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

共 11333 条记录，以下是1241-1250 订阅

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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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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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Blockchain-Enabled Decentralized Trust Management and Secure Voting system 7

Blockchain-Enabled Decentralized Trust Management and Secure...

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

作者： Minu, R.I. Nagarajan, G. SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372816

In the era of digital transformation, trust and security are paramount in ensuring the integrity of various online transactions and processes, including voting systems. Traditional centralized trust management and voting systems have faced challenges related to security, transparency, and accountability. To address these issues, this research introduces a novel Blockchain-Enabled Decentralized Trust Management and Secure Voting System. It leverages blockchain technology, a decentralized and immutable ledger, to enhance trust, transparency, and security in trust management and voting processes. this system combines several key components, including smart contracts, cryptographic techniques, and distributed ledger technology, to create a robust and tamper-resistant infrastructure. In the trust management aspect, the proposed system employs a decentralized reputation system where individuals and entities can build trust through transparent and verifiable interactions. Smart contracts automate trust-building processes and provide a reliable mechanism for dispute resolution. this system allows for trust to be quantified and established in a trustless environment. the secure voting component introduces a tamper-proof and transparent voting system. through the use of cryptographic keys and digital signatures, voters can securely cast their votes while ensuring anonymity and integrity. All voting transactions are recorded on the blockchain, providing a permanent and auditable record of the election process. Verification of election results becomes accessible to all stakeholders, enhancing transparency and trust in the electoral process. the Blockchain-Enabled Decentralized Trust Management and Secure Voting System represents a significant step towards enhancing trust and security in critical online processes, such as voting. By combining blockchain technology, cryptography, and decentralized trust mechanisms, it offers a promising solution to address the challenges associat

关键词： Decentralized finance

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Machine Learning in Economic Forecasting: A Comprehensive Review 7

Machine Learning in Economic Forecasting: A Comprehensive Re...

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

作者： Gupta, Arhaan Kumar, Kirana Asha, V. Kumar, S. Naveen Modern School Barakhamba Road New Delhi India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Master of Computer Application Karnataka Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

Economic experts investigate links between economic entities by analyzing large volumes of data. Econometrics is the process of using statistical methods on economic data to find these links through independent observations. Conventional econometric techniques evaluate economic data using only mathematical and statistical concepts. Conversely, applied econometrics concentrates on real-world observations, like economic results, market price swings, and forecasts. In recent years, Machine Learning models have become increasingly popular across numerous fields, including economics. When compared to typical econometric models, these models provide more accurate analysis of economic data and have shown to be more efficient in tasks like classification. this study examines some machine learning techniques are especially utilized with economic data. It also explores metrics for econometric model analysis and supervised machine learning techniques. the paper explores the possible future uses of the machine learning models that Economic experts find most appealing. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluation of Cryptographically Secure Pseudo Random Number Generators for Post Quantum Era 7

Evaluation of Cryptographically Secure Pseudo Random Number ...

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

作者： Kumar, Atul Mishra, Arun Defence Institute of Advanced Technology Computer Science and Engineering Pune India

ISBN: (纸本)9781665421683

the construction of a Pseudo-Random Number Generator (PRNG) is an extremely decisive task in the field of Cryptography, Gaming, Computational statistics, Simulation processes, Gambling, and other fields. But many of the PRNGs are not cryptographically secure so various kinds of models have been presented to generate Pseudo-Random numbers. As generators are used in the field of cryptography too therefore it is inescapable to assess it among scientists and researchers. In this study, we would evaluate different kinds of RNGs against quantum attacks including the one which is based on lattice-based cryptography. In the end, we would contrive to wind up how the lattice-based PRNGS are pragmatic for the Post Quantum Cryptography. © 2022 IEEE.

关键词： Random number generation

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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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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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Machine Transliteration for Indian languages: Survey 7

Machine Transliteration for Indian languages: Survey

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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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Interaural time difference based spatial release from masking with asymmetric hearing over a video conference app 7

Interaural time difference based spatial release from maskin...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Chen, Jiachen Zhou, Huali Yu, Guangzheng Meng, Qinglin South China University of Technology Acoustics Laboratory School of Physics and Optoelectronics Guangzhou China Shenzhen University College of Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9781665478571

Due to the difficulty of conducting offline laboratory experiments during the coronavirus outbreak, remote experiments such as experiments over video conference apps have become an important method to collect data for hearing researchers. For remote testing using headphone presentations, compared to monaural (i.e., unilateral or diotic) audio stimuli, dichotic stimuli (i.e., sounds with differences between left and right ears) are used in relatively less studies. In this study, a binaural hearing task of spatial release from masking (SRM) was tested in laboratory and over a video conference app, i.e., Tencent Meeting. In the experiment, the effects of interaural time difference (ITD) on SRM were compared between symmetric and asymmetric hearing which were realized by using a fixed interaural level difference (ILD) of 0 dB and -15 dB respectively. Results showed that 1) SRM was observed in the remote test but it was >4 dB smaller than the laboratory test;2) asymmetric hearing would lead to a ~2 dB significant decrease in the amount of masking release in both laboratory and remote conditions. the results indicate that binaural hearing could be measured remotely using the stereo sharing mode of video conference apps, but the effects of binaural cues especially ITDs may be degraded to some extent. © 2022 IEEE.

关键词： Audition

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Enhancing Disease Prediction through Random Forests in Healthcare Analytics 7

Enhancing Disease Prediction through Random Forests in Healt...

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

作者： Kukreti, Sanjeev Al-Attabi, Kassem Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Boob, Nandini Shirish Srivastava, Abhinav 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 New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Department of Electrical Engineering Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

this research investigates the application of Random Forest algorithms to enhance disease prediction within healthcare analytics. Using large healthcare datasets, the research compares Random Forests to other machine learning algorithms, such as Neural Networks and Support Vector Machines, and assesses their ability to forecast different illnesses. According to the findings, Random Forests have the ability to reliably forecast diseases since they provide greater accuracy, precision, recall, and F1-score. In addition, healthcare providers may benefit greatly from the model's feature significance metrics, which help them understand which aspects have the most impact on illness outcomes. this study emphasises the revolutionary power of Random Forests to improve healthcare diagnoses, personalised treatment regimens, and patient care overall. © 2024 IEEE.

关键词： Disease Prediction Healthcare Analytics Machine Learning Predictive Performance Random Forests

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