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Secured Entry Control: An IoT and Deep Learning based Mask Detection System for COVID-19 Suspected Patient Screening and Access Regulation 3

Secured Entry Control: An IoT and Deep Learning based Mask D...

引用

3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Sultana, Abida Peyal, Hasibul Islam Islam, Md. Injamul Abdal, Md. Nazmul Miraz, Shafiun Kontho, Md. Mahin Rahaman Leion, Zannatul Mawa Mamun, Abdul Al Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh Tejgaon College Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

The new coronavirus SARS-CoV-2, which triggered the COVID-19 pandemic, has had an unparalleled effect on economies, cultures, and world health. In response to the critical need for strict COVID-19 screening systems in public areas, this study presents a creative Secured Entry Control system. Detecting and controlling possible COVID-19 carriers attempting to enter the country is made possible by this system, which makes use of deep learning algorithms and IoT technology. The mask detection algorithm, MobileNetV2 model, has an exceptional validation accuracy of 98.96\%. The model's reliability is supported by performance evaluations using ROC curves, confusion matrix analysis, and an AUC value of 98.96\%, which is close to the optimal AUC score of 100\%. Because MobileNetV2 is well-suited for low-processing devices, it is easy to deploy it on Raspberry Pi, which helps to create an affordable system. Furthermore, by spotting increased body temperatures, a contactless temperature sensor improves the system's ability to identify possible COVID-19 carriers. The functioning of the system is confirmed by the working prototype that is presented in the Experimental Results section. The main goal of this research is to create an autonomous system that is affordable and selectively allows access to people who are less likely to transmit COVID-19 in public areas. To achieve the overall objective of reducing the spread of COVID-19 in public spaces, this research highlights the successful integration of mask identification algorithms and IoT. © 2024 IEEE.

关键词： COVID-19

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Implementation of Blockchain and IPFS to safeguard evidentiary data

Implementation of Blockchain and IPFS to safeguard evidentia...

引用

2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Srivastava, Supriya Kaur, GeetKiran Himank Singla, Sanjay Chandigarh University Dept. of Computer Science & Engineering Mohali140413 India Chandigarh University Dept. of Computer Science & Engineering Gharuan India

ISBN: (纸本)9798350359688

Nowadays due to increasing cases of evidence alteration the need of a robust evidence protection system is increasing rapidly. This paper presents an innovative solution that combines the InterPlanetary File System (IPFS) with blockchain technology to enhance the security, immutability, and transparency of evidential data obtained from crime *** proposed system initiates by utilizing IPFS, a decentralized and distributed file storage protocol, to securely store multimedia evidence, such as photographs. The content-addressed nature of IPFS guarantees tamper-resistant storage, making it an ideal choice for preserving the integrity of sensitive evidentiary materials. The IPFS addresses obtained from IPFS are then embedded within the blockchain, establishing a decentralized and verifiable link between the evidence and its storage location. We use multi-signature wallet to upload the data on blockchain so that we can make it more secure and monitored about who is uploading evidence state data. To verify links and integrity of evidence usage we set up a system of periodical updates of state of evidence whenever accessed. These updates serve as a mechanism to record the state of the evidence at different points in time, adding an additional layer of reliability and accountability. © 2024 IEEE.

关键词： Blockchain

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Breaking Barriers: Investigating the Sense of Belonging Among Women and Non-Binary Students in Software Engineering 46

Breaking Barriers: Investigating the Sense of Belonging Amon...

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46th International Conference on Software Engineering: Software Engineering Education and Training, ICSE-SEET 2024

作者： Boman, Lina Andersson, Jonatan de Oliveira Neto, Francisco Gomes University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden Chalmers and the University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798400704987

Women in computing were among the first programmers in the early 20th century and were substantial contributors to the industry. Today, men dominate the software engineering industry. Research and data show that women are far less likely to pursue a career in this industry, and those that do are less likely than men to stay in it. Reasons for women and other underrepresented minorities to leave the industry are a lack of opportunities for growth and advancement, unfair treatment and workplace culture. This research explores how the potential to cultivate or uphold an industry unfavourable to women and non-binary individuals manifests in software engineering education at the university level. For this purpose, the study includes surveys and interviews. We use gender name perception as a survey instrument, and the results show small differences in perceptions of software engineering students based on their gender. Particularly, the survey respondents anchor the values of the male software engineer (Hans) to a variety of technical and non-technical skills, while the same description for a female software engineer (Hanna) is anchored mainly by her managerial skills. With interviews with women and non-binary students, we gain insight on the main barriers to their sense of ambient belonging. The collected data shows that some known barriers from the literature such as tokenism, and stereotype threat, do still exist. However, we find positive factors such as role models and encouragement that strengthen the sense of belonging among these students. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Data Complexity and Stability Induced Predictive Distributed Learning Approach 19th

Data Complexity and Stability Induced Predictive Distributed...

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19th International Middle Eastern Simulation and Modelling Conference, MESM 2024

作者： Igodan, E.C. Ukaoha, K.C. Al-Akaidi, M. Dept. of Computer Science University of Benin Benin City Nigeria Dept. of Computer Science Wigwe University Isiokpo Nigeria College of Science & Computing Wigwe University Isiokpo Nigeria

ISBN: (纸本)9789492859341

The performance of most classifiers degrades because the majority of feature selection algorithms do not scale well, primarily due to the inherent characteristics in high-dimensional microarray datasets, making them eventually inapplicable. This research study proposed a novel predictive distributed FS model by sample partitioning while maintaining class distribution using data complexity and stability measures. For implementation, brain tumor microarray datasets were adopted. Six feature selection algorithms and classifiers, along with their ensemble learning methodology, were implemented. The dataset was preprocessed and the SMOTE technique applied, so as to balance the class distribution and the feature/sample size ratio. The study showed significant improvement in the proposed distributed model with an accuracy of 99.67% and demonstrated a sharp reduction both in the runtime complexity and the number of selected features respectively. © 2025 EUROSIS-ETI.

关键词： Contrastive Learning

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Exploring Climate Change Prediction Dynamics Using Machine Learning Approach: A Review 1

Exploring Climate Change Prediction Dynamics Using Machine L...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tiwari, Shashank Verma, Shikha Chauhan, Rosey Gupta, Neha Sharda Unviersity Dept. of Computer Science Gerater Noida India ABES Engineering College Dept. of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

Climate refers to the long-term weather patterns of a region. Variations in the weather have a lot of implications on our daily lives. Explore the application of deep learning techniques in climate prediction and related fields. It focuses on Long Short-Term Memory (LSTM) models for forecasting air quality, greenhouse temperature control, and urban flood water accumulation using Gradient Boosted Decision Trees (GBDT). The analysis concludes that deep learning, particularly LSTM, offers promising accuracy for various climate-related predictions. Challenges such as data scarcity, non-stationarity, and high dimensionality are addressed, highlighting the need for advanced models to capture complex climate system dynamics and human influences. © 2024 IEEE.

关键词： Prediction models

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Federated TD Learning in Heterogeneous Environments with Average Rewards: A Two-timescale Approach with Polyak-Ruppert Averaging 63

Federated TD Learning in Heterogeneous Environments with Ave...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Naskar, Ankur Thoppe, Gugan Koochakzadeh, Abbasali Gupta, Vijay Indian Institute of Science Dept. of Computer Science and Automation Bengaluru India Purdue University Dept. of Electrical and Computer Engineering IN United States

ISBN: (纸本)9798350316339

Federated Reinforcement Learning (FRL) provides a promising way to speedup training in reinforcement learning using multiple edge devices that can operate in parallel. Recently, it has been shown that even when these edge devices have access to different dynamic models, an optimal convergence rate that has a linear speedup proportional to the number of devices is achievable. However, this result requires that the stepsize in the algorithm be chosen in a manner dependent on the unknown model parameters. Also, it applies only to a discounted setting, which has been argued to fit episodic tasks better than continuing control tasks. In this paper, we obtain finite-time bounds for heterogeneous FRL with average rewards. We show that the optimal convergence rate with a linear speedup is possible even with a universal stepsize choice, independent of the underlying dynamics. To achieve our result, we modify the existing one-timescale FRL method to a novel two-timescale variant that additionally incorporates iterate averaging. © 2024 IEEE.

关键词： Federated learning

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Leveraging ML to Detect Fraudulent Customs Declaration: A Case of Logistic Regression and LightGBM Algorithms 4

Leveraging ML to Detect Fraudulent Customs Declaration: A Ca...

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4th International Conference on Next Generation Computing Applications, NextComp 2024

作者： Chilomo, Innocent Kankuzi, Bennett Malawi University of Science and Technology Dept. of Computer Science and IT Thyolo Malawi

ISBN: (纸本)9798331517403

Harmonised System (HS) code fraud, also known as Customs tariff classification fraud, is common in developed and developing countries. It occurs when traders intentionally mis-classify goods during the time of clearance. Classification fraud results in tax revenue loss, restricted and prohibited goods entry, and unfair market competition. Current manual verification techniques to detect classification fraud are time-consuming and error-prone;thus, an effective and precise strategy is required. In this study, we developed and evaluated the performance of Logistic Regression and LightGBM machine learning models in detecting HS Code fraud using a dataset obtained from a government Revenue Authority in a developing country. Results show that the LightGBM model outperformed Logistic Regression. The LightGBM model demonstrated superior accuracy (0.961), precision (0.890), recall (0.901), and F1 score (0.896) as compared to the Logistic Regression model, which had accuracy (0.932), precision (0.772), recall (0.900), and F1 score (0.831). Our contribution is threefold: Firstly, the dataset used is from a developing country, unlike other studies that use datasets from developed countries. Secondly, we worked with Customs import data collected at an 8-digit HS code level, whereas most similar studies typically used HS codes at 2, 4, or 6-digit levels. Lastly, unlike comparable studies, we have compared the performance of Logistic Regression and LightGBM machine learning algorithms. © 2024 IEEE.

关键词： Logistic regression

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Towards the Compilation of Digital Twin Models for Service-Oriented Systems 34

Towards the Compilation of Digital Twin Models for Service-O...

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34th Annual International Conference on Collaborative Advances in Software and Computing, CASCON 2024

作者： Jiang, Hao Kontogiannis, Kostas Western University Computer Science Dept. LondonON Canada York University Dept. of Electrical Eng. & Computer Science TorontoON Canada

ISBN: (纸本)9798331504830

Service-oriented architecture (SOA) serves as a foundational technology for complex IT systems, such as e-commerce platforms, banking solutions, public-sector applications, and gaming systems. As these systems become increasingly intricate, the need for models that enable software engineers to simulate component interactions early in the software lifecycle becomes crucial. This approach helps identify potential issues during the design phase. The goal of this research is to apply digital twin model principles to simulate component transactions, message exchanges, and ultimately evaluate whether a given design meets the goals and requirements of stakeholders. In this paper, we outline ongoing work towards a prototype framework that uses sequence diagrams and activity diagrams to build digital twin models of service-oriented systems. The framework aims to ensure that any design changes are reflected in the digital twin model early in the software lifecycle, potentially uncovering non-compliant or unintended interactions. © 2024 IEEE.

关键词： Service oriented architecture (SOA)

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Using Game Theory to Prioritize Goals in Requirements Engineering 4

Using Game Theory to Prioritize Goals in Requirements Engine...

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4th International Conference on Next Generation Computing Applications, NextComp 2024

作者： Munthali, Isaac Kankuzi, Bennett Malawi University of Science and Technology Dept. of Computer Science & IT Thyolo Malawi

ISBN: (纸本)9798331517403

Prioritizing competing goals among different stakeholders in requirements engineering is usually a daunting task for system architects as each of the stakeholders have their own interests. This study modeled this conflicting environment among stakeholders as a non-cooperative game in game theory with goals modeled as strategies and utility value of each goal as pay-offs in a pay-off matrix. In a user study that employed the NashPy algorithm to deduce the Nash equilibrium of the strategies in a two player game, the tool was able to automatically deduce goals which needed to be prioritized to achieve a win-win situation for two stakeholders. This was also confirmed by stakeholders. The paper has three contributions: modelling goals for stakeholders in requirements engineering as a non-cooperative game in game theory;automatically deducing goals that need prioritization among stakeholders in requirements engineering based on the Nash equilibrium;and performing a user study to validate the deduced prioritized goals. © 2024 IEEE.

关键词： Requirements engineering

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Privacy-Enhanced Image Restoration in Remote Sensing via Federated Learning 12

Privacy-Enhanced Image Restoration in Remote Sensing via Fed...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Jahanzeb Khan, M. Rath, Suman Zaib, Muhammad Hassan University of Nevada Reno Dept. of Computer Science and Engineering Reno United States Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350330366

This research addresses the dual challenges of image restoration quality and data privacy in optical remote sensing. Traditional restoration methods often fall short due to the complex nature of remote sensing images, and data privacy concerns further complicate the use of advanced techniques. By integrating the Deep Memory Connected Neural Network (DMCN) with the Data-Decoupled Federated Learning (DDFL) framework, our approach enables significant improvements in image restoration without requiring direct access to sensitive raw data. This method not only enhances data privacy by leveraging federated learning principles but also incorporates advanced techniques like Gaussian image denoising to maintain high restoration quality despite potential noise introduced by the federated process. The performance of the federated DMCN, particularly on the UCMERCED dataset, demonstrates minimal accuracy degradation, even in the presence of noise, while the strategic use of Downsampling Units within DMCN optimizes computational efficiency. Our comprehensive evaluations reveal the effectiveness of this approach in balancing data privacy with the need for high-quality image restoration, suggesting a promising direction for future advancements in remote sensing applications. © 2024 IEEE.

关键词： Image reconstruction

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