Physical Sciences and Mathematics Commons^™

Survey: A Study On Image Encryption Using Dna In Bioinformatics, Rana M. Zaki, Zaed S. Mahdi, Matheel E. Abdulmunim

Journal of Soft Computing and Computer Applications

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, …

New Feature Selection Using Principal Component Analysis, Zaid Mundher Radeef, Soukaena Hassan Hashem, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more efficient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features. In contrast, feature selection involves choosing a …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Enhancing Image Classification Using A Convolutional Neural Network Model, Zena M. Saadi, Ahmed T. Sadiq, Omar Z. Akif, Marwa M. Eid

Journal of Soft Computing and Computer Applications

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as …

Improved Rapidly-Exploring Random Tree Using Firefly Algorithm For Robot Path Planning, Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Yuhanis Yusof, Ahmed T. Sadiq, Faiz Al Alawy

Journal of Soft Computing and Computer Applications

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firefly Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for "Enhanced RRT with Firefly Algorithm", generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by the RRT algorithm and better paths …

Learning Paradigms For Rhythm Detection And Generation Using Mathematical Models, Biophysical And Artificial Neural Networks, Prianka Bose

Dissertations

Humans possess an inherent ability to recognize evenly-spaced rhythms, known as isochronous rhythms, owing to the brain's predisposition to entrain to external auditory stimuli with regular temporal intervals. The central focus of this research is to understand how the brain learns and retains rhythmic time intervals in the context of music. This dissertation studies rhythm detection and generation through mathematical models, biophysical networks, and artificial neural networks, addressing both isochronous and non-isochronous patterns.

A primary focus of the thesis is on isochronous rhythms. In particular, given a perturbation to an isochronous rhythm such as a tempo change or phase shift …

Pushing The Boundaries Of Large Language Models: Innovations And Limitations In Nlp, Finance, And Mathematics, A M Muntasir Rahman

Dissertations

Large Language Models (LLMs) have emerged as transformative tools across a spectrum of domains, yet their practical deployment reveals a blend of remarkable potential and notable limitations. This research explores innovative methodologies to extend the capabilities of LLMs while addressing critical challenges in their evaluation and application. By leveraging rule-based approaches, the in-context learning capabilities of LLMs, and human-in-the-loop validation across three focused studies, this research introduces robust strategies for dataset synthesis, model enhancement, and model assessment in three distinct domains: natural language processing, financial sentiment analysis, and mathematical reasoning

The first study proposes an efficient data augmentation framework, EASE, …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Ensemble Learning Models For Large-Scale Time Series Forecasting In Supply Chain, Minjuan Zhang

Dissertations

Machine learning and AI techniques are transforming supply chain forecasting, driven by the expanding availability of data assets. These advanced methods offer powerful opportunities to optimize management processes, reduce operational costs, and enhance strategic decision-making, which is crucial for enterprise success. However, conventional statistical approaches, such as Autoregressive Integrated Moving Average Models (ARIMA), dynamic regression, and Unobserved Component Models (UCMs)—which have long dominated time series forecasting—often fall short in accuracy and scalability. These traditional models face limitations in batch processing, handling large-scale data, addressing uncertainty-induced disruptions, and synchronizing demand-supply scenarios.

To address these challenges, a novel class of AI-powered ensemble …

Machine Learning Methods For Pattern Recognition Analysis Of Genomic And Molecular Data, Kuang Du

Dissertations

While immune therapies achieve remarkable success in treating various cancers, only a subset of patients achieves a durable clinical response, and many exhibit innate or acquired resistance. Precision medicine aims to tailor treatments to individual patients based on specific biological markers, ensuring that each patient receives the therapy most likely to be effective. Predictive biomarkers and gene signatures offer potential for more personalized treatment strategies by identifying patients likely to benefit. Recent studies suggest that gene signatures, comprising sets of genes, hold predictive value for certain clinical variables. Typically derived from biological expert knowledge, these signatures demonstrate substantial predictive potential, …

Knowledge Diffusion In Networks Of Artificial Learners, Ehsan Beikihassan

Dissertations

The dissertation draws inspiration from the topic of peer learning in the social sciences and the study of information dissemination and knowledge diffusion in network science. In particular, it introduces and studies a setting involving a population or network of artificial learners, with the objective of optimizing aggregate performance measures under constraints on training resources. In this context, natural knowledge diffusion processes in networks of interacting artificial learners are studied. The term "natural" refers to processes that emulate human peer learning, where the internal state and learning processes of students remain largely opaque, and the main degree of freedom lies …

Interactive Visualization Workflows For Mitigating Analytical Uncertainty, Kaustav Bhattacharjee

Dissertations

This dissertation takes a process-centric and stakeholder-first perspective for handling analytical uncertainty: the form of uncertainty that confronts data analysts' insight-generation processes in high-consequence decision-making scenarios. The cost of an incorrect decision when data is used for movie recommendations as opposed to when personal data is used to drive insights or when data-driven modeling is used to drive real-time decisions for maintaining the health of a grid are vastly different in terms of consequences. This dissertation looks at analytical uncertainty in two real-world scenarios: i) how sensitive information leakage can be prevented during the open data release process with data …

Visual Analytic Techniques For Interpretable Algorithmic Ranking Systems, Jun Yuan

Dissertations

Rankings have a profound impact on the increasingly data-driven society. From leisurely activities like the movies to watch, the restaurants to patronize; to highly consequential decisions, like making educational and occupational choices or getting hired by companies— these are all driven by sophisticated yet mostly opaque algorithmic rankers. A small change in how these rankers order the data items can have profound consequences, like deterioration of the prestige of a university or a job applicant missing out on being on the list of the top candidates for an organization. These scenarios necessitate data-driven and human-centered innovation to make rankers accessible, …

Crowd-Sourced Learning For Computer Graphics Applications, Yunhao Zhang

Dissertations

Computer Graphics (CG) revolves around virtual content creation using computational methods, spanning applications from games to visual effects. Typically, the creation of CG content is led by expert practitioners who guide computational algorithms towards satisfactory results. Thus, creating CG content often requires manual iterations encompassing algorithm design, parameter tuning, and aesthetic feedback. This work investigates how to leverage crowd-sourcing to streamline such creation processes, focusing on animation and simulation. In animation, a novel crowd-sourcing framework is proposed for combat animation, enabling users to analyze motion similarities, and retrieve matching motions using novel crowd-sourced motion features. Such features enable quantifying previously …

Mathematical Modelling And Analysis For The Co-Infection Of Viral And Bacterial Diseases: A Systematic Review Protocol, Timothy Kiprono Yano, Ebenezer Afrifa-Yamoah, Julia Collins, Ute Mueller, Steven Richardson

Research outputs 2022 to 2026

Introduction Breaking the chain of transmission of an infectious disease pathogen is a major public health priority. The challenges of understanding, describing and predicting the transmission dynamics of infections have led to a wide range of mathematical, statistical and biological research problems. Advances in diagnostic laboratory procedures with the ability to test multiple pathogens simultaneously mean that co-infections are increasingly being detected, yet little is known about the impact of co-infections in shaping the course of an infection, infectivity, and pathogen replication rate. This is particularly true of the apparent synergistic effects of viral and bacterial co-infections, which present the …

Multi-Sensor Data Fusion And Gis-Drastic Integration For Groundwater Vulnerability Assessment With Rainfall Consideration, Wu Jiazhe, Dai Xinrui, Su Yangcheng, Zheng Xiangtian, Bushra Ghaffar, Rabiya Nasir, Ahsan Jamil, Zeeshan Zafar, Mohammad Suhail Meer, M. Abdullah-Al-Wadud, Rahila Naseer, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

In many areas of the world, particularly in arid and semi-arid regions, groundwater is the primary source of fresh water, and it supplies around one-third of the world's fresh water. Agriculture is the primary economic sector on the coast in the southern district (Nowshera). More food productivity is required due to the expanding population and diminishing agricultural lands, which increases the use of chemical pesticides and fertilizers in farming. The current study was conducted in northwestern parts of Pakistan to evaluate the impacts of the frequent use of pesticides and fertilizers in agricultural fields. Nine hydrogeological parameters were considered, and …

Design Of Motivational-Realistic-Pancasila Learning In Mathematics, Marup Marup, Suradi Tahmir, Ahmad Talib, Nurzatulshima Kamaruddin

Jurnal Pendidikan Sains

Learning mathematics in class greatly influences students' learning success both in terms of knowledge and attitudes. In carrying out learning, a learning design is needed that can foster student motivation so that they provide learning outcomes to the learning objectives. Motivational-Realistic-Pancasila learning design is considered to be an alternative solution. Morela is a design that combines motivational, realistic indicators and Pancasila values. The teaching of mathematics in the classroom significantly influences students' academic success, both in terms of knowledge and attitude. To effectively carry out the teaching process, a well-designed instructional approach is necessary to foster students' motivation to achieve …

Shalom In Social Media Marketing Shalom In Social Media Marketing, Jill R. Risner, Thomas Betts

University Faculty Publications and Creative Works

This paper will examine social media and the ways in which it currently does and does not contribute to shalom through an examination of both the platforms themselves as well as the content shared through them. As Christ’s ambassadors in the world and the primary funders of social media, marketers have power and an obligation to consider social media’s impact on shalom in the world and to use it in ways that contribute to shalom. This paper will provide several recommendations of how marketers can do this including posting content that intentionally contributes to shalom, engaging on platforms that support …

Catalyzing Meteorological Insights With A Cost-Effective Weather Monitoring System, Ariful Alam, Sidratula Muntaha, Poonam Munshi, Israt Khan, Rakibul Islam, Jamil Bhuian, Md.Yasir Arafat, Md. Ridwanul Hasan

Journal of Environmental Science and Sustainable Development

Undoubtedly, one of the biggest alarming phenomena of this decade is the tremendous fluctuations in the weather and climate. Therefore, different types of surveys, investigations, and research are required in this regard in every region. A low-cost weather monitoring system can be implemented in every educational and research institute to collect and analyze different types of weather-related data. This study establishes the method of developing such a system and analyzing data in a simplified way which the data gathered during thunderstorms and cyclonic activity in Bangladesh. The system was designed with Proteus 8 professional software and developed by using a …

Impact Of Urban Development On Uv Exposure: A Clustering And Machine Learning Assessment, Taufik Roni Sahroni Mr., Verdi Yasin, Lulut Alfaris, Reza Ariefka, Ruben Cornelius Siagian, Mohammad Alfin Karim, Nana Rahdiana, Ade Suhara

Journal of Environmental Science and Sustainable Development

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling …

Developing A Roadmap For Green Port In Timor-Leste, Emanuel Da Silva Maia

World Maritime University Dissertations

No abstract provided.

The Radical Oxidation Of Hydroxyacetone: An Insight Into The Reaction Mechanism And Potential Tetraoxide Intermediates, Edward Buck

Master's Theses

This thesis presents the reaction of hydroxyacetone, a common volatile organic compound (VOC), with chlorine radicals in the presence of oxygen. Chapter one explores VOCs and their specific reactions and how tetraoxide intermediates could be formed from them. Chapter two analyses the experimental and computational methods used and the key theoretical concepts behind these methods. Experimental data was collected using multiplexed photoionization mass spectrometry at the Advanced Light Source Chemical Transformations Beamline, Lawrence Berkeley National laboratory. This data is combined with theoretical data using the CBS-QB3 composite method to determine the key reaction intermediates and mechanisms.

The main chapter of …

Automated Segmentation Of The Ulnar Nerve In Mri Using Deep Learning Techniques, Akhil Nagulapalli

Theses

Cubital Tunnel Syndrome (CuTS), a condition caused by compression of the ulnar nerve, results in numbness, tingling, pain, and even muscle atrophy, affecting fine motor skills and diminishing patient quality of life. Accurate diagnosis of CuTS is challenging, as current diagnostic methods—including clinical exams, nerve conduction studies, and unaided MRI—often lack the precision to reliably identify the nerve and detect compression in its early stages. Deep learning-based segmentation offers a promising solution, enabling precise and automated identification of nerve structures in MRI images, which could significantly improve diagnostic accuracy and support timely intervention.

A novel deep learning model for segmenting …

Surveying The Role Of Visual Analytics In Human-Machine Teaming, Naga Datha Saikiran Battula

Theses

Humans and machines both possess their unique capabilities and have their strengths and weaknesses, which can be complementary to one another and allow them to achieve a common goal. Teaming in the modern era involves text prompts, voice commands, gesture recognition, touch interfaces, and the latest visualization techniques that allow parties/agents to interact. Communication through visualization plays a vital role in allowing robust insights to be gained through a glance. Using visualization as a medium between humans and machines can increase the communication bandwidth. Human-machine teaming has witnessed much progress, with many theories and practical examples emerging. In the report, …

Advancing Prediction Of Stimulant Medication Misuse Through Graph Representation Learning, Hamid Razavi

Theses

The misuse of stimulant prescription medications poses a significant and escalating public health concern in the United States, particularly among young adults. Addressing this issue requires sophisticated methodologies capable of uncovering complex patterns and relationships in data. Geometric Deep Learning, a paradigm designed to analyze data with non-Euclidean structures, has achieved remarkable success across various domains, offering a powerful framework for tackling complex graph structure data challenges.

This study leverages Graph Convolutional Networks (GCNs) to predict the likelihood of stimulant medication misuse using data from the National Survey on Drug Use and Health (NSDUH). Individuals are represented as nodes in …

Ai-Assisted Academia: Unveiling Doctoral Students' Perspectives On Dissertation In Practice Innovation, Jennifer J. Lesh, Jévaughn J. Lancaster

Faculty and Staff Publications & Presentations

This action research study explores 73 doctoral students' perceptions of using Generative Artificial Intelligence (GAI) throughout their research journey in one educational doctorate (Ed.D) program. The first phase employed surveys, while the second incorporated semi-structured focus group interviews based on the survey data from a diverse sample of students across educational disciplines currently enrolled in the university's educational leadership doctoral program. In the study's first phase, the survey quantified educators' familiarity with, attitudes towards, perceived challenges, ethical considerations, and benefits of using GAI in doctoral research. The exploration of GAI in this practitioner-inspired doctoral program has uncovered essential insights into …

Single-Step Synthesis Of Activated Carbon From Arabica Spent Coffee Ground Using K2co3 As Activator Agent, Ghina Ivana Mieldan, Yuliusman Yuliusman

Journal of Materials Exploration and Findings

Activated carbon is a nanomaterial that is often used as an effective adsorbent. Activated carbon raw materials can use biomass, such as coffee grounds, which can be found along with the growth of public interest in coffee drinks. Chemical activators are used for activation to increase biomass carbon's adsorption capacity. Using K₂CO₃ activator to increase the specific surface area of activated carbon is more harmless than KOH. The use of spent coffee grounds as carbon source and food additive K₂CO₃ as an activator can make food-grade activated carbon that can be used for food. …

Effect Of Alkyd And Polyester Resin Compositions On Corrosion Resistance, Blistering, And Adhesion In Utilization Of Oily Sludge As Anti-Rust Coating Material, Gerets Land Kakalang, Yohanes David Kristianto, Johny Wahyuadi Mudaryoto

Journal of Materials Exploration and Findings

Oil sludge is a waste derived from upstream and downstream activities of the oil and gas industry which is estimated at 10,000 tonnes generated from all PERTAMINA downstream activities spread across various fields, processing units and depots throughout Indonesia. Oil sludge has the same characteristics as asphalt, where asphalt in previous studies can be used as an anti-rust coating, so that the handling of oily sludge can be topped up by reusing and having its own added value. The purpose of this research is to utilise waste oily sludge as an alternative anti-rust coating material and compare alkyd resin and …

A Comparative Study Of Conventional And Statistically Active Corrosion Methods For Corrosion Growth Assessment Of A 24-Inch Gas Pipeline, Rudi Rinaldi, Jaka Fajar Fatriansyah

Journal of Materials Exploration and Findings

Component failures in oil and gas pipelines can have fatal consequences, leading to operational downtimes and environmental damage. Knowledge of the corrosion growth rate is fundamental to pipeline integrity management, as it is essential for risk assessment and decisions related to asset management. This article aimed to compare two approaches for the corrosion growth estimation of the 24-inch offshore gas pipeline: the conventional method versus the Statistically Active Corrosion (SAC) method. This article is based on the in-line inspection (ILI) results of two consecutive assessments from 2020 to 2023 of the entire 73 km of the pipeline. The results show …