检索结果-内蒙古大学图书馆

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Faster approximate subgraph counts with privacy 37

Faster approximate subgraph counts with privacy

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Nguyen, Dung Srinivasan, Venkatesh Halappanavar, Mahantesh Vullikanti, Anil Department of Computer Science UVA Biocomplexity Institute Initiative University of Virginia United States Department of Mathematics and Computer Science Santa Clara University United States Data Sciences and Machine Intelligence Group Pacific Northwest National Laboratory United States

ISBN: (纸本)9781713899921

One of the most common problems studied in the context of differential privacy for graph data is counting the number of non-induced embeddings of a subgraph in a given graph. These counts have very high global sensitivity. Therefore, adding noise based on powerful alternative techniques, such as smooth sensitivity and higher-order local sensitivity have been shown to give significantly better accuracy. However, all these alternatives to global sensitivity become computationally very expensive, and to date efficient polynomial time algorithms are known only for few selected subgraphs, such as triangles, k-triangles, and k-stars. In this paper, we show that good approximations to these sensitivity metrics can be still used to get private algorithms. Using this approach, we much faster algorithms for privately counting the number of triangles in real-world social networks, which can be easily parallelized. We also give a private polynomial time algorithm for counting any constant size subgraph using less noise than the global sensitivity;we show this can be improved significantly for counting paths in special classes of graphs. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Polynomial approximation

来源：评论

学校读者我要写书评

暂无评论

MMD-ReID: A Simple but Effective Solution for Visible-Thermal Person ReID 32

MMD-ReID: A Simple but Effective Solution for Visible-Therma...

引用

32nd British Machine Vision Conference, BMVC 2021

作者： Jambigi, Chaitra Rawal, Ruchit Chakraborty, Anirban Department of Computational and Data Sciences Indian Institute of Science Bangalore India

Learning modality invariant features is central to the problem of Visible-Thermal cross-modal Person Reidentification (VT-ReID), where query and gallery images come from different modalities. Existing works implicitly align the modalities in pixel and feature spaces by either using adversarial learning or carefully designing feature extraction modules that heavily rely on domain knowledge. We propose a simple but effective framework, MMD-ReID, that reduces the modality gap by an explicit discrepancy reduction constraint. MMD-ReID takes inspiration from Maximum Mean Discrepancy (MMD), a widely used statistical tool for hypothesis testing that determines the distance between two distributions. MMD-ReID uses a novel margin-based formulation to match class-conditional feature distributions of visible and thermal samples to minimize intra-class distances while maintaining feature discriminability. MMD-ReID is a simple framework in terms of architecture and loss formulation. We conduct extensive experiments to demonstrate both qualitatively and quantitatively the effectiveness of MMD-ReID in aligning the marginal and class conditional distributions, thus learning both modality-independent and identity-consistent features. The proposed framework significantly outperforms the state-of-the-art methods on SYSU-MM01 and RegDB datasets. Code will be released at https://***/vcl-iisc/MMD-ReID. © 2021. The copyright of this document resides with its authors.

关键词： Statistical mechanics

来源：评论

学校读者我要写书评

暂无评论

Evaluating Multi-Instance DNN Inferencing on Multiple Accelerators of an Edge Device

arXiv

引用

arXiv 2025年

作者： Tayal, Mumuksh Simmhan, Yogesh Cyber Physical Systems Indian Institute of Science Bangalore India Department of Computational and Data Sciences Indian Institute of Science Bangalore India

Edge devices like Nvidia Jetson platforms now offer several on-board accelerators—including GPU CUDA cores, Tensor Cores, and Deep Learning Accelerators (DLA)—which can be concurrently exploited to boost deep neural network (DNN) inferencing. In this paper, we extend previous work by evaluating the performance impacts of running multiple instances of the ResNet50 model concurrently across these heterogeneous components. We detail the effects of varying batch sizes and hardware combinations on throughput and latency. Our expanded analysis highlights not only the benefits of combining CUDA and Tensor Cores, but also the performance degradation from resource contention when integrating DLAs. These findings, together with insights on precision constraints and workload allocation challenges, motivate further exploration of intelligent scheduling mechanisms to optimize resource utilization on edge platforms. © 2025, CC BY-NC-ND.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Empowering NGOs with Remote Sensing and CNN-LSTM Models for Social and Environmental Transformation

引用

Remote Sensing in Earth Systems sciences 2024年第4期7卷 554-568页

作者： Ramachandran, Jayakarthik Gupta, Ashwani Kumar Syamala, Maganti Banerjee, Sudipta Baburao, D. Ranadive, Amol Deivasigamani, Shanmugavel Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India School of Business and Commerce Chandigarh University Unnao India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India Campus Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science and Engineering Vidya Jyothi Institute of Technology (VJIT) Telangana Hyderabad India BIMHRD Sri Balaji University Pune India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Tamilnadu Chennai India

Non-governmental organizations (NGOs) play a pivotal role in addressing some of the most pressing environmental and social challenges across the globe. With the rise of advanced technologies, NGOs have an unprecedented opportunity to enhance their impact through the adoption of innovative tools. Among these, remote sensing technology has emerged as a cornerstone in environmental monitoring, offering real-time data on land, water, and atmospheric conditions. The integration of remote sensing with CNN-LSTM models offers a transformative approach for NGOs to tackle environmental and social challenges. This study explores the efficacy of these technologies in monitoring deforestation and habitat loss. By leveraging high-resolution satellite imagery and ground truth data, our proposed CNN-LSTM model successfully identifies and tracks changes in land cover. Throughout various tests, the model demonstrated a high accuracy of 0.97, showcasing its potential in providing reliable, real-time data for environmental conservation efforts. The findings suggest that combining CNNs and LSTMs enhances the ability to interpret complex spatiotemporal data, thus supporting NGOs in their decision-making processes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： CNN-LSTM models Deforestation Environmental monitoring Habitat loss Non-governmental organizations (NGOs) Remote sensing Satellite imagery Spatial pattern recognition Temporal dynamics

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning Model for Active-Break Spell Forecasting of Indian Summer Monsoon from Outgoing Longwave Radiation data

A Machine Learning Model for Active-Break Spell Forecasting ...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Apoorva Dinesh Singh Ritesh Jha Ravi Nanjundiah Deepak Subramani Department of Computational and Data Sciences Indian Institute of Science Bengaluru India Center for Atmospheric and Oceanic Sciences Indian Institute of Science Bengaluru India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Forecasting the active/break spell of the Indian Summer Monsoon is important for planning flood management and irrigation activities. In the present work, we develop a simple machine learning model to forecast the active/break spells at multiple lead times. The input to the model is a sequence of 15 days of daily 1°× 1° gridded Outgoing Longwave Radiation (OLR) spatial maps. The spatial extents are from 30°S to 30°N and 80°W to 150°E. The output from the model is whether the next 5-day, 10-day and 15-day periods have active, neutral or break spells. The active/neutral spells are defined as a 5-day period with cumulative rainfall more/less than one standard deviation of the long term 5-day mean. This simple model consists of a linear dimensionality reduction in space and multiple classical classification models that use the principal components in time as input. This simple model achieves an F1 score of 0.46 for the first, second and third pentad forecasts. These results will serve as a common sense baseline for the development of any model based on complex neural networks. The code can be found at https://***/Shikamaru-Nara1729/OLR_PCA_Baseline.

关键词： Irrigation Rain Neural networks Geoscience and remote sensing Monsoons Predictive models Planning

来源：评论

学校读者我要写书评

暂无评论

Cocoon morphological Features Based Silk Quality Prediction Using XG Boost Algorithm

Cocoon morphological Features Based Silk Quality Prediction ...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Nivetha, M. Sudha, I. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348460

Silk quality is a critical determinant in the silk industry, significantly influencing the market value of silk products. This study proposes an innovative method for forecasting silk quality by leveraging cocoon morphological features through the application of the XG Boost algorithm. The morphological characteristics of the cocoon, including dimensions, shape, and colour, are identified as key indicators of silk quality. The XG Boost algorithm, known for its effectiveness in handling complex datasets, is employed for building a predictive model. A comprehensive dataset is assembled, encompassing a diverse set of cocoon morphological features obtained from a representative sample of silkworms. These features comprise cocoon size, length, width, shape parameters, and colour attributes. The dataset is then utilized to train and validate the XG Boost model, fine-tuning its parameters to ensure accurate prediction of silk quality. The outcomes of this study showcase the effectiveness of the proposed approach, demonstrating high predictive accuracy in determining silk quality based on cocoon morphological features. Moreover, the XG Boost algorithm facilitates the identification of crucial features that significantly contribute to the prediction, offering valuable insights into the factors influencing silk quality. In conclusion, the integration of cocoon morphological features and the XG Boost algorithm presents a promising avenue for accurate silk quality prediction. This research contributes to the advancement of silk production methodologies, introducing innovative technologies to the silk industry and fostering a pathway for the adoption of cutting-edge practices. © 2024 IEEE.

关键词： data Security Deep Learning Machine Learning Quality Prediction Silk Quality XGBoost Algorithm

来源：评论

学校读者我要写书评

暂无评论

Exploring Classification Models for Video Source Device Identification: A Study of CNN-SVM and Softmax Classifier

Exploring Classification Models for Video Source Device Iden...

引用

2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Ottakath, Najmath Akbari, Younes Al-Maadeed, Somaya Bouridane, Ahmed Khelifi, Fouad Qatar University Department of Computer Science and Engineering Doha Qatar Center for Data Analytics and Cybernetics University of Sharjah Sharjah United Arab Emirates Northumbria University Department of Computer and Information Sciences Newcastle United Kingdom

ISBN: (纸本)9798350335590

Video Source device identification plays a crucial role in video forensics as the proliferation of video capturing devices has given rise to crimes with videos that are challenging to trace. Reliance on metadata extraction is insufficient as it can be corrupted or manipulated to conceal the source of the crime. Another technique employed for source identification is noise pattern extraction, which generates a unique identification for the video camera. However, this method is susceptible to capture faults and can produce diverse noise patterns for each video. In addressing these challenges, there is a need to identify distinctive features that are consistent across all videos captured by the same camera. This has led to the adoption of computer vision techniques utilizing machine learning and deep learning. Classifiers play a crucial role in machine learning and data analysis, as they are responsible for categorizing or predicting results based on input data. Our experiments show that the subject is sensitive to classifiers and developing a good classifier or classifier-level fusions can improve results in practice for all datasets. © 2023 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Simulation and Emulation Tools for Fog Computing

引用

Recent Advances in computer science and Communications 2022年第3期15卷 315-322页

作者： Singh, Simar P. Kumar, Rajesh Sharma, Anju Reddy, S. Raji Vashisht, Priyanka Landran Mohali India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Bathinda India Department of Mathematics & Humanities Mahatma Gandhi Institute of Technology Hyderabad India Computer Science and Engineering Department The NorthCap University Gurugram India

Background: Fog computing paradigm has recently emerged and gained increasing attention in the present era of the Internet of Things. The growth of a large number of devices all around, leads to the situation of the flow of packets everywhere on the Internet. To overcome this situation and to provide computations at network edge, fog computing is the need of the present time that enhances traffic management and avoids critical situations of jam, congestion etc. Methods: For research purposes, there are many methods to implement the scenarios of fog computing i.e. real-time implementation, implementation using emulators, implementation using simulators etc. The present study aims to describe the various simulation and emulation tools for implementing fog computing scenarios. Results: The review shows that iFogSim is the simulator that most of the researchers use in their research work. Among emulators, EmuFog is being used at a higher pace than other available emulators. This might be due to ease of implementation and user-friendly nature of these tools and language these tools are based upon. The use of such tools enhance better research experience and leads to improved quality of service parameters (like bandwidth, network, security etc.). Conclusion: There are many fog computing simulators/emulators based on many different platforms that use different programming languages. The paper concludes that the two main simulation and emulation tools in the area of fog computing are iFogSim and EmuFog. Accessibility of these simulation/emulation tools enhance better research experience and leads to improved quality of service parameters along with the ease of their usage. © 2022 Bentham science Publishers.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Scientific Research Transformation Needed for Comprehensive Artificial Intelligence Implementation 17

Scientific Research Transformation Needed for Comprehensive ...

引用

17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Medennikov, Viktor Bogatyreva, Larisa Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia

ISBN: (纸本)9798350375718

The paper deals with complementary changes in scientific research and real economy (exemplified by the farming industry) taking place when using the holistic approach to the industry's digital transformation resulting in the appearance of precise agrarian technologies that require a comprehensive, system-based combination of research and production resources that are able to ensure increased productivity in the industry. We demonstrate that among these new technologies, the artificial intelligence methods have a special significance. They have to, however, go through integration transformations to become standards for the proposed common digital farming management platform. © 2024 IEEE.

关键词： Agricultural economics

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：