Computer Sciences Commons^™

A Perspective Note On Μ_N Σ Baire’S Space, N. Raksha Ben, G. Hari Siva Annam, G. Helen Rajapushpam

Neutrosophic Systems with Applications

This paper presents an introduction to many novel types of sets, including μN strongly dense sets, μN strongly nowhere dense sets, μN strongly first category sets, and μN strongly nowhere residual sets. The features of these sets are briefly elucidated. In addition, by the use of these techniques, we have successfully obtained the highly Baire space μN, and it is imperative to elucidate its inherent features.

A Perspective Note On Μ_N Σ Baire’S Space, N. Raksha Ben, G. Hari Siva Annam, G. Helen Rajapushpam

Neutrosophic Systems with Applications

This paper presents an introduction to many novel types of sets, including μN strongly dense sets, μN strongly nowhere dense sets, μN strongly first category sets, and μN strongly nowhere residual sets. The features of these sets are briefly elucidated. In addition, by the use of these techniques, we have successfully obtained the highly Baire space μN, and it is imperative to elucidate its inherent features.

A Hybrid Metaheuristic And Computer Vision Approach To Closed-Loop Calibration Of Fused Deposition Modeling 3d Printers, Graig S. Ganitano, Shay V. Wallace, Benji Maruyama, Gilbert L. Peterson

Faculty Publications

Fused deposition modeling (FDM) is one of the most popular additive manufacturing (AM) technologies for reasons including its low cost and versatility. However, like many AM technologies, the FDM process is sensitive to changes in the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters. The experience required to efficiently calibrate a printer to a new feedstock acts as a barrier to entry. To enable greater accessibility to non-expert users, this paper presents the first system for autonomous calibration of low-cost FDM 3D printers that demonstrates optimizing …

A Newfangled Interpretation On Fermatean Neutrosophic Dombi Fuzzy Graphs, D. Sasikala, B. Divya

Neutrosophic Systems with Applications

Neutrosophic Dombi fuzzy graph is an advancement of the Dombi fuzzy graph and intuitionistic Dombi fuzzy graph. In this paper, we have initiated a new concept of the Fermatean neutrosophic Dombi fuzzy graph. Further, we identified a few products of the direct, cartesian, composition of Fermatean neutrosophic Dombi fuzzy graphs. Also, we examined the related proposition with suitable illustrations with graphs.

A Newfangled Interpretation On Fermatean Neutrosophic Dombi Fuzzy Graphs, D. Sasikala, B. Divya

Neutrosophic Systems with Applications

Neutrosophic Dombi fuzzy graph is an advancement of the Dombi fuzzy graph and intuitionistic Dombi fuzzy graph. In this paper, we have initiated a new concept of the Fermatean neutrosophic Dombi fuzzy graph. Further, we identified a few products of the direct, cartesian, composition of Fermatean neutrosophic Dombi fuzzy graphs. Also, we examined the related proposition with suitable illustrations with graphs.

The Characteristics Of Successful Military It Projects: A Cross-Country Empirical Study, Helene Berg, Jonathan D. Ritschel

Faculty Publications

In the armed forces, successful digitalization is crucial to ensure effective operations. Much of the existing literature on project factors during the planning and execution phases of public IT projects do not focus specifically on military sector projects. Therefore, the paper aims to provide empirical insights into the characteristics of successful military IT projects. Data from such projects in NATO countries and agencies were collected through interviews and project documents. The findings relating to the main variable of interest, “delivery of client benefit,” supported previous findings on IT project performance. Medium-sized projects performed better than small and large projects, and …

Mitigating Landslide Hazards In Qena Governorate Of Egypt: A Gis-Based Neutrosophic Paprika Approach, Nabil M. Abdelaziz, Safa Al-Saeed

Neutrosophic Systems with Applications

This paper presents a novel approach to landslide susceptibility assessment in the Qena Governorate, Egypt, integrating the neutrosophic Multi-Criteria Decision-Making (MCDM) method, the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA), and the ArcGIS weighted overlay technique. The research focuses on the quantification and prioritization of eight criteria: slope, aspect, proximity to road, soil type, proximity to river, land cover, elevation, and Lithology. These factors are evaluated under the uncertainty and indeterminacy of the neutrosophic environment by employing the PAPRIKA method. The results of the analysis are visualized and interpreted using ArcGIS weighted overlay, offering spatially explicit insights into …

Mitigating Landslide Hazards In Qena Governorate Of Egypt: A Gis-Based Neutrosophic Paprika Approach, Nabil M. Abdelaziz, Safa Al-Saeed

Neutrosophic Systems with Applications

This paper presents a novel approach to landslide susceptibility assessment in the Qena Governorate, Egypt, integrating the neutrosophic Multi-Criteria Decision-Making (MCDM) method, the Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA), and the ArcGIS weighted overlay technique. The research focuses on the quantification and prioritization of eight criteria: slope, aspect, proximity to road, soil type, proximity to river, land cover, elevation, and Lithology. These factors are evaluated under the uncertainty and indeterminacy of the neutrosophic environment by employing the PAPRIKA method. The results of the analysis are visualized and interpreted using ArcGIS weighted overlay, offering spatially explicit insights into …

Numerical Design And Optimization Of Near-Infrared Band- Pass Filter, Hafiza Syeeda Faiza, Ghazi Aman Nowsherwan, Basem A. Abu Izneid, Muhammad Azhar, Saira Riaz, Syed Sajjad Hussain, Saira Ikram, Mohsin Khan, Shahzad Naseem, Mohammad Kanan, Ibrahim M. Mansour

Applied Mathematics & Information Sciences

Band-pass filters functioning in the near-infrared (IR) range are desired for laser technology, multi-photon fluorescence, and IR imaging applications. In this study, we have designed four band-pass filters in the near Infrared spectrum (900-1200 nm) by vertically stacking different high and low-index materials. The band-pass filters are modelled by Essential Macleod software with different thicknesses. The layer’s thicknesses were optimized in such a way to provide the negligible reflectance and maximum transmission on the front side. All the simulated band-pass filters exhibit high transmittance, but TiO2/Al2O3 and Ta2O5/Al2O3 outperforms other modelled structure in terms of performance due to the better …

Balanced Blended Space: Foundational Human–Ai Dialogues In A Symmetry-Based Mediation Framework, David Smith

Publications and Research

This working paper documents the early development of the Balanced Blended Space (BBS) framework through a series of iterative interactions between a cognitive agent (human researcher) and a computational agent (AI system) conducted in 2023. The work is motivated by the need for a universal theoretical model capable of describing the integration of physical, virtual, and conceptual spaces, particularly in response to increasing fragmentation across contemporary communication systems.

BBS is proposed as a symmetry-based mediation framework in which relationships between domains—such as physical and virtual space, cognition and computation, and multiple sensory modalities—are treated as structurally equivalent and mappable. Central …

Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le

Research Collection School Of Computing and Information Systems

This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a …

Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai

Research Collection School Of Computing and Information Systems

Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a newimprovement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar or better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into …

A Hierarchical Optimization Approach For Dynamic Pickup And Delivery Problem With Lifo Constraints, Jianhui Du, Zhiqin Zhang, Xu Wang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider a dynamic pickup and delivery problem (DPDP) where loading and unloading operations must follow a last in first out (LIFO) sequence. A fleet of vehicles will pick up orders in pickup points and deliver them to destinations. The objective is to minimize the total over-time (that is the amount of time that exceeds the committed delivery time) and total travel distance. Given the dynamics of orders and vehicles, this paper proposes a hierarchical optimization approach based on multiple intuitive yet often-neglected strategies, namely what we term as the urgent strategy, hitchhike strategy and packing-bags strategy. These multiple strategies …

Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historicalvalue models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep timeindex models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a …

Well-Conditioned T-Matrix Formulation For Scattering By A Dielectric Obstacle, Murat Enes Hati̇poğlu, Fati̇h Di̇kmen

Turkish Journal of Electrical Engineering and Computer Sciences

The classic formulation of the extended boundary condition method is revisited to inject the regularization operators for the unknown coefficients of the eigen-function expansions for the travelling and standing waves throughout the dielectric scatterer. It is shown that, using the new definitions, the existing algorithm of the scattering field calculation can be kept the same for its well-conditioned version. This is exemplified for scalar 2D problems for both TM and TE polarization under illumination of a line source. The condition numbers of the matrix operators in the new version of the algorithm are drastically reduced when the regularization interfaces are …

Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …

A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç

Turkish Journal of Electrical Engineering and Computer Sciences

The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including …

Improving Unet Segmentation Performance Using An Ensemble Model In Images Containing Railway Lines, Mehmet Sevi̇, İlhan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims to make sense of the autonomous system and the railway environment for railway vehicles. For this purpose, by determining the railway line, information about the general condition of the line can be obtained along the way. In addition, objects such as pedestrian crossings, people, cars, and traffic signs on the line will be extracted. The rails and the rail environment in the images will be segmented with a semantic segmentation network. In order to ensure the safety of rail transport, computer vision, and deep learning-based methods are increasingly used to inspect railway tracks and surrounding objects. In …

Enhancing And Securing Wireless Medical Technology For Diagnosis And Treatment Of Lower Urinary Tract Dysfunction, Farhath Zareen

USF Tampa Graduate Theses and Dissertations

Lower urinary tract dysfunction (LUTD) is a debilitating medical condition that affects millions of individuals worldwide. Urodynamics is the current gold standard for diagnosing LUTD but uses non-physiologically fast, retrograde cystometric filling to obtain a brief snapshot of bladder function. Current state-of-the-art research in bladder monitoring includes ambulatory urodynamics using wireless implantable devices to evaluate bladder function during natural filling for long-term monitoring. However, there are various challenges and limitations to this multi-sensor approach. This research focuses on developing frameworks for automated event detection, data analysis, and optimization of long-term bladder recordingsto improve the diagnosis and treatment of LUTD. In …

A Comparative Effectiveness Study On Opioid Use Disorder Prediction Using Artificial Intelligence And Existing Risk Models, Sajjad Fouladvand, Jeffery Talbert, Linda Phyliss Dwoskin, Heather M. Bush, Amy L. Meadows, Lars E. Peterson, Yash R. Mishra, Steven K. Roggenkamp, Fei Wang, Ramakanth Kavuluru, Jin Chen

Markey Cancer Center Faculty Publications

Opioid use disorder (OUD) is a leading cause of death in the United States placing a tremendous burden on patients, their families, and health care systems. Artificial intelligence (AI) can be harnessed with available healthcare data to produce automated OUD prediction tools. In this retrospective study, we developed AI based models for OUD prediction and showed that AI can predict OUD more effectively than existing clinical tools including the unweighted opioid risk tool (ORT). Data include 474,208 patients’ data over 10 years; 269,748 were females with an average age of 56.78 years. Cases are prescription opioid users with at least …

Predicting Material Structures And Properties Using Deep Learning And Machine Learning Algorithms, Yuqi Song

Theses and Dissertations

Discovering new materials and understanding their crystal structures and chemical properties are critical tasks in the material sciences. Although computational methodologies such as Density Functional Theory (DFT), provide a convenient means for calculating certain properties of materials or predicting crystal structures when combined with search algorithms, DFT is computationally too demanding for structure prediction and property calculation for most material families, especially for those materials with a large number of atoms. This dissertation aims to address this limitation by developing novel deep learning and machine learning algorithms for effective prediction of material crystal structures and properties. Our data-driven machine learning …

An Ml Based Digital Forensics Software For Triage Analysis Through Face Recognition, Gaurav Gogia, Parag H. Rughani

Journal of Digital Forensics, Security and Law

Since the past few years, the complexity and heterogeneity of digital crimes has increased exponentially, which has made the digital evidence & digital forensics paramount for both criminal investigation and civil litigation cases. Some of the routine digital forensic analysis tasks are cumbersome and can increase the number of pending cases especially when there is a shortage of domain experts. While the work is not very complex, the sheer scale can be taxing. With the current scenarios and future predictions, crimes are only going to become more complex and the precedent of collecting and examining digital evidence is only going …

Extending The Convolution In Graph Neural Networks To Solve Materials Science And Node Classification Problems, Steph-Yves Mike Louis

Theses and Dissertations

The usage of graph to represent one's data in machine learning has grown in popularity in both academia and the industry due to its inherent benefits. With its flexible nature and immediate translation to real life observed objects, graph representation had a considerable contribution in advancing the state-of-the-art performance of machine learning in materials.

In this dissertation proposal, we discuss how machines can learn from graph encoded data and provide excellent results through graph neural networks (GNN). Notably, we focus our adaptation of graph neural networks on three tasks: predicting crystal materials properties, nullifying the negative impact of inferior graph …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Neutrosophic Decision Making Model For Investment Portfolios Selection And Optimizing Based On Wide Variety Of Investment Opportunities And Many Criteria In Market, Ayman H. Abdel-Aziem, Hoda K. Mohamed, Ahmed Abdelhafeez

Neutrosophic Systems with Applications

Investment portfolio selection is a difficult subject due to the presence of competing factors. Choosing a portfolio for one's investments is a major choice that may have far-reaching effects on one's financial well-being. Risk tolerance, time horizon, investing objectives, asset allocation, and investment selection are only a few of the factors that will be studied in this article. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) technique is the basis for our proposed integrated multi-criteria decision-making (MCDM) model. This paper used the single-valued neutrosophic set as a framework to deal with uncertain data. The purpose of the suggested SPOTIS–Neutrosophic model …

Optimization Techniques For Machine Learning Inference And Near Memory Image Processing In Hardware For Highly Constrained Iot Edge Nodes, Rajeev Joshi

USF Tampa Graduate Theses and Dissertations

The growing demand for fast and energy-efficient hardware for resource-constrained Internet of Things (IoT) edge devices has highlighted the limitations of conventional computing architectures. This research focuses on addressing the demand for fast, optimized, and energy-efficient machine learning inference engines as well as image processing in IoT edge applications. In this work, we address three challenging research problems and devise efficient solutions. Our investigation involved comprehensive exploration and analysis, leading to the proposal of effective approaches for overcoming these demanding issues. Through our work, we contribute novel solutions that offer improved efficiency and effectiveness in handling these research problems.

First, …

Product Costing And Pricing In Small And Medium Enterprises In Tanzania, Cosmas F Kindole

Tanzania Journal of Engineering and Technology (TJET)

metalwork small and medium enterprises located in Dar es Salaam with the aim of establishing methods used by SMEs in Tanzania in setting selling prices of their products. The study was necessitated by the apparent discrepancy of prices for similar products from SMEs. The approach used to gather information was mainly through direct interview and the use of questionnaires. Direct observation and literature review were also used to establish the whole costing and pricing practices in metal manufacturing SMEs. The findings of the study established that product selling practices of the surveyed SMEs were based on instinct and appearance of …

On The Use Of Wireless Technologies For Wildlife Monitoring: Wireless Sensor Network Routing Protocols, Lilian Mutalemwa

Tanzania Journal of Engineering and Technology (TJET)

Traditional methods for wildlife monitoring are labor-intensive and time-consuming. Therefore, advanced technologies and remote monitoring methods are becoming increasingly popular. This paper presents a study on wireless technologies for wildlife monitoring. In the study, a review of the literature was done to identify the most commonly used wireless technologies. Various technologies were explored including unmanned aerial vehicles (UAVs), Internet of Things (IoT), wireless sensor networks (WSNs), artificial intelligence (AI), global positioning system (GPS), and very high frequency (VHF) radio. Then, a more detailed study was done on WSN technology. Investigations were done to observe the performance of routing protocols in …

Neutrosophic Decision Making Model For Investment Portfolios Selection And Optimizing Based On Wide Variety Of Investment Opportunities And Many Criteria In Market, Ayman H. Abdel-Aziem, Hoda K. Mohamed, Ahmed Abdelhafeez

Neutrosophic Systems with Applications

Investment portfolio selection is a difficult subject due to the presence of competing factors. Choosing a portfolio for one's investments is a major choice that may have far-reaching effects on one's financial well-being. Risk tolerance, time horizon, investing objectives, asset allocation, and investment selection are only a few of the factors that will be studied in this article. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) technique is the basis for our proposed integrated multi-criteria decision-making (MCDM) model. This paper used the single-valued neutrosophic set as a framework to deal with uncertain data. The purpose of the suggested SPOTIS–Neutrosophic model …

Assessment Of Spatial Variability Of Groundwater Levels In Moroto District, Uganda, Augustina Clara Alexander Dr

Tanzania Journal of Engineering and Technology (TJET)

Globally, the variation of groundwater levels is increasingly overwhelming due to over exploitation resulting from population growth. The dynamic nature of socio-economic activities in Moroto District such as agriculture, settlements and increased trends in irrigation technology has been well-known worldwide as common parameters triggering groundwater variability. Nevertheless, determination of groundwater levels for groundwater management and development purpose is a challenge due to spatial variability in levels across Moroto District. In this study, geostatistical technique in Geographical Information System (GIS) was used to analyze spatial variability of water levels in the study area. The analysis utilized ordinary Kriging method to predict …