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Real-Time Gun Detection In Video Streams Using Yolo V8, Harish Kumar Reddy Kunchala 2024 California State University, San Bernardino

Real-Time Gun Detection In Video Streams Using Yolo V8, Harish Kumar Reddy Kunchala

Electronic Theses, Projects, and Dissertations

In this research, we advance the domain of public safety by developing a machine learning model that utilizes the YOLO v8 architecture for real-time detection of firearms in video streams. A diverse and extensive dataset, capturing a range of firearms in varying lighting and backgrounds, was meticulously assembled and preprocessed to enhance the model's adaptability to real-world scenarios. Leveraging the YOLO v8 framework, known for its real-time object detection accuracy, the model was fine-tuned to accurately identify firearms across different shapes and orientations.

The training phase capitalized on GPU computing and transfer learning to expedite the learning process while preserving …


Task Management Application, Dhaval Chaturbhai Hirpara 2024 California State University - San Bernardino

Task Management Application, Dhaval Chaturbhai Hirpara

Electronic Theses, Projects, and Dissertations

The Task Management Application is a web-based platform designed to facilitate efficient task and project management, similar to other Project Management Tools like Jira, Trello, ClickUp, Wrike, Zoho Projects, and Asana. The application features three distinct roles: Administrator, Project Manager, and Employee, each with specific functionalities and permissions to streamline workflow.

Administrator: This role encompasses comprehensive project oversight, including adding, viewing, and managing project managers, supervising ongoing projects, and viewing employee details.

Project Manager: Project Managers can manage employees, assign tasks, and oversee project progress effortlessly.

Employee: Employees have dedicated functionalities to view and manage tasks assigned …


Crime Data Prediction Based On Geographical Location Using Machine Learning, Sai Bharath Yarlagadda 2024 California State University, San Bernardino

Crime Data Prediction Based On Geographical Location Using Machine Learning, Sai Bharath Yarlagadda

Electronic Theses, Projects, and Dissertations

This project employs machine learning methods like K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree algorithms to monitor crime data based on location and pinpoint areas with risks. The project implements and tunes the four models to improve the precision of predicting crime levels. These models collaborate to offer a trustworthy evaluation of crime patterns. K Nearest Neighbors (KNN) categorizes locations by examining the proximity of data points considering coordinates and other factors to identify trends linked to increased crime data. Logistic Regression gauges the likelihood of crime incidents by studying the connection, between factors (like location …


Society Management App, RUCHIT RAKHOLIYA 2024 California State University, San Bernardino

Society Management App, Ruchit Rakholiya

Electronic Theses, Projects, and Dissertations

A comprehensive solution as native mobile application which is feasible economical and fast, which will establish the authenticity and reliability for society management overcoming the drawbacks of current system. In today's fast-paced technological ecosystem, the capacity to readily store and access information is becoming increasingly important. Residential societies, where individuals live together and manage collective resources, often require a large number of documents, registrations, vehicle parking records, and other forms of paperwork. The complexity and volume of these documents can lead to inefficiencies and frustrations among residents and management alike.


Future-Ready Digitalized Education: Unraveling The Dynamics Of Sustainable And Ethical Digital Transformation, Vaishnavi Rode 2024 California State University, San Bernardino

Future-Ready Digitalized Education: Unraveling The Dynamics Of Sustainable And Ethical Digital Transformation, Vaishnavi Rode

Electronic Theses, Projects, and Dissertations

Amid the brisk advancement of digital technologies, higher educational institutions and universities are finding themselves at a crucial turning point, with significant obstacles and new prospects in the realm of digital transformation. This culminating experience project delves deeply into the compounded terrain of digital transformation in higher education, emphasizing the need for sustainable practices in the face of rapidly evolving technical advancements. The research questions are: (Q1) What strategies can universities adopt to foster digital literacy among students and faculty while promoting sustainability values within their digital education programs and Why? (Q2) What ethical considerations, concerning data privacy and digital …


Service Connect, Namrata Bomble 2024 California State University, San Bernardino

Service Connect, Namrata Bomble

Electronic Theses, Projects, and Dissertations

ServiceConnect is an innovative web-based marketplace platform designed to revolutionize how local services are accessed and managed in the US. By connecting service providers and customers directly, ServiceConnect provides a simple, secure, user-friendly platform for a range of services such as home repairs, tutoring, pet care and more. Featuring convenient booking tools that increase efficiency while simultaneously building trust among both parties involved. ServiceConnect stands out with its comprehensive service range, user-friendly interface, secure payment processing and rigorous verification process for service providers. Leveraging advanced technologies like ReactJS on the frontend, Node.js & Express on the backend and MongoDB for …


Integrated Multi-Omics Analysis Of Cerebrospinal Fluid In Postoperative Delirium, Bridget A. Tripp, Simon T. Dillon, Min Yuan, John M. Asara, Sarinnapha M. Vasunilashorn, Tamara G. Fong, Sharon K. Inouye, Long H. Ngo, Edward R. Marcantonio, Zhongcong Xie, Towia A. Libermann, Hasan H. Otu 2024 University of Nebraska-Lincoln

Integrated Multi-Omics Analysis Of Cerebrospinal Fluid In Postoperative Delirium, Bridget A. Tripp, Simon T. Dillon, Min Yuan, John M. Asara, Sarinnapha M. Vasunilashorn, Tamara G. Fong, Sharon K. Inouye, Long H. Ngo, Edward R. Marcantonio, Zhongcong Xie, Towia A. Libermann, Hasan H. Otu

Department of Electrical and Computer Engineering: Faculty Publications

Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case–no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual …


Adaptable Quantum Education Platform Using Learning Objects, Krishna Puja Anumula 2024 Kennesaw State University

Adaptable Quantum Education Platform Using Learning Objects, Krishna Puja Anumula

Master's Theses

In recent years, the need to make classroom learning more interactive and engaging has become increasingly important. The lack of workforce in interdisciplinary fields such as quantum networking and quantum internet requires a new approach that addresses every learner’s individual needs. To address this challenge, this thesis introduces an adaptive learning platform rooted in the theory of learning objects and Kolb’s experiential learning model. The platform aids educators and learners in designing and utilizing various learning objects for quantum networking and quantum internet.

The platform enables educators and learners to build their own lessons and lesson plans using learning objects …


Efficient Deep Neural Network Compression For Environmental Sound Classification On Microcontroller Units, SHAN CHEN, Na MENG, HAOYUAN LI, WEIWEI FANG 2024 TÜBİTAK

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 …


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 2024 Abdullah Gül University

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, …


Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, OKAN ÇİFTÇİ, FATİH SOYGAZİ, SELMA TEKİR 2024 TÜBİTAK

Enrichment Of Turkish Question Answering Systems Using Knowledge Graphs, Okan Çi̇ftçi̇, Fati̇h Soygazi̇, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Recent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering …


A New Approach: Ordinal Predictive Maintenance With Ensemble Binary Decomposition (Opmeb), OZLEM ECE YUREK, DERYA BIRANT 2024 TÜBİTAK

A New Approach: Ordinal Predictive Maintenance With Ensemble Binary Decomposition (Opmeb), Ozlem Ece Yurek, Derya Birant

Turkish Journal of Electrical Engineering and Computer Sciences

Predictive maintenance (PdM), a fundamental element of modern industrial systems, employs machine learning to monitor equipment conditions, estimate failure probabilities, and optimize maintenance schedules. Its core objective is to enhance equipment reliability, extend lifespan, and minimize costs through data-driven insights by enabling efficient maintenance scheduling, reducing downtime, and optimizing resource allocation. In this paper, we propose a novel ordinal predictive maintenance with ensemble binary decomposition (OPMEB) method for the PdM domain, considering the hierarchical nature of class labels reflecting the machine's health status, including categories like healthy, low risk, moderate risk, and high risk. The proposed OPMEB method was validated …


A Real-Time Embedded System Designed For Nilm Studies With A Novel Competitive Decision Process Algorithm, SAİD MAHMUT ÇINAR, RASİM DOĞAN, EMRE AKARSLAN 2024 TÜBİTAK

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 …


Ensemble Learning For Accurate Prediction Of Heart Sounds Using Gammatonegram Images, SINAM ASHINIKUMAR SINGH, SINAM Ajitkumar SINGH, AHEIBAM DINAMANI SINGH 2024 TÜBİTAK

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 …


Multi-Label Voice Disorder Classification Using Raw Waveforms, GÖKAY DİŞKEN 2024 TÜBİTAK

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 …


Detection And Classification Of Unauthorized Use Of Irrigation Motors In Agricultural Irrigation, ÖNDER CİVELEK, SEDAT GÖRMÜŞ, HALİL İBRAHİM OKUMUŞ, ORHAN GAZİ KEDEROGLU 2024 TÜBİTAK

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 2024 TÜBİTAK

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. …


Data Lakes: A Survey Of Concepts And Architectures, Sarah Azzabi, Zakiya Alfughi, Abdelkader Ouda 2024 Western University

Data Lakes: A Survey Of Concepts And Architectures, Sarah Azzabi, Zakiya Alfughi, Abdelkader Ouda

Electrical and Computer Engineering Publications

This paper presents a comprehensive literature review on the evolution of data-lake technology, with a particular focus on data-lake architectures. By systematically examining the existing body of research, we identify and classify the major types of data-lake architectures that have been proposed and implemented over time. The review highlights key trends in the development of data-lake architectures, identifies the primary challenges faced in their implementation, and discusses future directions for research and practice in this rapidly evolving field. We have developed diagrammatic representations to highlight the evolution of various architectures. These diagrams use consistent notations across all architectures to further …


Enterprise Systems: Installing And Configuring Erpnext On Macos, Yazan Abbasi 2024 Liberty University

Enterprise Systems: Installing And Configuring Erpnext On Macos, Yazan Abbasi

Senior Honors Theses

Enterprise Resource Planning (ERP) systems integrate business processes across organizations onto unified digital platforms through data and workflow consolidation. However, high licensing costs of proprietary ERP solutions like SAP and Oracle limit adoption for small and medium enterprises. This led to the emergence of open-source ERP alternatives like ERPNext which provide sophisticated capabilities at much lower total cost of ownership. However, ERPNext faces documentation gaps that hamper onboarding, customization, and widespread adoption. Accelerating ERPNext implementation by developing a comprehensive installation and configuration guide tailored for developers using Mac environments will be examined furthermore.

The background on ERP systems explores critical …


Development And Evaluation Of An Expedited System For Creation Of Single Walled Carbon Nanotube Platforms, Ivon Acosta Ramirez, OMER SADAK, Wali Sohail, Xi Huang, Yongfeng Lu, Nicole M. Iverson 2024 Ardahan University, University of NebraskaLincoln

Development And Evaluation Of An Expedited System For Creation Of Single Walled Carbon Nanotube Platforms, Ivon Acosta Ramirez, Omer Sadak, Wali Sohail, Xi Huang, Yongfeng Lu, Nicole M. Iverson

Department of Electrical and Computer Engineering: Faculty Publications

Single-walled carbon nanotubes (SWNT) have a strong and stable near-infrared (nIR) fluorescence that can be used to selectively detect target analytes, even at the single molecule level, through changes in either their fluorescence intensity or emission peak wavelength. SWNTs have been employed as NIR optical sensors for detecting a variety of analytes. However, high costs, long fabrication times, and poor distributions limit the current methods for immobilizing SWNT sensors on solid substrates. Recently, our group reported a protocol for SWNT immobilization with high fluorescence yield, longevity, fluorescence distribution, and sensor response, unfortunately this process takes 5 days to complete. Herein …


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