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Fog Computing: applications in Smart Healthcare 3

Fog Computing: Applications in Smart Healthcare

引用

3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Dash, Yajnaseni Jajoria, Pooja School of Computer Science Engineering and Technology Bennett University Uttar Pradesh India Guru Gobind Singh Indraprastha University New Delhi Dwarka India

ISBN: (纸本)9798350330724

Internet of Things (IoT) applications in healthcare are revolutionizing conventional medical procedures through the integration of networked devices that gather real-time patient healthcare data. By relocating computational resources towards the network edge, fog computing provides an architectural basis that fortifies IoT technologies as there is minimal waiting for responses, less latency, and no bandwidth problems brought on by sending massive volumes of data to far-off cloud servers. The primary application domains where fog computing is transforming smart healthcare are highlighted in this study. The incorporation of fog computing for enabling real-time analysis and processing of information generated by IoT devices is an important milestone in this area of study. This paper investigates the concept of fog computing-based smart healthcare IoT applications, including its architecture, different application domains, and challenges that need to be addressed before it can be implemented. Improved patient outcomes, faster treatments, economic improvements and better decision-making processes are just a few advantages of fog computing. The healthcare industry can develop ground-breaking solutions that improve patient care and modernize healthcare practices by leveraging the capabilities of fog computing at the network edge. This article addresses how fog computing could impact intelligent health-related IoT applications by outlining its architecture, exploring several healthcare-related application domains, and assessing utilization challenges. © 2023 ieee.

关键词： Fog computing

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Classroom Teaching Management and Resource Optimization in Colleges and Universities Based on Big data Technology 3

Classroom Teaching Management and Resource Optimization in C...

引用

3rd ieee international conference on Social sciences and Intelligence Management, SSIM 2023

作者： Wang, Gengyan Sheng, Cao Wenhua College Wuhan China Wuhan Wanzhongzhilian Intelligent Technology Co. Ltd Wuhan China

ISBN: (纸本)9798350327786

With the rapid development of information technology, the importance of introducing advanced big data technology for classroom teaching management and resource optimization in university education was investigated. Through the extensive application of modern data science, artificial intelligence (AI) and cloud computing technologies, we collected data on the integration and efficient utilization of student information, teaching resources, and teaching environment. We used data mining technology for the in-depth analysis of students' learning behaviors and performance to support personalized teaching. In cloud computing and big data storage, we explored how to establish a powerful educational information platform for safe storage and efficient management of large data. In addition, in decision support, we introduced industry-leading business intelligence and decision analysis tools to help decision-makers optimize the management of classroom teaching and resources in colleges and universities and maintain their flexibility and adaptability. By integrating various advanced technologies into classroom teaching higher education, scientific and technological support can be provided for innovation and progress in higher education. © 2023 ieee.

关键词： Big data

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A Review of data science in Mechanical Engineering 3

A Review of Data Science in Mechanical Engineering

引用

3rd international conference on Digital data Processing, DDP 2023

作者： Saeweenan, Kwanchai Ketmaneechairat, Hathairat College of Industrial Technology King Mongkut's University of Technology North Bangkok Mechanical Engineering Technology Bangkok Thailand College of Industrial Technology King Mongkut's University of Technology North Bangkok Information and Production Technology Management Bangkok Thailand

ISBN: (纸本)9798350329018

Nowadays, intelligent systems and techniques are pivotal in advancing various components of machines and devices, aiming to enhance overall performance and optimization. Furthermore, accurate predictions are paramount to informed decision-making in mechanical engineering. The influence of data science has significantly shaped the approaches and strategies employed in research and development within the industrial sector. This underscores the imperative integration of data science into mechanical engineering. This paper highlights data science's crucial significance in mechanical engineering, where practitioners leverage these techniques to design, develop, and test products, systems, and processes. As data science rapidly becomes an integral component of mechanical engineering, the synergy between these two fields holds immense potential to revolutionize the efficiency and effectiveness of research endeavors. The amalgamation of data science and mechanical engineering facilitates better-informed decision-making and enables the optimization of processes, product quality improvement, and cost reduction. This harmonious integration stands as a testament to the substantial advancements occurring in the field of mechanical engineering. © 2023 ieee.

关键词： Product design

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Blockchain Based Traceability in computer Peripherals in Universities Scenarios 3

Blockchain Based Traceability in Computer Peripherals in Uni...

引用

3rd international conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Kumar, Jakka Raja Harshit Bhargavramu, Nettem Durga, Lella Saketh Naga Nimmagadda, Dinesh Bhattacharjya, Aniruddha Koneru Lakshmiah Education Foundation Computer Science Engineering Vijayawada India Sgt University Faculty of Engineering & Technology Department of Computer Science Engineering Gurugram India

ISBN: (纸本)9798350327762

Tracing and tracking of the peripheral devices help in terms of manufacturing and providing services to the users. However, most of the current systems which are suggested by Food and Drug Administration (FDA) are loT and Hardware-Assisted Tracing data which are being stored as required. The time and effort on duplicating record keeping and third-party validations are wasting of time and effort towards verification process. Record-keeping systems can be attacked by fraud and cyber-attacks whereas Blockchain is ideal for delivering that information securely and in a distributed fashion. This research has proposed a Blockchain based traceability of the computer Peripherals in Universities scenarios. It is well-known that the Blockchain is a shared, immutable ledger that facilitates the process of recording transactions and tracking assets in a business network. Smart contract is a good feature for maintaining this kind of peripheral data. In addition to providing quality information, the proposed traceability system enables transparency, safety, and security. We have done security analysis also. © 2023 ieee.

关键词： Blockchain

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A Survey Study of Social Media User Awareness Towards Personal Identifiable Information's data Security in Indonesian Millennial Generation 3

A Survey Study of Social Media User Awareness Towards Person...

引用

3rd international conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Christie, Gabriel Alexander Ivan Trevan, Javier Achmad, Said Junior, Franz Adeta Nadia School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

Online Social Network is a network communication platform where users have profiles that can be uniquely identified by the content sent. This content can be produced, consumed, and interacted with by other users. To connect with other users on social media, users must register by providing Personally Identifiable Information (PII) to social media platforms. PII is specific information that can identify or track individuals directly. This specific information may include your name, address, social security number, or other identifying code numbers such as telephone numbers, email addresses, and others. Personal identifiable information leakage is a problem in data security. Basically, every individual does not want their personal data to be known by anyone. Utilizing a sample size of 50 respondents, this study aims to ascertain the percentage of individuals who are aware of PII security on social media. This research will use quantitative methods by distributing questionnaires. The questionnaire in this study uses a social media attribute design. The results of the survey indicate that many respondents are unaware of the security of their data and have a limited understanding of how their personal data is managed by technology companies, particularly the 80% of non-IT respondents. © 2023 ieee.

关键词： Social networking (online)

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A Survey on Segmentation Techniques in Fractal Classification Systems 3

A Survey on Segmentation Techniques in Fractal Classificatio...

引用

3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Rana, Samriti Singh, Lovejit Chandigarh University Computer Science & Engineering India

ISBN: (纸本)9798350370867

Fractal-based classification systems have gained significant attention in recent years due to their ability to model complex data structures efficiently. These systems rely on the segmentation of data into self-replicating patterns, which are integral to their classification process. This survey explores the various segmentation techniques such as image pyramids, wavelet transform, region growing, k-means clustering, and fractal dimension-based segmentation employed in fractal classification systems, providing an in-depth analysis of their strengths, weaknesses, and applications. Each technique is evaluated based on its suitability for different data types and domains, highlighting their advantages and limitations. Finally, the survey outlines the current challenges and future directions in the field of fractal-based classification systems, emphasizing the need for improved segmentation techniques to handle large and complex datasets. By providing a comprehensive overview of segmentation methods in the context of fractal classification, this survey aims to contribute insights for improved pattern recognition in diverse applications. © 2023 ieee.

关键词： Textures

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Multimodal substorm recognition using UVI, SuperdARN and space physical parameters 3

Multimodal substorm recognition using UVI, SuperDARN and spa...

引用

3rd international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2023

作者： Ping, Wang Bing, Han Daohui, Ge Qufu Normal University School of Computer Science Qufu China Xidian University School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350302356

Substorm recognition method based on multimodal data is proposed in this paper. The multimodal data include auroral images from satellite imager, plasma velocities from ground-based radar stations and space parameters from aerocraft probes. Among the three modalities, the semantical features of auroral images are extracted by VGG-16 network. Then, LSTM group is utilized to extract sequential feature of independent modal data. And memory fusion network is used for multimodal sequential feature fusion. The experimental results of different embedding features on substorm recognition illustrate that particle precipitation recorded in auroral images is a key physical process in the substorm event process. © 2023 ieee.

关键词： Long short-term memory

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A depth completion method based on cross-feature extraction window 3

A depth completion method based on cross-feature extraction ...

引用

3rd international conference on Electronic Information Engineering and computer, EIECT 2023

作者： Dong, Yao Zhang, Dengfeng Zhang, Yinqiang Li, Lijuan Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China

ISBN: (纸本)9798350357707

A Dense depth maps with clear object boundaries are very important for robot mapping and navigation. However, today's depth sensors can only produce sparse depth data, so it is necessary to complete them. Traditional methods mostly rely on RGB images as an auxiliary modality to provide color information in the depth-filling process. However, RGB by itself is not enough to provide all the required color information, so we add semantic maps as a guide on this basis. In addition, we add a cross-feature extraction window (CFEW) based on the attention mechanism to the feature extraction stage of this model. The essence of the window is to use the channel attention mechanism, the spatial attention mechanism, and the mutual features attention mechanism to enhance the important features and suppress the irrelevant features, which can enhance the border of the object in the environment. © 2023 ieee.

关键词： Feature extraction

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Prediction of Bird Flapping Frequency Based on Least Squares Polynomial Fitting 3

Prediction of Bird Flapping Frequency Based on Least Squares...

引用

3rd international conference on Electronic Information Engineering and computer science, EIECS 2023

作者： Huang, Runqin Su, Min Liu, Jia Fu, Deli Guangxi Normal University School of Electronic and Information Engineering Guilin China Beihang University Research Institute for Frontier Science Beijing China

ISBN: (纸本)9798350316674

Identifying the movement state of flying birds by scattered echoes is a hot topic in radar bird detection. At present, the quantitative relationship between flapping frequency and its dynamic electromagnetic scattering characteristics is not clear, which brings challenges to flying bird detection. In view of this, this paper proposes a correlation modeling method based on least squares polynomials for bird flapping frequency prediction. This method uses least squares polynomials to fit the scattering characteristic data and flapping frequency, and uses MAE, MRE and RMSE to verify the fitting accuracy and explore the optimal order that minimizes the error between the fitted curves and the data points. Numerical experimental results show that the correlation model constructed by this method can effectively predict flapping frequency. Further experiments show that the prediction results using third-order polynomials have the minimum error and maximum fitting accuracy and are the optimal order. © 2023 ieee.

关键词： dynamic scattering characteristics flapping frequency least squares polynomial fitting

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Breast Histopathology Image Classification: A Comparative Evaluation of SVM, Random Forest and CNN 3

Breast Histopathology Image Classification: A Comparative Ev...

引用

3rd ieee international conference on Intelligent Technologies, CONIT 2023

作者： Yogitha Rachana, P. Milina, M. Sabhahit, Neel Ganapathi Ainain, Mohammad New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350338607

Artificial intelligence (AI) uses the impressions of human intelligence to wire machines to think and mimic the human behaviour. Deep Learning, Machine Learning, Neural Networks, are a few concepts included in AI among many others. Convolutional neural network (ConvNet or CNN) is a category of machine learning commonly applied for image recognition and processing pixel data. Support Vector Machine (SVM) algorithm is a Supervised Learning algorithm used often for Regression problems and Classification problems. Random Forest is a collaborative algorithm which is also used for both Regression and Classification problems. This paper provides a comparative analysis of CNN, SVM and Random Forest Algorithms. The dataset used is the Breast Histopathology Image dataset. © 2023 ieee.

关键词： Support vector machines

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