Digital Commons Network^™

Ensemble Learning For Accurate Prediction Of Heart Sounds Using Gammatonegram Images, Sinam Ashinikumar Singh, Sinam Ajitkumar Singh, Aheibam Dinamani Singh

Turkish Journal of Electrical Engineering and Computer Sciences

The analysis of heart sound signals constitutes a pivotal domain in healthcare, with the prediction of imbalanced heart sounds offering critical diagnostic insights. However, the inherent diversity in cardiac sound patterns presents a substantial challenge in predicting imbalanced signals. Many scientific disciplines have focused a great deal of emphasis on the problem of class inequality. We introduce an ensemble learning approach employing a convolutional neural network model-based deep learning algorithm to effectively tackle the challenges associated with predicting imbalanced heart sound signals. We use a Gammatone filter bank to extract relevant features from the heard sound signal. Our approach leverages …

Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, Önder Ci̇velek, Sedat Görmüş, Hali̇l İbrahi̇m Okumuş, Orhan Gazi̇ Kederoglu

Turkish Journal of Electrical Engineering and Computer Sciences

The decarbonisation of electricity generation requires the real-time monitoring and control of grid components in order to efficiently and timely dispatch demand. This highly automated system, known as the Smart Grid, relies on smart or sensor-equipped distribution network components to optimise energy flow and minimise losses. However, energy theft, a major obstacle to efficient resource utilisation, poses a significant challenge to achieving this goal. This study proposes and evaluates a real-time telemetry and control system designed to mitigate energy theft in agricultural irrigation applications. The system increases energy efficiency by tracking the energy use in agricultural irrigation. The key challenge …

A New Dynamic Classifier Selection Method For Text Classification, İsmai̇l Terzi̇, Alper Kürşat Uysal

Turkish Journal of Electrical Engineering and Computer Sciences

The primary objective of employing multiple classifier systems (MCS) in pattern recognition is to enhance classification accuracy. Dynamic classifier selection (DCS) and dynamic ensemble selection (DES) are two purposeful forms of multiple classifier systems. While DES involves the selection of a classifier set followed by decision combination, DCS opts for the choice of a single competent classifier, eliminating the necessity for classifier combination. As a consequence, DCS methods exhibit superior efficiency in terms of processing time and memory usage compared to DES methods. Moreover, a substantial performance gap exists between the performance of Oracle and both DES and DCS methods. …

A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, Sai̇d Mahmut Çinar, Rasi̇m Doğan, Emre Akarslan

Turkish Journal of Electrical Engineering and Computer Sciences

This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads …

Multi-Label Voice Disorder Classification Using Raw Waveforms, Gökay Di̇şken

Turkish Journal of Electrical Engineering and Computer Sciences

Automated voice disorder systems that distinguish pathological voices from healthy ones have been developed with the aid of machine learning methods. Both clinicians and patients can benefit from these systems as they provide many advantages, compared to the invasive techniques. These systems can produce binary (healthy/pathological) or multi-class (healthy/selected pathologies) decisions. However, multiple disorders might exist in an individual’s voice. Multi-label classification should be considered in such cases. By this time, only a single report is available on this topic, where hand-crafted features were used, and a data augmentation technique was utilized to overcome class imbalances. In this study, a …

Network Intrusion Detection Based On Machine Learning Strategies: Performance Comparisons On Imbalanced Wired, Wireless, And Software-Defined Networking (Sdn) Network Traffics, Hi̇lal Hacilar, Zafer Aydin, Vehbi̇ Çağri Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of computer networks emphasizes the urgency of addressing security issues. Organizations rely on network intrusion detection systems (NIDSs) to protect sensitive data from unauthorized access and theft. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances and classification performance for different types of network intrusions: wired, wireless, and software-defined networking (SDN). This research aims to fill this gap by examining these networks’ imbalances, feature selection, and binary classification to enhance intrusion detection system efficiency. Various techniques such as SMOTE, ROS, ADASYN, …

Detection And Mitigation Of Misleading Pheromone Trails In Foraging Robot Swarms, Ryan Luna, Qi Lu

Computer Science Faculty Publications

This study addresses the overlooked aspect of security in swarm robotics by exploring the vulnerabilities of stigmergic communication in foraging robot swarms. More specifically, we study the swarm's susceptibility to attacks that introduce misleading pheromone trails. Simulated scenarios in which detractor robots lay misleading trails to deceive benign foraging robots effectively reduce the foraging performance of the swarm. We analyze the impact of the attack on the swarm and evaluate the reduction of foraging efficiency. We introduce a defense mechanism using distance-based clustering (DBSCAN) along with a cluster grouping method to isolate large batches of detractors early in the simulation. …

Tuning Pid Controller For Quadrotor Using Particle Swarm Optimization, Eric X. Rodriguez, Qi Lu

Computer Science Faculty Publications

Energy expenditure for quadrotor control has a likelihood of being costly given parameter-dependent controllers that are less than optimal. The cost can grow proportionally when applied to multiple quadrotors for tracking and collaborative navigation tasks. This research aims to establish a basic approach to tuning PID (Proportional-Integral-Derivative) parameters for a simulated quadrotor drone. A PID controller for autonomy provides a straightforward method for correcting robotic movement based on its current state. However, applying a PID system to a flight controller poses challenges with an inherently under-actuated system, which includes the likelihood of large overshoots and lengthy adjustment times. To address …

Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, Shan Chen, Na Meng, Haoyuan Li, Weiwei Fang

Turkish Journal of Electrical Engineering and Computer Sciences

Environmental sound classification (ESC) is one of the important research topics within the non-speech audio classification field. While deep neural networks (DNNs) have achieved significant advances in ESC recently, their high computational and memory demands render them highly unsuitable for direct deployment on resource-constrained Internet of Things (IoT) devices based on microcontroller units (MCUs). To address this challenge, we propose a novel DNN compression framework specifically designed for such devices. On the one hand, we leverage pruning techniques to significantly compress the large number of model parameters in DNNs. To reduce the accuracy loss that follows pruning, we propose a …

Survey Of Space Professionals’ Perception Of Satellite Cybersecurity From 2012 To 2022: Decision-Makers’ Thoughts On Satellite Cybersecurity Evolving, Rachel C. Jones

Aerospace Sciences Student Publications

Cyberattacks on space assets are often portrayed in vague terms of doubt and mystery. Several claims depict satellites being compromised or attacked, but little corroboration has been published or made publicly available. As the commercial space industry grows, increased concern with space cybersecurity will prompt commercial satellite decision-makers to analyze the often-undefined risk of cyberattacks against satellites. This article identifies and characterizes the nature of cybersecurity risks to space assets and postulates why space professionals might not prioritize cybersecurity. Additional information was captured from a decadal survey of space professionals conducted in 2012 and 2022. The results show a rise …

The Potential Influence Of Immune Modulatory Molecules (Tgf-Βiii And Ctla-4) In Pathogenicity Of Pcos, Mustafa Riyadh Abdullah, Hazima Mossa Alabassi

Karbala International Journal of Modern Science

Polycystic ovary syndrome (PCOS) is characterised by chronic anovulation, hyperandrogenism, polycystic ovaries, and immunological dysregulation. Immune homeostasis and inflammation management require immune functions. T-regulatory cells (Tregs) regulate PCOS's immune system, inflammation, insulin resistance, ovarian function, and homeostasis. Transforming growth factor βIII (TGF-βIII) and Cytotoxic-T-lymphocyte-associated-protein-4 (CTLA-4) receptors on the surfaces of Treg cells are important for controlling the immune system. The study included 68 PCOS patients and 22 non-PCOS women controls aged 20–45. The waist-hip ratio (WHR) determines obesity. We evaluated serum levels and gene expression of TGF-βIII and CTLA-4 via ELISA and real-time PCR. Women with PCOS had significantly higher …

Enhanced Optoelectronics Performance Of Hybrid Self Power Photodetectors Go: Tio2- Ad / N-Si Heterojunctions, Mohammed J. Alsultani, Maysoon F. Alias

Karbala International Journal of Modern Science

A GO:TiO₂/n-Si heterostructure has been submerged in an anthocyanin dye solution that extracted from the red cabbage plant creates a high responsivity, self-powered UV, and visible photodetectors fabricated by the spray pyrolysis technique. The GO concentrations are varied, whereas TiO₂ is fixed. Thin films' structure, shape, and optical characteristics were examined using X-ray diffraction, field emission scanning electron microscopy, and UV-Vis spectrophotometers respectively. Island-like polycrystalline film powders with grain boundaries and granular shapes are created. Two direct energy gaps between 3.33-3.02 and 2.39-2.04 eV exist in all films. J-V characteristics were examined. The saturation current density (J …

A Scalable Parallel Processing Design For The Data Washing Machine: An Unsupervised Entity Resolution System, Nicholas Kofi Akortia Hagan

Theses and Dissertations

Entity Resolution (ER) has been one of the bedrocks in the creation of information systems by ensuring ambiguous entities are identified and resolved by linking. One common design approach of traditional ER systems is to run in single-threaded mode, which makes the system prone to out-of-memory error when processing larger datasets. The Data Washing Machine (DWM) as a proof-of-concept of an unsupervised cluster ER system is indifferent from this common design bottleneck. The original prototype design of the DWM requires shared memory tables and dictionaries of tokens, and its single-threaded nature makes it not scalable, hence not viable for real-world …

Transforming Libraries Through Engagement: Lessons From A Library Ai Interest Group, Lily Dubach, Rachel Vacek

Faculty Scholarship and Creative Works

Learn how one academic library is engaging with its employees to explore the latest trends, tools, and topics in AI through an interest group (IG). Through stimulating discussions, webinars, guest speakers, demos, and sharing of experiences with AI, the IG empowers its library community to explore and become more comfortable with AI. This session caters to varying levels of AI expertise. Challenges, successes, and valuable insights for deeper engagement will be shared so you can learn how to establish a similar initiative in your library.

Predicting Choroidal Nevus Transformation To Melanoma Using Machine Learning, Prashant D. Tailor, Piotr K. Kopinski, Haley S. D'Souza, David A. Leske, Timothy W. Olsen, Carol L. Shields, Jerry A. Shields, Lauren A. Dalvin

Wills Eye Hospital Papers

PURPOSE: To develop and validate machine learning (ML) models to predict choroidal nevus transformation to melanoma based on multimodal imaging at initial presentation.

DESIGN: Retrospective multicenter study.

PARTICIPANTS: Patients diagnosed with choroidal nevus on the Ocular Oncology Service at Wills Eye Hospital (2007-2017) or Mayo Clinic Rochester (2015-2023).

METHODS: Multimodal imaging was obtained, including fundus photography, fundus autofluorescence, spectral domain OCT, and B-scan ultrasonography. Machine learning models were created (XGBoost, LGBM, Random Forest, Extra Tree) and optimized for area under receiver operating characteristic curve (AUROC). The Wills Eye Hospital cohort was used for training and testing (80% training-20% testing) with …

Empowering Traditional Industries With Digital Intelligence For Transformation And Upgrading, Jiyuan Zang, Huanyong Ji, Qingxue Huang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Traditional industries are the basic support and driving force for the high-quality development of the economy. Digital intelligence is an important lever for traditional industries to achieve the transformation of old and new kinetic energy and the leapfrogging of the global value chain. This study firstly deconstructs the rich connotations of digital intelligence from the perspectives of digitalization, networkization, and intelligentization. Secondly, it analyzes the transformation mechanism and various pathways through which digital intelligence impacts traditional industries. Thirdly, it analyzes the barriers faced by traditional industries in the process of adopting digital intelligence technologies. Finally, this study puts forward four …

Strategic Study On Leading Construction Of Modern Agricultural System By Technology Innovation, Yan Yan, Xurong Mei, Xiudong Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Technology innovation is the fundamental solution to build a modern agricultural system, and it is also an important support for building the large base, large enterprise, and large industry of modern agriculture and achieving the modernization of material equipment, technology, management, agricultural informatization, and sustainable resource utilization of agriculture. This paper explains the scientific concepts and logical relationships of the agricultural production system, industrial system and management system, which are the main components of a modern agricultural system. It emphasizes that ensuring food security, promoting industrial integration and upgrading, and cultivating leading enterprises are current key tasks. Nevertheless, from the …

Innovation Path At Institute For Protein Design Of Washington University And Its Enlightenment For Construction Of New Life Sciences R&D Institutions, Runzhou Zhao, Ming Ni, Yunzhi Fa, Xiaochen Bo, Jian Jiao

Bulletin of Chinese Academy of Sciences (Chinese Version)

The Institute for Protein Design (IPD) at the University of Washington is a pioneering local and state-supported non-profit scientific research institution. Since its establishment in 2012, IPD has seized the opportunity of AI for Science and open science, and continuously enhanced its capabilities of fundamental innovations, breakthrough technologies, and industrial impact. We summarized five factors contributing to IPD’s development, including focusing on the cutting-edge issues of basic scientific research to gain a first-mover advantage and then further expand, integrating AI-enhanced digital tools and solid experimental validations, facilitating the integrated development of innovation and industrial chains, giving full play to the …

Constructing Problemology That Is Symmetrical To Methodology—Taking Computer Science As An Example, Yuhang Liu, Yunquan Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Institutionalized scientific research encourages free exploration while emphasizing the organization and coordination of research activities. Nevertheless, overcoming dispersion and repetition in the research process is an urgent problem to be addressed. Taking computer science as an example, to implement the problem-oriented concept, this study proposes constructing problemology that is symmetrical to methodology. Methodology is the important manifestation of scientific and technological achievements, and symmetrically, problemology is needed by the scientific research community. The problematique is a crucial component of problemology, representing the level of understanding of problems within the scientific research community. Through problematique as a medium, it connects, bridges, …

A New Optimization Approach Based On Neural Architecture Search To Enhance Deep U-Net For Efficient Road Segmentation, Narges Saeedizadeh, Seyed Mohammad Jafar Jalali, Burhan Khan, Parham Mohsenzadeh Kebria, Shady Mohamed

Research outputs 2022 to 2026

Neural Architecture Search (NAS) has significantly improved the accuracy of image classification and segmentation. However, these methods concentrate on finding segmentation structures for natural or medical applications. In this study, we introduce a NAS approach based on gradient optimization to identify ideal cell designs for road segmentation. To the best of our knowledge, this work represents the first application of gradient-based NAS to road extraction. Taking insight from the U-Net model and its successful variations in different image segmentation tasks, we propose NAS-enhanced U-Net, illustrated by an equal number of cells in both encoder and decoder levels. While cross-entropy combined …

Memetic Memory As Vital Conduits Of Troublemakers In Digital Culture, Alexander O. Smith, Jordan Loewen-Colón

School of Information Studies - Post-doc and Student Scholarship

Recent fears of data capitalism and colonialism often argue using implicit assumptions about cybernetic technology’s ability to automate data about culture. As such, the level of data granularity made possible by cybernetic engineering can be used to dominate society and culture. Here we unpack these implicit assumptions about the datafication of culture through memes, which both act as cultural data and cultural memory. Using Alexander Galloway’s critical method of protocological analysis and descriptions of media tactics, we respond to fears of cybernetic domination. Protocols – the source by which cybernetic technologies enable automated datafication – enables us to respond to …

Using Cache Files To Improve The Efficiency Of The Ucgretina Simulation Code, Blake Mcnulty

Physics and Astronomy Summer Fellows

UCGretina is a program that is used to simulate the Gretina gamma-ray tracking array used in experiments at the Facility for Rare Isotope Beams (FRIB) to collect gamma rays produced in reactions such as proton scattering. This is done by generating a simulation of the beam particles used and sending them into a simulated recreation that mimics the experimental environment. My job this summer was to tackle a major issue that the program had which is the speed or efficiency at which it ran. The main issue was that it would take days for certain simulation sets to run which …

A Qualitative Study On The Integration Of Artificial Intelligence In Cultural Heritage Conservation, Kholoud Ghaith, James Hutson

Faculty Scholarship

The widespread adoption of generative artificial intelligence (GAI) technologies heralds an era of expanding possibilities in the domain of cultural heritage conservation. This paradigm shift is marked by a confluence of innovative methodologies, including digital twin mapping, digital archiving, and enhanced preservation strategies, aimed at safeguarding the vestiges of our shared past. The application of AI within this field represents a frontier where technology and tradition intersect, offering new vistas for the preservation of historical structures and artifacts that are at risk of deterioration or oblivion. This article endeavors to elucidate the perspectives of professionals within the conservation domain on …

Gender-Specific Mental Health Outcomes In Central America: A Natural Experiment, Thea Nagasuru

Computer Science Summer Fellows

While COVID lockdown measures have had varying effects on the mental health of different demographics, several bodies of research have noted their disparate effect on women. Why is women's mental health more negatively impacted by lockdown measures, and how much more are they impacted than men? How can we predict and mitigate these negative effects on women? This paper aims to contribute to answering those questions by comparing COVID stringency measures and their effect on the gap in depression rates between men and women in two neighboring countries: Nicaragua and Honduras.

From Classroom Interaction To Academic Success: Tracing The Mediating Role Of Effective Communication In Faculty-Student Dynamics, Nadia Dahmani, Wael Ali, Mohammed Aboelenein, Mohammad A.K. Alsmairat, Mursal Faizi

All Works

This paper aimed to determine the impact of faculty communication style, student proactiveness, and academic discipline on student academic performance and student-faculty relationship quality in the United Arab Emirates (UAE) higher education context. This study also aimed to contribute to the literature by verifying the mediating impact of communication effectiveness between the selected factors. Using a cross-sectional survey design, the study sample comprised 193 university students, and it was analyzed using partial least squares structural equation modeling (PLS-SEM). The results revealed that academic discipline and the professor’s communication style enhanced communication effectiveness, whereas student proactiveness had a minimal effect. The …

Riesz Particle Markov Chain Monte Carlo Methods, Xiongming Dai

LSU Doctoral Dissertations

Markov chain Monte Carlo (MCMC) methods are simulations that explore complex statistical distributions, while bypassing the cumbersome requirement of a specific analytical expression for the target. This stochastic exploration of an uncertain parameter space comes at the expense of a large number of ``burn-in'' samples, and the computational complexity leads to the curse of dimensionality. Although at the exploration level, some methods have been proposed to accelerate the convergence of the algorithm, such as tempering, Hamiltonian Monte Carlo, Rao-redwellization, and scalable methods for better performance, they cannot avoid the stochastic nature of this exploration. We develop algorithms for the energy …

Effective Position Intelligent Decision Method Based On Model Fusion And Generative Network, Liqiang Guo, Liang Ma, Hui Zhang, Jing Yang, Lianfeng Li, Yaqi Zhai

Journal of System Simulation

Abstract: Military intelligence technology is currently the most dynamic frontier and the inevitable trend for the development of unmanned equipment in the future. Aiming at the dual requirements of reliability and real-time performance of unmanned platform autonomous decision-making in complex environments and the shortcomings of existing combat simulation technology based on rule reasoning in terms of dynamics and flexibility, a research method of principle analysis and experimental verification is adopted. Based on the shooting experiment dataset of an unmanned platform, the effective position recognition link of attack decision-making is transformed into a binary classification problem with imbalanced categories in the …

Digital Twin Modeling And Control Of Robots For Intelligent Manufacturing Scenarios, Ying Li, Lan Gao, Zhisong Zhu

Journal of System Simulation

Abstract: The introduction of Industry 4.0 and the Made in China 2025 development policy has accelerated the transformation of the manufacturing industry from automation to intelligence. Industrial robots, as the representative equipment of intelligent manufacturing, will also become more intelligent. Based on digital twin technology, digital modeling, and simulation debugging are conducted for such problems as interference and collision, tedious operation, and low efficiency of industrial robot spot welding debugging in production. Process Simulate from TECNOMATIX software is utilized to digitally model the robot spot welding station and define its motion, and TIA Portal and S7-PLCSIM Advanced are applied to …

Research On Learnable Wargame Agent Driven By Battle Scheme, Yifeng Sun, Zhi Li, Jiang Wu, Yubin Wang

Journal of System Simulation

Abstract: To enable the agent to cope with complex battle scenarios and objectives in wargame, a learnable wargame agent architecture driven by a battle scheme is proposed. By analyzing the "attachment characteristics" and "loose coupling characteristics" of the agent to wargame system, the learnable requirements of the agent are obtained. In the design of the agent framework, battle schemes are used to reduce the learning range of the agent. The finite state machine corresponds to the knowledge of the operational phase in the battle scheme, and the decision-making space of the agent is determined according to the framework of the …

A Deep Fuzzy Classifier Based On Feature Transform And Reconstruction, Rui Yin, Wei Lu, Jianhua Yang

Journal of System Simulation

Abstract: To obtain a classifier with good classification accuracy and interpretability, a deep fuzzy classifier based on feature transform and reconstruction (FR-DFC) is proposed. In FR-DFC, several fuzzy systems (FT_FS) for feature transform and a multi-prototype fuzzy classification system (MPRFD_FS) are stacked together to realize the classification process of the model, based on the hierarchically stacked thought originated from deep learning. Specifically, the stacked FT_FSs explore the hidden features in the data by transferring data from the original data space to the high-level feature space. MPRFD_FS, on the other hand, implements classification based on multiple prototypes that characterize the distribution …