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Application and Innovation of Cloud computing Technology in Computer data processing

Application and Innovation of Cloud Computing Technology in ...

引用

Applied Intelligence and Sustainable computing (ICAISC), international conference on

作者： Zhao Xiaolian Hubei Three Gorges Polytechnic Hubei China

Cloud computing technology has completely changed the way computer data is processed. It involves using remote servers to store, manage, and process data, rather than relying on local servers or personal computers. The application of cloud computing technology in computer data processing has many advantages. Firstly, it allows easy access to data from any location through an internet connection, making it easier for businesses and individuals to collaborate and share information. Secondly, cloud computing technology provides scalability as users can easily increase or decrease storage capacity according to their own needs. Thirdly, it provides a cost effective solution, as users only pay for what they use. However, there are also some concerns about the security and privacy of data stored in the cloud. Therefore, appropriate measures must be taken to ensure that sensitive information is not subject to unauthorized access. In short, the application of cloud computing technology in computer data processing has changed the way we store and manage information. Its benefits outweigh the challenges, making it a popular choice for businesses and individuals. The main advantage of using this technology is that it can provide multiple functions for multiple people to use simultaneously. This can more effectively utilize resources and reduce costs related to hardware maintenance. Cloud computing also provides access to information from different sources, allowing users to obtain the information they need faster than before.

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Intelligent transportation operation and maintenance integrated processing data collection and information analysis

Intelligent transportation operation and maintenance integra...

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6th international conference on Intelligent computing and Signal processing (ICSP)

作者： Lijuan Yao Xiandong Li Institute of Information Innovation and Network Security Industry Shandong Institute of Commerce & Technology Jinan Shandong CHN

During an observation season, for the representative of the transportation infrastructure, the intelligent monitoring of strain, deformation, cable force and mode of the urban river-crossing super large bridge will be carried out, and the information collection points will be arranged at the key parts to analyze the collected big data, which will serve as the proof that the bridge operation and maintenance are in a safe and controllable state, and ensure the smooth operation of the bridge. According to the monitoring information, the comprehensive evaluation of bridge health needs a reasonable evaluation method. In this paper, the evaluation system of bridge technical condition is established by using analytic hierarchy process (AHP) for extra-large bridges. This method is simple, practical and effective, and can also be used for intelligent operation and maintenance management of small and medium-sized bridges, slopes, tunnels and other transportation infrastructure.

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Examining Feasibility and Efficacy of Traditional Stream Clustering algorithms on Complex Human Activity Recognition data

Examining Feasibility and Efficacy of Traditional Stream Clu...

引用

IEEE Annual international Computer Software and Applications conference (COMPSAC)

作者： Martin Woo Farhana Zulkernine Hanady M. Abdulsalam School of Computing Queen’s University Kingston Canada Department of Information Science Kuwait University Kuwait City Kuwait

Modern adoption of network-connected devices have led to an abundance of high speed data streams and a call for the development of analytical models capable of processing them. Much of the real world data is unlabeled which requires development and application of unsupervised learning algorithms for advanced machine perception. However, the datasets that are used to train and validate these algorithms are often synthetic or simulated for the study and too simple, which leads to an inflation of reported performance. Based on the recent literature, there is a lack in research on developing deep learning models for clustering highly complex Human Activity Recognition (HAR) data streams. We, therefore, explore the area and apply three modern high-performing stream clustering algorithms on a complex HAR data stream. We evaluate the ability of these models to adapt and extract the volatile knowledge that is contained within the HAR data streams. Our goal is to set a baseline of performance for clustering timestamped HAR data streams. The results show that despite the reported performance of these algorithms in the literature, they lack the analytical depth to effectively extract sufficient knowledge from the complex HAR streams to construct viable clusters. So, new thoughts are needed to develop models that can efficiently handle HAR data streams.

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Screening Application for Possible Mental Health Issues in Adolescents and PWDs Using data Science 2nd

Screening Application for Possible Mental Health Issues in A...

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2nd international conference on Advances in data-driven computing and Intelligent Systems, ADCIS 2023

作者： Gunasundari, S. Nandhithaa, B. Mahalakshmi, L. Aparna, P. Velammal Engineering College Chennai India Fleet Studio Technologies Pvt Ltd Chennai India Power of N TechStudio Private Limited Chennai India Light and Wonder Chennai India

ISBN: (纸本)9789819995172

Mental health issues, such as depression, anxiety, and stress, are prevalent among adolescents and Persons with Disabilities (PwDs), yet access to mental health professionals is limited, especially in rural areas. A screening application for mental health issues in adolescents and PwDs is developed to address this challenge, utilizing Flutter, Dart, and a fine-tuned NLP model of GPT-4. The application consists of a questionnaire module that analyzes user responses, a Results module providing mental health assessments, and a Support and resources module offering over 100+ healthcare resources. data on mental health issues faced by adolescents is collected from Kilpauk Medical Hospital (KMC) and Velammal Medical College to inform the application’s development. The GPT-4 model is fine-tuned for phobia screening, resulting in an 80% accuracy rate in identifying phobia types and levels. The application effectively diagnoses various mental health issues, including anxiety disorders, bipolar disorder, depression, eating disorders, obsessive–compulsive disorder, post-traumatic stress disorder, schizophrenia, and various phobias. The application holds promise in enabling early identification and intervention for mental health issues, particularly in underserved areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Natural language processing systems

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Multi-GPU Parallel Pipeline Rendering with Splitting Frame 40th

Multi-GPU Parallel Pipeline Rendering with Splitting Frame

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40th Computer Graphics international conference (CGI)

作者： Zhang, Haitang Ma, Junchao Qiu, Zixia Yao, Junmei Al Sibahee, Mustafa A. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China Shenzhen Univ Coll Appl Technol Shenzhen Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Univ Basrah Coll Educ Pure Sci Dept Comp Sci Basrah Iraq Al Kunooze Univ Coll Comp Engn Dept Basrah Iraq Huazhong Univ Sci & Technol Shenzhen Inst Shenzhen Peoples R China Tom Mboya Univ Fac Biol & Phys Sci Homabay Kenya

ISBN: (纸本)9783031500718;9783031500725

In order to achieve real-time rendering of cloud gaming, a large amount of computing resources of graphics processing units (GPU) are required. In this paper, we propose a multi-GPU parallel pipeline rendering approach that makes full use the computing power of multiple GPUs to accelerate real-time ray tracing rendering effectively. This approach enables heterogeneous GPUs to render the same frame cooperatively through a dynamic splitting frame load balancing scheme, and ensures that each GPU is assigned with the suitable size of splitting frame based on its rendering ability. A fine-grained parallel pipeline method divides the process of rendering into more detailed steps that enable multiple frames to be rendered in parallel, which improves the utilization of each step and speeds up the output of frames. With the experiments on various dynamic scenes, the results show that the number of frames per second (FPS) of the multi-GPU system composed of two GPUs is 2.2 times higher than that of the single GPU system, while the multi-GPU system composed of three GPUs has increased to 3.3 times.

关键词： Multi-GPU Ray tracing Split frame rendering Parallel pipeline 3D render

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4th international conference on computing, Communications, and Cyber-Security, IC4S 2022

4th International Conference on Computing, Communications, a...

引用

4th international conference on computing, Communications, and Cyber-Security, IC4S 2022

ISBN: (纸本)9789819914784

The proceedings contain 67 papers. The special focus in this conference is on computing, Communications, and Cyber-Security. The topics include: Resource Sharing in Back Haul Satellite-Based NOMA Network;a Review on Various Deepfakes’ Detection Methods;Proposed Framework for Implementation of Biometrics in Banking KYC;paddy Pro: A MobileNetV3-Based App to Identify Paddy Leaf Diseases;Cryptanalysis of RSEAP2 Authentication Protocol Based on RFID for Vehicular Cloud computing;a Survey on Code-Mixed Sentiment Analysis Based on Hinglish dataset;An Alternative to PHP for the Development of Web Applications: Java Server Pages Engine;Drone Ecosystem: Architecture for Configuring and Securing UAVs;novel Load Balancing Technique for Microservice-Based Fog Healthcare Environment;Self-improved COOT Algorithm for Resource Allocation in Cloud data Centers;XGBoost-Based Prediction and Evaluation Model for Enchanting Subscribers in Industrial Sector;cleanO-Renewable Energy-Based Robotic Floor Cleaner;Impact of "COVID-19 Pandemic" on Children Online Education: A Review and Bibliometric Analysis;digital-Based Learning in Indian Government’s Higher Education: Initiatives and Insights;Autonomous Vehicles Adoption Classification for Future Mobility in UAE Using Machine Learning;an Augmented Reality Framework as a Solution to Enhance the Experience of Visiting a Museum;Hybrid Real-Time Implicit Feedback SOM-Based Movie Recommendation Systems;Automatic SMS Spam Filtering for Disaster Response Using Classification algorithms;an Improved Neural Network-Based Routing Algorithm for Mobile Ad Hoc Networks;Lung Cancer Diagnosis Using X-Ray and CT Scan Images Based on Machine Learning Approaches;hybrid Machine Learning Algorithm for Prediction of Malaria;yoga Pose Estimation Using Machine Learning;COVID Detection from Chest X-Ray Images Using Deep Learning Model;preface.

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data Driven Joint Sensor Fusion and Regression Based on Geometric Mean Squared Error

Data Driven Joint Sensor Fusion and Regression Based on Geom...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Carlos Alejandro Lopez Jaume Riba Signal Theory and Communications Department Technical University of Catalonia (SPCOM/UPC)

This paper explores the problem of estimating a temporal series measured from multiple independent sensors with unequal and stationary measurement errors with unknown variances. By formulating the data fusion problem as a joint Maximum Likelihood estimation of sensor covariances and a fusion rule, a batch data driven method is derived involving a residual covariance determinant minimization of a diagonal matrix. It is shown that yielding useful learning from data with good generalization properties in the joint regression and fusion approach requires the assumption of some structure on the sensor noises and/or on the temporal series to be estimated. An efficient data driven algorithm is proposed to obtain the best linear sensor combiner, whose performance is numerically analyzed and compared with the Cramer-Rao Lower Bound of the estimated parameters.

关键词： Training Redundancy Measurement uncertainty Signal processing algorithms Sensor fusion Signal processing Minimization

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Simulation of CPU Scheduling algorithms for Efficient Execution of Processes

Simulation of CPU Scheduling Algorithms for Efficient Execut...

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Advancement in Technology (ICONAT), international conference for

作者： Kuldeep Vayadande Parth Sheth Dhananjay Pawal Atharva Pathak Kaustubh Paralkar Sankalp Patil Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India

In this paper, our main objective is to study, implement and compare different scheduling algorithms which is very important in increasing the Central processing Unit’s efficiency. There are a total of 8 algorithms that are opted for the study. They are First Come First Serve (FCFS), Priority Scheduling (PP), Shortest Job First (SJF), Longest Remaining Job First (LRJF), Longest Job First (LJF), Shortest Remaining Job First (SRJF), Round Robin (RR), Highest Response Ratio Next (HRRN)). In this work, various parameters have been considered for the comparison of Central processing Unit scheduling algorithms. In the end, after the comparative analysis, a conclusion is made regarding which algorithm has what qualities and which one can be treated as the best algorithm for Central processing Unit scheduling. This paper will be helpful to decide the right algorithm for Central processing Unit scheduling.

关键词： Schedules Scheduling Central processing Unit Round robin

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Multi-party Secure Comparison of Strings Based on Outsourced Computation 1

引用

4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhang, Xin Shan, Chao Zou, Yunfeng State Grid Jiangsu Marketing Service Center State Grid Jiangsu Electric Power Co. Ltd. Nanjing China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

data is an important production factor in the era of digital economy. Privacy computing can ensure that data providers do not disclose sensitive data, carry out multi-party joint analysis and computation, securely and privately complete the full excavation of data value in the process of circulation, sharing, fusion, and calculation, which has become a popular research topic. String comparison is one of the common operations in data processing. To address the string comparison problem in multi-party scenarios, we propose an algorithm for secure string comparison based on outsourced computation. The algorithm encodes the strings with one hot encoding scheme and encrypts the encoded strings using an XOR homomorphic encryption scheme. The proposed algorithm achieves efficient and secure string comparison and counts the number of different characters with the help of a cloud-assisted server. The proposed scheme is implemented and verified using the new coronavirus gene sequence as the comparison string, and the performance is compared with that of a state-of-the-art security framework. Experiments show that the proposed algorithm can effectively improve the string comparison speed and obtain correct comparison results without compromising data privacy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sensitive data

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A data Propagation Method of Internet of Vehicles Based on Sharding Blockchain 36th

A Data Propagation Method of Internet of Vehicles Based on S...

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36th international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2023

作者： Chen, Chen Shi, Quan School of Information Science and Technology Informatization Center Nantong University Nantong China School of Transportation and Civil Engineering Nantong University Nantong China

ISBN: (纸本)9783031368219

Blockchain technology has been successfully applied to finance and medical treatment recently. It is the prototype of the next generation of cloud computing, which is expected to reconstruct human social activities. IoV is an essential part of human social activities. Traditional centralized management and data storage are not suitable for IoV, which satisfies large-scale and low latency. Therefore, decentralization, distributed management, and distributed storage may become the future technology trends of IoV. However, when we take decentralized techniques, the data and communication must have high-security requirements. According to the characteristics of IoV, this paper discussed the sharding design of Blockchain and proposed an IoV model-based sharding Blockchain. Based on this model, we designed a Sharding algorithm for the RSU Blockchain layer (RSU-SA) to elect full nodes and sharding with good scalability and stability. Secondly, by introducing the correlation degree of light nodes, we adopted the light node evaluation matrix, established the data propagation subtree, and implemented a tree-based data propagation algorithm (TDPA). Finally, we simulated the above two algorithms. Simulation results showed that the Sharding algorithm for the RSU Blockchain layer (RSU-SA) is more effective with a stable network life cycle. The experimental results verified the effect of tree depth on data block propagation. It revealed that the tree-based partition data propagation algorithm effectively reduced the block data transmission delay. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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