Computer Engineering Commons^™

Time Series Forecasting With Lstm: An Extensive Content Analysis, Andri Pranolo, Xiaofeng Zhou, Yingchi Mao

Knowledge Engineering and Data Science

This paper presents a comprehensive bibliometric and content review of the trend, architecture, and application of long short-term memory (LSTM) models for time series forecasting. The study aims to provide insights into the overall statistics and distribution of papers focused on LSTM for forecasting. Additionally, the research questions address the most highly cited papers based on LSTM approaches in forecasting, the most productive journals in this field, and identifying trends, gaps, summary tasks and their performance, datasets availability, and future research directions for LSTM in forecasting. This paper is a comprehensive review of LSTM for forecasting from 2017 to 2023 …

Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar

Knowledge Engineering and Data Science

In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means …

Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra

Knowledge Engineering and Data Science

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score …

Ahp–Python Framework For Multicriteria Modeling Of Rice Production In Asean, Mayang Anglingsari Putri, Risqy Siwi Pradini, Anuraga Jayanegara, Alexander Dimas Yonanta Putra

Knowledge Engineering and Data Science

Rice production is a key indicator of food security and agricultural stability in Southeast Asia, especially among Association of Southeast Asian Nations (ASEAN) countries. Despite shared regional goals, disparities in rice production remain, and previous studies mainly rely on descriptive statistics, lacking structured multicriteria decision-making frameworks and computational tools for cross-country comparisons. This study addresses these gaps by proposing an integrated Analytic Hierarchy Process (AHP)–Python framework to evaluate and rank ASEAN rice production from 2013 to 2022. Three criteria are used: Total Production Volume (K1), Production Growth Trend (K2), and Recent Year Performance (K3), capturing both long-term consistency and short-term …

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

Assessing Deep Learning Models And Hyperparameter Optimization For Stable Time-Series Electricity Load Forecasting, Sukma Patrya, Aji Prasetya Wibawa, Aripriharta Aripriharta

Knowledge Engineering and Data Science

Long-term electricity load forecasting plays an important role in ensuring system reliability, optimizing energy management, and making operational plans in face of continuously rising electricity demands. This study suggests a complete deep learning method for univariate forecasting of future electricity loads based on climatology and electricity consumption data for the period between 2019 and 2023. The initial dataset was cleaned, normalized, and partitioned chronologically into train/test datasets. Four train/test split cases (20/80, 40/60, 60/40, 80/20) were considered to explore the impact of different levels of historical data availability on the performance of the suggested framework from data-poor to data-rich situations. …

Classification Of Indonesian Sign Language (Sibi) Using Data Mining Algorithms K-Nearest Neighbor And Random Forest, Muhammad Zaki Wirawan, Achmad Afif, Anik Nur Handayani, Imanuel Hitipeuw, Osamu Fukuda

Knowledge Engineering and Data Science

This study aims to address the communication hallenges faced by the Indonesian deaf community by developing an automatic classification model for Sistem Bahasa Isyarat Indonesia (SIBI) using data mining techniques. The main objective is to identify a practical algorithm for recognizing SIBI hand gestures to enhance accessibility and inclusiveness in digital communication. A comprehensive dataset consisting of 32,850 gesture samples representing SIBI alphabet signs was collected and processed through feature extraction, data cleaning, and normalization using Z-Transform and Min-Max methods. Two classification algorithms, K-Nearest Neighbor (KNN) and Random Forest, were implemented and evaluated using metrics such as accuracy, precision, recall, …

Exploring The Benefits, Barriers, And Sustainability Of Community Stem Partnerships: A Qualitative Case Study On Stem Collaboration In Elementary Education, Katherine M. Blagden

Open Access Dissertations

In an era of increasing emphasis on science, technology, engineering, and mathematics (STEM) education, elementary schools often face significant challenges in implementing high-quality, integrated STEM instruction. These challenges include limited resources, inconsistent professional development, and a lack of access to real-world, community-based learning experiences (Dorph et al., 2018; National Science Board, 2024). This dissertation explores how collaborative partnerships among elementary schools, community organizations, and industry professionals can overcome these barriers and foster meaningful, sustainable STEM learning opportunities for young students. Drawing on the Asset-Based Community Development (ABCD) model (Kretzmann & McKnight, 1993) and informed by a constructivist theoretical framework (Vygotsky, …

Predicting Biomechanical Risk Factors For Division - I Women’S Basketball Athletes, Aayushi Shah, Vanaja Agarwal, Dhairya Shah, Harman Jani, Sristi Sharma, Kaya Tolga, Christopher Taber, Mehul Raval

School of Computer Science & Engineering Faculty Publications

Collegiate basketball is characterized by high-impact movements such as jump landings, making athletes more susceptible to injuries. Critical biomechanical factors like knee flexion, lateral trunk flexion, and foot landing asymmetry are strongly associated with injury risk. This study aims to predict six biomechanical risk factors in the landing error scoring system (LESS). The dataset comprises 8600 video frames of counter-movement jumps (CMJs) from 17 NCAA Division I female basketball athletes, recorded from frontal and lateral perspectives and annotated using a customized error annotation algorithm. The study uses the You Only Look Once (YOLOv5nu) model to analyze the basketball athletes’ CMJ …

Smart Qos-Aware Resource Management For Edge Intelligence Systems, Minoo Hosseinzadeh

Theses and Dissertations--Computer Science

There are several definitions for Smart Cities. One common key point of these definitions is that smart cities are technologically advanced cities which connect everything in a complex urban environment including infrastructure, information, and even people to cope with the crucial problems linked with the urban life such as traffic, pollution, city crowding, health, and poverty. Central to this vision are the Internet of Things (IoT) and Big Data, where interconnected devices with sensors collect vast amounts of data for informed decision-making. However, the rapid expansion of IoT devices challenges efficient data processing while meeting diverse Quality-of-Service (QoS) requirements; for …

Optimization, Machine Learning, And Networking Solutions For Cyber-Physical Systems, Xu Tao

Theses and Dissertations--Computer Science

Cyber-Physical Systems (CPS) represent a transformative paradigm that integrates sensing, computation, and actuation through networked systems to enable intelligent, context-aware applications across various domains. Despite their growing potential, CPS still face critical challenges, particularly in maintaining reliable, low-latency communication and efficient data processing in resource-constrained and dynamic environments. These challenges are further magnified in rural or remote deployments, where traditional network infrastructure is often unavailable or unreliable. This dissertation addresses these challenges through the development of optimization and inference algorithms that enhance network performance in Software-Defined Networks (SDN), using techniques such as reinforcement learning and network tomography for efficient routing. …

Cnns And Transformers For Visual Understanding: From Feature Alignment To Image Captioning, Xuehao Liu

Doctoral

Deep learning has developed rapidly since the introduction of Deep Belief Networks during the past decade. As an area of machine learning, it still has many open challenges. Among these open challenges is the issue of transparency, with deep learning models known as black boxes. Both explainability of a model for understanding the decision making process, and the transparency of the relationship between the input and output are crucial for understanding a model. The understanding of models can build trust between AI systems and humans, verify models behavior, and identify potential biases or errors.

Navigating Human-Robotic Interaction Challenges In Teaching-By-Demonstration, Shakra Mehak

Doctoral

The advancement in interdisciplinary research domains, like robotics and HRI, presents a challenging task. It is an exception rather than the norm for research to extend beyond the boundaries of individual disciplines and encompass the challenges presented by other fields of study. This project is part of ”Collaborative Intelligence for Safety Critical Systems” (CISC), which is a Marie Curie Training Network funded by the European Commission to hire and train researchers with the expertise and skillset necessary to carry out the major tasks required to develop a Collaborative Intelligence system. The program encompasses four overarching themes: Artificial Intelligence (AI), Human …

Personalized Persuasion In The Digital Age: A Data-Driven Approach To Effective Communication, Annye Braca

Doctoral

This thesis investigates the potential of Machine Learning (ML) to personalize persuasive marketing messages. It explores the identification of individuals receptive to specific persuasion techniques based on their psychometric profiles. By developing ML models that incorporate these profiles, the thesis aims to predict the impact of tailored messages and improve the effectiveness of marketing communication.

Machine Learning Applications In Epigenomics And Its Association With Health And Disease, Trevor Doherty

Doctoral

Epigenetic modifications can lead to altered phenotypes without a change in the DNA sequence itself. Disrupted gene expression regulated by epigenetic processes can result in cancers, autoimmune diseases and various other maladies. Machine learning (ML) involves the use of algorithms and models which are trained to learn patterns in data, and has demonstrated remarkable success in solving diverse, complex challenges. Epigenomic studies, such as those that use DNA methylation (DNAm) data, increasingly make use of ML techniques to process extremely high dimensional data obtained from high throughput platforms e.g., DNAm arrays. These datasets suffer from the curse of dimensionality, increased …

Deep Learning For Computer Vision Applications In Medical Diagnostics And Wildlife Monitoring, Mostapha Al Saidi

Electronic Theses and Dissertations

This dissertation explores innovative applications of deep learning and computer vision techniques across three distinct domains: medical imaging, dermatological diagnostics, and wildlife monitoring. The research addresses critical challenges in each field through the development and optimization of convolutional neural networks and other deep learning architectures.

The first study examines COVID-19 classification from X-ray images, comparing one-shot versus two-stage classification approaches using transfer learning with pre-trained models such as VGG16 and VGG19. The initial hypothesis was that breaking down the classification task into two optimized tasks would yield better results than one-shot classification. Results demonstrated that the single-stage approach achieved superior …

C 3 An: Custom, Compact And Composite Ai Systems - A Neurosymbolic Approach: 4Th-Generation Evolution Of Intelligent Systems, Amit P. Sheth, Kaushik Roy, Revathy Venkataramanan, Venkatesan Nadimuthu

Publications

Artificial Intelligence (AI) systems continue to evolve rapidly. From the architecture perspective, it is evolving from large, monolithic models trained on massive internet data to complex, multi-component “compound” systems and “agentic” frameworks capable of semi-autonomous decision-making. These systems show immense promise yet face numerous challenges in reliability, consistency, transparency, and alignment with user goals. In this article, we propose Custom, Compact and Composite AI with Neurosymbolic (C3AN) approach, a framework that paves way to 4th-generation of AI that integrates data, knowledge, and human expertise to build robust, intelligent and trustworthy AI systems defined by 14 foundation elements.

Custom emphasizes …

Learning From Leads: A 1d Dilated Resnet For Ecg Chagas Disease Screening, Somesh Saini, Matheus Lima Diniz Araujo

Student Scholarship

No abstract provided.

An Educational Framework For The Disruptive Technologies And Their Integration In The Un Sdgs Curriculum, Tulsi Pawan Fowdur, Aishani Radhakeesoon

Journal of Educational Technology Development and Exchange (JETDE)

Disruptive technologies such as 5G, AI, IoT, cloud computing, and blockchain are revolutionizing life on our planet and at the same time contributing immensely towards sustainable development and the achievement of the UN SDGs. Nowadays, optimizing the management of resources in various fields such as manufacturing, education, health, transportation etc. in a much more efficient way is possible. This can be achieved by capturing a wealth of real-time data via IoT sensors with the ultra-reliable and low latency connections provided by 5G. Additionally, with cloud computing, blockchain, and AI, these real-time systems can securely transmit, store, and analyze a massive …

Utilizing Information Technology And Hands-On Learning Practices To Improve Student Learning Outcomes In A High Failure Rate Introductory Programming Course, Todd Edward Thomas

Theses and Dissertations

The purpose of this study was to introduce and examine the impact that two interventions have on a high failure rate introductory programing course: a pedagogical approach of introducing Live Coding instruction technique to the in-person lecture portion, and a technological approach of introducing remote collaboration software (VS Code Liveshare) to the online lab portion. This study used convergent parallel mixed methods approach for both data collection and analysis; utilizing four data collection methods: 1) online survey questionnaires 2) in-depth interviews 3) in class observations and 4) quantifiable data collection (ie., student demographic, IT experience, GPA data). The Live Coding …

Heuristic Approaches For Coordination Of Heterogeneous Robotic Systems In Harvesting Automation With Size Constraints, Hyeseon Lee

Dissertations, Master's Theses and Master's Reports

This thesis presents the development of path planning algorithms for the coordination of heterogeneous robotic systems while considering size constraints. The objective is to generate practical and efficient solutions for real-world applications. The use of heterogeneous collaborative robots is beneficial in many applications, such as transportation operations in warehouses or manufacturing environments, surveillance, and monitoring, and task allocation and path planning are critical techniques that need to be addressed to deploy in real-world applications. This research focuses on automating lavender harvesting, where robots with varying capabilities must collaboratively navigate complex field layouts to efficiently complete harvesting tasks.

The problem considers …

Navigating Big Data In Cyber Archaeology: An Updated Four-Field Approach Applied To San Miceli, Jared Wilson

Dissertations

Problem

This dissertation examines the evolving role of digital technologies in archaeological research, focusing on the management and interpretation of extensive digital datasets generated by modern excavations. Drawing on six years of comprehensive fieldwork at San Miceli, Sicily, this study critically evaluates and updates the established four-field methodology proposed by Levy et al. (2012), encompassing acquisition, analysis, dissemination, and curation. While traditional archaeological documentation methods rely heavily on analog techniques, these approaches increasingly fall short due to the rapid expansion and complexity of digital data.

Method

To address this gap, the dissertation refines and expands the current methodological framework by …

Agency, Index & Process: Investigating The Role Of The Artist’S Body In Digital Sculpture Production, Alan Magee

Doctoral

Contemporary Digital Sculpture has emerged out of recent developments in digital art, 3d modelling and virtual modes of production. These advances range from the creation of more powerful software and hardware systems to the to nascent XR sectors, and the potential for materialisation through technologies such as 3d printing, laser-cutting, or CNC1 machining. However, critical discourse remains predominantly focused on the end product, often overlooking the embodied labour processes inherent in its creation. As revealed through indexical traces of the artist’s body, these processes encapsulate the gestures, actions and subjective agency of artistic activity. Consequently, with the limitations of digital …

Deep Target Recognition: Semi-Supervised Annotation, Sensor Fusion And Super-Resolution, Shoaib Meraj Sami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Despite the recent expansion of machine learning algorithms to cover a wide range of disciplines, several areas of automatic target recognition (ATR) remain underexplored. This dissertation presents tools developed to improve performance in three significant aspects of ATR: semi-supervised annotation, sensor fusion, and image super-resolution. The aim of the semi-supervised methods is to automatically annotate targets in scenarios where labeled data are scarce in the target domain but available in the source domain. Secondly, to address the limitations of individual image sensors and enhance robustness under different environmental conditions and man-made constraints, a sensor fusion algorithm was developed to improve …

Evolving Secure Authentication From 5g To 6g: Advancing Privacy And Resilience In Next-Generation Networks, Isabella Deanne Lutz

Graduate Theses, Dissertations, and Problem Reports (ETD)

The fifth generation (5G) of mobile networks introduced groundbreaking improvements in connectivity, latency, and reliability. As 5G continues to expand across commercial and de- fense sectors, ensuring the privacy and integrity of its authentication mechanisms remains paramount. The foundation of 5G security lies in the Authentication and Key Agreement (AKA) protocol, which enhances user identity protection and establishes mutual authenti- cation between the user equipment (UE) and the network. Despite these advances, several weaknesses persist, including replay-based desynchronization, linkability, and correlation at- tacks under realistic adversary models. This thesis provides a unified analysis of these vulnerabilities and introduces a lightweight …

Précis Of The Edge Of Sentience: Risk And Precaution In Humans, Other Animals, And Ai, Jonathan Birch

Animal Sentience

We often face grave practical decisions that seem to hinge on whether a system is sentient. This family of cases includes invertebrate animals, people who are unresponsive after brain injury, fetuses, neural organoids, and now AI technologies. We must decide what to do despite ongoing disagreement about the nature of sentience. In our state of uncertainty, we should pragmatically transform the question from “Is it sentient?” to “Is it a sentience candidate, an investigation priority, or neither?”. When a system is a sentience candidate, it is negligent to fail to consider precautions. We should instead evaluate precautions for their proportionality …

Enhancing Trust In Ai For Healthcare: A Quantative Evaluation Of Explainable Methods In Clinical Decision Support Systems, Abdul Aziz Noor

Masters

The integration of Artificial Intelligence (AI) into healthcare has revolutionized Clinical Decision Support Systems (CDSS) by enabling sophisticated predictive capabilities. However, the opaque nature of many machine learning models, commonly referred to as "black-box" systems, poses significant challenges to their adoption in critical clinical settings where transparency, interpretability, and trust are paramount. This thesis addresses these challenges by developing a rigorous, mathematically grounded framework to evaluate Explainable AI (XAI) methods and enhance their integration into CDSS.

Multi-Dimensional Iot-Based Energy Management Approach For Smart Homes: A Unified Model For Comfort And Energy Efficiency, Muhammad Ans, Teodoro Montanaro, Ilaria Sergi, Ahmad Alsharoa, Miriam Pezzuto, Luigi Patrono

Electrical and Computer Engineering Faculty Research & Creative Works

As smart home technologies evolve, achieving energy-efficient indoor climate management while maintaining comfort and air quality is a growing priority. This paper introduces a novel optimization framework for smart buildings that minimizes energy costs and dynamically manages indoor environmental conditions, specifically temperature, CO₂ concentration, and illuminance. Unlike conventional systems, our model incorporates dynamic constraints that respond to day-night comfort requirements and leverage real-time variations in electricity prices and environmental conditions. By optimally controlling the power levels of air conditioning, air purification, and lighting systems, the framework ensures indoor comfort while significantly reducing operational costs.A nonlinear optimization approach with dynamic …

Ai-Driven Traffic Scene Understanding Using Static Lidar Sensors, Elham Binshaflout, Chaima Zaghouani, Charalampos Antoniadis, Hakim Ghazzai, Nawfal Guefrachi, Ahmad Alsharoa, Sameh Najeh, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

Traffic congestion and road safety remain critical challenges in urban environments, driving the need for more effective traffic monitoring solutions. While recent advancements in computer vision have enhanced traffic perception, the dynamic viewpoint of autonomous vehicles is often insufficient for comprehensive traffic management. To address this gap, we propose an AI-driven framework for enhanced traffic scene understanding using static LiDAR sensors at road intersections. The system collects 3D point clouds from roadside static LiDAR sensors, providing a complete view of vehicles and pedestrians. We integrate state-of-the-art 3D object detection (i.e., PV-RCNN) and instance segmentation models (i.e., PointGroup3heads) to accurately identify …

Annotated 3d Point Cloud Dataset For Traffic Management In Simulated Urban Intersections, Elham Binshaflout, Chaima Zaghouani, Nawfal Guefrachi, Charalampos Antoniadis, Hakim Ghazzai, Ahmad Alsharoa, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

Ensuring accurate traffic perception and road safety in complex urban environments remains a significant challenge. Advanced traffic monitoring increasingly relies on deep learning, which requires large data volumes. However, existing datasets are often limited to CCTV video footage or focus on dynamic scenarios captured by sensors mounted on ego vehicles. This narrow perspective reduces the effectiveness of comprehensive traffic monitoring, particularly for LiDAR sensors, which typically capture only the vehicle's viewpoint and miss critical areas such as intersections and pedestrian crossings. To address these limitations, we propose a holistic strategy for rapid data collection in urban settings using simulated 3D …