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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8166 条记录，以下是1041-1050 订阅

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Unlocking Carbide Vacancy Energy in Molybdenum Carbide: Predictive Insights from Numerical Descriptors and Neural networks

Unlocking Carbide Vacancy Energy in Molybdenum Carbide: Pred...

引用

BioInspired processing (BIP), IEEE international conference on

作者： Karen Tovar Marvin Coto-Jiménez Mauricio Gutierrez Ignacio Borge School of Electrical Engineering University of Costa Rica San José Costa Rica School of Chemistry University of Costa Rica San José Costa Rica School of Chemistry Bar-Ilan University Ramat Gan Israel

Carbides, as exemplified by Mo 2 C, have various valuable properties that affect various industries. Understanding the influence of carbon vacancy patterns on these properties is an essential yet challenging task. this work presents a two-stage approach. First, novel numerical descriptors linked to molecular structure are predicted using Neural networks. Subsequently, using these descriptors alone, carbide vacancy energy is accurately forecasted through Neural networks and an Attention mechanism. the correlation unveiled hints at a numerical relationship between these descriptors and energy, opening avenues for interpreting their significance. this research contributes to computational materials science, offering insights into carbide intricacies and inspiring innovative applications.

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Structural networks for Brain Age Prediction 5

Structural Networks for Brain Age Prediction

引用

5th international conference on Medical Imaging with Deep Learning (MIDL)

作者： Pina, Oscar Cumplido-Mayoral, Irene Cacciaglia, Raffaele Gonzalez-de-Echavarri, Jose Maria Gispert, Juan Domingo Vilaplana, Veronica Tech Univ Catalonia UPC Barcelona Spain Barcelona Brain Res Ctr BBRC Barcelona Spain

Biological networks have gained considerable attention within the Deep Learning community because of the promising framework of Graph Neural networks (GNN), neural models that operate in complex networks. In the context of neuroimaging, GNNs have successfully been employed for functional MRI processing but their application to ROI-level structural MRI (sMRI) remains mostly unexplored. In this work we analyze the implementation of these geometric models with sMRI by building graphs of ROIs (ROI graphs) using tools from Graph Signal processing literature and evaluate their performance in a downstream supervised task, age prediction. We first make a qualitative and quantitative comparison of the resulting networks obtained with common graph topology learning strategies. In a second stage, we train GNN-based models for brain age prediction. Since the order of every ROI graph is exactly the same and each vertex is an entity by itself (a ROI), we evaluate whether including ROI information during message-passing or global pooling operations is beneficial and compare the performance of GNNs against a Fully-Connected Neural Network baseline. the results show that ROI-level information is needed during the global pooling operation in order to achieve competitive results. However, no relevant improvement has been detected when it is incorporated during the message passing. these models achieve a MAE of 4.27 in hold-out test data, which is a performance very similar to the baseline, suggesting that the inductive bias included with the obtained graph connectivity is relevant and useful to reduce the dimensionality of the problem.

关键词： Graph Neural Network Graph Signal processing Structural MRI Brain Age

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Efficient Business Meeting Summarization: Leveraging Natural Language processing for Enhanced Productivity 5th

Efficient Business Meeting Summarization: Leveraging Natural...

引用

5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Shanthamalar, J. Jeslin Nithya, S. Kumar, Alle Nithin Chandra, Beraboina Jaya Nancy Noella, R.S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819788644

In the contemporary corporate landscape, efficient utilization of time is paramount for organizational success. Business meetings, a fundamental component of decision-making processes, often result in copious amounts of information exchange. this project aims to streamline and enhance the post-meeting experience through the development of an Automated Business Meeting Summarization system. Additionally, the system employs sentiment analysis to gauge the overall tone and mood of the meeting, providing valuable insights into team dynamics and engagement levels. the project aims to empower organizations with an efficient tool that not only accelerates the decision-making process but also fosters better collaboration and understanding among team members. In contrast, zero-shot Whisper models demonstrate a robustness frontier that includes the 95% confidence interval for human performance. the BART-large-CNN-SAMSum model demonstrates promising performance on the SAMSum summarization dataset, achieving a ROGUE-1 score of 42.621. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sentiment analysis

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Comparative Analysis of CNN-based Deep Learning Approaches on Complex Activity Recognition 7

Comparative Analysis of CNN-based Deep Learning Approaches o...

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7th international conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9781665495103

For those with Parkinson's disease, freezing of gait (FOG) could be a distressing condition. In individuals with diabetes, FOG could have a significant influence on their quality of life and cause falls, which can be dangerous. A questionnaire has traditionally been used to measure the severity of FOG symptoms;nevertheless, this technique is subjective and can not accurately reflect the intensity of this illness. the adoption of sensor-based devices may give reliable and objective information to follow the progression of symptoms, allowing for better Parkinson's disease management and therapy. A compact deep convolutional neural network comprising squeeze and excitation components, which we called SE-DeepConV model, was designed to identify FOG utilizing wearable data from the sensors. the SE-DeepConV model was constructed and evaluated using a publicly accessible standard FOG dataset, particularly the Daphnet dataset. the SE-DeepConV model outperforms other benchmark deep learning models in terms of accuracy, with the most outstanding accuracy of 95.65%, according to research observations. © 2022 IEEE.

关键词： Deep neural networks

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Simulation Analysis of Inter-digital Electrodes sensor Based on HFSS 1

引用

5th EAI international conference on 6G for Future Wireless networks, 6GN 2022

作者： Yuan, Hanxiao Shi, Yao Harbin Institute of Technology Guangdong Shenzhen51800 China

ISBN: (数字)9783031360145

ISBN: (纸本)9783031360138

In the past few decades, as people have become more and more concerned about physical health, a large number of studies have been used to measure physiological information (such as heart rate, respiration, and blood pressure), which provides great value for predicting diseases. As a component of sensors, interdigital electrodes have developed rapidly in the field of sensors and are widely used in the field of biological detection. Moreover, interdigital electrodes can be non-invasive, low-cost, reliable, and not affected by environmental conditions, making interdigital electrodes’ performance more important. this paper proposes a method to simulate and optimize interdigital electrodes’ performance through HFSS. Firstly, the equivalent circuit model of the interdigital electrode is studied and calculated, and the initialization sensor parameters are obtained. then the effects of electrode length, electrode width, spacing, and substrate thickness on the performance of interdigital electrodes are analyzed by simulation, and the interdigital electrode model is optimized to better detect some physiological information about the human body. In addition, the detection of human physiological information is simulated, which can provide a reference for future research. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Blood pressure

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Accessing the Performance of K-Medoid, K-Means and FCM Clustering Techniques for Wireless sensor networks

Accessing the Performance of K-Medoid, K-Means and FCM Clust...

引用

India Council international Subsections conference (INDISCON), IEEE

作者： N. thiyagarajan N. Shanmugasundaram Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

the real-time data collection and monitoring process demands Wireless sensor networks (WSNs) as a pivotal technology that employs randomly distributed battery-operated sensor modules to facilitate seamless data transmission from remote and often hostile environments. the versatility of WSNs extends across various domains, including monitoring of smart cities. By harnessing the power of WSNs, we can optimise processes, enhance productivity, and mitigate risks more effectively. Clustering is the widely discussed concrete part of the energy mitigating process. We wish to evaluate the clustering in WSN with the Fuzzy C-Means Clustering, K-medoid clustering, and K-means. the clustering process was analysed with the estimation of the silhouette score. In order to assess their performance, numerous simulations are conducted in the MATLAB environment. Furthermore, we discussed their mean silhouette score, clustering ability for different scenarios in WSN.

关键词： Productivity Wireless sensor networks Smart cities Estimation Routing Real-time systems Energy efficiency MATLAB Monitoring Optimization

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Deformation Reconstruction of Plate Structure Based on FBG tilt sensor

Deformation Reconstruction of Plate Structure Based on FBG t...

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sensors and Information Technology (ICSI), international conference on

作者： Jianjun Pan Yahong Li Wei Hou Renxin Wu National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan China School of Mechanical and Electrical Engineering Wuhan University of Technology Wuhan China School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

In this paper, a high sensitivity fiber Bragg grating (FBG) tilt sensor with 281.7 pm/° is designed, which is used to monitor plane deformation. To make the reconstruction result more accurate, this paper optimizes the trigonometric function reconstruction algorithm, and calculates the plane deformation from the x direction and the y direction respectively. In the experiment, the force is applied at the loading position of the thin plate to deform it, and the optimized trigonometric function algorithm is used to reconstruct the thin plate. the error of the final reconstruction result is within 5 %. the results show that the use of FBG tilt sensor can accurately monitor the deformation of the plane.

关键词： Sensitivity Deformation Loading Force Metals Reconstruction algorithms Logic gates Fiber gratings Information technology Monitoring

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An In-depth Analysis of Transformer Models for Enhanced Performance in Fake News Detection

An In-depth Analysis of Transformer Models for Enhanced Perf...

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Image processing and Capsule networks (ICIPCN), international conference on

作者： Shailender Kumar Naman Malhotra Naman Garg Md Asjad Shakil Dept. of Comp. Science & Engineering Delhi Technological University Delhi India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

the internet has revolutionized global communication, connecting the entire world. However, a significant challenge it faces is the rampant spread of fake news. Transformer models have transformed Natural Language processing (NLP). this study aims to assess the effectiveness of various transformer models in identifying false information using the LIAR benchmark datasets, which are sourced from credible fact-checking and claim verification platforms. Additionally, this study proposes a modified transformer model to enhance the efficiency in detecting the fake news. By integrating advanced NLP techniques, this research improves the accuracy and reliability of fake news detection, addressing a critical issue in this digital era.

关键词： Training Large language models Image processing Semantics Transformers Natural language processing Reliability

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Framework Design of Fault Diagnosis System Based on Improved LSTM networks

Framework Design of Fault Diagnosis System Based on Improved...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Yixin Wang University of Geosciences (Wuhan) Wuhan China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In the current era of high industrialisation, fault diagnosis of complex industrial systems is facing unprecedented challenges. Traditional fault diagnosis methods based on physical models and signal processing gradually show their limitations, especially when facing nonlinear and nonsmooth signals as well as fusion of multi-source information. In this paper, we propose a framework design of a fault diagnosis system based on a long short-term memory network (LSTM) with improved attention mechanism. the system employs high-precision sensors to collect real-time operating signals from key components, and analyses and diagnoses these signals through a deep learning model to quickly and accurately determine whether there is a fault in the equipment and provide detailed fault type and location information. In particular, an attention mechanism is introduced to enhance the model's focus on key features in the fault signals, thereby improving the accuracy and reliability of fault diagnosis. Experimental results show that the proposed LSTM-Attention network performs well on the bearing fault dataset and achieves high classification accuracy.

关键词： Fault diagnosis Deep learning Attention mechanisms Accuracy Signal processing sensor systems Real-time systems Power electronics sensors Long short term memory

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Salp Swarm Algorithm for Hierarchical Cluster Head Selection in Wireless sensor networks 5

Salp Swarm Algorithm for Hierarchical Cluster Head Selection...

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5th international conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Pitchaimanickam, B. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnan koil Tamilnadu 626126 India

ISBN: (纸本)9781665435246

the sensor nodes are positioned in random locations for monitoring the environmental conditions in WSN. the sensor nodes are cooperatively passed the entire information to BS. the sensor node is operated by using the battery sources and also used to perform sensing, computing, and communication. these processes need additional energy for data processing and affect the lifetime of the network. the clustering method provides an efficient solution for handling the network lifetime issue. In this paper, the salp swarm optimization is introduced in the LEACH-C to choose an effective CH. the proposed algorithm is mimicked by the salp behavior in the ocean for generating the salp chain. the salp chain is generated by using the leaders and followers for balancing the exploitation and exploration phases of optimization. Salp swarm algorithm is used to select the higher residual energy of the node and minimum distance between node and base station for CH selection. the proposed methodology is evaluated with energy consumption and network lifetime. the proposed method extends the lifetime of the network and minimizes energy utilization. © 2021 IEEE.

关键词： Energy utilization

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