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作者： Qian, Yufei Xie, Shipeng Zhuang, Wenqin Li, Haibo College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications NanjingJiangsu210003 China Department of Media Technology and Interaction Design School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

With the development of CT imaging, people have higher requirements for the quality of CT image reconstruction. It is desirable to use as low as reasonably achievable X-ray dose while meeting the quality of imaging requirements. Sparse-view reconstruction is a valid measure to resolve the radiation dose problem. Owing to the angular range of projection data does not satisfy the data completeness condition, sparse-view reconstruction has always been a conundrum in CT image reconstruction. In this paper, we introduces a new CT sparse-view reconstruction algorithm, which bases on the residual network. We optimize traditional residual models by improving the superfluous modules and reducing unnecessary calculations. Compared to several other classic methods, the experimental results with our network obtained better consequent, regarding artifact reduction, feature preservation, and computational speed. © Springer Nature Switzerland AG 2020.

关键词： Image reconstruction

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Using artificial intelligence to identify emergency messages on social media during a natural disaster: A deep learning approach

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International Journal of Information Management Data Insights 2023年第1期3卷

作者： Powers, Courtney J. Devaraj, Ashwin Ashqeen, Kaab Dontula, Aman Joshi, Amit Shenoy, Jayanth Murthy, Dhiraj Department of Communication College of Arts Communication The University of Wisconsin-Whitewater 180 North Prairie Street Whitewater 53190 WI United States Department of Computer Science The University of Texas at Austin United States Department of Mathematics The University of Texas at Austin United States Department of Electrical and Computer Engineering The University of Texas at Austin United States School of Journalism and Media The University of Texas at Austin United States

During natural disasters, emergency communication systems become overloaded, and people are forced to turn to social media to make requests for help. This study employs machine learning and artificial intelligence to automatically detect, identify, and categorize tweets relevant to first responders during Hurricane Harvey. We curate a dataset of tweets, present a labeling scheme based on relevance and urgency, and develop neural and non-neural machine learning models to automatically categorize tweets. Our best relevance classifiers, language models BERT and XLNet, perform significantly better than non-neural models and the deep convolutional neural network (CNN) and achieve comparable F1 scores. Ultimately, this study furthers machine learning and crisis communication research by developing methods to automatically categorize tweets that can signal to first responders of individuals’ requests for help in urgent, life-threatening disasters. Our work also finds large pretrained language models promising for the development of well-performing disaster tweet classifiers in future work. © 2023

关键词： Artificial intelligence Computational methods Crisis communication Disaster Machine learning Supervised learning Twitter

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Deep Learning Approach to Predict Alzheimer's Disease through Magnetic Resonance Images

Deep Learning Approach to Predict Alzheimer's Disease throug...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Gilang Titah Ramadhan Yori Pusparani Isack Farady Elvin Nur Furqon Chih-Yang Lin Wen-Hung Chao John Sahaya Rani Alex Jeetashree Aparajeeta Chi-Wen Lung Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Visual Communication Design Budi Luhur University Jakarta Indonesia Department of Digital Media Design Asia University Taichung Taiwan Electrical Engineering Mercu Buana University Jakarta Indonesia Department of Electrical Engineering Yuan Ze University Chung-Li Taiwan Department of Mechanical Engineering National Central University Taoyuan Taiwan Division of Healthcare Advancement Innovation and Research Vellore Institute of Technology Tamil Nadu India School of Electronics Engineering Vellore Institute of Technology Tamil Nadu India Department of Creative Product Design Asia University Taichung Taiwan

Alzheimer's disease is the most common type of dementia that causes many of the functions of the human brain to be severely weakened. To date, there has not been a cure for Alzheimer's disease. Therefore, early diagnosis is needed using MRI images with the help of a classification program. Deep learning using the Convolutional Neural Network (CNN) method is receiving increasing attention because of its excellent performance. Its architecture can be modified according to user needs based on the data to be processed and the approach for classifying, detecting, and segmenting visual objects. In this paper, we offer a classification of Alzheimer's disease using one of the architectures on CNN, namely Visual Geometry Group-19 (VGG-19), with a sagittal view of MRI images with an image size of 229 x 229 pixels. The classification accuracy of the described method is 94% for the validation set.

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Algebraic Properties of Riemannian Manifolds

arXiv

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arXiv 2022年

作者： Chung, Youngjoo Hwang, Chi-Ok Yang, Hyun Seok School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Division of Liberal Arts and Sciences Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Physics and Photon Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

Algebraic properties are explored for the curvature tensors of Riemannian manifolds, using the irreducible decomposition of curvature tensors. Our method provides a powerful tool to analyze the irreducible basis as well as an algorithm to determine the linear dependence of arbitrary Riemann polynomials. We completely specify 13 independent basis elements for the quartic scalars and explicitly find 13 linear relations among 26 scalar invariants. Our method provides several completely new results, including some clues to identify 23 independent basis elements from 90 quintic scalars, that are difficult to find otherwise. © 2022, CC BY.

关键词： Tensors

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Implementation of Video-Based Human Anomalous Activity Detection Using LSTM-RNN Network

Implementation of Video-Based Human Anomalous Activity Detec...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S L Prathapareddy Neelam Sharma Jayalakshmi V D. Suganthi S.Sathya Naveena Rajanish Kumar Kaushal Department of Electronics and Communication Engineering KSRM College of Engineering Kadapa Andhra Pradesh India Department of Physical Education Lovely Professional University Punjab India Science and Humanities Department (English) RMK College of Engineering and Technology Thiruvallur Chennai India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Master of Business Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Electrical Engineering Chandigarh University Mohali Punjab India

Increased interest in video analysis technology, and in particular automatic human activity recognition, has arisen in response to the rising requirements of a wide range of applications, including surveillance, entertainment, and healthcare systems. Using automated reporting, authorities might be alerted to the presence of a possible criminal or dangerous actor, such as a person hanging about an airport or railway station with a bag. Similarly, activity identification can improve human-computer interaction in the gaming industry by, say, automatically identifying the motions of different players in a tennis match so that an avatar can assume control of the game on the player's behalf. Steps one through four of the proposed procedure are preprocessing the data, segmenting the data, selecting features, and training the model. Preprocessing videos is used to observe people's actions. Background subtraction and GMM both play a role in the segmentation process. Parameters such as speed, measurement, and movement play a significant role in feature extraction. The models are then trained with LSTM-RNN after feature extraction. The proposed method outperforms the two most used alternatives, LSTM and RNN.

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Towards a Prosodic Model for Synthesized Speech of Mathematical Expressions in MathML 9

Towards a Prosodic Model for Synthesized Speech of Mathemati...

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9th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-Exclusion, DSAI 2020

作者： Souza, Adriana Freitas, Diamantino Bahia Federal Institute of Education Science and Technology Porto Seguro Brazil Doctoral Program in Digital Media Faculty of Engineering University of Porto Porto Portugal Department of Electrical and Computer Engineering Faculty of Engineering University of PortoPorto Portugal

ISBN: (纸本)9781450389372

The use of the MathML language made possible to improve the accessibility of mathematics for blind or low-vision persons in digital media. Synthetic speech technologies have advanced significantly using MathML, however, the speech synthesizers' standard reading style is still not suitable for mathematics. Making mathematical reading of the speech synthesizers more natural and expressive is still a challenge. The creation of models to produce the appropriate prosody in the synthesized speech of math content is therefore necessary, as shown in previous research. This article presents a proposal for a model to improve prosody in the synthesized speech of mathematical expressions based on MathML. A corpus of mathematical expressions spoken by Mathematics teachers was created to support the model's development. The Fujisaki intonation model was adopted for intonation control, accent and phrase commands have been extracted from the corpus, and some adjustments have been made to manipulate prosodic parameters in the speech of mathematical expression in correlation with the MathML tree;additionally, a pattern of pauses control is being created. © 2020 ACM.

关键词： Digital storage

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Multiple Target Research of Construction Progress on the Basis of on BIM and Decide Tree Algorithms

Multiple Target Research of Construction Progress on the Bas...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： S. Selvakanmani E. Manigandan C. Hazarathaiah Yadav Satheesh Kumar S Brijesh Singh B. Karthik Department of Information Technology RMK Engineering College RSM Nagar Chennai Department of Computer Applications Saveetha College of Liberal Arts and Sciences Thandalam Chennai Tamilnadu India Department of Chemistry Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Sathyamangalam Tamilnadu India Department of MBA CMR Institute Of Technology Bangalore India Department of Electrical and Electronics Engineering Sona College of Technology Salem India

Because of India's growing urbanization, coupled with dwindling urban land supply and rising land costs, the construction of high-rise structures has emerged as the dominant method to urban planning and development. The rapid growth of India's economy and the rapid pace of urbanization have resulted in an increasing urban population, raising the requirement for effective land usage. High-rise structures have gradually been the main center of urban construction projects in response to these *** primary goal of this study is to look into the use of Building Information Modeling (BIM) in building projects with the goal of optimizing the use of information received from it. Numerous graphs and algorithmic formulas are developed during the study to examine and illustrate these features. According to the findings of the study, a significant amount, accounting for 28.2% of the total, is related to various focused research procedures. As a result, the interest and significance of this field of investigation are expected to endure and develop in the coming years..

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Optimal stochastic coded computation offloading in unmanned aerial vehicles network

arXiv

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arXiv 2021年

作者： Ng, Wei Chong Lim, Wei Yang Bryan Ng, Jer Shyuan Sawadsitang, Suttinee Xiong, Zehui Niyato, Dusit Alibaba Group Alibaba-NTU JRI College of Arts Media and Technology Chiang Mai University Singapore University of Technology and Design Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Today, modern unmanned aerial vehicles (UAVs) are equipped with increasingly advanced capabilities that can run applications enabled by machine learning techniques, which require computationally intensive operations such as matrix multiplications. Due to computation constraints, the UAVs can offload their computation tasks to edge servers. To mitigate stragglers, coded distributed computing (CDC) based offloading can be adopted. In this paper, we propose an Optimal Task Allocation Scheme (OTAS) based on Stochastic Integer Programming with the objective to minimize energy consumption during computation offloading. The simulation results show that amid uncertainty of task completion, the energy consumption in the UAV network is minimized. © 2021, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Adaptive Ant Selection for Pheromone Update in Ant Colony Optimization

Adaptive Ant Selection for Pheromone Update in Ant Colony Op...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Biao Wang Dan-Ting Duan Qiang Yang Xiao-Yan Zhao Tao Li Dong Liu Jun Zhang College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China Key Laboratory of Media Audio & Video Ministry of Education Communication University of China Beijing China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China College of Artificial Intelligence Nankai University Tianjin China Department of Electrical and Electronic Engineering Hanyang University Ansan South Korea

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Ant selection for updating the pheromone is one most crucial operation in ant colony optimization (ACO). In this direction, this paper designs an adaptive ant selection strategy (AAS) to adaptively and dynamically select ants to update the pheromone for ACO. Therefore, a new ACO, called AAS-ACO is developed. Specifically, AAS-ACO first assigns a non-linear selection probability to each ant based on its path ranking. As a result, better ants preserve exponentially higher selection probabilities. Then, based on the selection probabilities, ants are adaptively selected for the pheromone update. By this means, on the one hand, the number of ants involved in the pheromone update is uncertain; on the other hand, relatively better ants instead of absolutely better ones are adaptively selected to update the pheromone, leading to the promotion of search diversity. Subsequently, a dynamic weighting strategy is designed to adjust the amount of the pheromone deposited by the best ant in the current iteration to enhance the search convergence. With the two schemes, AAS-ACO is expected to maintain a suitable compromise between search diversity and search convergence to seek the optimal solutions to TSP. Experiments on 10 classical TSP instances varying from 100 to 1000 cities have proven the significant superiority of AAS-ACO to 5 classic ACOs, especially on high-dimensional TSP problems.

关键词： Hands Ant colony optimization Heuristic algorithms Urban areas Optimization Cybernetics Convergence

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Domain generalization by marginal transfer learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 46-100页

作者： Gilles Blanchard Aniket Anand Deshmukh Ürun Dogan Gyemin Lee Clayton Scott Université Paris-Saclay CNRS Inria Laboratoire de mathématiques d'Orsay Microsoft AI & Research Dept. Electronic and IT Media Engineering Seoul National University of Science and Technology Electrical and Computer Engineering Statistics University of Michigan

In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner. This problem arises in several applications where data distributions fluctuate because of environmental, technical, or other sources of variation. We introduce a formal framework for DG, and argue that it can be-viewed as a kind of supervised learning problem by augmenting the original feature space with the marginal distribution of feature vectors. While our framework has several connections to conventional analysis of supervised learning algorithms, several unique aspects of DG require new methods of *** work lays the learning theoretic foundations of domain generalization, building on our earlier conference paper where the problem of DG was introduced (Blanchard et al., 2011). We present two formal models of data generation, corresponding notions of risk, and distribution-free generalization error analysis. By focusing our attention on kernel methods, we also provide more quantitative results and a universally consistent algorithm. An efficient implementation is provided for this algorithm, which is experimentally compared to a pooling strategy on one synthetic and three real-world data sets.

关键词： domain generalization generalization error bounds Rademacher complexity kernel methods universal consistency kernel approximation

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