Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons

Open Access. Powered by Scholars. Published by Universities.®

54,491 Full-Text Articles 70,579 Authors 20,935,862 Downloads 374 Institutions

All Articles in Computer Sciences

Faceted Search

54,491 full-text articles. Page 1 of 1975.

Detection And Classification Of Diabetic Retinopathy Using Deep Learning Models, Aishat Olatunji 2024 East Tennessee State University

Detection And Classification Of Diabetic Retinopathy Using Deep Learning Models, Aishat Olatunji

Electronic Theses and Dissertations

Healthcare analytics leverages extensive patient data for data-driven decision-making, enhancing patient care and results. Diabetic Retinopathy (DR), a complication of diabetes, stems from damage to the retina’s blood vessels. It can affect both type 1 and type 2 diabetes patients. Ophthalmologists employ retinal images for accurate DR diagnosis and severity assessment. Early detection is crucial for preserving vision and minimizing risks. In this context, we utilized a Kaggle dataset containing patient retinal images, employing Python’s versatile tools. Our research focuses on DR detection using deep learning techniques. We used a publicly available dataset to apply our proposed neural network and …


Detection Of Jamming Attacks In Vanets, Thomas Justice 2024 East Tennessee State University

Detection Of Jamming Attacks In Vanets, Thomas Justice

Undergraduate Honors Theses

A vehicular network is a type of communication network that enables vehicles to communicate with each other and the roadside infrastructure. The roadside infrastructure consists of fixed nodes such as roadside units (RSUs), traffic lights, road signs, toll booths, and so on. RSUs are devices equipped with communication capabilities that allow vehicles to obtain and share real-time information about traffic conditions, weather, road hazards, and other relevant information. These infrastructures assist in traffic management, emergency response, smart parking, autonomous driving, and public transportation to improve roadside safety, reduce traffic congestion, and enhance the overall driving experience. However, communication between the …


Decentralized Unknown Building Exploration By Frontier Incentivization And Voronoi Segmentation In A Communication Restricted Domain, Huzeyfe M. Kocabas 2024 Utah State University

Decentralized Unknown Building Exploration By Frontier Incentivization And Voronoi Segmentation In A Communication Restricted Domain, Huzeyfe M. Kocabas

All Graduate Theses and Dissertations, Fall 2023 to Present

Exploring unknown environments using multiple robots poses a complex challenge, particularly in situations where communication between robots is either impossible or limited. Existing exploration techniques exhibit research gaps due to unrealistic communication assumptions or the computational complexities associated with exploration strategies in unfamiliar domains. In our investigation of multi-robot exploration in unknown areas, we employed various exploration and coordination techniques, evaluating their performance in terms of robustness and efficiency across different levels of environmental complexity.

Our research is centered on optimizing the exploration process through strategic agent distribution. We initially address the challenge of city roadway coverage, aiming to minimize …


Pedestrian Pathing Prediction Using Complex Contextual Behavioral Data In High Foot Traffic Settings, Laurel Bingham 2024 Utah State University

Pedestrian Pathing Prediction Using Complex Contextual Behavioral Data In High Foot Traffic Settings, Laurel Bingham

All Graduate Theses and Dissertations, Fall 2023 to Present

Ensuring the safe integration of autonomous vehicles into real-world environments requires a comprehensive understanding of pedestrian behavior. This study addresses the challenge of predicting the movement and crossing intentions of pedestrians, a crucial aspect in the development of fully autonomous vehicles.

The research focuses on leveraging Honda's TITAN dataset, comprising 700 unique clips captured by moving vehicles in high-foot-traffic areas of Tokyo, Japan. Each clip provides detailed contextual information, including human-labeled tags for individuals and vehicles, encompassing attributes such as age, motion status, and communicative actions. Long Short-Term Memory (LSTM) networks were employed and trained on various combinations of contextual …


Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, Tianqi LI, Guansong PANG, Xiao BAI, Jin ZHENG, Lei ZHOU, Xin NING 2024 Singapore Management University

Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng, Lei Zhou, Xin Ning

Research Collection School Of Computing and Information Systems

Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is known during training. Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes. To tackle this combined ZSL and OSR problem, we consider the case of “Zero-Shot Open-Set Recognition” (ZS-OSR), where a model is trained under the ZSL …


Exploring Practical Measures As An Approach For Measuring Elementary Students’ Attitudes Towards Computer Science, Umar Shehzad, Mimi M. Recker, Jody E. Clarke-Midura 2024 Utah State University

Exploring Practical Measures As An Approach For Measuring Elementary Students’ Attitudes Towards Computer Science, Umar Shehzad, Mimi M. Recker, Jody E. Clarke-Midura

Publications

This paper presents a novel approach for predicting the outcomes of elementary students’ participation in computer science (CS) instruction by using exit tickets, a type of practical measure, where students provide rapid feedback on their instructional experiences. Such feedback can help teachers to inform ongoing teaching and instructional practices. We fit a Structural Equation Model to examine whether students' perceptions of enjoyment, ease, and connections between mathematics and CS in an integrated lesson predicted their affective outcomes in self-efficacy, interest, and CS identity, collected in a pre- post- survey. We found that practical measures can validly measure student experiences.


Stopguess: A Framework For Public-Key Authenticated Encryption With Keyword Search, Tao XIANG, Zhongming WANG, Biwen CHEN, Xiaoguo LI, Peng WANG, Fei CHEN 2024 Singapore Management University

Stopguess: A Framework For Public-Key Authenticated Encryption With Keyword Search, Tao Xiang, Zhongming Wang, Biwen Chen, Xiaoguo Li, Peng Wang, Fei Chen

Research Collection School Of Computing and Information Systems

Public key encryption with keyword search (PEKS) allows users to search on encrypted data without leaking the keyword information from the ciphertexts. But it does not preserve keyword privacy within the trapdoors, because an adversary (e.g., untrusted server) might launch inside keyword-guessing attacks (IKGA) to guess keywords from the trapdoors. In recent years, public key authenticated encryption with keyword search (PAEKS) has become a promising primitive to counter the IKGA. However, existing PAEKS schemes focus on the concrete construction of PAEKS, making them unable to support modular construction, intuitive proof, or flexible extension. In this paper, our proposal called “StopGuess” …


Transiam: Aggregating Multi-Modal Visual Features With Locality For Medical Image Segmentation, Xuejian LI, Shiqiang MA, Junhai XU, Jijun TANG, Shengfeng HE, Fei GUO 2024 Central South University

Transiam: Aggregating Multi-Modal Visual Features With Locality For Medical Image Segmentation, Xuejian Li, Shiqiang Ma, Junhai Xu, Jijun Tang, Shengfeng He, Fei Guo

Research Collection School Of Computing and Information Systems

Automatic segmentation of medical images plays an important role in the diagnosis of diseases. On single-modal data, convolutional neural networks have demonstrated satisfactory performance. However, multi-modal data encompasses a greater amount of information rather than single-modal data. Multi-modal data can be effectively used to improve the segmentation accuracy of regions of interest by analyzing both spatial and temporal information. In this study, we propose a dual-path segmentation model for multi-modal medical images, named TranSiam. Taking into account that there is a significant diversity between the different modalities, TranSiam employs two parallel CNNs to extract the features which are specific to …


Simulated Annealing With Reinforcement Learning For The Set Team Orienteering Problem With Time Windows, Vincent F. YU, Nabila Y. SALSABILA, Shih-W LIN, Aldy GUNAWAN 2024 Singapore Management University

Simulated Annealing With Reinforcement Learning For The Set Team Orienteering Problem With Time Windows, Vincent F. Yu, Nabila Y. Salsabila, Shih-W Lin, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This research investigates the Set Team Orienteering Problem with Time Windows (STOPTW), a new variant of the well-known Team Orienteering Problem with Time Windows and Set Orienteering Problem. In the STOPTW, customers are grouped into clusters. Each cluster is associated with a profit attainable when a customer in the cluster is visited within the customer's time window. A Mixed Integer Linear Programming model is formulated for STOPTW to maximizing total profit while adhering to time window constraints. Since STOPTW is an NP-hard problem, a Simulated Annealing with Reinforcement Learning (SARL) algorithm is developed. The proposed SARL incorporates the core concepts …


Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen YUAN, Heyan HUANG, Yixin CAO, Qianwen CAO 2024 Singapore Management University

Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao

Research Collection School Of Computing and Information Systems

Lexically constrained text generation (CTG) is to generate text that contains given constrained keywords. However, the text diversity of existing models is still unsatisfactory. In this paper, we propose a lightweight dynamic refinement strategy that aims at increasing the randomness of inference to improve generation richness and diversity while maintaining a high level of fluidity and integrity. Our basic idea is to enlarge the number and length of candidate sentences in each iteration, and choose the best for subsequent refinement. On the one hand, different from previous works, which carefully insert one token between two words per action, we insert …


Investigations Of The Eutectic Formation And Skin Rejuvenation By Hyaluronan - Kojic Acid Dipalmitate System, Syed Waqar Hussain Shah, Sumbal Imran, Iram Bibi, Kashif Ali, Nadia Bashir 2024 Department of Chemistry, Hazara University, Mansehra, Pakistan

Investigations Of The Eutectic Formation And Skin Rejuvenation By Hyaluronan - Kojic Acid Dipalmitate System, Syed Waqar Hussain Shah, Sumbal Imran, Iram Bibi, Kashif Ali, Nadia Bashir

Karbala International Journal of Modern Science

Eutectic phenomenon has been investigated in binary system based on biopolymer hyaluronan (HN) and kojic acid dipalmitate (KAD). Solid-liquid phase diagram showed a significant dependence of melting points on weight fraction of KAD up to KAD < 0.5. A negligible regain to melting temperature of pure KAD occurred later. Simulations of molecular mechanics using a four-unit segment of HN and KAD revealed the interaction between carbonyl of KAD with 4-OH on N-acetylglucosamine unit of oligomer. Infrared vibrational spectroscopy also endorsed the existence of a weakly interacting system. Such behavior was expected due to steric hinderance and rigidity of biopolymer. The thermal decomposition temperature of HN (i.e., 215 °C) was increased to 322 °C in HK50 having HN and KAD in 1:50 w/w. Bioelectric impedance analysis revealed that these green materials could promote skin health in humans.


Synthesis And Characterization Of Renewable Heterogeneous Catalyst Zno Supported Biogenic Silica From Pineapple Leaves Ash For Sustainable Biodiesel Conversion, Nadila Pratiwi, Suriati Eka Putri, Yulia Shinta, Arya Ibnu Batara, Diana Eka Pratiwi, Abd Rahman, Nur Ahmad, Heryanto Heryanto 2024 Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, Makassar, South Sulawesi, Indonesia;

Synthesis And Characterization Of Renewable Heterogeneous Catalyst Zno Supported Biogenic Silica From Pineapple Leaves Ash For Sustainable Biodiesel Conversion, Nadila Pratiwi, Suriati Eka Putri, Yulia Shinta, Arya Ibnu Batara, Diana Eka Pratiwi, Abd Rahman, Nur Ahmad, Heryanto Heryanto

Karbala International Journal of Modern Science

This study reports on the first case of the low-cost and environmentally friendly ZnO/SiO2 heterogeneous catalyst from pineapple leaves ash (PLA). Catalyst shows excellent performance in catalyzing the transesterification of waste cooking oil (WCO) with methanol for biodiesel conversion. This study focuses on assessing the influence of Zn content on physicochemical characteristics, using XRD, FTIR, SEM, and N2 adsorption-desorption methods. In addition, three different Zn content levels (20, 25, and 30 %wt) were applied. The results showed that all ZnO/SiO2 samples exhibited characteristics suitable for use as catalyst with an average crystallite size of 31.83-34.15 nm, and a surface area …


Music Genre Classification Capabilities Of Enhanced Neural Network Architectures, Joshua Engelkes 2024 University of Minnesota Morris

Music Genre Classification Capabilities Of Enhanced Neural Network Architectures, Joshua Engelkes

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

With the increase of digital music audio uploads, applications that deal with music information have been widely requested by streaming platforms. Automatic music genre classification is an important function of music recommendation and music search applications. Since the music genre categorization criteria continually shift, data-driven methods such as neural networks have been proven especially useful to music information retrieval. An enhanced CNN architecture, the Bottom-up Broadcast Neural Network, uses mel-spectrograms to push music data through a network where important low-level information is preserved. An enhanced RNN architecture, the Independent Recurrent Neural Network for Music Genre Classification, takes advantage of the …


Butterworth Filter To Reduce Reactivity Fluctuations, Daniel Suescún-Díaz, Geraldyne Ule-Duque, Luis E. Cardoso-Páez 2024 Department of Natural Sciences, Avenida Pastrana, Universidad Surcolombiana, Neiva, Huila, Colombia

Butterworth Filter To Reduce Reactivity Fluctuations, Daniel Suescún-Díaz, Geraldyne Ule-Duque, Luis E. Cardoso-Páez

Karbala International Journal of Modern Science

In this study, we introduce the calculation of reactivity in nuclear reactors. The proposed method uses the Euler-Maclaurin series to approximate the integral in the inverse equation of point kinetics. The approximation is done with the first three terms, the first term represents the approximation of a zero-order sum, the second term the trapezoidal rule and the third term the first Bernoulli number. These three terms improve the approximation, along with an estimate of the neutron density using the prompt jump approximation. To reduce neutron density fluctuations, a second-order Butterworth filter for the reactivity calculation was implemented, which offers the …


Sustainability Considerations Of Generative A.I., Thomas Pantazes 2024 West Chester University of Pennsylvania

Sustainability Considerations Of Generative A.I., Thomas Pantazes

Sustainability Research & Practice Seminar Presentations

Dr. Thomas Pantazes of the WCU Teaching and Learning Center shares Sustainability Considerations of Generative A.I.


Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa 2024 Rowan University

Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa

Theses and Dissertations

Bone marrow lesions (BMLs), occurs from fluid build up in the soft tissues inside your bone. This can be seen on magnetic resonance imaging (MRI) scans and is characterized by excess water signals in the bone marrow space. This disease is commonly caused by osteoarthritis (OA), a degenerative join disease where tissues within the joint breakdown over time [1]. These BMLs are an emerging target for OA, as they are commonly related to pain and worsening of the diseased area until surgical intervention is required [2]–[4]. In order to assess the BMLs, MRIs were utilized as input into a regression …


Machine Learning Model And Molecular Docking For Screening Medicinal Plants As Hiv-1 Reverse Transcriptase Inhibitors, Muthia Rahayu Iresha, Firdayani Firdayani, Agam Wira Sani, Nihayatul Karimah, Shelvi Listiana, Irfansyah Yudhi Tanasa, Arief Sartono, Ayu Masyita 2024 Research Center for Vaccine and Drugs, National Research and Innovation Agency (BRIN), South Tangerang, Indonesia

Machine Learning Model And Molecular Docking For Screening Medicinal Plants As Hiv-1 Reverse Transcriptase Inhibitors, Muthia Rahayu Iresha, Firdayani Firdayani, Agam Wira Sani, Nihayatul Karimah, Shelvi Listiana, Irfansyah Yudhi Tanasa, Arief Sartono, Ayu Masyita

Karbala International Journal of Modern Science

The human immunodeficiency virus type 1 reverse transcriptase (HIV-1 RT) plays a significant role in viral replication and is one of the targets for anti-HIV. However, a mutation in viral strains rapidly developed the resistance of the com-pounds to the protein, reducing the effectiveness of the inhibitors. This work seeks to utilize machine learning-based quantitative structure-activity relationship (QSAR) analysis in combination with molecular docking simulations to forecast the presence of active compounds derived from medicinal plants. Specifically, the objective is to identify com-pounds that have the potential to operate as inhibitors of HIV-1 reverse transcriptase (RT), encompassing both wild-type and …


Emergent Ai, Jillian A. Bick 2024 Gettysburg College

Emergent Ai, Jillian A. Bick

CAFE Symposium 2024

For many years, artificial intelligence (AI) was considered to be limited in its abilities due to being confined to a pre-defined set of data. Currently, however, AI models have grown in complexity and size, leading to some previously impossible behaviors. These behaviors, known as "emergent AI behaviors," are unpredictable and not pre-programmed. Their existence suggests that AI is expanding in adaptability and may one day rival human intelligence. Media often portrays AI as having emotions and having the ability to operate autonomously, but what behaviors are AI really capable of?


Cybernetics: How It Compares To Science-Fiction And Future Possibilities, Anindo Majumder 2024 Gettysburg College

Cybernetics: How It Compares To Science-Fiction And Future Possibilities, Anindo Majumder

CAFE Symposium 2024

Cybernetics is a branch of science that studies how information is communicated in machines and electronic equipment compared to how information is communicated in the brain and nervous system. It also relates to the theory of automatic control and physiology, particularly the physiology of the nervous system. Usage of cybernetics is very popular in various science-fiction medium. This naturally leads one to be curious if its depictions might turn into reality one day. This research paper delves into the growth of cybernetics since its inception, current applications of cybernetics, and what the future might hold.


Traffic Signal Optimization Using Multiobjective Linear Programming For Oversaturated Traffic Conditions, Mustafa Murat COŞKUN, Cevat ŞENER, İsmail Hakkı TOROSLU 2024 TÜBİTAK

Traffic Signal Optimization Using Multiobjective Linear Programming For Oversaturated Traffic Conditions, Mustafa Murat Coşkun, Cevat Şener, İsmail Hakkı Toroslu

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we present a framework designed to optimize signals at intersections experiencing oversaturated traffic conditions, utilizing mixed-integer linear programming (MILP) techniques. The proposed MILP solutions were developed with different objective functions, namely a reduction in the total remaining queue and fair distribution of the remaining queue after each signal cycle. Our framework contains two distinct stages. The initial stage applies two distinct MILP methodologies, while the subsequent stage employs a neighborhood search method to further reduce the delays associated with the green signal timings derived from the first stage. Ultimately, to evaluate their effectiveness across various intersections, we …


Digital Commons powered by bepress