检索结果-内蒙古大学图书馆

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shukla, Sakshi Gupta, Kshitij Pasari, Sumanta Gupta, Vishal Birla Institute of Technology and Science Department of Mathematics Rajasthan Pilani India Birla Institute of Technology and Science Department of Computer Science and Information Systems Rajasthan Pilani India

ISBN: (纸本)9798350370249

Accurate forecasting of wind speed is crucial for optimal extraction of energy, enabling integration of power grid, planning and management of renewable energy resources. To overcome the unpredictability of long-term trends and seasonal variation of wind, this study proposes a deterministic framework utilizing a hybrid model based on empirical wavelet transform (EWT), bidirectional gated recurrent neural network (BiGRU), and Bayesian optimization algorithm (BOA) for an hour-ahead wind speed prediction. Firstly, the EWT is used for preprocessing the wind speed data with enabling wavelet charaterticstics. Then, the BiGRU model is employed for regression using optimal values determined by the BOA method. The robustness of the proposed integrative framework is regressively evaluated over seven years (2015-2021) of hourly wind speed data across four locations in India. The evidence of numerical results of the proposed model demonstrates its effectiveness with a maximum improvement of 70% - 80% in terms of RMSE values across all the studied regions. Furthermore, the model evaluation and pictorial results indicate that the proposed model is a potent tool for generating wind energy and its integration into the smart grid. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Air Quality Index (AQI) Prediction using Automated Machine Learning with TPOT-ANN∗

Air Quality Index (AQI) Prediction using Automated Machine L...

引用

2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Sharma, Meghna Gupta, Eishita Viji, D. Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350337426

Pollution is a critical and disturbing problem that people encounter daily in today's world and also has an impact on the quality of air. The issue is so crucial that it cannot be overlooked and its effects are felt everywhere. The climatic variables that affect the AQI, such as NO2, NH3, SO2, CO, O3, fog ,temperature, smoke, dew, mist, benzene, toluene, xylene, etc. The AQI measures the severity of the pollutants present in the air. It classifies the severity of air quality into six categories, each with its own range of values. The categories are as follows: Good, which ranges from 0-50 on the AQI scale, indicating that the air quality is generally safe and healthy for everyone to breathe. Moderate, which ranges from 51-100 on the AQI scale, indicating that the air quality is acceptable but may pose a moderate risk for certain individuals, such as those with respiratory issues. Unhealthy for Sensitive Groups, which ranges from 101-150 on the AQI scale, indicating that the air quality is dangerous for certain individuals, for example the youth or the younger and older ones or people having respiratory problems. Unhealthy, which ranges from 151-200 on the AQI scale, indicating that the air quality is hazardous and can cause serious health problems for everyone. Very Unhealthy, which ranges from 201-300 on the AQI scale, indicating that the air quality is extremely dangerous and can cause severe respiratory and cardiovascular problems. Hazardous, which ranges from 301 and higher on the AQI scale, indicating that the air quality is life-threatening and can cause serious health problems even for those who are otherwise healthy. Overall, the AQI is an essential tool for assessing the severity of air pollution levels and determining the appropriate measures that need to be taken to protect public health. The suggested model aims to evaluate the air quality. The proposed model suggests a strategy for measuring future AQI data from the present and historical AQI data by us

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Secure Data Education: Leveraging Big Data for Enhanced Academic Performance and Student Success in Educational Institutions 1

引用

8th International Conference on ICT for Sustainable Development, ICT4SD 2023

作者： Shylaja, A.R. Shubhashree, D.A. Shrihari, M.R. Manjunath, T.N. Ajay, N. S J C Institute of Technology/Computer Science and Technology Chickballapur India BMSIT&M/Computer Science and Technology Bengaluru India

ISBN: (数字)9789819949328

ISBN: (纸本)9789819949311

Big data applications are employed in a variety of industries, including academia, healthcare, marketing, industry, government, and public administration, among others, to address societal issues and progress society (Munshi and Alhindi in Big data platform for educational analytics, 2021). Big data in education has significant benefits since it can be managed at an infinite scale. Big data in education helps students achieve better grades, enhance the grading system, gain attention, and lower dropout rates (Wang and Yang in 2020 International conference on communications, information system and computer engineering (CISCE), 2020). The evaluation of student performance is based on their results on tests, evaluations, quizzes, projects, etc. By analyzing this data, mentors can recognize student behavior and performance and establish the best learning environment. The feedback is highly useful for enhancing the outcomes. Students’ heart rates, facial characteristics, and behavioral clues may all be seen visually. Educational despite the number of students in institutions, big data makes it possible to create a specific curriculum for each student. With no compromises, this enables students to enroll in the classes they are interested in and progress at their own speed. All of this data on students’ grades, tardiness in submitting tasks, and desire in learning aid mentors and institutions in pinpointing the precise causes of dropouts. The security and privacy of data created in universities, however, are the major drawbacks of big data applications in the educational industry. In this paper, we investigate how Hadoop employs Kerberos for authentication and authorization as well as how big data benefits students’ academic performance. A network authentication system called Kerberos offers a safe authentication service based on reliable outside sources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Classification of Autism Spectrum Disorder using CNN & Transfer Learning 2

Classification of Autism Spectrum Disorder using CNN & Trans...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Das, Shyamali Chaudhury, Pamela Tripathy, Hrudaya Kumar Sillicon Institute of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (纸本)9798350370188

Autism spectrum disorder (ASD) is a neurodevelopment condition that impacts a person's ability to communicate and interact socially. Diagnosing autism spectrum disorder (ASD) early and correctly is critical for providing appropriate help to those who need it. Transferring information or learned representations from one task or domain to another is possible using a computerized learning technique known as transfer learning. In this study, we proposed a method for classifying ASD using an Enhanced Layerwise Sequential Convolutional Neural Network (ELS-CNN) with transfer learning. Transfer learning involves leveraging pre-trained models on large-scale datasets used for the Kaggle platform and fine-tuning them for specific tasks. A dataset that tested these models was collected and consisted of 2,928 face images. These images are preprocessed using Adaptive Median Filtering to remove noise from an image and Histogram Equalization to improve image contrast. Otsu's Binary Threshold can be used for image segmentation. Extracting the features can be Principal Component Analysis (PCA) for image compression. We suggested the ELS-CNN algorithm, which has demonstrated good performance in a number of image identification applications. The output of the deep learning neural network models was assessed using common assessment measures like accuracy, precision, recall, and fl-score. The Enhanced Layerwise Sequential CNN algorithm achieved the highest accuracy. © 2024 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

A Survey on Remote Sensing-Based Climate Change Prediction Using Artificial Intelligence Techniques 4

A Survey on Remote Sensing-Based Climate Change Prediction U...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Suhana, S. Karthiga, M. Bannari Amman Institute of Technology Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Climate change prognosis is one of the most important fields of study that has attracted much interest in the last decade because of its implication for environmental and social development. The developments in remote sensing technologies that deliver data on various climatic factors have been integrated with Artificial Intelligence techniques to improve climate change prediction models. This paper aims to review the research done between the years 2014-2024 and how the AI methods were applied along with the remote sensing data for climate change prediction. The survey covers different AI techniques, including machine learning, deep learning, combined approaches, and their application to vast RS datasets. Major trends and issues in this rapidly developing multidisciplinary area are discussed, and the strengths and weaknesses of different AI methods are compared. Furthermore, recent AI techniques used in remote-sensing-based climate change prediction include Neural Networks, Support Vector Machines (SVMs), Random Forests and Decision trees with physical climate models. However, these models have limitations like difficulties in handling heterogeneous information from different sensors, high computational costs, limited labelled datasets, and issues interpreting complicated AI model results. It also points to the directions for future research, noting that there is a need for better, more powerful and easily understandable AI frameworks to handle the issue of climate change. This paper seeks to give the reader an insight into the current state of AI in climate change prediction and the use of remote sensing data in this area of research. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Deformation Correction in Laparoscopic Liver Surgical Navigation Using Point Cloud Completion and Biomechanical Model

Deformation Correction in Laparoscopic Liver Surgical Naviga...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Qian Xiao, Deqiang Ai, Danni Fan, Jingfan Fu, Tianyu Yang, Shuo Song, Hong Yang, Jian Beijing Institute of Technology School of Optics and Photonics Beijing China Beijing Institute of Technology School of Medical Technology Beijing China Beijing Institute of Technology School of Computer Science Beijing China

ISBN: (纸本)9798350386226

In the minimally invasive liver resection surgery, deformation estimation of liver is required to correct the preoperative virtual model to match the intraoperative scenarios, in which the liver deforms due to respiration and surgical operations. Existing methods on liver deformation estimation often struggle to achieve high accuracy when the intraoperative liver surface is limited in size. To overcome the challenge of sparse intraoperative point cloud data and improve the accuracy of liver deformation predictions, this paper introduces an innovative method for estimating liver deformation. This method comprises two main components: intraoperative point cloud completion and liver deformation estimation. Intraoperative point cloud completion uses registration techniques to integrate preoperative topological structures into the intraoperative phase. Liver deformation estimation combines optimization control with biomechanical modeling to accurately align the preoperative liver model with its intraoperative counterpart. Comparative and ablation experiments, as well as investigations into the impact of different completion ratios, were conducted. The results demonstrate that this method effectively utilizes preoperative liver geometric features to enhance intraoperative visualization, even with limited intraoperative data. Additionally, the opti-mization control method provides reliable deformation estimates with acceptable accuracy. This study offers new insights and methodologies for the development of augmented reality surgical navigation systems, contributing to the computer assisted liver surgey. © 2024 IEEE.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Improved Wild Horse Optimizer with Deep Learning Model for Skin Lesion Detection and Classification on Dermoscopic Images 23th

Improved Wild Horse Optimizer with Deep Learning Model for S...

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Maranco, M. Tyagi, Amit Kumar Sivakumar, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9783031648120

Melanoma is a deadly kind of skin cancer which can spread to other parts of the body. Therefore, it is necessary to identify melanoma at the beginning level. Visual examinationat the time of medical examination of skin lesion is a challenging process as there exist high resemblance among the lesions. Besides, dermoscopy is a non-invasive imaging tool which enables visualizationof the skin surface by light magnifying device and immersion fluid. Traditional image processing models such as histogram thresholding, clustering, or active contours are employed to segment skin lesions. This motivates the study of how to best employ AI, and deep learning (DL) algorithms in particular, to identify skin lesions in dermoscopic pictures. By using these images, current research refines the existing wild horse optimizer (IWHO) by using a deep learning driven skin lesion detection and classification (IWHODL-SLDC) *** proposed model utilizes optimal Tsallis entropy based image segmentation. Besides, Squeeze Netapproach can be employed to derive feature vectors. Also, IWHO with deep wavelet neural network (DWNN) technique is used to classify the dermoscopic images. Numerous tests are carried out to guarantee the enhancement of the IWHODL-SLDC method on the reference dataset. It was found via experimentation that the IWHODL-SLDC model outperformed the other approaches in a wide range of ways. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Prioritization and meta-heuristic approach for efficient congestion control in vehicular ad hoc networks (VANETs)

引用

International Journal of Information technology (Singapore) 2025年 1-9页

作者： Sharma, Swati Khanum, Saba Madhumala, R.B. Maroju, Abhilash Aggarwal, Saurabh Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab India Department of Computer Science and Engineering MSIT Janakpuri Delhi India Department of Computer Science and Engineering Dayanand Sagar Academy of Technology and Management (DSATM) Bengaluru India Department of Computer Science and Engineering University of Cumberlands Williamsburg United States Department of Information Technology University of the Cumberlands Williamsburg United States

Vehicular ad hoc networks (VANETs) have emerged as a key area of interest in the research community due to their wide range of applications. As the number of vehicles increases, VANETs encounter challenges with access control, which affects communication between the source vehicle and other vehicles. To overcome this issue, the prioritization and meta-heuristic-based congestion control (PMCC) scheme is proposed in the given paper. It addresses this congestion by prioritizing messages and dynamically adjusting the transmission rate of safety messages. In the prioritization phase, message priority is determined using both dynamic and static factors. Then, to control channel load within VANETs, the PMCC scheme employs a Tabu Search optimization (TSO) algorithm to adjust the transmission rate of beacon messages. Comparative analysis of the PMCC scheme with existing schemes such as urban vehicular broadCAST (UV-CAST), distributed-fair power adjustment for the vehicular environment (D-FPAV), and loss differentiation rate adaptation (LORA) is conducted based on metrics including reception probability, average delay, and vehicle coverage ratio. The results show the superior performance of the proposed PMMC scheme in all aspects. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Decentralized congestion control (DCC) Distributed-fair power adjustment for the vehicular environment (D-FPAV) Loss differentiation rate adaptation (LORA) PMCC TSO Urban vehicular broadCAST (UV-CAST) VANETs

来源：评论

学校读者我要写书评

暂无评论

SECURE TRUST: a blockchain-enabled trust and reputation system for malicious node detection in IOT networks

引用

International Journal of Information technology (Singapore) 2023年第8期15卷 4323-4336页

作者： Simpson, Serin V. Ravi Raju, Y. Bhanu Rajesh Naidu, K. Venu, Gopika Department of Computer Science & Technology Madanapalle Institute of Technology & Science Kadiri Road Angallu Andhra Pradesh Madanapalle India

This study presents SECURE TRUST, a novel blockchain-enabled trust and reputation system designed to address the growing security risks associated with the proliferation of IoT devices. By leveraging blockchain technology, our system effectively evaluates the reliability of nodes in IoT networks and mitigates harmful activities. Our approach employs smart contracts to facilitate collaborative detection and mitigation of malicious behavior among nodes. Furthermore, we integrate a trust and reputation system that assigns trust ratings to nodes based on their past interactions and behavior. Our system is compatible with various hardware platforms and utilizes open-source software components. Extensive testing demonstrates that our proposed approach reliably detects and reduces security risks caused by malicious nodes, offering robust protection against unwanted activities. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Blockchain Intrusion detection IoT Security Trust management

来源：评论

学校读者我要写书评

暂无评论

An Automated and Fine- Tuned Image Detection and Classification System for Plant Leaf Diseases

An Automated and Fine- Tuned Image Detection and Classificat...

引用

2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Amritraj, Shoibam Hans, Nitish Diana Cyril, C. Pretty Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350337426

Plant leaf disease detection is the process of discovering and diagnosing diseases that damage the plant leaves. This can be done using a variety of approaches including visual inspection, laboratory tests, computer vision techniques etc. Plant leaf diseases must be detected and categorize the cause in order to take corresponding counter measures to manage and control them for healthy and achieve high-yielding crops. In this contemporary Deep Learning era traditional object detection methods have become obsolete due to limitations such as the need for manual crafted features, lack of robustness, and inability to handle large datasets. Deep learning-based approaches are more robust, can account for differences in brightness, interference, and perspective, and can automatically learn features from enormous datasets. These techniques are practical because they can efficiently process large datasets in a brief time frame. Several research studies have developed expert and automatic disease detection methods. Most of this research are limited to one or two plant species and a few disease types. In this research study, we presented a computer vision approach applying the state-of-the-art YOLO algorithm with our own dataset consisting of 8 different classes of pant leaf diseases caused by fungi, bacteria, viruses and pest. Our system achieved promising results and effectively predicted the diseases with the bounding boxes and class probabilities. Overall, this research paper provides a solution for plant disease detection using YOLOv5 and contributes to the development of automated agricultural management systems. © 2023 IEEE.

关键词： Object detection

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：