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检索条件"任意字段=International Conference on Advances in Information Communication Technology and Computing"

共 82710 条记录，以下是141-150 订阅

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A Comparative Analysis for Image Reconstruction Using Simple, Vanilla, and Convolutional Autoencoder 5th

A Comparative Analysis for Image Reconstruction Using Simple...

引用

5th international conference on advances in information communication technology and computing, AICTC 2024

作者： Kumar, Amit Soni, Rituraj Poonia, Rakesh Department of Computer Science and Information Technology TMU Uttar Pradesh Moradabad India CSE Department Engineering College Rajasthan Bikaner India MCA Department Engineering College Rajasthan Bikaner India

ISBN: (纸本)9789819761050

In recent days, deep learning technologies have gained more and more interest in computer related task. In generative models, autoencoder (AE) has achieved a tremendous success in many fields, especially in image generation due to ability to produce highly realistic looking images. In synthetic image generation, this learning framework is very effective. In this paper, we take a broad view of autoencoder technique and compare and implement some autoencoders model with different architectures. The first autoencoder is simple autoencoder with only bottleneck layer, second autoencoder is with one hidden layer and bottleneck layer and third autoencoder is the convolutional autoencoder. We use convolutional layers in convolutional autoencoder. We have generated the images using these autoencoders and compare the training loss, validation loss, and accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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DenseNet-Transformer Model: A Hybrid Architecture for Improved Human Action Recognition in Videos 5

DenseNet-Transformer Model: A Hybrid Architecture for Improv...

引用

5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Athilakshmi, R. Indugapalli, Lakshmi Gnaneswara Prasadu Sai, Anusuri Bhuvan Teja, M. Chandra Kiran Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence SRM Nagar Kattankulathur603203 India

ISBN: (纸本)9798350356236

Human Action Recognition (HAR) in video sequences is a critical yet challenging task in computer vision, requiring the capture of both spatial and temporal features for accurate classification of videos. While traditional Convolutional Neural Networks (ConvNets) have achieved considerable success in image recognition, they often struggle with HAR due to their focus on spatial features and inability to effectively model temporal dynamics. To overcome this limitation, we propose a novel DenseNet-Transformer model that combines the strengths of both DenseNet and Transformers. DenseNet is used for robust feature extraction from video frames, while Transformers are leveraged for their powerful sequence modeling capabilities to capture temporal dependencies across frames. Our hybrid DenseNet-Transformer architecture achieves state-of-the-art performance on benchmark datasets, with 93.75% accuracy on the UCF-101 dataset and 87.5% accuracy on the Kinetics dataset. These results highlight the effectiveness of our approach in enhancing the accuracy and reliability of HAR systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Design and Analysis of a Boost Type Nine-Level Inverter with Reduced Switch and Capacitor Voltage Stress 5

Design and Analysis of a Boost Type Nine-Level Inverter with...

引用

5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Gobinath, Harsha Saivarshini, R. Gopinath, N.P. Manickam, Kannan Shunmugam, Prakash St.Joseph's Institute of Technology Department of Eee Chennai India Department of Eee Chennai India Aarupadai Veedu Institute of Technology Department of Eee Chennai India

ISBN: (纸本)9798350356236

This article proposes a boost type (BT) nine-level (9L) switched capacitor (SC) based transformerless inverter topology fo grid connected solar photovoltaic systems. It uses a single source, 10 switches, 2 diodes and 3 SCs. The voltage across the Sc is even which is only half of the output voltage vo/2 and does not employ any complex algorithms for their voltage balancing. Power switches voltage stress is significantly low for the proposed topology and hence low total standing voltage per unit (TSVp.u.). The different modes of operation and design of SC are presented. The simulation verification and prototype results are presented to confirm the operability of the proposed topology. Further, a detailed comparison of same art of topologies has been presented to highlight the features of the proposed topology. © 2025 IEEE.

关键词： Integrated circuit design

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Partial Distributed Deep Learning Inference Model for Image based Edge Device Cluster 24

Partial Distributed Deep Learning Inference Model for Image ...

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8th international conference on Electronic information technology and Computer Engineering, EITCE 2024

作者： Xiao, Dun Wang, Xiaogang Yang, Zhihe Huang, Chao School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In recent years, the development of AI technology has driven the development of network edge applications such as smart manufacturing, smart factories, and smart cities. Deep neural networks are increasingly being deployed in smart manufacturing edge application scenarios to carry out edge detection tasks. However, there is a gap between the expensive DNN computing network and the limited computing power of the edge devices. To deploy compute-intensive neural networks on resource-constrained edge devices, traditional approaches rely either on offloading workloads to a remote central cloud or on optimizing compute on local edge devices. However, the method of uploading tasks to the central cloud is significantly affected by the unreliability and delay of the WAN, and the sensitive information of users is easy to be leaked during transmission, and the local computing method is also limited by the computing power. The latest machine learning models have great demands on memory, computing, and energy, which poses challenges to distributed computing and data aggregation for heterogeneous and resource-constrained edge devices. In order to address these challenges, this paper proposes solutions for different image types in intelligent manufacturing scenarios. When images are processed in the production process of intelligent manufacturing, this paper adopts the method of horizontal partitioning of the DNN model to perform DNN-based inference tasks on the edge device cluster. In order to reduce the consumption of network bandwidth, a dynamic priority scheduling algorithm is used to optimize the distribution of inference tasks at the edge nodes to improve the inference speed. Finally, experiments show that the edge node optimization scheduling method proposed in this paper has a good effect on the performance of running time, communication cost and energy consumption of edge devices. © 2024 Copyright held by the owner/author(s).

关键词： Smart manufacturing

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CorLMI-FSA: An Efficient Feature Selection Approach for Stress Classification Using Physiological Signals 5

CorLMI-FSA: An Efficient Feature Selection Approach for Stre...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Electronics and Communication New Delhi India

ISBN: (纸本)9798350356236

Mental stress in academic settings significantly impacts students' health and performance. Physiological signals such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV) offer valuable insights for stress classification. However, the multidomain nature of these signals often introduces redundancy and irrelevant data, reducing both classification accuracy and computational efficiency [1]. Hence, this paper proposes an efficient feature selection algorithm, CorLMI-FSA (Correlation-Logistic Mutual information Feature Selection Algorithm), which dynamically adjusts redundancy weights through a logistic function to capture nonlinear dependencies among features. The proposed algorithm achieves accuracies of 96.82% and 95.84% for binary and three-level stress classification tasks using Gradient Boosting (GB), enhancing computational efficiency by selecting only the most relevant features. These results underscore CorLMI-FSA's potential as a low-complexity solution for realtime stress monitoring in wearable devices, facilitating timely intervention. © 2025 IEEE.

关键词： Stress analysis

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Unveiling the Connection: Music Preferences and Mental Health Through Machine Learning Analysis 5

Unveiling the Connection: Music Preferences and Mental Healt...

引用

5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Thakeria, Karunya Jain, Kritika Sain, Khushi Rani, Ritu Sharma, Arun Sharma, Rajiv Indira Gandhi Delhi Technical University for Women Department of Ai & Ds Delhi India Bhagwan Parshuram Institute of Technology Delhi India Bhagwan Parshuram Institute of Technology Department of Information Technology Delhi India

ISBN: (纸本)9798350356236

A song can find its way into your heart, and then transform the way you think, as famously conveyed by Elvis Costello. Our emotions shape the type of music we are listening to, and our music preferences impact our emotions as well. During rainy days, we feel sadness gravitating us toward slower music echoing the sentiments. To find inspiration, we listen to pop and rock to boost us up. Music therapy brings benefits to people of all ages, regardless of their musical expertise or health. Music can change our mood, alter body rhythms, and divert our focus, affecting our behavior. The objective of this research is to design a model that determines the possible state of a person's mind based on their usual music preferences. The data we used focused on the effects of music primarily on depression, anxiety, OCD, and insomnia. During our research, we compared 5 different models and saw that Random Forest and K-Nearest Neighbor gave the best results with accuracy reaching up to 85%. Our exploratory data analysis identified that individuals reporting high levels of depression, OCD, and insomnia prefer Lofi music, while those with higher anxiety levels tend toward Folk and K-pop. In conclusion, specific musical preferences can be linked to different conditions, such as anxiety and depression. The model created in this research enables identifying the effect of music on mental health with good precision. Eventually, this process of incorporating music into mental health management can be personalized. © 2025 IEEE.

关键词： k-nearest neighbors

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Group authentication algorithm based on region grouping and aggregate signcryption 4

Group authentication algorithm based on region grouping and ...

引用

4th international conference on Network communication and information Security, ICNCIS 2024

作者： Li, Yong Gu, Jian Qiu, Jinyi He, Wenbo Zuo, Jiashuo State Grid Shaoxing Power Supply Company Zhejiang Shaoxing312000 China Department of Electronic and Communication Engineering North China Electric Power University Hebei Baoding071003 China

ISBN: (数字)9781510688261

ISBN: (纸本)9781510688254

With the rapid development of the power Internet of Things and the increasing number of IoT devices and application scenarios, new requirements are put forward for the reliability and efficiency of equipment access certification. Aiming at the problems of low efficiency and malicious attack of traditional authentication methods, this paper proposes a group authentication algorithm based on regional grouping and aggregate signcryption. Firstly, according to the characteristics of IoT devices, a distributed authentication scheme based on regional grouping is designed. Then each group relies on aggregation signcryption technology for group authentication of IoT devices. Finally, the calculation and communication cost of the algorithm are compared and analyzed. The results show that the proposed algorithm can improve the authentication efficiency. © 2025 SPIE.

关键词： Network security Computer security Internet of things Local area networks Data transmission information security Telecommunications Zinc computing systems Fiber optic gyroscopes

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Time Series Forecasting for COVID-19 Confirmed Cases Using Transformer Based Stacked LSTM Model 8th

Time Series Forecasting for COVID-19 Confirmed Cases Using T...

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8th IFIP TC 12 international conference on Computer, communication and Signal Processing, ICCCSP 2024

作者： Penumadi, Sri Naga Jathin Nambiar, Vaishakh Vignesh, G.S. Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Perak Malaysia

ISBN: (纸本)9783031736162

The worldwide impact of COVID-19 necessitates accurate forecasting for informed decision-making by governments and health entities. This study proposes neural network architectures for time series forecasting of confirmed COVID-19 cases based on historical data. The models, including variants of LSTM networks, CNN-GRU hybrids, RBMs, DBNs, and self-attention mechanisms, are trained on comprehensive datasets of confirmed cases, deaths, recoveries, and other variables across various countries. Comparative analysis indicates that CNN-GRU and LSTM models exhibit superior performance in forecasting accuracy, surpassing RBM, DBN, and self-attention models across metrics like Mean Absolute Error and Root Mean Squared Error. This research emphasizes the viability of employing advanced neural networks for precise COVID-19 spread predictions, pivotal in shaping public health policies during global crises. © IFIP international Federation for information Processing 2025.

关键词： Time series

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Research on Precision Enhancement of Wheat Pest and Disease Image Recognition through Deep Learning Feature Fusion 5

Research on Precision Enhancement of Wheat Pest and Disease ...

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5th international conference on advances in Electrical, Electronics and computing technology, EECT 2025

作者： Wang, Qi Lv, Yuanyuan Hao, Buwei Li, Yanxin Taiyuan University of Technology Shanxi China

ISBN: (纸本)9798331541545

This study proposes a method combining deep learning and feature fusion to improve wheat pest and disease image recognition accuracy. The uneven distribution of pests and diseases leads to varied image features, challenging traditional recognition methods. By using image segmentation to locate affected regions, the study extracts key features (e.g., edges and gradients) with deep learning models. Feature fusion then integrates multi-scale features to capture comprehensive information. The fused features are used to train deep learning models for precise classification. Experiments show that the proposed method achieves 99% - 100% accuracy and near-perfect F1 scores, significantly outperforming traditional methods. This approach provides robust support for early diagnosis and control of wheat pests and diseases. © 2025 IEEE.

关键词： Image segmentation

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Analysis for Predicting English Premier League Matches Using Clifford-Steerable Convolutional Networks 5

Analysis for Predicting English Premier League Matches Using...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Mohideen AbdulKader, M. Vaithieswaran, S. Advay Rao, Tanuj School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Tamilnadu Chengalpattu India

ISBN: (纸本)9798350356236

The most widely watched football league globally is the Barclays English Premier League (EPL), the future performance of which attracts attention from both scholars and practitioners in terms of outcome prediction. It is still challenging to provide accurate predictions per match result because of the many attempts tried in the past due to complicated interference of many factors that entice game dynamics. In order to predict the EPL matches, this paper proposed a Cubature Kalman Filter Haar Wavelet Transform Clifford-Steerable Convolutional Neural Network with Mud Ring Algorithm (CKFHWTCSCNNet-MRA). The data was downloaded from Kaggle and the OCRCKF was used for data pre-processing. For feature selection the Q-DHWT was used, which stands for Quantized Discrete Haar Wavelet Transform. CS-CNNs were then applied to predict match results while The Mud Ring Algorithm (MRA) was applied to the analysis of hyperparameters. The rationale and validation of the proposed model is established based on the performance measures employed in predicting wins/loses of the match which yielded an outstanding accuracy, specificity, sensitivity and recall of 99%, F1-Score of 99% and error of 0.25%. © 2025 IEEE.

关键词： Image reconstruction

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