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A Critical Review of Cloud computing Adoption, data Security Concerns, and Impact in the Healthcare Landscape

A Critical Review of Cloud Computing Adoption, Data Security...

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2024 ASU international conference in Emerging Technologies for Sustainability and intelligent systems, ICETSIS 2024

作者： Yin, Boaz Lau Buong Rana, Muhammad Ehsan School of Computing Asia Pacific University of Technology & Innovation Technology Park Malaysia Bukit Jalil Kuala Lumpur Malaysia

ISBN: (纸本)9798350372229

In recent years, cloud computing has become the buzzword in a lot of industries including healthcare. Cloud computing can be simplified as a computation infrastructure that can be accessed on-demand with high availability and uptime through networks. Especially after the outbreak of Covid-19, the growth in adopting cloud computing has shown an exponential rise. This shows how cloud computing can be very useful to mitigate certain problems during a pandemic outbreak such as providing telehealth support, automation in healthcare management systems, offering online consultations, etc., without the need to show up in person to receive physical treatment except purchasing medicine. Storage and data security as well as privacy is the most detrimental factor that delays the acceptance of cloud solutions for healthcare. This also addresses the issues that come with data availability in cloud storage. With the advent of big data, migration to clouds has become more challenging. Therefore, this paper reviews the adaptation of cloud computing in healthcare as well as the data security concerns related to cloud storage. Additionally, cloud computing characteristics, deployment options and cloud service providers are discussed and compared. Concretely, this paper aims to discuss and explore existing research and works on cloud solutions for healthcare security, strategies, and the impact of cloud computing in the context of healthcare which provides a foundation for medical practitioners, organizations, and specialists for future studies. © 2024 ieee.

关键词： cloud computing healthcare cloud storage security

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intelligent Auditing System Based on Deep Belief Network Density based Spatial Clustering of Applications 1

Intelligent Auditing System Based on Deep Belief Network Den...

引用

1st international conference on Software, systems and Information Technology, SSITCON 2024

作者： He, Ming Meng, Gege Xi'an Eurasia University School of Accounting Xi'an China

ISBN: (纸本)9798350352931

In recent years, as the traditional manual audit systems disposed to errors and frequently focused on compliance, rather than risk valuation the need of advanced audit systems had increased. Additionally, increased complex financial transactions, advanced technologies and regulators requirements in sectors and industry firms have necessitated the developing intelligent audit systems. The existing methods for developing intelligent audit systems failed in providing valuable indicators for audit process. Therefore, this paper proposes Deep Belief Network (DBN) - Density Based Spatial Clustering of Applications with Noise (DBSCAN) for designing an intelligent audit system by detecting fraudulent firms. Initially, audit data dataset is taken which incorporated audit data information of various firms. The collected audit data is preprocessed by utilizing data cleaning and feature engineering, for obtaining efficient financial audit data. Then this financial audit data features are extracted by employed DBN method for revealing hidden patterns. The extracted relevant features are evaluated for detecting the fraudulent firms by utilizing DBSCAN data mining method. The proposed DBN-DBSCAN achieved better results which comprises accuracy (96.58%), Specificity (97.85%), Sensitivity (95.69%), and F1-score (9 5. 2 0 %)when compared with existing Long Short-Term Memory (LSTM). © 2024 ieee.

关键词： Decentralized finance

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Optimizing Motion Capture data in Animation Sequences Using Machine Learning Techniques 6

Optimizing Motion Capture Data in Animation Sequences Using ...

引用

6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Zhang, Aihua Li, Yufei School of Art & Design Hubei University of Technology Wuhan430070 China

ISBN: (纸本)9798350374315

During the early 20th Century, producing a real-world animated sequence motion was a process done by hand. Various techniques have emerged to compute and complete multiple actions in animation. Traditional MoCap systems, reliant on extensive hardware and physical markers, face significant limitations in accuracy and efficiency. We employ a two-tier machine learning framework comprising Deep Convolutional Neural Networks (DCNN) and Generative Adversarial Networks (GAN). The DCNN model's role is to process raw video footage to extract motion data initially. While the first part of this process provides sufficient visual attention, the GAN moves it to the next level, enhancing realism and creating a more accurate feel of animation sequence. The experiment gets proved by the fact that there is a huge improvement in the joint position accuracy, flowing natural motion of animated sequences, as well the overall realism of animated sequences. All these are evidenced by Mean Squared Error (MSE) and Structural Similarity Index (SSI) metrics. These advancements indicate that ML can significantly enhance traditional MoCap systems. Also, it offers a more accessible and less equipment-intensive approach that broadens the potential applications in animation, virtual reality, and biomechanical analysis. © 2024 ieee.

关键词： Adversarial machine learning

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Curriculum for a New Five-Year Academic Program in intelligent systems Engineering and Software Engineering

Curriculum for a New Five-Year Academic Program in Intellige...

引用

international conference on Computational Science and Computational Intelligence (CSCI)

作者： Solo, Ashu M. G. Maverick Trailblazers Inc Wilmington DE 19805 USA

ISBN: (纸本)9798350361513;9798350372304

This research paper proposes a curriculum for a five-year academic program with a bachelor's degree (honors) in intelligent systems engineering and software engineering and a master's degree in intelligent systems engineering and software engineering. This program includes courses in data structures, compiler design, operating system design, firmware design, database systems, computer graphics and virtual reality design, static and dynamic website design, development of chatbots and voice assistants, software engineering methodology, knowledge-based systems, fuzzy logic, neural networks, evolutionary computation, evolutionary multiojective optimization, machine learning, image processing, computer vision, pattern recognition, voice recognition, natural language processing, data science, control systems, intelligent control systems, robotics, digital signal processing, mathematics, engineering physics, biology, etc. These degrees will allow graduates to have a good understanding of all of the main branches of intelligent systems engineering and software engineering as well as other relevant subjects in electrical and computer engineering.

关键词： education intelligent systems computational intelligence soft computing artificial intelligence machine learning software development firmware development

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Artificial Intelligence Based System and Method for data Augmentation in Biomedical Computer Vision Tasks

Artificial Intelligence Based System and Method for Data Aug...

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2024 ieee international conference on intelligent systems and advanced Applications, ICISAA 2024

作者： Selvaraj, Jothiraj Umapathy, Snekhalatha SRM Institute of Science and Technology Kattankulathur College of Engineering and Technology Department of Biomedical Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331539948

Deep learning (DL) has gained significant traction in computer vision tasks, particularly within the healthcare domain. Medical image analysis using DL models for disease segmentation and classification often requires vast amounts of annotated data. Nevertheless, obtaining annotated data can be a laborious and costly process. data augmentation techniques address limited annotated data, overfitting, and class imbalance issues during DL model training. While existing libraries facilitate code-based data augmentation, we present a novel graphical user interface (GUI) designed to streamline this process for researchers. This user-friendly GUI allows intuitive selection of data augmentation techniques tailored to specific datasets and applications. The GUI integrates the Albumentation library and leverages Google Colab for cloud-based processing. Our implementation demonstrates successful data augmentation on medical images, generating 92 augmented versions from a single input image, significantly reducing researcher effort in data preparation. © 2024 ieee.

关键词： Graphical user interfaces

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Real-Time Monitoring and data Analysis of intelligent Monitoring and Diagnosis System in Coastal Soft Soil Foundation Treatment 2

Real-Time Monitoring and Data Analysis of Intelligent Monito...

引用

2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Li, Mingyu Qiao, Xiaoli Wang, Luyao Zhang, Yong Tianjin Research Institute for Water Transport Engineering Experiment & Inspection Center for Water Transport Engineering M.O.T.Binhai New Area Tianjin China

ISBN: (纸本)9798350382952

In the past few decades, intelligent monitoring and diagnosis systems have been widely applied in the field of land foundation. Although these systems have been widely used in the field of land foundation treatment, their application in coastal soft soil foundation treatment is relatively limited, especially in special situations such as seawater erosion in coastal environments, which makes the research on monitoring and diagnosis systems insufficient. This article proposes a solution-based on intelligent monitoring and diagnosis system for real-time monitoring and data analysis in coastal soft soil foundation treatment. This article also analyzes the characteristics of coastal soft soil foundation and proposes real-time monitoring requirements for coastal soft soil foundation treatment. The components and working principles of an intelligent monitoring and diagnosis system are introduced and applied to soft soil foundation treatment to achieve more accurate monitoring and data analysis capabilities. The specific process of field application and laboratory testing is then described, and the monitoring data and data analysis results are provided. The importance and value of the intelligent monitoring and diagnosis system in coastal soft soil foundation treatment have been proven, providing a new technical approach for real-time monitoring and data analysis. Further optimization of system performance can be applied in more practical engineering projects. © 2024 ieee.

关键词： Soils

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Biometric systems in Focus: A Review of Methods, Challenges, and Future Directions 3

Biometric Systems in Focus: A Review of Methods, Challenges,...

引用

3rd international conference on intelligent computing and Next Generation Networks, ICNGN 2024

作者： Fatima, Masroor Banu, Ruqiya Chaeikar, Saman Shojae Najafabadi, Maryam Khanian Dept. of Cyber Security Sydney International School of Technology and Commerce Sydney Australia

ISBN: (纸本)9798331529222

Biometric authentication has emerged as an important component in strengthening cyber security, providing a superior alternative to conventional password-based systems because of their distinctive and difficult-to-duplicate attributes. This study examines diverse biometric techniques—including fingerprint, facial recognition, palm vein, and behavioral biometrics—and the incorporation of Artificial Intelligence (AI) methodologies such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to improve their dependability. Innovations including multi-modal biometrics, cancelable biometrics, and Genetic Encryption Algorithms (GEA) are examined, emphasizing their capacity to enhance security while mitigating privacy issues. The research additionally investigates continuous authentication inside IoT systems and future themes such as federated learning and quantum-resistant algorithms. Despite these developments, issues of computing complexity, scalability, consumer acceptance, and data privacy remain. The report concludes with recommendations for future research focused on optimizing biometric systems for broad implementation, improving data privacy, and creating adaptive, AI-driven methodologies to enhance biometric security. ©2024 ieee.

关键词： Differential privacy

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Privacy-preserving data Sharing with Incomplete data for Smart Grids: A Federated Imputation Approach 6

Privacy-preserving Data Sharing with Incomplete Data for Sma...

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6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Lu, Jiangang Tang, Yi Yang, Chenwei Lu, Mingxin Power Dispatching and Controlling Center Guangdong Power Grid Company Limited Guangzhou China Nanjing University Nanjing China

ISBN: (纸本)9798350374315

In smart grids, the challenge of handling incomplete data while maintaining privacy is critical for effective data sharing and analysis. This paper presents a novel framework for Privacy- Preserving data Sharing with Incomplete data using a Federated Imputation Approach. The proposed system enables decentralized nodes in a smart grid to collaboratively impute missing data without directly sharing their raw data, thereby preserving privacy. The framework integrates a central coordinator that manages the federated imputation process, leveraging local imputation models from each node. To ensure privacy, differential privacy mechanisms are employed, adding noise to local model updates and managing privacy budgets. The communication between nodes and the coordinator is secured and optimized for efficiency. Our approach demonstrates that high-quality data imputation can be achieved without compromising privacy, facilitating accurate and secure data sharing in smart grids. Experimental results show the effectiveness of the federated imputation model in improving data utility while adhering to stringent privacy standards. © 2024 ieee.

关键词： data assimilation

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Harnessing CNN Architecture for Accurate Traffic Sign Classification: Findings from the GTSRB dataset

Harnessing CNN Architecture for Accurate Traffic Sign Classi...

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2024 ieee international conference on computing, Applications and systems, COMPAS 2024

作者： Shorna, Sumiya Aktar Ayon, Walid Ibn Zinnah Apu, Khalid Ibn Zinnah Sheikh Fazilatunnesa Mujib University Department of Cse Jamalpur Bangladesh Chittagong University of Engineering and Technology Department of Cse Chittagong Bangladesh

ISBN: (纸本)9798331529765

Traffic signs are vital components of road infrastructure, serving as visual cues that communicate essential information to drivers, pedestrians, and cyclists. They play a crucial role in maintaining road safety by guiding traffic, indicating hazards, and regulating speed limits and right-of-way. Additionally, traffic signs help to establish order and predictability on the roads, reducing the likelihood of accidents and promoting smooth traffic flow. The importance of traffic sign classification lies in its ability to enhance the accuracy and efficiency of sign recognition systems, contributing to safer driving environments and the development of intelligent transportation systems. In this research we have taken the GTSRB - German Traffic Sign Recognition Benchmark dataset and included 516 more images in the dataset. After some preprocessing steps, we split the dataset into train and test data. Finally, we have trained the data with GRU, VGG-16, VGG-19, and scratch CNN architecture. Accuracies found are 91.91%, 87.19%, 86.53%, and 99.62%. © 2024 ieee.

关键词： intelligent systems

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Deep Reinforcement Learning-Based Symbolic Regression for PDE Discovery Using Spatio-Temporal Rewards 20

Deep Reinforcement Learning-Based Symbolic Regression for PD...

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ieee 20th international conference on Automation Science and Engineering (CASE)

作者： Wang, Xizhe Zhao, Hongbo Zhao, Qianchuan Jiang, Benben Tsinghua Univ Ctr Intelligent & Networked Syst Dept Automat Beijing Peoples R China Princeton Univ Dept Chem & Biol Engn Princeton NJ 08544 USA

ISBN: (纸本)9798350358513;9798350358520

The discovery of partial differential equations (PDEs) directly from data has significant implications for automation and manufacturing fields. This approach leverages advanced computational techniques and data-driven methodologies to identify the underlying mathematical models that govern the physical processes in various systems. In this article, a deep symbolic regression framework is developed for PDE discovery which is enhanced by reinforcement learning with spatiotemporal rewards. Within this framework, a dense encoderdecoder network is proposed to adeptly extract the information of temporal dynamics related to PDEs from the data, and a refined reinforcement learning reward shaping technique is further put forward that incorporates the extracted dynamics, specifically designed for PDE symbolic regression tasks. An example of Cahn-Hilliard equation, which is widely used to describe the phase separation process in multi-component mixture systems, is used to validate the effectiveness of the proposed approach. The experimental results show that the proposed deep symbolic regression approach outperforms the baselines in identifying PDEs, achieving higher accuracy with less data. Additionally, we provide a thorough analysis on how adjustable parameters affect the PDE discovery performance, further verifying the effectiveness of the proposed approach.

关键词： Deep reinforcement learning

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