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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

共 11096 条记录，以下是1231-1240 订阅

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Feature engineering Framework based on Secure Multi-Party Computation in Federated Learning 23

Feature Engineering Framework based on Secure Multi-Party Co...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Sun, Litong Du, Runmeng He, Daojing Zhu, Shanshan Wang, Rui Chan, Sammy East China Normal University Dept. School of Software Engineering Shanghai China Technische Universiteit Delft Delft Netherlands City University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Data and features often determine the upper limit of results, so that feature engineering is an important stage of federated learning. the existing research schemes all carry out feature engineering based on publicly sharing data. One is plaintext data sharing, the other is ciphertext data sharing, but both types of sharing bring security and efficiency problems. To address these challenges, we propose a feature engineering framework based on Secure Multi-party Computation, which supports multi-party participation in feature engineering and confines feature data locally to ensure data security. Moreover, the computational efficiency of the core algorithm of the framework is also improved compared with the existing methods. © 2021 IEEE.

关键词： Smart cities Data security Collaborative work Multi-party computation Safety Computational efficiency

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Smart Surveillance System Using Face Detection for Residentials 7

Smart Surveillance System Using Face Detection for Residenti...

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7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Doshi, Mitesh Jawalkar, Priyanka Shinde, Shreekant Hire, Siyal Sukte, Chudaman Mit Wpu School of Computer Engineering & Technology Pune India

ISBN: (纸本)9781665421683

the Internet of things is helping humans to automate life by providing different tools and making it more easier. It can also be viewed as things connected in a network. this network can provide several smart services. On the other hand, face recognition is something which provides us a way to make our system intelligent enough to recognize people, which makes it easier to find the potential suspects from a security point of view. Nowadays security cameras are used to build safety, where a person is responsible to detect any problems/ suspects and report the same to concerned authorities. this paper presents an idea to automate processes using IoT and face recognition methods. © 2022 IEEE.

关键词： Face recognition

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Empowering IoT Devices with Energy-Efficient AI and Machine Learning 7

Empowering IoT Devices with Energy-Efficient AI and Machine ...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Anusha, P. Chouhan, Nitesh Chandhok, Gunita Arun Sugumaran, D. Aswal, Upendra Suganya, A. R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India MLV Textile and Engineering College Department of IT Rajasthan Bhilwara India SIMATS Saveetha College of Liberal Arts and Sciences Department of Commerce General Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Information Technology Tami Nadu Chennai India Graphic Era Deemed to Be University Uttarakhand Dehradun India Karpagam College of Engineering Department of Information Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372816

Existing IoT devices frequently exhibit inefficiencies in energy consumption and decision-making processes, resulting in inadequate efficiency and environmental impact. the study describes a unique framework that uses artificial intelligence (AI) and machine learning (ML) methods to provide Internet of things (IoT) devices with cognitive decision-making capabilities, hence improving energy efficiency and overall performance. the proposed method enables real-time autonomous decision-making in IoT contexts by collecting extensive data, preprocessing it, and designing features, then selecting, training, and deploying algorithms. Continuous learning mechanisms enable flexibility in changing situations, while thorough testing reveals considerable gains in energy efficiency, proposed system dependability, and user pleasure. the results show a 30% reduction in energy consumption, a 25% increase in resource utilization, a 5% improvement in proposed system uptime, and significant improvements in user satisfaction indicators. the proposed method is an exciting possibility for making IoT deployments more sustainable and efficient, paving the path for intelligent technology ecosystems. © 2024 IEEE.

关键词： Energy efficiency Intelligent decision-making IoT devices Performance improvement Resource allocation Sustainability

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Autonomous AI Chat Bot therapy for Patient with Insomnia 7

Autonomous AI Chat Bot Therapy for Patient with Insomnia

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7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Shaikh, Talha Abdul Hakeem Mhetre, Manisha Vishwakarma Institute of Technology Department of Instrumentation and Control Engineering Pune India

ISBN: (纸本)9781665421683

this paper's endeavor is concentrated primarily aiding insomniacs. the importance of accomplishing the work is critical since insomnia is a common sleep condition that stops people from sleeping and therefore is common in today's society. Because of its chronic nature, insomnia is connected to a considerable deterioration in an individual's quality of life. the system is powered via Artificial Intelligence and Deep Learning. the goal of this system is to increase the number of encounters with these individuals as they get more sad and anxious. So, essentially, a friendly Chat Bot is created to better understand and treat insomniacs. the algorithm, how it works, and why it has to be implemented are briefly discussed in the paper. © 2022 IEEE.

关键词： Deterioration

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A Hybrid SARIMAX Model in Conjunction with Neural Networks for the Forecasting of Life Insurance Industry Growth in thailand 9

A Hybrid SARIMAX Model in Conjunction with Neural Networks f...

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Joint 9th international conference on Digital Arts, Media and technology with 7th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Huadsri, Supika Mekruksavanich, Sakorn Jitpattanakul, Anuchit Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao56000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798350318241

this article is conducted with the primary aim of comparing various forecasting models to identify the optimal model for forecasting the growth of the life insurance industry in thailand. the models under consideration include the SARIMAX model, the Recurrent Neural Network (RNN), and a hybrid model introduced as RNN-SARIMAX. the dataset utilized for this investigation is extensive, consisting of monthly direct premium data from the thai life insurance sector spanning January 2003 to December 2022. the dataset is segregated into two distinct segments. Initially, data from January 2003 to December 2021 is employed for constructing the forecasting model. Subsequently, data from January 2022 to December 2022 is utilized for a comparative analysis of the forecasting models, to determine the most suitable and accurate model for predicting the growth of the life insurance industry in thailand. the evaluation criteria for comparing models include the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R-squared). the consistent findings from our analysis indicate that the SARIMAX model consistently outperforms both the RNN and RNN-SARIMAX models. © 2024 IEEE.

关键词： Recurrent neural networks

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Machine Transliteration for Indian languages: Survey 7

Machine Transliteration for Indian languages: Survey

引用

7th international conference on Parallel, Distributed and Grid computing, PDGC 2022

作者： Kaur, Palakpreet Garg, Kamal Deep Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781665454018

Transliteration is an important aspect when conversing from one language to another. this paper addresses several models designed for the Transliteration of Indian languages. It deals with the methods and approaches used for the languages like Marathi, Hindi, and Punjabi. A Transliteration model is designed on the basis of grapheme, phoneme, correspondence and hybrid methods. Various models have been designed for top-level languages but for Indian languages, the researchers are yet in an infant state as fewer resources are available for them as compared to other languages. Initial research work was rule-based which means rules had to be created for every language taking into consideration of rules and grammar of that language. Later, technology transfer occurred, and alignment models and hybrid models gained popularity. Technological progress in the last few years have improved Transliteration results manifold. Advances in this area contributed to the advancement of communication technologies immensely. To improve data retrieval processing, research developments and Transliteration results, the future scope and requirements of several models are discussed briefly in this paper. the analysis of various models in this paper can be further used to improve research in this field. © 2022 IEEE.

关键词： technology transfer

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A Refined Lane Recognition Method Based on LiDAR and its Verification 23

A Refined Lane Recognition Method Based on LiDAR and its Ver...

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作者： He, Dong Gao, Kai Xie, Jin Du, Ronghua Li, Haojie Li, Xunhao Huang, Shuo College of Automotive and Mechanical Engineering Changsha University of Science & Technology Changsha China

ISBN: (纸本)9781665494571

Lane line recognition and classification is an important content in the field of advanced assisted driving and autonomous driving. Based on LiDAR (Light Detection and Ranging), this paper proposes a fine lane recognition method, namely dashed and solid lane lines classification, and verifies its feasibility. First, the collected LiDAR data is preprocessed, then the lane points in the point cloud are detected, and finally the lane line fitting is carried out for the detected lane points. In the preprocessed process, KD-Tree is used to search the point cloud in the ROI (Region of interest), and then RANSAC (Random Sample Consensus) algorithm is used to fit it. An appropriate threshold is set to extract the optimal plane to get the ground data, and then the window search is used to iteratively search lane points one by one along the road direction, and the final lane lines are obtained by parabolic fitting and parallel fitting of lane points. Dashed and solid lines can be distinguished according to the mean reflection intensity of point clouds on the lane line. Based on MATLAB, the method is verified, and the results show that the method can accurately identify the lane line, and can classify the dashed lines and solid lines. © 2021 IEEE.

关键词： Point cloud compression Laser radar Roads Fitting Solids Reflection Classification algorithms

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Analysis of Apt Attack for Source Tracing in Industrial Internet Environment 7

Analysis of Apt Attack for Source Tracing in Industrial Inte...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Mittal, Tanusha Almusawi, Muntather Kumar, Yogendra Anandhi, R.J. Bhavana, Munugapati Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Electrical Engineering Mathura India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

As businesses become more dependent on networked digital devices, advanced persistent threats (APT) attacks are progressively becoming a component of the threat landscape. these types of attacks target essential pieces of infrastructure. this research paper presents an in-depth examination of source tracing in an industrial internet context, often known as an internet environment in the workplace. the purpose of this analysis is to reinforce the security posture against APT attacks. We are able to investigate a variety of strategies and procedures for tracing the origin of these assaults if we look closely at the fundamental aspects of advanced persistent threats (APT) attacks, such as entrance techniques, the spread of malware, and the removal of data. As a result of this, we investigate the essential aspects that comprise APT attacks. In addition, we discuss the challenges and limitations that are inherent to source tracing in industrial settings and provide workable solutions to these issues and limitations. Industrial companies will have the expertise and resources necessary to protect key infrastructure if advanced persistent threats can be effectively tracked down and neutralised. © 2024 IEEE.

关键词： Network intrusion

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Fake Packet Generation, Detection and its analysis using Network Security 7

Fake Packet Generation, Detection and its analysis using Net...

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7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Pansari, Nikunj Saiya, Rishit Indian Institute of Technology Computer Science and Engineering Dharwad India

ISBN: (纸本)9781665421683

the threat of network Trojans looms largely on mission-critical applications. this research work illustrates the generation of fake packets distinct from general conventional network traffic and their detection using tools like Scapy, Snort, and simulation of an IDS (Intrusion Detection System). It caters to the execution of the Payload (packet generation) and its real-time analytical understanding based on MITM (Man-in-the-middle) attack and its illustration using Scapy, Wireshark & Snort, thus utilizing the network analysis techniques. Furthermore, Port Security strategies to mitigate the most-vulnerable threats are also defined, possible DNA cryptographic techniques, and state-of-the-art Quantum Cryptography is also explored comprehensively. © 2022 IEEE.

关键词： Network security

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Probing Aberrant Heart Rhythms Utilizing CNN Autoencoders for Enhanced ECG Anomaly Recognition 7

Probing Aberrant Heart Rhythms Utilizing CNN Autoencoders fo...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Kaushik, Pratham Singh Gill, Kanwarpartap Chattopadhyay, Saumitra Singh, Mukesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350372816

An application of Convolutional Neural Network (CNN) Autoencoders for anomaly identification in electrocardiogram (ECG) data is investigated in this study using the PTB Diagnostic ECG Database. Heartbeats affected by cardiac abnormalities and normal heartbeats make up the two groups of 14,552 samples in all. Transposed convolution has been used to greatly enhance model performance. this work highlights the need of early cardiac issue detection and introduces a CNN Autoencoder model specifically made to effectively encode and decode ECG data, therefore allowing for the identification of abnormal patterns. Construction of a strong Autoencoder with trained encoder and decoder components to lower reconstruction errors is the process. the astonishing F1 score (65.40%), recall (89.81%), precision (55.23%), and accuracy (76.93%) of the model are demonstrated by the assessment measures. this paper emphasises the importance of early anomaly identification in ECG data using deep learning methods. Higher diagnostic skills and improved patient outcomes are very much possible with this approach. © 2024 IEEE.

关键词： Heart

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