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Assessing the spatial-temporal variability of net primary productivity in Qinghai-Tibet Plateau based on Google Earth Engine platform 7

Assessing the spatial-temporal variability of net primary pr...

引用

2022 7th international conference on information Science, computer technology and Transportation, ISCTT 2022

作者： Wang, Yuwei Meng, Xiaoliang Wu, Kaicheng Gao, Wang School of Artificial Intelligence Jianghan University Wuhan430056 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China

ISBN: (纸本)9783800760565

Based on Google Earth Engine (GEE) platform, combined with NPP, land use and climate datasets, this study analyzed the spatial-temporal patterns of NPP variations in Qinghai-Tibet Plateau from 2001 to 2020 using linear trend analysis, and explored the response of NPP to climate change and human activities. the findings are as follows: (1) From 2001 to 2020, the spatial distribution of NPP in the Qinghai-Tibet Plateau demonstrated a decreasing trend from southeast to northwest. Except for forested areas in the eastern and southeastern parts, NPP values were relatively low in other regions. (2) the evolution trajectory of vegetation NPP in Qinghai-Tibet Plateau during the past 20 years exhibited significant spatial heterogeneity. Approximately 94% of the vegetation in the study area had been restored to some extent. (3) there was significant spatial-temporal variability in the comparative roles of climate change and human activities during the NPP transformation in Qinghai-Tibet Plateau, and the restoration effect of climate change and human activities on vegetation was significantly more pronounced than the degradation effect. (4) Compared with the traditional desktop remote sensing processing platform, GEE platform enabled rapid invocation of massive online resources, thus providing strong technical support for large scale and long time series remote sensing data. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Climate change

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Development of flexible Text Input Device Based on Image processing for Each Level of Disability Person 7

Development of flexible Text Input Device Based on Image Pro...

引用

7th international conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2022

作者： Kinjo, Hiroki Mbaitiga, Zacchari Okinawa College National Institute of Technology Dept. of Medeia Information Engineering Japan

ISBN: (数字)9781665482295

ISBN: (纸本)9781665482295

this research aims to develop a flexible input text device for physically disable person using image processing. In propose method we use a camera to detect the disable person arm and hand movements via image processing technics, in order to identify these movements with exact disable person intention on what he/she wants to convey. When the intention is detected, the disabled person can then input his/her intention or thought data through the device. the input method can be changed according to the user's disability level, and is expected to have a positive impact for the rehabilitation of the user. © 2022 IEEE.

关键词： Image processing

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Cross-Corpus Speech Emotion Recognition Based on Causal Feature Disentanglement 24

Cross-Corpus Speech Emotion Recognition Based on Causal Feat...

引用

7th international conference on Signal processing and Machine Learning, SPML 2024

作者： Jiang, Xiaoying Cui, Lingling Chen, Lihang Song, Shanyou College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Foundation Department Shandong Water Conservancy Vocational College Shandong Rizhao276826 China

ISBN: (纸本)9798400717192

the purpose of cross-corpus speech emotion recognition is to transfer the capability of recognizing emotions from speech, learned in a richly annotated corpus, to an unannotated corpus. Due to significant differences between various corpora, this task is highly challenging. Current methods often predict the emotions of unseen samples by learning the correlation between inputs and labels, but their generalization performance is not ideal. therefore, this paper proposes a cross-corpus speech emotion recognition method based on causal feature disentanglement (CFDM). Specifically, we designed a causal feature disentanglement framework that includes two feature encoders, ingeniously crafting loss functions to learn causal features and confounding features separately. then, a feature self-enhancement method was developed by disordering and summing the confounding features to further strengthen the disentanglement of causal and confounding features. this framework is versatile and can be adapted to existing emotion recognition models to extract causal features for emotion recognition. Additionally, extended experiments conducted on five widely used speech corpora confirmed the effectiveness of the proposed method. © 2024 ACM.

关键词： Emotion Recognition

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Deep Learning Algorithms for Detecting Alzheimer's Disease using WBSN 7

Deep Learning Algorithms for Detecting Alzheimer's Disease u...

引用

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

作者： Sood, Vandana Mohindru Hitakshi, Hitakshi Das, Moushumi Narang, Sushil Kumar Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665454018

Brain-related diseases are among the most difficult diseases to cure due to their sensitivity, the danger associated with performing operations, and their unanticipated costs. In contrast, the surgery is not required to be successful, as its effects may be unsuccessful. therefore, it should be diagnosed at an early stage and the patient should be treated so that they can lead a normal life on their own. WBSN is a novel technology that is applicable in a range of healthcare contexts. WBSNs are made up of several sensors that are implanted in, around, or on the human body to track things like temperature, blood pressure, ECG, and EEG. In developed countries, Alzheimer's disease (AD) is one of the major causes of death. Using computer-aided algorithms has produced outstanding scientific findings, but no practically applicable diagnostic approach is currently available. In recent years, deep models have gained popularity, particularly for image processing. Deep learning methods surpass conventional machine learning algorithms in the recognition of complex patterns in vast quantities of high-dimensional medical imaging data. As a result, there is a growing interest in applying deep learning to medical diagnoses. In this paper, we will examine the most prevalent deep learning algorithms for diagnosing Alzheimer's illness using WBSN. © 2022 IEEE.

关键词： Recurrent neural networks

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Non-uniform illumination-guided probe placement 7

Non-uniform illumination-guided probe placement

引用

2022 7th international conference on Electronic technology and information Science, ICETIS 2022

作者： Xia, Bo Kuang, Fen College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9783800757794

In real-time rendering, the probe-based global illumination algorithms are one of the most widely used algorithms to produce indirect diffuse illumination. In most algorithms, probes are placed uniformly in the scene volume, resulting in memory waste, or even incorrect shading results due to probes inside the object. this paper presents a non-uniform illumination-guided probe placement method. Our algorithm performs gradient descent on the irradiance errors between the probe and its surrounding shading points to drive the placement of the probe. We also propose a local distance variance to prevent the probe from getting too close to the object surface. Experimental results demonstrate that our algorithm can achieve similar rendering results to DDGI (Dynamic Diffuse Global Illumination) with fewer probes. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Gradient methods

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Design and Application of Temperature Control System Based on Fuzzy PID Algorithm 7

Design and Application of Temperature Control System Based o...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Wang, Jianpeng Lu, Zhongliang Wang, Gaojie Jiangxi University of Science and Technology College of Information Engineering Ganzhou China Guangdong Shunde Innovation Design Institute Department of Electronic Information Foshan China

ISBN: (纸本)9781665478571

In order to improve the control accuracy, response speed, anti-interference ability and adaptive ability of the temperature control system in the PCR amplification reaction stage during the detection of African swine fever (ASF) virus, a temperature control system based on fuzzy PID control algorithm was designed. the system combines the fuzzy theory with the traditional PID control theory, which makes up for the shortcomings of the traditional PID algorithm, such as difficulty in parameter tuning, slow response speed, and weak self-adaptive ability. the mathematical model was established and tested by MATLAB software. At the three temperatures of 25°C, 64°C and 85°C required by the PCR amplification reaction, the time taken by the fuzzy PID control algorithm to reach the target temperature was shortened compared with the traditional PID control algorithm. More than 32s, and the recovery time is more than 23s after adding external interference, which meets the requirements of temperature control accuracy, response speed, anti-interference ability and adaptive ability required by PCR reaction in the process of African swine fever (ASF) virus detection. the results highlight the fuzzy Compared with the traditional PID control algorithm, the PID control algorithm has the advantages of temperature control. © 2022 IEEE.

关键词： Polymerase chain reaction

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Rural Health Helper using NLP and IoT System 7

Rural Health Helper using NLP and IoT System

引用

7th international conference on Communication and Electronics Systems, ICCES 2022

作者： Selvaraj, Kailasam Priyadharshini, R. Adith, T.G. Vishal Usha, R. Kalasalingam Academy of Research and Education Department of Information Technology Krishnankoil India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

this system is a prototype through which the doctor can experience the patient current health from a collection of databases. the patient and the helper will be available where this system is implemented. the helper can monitor the temperature, pulse rate, and blood oxygen level in the LCD monitor through the real-time monitoring system. After the patient uses this IoT device, the device will monitor the patient's health condition and update the health information into the connected system and it is stored in a database and generate the report. the website is used to communicate with the doctor which has the facility to upload the report update in the system using the IoT device and help is availed after the doctor views it. For overcoming the communication lag during this online consultation between the doctor and patient natural Language processing is used. © 2022 IEEE.

关键词： Natural language processing systems

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Augmented Analytics: Modern Business Intelligence and Data Analytics 7

Augmented Analytics: Modern Business Intelligence and Data A...

引用

2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Ukhalkar, Prakash Bhate, Manasi Hingane, Suraj Hingane, Swati Pimpri Chinchwad College of Engineering Mca Department Pune India MES's Institute of Management and Career Courses Mca Department Pune India Abhinav Education Society's Institute of Management and Research Mca Department Pune India Rmd Sinhgad School of Management Studies Mba Department Pune India

ISBN: (纸本)9798350304268

Analytics that define the most valuable and possible automation are referred to as augmented analytics. Analytics is the name given to the scientific method of identifying and communicating critical patterns in data. To improve decision-making, gain understanding, prepare data, and exchange data, augmented analytics focused on turning raw data into information. Measurement and insights of data are obtained through augmented analytics. It employs operations research, computer programming, and statistics. It is functional in places with an enormous amount of information. When it comes to analytics and decision-making, augmented analytics can be supplemented and eventually fully automated. Augmented analytics is the most recent method for considering data and analytics. Artificial intelligence (AI) and analytics are combined. It involves combining artificial intelligence (AI) with conventional analytics, the most popular machine learning (ML), and natural language processing (NLP). It differs from typical analytics or business intelligence (BI) tools in that ML technology is constantly learning and improving outcomes behind the scenes. Augmented analytics, in particular, allows for faster access to insights gained from vast volumes of structured and unstructured data to provide ML-based recommendations. this intelligence contributes to discovering hidden patterns and deviations in data, as well as the removal of human bias and the development of forecasting capabilities that can help an organization decide the future course of action. © 2023 IEEE.

关键词： Artificial Intelligence Augmented Analytics Business Intelligence Machine Learning Natural Language processing

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Robust Feature Graph for Point Cloud Denoising 7

Robust Feature Graph for Point Cloud Denoising

引用

7th international conference on Communication, Image and Signal processing, CCISP 2022

作者： Shang, Xin Ye, Rong Feng, Hui Jiang, Xueqin Donghua University School of Information Science and Technology Shanghai China Fudan University School of Information Science and Technology Shanghai China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

Point cloud is an important and commonly used signal representation for volume objects or scenes in the real world. Due to the imperfect acquisition of the point cloud, there is nonnegligible noise in the point cloud. Most literatures that use graph signal processing (GSP) for point cloud denoising (PCD) generally construct k-NN graph to represent the point cloud. However, the graph constructed based on this scheme can not compactly represent the underlying structure of a noisy point cloud. In this paper, we propose a feature graph that can effectively and naturally represent the structure of the point cloud. To construct the feature graph, a feature sampling method is exploited to obtain the feature points. then, patches are built based on the feature points. After that, the feature graph is constructed by connecting all the points in the patches. Finally, we apply the feature graph to the PCD problem and exploit graph Laplacian regularization (GLR) as smoothing prior information for denoising. Experimental results show that our proposed PCD method not only outperforms the existing PCD methods in objective evaluation metrics, but also performs better in processing the inner and edge structure of the point cloud. © 2022 IEEE.

关键词： Laplace transforms

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ELDA: Enhancing Multi-Modal Machine Translation via Large Language Model-Driven Data Augmentation 7

ELDA: Enhancing Multi-Modal Machine Translation via Large La...

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7th international conference on Machine Learning and Natural Language processing, MLNLP 2024

作者： Wang, Yusong Li, Dongyuan Shen, Jialun Xu, Yicheng Zhong, Shuai Xu, Mingkun Tokyo Institute of Technology Japan The University of Tokyo Japan Guangdong Institute of Intelligence Science and Technology China

ISBN: (纸本)9798350354973

Multi-modal machine translation (MMT) leverages auxiliary information to reduce ambiguity and semantic distortion compared to traditional neural machine translation. However, a significant challenge for current MMT methods is their inability to surpass strong pre-trained machine translation methods, mainly due to the scarcity of triplet training data. A common approach involves incorporating extensive parallel and monolingual data to train the text and visual models separately. However, collecting external data is labor-intensive and time-consuming, often leading to distribution deviations. To address this challenge, we introduce ELDA, a novel low-cost data augmentation method driven by large language models, which can automatically expand the dataset. We introduce a carefully crafted prompt based on multi-attributes, combined with in-context learning, to effectively guide the GPT-3.5 model in generating diverse yet consistent-style samples. Extensive experiments on three benchmark datasets show obvious improvement up to 3.26 BLEU score and 2.64 METEOR score on the current leading MMT methods in downstream MMT tasks, confirming the effectiveness of ELDA. © 2024 IEEE.

关键词： computer aided language translation

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