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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6952 条记录，以下是281-290 订阅

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Hierarchical Federated Learning-Based Method for privacy-preserving in the Healthcare Environment

Hierarchical Federated Learning-Based Method for privacy-pre...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Manoj Kumar, D.P. Babu. J, Ananda Nagasireesha, Jaladi Venkatesh, Bhattu Siddiqua, Ayesha Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Malnad College of Engineering Department of Information Science and Engineering Hassan India Malla Reddy Engineering College for Women Autonomous India Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The COVID-19 pandemic has led to increase the demand for medical equipment with internet of things (IoMT) capabilities forcing doctors to diagnose and treat patients remotely. This has increased awareness of health, leading to a surge in purchasing of IoMT-capable medical equipment. However, medical data is sensitive and valuable on the dark web, making it vulnerable to cybercriminals. Due to limited storage and processing capabilities of network devices, system administrators struggle to enhance security measures. This work aims to investigate rapidly spreading attacks before they compromise wellness systems. Hierarchical federated learning is (HFL) proposed using a model built on Dew-cloud, which offers improved data privacy due to the increased availability of IoMT critical applications. Dew servers with distributed architecture and a cloud computing-backed backend implement hierarchical long-term memory (HLSTM) method. The implemented HFL-HLSTM method's performance is evaluated using NSL-KDD dataset. Implemented method achieved better performance than the existing intrusion detection methods including MiTed, LS2DNN, and MetaCID and the metrics are 98.62% of f-measure, 99.53% of accuracy, 99.21% of precision, and 99.10% of recall. © 2023 IEEE.

关键词： and Intrusion detection data privacy Dew computing Federated Learning Hierarchical Long Short-Term Memory Internet of Medical Things (IoMT)

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A Dynamic Deep Learning Ecosystem for Real-Time Cognitive data processing on Industrial Edge Devices 3

A Dynamic Deep Learning Ecosystem for Real-Time Cognitive Da...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Das, Sidhant Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350343274

The capacity to manage cognitive data in real time is crucial in the rapidly increasing field of industrial edge computing. This criterion must be fully completed to proceed. Our research resulted in the creation of a new platform dubbed the Dynamic Deep Learning Ecosystem, or DDLE, which strives to achieve these goals. The Deep Learning Edge (DDLE) is a strong architecture that ensures deep learning models run as well as possible on edge devices. The dynamic data-driven learning environment (DDLE) includes approaches such as dynamic model scaling (DMS), edge-based pruning (EBP), and real-time adaptive inference (RAI). The entire goal of the operation is dependent on these algorithms accomplishing all this work. Deep learning models may face dynamic changes in complexity and size if DMS is used. The way these changes are implemented is determined in part by the type of data and the amount of processing capacity presently available. The proposed DDLE has been thoroughly tested, and the results demonstrate that it beats more standard approaches in certain crucial measures, such as accuracy and latency. The findings of this study highlight the necessity of deep learning at the edge as well as the potential benefits of DDLE. Furthermore, it demonstrates that DDLE can be a viable option for businesses looking for a real-time cognitive data processing solution. © 2023 IEEE.

关键词： Edge computing

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Learning suitable data representation for credit card fraud detection algorithms 6

Learning suitable data representation for credit card fraud ...

引用

6th international conference on data Science and Information Technology, DSIT 2023

作者： Lejamble, Sylvain Vernier, Flavien Alloui, Ilham Monnet, Sebastien SaGa Corp Research & Development Department Paris France Laboratoire d'Informatique Systèmes Annecy France

ISBN: (纸本)9798350304442

With the recent pandemic, credit card and even contactless payment have gained significant popularity. The elevated frequency of card usage, along with the lack of diligence among customers, has resulted in an increase in stolen or counterfeit cards, often leading to fraudulent activities. This emphasized the importance of real-time detection of abnormal banking transactions for card issuers. Automated analysis of transaction logs is a prevalent approach to address this challenge, often involving the comparison of incoming transactions to a database containing genuine and fraudulent transactions. Methods such as the Mahalanobis distance have proven to be efficient when seeking for similarities between high dimensional data. However, the challenge lies in the fact that credit card logs contain both categorical and tabular data, which poses compatibility issues with the Mahalanobis algorithm. This study explores the effectiveness of finding alternative data representations as a pre-processing step to enable the utilization of algorithms that were previously unsuitable for such data types. The research conducted in this work is established on an exclusive credit card logs dataset. © 2023 IEEE.

关键词： Principal component analysis

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Survey of Neuromorphic computing: A data Science Perspective 3

Survey of Neuromorphic Computing: A Data Science Perspective

引用

3rd IEEE international conference on Computer Communication and Artificial Intelligence, CCAI 2023

作者： Clark, Kasey Wu, Yalong University of Houston Department of Computing Sciences Clear LakeTX United States

ISBN: (纸本)9798350335262

Neuromorphic computing utilizes memristors to mimic biological neurons and processes data in brain-like way. Given the intrinsic benefits of non-volatile high-density memory, bio-compatibility, and energy efficiency of memristors, neuro-morphic computing has opened many opportunities for various fields of data science. In this paper, we conduct a brief survey of neuromorphic computing through the lens of data storage, data processing, artificial intelligence, and data security and privacy. © 2023 IEEE.

关键词： Energy efficiency

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Lightweight Cryptography based Deep Learning Techniques for Securing IoT Based E-Healthcare System 2

Lightweight Cryptography based Deep Learning Techniques for ...

引用

2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Maram, Balajee Majji, Ramachandro Gopisetty, Guru Kesava Dasu Garg, Ankit Daniya, T. Santhosh Kumar, B. AIT-CSE Chandigarh University Gharuan Punjab Mohali140055 India Vardhaman College Of Engineering Kacharam Village Shamshabad Telengana State Hyderabad501218 India Department of IT KKR & KSR Institute of Technology and Sciences Guntur India Department of Research And Development AIT-CSE Chandigarh University Gharuan Punjab Mohali140055 India Department of CSE - AI & ML GMR Institute of Technology Rajam532127 India Department of CSE Guru Nanak Institute of Technology Telangana Ibrahimpatnam India

ISBN: (纸本)9798350340235

The integration of deep learning algorithms with lightweight cryptography for boosting the security and effectiveness of an Internet of Things (IoT) based E-Healthcare system is likely discussed in the paper's abstract. The article might describe the difficulties in protecting private healthcare data sent via IoT networks and suggest a remedy that blends deep learning algorithms with simple encryption techniques. The E-Healthcare system may benefit from increased data privacy, authentication, and integrity as a result of this integration, ensuring that patient information is kept secure during transmission and processing. The potential advantages of this strategy, such as decreased processing overhead, energy usage, and latency, which are essential elements for IoT applications, might be highlighted in the abstract. Overall, by utilizing the synergy of deep learning and lightweight cryptography in the context of IoT, the research hopes to develop secure and effective E-Healthcare systems. © 2023 IEEE.

关键词： authentication computation overhead data integrity data privacy deep learning E-Healthcare system efficiency energy consumption IoT latency lightweight cryptography security

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A Multi - User Effective Computation Offloading Mechanism for MEC System: Batched Multi-Armed Bandits Approach 25

A Multi - User Effective Computation Offloading Mechanism fo...

引用

25th IEEE international conferences on High Performance computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Li, Hangfan Shi, Lin Zhong, Xiaoxiong Wang, Xinghan Ji, Yun Zhang, Sheng Guilin University of Electronic Technology Guilin541004 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518000 China

ISBN: (纸本)9798350330014

With the development of 5G technology, mobile edge computing (MEC) is becoming a useful architecture, which is envisioned as a cloud extension version. Users within MEC system could save plenty of time on data transmission. algorithms within Multi-armed bandits (MAB) framework are powerful tools helping users offloading tasks to their best servers. However, as the system scale growing, servers will face higher pressure and users will waste more time on offloading tasks. The situation by applying traditional MAB algorithms is more serious. Therefore, in this paper, we propose a Batch-based Multi-user Server Elimination (BMSE) to solve such problem. BMSE contains two sub-algorithms, BMSE in User Level (BMSE-UL) and BMSE in System Level (BMSE-SL). BMSE- UL is applied by users for initial purpose which will help themselves discard servers with obvious bad performance. BMSE-SL is applied in the MEC system which could offload tasks from different users collectively. Furthermore, we establish the optimality of the proposed algorithms by proving the sub-linearity convergence of their regrets and demonstrate the effectiveness by extensive experiments. © 2023 IEEE.

关键词： Mobile edge computing

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Towards a Framework for the Preparation of High Quality data for Use by Machine Learning algorithms 15th

Towards a Framework for the Preparation of High Quality Data...

引用

15th international conference on e Infrastructure and e Services for Developing Countries

作者： Nabi, Rasidatou Traore, Yaya Thiombiano, Julie Univ Joseph KI ZERBO Ouagadougou Burkina Faso Univ Nazi BONI Bobo Dioulasso Burkina Faso

ISBN: (纸本)9783031815720;9783031815737

Nowadays, companies and organizations have access to various data collection tools that enable them to amass vast amounts of data, which can be stored in databases. This data can be leveraged by machine learning algorithms to extract valuable information for decision-makers. However, this raw data is often of poor quality, containing errors such as missing data and outliers, requiring the intervention of technicians and domain specialists to prepare the data to ensure the F1_Score of the analysis. This article proposes a framework for preparing high-quality data for machine learning algorithms, as manually identifying reliable data from a large pool can be challenging and time-consuming. Our approach is an architectural method that combines data preparation techniques to generate dataset quality.

关键词： data processing Quality data Missing data Encoding Normalization

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Research on Key Technologies of Big data Analysis 3

Research on Key Technologies of Big Data Analysis

引用

3rd international conference on Artificial Intelligence, Big data and algorithms, CAIBDA 2023

作者： Cai, Jianjun Ren, Nver Sun, Erxin Tian, Yang Zhang, Jingming China Automotive Technology Research Center Co. Ltd. China

ISBN: (纸本)9781643684444

With the advent of emerging computing services such as cloud computing and the decline in hardware prices, the unit price per GB of mechanical hard drives has plummeted by 87%, the amount of information on the Internet has shown explosive growth. Furthermore, the scale of data in large enterprises has reached EB level at present. These massive data form subject-oriented, integrated, relatively stable data warehouses which can reflect historical changes. How to process and analyze these data has gradually become the key technology of Big data. This paper will discuss the key technologies of Big data processing and analysis, such as data preprocessing and Big data analysis framework. © 2023 The Authors.

关键词： Big data

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Resource-Limited Localized Adaptive Adversarial Training for Machine Learning Model 21

Resource-Limited Localized Adaptive Adversarial Training for...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Rajhi, Mohammed Pissinou, Niki Florida International University School of Computing and Information Sciences MiamiFL United States Jazan University Faculty of Computer Science & Information Technology Jazan Saudi Arabia

ISBN: (纸本)9798350329223

Balancing robustness and computational efficiency in machine learning models is challenging, especially in settings with limited resources like mobile and IoT devices. This study introduces Adaptive and Localized Adversarial Training (ALAT), an optimization approach that balances these competing needs. ALAT combines generalized models with localized adversarial perturbations and adaptive data augmentation. As a result, the model strengthens its weak points without needing to explore all possible adversarial threats, saving computational effort Our data shows that ALAT-trained models perform robustly with less computational cost compared to traditional adversarial training methods. This adaptability makes ALAT suitable for various machine learning architectures and particularly valuable in resource-constrained settings requiring resilience to adversarial threats. © 2023 IEEE.

关键词： Computational efficiency

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Evaluation of data Mining Classification Algorithm by Monitoring Fall Detection Anomaly using Discriminative Approach 14

Evaluation of Data Mining Classification Algorithm by Monito...

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14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Poorni, R. Madhavan, P. School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India

data mining plays a vital role in various sectors such as health care, finance, business, intrusion detection, scientific analysis and research. The user health behavior can be classified using various data mining models so that the analysis can be used for future predictions on the healthcare unit. The proposed model compares and evaluates various discriminative classification algorithms and shows the result of best classification model by applying it to the fall detection anomaly dataset. © Grenze Scientific Society, 2023.

关键词： data mining

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