检索结果-内蒙古大学图书馆

1st International Conference on Deep sciences for Computing and Communications, IconDeepCom 2022

作者： Amudha, S. Shobana, J. Satheesh Kumar, M. Chitra, P. Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Information Technology K.L.N. College of Engineering Tamil Nadu Madurai India Department of Electrical and Electronics Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031276217

As we know, air pollution is one of the major problems in the metropolis. Due to this a lot of consequences have to be faced by all people. One of the main reasons for this are the vehicles. Most of the time people use their vehicles unnecessarily without any certain reasons. Due to the same reason another problem of traffic congestion arises. And this can be reduced if people become a bit more serious about it. They should prefer using public transport rather than personal vehicles for travelling large distances. By this the number of vehicles on the road will be turned down and it might solve the above two problems directly. So, the main approach is based on this theory itself to reduce the number of transports on the road. The solution is an application which runs on phone and keep track of the fuel used by the vehicle. A certain fuel limit shall be set based on vehicle type, its fuel consumption and other constraints. Once the limit got exceeded, fuel price shall differ and automated calculation would get you actual fuel amount. This aims at reducing the unnecessary usage of vehicles. There is a CO2 emission model in an application which can predict the amount of CO2 produced by the vehicle. The objective of the proposed work is to control the usage of vehicles based on the fuel used by a particular vehicle for a period of time. As the module has been tested with ML model for mileage prediction and CO2 emission with different algorithms and got the best accuracy of around 85%. There will be no hardware in this work, if any hardware implemented all over India then that prototype model will produce good impact in the vehicle fuel usage prediction and CO2 emissions. Hardware based projects using IOT devices, VTRS etc. is something which is very hard to implement in India as CCTV are still not installed and used properly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Decision-Making in Dynamic Environment

arXiv

引用

arXiv 2024年

作者： Chowdhury, Jayabrata Shivaraman, Venkataramanan Dangi, Sumit Sundaram, Suresh Sujit, P.B. Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Science Bangalore India The Department of Aerospace Engineering Indian Institute of Science Bangalore India The Department of Data Science & Engineering and Electrical Engineering & Computer Science Indian Institute of Science Education and Research Bhopal India

Autonomous Vehicle (AV) decision-making in urban environments is inherently challenging due to the dynamic interactions with surrounding vehicles. For safe planning, AV/ego must understand the weightage of various spatiotemporal interactions in a scene. Contemporary works use colossal transformer architectures to encode interactions mainly for trajectory prediction, resulting in increased computational complexity. To address this issue without compromising spatiotemporal understanding and performance, we propose the simple Deep Attention Driven Reinforcement Learning (DAD-RL) framework, which dynamically assigns and incorporates the significance of surrounding vehicles into the ego’s RL-driven decision-making process. We introduce an AV-centric spatiotemporal attention encoding (STAE) mechanism for learning the dynamic interactions with different surrounding vehicles. To understand map and route context, we employ a context encoder to extract features from context maps. The spatiotemporal representations combined with contextual encoding provide a comprehensive state representation. The resulting model is trained using the Soft-Actor Critic (SAC) algorithm. We evaluate the proposed framework on the SMARTS urban benchmarking scenarios without traffic signals to demonstrate that DAD-RL outperforms recent state-of-the-art methods. Furthermore, an ablation study underscores the importance of the context-encoder and spatio-temporal attention encoder in achieving superior performance. © 2024, CC BY-SA.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent Personalized Nutrition Guidance System Using IoT and Machine Learning Algorithm 2

Intelligent Personalized Nutrition Guidance System Using IoT...

引用

2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Santhuja, P. Choudri, Subramani Roy Balaji, S. Reddy, Elangovan Guruva Muthulekshmi, M. Department of Computer Science Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Information Technology Sri Shanmugha College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Nowadays, dietary issues are increasing around the world. Numerous problems, such as weight gain, obesity, diabetes, etc., can arise from an unbalanced diet. By integrating image processing, the system can assess food images in new ways to provide personalized solutions for better eating habits. The system uses machine learning, IoT, and image processing techniques to evaluate food image data and extract useful information. IoT devices like smartphones or specialized cameras take images of food and send to the cloud for analysis. The Support Vector Machine (SVM) is a machine learning technique that is integrated with Internet of Things (IoT) technology in this study to propose a novel method for creating a system that uses personalized nutritional suggestions. The SVM algorithm is then used to analyze and analyze this data to find patterns, correlations, and individual dietary requirements. A cloud database is used to process and store all the data related to food consumption. The system analyzes food images and determines the nutrition and caloric content of the food using image processing and segmentation. The system takes up new abilities using an extensive collection of dietary records that have been labeled and include details about the sorts of foods, portion sizes, and nutritional value. The system can predict and identify users' nutritional needs, deficits, and personalized dietary objectives by using this trained SVM model to their nutritional data. © 2023 IEEE.

关键词： Dietetics IoT Machine Learning Nutrition Guidance SVM Algorithm

来源：评论

学校读者我要写书评

暂无评论

Exploring Cutting-Edge Machine Learning and data Mining Techniques for Enhancing Big data Management with Advanced Algorithmic Strategies for Optimal data Processing and Analysis 2

Exploring Cutting-Edge Machine Learning and Data Mining Tech...

引用

2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anitha, M. Kumari, V. Sheeja Pillai, N. Muthuvairavan Jayarin, P. Jesu David, D. Beulah Department of Artificial Intelligence & Data Science CARE College of Engineering Tamil Nadu Trichy India Department of Computational Intelligence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Thandalam Chennai India Department of Computer Science and O. Business Systems R.M.D Engineering College Tamil Nadu Thiruvallur India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Thandalam Chennai India

ISBN: (纸本)9798350383867

This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model selection, hyperparameter tuning, and model evaluation are a focus. A number of algorithms including Random Forests, SVM (Support Vector Machines) Gradient Boosting Machines (GBM), Neural Networks, and ensemble methods (Bagging and Boosting) have their performance evaluated. Hyperparameter tuning applies the Grid Search and Random Search algorithms while model evaluation is done by the K-fold Cross-validation. Fleiss' kappa provide differing levels of accuracy, precision, recall, and F1-score for different models and techniques. Ensemble methods actually show how they have all the necessary qualities to win and perform better than individual models. repeatedly testing improve the models' reliability and effectiveness for practical use. The study highlights the crucial significance of choices on the methods and algorithms that suit the peculiarities of the dataset and problem domain. Capitalizing on the machine learning techniques and in-depth evaluation methods, organizations can get the actionable insights from the big data that will ensure the headquarters' decision-making process and accomplish the meaningful outcomes. Future research could instead be exploring new algorithms and techniques to improve the prediction and scalability in big data analytics setting. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Mo...

引用

Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Bhanu Pratap Singh Rathore Rutul Dave Dhanasekaran K Priyadarsini K Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Structure-Aware Argument Encoder for Literature Discourse Analysis 29

A Structure-Aware Argument Encoder for Literature Discourse ...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Yinzi Chen, Wei Wei, Zhongyu Huang, Yujun Wang, Chujun Wang, Siyuan Zhang, Qi Huang, Xuanjing Wu, Libo School of Data Science Fudan University China Research Institute of Intelligent Complex Systems Fudan University China Department of Environmental Science and Engineering Fudan University China School of Computer Science Fudan University China

Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context. In this paper, we propose to separate tokens into two groups, namely framing tokens and topic ones, to capture structural information of arguments. In addition, we consider high-level structure by incorporating paragraph-level position information. A novel structure-aware argument encoder is proposed for literature discourse analysis. Experimental results on both a self-constructed corpus and a public corpus show the effectiveness of our model. Resources are available at https://***/lemuria-wchen/SAE. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

A Novel Architecture for Efficient Mobile Communication based on Scattered and Diverse Combination using Prioritize and Postoperative Soft Modulation 7

A Novel Architecture for Efficient Mobile Communication base...

引用

7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Sumithra, J. Mercy Theresa, M. Geetha Priya, S. Jackson, Beulah Samuthira Pandi, V. Balamurugan, A. R. M. D Engineering College Department of Electronics and Communication Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu India R. M. D Engineering College Department of Computer Science and Engineering Tamil Nadu India Vel Tech Rangarajam Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350397253

The proposed work suggests a novel approach for the cooperation of geographically scattered individuals and the blending of different points of view. This work ensures that the center on heterogeneous networks in which nodes support both long-range low-rate Radio Frequency (RF) interfaces (like cellular) and short-range high-rate RF interfaces (such as IEEE802.11) that communicate over urban Rayleigh fading channels. These interfaces are used to connect the devices. The interfaces are what are used to link the various gadgets. This research work proposes and evaluates a wide variety of hierarchically distributed cooperation strategies that can function despite resource restrictions such as limited short-range RF bandwidth. These strategies are contained within the confines of this paradigm, which serves as the framework for proposed work. Post Soft-Demodulation Combining, often referred to as PSDC, and Priority Maximum Ratio Combining are the two approaches that come highly recommended by our team (PMRC). This work provides evidence that using the offered methods increases throughput, reliability, and signal-to-noise ratio. Moreover, this study found that PMRC and PSDC have the ability to improve throughput performance by more than an order of magnitude across a wide range of communication circumstances. © 2023 IEEE.

关键词： Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Evaluation Method of Motor Coordination Ability in Children Based on Machine Vision

引用

Tsinghua science and Technology 2025年第2期30卷 633-649页

作者： Yi Lei Dawei Shu Miao Yu Donglin Shi Jianqiang Li Yanjie Chen Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Department of Automation Tsinghua UniversityBeijing 100084China Pharmacovigilance Research Center for information technology and Data Science Cross-strait Tsinghua Research InstituteXiamen 361000China Institute of Systems Engineering Dalian University of TechnologyDalian 116024China Physical Education Department Hebei Sports UniversityShijiazhuang 050049China Department of Children’s Health Care Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijing 100045China

Motor coordination is crucial for preschoolers’development and is a key factor in assessing childhood *** diagnostic methods often rely on subjective manual *** paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of *** method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network,thereby transforming video assessments into evaluations of keypoint *** study uses different methods to handle static and dynamic actions,including regularization and Dynamic Time Warping(DTW)for spatial alignment and temporal discrepancies.A penalty-adjusted single-frame pose similarity method is used to evaluate *** lightweight pose estimation model reduces parameters by 85%,uses only 6.6%of the original computational load,and has an average detection missing rate of less than 1%.The average error for static actions is 0.071 with a correlation coefficient of 0.766,and for dynamic actions it is 0.145 with a correlation coefficient of *** results confirm the proposed method’s effectiveness,which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.

关键词： developmental coordination disorder action quality assessment pose estimation dynamic time warping decision support system

来源：评论

学校读者我要写书评

暂无评论

Network Learning with Directional Sign Patterns 63

Network Learning with Directional Sign Patterns

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Dong, Anqi Chen, Can Georgiou, Tryphon T. Kth Royal Institute of Technology Division of Decision and Control Systems Department of Mathematics StockholmSE-10044 Sweden University of North Carolina Chapel Hill School of Data Science and Society Department of Mathematics Chapel HillNC27599 United States University of California Department of Mechanical and Aerospace Engineering IrvineCA92697 United States

ISBN: (纸本)9798350316339

Complex systems can be effectively modeled via graphs that encode networked interactions, where relations between entities or nodes are often quantified by signed edge weights, e.g., promotion/inhibition in gene regulatory networks, or encoding political of friendship differences in social networks. However, it is often the case that only an aggregate consequence of such edge weights that characterize relations may be directly observable, as in protein expression of in gene regulatory networks. Thus, learning edge weights poses a significant challenge that is further exacerbated for intricate and large-scale networks. In this article, we address a model problem to determine the strength of sign-indefinite relations that explain marginal distributions that constitute our data. To this end, we develop a paradigm akin to that of the Schrödinger bridge problem and an efficient Sinkhorn type algorithm (more properly, Schrödinger-Fortet-Sinkhorn algorithm) that allows fast convergence to parameters that minimize a relative entropy/likelihood criterion between the sought signed adjacency matrix and a prior. The formalism that we present represents a novel generalization of the earlier Schrödinger formalism in that marginal computations may incorporate weights that model directionality in underlying relations, and further, that it can be extended to high-order networks - the Schrödinger-Fortet-Sinkhorn algorithm that we derive is applicable all the same and allows geometric convergence to a sought sign-indefinite adjacency matrix or tensor, for high-order networks. © 2024 IEEE.

关键词： Gene expression

来源：评论

学校读者我要写书评

暂无评论

Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

引用

NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：