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检索条件"任意字段=5th International Conference on Computing in Engineering and Technology,ICCET 2020"

共 13518 条记录，以下是121-130 订阅

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Predictive Parenting: An IoT-Enabled Cradle System with AI-Driven Sleep Pattern Analysis 5

Predictive Parenting: An IoT-Enabled Cradle System with AI-D...

引用

5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Sakthidevi, I. Vijaya Shankar, S. Santhana Krishnan, R. Kuruvilla, Sincy Elezebeth Bharath, M. Gopikumar, S. Adhiyamaan College of Engineering Department of Information Technology Hosur India Sona College of Technology Department of Electrical & Electronics Engineering Salem India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Civil Engineering Chennai India

ISBN: (纸本)9798350395235

this research study introduces an IoT-Enabled Cradle System with the "DreamFlowRNN"algorithm, combining Recurrent Neural Networks (RNN) and Flow-based models. Equipped with smart sensors, the cradle monitors infants' physiological and environmental data during sleep. DreamFlowRNN analyzes this information, classify sleep stages, detect disturbances, and predict future patterns. the proposed system aims to provide predictive parenting capabilities by offering valuable insights into infants' sleep behavior. Extensive simulations, comparing DreamFlowRNN to existing algorithms like LSTM, CNN, and SVM, reveal its superior performance in accurate sleep stage classification, disturbance detection, and generation of realistic synthetic sleep patterns. the proposed system stands out with higher F1-score, precision, inception score, and lower Frechet Inception Distance (FID) value, emphasizing its efficacy in enhancing the well-being of infants through advanced AI-driven sleep pattern analysis. © 2024 IEEE.

关键词： Internet of things

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Parallel Optimization of Remote Sensing Image Geometric Correction on DCU 5

Parallel Optimization of Remote Sensing Image Geometric Corr...

引用

5th international conference on Computer engineering and Application (ICCEA)

作者： Liu, Runjie Wang, Siqi Zhengzhou Univ Natl Supercomp Ctr Zhengzhou Zhengzhou Henan Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Henan Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Geometric correction is essential in the preprocessing of remote sensing images, ensuring the precision and reliability of spatial data, crucial for further analysis such as target recognition and feature extraction. Nevertheless, handling a lot of high-resolution images often encounters computational inefficiencies and limited real-time processing capabilities. this paper introduces a parallel optimization approach for geometric correction on multi-DCU heterogeneous clusters, leveraging a detailed thread mapping strategy tailored to the DCU architecture. It capitalizes on the specific hardware characteristics and execution models of the DCU for parallel processing. the study delves into the common bicubic interpolation resampling method used in geometric correction, optimizing thread mapping to match the DCU's computational and storage architecture. this method allows a single thread to handle multiple pixel computations, enhancing data reuse during interpolation. the research accomplishes parallel geometric correction processing on DCU and significantly curtails memory access through optimization, improving algorithmic efficiency. Comparative experimental outcomes show that the proposed parallel strategy markedly outperforms traditional serial CPU processing, multicore CPU processing, and unoptimized DCU acceleration, evidencing substantial speedup across processing different image sizes.

关键词： Geometric correction Parallel computing DCU HIP

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ASIC acceleration of floating-point arithmetic: Empowering fast and reliable computations 5th

ASIC acceleration of floating-point arithmetic: Empowering f...

引用

5th international conference on Advances in AI for Biomedical Instrumentation, Electronics and computing, ICABEC 2023

作者： Siddireddy, Kapileswarreddy Hegde, Ganapathi Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9781032642987

Modern computer systems are fundamentally based on floating-point arithmetic, which enables high precision computations and the execution of intricate algorithms in hardware. Floating-point units (FPUs) are designed and implemented for use in Application Specific Integrated Circuits (ASICs). In this study, I focused on the difficulties and trade-offs associated with obtaining high performance and low power consumption. Here, I discuss several methods for incorporating FPUs in ASIC designs, such as dedicated units and floating-point arithmetic and its benefits. the performance and power use of several FPU designs on a 45nm technology node are also shown in the experimental findings that we have presented. the study of binary operations is crucial to engineering science. Verilog-HDL is used to design, and Cadence is used to implement them. this paper also includes an analysis on suggested method’s area, power, and latency for various specifications. To effectively handle FPUs’ implementation by using tools like Genus, Tempus, Innovus. © 2024 the Author(s).

关键词： Application specific integrated circuits

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Improvement of Image Classification Technique Using Transfer Learning 5

Improvement of Image Classification Technique Using Transfer...

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5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Bhonsle, Devanand Uzma, Ruhi Prasad, Priyanka Bankar, Rushikesh Nayak, Manjushree Massey, Sampada Vishwas Rizvi, Tanu Shri Shanakaracharya Technical Campus Bhilai India Anjuman College of Engineering & Technology Nagpur India Shri Shanakaracharya Institute of Professional Management & Tech Bhilai India G. H. Raisoni Institute of Engineering and Technology Nagpur India Nist University Odisha Berhampur India Shri Shanakaracharya Technical Campus Bhilai India

ISBN: (纸本)9798350356236

Image classification is one of the fundamental task in the digital image processing and computer vision. It is very essential to get high accuracy in it which requires substantial computational resources and a large dataset for the training using deep neural networks. Transfer learning technique is one of the solution which mitigate various challenges by leveraging pre-trained models on large datasets. In this paper;the effectiveness of the transfer technique is exhibited for the purpose of enhancing the image classification. A comprehensive survey has been done of the recent advancement for the purpose to investigate the strategies of transfer learning technique including feature extraction, fine tuning and domain adaption. Experimental results exhibits the efficacy of the transfer learning in the improvement of classification accuracy with less computational complexity. this paper also discusses practical consideration, challenges and future research scope for the utilization of transfer learning for image classification applications. © 2025 IEEE.

关键词： Transfer learning

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Early Detection of Pancreatic Cancer using Machine Learning Algorithm 5

Early Detection of Pancreatic Cancer using Machine Learning ...

引用

5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Bhoomika, B.K. Kanmani, K. Smilarubavathy, G. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350395235

Pancreatic cancer is very challenging since it has poor results when it is diagnosed at the later stages, and proper testing tools are lacking to treat patients. Endoscopic harmonic ultrasound imaging is used to characterize pancreatic masses. For detecting the pancreatic cancer machine learning techniques are mostly used. the main aim of this work is to compare and explain how NN and logistic algorithms give better results when it works with ensemble ML algorithms for diagnosing pancreatic cancer. the datasets are collected through the Kaggle platform. When compared to related work from the literature, it is shown that the ANN approach with a logistic algorithm achieved 90.50% accuracy from another machine learning algorithm. © 2024 IEEE.

关键词： Diseases

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Enhancing the Security and Integrity of Intrusion Detection Systems based on CNN and LSTM Approach for Cloud computing 5

Enhancing the Security and Integrity of Intrusion Detection ...

引用

5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Arunkumar, K. Kumar, S. Sai Vulapula, Sridhar Reddy Inayathulla, Mohammed Venkatesh, N. Babu, G. Charles Malla Reddy Engineering college Department of Cse Telangana Secunderabad India Pvp Siddhartha Institute of Technology Department of It Andhrapradesh Vijayawada India Vignana Bharathi Institute of Technology Department of It Telangana Hyderabad India Gates Institute of Technology Department of Cse Andhra Pradesh Gooty India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798331504403

As cloud computing environments become increasingly prevalent, ensuring the security and integrity of these systems is paramount. Traditional intrusion detection systems (IDS) often fall short in addressing the dynamic and complex nature of cloud infrastructures. this study presents a enhanced security and integrity of Deep Learning (DL) approach for intrusion detection in cloud computing environments, leveraging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. By incorporating an attention mechanism into the deep learning framework, the suggested system is able to recognize temporal patterns that point to intrusions and efficiently record spatial characteristics. Comprehensive tests are carried out on standard cloud computing datasets, proving that the system performs better than traditional techniques in identifying a broad variety of intrusion types. the findings point to the possibility of using deep learning methods to greatly enhance the responsiveness and resilience of IDS in cloud environments, opening up opportunities for more reliable and secure cloud computing services. © 2024 IEEE.

关键词： Network intrusion

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Deep Learning-Based Road Sign Detection & Recognition System for Autonomous Vehicles 5

Deep Learning-Based Road Sign Detection & Recognition System...

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5th international conference on Advanced Research in computing, ICARC 2025

作者： Jayarathne, P.G.B.A.H. Prabhath Buddhika, R.A. Nsbm Green University Faculty of Engineering Department of Electrical Electronic and Systems Engineering Homagama Sri Lanka

ISBN: (纸本)9798331530983

the rapid evolution of AV technology demands reliable systems for the detection and recognition of road signs. Especially in unique traffic environments like Sri Lanka. this study addresses a gap in localized research by developing a deep learning-based road sign recognition system. the system is designed specifically for Sri Lankan traffic scenarios. A robust dataset of Sri Lankan Road signs was created through manual and online data collection. the data was annotated using Roboflow. the model, based on YOLOv8m, was trained and integrated into a moving vehicle for real-time detection and recognition. the experimental setup integrates the trained model into a moving vehicle for real-time detection and recognition. the results show very good accuracy and reliability. thus, proving the potential of this system to improve road traffic safety and efficiency. this study contributes to autonomous navigation research in Sri Lanka. It also provides a foundation for future work on dataset expansion and hybrid models to enhance efficiency and robustness. © 2025 IEEE.

关键词： Deep learning

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Transforming High-Frequency Trading Predictions with CNN-LSTM Networks 5

Transforming High-Frequency Trading Predictions with CNN-LST...

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5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Priya, S. Baghavathi Priyanga, S. Kamisetty, Sri Surya Shobith Neelapala, Venkata Sai Vinay Nithila, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Chennai India Vellore Institute of Technology Department of Electronics & Communication Engineering Vellore India Vellore Institute of technology Department of Computer Science & Engineering Vellore India

ISBN: (纸本)9798350356236

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. this paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. this work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets. © 2025 IEEE.

关键词： Deep learning

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Enhancing Low-Power Performance of GAA-TFETs through Gate Work Function Optimization 5

Enhancing Low-Power Performance of GAA-TFETs Through Gate Wo...

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5th IEEE international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Dandu, Obulesu Sathish, Anchula Jntua Department of Ece Andhra Pradesh Anantapuramu515002 India Govt.Polytechnic Department of Ece Andhra Pradesh Tadipatri515411 India Rajeev Gandhi Memorial college of Engineering and Technology Department of Ece Andhra Pradesh Nandyal518501 India

ISBN: (纸本)9798350356236

In this work, we analyze the effect of gate work function engineering on the performance of cylindrical GAA-TFETs. Using a non-local BTBT model device characteristics are simulated for several gate work functions. A clear tradeoff between ION and IOFF is shown, while higher work functions result in lower IOFF with favourable ION. For a work function of 3.9 eV, ' we observe well-balanced performance with a threshold voltage of 0.56 V, while the ION/IOFF ratio is around 1.02× 1010. Increasing the work function up to 4.0 eV increases the threshold voltage up to 0.66 V, while increasing the ION/IOFF ratio up to 1.97× 1011 and further reduces the leakage current. these results underscore the significant interrelation between optimized gate work function and energy efficiency balanced by performance for desirable low-power applications. © 2025 IEEE.

关键词： Leakage currents

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5th IEEE international conference on Artificial Intelligence in engineering and technology, IICAIET 2023

5th IEEE International Conference on Artificial Intelligence...

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5th IEEE international conference on Artificial Intelligence in engineering and technology, IICAIET 2023

ISBN: (纸本)9798350304152

the proceedings contain 71 papers. the topics discussed include: the development of simultaneous Congkak game with intelligent agents;energy efficiency of iot-edge computing in smart grid using deep reinforcement learning;day-old chick sexing using convolutional neural network (CNN) and Computer Vision;Efficient Robot Following for Enhanced Formation Control in Limited computing Environments;Nondestructive Freshness Classification System for Cultured Pacific White Shrimp using Electronic Nose and Artificial Neural Network;and Data Augmentation Approach for Language Identification in Imbalanced Bilingual Code-Mixed Social Media Datasets.

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