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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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Deep Learning-based Cluster Analysis for Healthcare Data Classification

Deep Learning-based Cluster Analysis for Healthcare Data Cla...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Gayathri, M. Kavitha, V. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Kattankulathur India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India

ISBN: (纸本)9798350348910

The use of cutting-edge technology in the medical field results in the production of massive volumes of data on a daily basis. Various categories of information are applied in the domain of healthcare, including clinic information, medical record data, and genetic information. In addition, real-time monitoring in the medical industry produces enormous volumes of data, and properly interpreting such enormous amounts of data is a significant problem. The evaluation of medical information becomes more required so that suitable drugs may be supplied and issues can be avoided by taking appropriate measures depending on the history of the patient. Automation makes data analysis more effective, but performance might deteriorate when there are problems with data integrity, diversity, or consistency. Automation makes data analysis more effective. In order to handle the management of massive amounts of data, many models that are powered by neural networks have been developed;still researchers are currently attempting to develop a superior model that is more accurate. Hence, fuzzy c denotes the process of clustering, whereas generative adversarial networks are used in this study to clusters and classify medical data, aiming to achieve the maximum degree of efficiency in classification. The experiment will use both the benchmark lung cancer sample and the arrhythmias sample. The proposed model achieves a maximum accuracy of 97.3% for dataset 1 and 98.2% for dataset 2, outperforming other methods such as support vector machine, decision tree, and random forest algorithms. © 2023 IEEE.

关键词： Classification (of information)

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Revolutionalize Your Learning Experience with EQU ACCESS 4

Revolutionalize Your Learning Experience with EQU ACCESS

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Raveenthiran, Gawsan Sivarajah, Kaanushan Mohamed Riyal, Akash Ahamed Kugathasan, Vethurja Chandrasiri, Sanjeevi Rajendran, Karthiga Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350395914

This paper introduces a novel approach aimed at enhancing online education by placing a central focus on students' emotional well-being and improving their learning experiences. The approach integrates four key machine learning technologies: behavioral expression analysis, a personalized chatbot for emotional support, voice stress detection, and visual content description. Through empirical findings, the study illustrates the effectiveness of these methods in bolstering students' emotional well-being and academic performance. By providing a roadmap for the advancement of online education and emotional support, this research holds promise for delivering substantial benefits to learners worldwide. The study showcases notable advancements in online education, reporting a 30% rise in perceived emotional support and a 25% increase in overall satisfaction. The personalized emotional support chatbot achieved an 85% accuracy in addressing students' emotional needs, while voice stress detection boasted a 90% accuracy in identifying anxiety. Additionally, visual content description led to a 20% improvement in comprehension. These findings highlight the approach's potential to elevate both emotional well-being and academic performance in online learners. © 2024 IEEE.

关键词： Contrastive Learning

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An Improved Ant Lion Optimization Algorithm Based Convolutional Neural Network for Big data Classification

An Improved Ant Lion Optimization Algorithm Based Convolutio...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Umaeswari, P. Vellingiri, J. Kumar, Gotte Ranjith Devendhiran, A. Naik, Ashitha V R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Institute of Technology School of Computer Science Engineering and Information Systems Vellore India Sr University School of Computer Science & Artificial Intelligence Warangal India Vivekanandha College of Engineering for Women Department of Computer Science and Technology Namakkal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India

ISBN: (纸本)9798350313413

The continuous advancement of remote sensor technology is contributing to a daily surge in data production, necessitating improvements in the accuracy of big data classification. This research proposes a unique feature selection method named as Improved Ant Lion Optimization (IALO) algorithm that integrates the behavior of animal migration with Ant Lion Optimization (ALO) algorithm. The proposed feature selection method enhances the performance of the ALO algorithm. The datasets utilized for the research are iris, wine, Cleveland, and Switzerland datasets. Next, the map reduces framework is utilized and then feature extraction is processed by Linear Discriminant Analysis (LDA). The feature selection is performed by the proposed IALO algorithm and the selected features are given to the Convolutional Neural Network (CNN) classifier for the big data classification. The performance of the proposed method is evaluated by parameters such as accuracy, computation time, specificity, and sensitivity. The proposed method attained a high accuracy of 98.05% with less computation time of 4.8 secs in the wine dataset which is comparatively higher than other existing methods such as Grey Wolf Optimization based CNN (GWO-CNN), Particle Swarm Optimization based CNN (PSO-CNN), Cuckoo Search Optimization based CNN (CSO-CNN), Ant Lion Optimization based CNN (ALO-CNN). © 2023 IEEE.

关键词： Ant Lion Optimization Big data Convolutional Neural Network MapReduce and Migration behavior

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Diagnosing Spinal Abnormalities Using Machine Learning: A Data-Driven Approach 3

Diagnosing Spinal Abnormalities Using Machine Learning: A Da...

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3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Ul Islam Nasir, Zia Khan, Khalil Asghar, Momna Islamabad Pakistan Pak-Austria Institute of Applied Science & Technology Department of Computer Software Engineering & Computer Science Haripur Pakistan University of Azad Jammu & Kashmir Department of Electrical Engineering A.K Muzaffarabad Pakistan

ISBN: (纸本)9798350322125

Low back pain is a predominant condition which can affects people from different diaspora. The goal of this work is to use machine learning approach to forecast spinal abnormalities. Extratreesclassifier is utilized as a data preprocessing stage to choose the dataset's most prominent features. On a dataset of 310 samples, spinal anomalies are diagnosed using machine learning algorithms like the Support Vector Machine (SVM) and the multilayer perceptron (MLP). The purpose of this study is to determine the most crucial factors that produce backbone abnormalities and to predict them using supervised machine learning techniques. The classification of normal and abnormal spinal patients is investigated in terms of various aspects, including testing and training accuracy, precision, and recall. The observed accuracies for SVM and MLP with 80% training data are 92% and 90%, respectively. The result shows that these models can achieve high accuracy in predicting spinal abnormalities, with the SVM model performing the better. The result suggest that this approach has the potential to significantly improve the efficiency and accuracy of spinal abnormality diagnosis, leading to better patient outcomes. © 2023 IEEE.

关键词： Support vector machines

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Helmet Detection on Two Wheelers and Penalty Generation using Artificial Intelligence 7

Helmet Detection on Two Wheelers and Penalty Generation usin...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Gomathy, B. Meenu, R. Mohammed Sugail, M. Niranjana, S. Vignesh, K. Dr. Ngp Institute of Technology Department of Computer Science and Business Systems Coimbatore India Computer Science and Engineering Dr. Ngp Institute of Technology Kalapatti road Coimbatore India

ISBN: (纸本)9798350397284

The fundamental concept is to implement a helmet detecting mechanism Because headgear failure accounts for most fatalities, the basic idea is to create a helmet detecting system. According to research, direct observation is labour-intensive and time-consuming. This study seeks to build a detection mechanism using a number of machine learning algorithms and pre-defined libraries. Since it determines whether someone is wearing a helmet or not and notifies about the formation of penalties, this system enforces the communication to management of the found candidates. Motorcycle accidents are the leading cause of fatalities in traffic accidents, particularly in developing countries. The majority of fatal injuries in motorcycle incidents are caused by passengers who don't wear helmets. This paper offers a technique for automatically identifying motorcycle helmets that makes use of artificial intelligence and an auto-intimation system that relies on licence information. © 2023 IEEE.

关键词： Motorcycles

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Wavelet-Based Hybrid Ensemble Forecasting for Accurate Web Traffic Prediction 1

Wavelet-Based Hybrid Ensemble Forecasting for Accurate Web T...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Manoj Kumar, M.V. Puneetha, B.H. Prashanth, B.S. Sneha, H.R. Patil, N.S. Ranjana, B.J. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Davanagere577004 India Karnataka Davanagere577004 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

This paper presents a novel approach for accurate web traffic prediction on the websites using a Wavelet-Based Hybrid Ensemble Forecasting method. The proposed method decomposes the web traffic time series into time-shifted discrete wavelets, enabling individual forecasting on each decomposed series. Four models - SARIMA, ARIMA, LSTM, and KNN - were evaluated, and their forecasts were combined in a hybrid ensemble model. Evaluation metrics such as accuracy percentage, MAE, RMSE, and MAPE were employed. The hybrid model exhibited superior performance, achieving an accuracy of 99.39%, along with the lowest MAE and RMSE values. Combining forecasts from multiple models significantly improved accuracy and reduced error rates in web traffic prediction. © 2024 IEEE.

关键词： Prediction models

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EPipe: Pipeline Inference Framework with High-quality Offline Parallelism Planning for Heterogeneous Edge Devices 24

EPipe: Pipeline Inference Framework with High-quality Offlin...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Xiong, Yi Liu, Weihong Zhang, Rui Zu, Yulong Zhu, Zongwei Zhou, Xuehai School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China School of Software Engineering University of Science and Technology of China China

ISBN: (纸本)9798400710773

Pipeline parallelism is essential for edge computing as it effectively consolidates the limited resources of edge devices, enabling the deployment of large Deep Neural Network (DNN) models and accelerating inference processes without compromising the performance of models. Accurate computation and communication latency estimation on heterogeneous edge devices is essential for searching for a superior parallelism plan. However, existing heterogeneous pipeline inference approaches either incur substantial resource wastage during online parallelism planning, as they utilize profiling strategies that occupy physical devices;or rely on cost models with inadequate representational capabilities, leading to inaccurate predictions, thereby harming the result of pipeline planning. This paper proposes EPipe, a novel pipeline inference framework that supports high-quality offline planning in heterogeneous edge environments. EPipe integrates two core components: the Task-Device Co-analyzer (TDC) and the Multi-pipeline Parallelism Planner (MPP). TDC utilizes an undirected connected graph to depict the compatibility of DNNs across device groups and precisely estimates inference and communication latencies through fine-grained modeling. Based on TDC, MPP utilizes a dynamic programming-based genetic algorithm to explore multi-pipeline solutions, extending beyond traditional single-pipeline methods. A comprehensive experimental evaluation on an edge testbed confirms the effectiveness of EPipe, demonstrating significant speedups in inference tasks for both task streams and single tasks. © 2024 Copyright is held by the owner/author(s).

关键词： Edge detection

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Multimodal Deep Learning for Cardiovascular Risk Stratification: Integrating Retinal Biomarkers and Cardiovascular Signals for Enhanced Heart Attack Prediction

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IEEE Access 2025年 13卷 99672-99689页

作者： K. Sathya G. Magesh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Cardiovascular diseases, particularly myocardial infarction, have remained a significant cause of death around the world. Therefore, dedicated non-invasive risk prediction frameworks are required. Conventional measures for risk prediction among most heterogeneous patients, such as the Framingham Risk Score and ASCVD calculator, are often less precise and poorly generalizable as they fail to capture individual variations in the mechanisms. This study seeks to present a new multimodal deep learning model developed for cardiovascular risk stratification by fusing retinal microvascular features with cardiovascular physiological signals. The pipeline includes a Hierarchical Retinal Vessel Graph Transformer (HRV-GT) for graph-based vascular representation, Spectro-Temporal Cardiovascular Transformer (STC-T) for capturing short- and long-term signal variability, Sparse Manifold Multimodal Fusion (SMM Fusion) for joint alignment of features, Evolutionary Feature Selection with Clinical Prior Constraints (EFS-CP), and a Contrastive Hierarchical Risk Classifier (CHRC-Net) for decision modeling structured. The model was evaluated across the UK Biobank and SEED datasets (n = 55,000), achieving an AUC of 0.97, sensitivity of 91%, and specificity of 89%, with more than 35% better performance than existing models in reducing misclassification. Clinically interpretable biomarkers that combine high computational efficiency (inference time $\approx \; 37$ ms) affirm its design for real-world deployment in preventive cardiovascular screening processes.

关键词： Feature extraction Deep learning Biomarkers Biological system modeling Transformers Predictive models Imaging Heart rate variability Accuracy Retinal vessels

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Crafting Tomorrow's Trends with Automated Precision, Efficiency, and Faultless Excellence in Apparel Industry for Medium Scale Businesses 6

Crafting Tomorrow's Trends with Automated Precision, Efficie...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Hiruni Herath, H.M. Jayaweera, J.M.M.G. Jayasinghe, J.M.Y. Randeni, K.M.T.D. Amarasena, Nelum Chathuranga Rathnayake, Samadhi Chandanathilaka, S.N. Sri Lankan Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lankan Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

In the evolving landscape of Sri Lanka's apparel industry, the predominance of manual methods in the pre-production phase necessitates innovative technological interventions to enhance efficiency. This research explores the complexities of garment production, addressing critical challenges such as machine breakdowns, maintenance, and workforce management. By employing advanced technologies such as image recognition, loT devices, and machine learning, this study aims to transform pre-production decision-making, optimize sewing machine performance, implement predictive maintenance strategies, and refine workforce optimization. These initiatives, driven by domain expertise and cutting-edge methodologies, not only aim to bridge existing gaps in the industry but also pave the way for significant advancements, ensuring a future characterized by precision, efficiency, and operational excellence. © 2024 IEEE.

关键词： Predictive maintenance

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