检索结果-内蒙古大学图书馆

2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Shrutha, P. Mary, Shobha Sarkar, Sayantika Das, Thrisha Santhosh Kumar, B.J. Amrita Vishwa Vidyapeetham Department of Computer Science School of Computing Mysuru India

ISBN: (纸本)9798350367461

The proposed work landscape of assistive devices available for differently-abled individuals in India. The study encompasses a comprehensive market analysis of existing assistive devices based on their import and export details for the current year with offering insights into the growth in the usage of assistive devices for the current year 2024. Work investigates the wide knowledge of the existence of such devices in the society by carrying out a manual survey and comparing the obtained results which aids in identifying patterns within this critical domain along with the prototype development of an assistive device "Smart Stick"for the visually impaired. Showcasing the market growth of various assistive devices purchased across various countries like India, Vietnam, US etc through implementation of one such robust machine learning technique like ARIMA integrated auto regressive moving average (ARIMA) for predicting time series data. The entire work for dying need for differently abled. © 2024 IEEE.

关键词： accessibility affordability assistive devices differently-abled individuals emerging market trends market analysis user preferences

来源：评论

学校读者我要写书评

暂无评论

An IoT Assisted Weather Prediction and Information Monitoring Scheme based on Intensive Learning Strategy 2

An IoT Assisted Weather Prediction and Information Monitorin...

引用

2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Rayen, Sonia Jenifer Susan Jacob, Minu Nivetha, S. Raveendran, Lekshmi S Sarojini Premalatha, J. Clarinda, S. Scinthia Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798350315905

Several of the environmental characteristics we hold dear, including our fundamental ecology, regional climatic variances, and global variety, are threatened caused by widespread industry and increasing urbanization. Problems arise in our everyday lives as a consequence of the challenges we face due of the rapid pace at which things are changing. These problems have their origins in the rapid pace of digitalization and the lack of enough infrastructure for handling and analyzing extremely large data sets. When data generated by the IoT detection layer is inaccurate, partial, or irrelevant, it affects weather forecast reports, which in turn disrupts activities that rely on precise and reliable weather predictions. Some of the main problems in the modern world is getting accurate readings of the surroundings in real time, as doing so presents a number of obstacles. The proposed technology could avoid such events from occurring by constantly monitoring the weather. The suggested solution is built on the cutting edge of embedded system technology and the IoT. Electronic components, sensor nodes, and wireless infrastructure are also part of the system. The primary function of the sensors in this framework is to monitor environmental factors such as temperature, humidity, and gas concentrations. Data collected in this way can be transmitted to external systems, such as databases. Random Forest, Logistic Regression, AdaBoost, and Naive Bayes were all used to compare their respective identification of anomalies scores. These algorithms were applied to the time-based, temperature-based, pressure-based, and humidity-based data produced by the sensors. © 2023 IEEE.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

Gradient Boosting classifier performance evaluation using Generative Adversarial Networks 2

Gradient Boosting classifier performance evaluation using Ge...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Lavanya, P. Singh, Rimjhim Padam Kumaran, U. Kumar, Priyanka Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science University of Texas at San Antonio TX United States

Increasing amount of cyber-attacks in past few years have necessitated the development of automatic threat detection systems for ensuring network and data security. Such automated threat detection models for handling cyber threats are subjected to adversarial attacks. Developing such robust detection models resilient to the attacks require rigorous training and testing with ample number of adversarial examples (AEs) prior to deployment in real-world scenarios where the models are subjected tothe new and unseen data and attacks. Generating adversarial examples with Generative Adversarial Networks (GANs) and its variants is a widely adopted technique. While many notable works generate adversarial examples using the confidence scores of the features that require extensive knowledge about the underlying features of the dataset. This work focuses on generating adversarial examples using Wasserstein condition generative adversarial network and analyzing the robustness of several Gradient-boosted classifiers namely, Adaptive boosting classifier, Categorical Boosting Classifier and Light gradient-boosting classifier on the synthetic and real data. A comparison is also made in the process of generating synthetic data with both GANs and Confidence scores on benchmark dataset NSL-KDD. The robustness of Gradient-boosted classifiers Adaboost, Catboost and LightGBM are tested with these generated data that claim for the superiority of variants of GAN over Confidence scores using features. © 2024 Elsevier B.V.. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Editorial: Artificial intelligence in biomedical big data and digital healthcare

引用

Future Generation computer Systems 2024年 152卷 343-345页

作者： Lim, Kiho Esposito, Christian Wang, Tian Choi, Chang Department of Computer Science William Paterson University of New Jersey United States Department of Computer Science University of Salerno Italy School of Automation Science and Electrical Engineering Beihang University China Department of Computer Engineering Gachon University Korea Republic of

来源：评论

学校读者我要写书评

暂无评论

Word Segmentation and Sandhi Resolution on Ayurveda Classical Scriptures 6

Word Segmentation and Sandhi Resolution on Ayurveda Classica...

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Amrita Varshini, E.R. Nair, Jayashree Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350398496

Sanskrit is one of the oldest of the Indo-Aryan languages which has an abundant amount of texts in literature. One of the main sources of these Sanskrit texts is the textbooks written about the field of Ayurveda, the ancient Indian medical system. However, proper computational systems for processing this language are bare. A very challenging part of processing the Sanskrit language is the task of Sandhi resolution, which means the splitting of phonetically merged words. This study mainly focuses on this complex task of joint compound splitting and Sandhi resolution in Sanskrit specific to Ayurveda texts. This paper also surveys and compares all the existing research on Sandhi resolution in Sanskrit. In this work, an Ayurvedic dataset is compiled from some popular Ayurvedic texts, and a performance analysis of the state-of-the-art word segmentation model is carried out using this dataset. © 2023 IEEE.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Identification of DNA N4-methylcytosine Sites via Multiview Kernel Sparse Representation Model

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1236-1245页

作者： Ai, Chengwei Tiwari, Prayag Yang, Hongpeng Ding, Yijie Tang, Jijun Guo, Fei Tianjin University School of Computer Science and Technology College of Intelligence and Computing Tianjin300072 China Aalto University Department of Computer Science Espoo02150 Finland Quzhou610056 China University of South Carolina Department of Computational Science and Engineering CO29208 United States Central South University School of Computer Science and Engineering Changsha410017 China

Identifying DNA N4-methylcytosine (4mC) sites is of great significance in biological research, such as chromatin structure, DNA stability, DNA-protein interaction, and controlling gene expression. However, the traditional sequencing technology to identify 4mC sites is very time-consuming. In order to detect 4mC sites, we develop a multiview learning method for achieving more effectively via merging multiple feature spaces. Furthermore, we think about whether the multiview learning method can improve the across species classification ability by fusing data of multiple species. In our study, we propose a multiview Laplacian kernel sparse representation-based classifier, called MvLapKSRC-HSIC. First, we make use of three feature extraction methods [position-specific trinucleotide propensity, nucleotide chemical property, and DNA physicochemical properties) to extract the DNA sequence features. MvLapKSRC-HSIC uses a kernel sparse representation-based classifier with graph regularization. In order to maintain the independence between various views, we add a multiview regularization term constructed by Hilbert-Schmidt independence criterion (HSIC). In the experiments, MvLapKSRC-HSIC is applied on six datasets, so as to compare with other popular methods in single-species and cross-species experiments. All experimental results show that MvLapKSRC-HSIC is superior to other outstanding methods on both single species and cross species. Importantly, MvLapKSRC-HSIC can identify a series of potential DNA 4mC sites, which have not yet been experimentally evaluate on multiple species and merit further research. © 2020 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Performance of Wireless Ad-Hoc Network: Through TCP/IP Protocols

Enhancing the Performance of Wireless Ad-Hoc Network: Throug...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Chaudhary, Lalitesh Sharma, Sushil Kumar Kumar, Santosh Institute of Technology and Management Department of Computer Science Aligarh India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350394412

Remote correspondence is quickly taking the place of wired correspondence, posing new difficulties for Transmission Control Protocol (TCP). TCP is enhanced in a few ways to detect this issue. This paper addresses the protocol and display upgrade model for remote, specially designated computers over TCP. TCP is essentially a transport protocol created for web applications that are wired. However, the TCP misinterprets some conditions, such obstructions that slow down transmission and taint presentation quality, resulting in decreased throughput. This is because remote specially designated systems are unstable and vulnerable to unforeseen faults associated to geography. Because of poor connections and high congestion, TCP must be modified in order to prevent package loss. Accordingly, the evaluation of the recommended TCP presentation for a remote specially defined system is managed by this study. © 2024 IEEE.

关键词： Transmission control protocol

来源：评论

学校读者我要写书评

暂无评论

Ensuring safety of vehicular cyber physical systems using machine learning and MQTT

引用

International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 165-183页

作者： Bagga, Neha Kalra, Sheetal Kaur, Parminder Department of Computer Science Guru Nanak Dev University Punjab Amritsar India School of Computer Science Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering G.N.D.U. Regional Campus Punjab Jalandhar India

One of the pressing concerns for emerging nations is maintenance of roads, including identification and repair of pavement distress. Previous research has focused on pothole detection and lane identification, with the distress details being shared with drivers via database or an Android application. However, this approach is battery-intensive for sensors in smart vehicles and requires a regular internet connection. To address these issues, we have proposed a model trained using Python and TensorFlow to identify road distress and steep curves with an accuracy of 85.2% and 83.1%, respectively. The simulation uses Geocoder to capture the geographical coordinates of the distress, and the collected data is transferred to other CPS devices in cars using MQTT which outperforms databases and Android applications in terms of efficiency, sensor load, and internet connectivity. Drivers receive alerts within 10 seconds, allowing them to make informed decisions which helps prevent accidents and fatalities on the road. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

引用

Neural computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

MeshWGAN: Mesh-to-Mesh Wasserstein GAN with Multi-Task Gradient Penalty for 3D Facial Geometric Age Transformation

引用

IEEE Transactions on Visualization and computer Graphics 2024年第8期30卷 4927-4940页

作者： Zhang, Jie Zhou, Kangneng Luximon, Yan Lee, Tong-Yee Li, Ping The Hong Kong Polytechnic University Department of Computing The School of Design Hong Kong University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China The Hong Kong Polytechnic University School of Design Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong National Cheng-Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

As the metaverse develops rapidly, 3D facial age transformation is attracting increasing attention, which may bring many potential benefits to a wide variety of users, e.g., 3D aging figures creation, 3D facial data augmentation and editing. Compared with 2D methods, 3D face aging is an underexplored problem. To fill this gap, we propose a new mesh-to-mesh Wasserstein generative adversarial network (MeshWGAN) with a multi-task gradient penalty to model a continuous bi-directional 3D facial geometric aging process. To the best of our knowledge, this is the first architecture to achieve 3D facial geometric age transformation via real 3D scans. As previous image-to-image translation methods cannot be directly applied to the 3D facial mesh, which is totally different from 2D images, we built a mesh encoder, decoder, and multi-task discriminator to facilitate mesh-to-mesh transformations. To mitigate the lack of 3D datasets containing children's faces, we collected scans from 765 subjects aged 5-17 in combination with existing 3D face databases, which provided a large training dataset. Experiments have shown that our architecture can predict 3D facial aging geometries with better identity preservation and age closeness compared to 3D trivial baselines. We also demonstrated the advantages of our approach via various 3D face-related graphics applications. © 1995-2012 IEEE.

关键词： Face recognition

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：