autonomous robotic systems. However, the current methodologies are predominantly constrained to singular setups, necessitating substantial data and extensive training periods. Moreover, these methods have exhibited suboptimal performance in tasks requiring long-horizontal maneuvers, such as Radio Frequency Identification (RFID) inventory, where a robot requires thousands of steps to complete.

In this thesis, we address the aforementioned challenges by presenting the Cross-modal Reasoning Model (CMRM), a novel zero-shot Imitation Learning policy, to tackle long-horizontal robotic …

Evaluating Neuroimaging Modalities In The A/T/N Framework: Single And Combined Fdg-Pet And T1-Weighted Mri For Alzheimer’S Diagnosis, Peiwang Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

With the escalating prevalence of dementia, particularly Alzheimer's Disease (AD), the need for early and precise diagnostic techniques is rising. This study delves into the comparative efficacy of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and T1-weighted Magnetic Resonance Imaging (MRI) in diagnosing AD, where the integration of multimodal models is becoming a trend. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we employed linear Support Vector Machines (SVM) to assess the diagnostic potential of these modalities, both individually and in combination, within the AD continuum. Our analysis, under the A/T/N framework's 'N' category, reveals that FDG-PET consistently outperforms T1w-MRI across …