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32nd ACM International Conference on Multimedia, MM 2024

作者： Steinert, Patrick Wagenpfeil, Stefan Frommholz, Ingo Hemmje, Matthias L. Faculty of Mathematics and Computer Science University of Hagen Hagen Germany Faculty of Business Computing and Software Engineering Pfh University of Applied Sciences Göttingen Germany School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom

ISBN: (纸本)9798400706868

The metaverse is a developing field that crosses several areas of multimedia research. In this paper, we introduce the 256-MetaverseRecords dataset, a novel and extensive collection of annotated screen recordings in the form of videos from various virtual worlds of the metaverse. We describe the process of creating the dataset, the quality criteria for the annotations, and the exploration of the dataset. We also show four experiments to evaluate the performance of different feature extraction methods for Metaverse Recordings (MVRs): MVR segmentation, audio event detection, and object and interaction detection based on this dataset. Our results demonstrate that existing methods have limitations and leave challenges in dealing with the diversity and complexity of metaverse data, and that more research is needed to develop metaverse-specific techniques. Our dataset can serve as a valuable resource for the research community and foster the development of new applications and solutions for the metaverse. © 2024 Owner/Author.

关键词： dataset

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Concrete Strength Determination via S-Band Operating Microstrip Patch Sensing Antenna 21

Concrete Strength Determination via S-Band Operating Microst...

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21st IEEE Student Conference on Research and Development, SCOReD 2023

作者： Emin, Benjamin Then, Yi Lung Kho, Anderson Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing and Science Sarawak Kuching93350 Malaysia Universiti Malaysia Sarawak Faculty of Engineering Sarawak Kota Samarahan94300 Malaysia

ISBN: (纸本)9798350318821

Compressive strength of concrete is very important to provide good quality of concrete, ensuring public safety and increase the lifespan of concrete. One of the most important parameters influencing the concrete strength is moisture content. This paper proposed a portable microwave non-destructive method by designing a microwave patch antenna using Computer Simulation Technology (CST) to determine the moisture content of concrete. Two experimental studies are conducted where concrete cubes with various moisture content and concrete cubes cured for 28 days are prepared as testing materials. The microstrip patch antenna is connected to vector network analyzer via coaxial cable to obtain the reflection coefficient. The reflection coefficient is correlated with moisture content, which then related to the compressive strength of concrete cubes. Two equations are obtained from the relationship between reflection coefficient with moisture content and compressive strength. The equations show great potential of the microwave method in determination of moisture content and compressive strength of concrete cubes. The accuracy of predicting the moisture content and compressive strength is 93.37% and 97.43% respectively. © 2023 IEEE.

关键词： Reflection

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Building a Data Lake for Power BI in the Cloud: A Review on Utilizing Cloud Storage Services for Large Datasets 2

Building a Data Lake for Power BI in the Cloud: A Review on ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gurbade, Viraj Vijay Verma, Prateek Gundewar, Swapnil Bramhe, Vijendra Singh Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Vit Bhopal University School of Computing Science and Engineering Kothrikalan Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331528713

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business Intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure Data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets. © 2024 IEEE.

关键词： Decision making

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Sociodemographic Factors Associated with Cyberbullying Experience and Practice Among Youths in Malaysia

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Journal of Technology in Behavioral science 2024年 1-11页

作者： Lim, Willone Lau, Bee Theng Fu, Swee Tee Tee, Mark Kit Tsun Islam, Fakir M. Amirul Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Kuching 93350 Malaysia School of Health Sciences Swinburne University of Technology Hawthorn 3122 VIC Australia

Cyberbullying is a complex issue among youth, yet, while adolescent victimization is well-studied, little is known about what causes youth to perpetrate cyberbullying in Malaysia. This quantitative study examines the association of sociodemographic factors and the use of social media with cyberbullying experiences and practices among youth in Malaysia. A baseline survey, conducted from July to December 2023 as part of an intervention program, gathered data from 309 participants aged 18 to 30 via an online survey. Statistical analyses, including logistic regression, were conducted to determine the relationships between sociodemographic factors, social media use, and cyberbullying behaviors. Of the total participants, the mean age was 21 years, 201 (65%) were female, 241 (78%) were undergraduate students. Among them, 302 (97.7%) were aware of cyberbullying, 252 (81.5%) witnessed it, 179 (58%) experienced it, and 51 (17%) participated in it. Females had higher odds of experiencing cyberbullying than males (odds ratio (OR), 2.67;95% confidence interval (95% CI), 1.64–4.33), while males were more likely to engage in cyberbullying. Students and unemployed individuals had higher risks of experiencing cyberbullying. Heavy social media users (8 + hours/day) were at increased risk (OR, 16.2;95% CI, 5.31–49.4), with Facebook use linked to higher cyberbullying exposure. There is a need for targeted interventions and policies to safeguard females and students to support their well-being. Limiting social media usage may serve as a preventive measure. Further longitudinal research to deepen understanding and collaboration among researchers, educators, and policymakers is crucial to creating safer digital environments for youth. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Cyberbullying Experience Practice Risk factors Youth

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Implementation of ResNet-50 with the Skip Connection Principle in Transfer Learning Models for Lung Disease Prediction

Implementation of ResNet-50 with the Skip Connection Princip...

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International Conference on Intelligent Systems and Sustainable computing, ICISSC 2022

作者： Prabakaran, J. Selvaraj, P. Department of Networking and Communications College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9789819947164

The adoption of advanced technologies is crucially essential for medical progress. To identify or predict any diseases in humans, we must first have a proper diagnosis to predict the disease in that human body. Cancer is an illness that stands uncontrolled in cells. The illness begins in the lungs, lung node is also known as lung disease. Cancer of the pulmonary system may cause an infection or other organs, such as the brain and lungs. In addition, cancer from other organs can affect the lungs. An accurate diagnosis is essential to identify and anticipate the disease within a person's body. Machine learning, deep learning, and transfer learning models are frequently used to forecast and characterize lung disease utilizing data from chest X-ray, positron emission tomography scans, and magnetic resonance imaging. Residual neural network-50, MobileNet-V2, Visual Geometry Group-19, and DenseNet201 Cable News Network-based transfer learning algorithms reveal which image classification algorithm is better suited to forecast and categorize the classification problem of lung disease. ResNet-50 transfer learning enabled efficient lung disease categorization and prediction. The main point of this work is to utilize the ResNet-50 algorithm with the skip connection principle for lung disease identification and classification. This algorithm has been trained and evaluated on benchmark X-ray datasets. This methodology obtained 95.49 F1 measure, 97.45 accuracy, 93.41 precision, and 97.31 recall. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Optimizing PV Array Performance: Comparative Study of Shading Mitigation Techniques and Row-Constrained Swapping 4

Optimizing PV Array Performance: Comparative Study of Shadin...

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4th IEEE Industrial Electronics and Applications Conference, IEACon 2023

作者： Ting, Lawrence Sii Ying Then, Yi Lung Basri, Hazrul Mohamed Azhar, Abadi Chanik Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing & Science Sarawak Kuching93350 Malaysia Universiti Malaysia Sarawak Faculty of Engineering Sarawak Kota Samarahan94300 Malaysia

ISBN: (纸本)9798350347517

Partial shading presents a recurring problem for PV modules, leading to uneven exposure and reduced power generation. Various setups have been proposed to mitigate shading effects by creating alternative paths for current flow. This comprehensive review examines different photovoltaic (PV) configurations like total-cross-tied (TCT), Sudoku puzzle pattern, and Futoshiki puzzle pattern, assessing their advantages and limitations. To overcome existing limitations, a new configuration pattern called Row-Constrained Swapping (RCS) is proposed. The performance of the RCS pattern is evaluated in five partial shading scenarios through MATLAB Simulink simulations, and the resulting power output characteristics are analyzed. The findings demonstrate the superiority of the proposed RCS configuration, surpassing other PV setups and establishing it as an optimal choice for large-scale PV applications. By optimizing PV array performance, this study contributes to maximizing power generation and addressing the negative impacts of partial shading. The RCS configuration decreases the number of shaded panels required for relocation by 30%, resulting in a corresponding increase in power generation. © 2023 IEEE.

关键词： MATLAB

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Intense Triad Defender for End-User Security in Cyber Physical System 2

Intense Triad Defender for End-User Security in Cyber Physic...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Sheryl Sharon, G. Maranco, M. Nidhya, R. Sivakumar, M. Coimbatore Institute of Technology Department of Computer Science and Engineering Coimbatore641014 India SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

The rapid advancement of technology has undoubtedly brought comfort to humanity, but it also necessitates robust authentication measures to ensure security in the ever-expanding e-world. This research aims to address the critical aspects of security, focusing on end-user authentication. The proposed solution incorporates multiple factors, including location-based authentication, text-based authentication with enhanced encryption standards, and facial recognition authentication. Location-based authentication serves to restrict access geographically, mitigating the risk of impersonation by enforcing strict geographic limits on facility access. Strong text passwords, utilizing a unique algorithm with a classic salt method, add an extra layer of complexity for potential attackers, making it challenging to compromise user credentials. The incorporation of facial recognition technology acts as a deterrent against non-human entities attempting unauthorized access. Leveraging deep learning and API servers ensures precision and scalability in the model, enhancing its effectiveness in real-world scenarios. These multi-factor authentication techniques collectively contribute to a secure, reliable, and forward-looking solution to address the evolving landscape of digital security threats. In adhering to the age-old adage "prevention is better than cure,"the research emphasizes the importance of proactive measures to safeguard against potential security breaches. © 2024 IEEE.

关键词： Network security

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A Review of Sustainable Farming with Robotics in Indoor Greenhouse Environments

A Review of Sustainable Farming with Robotics in Indoor Gree...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Josiah Yang Qi Lai Chean Hung Hudyjaya Siswoyo Jo Justin Sia Wei Kiat Evon Lim Wan Ting Michelle Dunn Faculty of Engineering Computing and Science Swinburne University of Technology Kuching Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Melbourne Australia

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

Current trend projected the global population to increase to 9 billion by the year 2050 which will increase the demand of food production by around 70%. The agricultural sector also supplies produce to bio-based products such as bioenergy and bioplastics which is also increasing the demand for crops. However, the situation is not as simple as planting more crops, as current agricultural practices are not sustainable. Environmental impact and climate changes could be reduced with the utilization of indoor farming could bring about a more sustainable farming solution. Combined with the utilization of precision farming solutions such as IoT based sensors and robotic systems indoor farming could become the next step in sustainable farming. The implementation of precision farming in indoor farming comes with its problems which may cause a challenge in the development and deployment period. This paper serves as the preliminary research for the development of a robotic system to be deployed at Sarawak’s Digital Economy Corporation (SDEC)’s greenhouse.

关键词： Climate change Precision agriculture Greenhouses Agricultural robots Sustainable development Indoor environment Farming Globalization Forecasting

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Computer Aided Diagnosis Multi-model System using Late Fuison and Ensemble Learning

Computer Aided Diagnosis Multi-model System using Late Fuiso...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Priya, M.Kani Aruru, Gunabhiram Vanam, Nikhil Suganiya, M. Salomi, M. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India

ISBN: (纸本)9798331508685

Lung Cancer and brain tumors are the well-known causes of cancer deaths universal. Therefore, appropriate and accurate diagnosis is an important issue that affects better and more reliable treatment and the patient's chance of survival. Present diagnostic strategies are mostly based on a single piece of information. This often leads to missing necessary information, which can lead to Misidentification of condition or delay in timely treatment. This study presents a novel Computer-Aided-Diagnosis system designed to enhance the detection of lung cancer and brain tumors by integrating multimodal data, including medical images (CT and MRI scans) and clinical information. The proposed approach employs late fusion and ensemble learning techniques, utilizing CNN for processing CT images, VGG19 for analyzing MRI scans, and Random Forest classifiers for evaluating clinical data. By separately training these models, the system capitalizes on the distinct characteristics of each data modality, overcoming the limitations of traditional CAD systems that typically rely on a single data source. This comprehensive analysis not only improves diagnostic accuracy but also provides reliable predictions, ultimately facilitating better clinical decision-making for lung cancer and brain tumor detection. © 2025 IEEE.

关键词： Lung cancer

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