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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Naz, Ammerha Ali, Muhammad Cheema, Sehrish Munawar Pires, Ivan Miguel Dept. of Computer Science University of Sialkot Punjab Sialkot Pakistan Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries. © 2024 The Authors. Published by Elsevier B.V.

关键词： Electronic health record

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Synthetic Data: Revisiting the Privacy-Utility Trade-off

arXiv

引用

arXiv 2024年

作者： Sarmin, Fatima Jahan Sarkar, Atiquer Rahman Wang, Yang Mohammed, Noman Dept. of Computer Science University of Manitoba Canada Dept. of Computer Science and Software Engineering Concordia University Canada

Synthetic data has been considered a better privacy-preserving alternative to traditionally sanitized data across various applications. However, a recent article challenges this notion, stating that synthetic data does not provide a better trade-off between privacy and utility than traditional anonymization techniques, and that it leads to unpredictable utility loss and highly unpredictable privacy gain. The article also claims to have identified a breach in the differential privacy guarantees provided by PATE-GAN and PrivBayes. When a study claims to refute or invalidate prior findings, it is crucial to verify and validate the study. In our work, we analyzed the implementation of the privacy game described in the article and found that it operated in a highly specialized and constrained environment, which limits the applicability of its findings to general cases. Our exploration also revealed that the game did not satisfy a crucial precondition concerning data distributions, which contributed to the perceived violation of the differential privacy guarantees offered by PATE-GAN and PrivBayes. We also conducted a privacy-utility tradeoff analysis in a more general and unconstrained environment. Our experimentation demonstrated that synthetic data indeed achieves a more favorable privacy-utility trade-off compared to the provided implementation of k-anonymization, thereby reaffirming earlier conclusions. © 2024, CC BY.

关键词： Anonymity

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Decision Support System: Analysis and Design Methodology 2

Decision Support System: Analysis and Design Methodology

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2nd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Darbi, Huweida A. Saleh, Eiman M. University of Derna Dept. Computer Science Derna Libya Benghazi University Dept. Software Engineering Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

The success of any organization depends on the quality of the decisions made, and good decisions can be made by analyzing all the available data and knowledge of the organization. Decision Support Systems (DSSs) are used to analyze this data and aid in decision-making in the formulation of well-studied decisions based on knowledge. However, the analysis and design of decision support systems (DSS) is a complex process that involves the use of appropriate methodologies, methods, and tools to model decision-making processes. This paper presents an architectural design of DSS framework. The proposed approach helps developers develop decision-making systems through models that detail the analysis and design. © 2022 IEEE.

关键词： Decision support systems

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Handcrafted and Deep features based Classification of Scoliosis 2

Handcrafted and Deep features based Classification of Scolio...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Fatima, Joddat Mohsan, Mashood M. Qaisar, Muhammad Umair Hamza, Muhammad Tahir, Muhammad Zeshan Zaman, Ghazzal Bahria University Dept. of Computer Science Islamabad Pakistan National University of Sciences & Technology Dept. of Computer & Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9781665498197

The Spinal cord acts as the central transmission line connecting the Brain with all other body organs. Vertebrae are 33 uneven bones stacked over one another that holds the whole skeleton structure. Scoliosis is the three-dimensional spinal deformity which commonly occurs during the growing age and erupts before puberty. It is further classified in two Shapes C and S. Our research work has two stages, in first stage we segment out the vertebral column using Mask-RCNN. The segmented column is used for features extraction and in stage two feature based classification is done for normal, C and S shape of scoliosis using AASCE2019 dataset. A comparative study on multiple image classification networks is also conducted and based on results EfficientNet-B4 is selected for formulation of hybrid feature set. The accuracy achieved using Random forest classifier, for handcrafted and deep features was up to 94.32% and 89.66%. Hybrid feature set formulated with combination of deep and handcrafted features attained accuracy up to 94.45%. © 2022 IEEE.

关键词： Decision trees

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Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning 3

Robust Phishing URL Classification Using FastText Character ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Al-Sakib, Abdullah Limon, Zishad Hossain Sakib, Anamul Pranto, Md Nuruzzaman Islam, Md Ariful Sultana, Shayma Ahmed, Md. Redwan Rahman, Shafiur Westcliff University Dept. of Information Technology CA92614 United States Westcliff University Dept. of Computer Science CA92614 United States International American University Dept. of Business Administration CA90010 United States International American University Dept. of Management Information System CA90010 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizations. Being able to identify fraudulent websites is crucial in order to effectively address these potential risks. This study introduces a novel method for detecting phishing URLs by using word and character embeddings to capture complex URL patterns. We used a dataset of 80,000 URLs, including 50,000 legitimate ones and 30,000 phishing instances, and applied thorough preprocessing techniques. We utilized word embeddings in FastText to handle unseen words, with the added advantage of n-gram representations. Additionally, we captured character-level features through dense character embeddings. We trained several machine learning and deep learning models, and one model, the Convolutional Bidirectional LSTM (CBiLSTM), stood out with an accuracy of 99.01% and an F1-score of 99.08%. Furthermore, we made a thorough comparison with the most advanced techniques available, and our findings demonstrated clear superiority over previous research. This study presents an effective approach for classifying phishing URLs, providing a valuable tool to combat fraud and protect against identity theft, thereby helping to minimize the financial and emotional harm experienced by victims. © 2024 IEEE.

关键词： Phishing

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Utilizing Machine Learning to Enhance Infrastructure Resilience in Cold Regions 20

Utilizing Machine Learning to Enhance Infrastructure Resilie...

引用

20th International Conference on Cold Regions engineering: Sustainable and Resilient engineering Solutions for Changing Cold Regions, ICCRE 2024

作者： Shohel Rana, Md Gudla, Charan Ahmed, Feroz Nobi, Mohammad Nur Dept. of Computing and Software Engineering Florida Gulf Coast Univ. Fort MyersFL United States Dept. of Computer Science and Engineering Mississippi State Univ. StarkvilleMS United States Prediction 3D Technologies BiloxiMS United States Dept. of Computer Science Univ. of Texas at San Antonio San AntonioTX United States

ISBN: (纸本)9780784485460

In the challenging domain of engineering, where cold regions present formidable challenges, we confront the relentless forces of nature. From sub-zero temperatures to the unpredictable dance of snowfall and the silent buildup of ice, these regions demand innovative solutions to fortify the resilience of critical infrastructure. This initiative harnesses the potential of cutting-edge technology and leverages the extensive historical weather data tapestry. It introduces a pioneering strategy by integrating machine learning algorithms with extensive weather data, steering cold region engineering into an era defined by foresight and adaptability. This paper studies a transformative approach designed to forecast, prevent, and ultimately enhance infrastructure resilience in the face of rigid cold. Addressing the distinct challenges of cold region engineering, arising from harsh winter conditions such as extreme cold temperature, snowfall, and ice accumulation, we offer a comprehensive study using machine learning algorithms applied to historical weather data to construct a deeper analysis model capable of highlighting adverse weather effects. This, in turn, covers the way for optimized resource allocation, streamlined maintenance planning, and design enhancements. Our proposed study follows a systematic process, encompassing meticulous data collection, appropriate feature selection, and aiming seamless integration of the model into existing infrastructure management systems. Additionally, it facilitates the implementation of efficient and proactive measures to mitigate the impact of severe weather conditions on infrastructure. The paper also conducts three different hypotheses testing: temperature impact hypothesis, precipitation influence hypothesis, and ice accumulation and infrastructure resilience hypothesis, propelling engineering practices to new heights, particularly in the face of challenging cold environments. © ASCE.

关键词： Snow

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Robust Psoriasis Severity Classification by using Data Augmentation

引用

Transactions of the Korean Institute of Electrical Engineers 2022年第12期71卷 1841-1847页

作者： Moon, Cho-I Baek, Yoo Sang Choi, Min Hyung Lee, Onseok Dept. of Software Convergence Graduate School Soonchunhyang University Korea Republic of Dept. of Dermatology Guro Hospital Korea University College of Medicine Korea Republic of Dept. of Computer Science and Engineering College of Engineering Design and Computing University of Colorado Denver United States

Psoriasis is a chronic recurrent disease formed by lesions such as erythema and scale. To evaluate the severity of psoriasis, the psoriasis area and severity index (PASI) score have been used in clinical trials and studies. This clinical indicator is subjective, so to overcome these shortcoming, various automatic psoriasis analysis methods based on deep learning have been studied. However, the limited number of data and psoriasis characteristic such as ambiguity of severity deteriorate model performance. One of the simple and powerful methods to overcome these problem is data augmentation. Data augmentation should be used according to data characteristics. Therefore, we analyzed and compared the classification results applied with five data augmentation methods, Geometric transformation, CutMix, Visual Corruptions, AutoAugment, RandAugment, and explored data augmentation method suitable for psoriasis severity classification. We used the EfficientNet B2 for psoriasis severity classification. As a result, when RandAugment or the combination of Geometric transform and Visual Corruptions were used, it showed the best classification performance with an accuracy of 87.5%. In addition, we confirmed the effect of data augmentation for improving model performance and the difference in performance according to single or multiple applications of the data augmentation methods. Through these results, our study can be applied to various studies as a data augmentation method suitable for psoriasis disease image. Copyright © The Korean Institute of Electrical Engineers.

关键词： Deep learning

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Designing and Simulating a Smart Water Harvesting System Using PLC Techniques

Designing and Simulating a Smart Water Harvesting System Usi...

引用

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023

作者： Thabet, Hassaan Th. H. Al-Hilali, Aqeel A. Qasim, Haitham B. Thabit, Thabit H. Mahmood, Firas M. Al-Hadba University College Techniques Engineering Dept. of Medical Instruments Mosul Iraq Al-Farahidi University Dept. of Medical Instruments Engineering Techniques Baghdad Iraq Al-Esraa University College Dept. of Computer Science Baghdad Iraq Ninevah University Dept. of Computer Engineering and Information Mosul Iraq Cihan University-Erbil Dept. of Informatics and Software Engineering Iraq

ISBN: (纸本)9798350307771

The presence of water in human life is crucial, and God has blessed humans much with its availability. This paper tries to reduce the amount of water that people use for washing in all aspects of daily life and instead utilize it to irrigate gardens and maintain hygienic facilities. The importance of water as a fundamental ingredient and the range of social and age groups that regularly take advantage of the washing process made it an ideal location for water waste during the washing process. This is why the authors choose this region to conduct their research. The monitoring and management of water usage during the washing process are simulated using PLC techniques. © 2023 IEEE.

关键词： Washing

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Visualizing the Path of a Photo Taker from Image Metadata 4

Visualizing the Path of a Photo Taker from Image Metadata

引用

4th International Informatics and software engineering Conference, IISEC 2023

作者： Majid, Rasty Shakhawan Varol, Cihan Varol, Asaf Nasab, Ahad Firat University Department of Software Engineering Erbil Iraq Sam Houston State University Computer Science Department HuntsvilleTX United States College of Engineering and Computer Science Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (纸本)9798350318036

One of the most challenging investigations in forensic analysis is the work conducted on smartphones. Specifically using smartphone data to provide evidence for a case is becoming a norm nowadays. Many Android device applications use and work with location services that assist investigators in working with log files that store valuable information. Images captured on Android devices will save geolocation information, date, and time in the properties. In this work, a tool is developed to acquire the date and geolocation for each taken photo so that the path of the photo taker can be displayed on Google Maps. The work showed that the application can take about 4 and a half seconds to process four images and display the map to the investigator. © 2023 IEEE.

关键词： Metadata

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Report on the 28th Foundation of software engineering Workshop (FOSE2021)

Report on the 28th Foundation of Software Engineering Worksh...

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作者： Nagura, Masataka Sawada, Atsushi Sekizawa, Toshifusa Dept. of Software Engineering Fuculty of Sciense and Technology Nanzan University Japan Dept. of Computer Science College of Engineering Nihon University Japan

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