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<h2 class="mb-1" style="font-size:1.1rem;">[Computer and Information Engineering]</h2> <h3 class="mb-1" style="font-size:1rem;">Online ISSN : 1307-6892</h3> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3676</span> Digital Games as a Means of Cultural Communication and Heritage Tourism: A Study on Black Myth-Wukong</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kung%20Wong%20Lau">Kung Wong Lau</a> </p> <p class="card-text"><strong>Abstract:</strong></p> On August 20, 2024, the global launch of the Wukong game generated significant enthusiasm within the gaming community. This game provides gamers with an immersive experience and some digital twins (the location) that effectively bridge cultural heritage and contemporary gaming, thereby facilitating heritage tourism to some extent. Travel websites highlight locations featured in the Wukong game, encouraging visitors to explore these sites. However, this area remains underexplored in cultural and communication studies, both locally and internationally. This pilot study aims to explore the potential of in-game cultural communication in Wukong for promoting Chinese culture and heritage tourism. An exploratory research methodology was employed, utilizing a focus group of non-Chinese active gamers on an online discussion platform. The findings suggest that the use of digital twins as a means to facilitate cultural communication and heritage tourism for non-Chinese gamers shows promise. While this pilot study cannot generalize its findings due to the limited number of participants, the insights gained could inform further discussions on the influential factors of cultural communication through gaming. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20game" title="digital game">digital game</a>, <a href="https://publications.waset.org/abstracts/search?q=game%20culture" title=" game culture"> game culture</a>, <a href="https://publications.waset.org/abstracts/search?q=heritage%20tourism" title=" heritage tourism"> heritage tourism</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20communication" title=" cultural communication"> cultural communication</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Chinese%20gamers" title=" non-Chinese gamers"> non-Chinese gamers</a> </p> <a href="https://publications.waset.org/abstracts/193092/digital-games-as-a-means-of-cultural-communication-and-heritage-tourism-a-study-on-black-myth-wukong" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193092.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">18</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3675</span> The Interdisciplinary Synergy Between Computer Engineering and Mathematics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitat%20Uysal">Mitat Uysal</a>, <a href="https://publications.waset.org/abstracts/search?q=Aynur%20Uysal"> Aynur Uysal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computer engineering and mathematics share a deep and symbiotic relationship, with mathematics providing the foundational theories and models for computer engineering advancements. From algorithm development to optimization techniques, mathematics plays a pivotal role in solving complex computational problems. This paper explores key mathematical principles that underpin computer engineering, illustrating their significance through a case study that demonstrates the application of optimization techniques using Python code. The case study addresses the well-known vehicle routing problem (VRP), an extension of the traveling salesman problem (TSP), and solves it using a genetic algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VRP" title="VRP">VRP</a>, <a href="https://publications.waset.org/abstracts/search?q=TSP" title=" TSP"> TSP</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20engineering" title=" computer engineering"> computer engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/193090/the-interdisciplinary-synergy-between-computer-engineering-and-mathematics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193090.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">13</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3674</span> Machine Learning Approaches to Water Usage Prediction in Kocaeli: A Comparative Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kasim%20G%C3%B6renekli">Kasim Görenekli</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20G%C3%BClba%C4%9F"> Ali Gülbağ</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a comprehensive analysis of water consumption patterns in Kocaeli province, Turkey, utilizing various machine learning approaches. We analyzed data from 5,000 water subscribers across residential, commercial, and official categories over an 80-month period from January 2016 to August 2022, resulting in a total of 400,000 records. The dataset encompasses water consumption records, weather information, weekends and holidays, previous months' consumption, and the influence of the COVID-19 pandemic.We implemented and compared several machine learning models, including Linear Regression, Random Forest, Support Vector Regression (SVR), XGBoost, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Particle Swarm Optimization (PSO) was applied to optimize hyperparameters for all models.Our results demonstrate varying performance across subscriber types and models. For official subscribers, Random Forest achieved the highest R² of 0.699 with PSO optimization. For commercial subscribers, Linear Regression performed best with an R² of 0.730 with PSO. Residential water usage proved more challenging to predict, with XGBoost achieving the highest R² of 0.572 with PSO.The study identified key factors influencing water consumption, with previous months' consumption, meter diameter, and weather conditions being among the most significant predictors. The impact of the COVID-19 pandemic on consumption patterns was also observed, particularly in residential usage.This research provides valuable insights for effective water resource management in Kocaeli and similar regions, considering Turkey's high water loss rate and below-average per capita water supply. The comparative analysis of different machine learning approaches offers a comprehensive framework for selecting appropriate models for water consumption prediction in urban settings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mMachine%20learning" title="mMachine learning">mMachine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20consumption%20prediction" title=" water consumption prediction"> water consumption prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20resource%20management" title=" water resource management"> water resource management</a> </p> <a href="https://publications.waset.org/abstracts/193028/machine-learning-approaches-to-water-usage-prediction-in-kocaeli-a-comparative-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193028.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">15</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3673</span> Repository Blockchain for Collaborative Blockchain Ecosystem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Razwan%20Ahmed%20Tanvir">Razwan Ahmed Tanvir</a>, <a href="https://publications.waset.org/abstracts/search?q=Greg%20Speegle"> Greg Speegle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collaborative blockchain ecosystems allow diverse groups to cooperate on tasks while providing properties such as decentralization and transaction security. We provide a model that uses a repository blockchain to manage hard forks within a collaborative system such that a single process (assuming that it has knowledge of the requirements of each fork) can access all of the blocks within the system. The repository blockchain replaces the need for Inter Blockchain Communication (IBC) within the ecosystem by navigating the networks. The resulting construction resembles a tree instead of a chain. A proof-of-concept implementation performs a depth-first search on the new structure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hard%20fork" title="hard fork">hard fork</a>, <a href="https://publications.waset.org/abstracts/search?q=shared%20governance" title=" shared governance"> shared governance</a>, <a href="https://publications.waset.org/abstracts/search?q=inter%20blockchain%20communication" title=" inter blockchain communication"> inter blockchain communication</a>, <a href="https://publications.waset.org/abstracts/search?q=blockchain%20ecosystem" title=" blockchain ecosystem"> blockchain ecosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=regular%20research%20paper" title=" regular research paper"> regular research paper</a> </p> <a href="https://publications.waset.org/abstracts/192920/repository-blockchain-for-collaborative-blockchain-ecosystem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192920.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">17</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3672</span> Optimizing Machine Learning Algorithms for Defect Characterization and Elimination in Liquids Manufacturing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tolulope%20Aremu">Tolulope Aremu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The key process steps to produce liquid detergent products will introduce potential defects, such as formulation, mixing, filling, and packaging, which might compromise product quality, consumer safety, and operational efficiency. Real-time identification and characterization of such defects are of prime importance for maintaining high standards and reducing waste and costs. Usually, defect detection is performed by human inspection or rule-based systems, which is very time-consuming, inconsistent, and error-prone. The present study overcomes these limitations in dealing with optimization in defect characterization within the process for making liquid detergents using Machine Learning algorithms. Performance testing of various machine learning models was carried out: Support Vector Machine, Decision Trees, Random Forest, and Convolutional Neural Network on defect detection and classification of those defects like wrong viscosity, color deviations, improper filling of a bottle, packaging anomalies. These algorithms have significantly benefited from a variety of optimization techniques, including hyperparameter tuning and ensemble learning, in order to greatly improve detection accuracy while minimizing false positives. Equipped with a rich dataset of defect types and production parameters consisting of more than 100,000 samples, our study further includes information from real-time sensor data, imaging technologies, and historic production records. The results are that optimized machine learning models significantly improve defect detection compared to traditional methods. Take, for instance, the CNNs, which run at 98% and 96% accuracy in detecting packaging anomaly detection and bottle filling inconsistency, respectively, by fine-tuning the model with real-time imaging data, through which there was a reduction in false positives of about 30%. The optimized SVM model on detecting formulation defects gave 94% in viscosity variation detection and color variation. These values of performance metrics correspond to a giant leap in defect detection accuracy compared to the usual 80% level achieved up to now by rule-based systems. Moreover, this optimization with models can hasten defect characterization, allowing for detection time to be below 15 seconds from an average of 3 minutes using manual inspections with real-time processing of data. With this, the reduction in time will be combined with a 25% reduction in production downtime because of proactive defect identification, which can save millions annually in recall and rework costs. Integrating real-time machine learning-driven monitoring drives predictive maintenance and corrective measures for a 20% improvement in overall production efficiency. Therefore, the optimization of machine learning algorithms in defect characterization optimum scalability and efficiency for liquid detergent companies gives improved operational performance to higher levels of product quality. In general, this method could be conducted in several industries within the Fast moving consumer Goods industry, which would lead to an improved quality control process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=liquid%20detergent%20manufacturing" title="liquid detergent manufacturing">liquid detergent manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=defect%20detection" title=" defect detection"> defect detection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=defect%20characterization" title=" defect characterization"> defect characterization</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20maintenance" title=" predictive maintenance"> predictive maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=fast-moving%20consumer%20goods" title=" fast-moving consumer goods"> fast-moving consumer goods</a> </p> <a href="https://publications.waset.org/abstracts/192911/optimizing-machine-learning-algorithms-for-defect-characterization-and-elimination-in-liquids-manufacturing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192911.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">18</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3671</span> Architectural Adaptation for Road Humps Detection in Adverse Light Scenario</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Padmini%20S.%20Navalgund">Padmini S. Navalgund</a>, <a href="https://publications.waset.org/abstracts/search?q=Manasi%20Naik"> Manasi Naik</a>, <a href="https://publications.waset.org/abstracts/search?q=Ujwala%20Patil"> Ujwala Patil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Road hump is a semi-cylindrical elevation on the road made across specific locations of the road. The vehicle needs to maneuver the hump by reducing the speed to avoid car damage and pass over the road hump safely. Road Humps on road surfaces, if identified in advance, help to maintain the security and stability of vehicles, especially in adverse visibility conditions, viz. night scenarios. We have proposed a deep learning architecture adaptation by implementing the MISH activation function and developing a new classification loss function called "Effective Focal Loss" for Indian road humps detection in adverse light scenarios. We captured images comprising of marked and unmarked road humps from two different types of cameras across South India to build a heterogeneous dataset. A heterogeneous dataset enabled the algorithm to train and improve the accuracy of detection. The images were pre-processed, annotated for two classes viz, marked hump and unmarked hump. The dataset from these images was used to train the single-stage object detection algorithm. We utilised an algorithm to synthetically generate reduced visible road humps scenarios. We observed that our proposed framework effectively detected the marked and unmarked hump in the images in clear and ad-verse light environments. This architectural adaptation sets up an option for early detection of Indian road humps in reduced visibility conditions, thereby enhancing the autonomous driving technology to handle a wider range of real-world scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Indian%20road%20hump" title="Indian road hump">Indian road hump</a>, <a href="https://publications.waset.org/abstracts/search?q=reduced%20visibility%20condition" title=" reduced visibility condition"> reduced visibility condition</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20light%20condition" title=" low light condition"> low light condition</a>, <a href="https://publications.waset.org/abstracts/search?q=adverse%20light%20condition" title=" adverse light condition"> adverse light condition</a>, <a href="https://publications.waset.org/abstracts/search?q=marked%20hump" title=" marked hump"> marked hump</a>, <a href="https://publications.waset.org/abstracts/search?q=unmarked%20hump" title=" unmarked hump"> unmarked hump</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv9" title=" YOLOv9"> YOLOv9</a> </p> <a href="https://publications.waset.org/abstracts/192675/architectural-adaptation-for-road-humps-detection-in-adverse-light-scenario" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192675.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">23</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3670</span> Source Identification Model Based on Label Propagation and Graph Ordinary Differential Equations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuyuan%20Ma">Fuyuan Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuhan%20Wang"> Yuhan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Junhe%20Zhang"> Junhe Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying%20Wang"> Ying Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identifying the sources of information dissemination is a pivotal task in the study of collective behaviors in networks, enabling us to discern and intercept the critical pathways through which information propagates from its origins. This allows for the control of the information’s dissemination impact in its early stages. Numerous methods for source detection rely on pre-existing, underlying propagation models as prior knowledge. Current models that eschew prior knowledge attempt to harness label propagation algorithms to model the statistical characteristics of propagation states or employ Graph Neural Networks (GNNs) for deep reverse modeling of the diffusion process. These approaches are either deficient in modeling the propagation patterns of information or are constrained by the over-smoothing problem inherent in GNNs, which limits the stacking of sufficient model depth to excavate global propagation patterns. Consequently, we introduce the ODESI model. Initially, the model employs a label propagation algorithm to delineate the distribution density of infected states within a graph structure and extends the representation of infected states from integers to state vectors, which serve as the initial states of nodes. Subsequently, the model constructs a deep architecture based on GNNs-coupled Ordinary Differential Equations (ODEs) to model the global propagation patterns of continuous propagation processes. Addressing the challenges associated with solving ODEs on graphs, we approximate the analytical solutions to reduce computational costs. Finally, we conduct simulation experiments on two real-world social network datasets, and the results affirm the efficacy of our proposed ODESI model in source identification tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=source%20identification" title="source identification">source identification</a>, <a href="https://publications.waset.org/abstracts/search?q=ordinary%20differential%20equations" title=" ordinary differential equations"> ordinary differential equations</a>, <a href="https://publications.waset.org/abstracts/search?q=label%20propagation" title=" label propagation"> label propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20networks" title=" complex networks"> complex networks</a> </p> <a href="https://publications.waset.org/abstracts/192604/source-identification-model-based-on-label-propagation-and-graph-ordinary-differential-equations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192604.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">20</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3669</span> Ontology Expansion via Synthetic Dataset Generation and Transformer-Based Concept Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20Khalov">Andrey Khalov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid proliferation of unstructured data in IT infrastructure management demands innovative approaches for extracting actionable knowledge. This paper presents a framework for ontology-based knowledge extraction that combines relational graph neural networks (R-GNN) with large language models (LLMs). The proposed method leverages the DOLCE framework as the foundational ontology, extending it with concepts from ITSMO for domain-specific applications in IT service management and outsourcing. A key component of this research is the use of transformer-based models, such as DeBERTa-v3-large, for automatic entity and relationship extraction from unstructured texts. Furthermore, the paper explores how transfer learning techniques can be applied to fine-tune large language models (LLaMA) for using to generate synthetic datasets to improve precision in BERT-based entity recognition and ontology alignment. The resulting IT Ontology (ITO) serves as a comprehensive knowledge base that integrates domain-specific insights from ITIL processes, enabling more efficient decision-making. Experimental results demonstrate significant improvements in knowledge extraction and relationship mapping, offering a cutting-edge solution for enhancing cognitive computing in IT service environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ontology%20expansion" title="ontology expansion">ontology expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20dataset" title=" synthetic dataset"> synthetic dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer%20fine-tuning" title=" transformer fine-tuning"> transformer fine-tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=concept%20extraction" title=" concept extraction"> concept extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=DOLCE" title=" DOLCE"> DOLCE</a>, <a href="https://publications.waset.org/abstracts/search?q=BERT" title=" BERT"> BERT</a>, <a href="https://publications.waset.org/abstracts/search?q=taxonomy" title=" taxonomy"> taxonomy</a>, <a href="https://publications.waset.org/abstracts/search?q=LLM" title=" LLM"> LLM</a>, <a href="https://publications.waset.org/abstracts/search?q=NER" title=" NER"> NER</a> </p> <a href="https://publications.waset.org/abstracts/192579/ontology-expansion-via-synthetic-dataset-generation-and-transformer-based-concept-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192579.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">14</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3668</span> Ontology Mapping with R-GNN for IT Infrastructure: Enhancing Ontology Construction and Knowledge Graph Expansion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20Khalov">Andrey Khalov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid growth of unstructured data necessitates advanced methods for transforming raw information into structured knowledge, particularly in domain-specific contexts such as IT service management and outsourcing. This paper presents a methodology for automatically constructing domain ontologies using the DOLCE framework as the base ontology. The research focuses on expanding ITIL-based ontologies by integrating concepts from ITSMO, followed by the extraction of entities and relationships from domain-specific texts through transformers and statistical methods like formal concept analysis (FCA). In particular, this work introduces an R-GNN-based approach for ontology mapping, enabling more efficient entity extraction and ontology alignment with existing knowledge bases. Additionally, the research explores transfer learning techniques using pre-trained transformer models (e.g., DeBERTa-v3-large) fine-tuned on synthetic datasets generated via large language models such as LLaMA. The resulting ontology, termed IT Ontology (ITO), is evaluated against existing methodologies, highlighting significant improvements in precision and recall. This study advances the field of ontology engineering by automating the extraction, expansion, and refinement of ontologies tailored to the IT domain, thus bridging the gap between unstructured data and actionable knowledge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ontology%20mapping" title="ontology mapping">ontology mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20graphs" title=" knowledge graphs"> knowledge graphs</a>, <a href="https://publications.waset.org/abstracts/search?q=R-GNN" title=" R-GNN"> R-GNN</a>, <a href="https://publications.waset.org/abstracts/search?q=ITIL" title=" ITIL"> ITIL</a>, <a href="https://publications.waset.org/abstracts/search?q=NER" title=" NER"> NER</a> </p> <a href="https://publications.waset.org/abstracts/192575/ontology-mapping-with-r-gnn-for-it-infrastructure-enhancing-ontology-construction-and-knowledge-graph-expansion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192575.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">15</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3667</span> Singularization: A Technique for Protecting Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Robert%20Poenaru">Robert Poenaru</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihail%20Ple%C5%9Fa"> Mihail Pleşa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, a solution that addresses the protection of pre-trained neural networks is developed: Singularization. This method involves applying permutations to the weight matrices of a pre-trained model, introducing a form of structured noise that obscures the original model’s architecture. These permutations make it difficult for an attacker to reconstruct the original model, even if the permuted weights are obtained. Experimental benchmarks indicate that the application of singularization has a profound impact on model performance, often degrading it to the point where retraining from scratch becomes necessary to recover functionality, which is particularly effective for securing intellectual property in neural networks. Moreover, unlike other approaches, singularization is lightweight and computationally efficient, which makes it well suited for resource-constrained environments. Our experiments also demonstrate that this technique performs efficiently in various image classification tasks, highlighting its broad applicability and practicality in real-world scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=ANE" title=" ANE"> ANE</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a> </p> <a href="https://publications.waset.org/abstracts/192521/singularization-a-technique-for-protecting-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192521.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">14</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3666</span> Reactive and Concurrency-Based Image Resource Management Module for iOS Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shubham%20V.%20Kamdi">Shubham V. Kamdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to serve as an introduction to image resource caching techniques for iOS mobile applications. It will explain how developers can break down multiple image-downloading tasks concurrently using state-of-the-art iOS frameworks, namely Swift Concurrency and Combine. The paper will explain how developers can leverage SwiftUI to develop reactive view components and use declarative coding patterns. Developers will learn to bypass built-in image caching systems by curating the procedure to implement a swift-based LRU cache system. The paper will provide a full architectural overview of a system, helping readers understand how mobile applications are designed professionally. It will cover technical discussion, helping readers understand the low-level details of threads and how they can switch between them, as well as the significance of the main and background threads for requesting HTTP services via mobile applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=main%20thread" title="main thread">main thread</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20thread" title=" background thread"> background thread</a>, <a href="https://publications.waset.org/abstracts/search?q=reactive%20view%20components" title=" reactive view components"> reactive view components</a>, <a href="https://publications.waset.org/abstracts/search?q=declarative%20coding" title=" declarative coding"> declarative coding</a> </p> <a href="https://publications.waset.org/abstracts/192451/reactive-and-concurrency-based-image-resource-management-module-for-ios-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192451.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">25</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3665</span> Enhancing Information Technologies with AI: Unlocking Efficiency, Scalability, and Innovation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdal-Hafeez%20Alhussein">Abdal-Hafeez Alhussein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial Intelligence (AI) has become a transformative force in the field of information technologies, reshaping how data is processed, analyzed, and utilized across various domains. This paper explores the multifaceted applications of AI within information technology, focusing on three key areas: automation, scalability, and data-driven decision-making. We delve into how AI-powered automation is optimizing operational efficiency in IT infrastructures, from automated network management to self-healing systems that reduce downtime and enhance performance. Scalability, another critical aspect, is addressed through AI’s role in cloud computing and distributed systems, enabling the seamless handling of increasing data loads and user demands. Additionally, the paper highlights the use of AI in cybersecurity, where real-time threat detection and adaptive response mechanisms significantly improve resilience against sophisticated cyberattacks. In the realm of data analytics, AI models—especially machine learning and natural language processing—are driving innovation by enabling more precise predictions, automated insights extraction, and enhanced user experiences. The paper concludes with a discussion on the ethical implications of AI in information technologies, underscoring the importance of transparency, fairness, and responsible AI use. It also offers insights into future trends, emphasizing the potential of AI to further revolutionize the IT landscape by integrating with emerging technologies like quantum computing and IoT. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20technology" title=" information technology"> information technology</a>, <a href="https://publications.waset.org/abstracts/search?q=automation" title=" automation"> automation</a>, <a href="https://publications.waset.org/abstracts/search?q=scalability" title=" scalability"> scalability</a> </p> <a href="https://publications.waset.org/abstracts/192446/enhancing-information-technologies-with-ai-unlocking-efficiency-scalability-and-innovation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192446.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">17</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3664</span> 'CardioCare': A Cutting-Edge Fusion of IoT and Machine Learning to Bridge the Gap in Cardiovascular Risk Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arpit%20Patil">Arpit Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Atharav%20Bhagwat"> Atharav Bhagwat</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajas%20Bhope"> Rajas Bhope</a>, <a href="https://publications.waset.org/abstracts/search?q=Pramod%20Bide"> Pramod Bide</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research integrates IoT and ML to predict heart failure risks, utilizing the Framingham dataset. IoT devices gather real-time physiological data, focusing on heart rate dynamics, while ML, specifically Random Forest, predicts heart failure. Rigorous feature selection enhances accuracy, achieving over 90% prediction rate. This amalgamation marks a transformative step in proactive healthcare, highlighting early detection's critical role in cardiovascular risk mitigation. Challenges persist, necessitating continual refinement for improved predictive capabilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardiovascular%20diseases" title="cardiovascular diseases">cardiovascular diseases</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things" title=" internet of things"> internet of things</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=cardiac%20risk%20assessment" title=" cardiac risk assessment"> cardiac risk assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=heart%20failure%20prediction" title=" heart failure prediction"> heart failure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=early%20detection" title=" early detection"> early detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cardio%20data%20analysis" title=" cardio data analysis"> cardio data analysis</a> </p> <a href="https://publications.waset.org/abstracts/192429/cardiocare-a-cutting-edge-fusion-of-iot-and-machine-learning-to-bridge-the-gap-in-cardiovascular-risk-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192429.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">11</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3663</span> SynKit: A Event-Driven and Scalable Microservices-Based Kitting System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bruno%20Nascimento">Bruno Nascimento</a>, <a href="https://publications.waset.org/abstracts/search?q=Cristina%20Wanzeller"> Cristina Wanzeller</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Silva"> Jorge Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Jo%C3%A3o%20A.%20Dias"> João A. Dias</a>, <a href="https://publications.waset.org/abstracts/search?q=Andr%C3%A9%20Barbosa"> André Barbosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Ribeiro"> José Ribeiro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing complexity of logistics operations stems from evolving business needs, such as the shift from mass production to mass customization, which demands greater efficiency and flexibility. In response, Industry 4.0 and 5.0 technologies provide improved solutions to enhance operational agility and better meet market demands. The management of kitting zones, combined with the use of Autonomous Mobile Robots, faces challenges related to coordination, resource optimization, and rapid response to customer demand fluctuations. Additionally, implementing lean manufacturing practices in this context must be carefully orchestrated by intelligent systems and human operators to maximize efficiency without sacrificing the agility required in an advanced production environment. This paper proposes and implements a microservices-based architecture integrating principles from Industry 4.0 and 5.0 with lean manufacturing practices. The architecture enhances communication and coordination between autonomous vehicles and kitting management systems, allowing more efficient resource utilization and increased scalability. The proposed architecture focuses on the modularity and flexibility of operations, enabling seamless flexibility to change demands and the efficient allocation of resources in realtime. Conducting this approach is expected to significantly improve logistics operations’ efficiency and scalability by reducing waste and optimizing resource use while improving responsiveness to demand changes. The implementation of this architecture provides a robust foundation for the continuous evolution of kitting management and process optimization. It is designed to adapt to dynamic environments marked by rapid shifts in production demands and real-time decision-making. It also ensures seamless integration with automated systems, aligning with Industry 4.0 and 5.0 needs while reinforcing Lean Manufacturing principles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microservices" title="microservices">microservices</a>, <a href="https://publications.waset.org/abstracts/search?q=event-driven" title=" event-driven"> event-driven</a>, <a href="https://publications.waset.org/abstracts/search?q=kitting" title=" kitting"> kitting</a>, <a href="https://publications.waset.org/abstracts/search?q=AMR" title=" AMR"> AMR</a>, <a href="https://publications.waset.org/abstracts/search?q=lean%20manufacturing" title=" lean manufacturing"> lean manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=industry%204.0" title=" industry 4.0"> industry 4.0</a>, <a href="https://publications.waset.org/abstracts/search?q=industry%205.0" title=" industry 5.0"> industry 5.0</a> </p> <a href="https://publications.waset.org/abstracts/192380/synkit-a-event-driven-and-scalable-microservices-based-kitting-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192380.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">22</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3662</span> Emerging Threats and Adaptive Defenses: Navigating the Future of Cybersecurity in a Hyperconnected World</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olasunkanmi%20Jame%20Ayodeji">Olasunkanmi Jame Ayodeji</a>, <a href="https://publications.waset.org/abstracts/search?q=Adebayo%20Adeyinka%20Victor"> Adebayo Adeyinka Victor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a hyperconnected world, cybersecurity faces a continuous evolution of threats that challenge traditional defence mechanisms. This paper explores emerging cybersecurity threats like malware, ransomware, phishing, social engineering, and the Internet of Things (IoT) vulnerabilities. It delves into the inadequacies of existing cybersecurity defences in addressing these evolving risks and advocates for adaptive defence mechanisms that leverage AI, machine learning, and zero-trust architectures. The paper proposes collaborative approaches, including public-private partnerships and information sharing, as essential to building a robust defence strategy to address future cyber threats. The need for continuous monitoring, real-time incident response, and adaptive resilience strategies is highlighted to fortify digital infrastructures in the face of escalating global cyber risks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cybersecurity" title="cybersecurity">cybersecurity</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperconnectivity" title=" hyperconnectivity"> hyperconnectivity</a>, <a href="https://publications.waset.org/abstracts/search?q=malware" title=" malware"> malware</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20defences" title=" adaptive defences"> adaptive defences</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-trust%20architecture" title=" zero-trust architecture"> zero-trust architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things%20vulnerabilities" title=" internet of things vulnerabilities"> internet of things vulnerabilities</a> </p> <a href="https://publications.waset.org/abstracts/192296/emerging-threats-and-adaptive-defenses-navigating-the-future-of-cybersecurity-in-a-hyperconnected-world" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192296.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">20</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3661</span> Reviewing Image Recognition and Anomaly Detection Methods Utilizing GANs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Agastya%20Pratap%20Singh">Agastya Pratap Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This review paper examines the emerging applications of generative adversarial networks (GANs) in the fields of image recognition and anomaly detection. With the rapid growth of digital image data, the need for efficient and accurate methodologies to identify and classify images has become increasingly critical. GANs, known for their ability to generate realistic data, have gained significant attention for their potential to enhance traditional image recognition systems and improve anomaly detection performance. The paper systematically analyzes various GAN architectures and their modifications tailored for image recognition tasks, highlighting their strengths and limitations. Additionally, it delves into the effectiveness of GANs in detecting anomalies in diverse datasets, including medical imaging, industrial inspection, and surveillance. The review also discusses the challenges faced in training GANs, such as mode collapse and stability issues, and presents recent advancements aimed at overcoming these obstacles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title="generative adversarial networks">generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20recognition" title=" image recognition"> image recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data%20generation" title=" synthetic data generation"> synthetic data generation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20evaluation" title=" model evaluation"> model evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20applications" title=" machine learning applications"> machine learning applications</a> </p> <a href="https://publications.waset.org/abstracts/192253/reviewing-image-recognition-and-anomaly-detection-methods-utilizing-gans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192253.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">25</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3660</span> The Role of Information and Communication Technology in Curbing Electoral Malpractices in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fred%20Fudah%20Moveh">Fred Fudah Moveh</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Abba%20Jallo"> Muhammad Abba Jallo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electoral fraud remains a persistent threat to democracy in Nigeria, undermining public trust and stalling political development. This study explores the role of Information and Communication Technology (ICT) in curbing electoral fraud, focusing on its application in recent Nigerian elections. The paper identifies the main forms of electoral fraud, evaluates the effectiveness of ICT-based interventions like the Permanent Voter Card (PVC) and the Bi-modal Voter Accreditation System (BVAS), and discusses challenges such as poor infrastructure, voter intimidation, and legal inadequacies. Data was collected through structured questionnaires and interviews and analyzed using SPSS software. Results reveal that while ICT has mitigated some forms of fraud, systemic issues continue to hinder its full potential. The study concludes with recommendations for enhancing the application of ICT in Nigeria’s electoral process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ICT" title="ICT">ICT</a>, <a href="https://publications.waset.org/abstracts/search?q=electoral%20fraud" title=" electoral fraud"> electoral fraud</a>, <a href="https://publications.waset.org/abstracts/search?q=election%20process" title=" election process"> election process</a>, <a href="https://publications.waset.org/abstracts/search?q=Nigeria" title=" Nigeria"> Nigeria</a>, <a href="https://publications.waset.org/abstracts/search?q=political%20instability" title=" political instability"> political instability</a> </p> <a href="https://publications.waset.org/abstracts/192207/the-role-of-information-and-communication-technology-in-curbing-electoral-malpractices-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192207.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">24</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3659</span> Parallelization by Domain Decomposition for 1-D Sugarcane Equation with Message Passing Interface</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ewedafe%20Simon%20Uzezi">Ewedafe Simon Uzezi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we presented a method based on Domain Decomposition (DD) for parallelization of 1-D Sugarcane Equation on parallel platform with parallel paradigms on Master-Slave platform using Message Passing Interface (MPI). The 1-D Sugarcane Equation was discretized using explicit method of discretization requiring evaluation nof temporal and spatial distribution of temperature. This platform gives better predictions of the effects of temperature distribution of the sugarcane problem. This work presented parallel overheads with overlapping communication and communication across parallel computers with numerical results across different block sizes with scalability. However, performance improvement strategies from the DD on various mesh sizes were compared experimentally and parallel results show speedup and efficiency for the parallel algorithms design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sugarcane" title="sugarcane">sugarcane</a>, <a href="https://publications.waset.org/abstracts/search?q=parallelization" title=" parallelization"> parallelization</a>, <a href="https://publications.waset.org/abstracts/search?q=explicit%20method" title=" explicit method"> explicit method</a>, <a href="https://publications.waset.org/abstracts/search?q=domain%20decomposition" title=" domain decomposition"> domain decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=MPI" title=" MPI"> MPI</a> </p> <a href="https://publications.waset.org/abstracts/192206/parallelization-by-domain-decomposition-for-1-d-sugarcane-equation-with-message-passing-interface" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192206.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">21</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3658</span> Factors Affecting Citizens’ Behavioural Intention to Use E-voter Registration and Verification System Towards the Electoral Process in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aishatu%20Shuaibu">Aishatu Shuaibu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is expected that electronic voter registration and verification in Nigeria will enhance the integrity of elections, which is vital for democratic development; it is also expected to enhance efficiency, transparency, and security. However, the reasons for citizens' intentions with respect to behavioral use of such platforms have not been studied in the literature much. This paper, therefore, intends to look into significant characteristics affecting the acceptance and use of e-voter technology among Nigerian residents. Data will be collected using a structured questionnaire from several local government areas (LGAs) around Nigeria to evaluate the influence of demographic characteristics, technology usability, security perceptions, and governmental education on the intention to implement e-voter systems. The results will offer vital insights into the barriers and drivers of voter technology acceptance, aiding in policy suggestions to enhance voter registration and verification processes within Nigeria's electoral framework. This study is designed to aid electoral stakeholders in devising successful strategies for encouraging the broad deployment of e-voter systems in Nigeria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=e-governance" title="e-governance">e-governance</a>, <a href="https://publications.waset.org/abstracts/search?q=e-voting" title=" e-voting"> e-voting</a>, <a href="https://publications.waset.org/abstracts/search?q=e-democracy" title=" e-democracy"> e-democracy</a>, <a href="https://publications.waset.org/abstracts/search?q=INEC" title=" INEC"> INEC</a>, <a href="https://publications.waset.org/abstracts/search?q=Nigeria" title=" Nigeria"> Nigeria</a> </p> <a href="https://publications.waset.org/abstracts/192203/factors-affecting-citizens-behavioural-intention-to-use-e-voter-registration-and-verification-system-towards-the-electoral-process-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192203.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">19</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3657</span> Identifying the Knowledge Management and its Capabilities in Universities: A Case Study of Public Universities in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hilary%20Joseph%20Watsilla">Hilary Joseph Watsilla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Research work is a vital part of the university system; in Nigeria public universities, research is used in measuring the development of individuals and departments within the academic system. Information technology has impacted the way research is carried out by providing easy access to information and improved collaboration between research and other instruments necessary for research activities. However, access to some of these IT facilities is not readily available in most of the public institutions in Nigeria. Research activities are usually tedious and rigorous and any inadequacy in research resources might affect the quality of research outcome. This study aims to identify the IT capability and knowledge management capabilities necessary for academic researchers in public universities in Nigeria, as it will provide more incite to the knowledge creation processes of research. The research will be conducted using an interpretive lens, which will provide a more qualitative understanding of the subject matter. The outcome of the research will provide an empirical understanding of the IT capabilities, which help in the optimization of the knowledge management capabilities of the university. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IT%20capabilities" title="IT capabilities">IT capabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=KM%20capabilities" title=" KM capabilities"> KM capabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=universities" title=" universities"> universities</a>, <a href="https://publications.waset.org/abstracts/search?q=academic%20research" title=" academic research"> academic research</a> </p> <a href="https://publications.waset.org/abstracts/192191/identifying-the-knowledge-management-and-its-capabilities-in-universities-a-case-study-of-public-universities-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192191.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">22</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3656</span> Autism Spectrum Disorder Interventions, Problems and Solutions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ammara%20Jabeen">Ammara Jabeen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This survey report aims to find the interventions and their effectiveness that are being used globally as well as in Pakistan to treat autistic kids. ‘Autism spectrum disorder (ASD) is a state associated with brain development that shows ‘how a person perceives and socializes with others, causing problems in social interaction and communication’. Besides these problems, these children suffer from restricted and repetitive behaviors too. The term ‘Spectrum’ in Autism Spectrum Disorder refers to the wide range of symptoms and severity. The main cause of this Autism Spectrum Disorder is not known yet, but the research showed that genetics and environmental factors play important roles. In this survey report, after a literature review, some of the possible solutions are suggested based on the most common problems that these children are currently facing in their daily lives. Based on this report, we are able to overcome the lack of the resources (e.g. language, cost, training etc.) that mostly exist in Pakistani culture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autism" title="autism">autism</a>, <a href="https://publications.waset.org/abstracts/search?q=interventions" title=" interventions"> interventions</a>, <a href="https://publications.waset.org/abstracts/search?q=spectrum" title=" spectrum"> spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=disorder" title=" disorder"> disorder</a> </p> <a href="https://publications.waset.org/abstracts/192186/autism-spectrum-disorder-interventions-problems-and-solutions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192186.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">22</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3655</span> Leveraging SHAP Values for Effective Feature Selection in Peptide Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sharon%20Li">Sharon Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhonghang%20Xia"> Zhonghang Xia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Post-database search is an essential phase in peptide identification using tandem mass spectrometry (MS/MS) to refine peptide-spectrum matches (PSMs) produced by database search engines. These engines frequently face difficulty differentiating between correct and incorrect peptide assignments. Despite advances in statistical and machine learning methods aimed at improving the accuracy of peptide identification, challenges remain in selecting critical features for these models. In this study, two machine learning models—a random forest tree and a support vector machine—were applied to three datasets to enhance PSMs. SHAP values were utilized to determine the significance of each feature within the models. The experimental results indicate that the random forest model consistently outperformed the SVM across all datasets. Further analysis of SHAP values revealed that the importance of features varies depending on the dataset, indicating that a feature's role in model predictions can differ significantly. This variability in feature selection can lead to substantial differences in model performance, with false discovery rate (FDR) differences exceeding 50% between different feature combinations. Through SHAP value analysis, the most effective feature combinations were identified, significantly enhancing model performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=peptide%20identification" title="peptide identification">peptide identification</a>, <a href="https://publications.waset.org/abstracts/search?q=SHAP%20value" title=" SHAP value"> SHAP value</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20tree" title=" random forest tree"> random forest tree</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/192174/leveraging-shap-values-for-effective-feature-selection-in-peptide-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192174.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">23</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3654</span> Automatic Calibration of Agent-Based Models Using Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sima%20Najafzadehkhoei">Sima Najafzadehkhoei</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20Vega%20Yon"> George Vega Yon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an approach for calibrating Agent-Based Models (ABMs) efficiently, utilizing Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. These machine learning techniques are applied to Susceptible-Infected-Recovered (SIR) models, which are a core framework in the study of epidemiology. Our method replicates parameter values from observed trajectory curves, enhancing the accuracy of predictions when compared to traditional calibration techniques. Through the use of simulated data, we train the models to predict epidemiological parameters more accurately. Two primary approaches were explored: one where the number of susceptible, infected, and recovered individuals is fully known, and another using only the number of infected individuals. Our method shows promise for application in other ABMs where calibration is computationally intensive and expensive. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABM" title="ABM">ABM</a>, <a href="https://publications.waset.org/abstracts/search?q=calibration" title=" calibration"> calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemiology" title=" epidemiology"> epidemiology</a> </p> <a href="https://publications.waset.org/abstracts/192172/automatic-calibration-of-agent-based-models-using-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192172.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">24</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3653</span> Umbrella Reinforcement Learning – A Tool for Hard Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Egor%20E.%20Nuzhin">Egor E. Nuzhin</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikolay%20V.%20Brilliantov">Nikolay V. Brilliantov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an approach for addressing Reinforcement Learning (RL) problems. It combines the ideas of umbrella sampling, borrowed from Monte Carlo technique of computational physics and chemistry, with optimal control methods, and is realized on the base of neural networks. This results in a powerful algorithm, designed to solve hard RL problems – the problems, with long-time delayed reward, state-traps sticking and a lack of terminal states. It outperforms the prominent algorithms, such as PPO, RND, iLQR and VI, which are among the most efficient for the hard problems. The new algorithm deals with a continuous ensemble of agents and expected return, that includes the ensemble entropy. This results in a quick and efficient search of the optimal policy in terms of ”exploration-exploitation trade-off” in the state-action space. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=umbrella%20sampling" title="umbrella sampling">umbrella sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=policy%20gradient" title=" policy gradient"> policy gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20programming" title=" dynamic programming"> dynamic programming</a> </p> <a href="https://publications.waset.org/abstracts/192151/umbrella-reinforcement-learning-a-tool-for-hard-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192151.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">21</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3652</span> Augmented Reality Applications for Active Learning in Geometry: Enhancing Mathematical Intelligence at Phra Dabos School</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nattamon%20Srithammee">Nattamon Srithammee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratchanikorn%20Chonchaiya"> Ratchanikorn Chonchaiya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores the impact of Augmented Reality (AR) technology on mathematics education, focusing on Area and Volume concepts at Phra Dabos School in Thailand. We developed a mobile augmented reality application to present these mathematical concepts innovatively. Using a mixed-methods approach, we assessed the knowledge of 79 students before and after using the application. The results showed a significant improvement in students' understanding of Area and Volume, with average test scores increasing from 3.70 to 9.04 (p < 0.001, Cohen's d = 2.05). Students also reported increased engagement and satisfaction. Our findings suggest that augmented reality technology can be a valuable tool in mathematics education, particularly for enhancing the understanding of abstract concepts like Area and Volume. This study contributes to research on educational technology in STEM education and provides insights for educators and educational technology developers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=augmented%20reality" title="augmented reality">augmented reality</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematics%20education" title=" mathematics education"> mathematics education</a>, <a href="https://publications.waset.org/abstracts/search?q=area%20and%20volume" title=" area and volume"> area and volume</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20technology" title=" educational technology"> educational technology</a>, <a href="https://publications.waset.org/abstracts/search?q=STEM%20education" title=" STEM education"> STEM education</a> </p> <a href="https://publications.waset.org/abstracts/192127/augmented-reality-applications-for-active-learning-in-geometry-enhancing-mathematical-intelligence-at-phra-dabos-school" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192127.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">24</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3651</span> Expanding Trading Strategies By Studying Sentiment Correlation With Data Mining Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ved%20Kulkarni">Ved Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=Karthik%20Kini"> Karthik Kini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This experiment aims to understand how the media affects the power markets in the mainland United States and study the duration of reaction time between news updates and actual price movements. it have taken into account electric utility companies trading in the NYSE and excluded companies that are more politically involved and move with higher sensitivity to Politics. The scrapper checks for any news related to keywords, which are predefined and stored for each specific company. Based on this, the classifier will allocate the effect into five categories: positive, negative, highly optimistic, highly negative, or neutral. The effect on the respective price movement will be studied to understand the response time. Based on the response time observed, neural networks would be trained to understand and react to changing market conditions, achieving the best strategy in every market. The stock trader would be day trading in the first phase and making option strategy predictions based on the black holes model. The expected result is to create an AI-based system that adjusts trading strategies within the market response time to each price movement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20processing" title=" language processing"> language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a> </p> <a href="https://publications.waset.org/abstracts/192108/expanding-trading-strategies-by-studying-sentiment-correlation-with-data-mining-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">17</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3650</span> Using Machine Learning Techniques to Extract Useful Information from Dark Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nigar%20Hussain">Nigar Hussain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is a subset of big data. Dark data means those data in which we fail to use for future decisions. There are many issues in existing work, but some need powerful tools for utilizing dark data. It needs sufficient techniques to deal with dark data. That enables users to exploit their excellence, adaptability, speed, less time utilization, execution, and accessibility. Another issue is the way to utilize dark data to extract helpful information to settle on better choices. In this paper, we proposed upgrade strategies to remove the dark side from dark data. Using a supervised model and machine learning techniques, we utilized dark data and achieved an F1 score of 89.48%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=dark%20data" title=" dark data"> dark data</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=heatmap" title=" heatmap"> heatmap</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/191942/using-machine-learning-techniques-to-extract-useful-information-from-dark-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191942.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">28</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3649</span> Data-Driven Insights Into Juvenile Recidivism: Leveraging Machine Learning for Rehabilitation Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saiakhil%20Chilaka">Saiakhil Chilaka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Juvenile recidivism presents a significant challenge to the criminal justice system, impacting both the individuals involved and broader societal safety. This study aims to identify the key factors influencing recidivism and successful rehabilitation outcomes by utilizing a dataset of over 25,000 individuals from the NIJ Recidivism Challenge. We employed machine learning techniques, particularly Random Forest Classification, combined with SHAP (SHapley Additive exPlanations) for model interpretability. Our findings indicate that supervision risk score, percent days employed, and education level are critical factors affecting recidivism, with higher levels of supervision, successful employment, and education contributing to lower recidivism rates. Conversely, Gang Affiliation emerged as a significant risk factor for reoffending. The model achieved an accuracy of 68.8%, highlighting its utility in identifying high-risk individuals and informing targeted interventions. These results suggest that a comprehensive approach involving personalized supervision, vocational training, educational support, and anti-gang initiatives can significantly reduce recidivism and enhance rehabilitation outcomes for juveniles, providing critical insights for policymakers and juvenile justice practitioners. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=juvenile" title="juvenile">juvenile</a>, <a href="https://publications.waset.org/abstracts/search?q=justice%20system" title=" justice system"> justice system</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=SHAP" title=" SHAP"> SHAP</a> </p> <a href="https://publications.waset.org/abstracts/191935/data-driven-insights-into-juvenile-recidivism-leveraging-machine-learning-for-rehabilitation-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191935.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">21</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3648</span> Secure Data Sharing of Electronic Health Records With Blockchain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kenneth%20Harper">Kenneth Harper</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The secure sharing of Electronic Health Records (EHRs) is a critical challenge in modern healthcare, demanding solutions to enhance interoperability, privacy, and data integrity. Traditional standards like Health Information Exchange (HIE) and HL7 have made significant strides in facilitating data exchange between healthcare entities. However, these approaches rely on centralized architectures that are often vulnerable to data breaches, lack sufficient privacy measures, and have scalability issues. This paper proposes a framework for secure, decentralized sharing of EHRs using blockchain technology, cryptographic tokens, and Non-Fungible Tokens (NFTs). The blockchain's immutable ledger, decentralized control, and inherent security mechanisms are leveraged to improve transparency, accountability, and auditability in healthcare data exchanges. Furthermore, we introduce the concept of tokenizing patient data through NFTs, creating unique digital identifiers for each record, which allows for granular data access controls and proof of data ownership. These NFTs can also be employed to grant access to authorized parties, establishing a secure and transparent data sharing model that empowers both healthcare providers and patients. The proposed approach addresses common privacy concerns by employing privacy-preserving techniques such as zero-knowledge proofs (ZKPs) and homomorphic encryption to ensure that sensitive patient information can be shared without exposing the actual content of the data. This ensures compliance with regulations like HIPAA and GDPR. Additionally, the integration of Fast Healthcare Interoperability Resources (FHIR) with blockchain technology allows for enhanced interoperability, enabling healthcare organizations to exchange data seamlessly and securely across various systems while maintaining data governance and regulatory compliance. Through real-world case studies and simulations, this paper demonstrates how blockchain-based EHR sharing can reduce operational costs, improve patient outcomes, and enhance the security and privacy of healthcare data. This decentralized framework holds great potential for revolutionizing healthcare information exchange, providing a transparent, scalable, and secure method for managing patient data in a highly regulated environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blockchain" title="blockchain">blockchain</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20health%20records%20%28ehrs%29" title=" electronic health records (ehrs)"> electronic health records (ehrs)</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20healthcare%20interoperability%20resources%20%28fhir%29" title=" fast healthcare interoperability resources (fhir)"> fast healthcare interoperability resources (fhir)</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20information%20exchange%20%28hie%29" title=" health information exchange (hie)"> health information exchange (hie)</a>, <a href="https://publications.waset.org/abstracts/search?q=hl7" title=" hl7"> hl7</a>, <a href="https://publications.waset.org/abstracts/search?q=interoperability" title=" interoperability"> interoperability</a>, <a href="https://publications.waset.org/abstracts/search?q=non-fungible%20tokens%20%28nfts%29" title=" non-fungible tokens (nfts)"> non-fungible tokens (nfts)</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy-preserving%20techniques" title=" privacy-preserving techniques"> privacy-preserving techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=tokens" title=" tokens"> tokens</a>, <a href="https://publications.waset.org/abstracts/search?q=secure%20data%20sharing" title=" secure data sharing"> secure data sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=" title=""></a> </p> <a href="https://publications.waset.org/abstracts/191930/secure-data-sharing-of-electronic-health-records-with-blockchain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191930.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">21</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3647</span> Transforming Healthcare Data Privacy: Integrating Blockchain with Zero-Knowledge Proofs and Cryptographic Security</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kenneth%20Harper">Kenneth Harper</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Blockchain technology presents solutions for managing healthcare data, addressing critical challenges in privacy, integrity, and access. This paper explores how privacy-preserving technologies, such as zero-knowledge proofs (ZKPs) and homomorphic encryption (HE), enhance decentralized healthcare platforms by enabling secure computations and patient data protection. An examination of the mathematical foundations of these methods, their practical applications, and how they meet the evolving demands of healthcare data security is unveiled. Using real-world examples, this research highlights industry-leading implementations and offers a roadmap for future applications in secure, decentralized healthcare ecosystems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blockchain" title="blockchain">blockchain</a>, <a href="https://publications.waset.org/abstracts/search?q=cryptography" title=" cryptography"> cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20privacy" title=" data privacy"> data privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=decentralized%20data%20management" title=" decentralized data management"> decentralized data management</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20privacy" title=" differential privacy"> differential privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare%20data%20security" title=" healthcare data security"> healthcare data security</a>, <a href="https://publications.waset.org/abstracts/search?q=homomorphic%20encryption" title=" homomorphic encryption"> homomorphic encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy-preserving%20technologies" title=" privacy-preserving technologies"> privacy-preserving technologies</a>, <a href="https://publications.waset.org/abstracts/search?q=secure%20computations" title=" secure computations"> secure computations</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-knowledge%20proofs" title=" zero-knowledge proofs"> zero-knowledge proofs</a> </p> <a href="https://publications.waset.org/abstracts/191929/transforming-healthcare-data-privacy-integrating-blockchain-with-zero-knowledge-proofs-and-cryptographic-security" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191929.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">18</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/computer-and-information-engineering?page=1" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/computer-and-information-engineering?page=1">1</a></li> <li class="page-item active"><span class="page-link">2</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/computer-and-information-engineering?page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/computer-and-information-engineering?page=4">4</a></li> <li class="page-item"><a class="page-link" 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