检索结果-内蒙古大学图书馆

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, R. Prakash, B. Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Kattankulathur India Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu Kattankulathur India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Vehicular Ad Hoc Networks (VANETs) are important for intelligent transportation systems, permitting vehicles to communicate with one another and nearby infrastructure in order to enhance road safety, traffic management and infotainment services. But the VANET communication is a high energy consumption technology, which may become a big challenge of large-scale power efficiency being in some real scenarios where huge number vehicles are needed to implement such V2I or V2V services sustaining. This paper conducts a analysis of different transmission stratagems for energy-efficient communication strategies among vehicles in VANETs, along with the focus to incorporate deep learning accelerators into an optimized architecture framework for enhancing communication processes. The paper presents an overview of VANET architecture and communication protocols, and then reviews the techniques to save energy in a VANET including efficient mechanisms for Transmission Power Control (TPC), some energy-aware routing protocols, as well as data aggregation realizations. Next, the paper introduces deep learning models that can adaptively optimize VANET transmission by forecasting network states and updating strategies on-the-fly. This analysis emphasizes hybrid solutions where deep learning is combined with traditional communication strategies to improve the energy efficiency apparent on a system level. Finally addresses the current challenges in the field and the need for advanced AI-driven techniques. This bibliometric analysis provides a comprehensive overview of energy-efficient communication strategies in VANETs, offering insights into how deep learning can contribute to the development of more sustainable vehicular networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Accident Forecasting using IoT and Deep Learning Techniques 2

Accident Forecasting using IoT and Deep Learning Techniques

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Paul, Ann Rija Kanaga, E Grace Mary Sahrdaya College of Engineering and Technology Computer Science and Engineering Department Thrissur India Karunya Institute of Technology and Sciences India Karunya Institute of Technology and Sciences Computer Science and Engineering Department Coimbatore India

ISBN: (纸本)9798350325799

Road accidents are a major cause of injury and death worldwide, and there is a need for accurate and proactive accident prevention measures. In recent years, Internet of Things (IoT) devices have been increasingly used for collecting real-time data on various parameters that affect the likelihood of accidents, such as weather conditions, traffic volume, and road conditions. Additionally, deep learning techniques have shown promise in predicting the likelihood of accidents based on these parameters. This research work proposes a novel framework for accident forecasting that combines IoT devices and deep learning techniques. The proposed framework includes a network of IoT devices placed on roads that collect real-time data on various parameters, and a deep learning ensemble methods that uses these parameters to forecast the occurrences of accidents in the traffic. Here, this study utilizes the deep learning ensemble methods and LSTM neural network to process the time-series data from the IoT devices and make predictions. The model is trained and tested using a large dataset of past accidents and corresponding IoT data. Our model uses 31 different parameters to forecast accidents. Finally, the proposed model achieves high accuracy in predicting the likelihood of accidents, with an accuracy of 93.6 percent. The proposed platform can be used to improve road safety by enabling the implementation of more precise and proactive accident prevention measures by providing real-time accident forecasting. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Reducing the vacancies associated with ferroelectric polarization to promote photocatalytic overall water splitting

引用

science China Chemistry 2024年第10期67卷 3258-3264页

作者： Haozhi Qi Yuyang Kang Jian-An Liu Lichang Yin Wenyu Zhang Shangyi Ma Jianhang Qiu Lingli Li Weijin Hu Lianzhou Wang Gang Liu Institute of Metal Research Chinese Academy of SciencesShenyang 110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China Nanomaterials Centre School of Chemical Engineering and Australian Institute for Bioengineering and NanotechnologyThe University of QueenslandSt LuciaBrisbane QLD 4072Australia

Ferroelectric materials hold great promise in photocatalytic water splitting because their built-in electric field induced by the depolarization field can fulfill the separation of photogenerated ***,a number of intrinsic charged vacancy defects are simultaneously generated to screen the depolarized field for stabilizing the crystal structure,always resulting in severe recombination of photogenerated carriers and thus poor overall water splitting ***,we proposed a strategy to promote the separation and transport of photogenerated carriers of ferroelectric photocatalysts by adjusting the ferroelectric polarization and altering the coordination environment of elements to reduce the defect ***,we prepared a series of Ta-doped PbTiO_(3)with low Pb(V_(Pb))and O(V_(O))vacancy concentrations by reducing the polarization intensity and strengthening the Pb–O *** Ta-doped PbTiO_(3)shows efficient charge separation and greatly enhanced photocatalytic overall water splitting activity with the assistance of *** work highlights the importance of regulating ferroelectric polarization and vacancy defect concentration by the doping strategy in charge separation for photocatalytic water splitting.

关键词： PbTiO_(3) ferroelectric polarization vacancy defect element doping photocatalytic overall water splitting

来源：评论

学校读者我要写书评

暂无评论

Enhancement of Alzheimer’s Disease Detection Technique by Fusing Features of Deep Learning and Handcrafted Methods on sMRI Images 3

Enhancement of Alzheimer’s Disease Detection Technique by F...

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Bhadra, Sampa Rani Maity, Santi Prasad Sil, Jaya Department of Computer Science Kalyani Mahavidyalaya West Bengal Kalyani741235 India Department of Information Technology Indian Institute of Engineering Science and Technology West Bengal Howrah711103 India Department of Computer Science and Technology Indian Institute of Engineering Science and Technology West Bengal Howrah711103 India

ISBN: (纸本)9798331532420

Automated detection of Alzheimer disease (AD) is evolving rapidly with the advent of artificial intelligence (AI). Machine learning and deep learning models. Detection of AD at an early stage provides advance planning of treatment to the patients that arrest progression of the disease. The aim of this paper is to improve performance of diagnosing AD by fusing features extracted mainly from two sources, such as deep and nondeep frameworks applied on the sMRI images. This paper introduces two customized convolutional neural networks (CNN) (baseline model and hybrid model) and extracted two different sets of deep features. As an alternative source of features, a set of handcrafted features are extracted from the sMRI images by applying the stack assembling of Gray Level Co-occurrence Matrix (GLCM) and Gabor algorithms. In order to select nonredundant important features, we apply cosine similarity metric between the handcrafted and deep features. Next, a Feature Pyramid Network-based feature fusion technique namely- FusionNet is also proposed to fuse the deep features and the handcrafted features in order to further improve the classification accuracy. For comparing the performance of AD diagnosis, fused feature sets without feature selection and with feature selection are considered. It has been observed that the fused features without feature selection achieves the best classification accuracy implying that for AD diagnosis all the features are important and therefore distinct. All the experiments have been applied on a 5-class dataset obtained from ADNI and classified by Support Vector Machine classifier. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Pothole Detection Using YOLOv3 Model

Pothole Detection Using YOLOv3 Model

引用

2023 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2023

作者： Sathya, R. Saleena, B. Prakash, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Chennai India

ISBN: (纸本)9798350398748

When it comes to moving people and goods around a country, road transports are like the heart and arteries. It is essential that roads need be kept in good shape at all times. Most of the times, potholes on the road surface causes major damages and they must be repaired for maintaining the roads in usable condition. Countries like India, where roads span millions of kilometers and require constant upkeep, it may be difficult to locate potholes. Since this is the case, rapid, accurate, and real-time automated pothole detection is essential. The primary motivation of this proposed work is to explain the pothole prediction models that are created using object detection algorithm called YOLOv3. The YOLOv3 model is trained on a dataset of potholes, and the results are analyzed and compared with variants of YOLO by computing the model's precision, recall, and mAP. When compared to other models, the mAP of this one is improved while the computing cost is reduced significantly. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Identification of Cardiovascular Disease using Machine Learning and Ensemble Learning

Identification of Cardiovascular Disease using Machine Learn...

引用

2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Chopra, Shreya Kalra, Nidhi Rani, Rinkle Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350397208

Cardiovascular disease is a very dangerous disease among humans and can be fatal. It describes a condition where blood arteries get constricted or clogged, which can cause a heart attack, chest symptoms, or a stroke. One of the significant challenges faced in research is accurately diagnosing heart disease using health data. Existing techniques have poor performance, potential to bias and limited interpretability. In the proposed study, a system has been developed to identify the presence of cardiovascular disease in a person by applying machine learning algorithms to a heart disease dataset containing information about clinical parameters. The model algorithms used are Random Forest, k Nearest Neighbours, Decision Trees, Logistic Regression, Naive Bayes and Ensemble Learning. For comparison, Principal Component Analysis has been applied for dimensionality reduction on the dataset and then the classification algorithms are applied and the results are compared based on various model performance metrics. The authors of this paper aim to propose system can identify the presence of cardiovascular disease among people efficiently and accurately in a way that is easy to use for all. © 2023 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Leveraging DenseNet and Genetic Algorithms for Lung Cancer Severity Classification 1

Leveraging DenseNet and Genetic Algorithms for Lung Cancer S...

引用

1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Arthi, Ts. Premkumar, S. Sivakumar, S. Partheeban, N. Galgotia College of Engineering and Technology Dept of Computer Science and Engineering Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India Srm Institute of Science and Technology Computer Science and Engineering India

ISBN: (纸本)9798350328042

Lung cancer is an alarming global health crisis. Affecting both men and women equally, its incidence has skyrocketed to five million new cases every year and boasting an alarming annual death rate. CT scans can be invaluable tools in diagnosing lung disorders;therefore, this research seeks to develop a method for gauging malignancy severity by counting nodules found on pulmonary images. This study's primary goal is to use advanced Deep Learning techniques in order to increase accuracy and efficiency for lung cancer classification. DenseNet is an advanced Deep Learning architecture which has proven adept at learning complex patterns from medical images. By employing its capabilities, we hope to maximize lung cancer classification accuracy thereby allowing early and more effective interventions. Genetic Algorithm is used to optimize the DenseNet Model settings. This sophisticated optimization process gradually adapts hyperparameters of neural networks over time, in order to enhance its accuracy of identifying benign and malignant lung lesions, leading to improved patient outcomes and clinical decisions. This research represents a significant advancement in lung cancer classification and diagnosis. A combination of Deep Learning and Genetic Algorithms could improve accuracy and efficiency when it comes to lung cancer severity assessments, leading to earlier diagnoses and better management. The findings from this study may pave the way towards earlier diagnoses and better treatments of lung cancer. © 2023 IEEE.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19

引用

Materials Today: Proceedings 2023年 80卷 3709-3713页

作者： Modi, Shraddha Guhathakurta, Rajib Praveen, Sheeba Tyagi, Sachin Bansod, Saket Narendra Department of Computer Science & Engineering Government Engineering College Patan Katpur India Sri Venkateswara College of Engineering and Technology Department of IT Andhra Pradesh Chittoor India Department of Computer Science & Engineering Integral University Lucknow226026 India Bharat Institute of Technology School of Pharmacy Meerut India Dr. Ambedkar Institute of Management Studies and Research Deekshabhoomi Nagpur India

COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity. © 2021

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Harnessing the Potential of Visual and Audio Data using IoT in Multimedia Analytics 1

Harnessing the Potential of Visual and Audio Data using IoT ...

引用

1st IEEE International Conference on Emerging research in Computational science, ICERCS 2023

作者： Doni, A. Ronald Nirmalrani, V. Franklin, Ramya G Poornima, D. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

In the cutting-edge digital panorama, the amalgamation of multimedia analytics and the Internet of Things (IoT) has ignited a paradigm shift in information usage. This transformation leverages the abundance of visible and audio records, encompassing photos, films, and sound recordings, facilitated via the interconnected IoT community. This convergence gives challenges, which includes information privacy troubles, bandwidth limitations, and the need for superior information processing algorithms. Multimedia analytics, as a subject, revolves throughout the extraction of significant insights from multimedia records. IoT, then again, constitutes a widespread array of interconnected devices and sensors that generate copious portions of visible and audio facts. This mixture permits the actual-time collection, transmission, and evaluation of multimedia information, thereby empowering decision-makers to make extra informed alternatives and benefit profound insights at some point of various domain names. Hence, this paper proposes harnessing the potential of the Internet of Things in Multimedia Analytics (IoT-MA) to enhance the visible and audio facts. The convergence of IoT and multimedia analytics opens up a realm of exceptional possibilities spanning numerous industries and programs. This synergy has a long way-reaching implication in sectors along side healthcare, production, transportation, agriculture, and safety. IoT in multimedia analytics plays a pivotal feature in protection and surveillance, improving public protection, catastrophe response, and danger control. This article sets the stage for a comprehensive exploration of the potential of visible and audio information harnessed through IoT in multimedia analytics. The studies delves into the technologies, applications, and implications of this integration, shedding light on how it's miles reshaping the interactions with the sector and revolutionizing decision-making in an more and more interconnected and informat

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

U-Net Architecture for Detecting Glaucoma with Retinal Fundus Images 1

U-Net Architecture for Detecting Glaucoma with Retinal Fundu...

引用

1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Nandhini, S. Jagadeesan, J. Anitha, E. Vinayaka Mission's Research Foundation Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Tamil Nadu India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Loyola - ICAM College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350342796

Ophthalmologists use retinal images to determine eye disease, for example glaucoma disease. This diagnosis is made by measuring changes in the cup-to-disc ratio. Eye Cup Segmentation Helps Fossil Ophthalmologists Calculate CDRs of Retinal Background Images. In this article, we propose a machine learning approach to perform segmentation tasks using the U-Net architecture. This proposed technique was evaluated using 650 colour retinal fundus images. Next, U-Net has 160 opportunities. Additionally, we compared the segmentation results with ground reality images. According to the test results, the eyecup's segmentation performance reaches 98.42% of the membrane, with a loss of 0.15. Fundus images help detect glaucoma. In the past, several experiments have been conducted to detect this disease. This paper proposes an ideal framework aimed at building intelligent solutions that help identify glaucoma using MATLAB software tools. The diagnosis of glaucoma is made by measuring changes in the bowl-to-disc ratio. Eye Cup Segmentation Helps Fossil Ophthalmologists Calculate CDRs of Retinal Background Images. As we propose a deep learning method using U-Net architecture to perform segmentation tasks, we collected many fundus images as data. Subsequent pre-processing of the occlusion figure is accompanied by classification and segmentation. The pre-processing module uses several filters such as Gaussian filter and guided image filter to remove noise. The division into healthy and sick eyes helps with further administration. © 2023 IEEE.

关键词： Deep Learning Glaucoma detection KNN Algorithm Machine Learning U-Net Architecture

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：