检索结果-内蒙古大学图书馆

7th International Conference on Advances in Artificial Intelligence, ICAAI 2023

作者： Baniya, Pashupati Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Department of Computer Science and Engineering School of Engineering & Technologynce and Enginerring Sharda University Greater Noida India Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Engineering Sciences Islamabad Pakistan

ISBN: (纸本)9798400708985

The growing demand for Internet of Medical Things (IoMT) sensors within the healthcare sector has emphasized the necessity for robust security measures. However, these devices, often constrained by limited resources, frequently require external assistance to guarantee the security of data. Blockchain technology emerges as a practical solution, offering a way to enhance system security and safeguard patients' sensitive health records against unauthorized access and data breaches. The fusion of IoMT and blockchain presents a range of benefits, such as cost reduction, improved speed, automation, and the guarantee of unmodifiable data. Nevertheless, security concerns persist, particularly in light of the ever-evolving encryption techniques. The effective implementation of IoMT hinges on the delivery of personalized, patient-centered care, which has the potential to enhance affordability, simplicity, and overall convenience, ultimately contributing to improved quality of life and extended lifespans. In this comprehensive exploration, we delve into the historical context, architecture, components, and challenges associated with IoMT. Additionally, we conduct an in-depth examination of blockchain technology, covering its essential elements and categories, as well as its seamless integration into IoMT, underscoring its critical role in reinforcing security measures within the healthcare domain. Furthermore, this paper outlines recent developments in blockchain technology within the IoMT framework, highlighting its ongoing evolution and sustained significance in the healthcare field. © 2023 ACM.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Advanced Machine Learning Techniques for Fake News Detection: A Comprehensive Analysis Using the LIAR Dataset 3rd

Advanced Machine Learning Techniques for Fake News Detectio...

引用

3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Paul, Subrata Ghosh, Shivnath Mitra, Anirban Bhattacharya, Pronaya Department of CSE-AI Brainware University West Bengal Barasat India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University West Bengal Kolkata India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India

ISBN: (纸本)9789819624676

In today’s digital world, fake news presents a serious challenge to social cohesiveness, trust among individuals, and the functioning of democracy. Overcoming this issue demands novel solutions that make efficient use of machine learning (ML) for identifying and battle disinformation. This paper examines the important problem of fake news, including its meaning, its consequences, and the significance of social media in its rapid spread. The study emphasises the complexities of identifying fake news, focusing on sophisticated techniques like AI-generated content and the widespread dissemination of misinformation. In order to tackle such obstacles, the paper uses ML approaches, particularly the LIAR dataset and the XGBoost model, to create a reliable fake news detection system. The outcomes show that these methods have been successful at successfully recognising false information, highlighting ML’s prospective in reducing misinformation. This research contributes to the broader discourse on media literacy and the need for reliable information in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Ocean Wave Height Forecasting using Deep Learning Neural Networks and Optimization Techniques

Ocean Wave Height Forecasting using Deep Learning Neural Net...

引用

2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Lohitha Chowdary, M. Premsai, P. Dasharna, M. Sri Kavya, I. Swapna, T.R. Dept of Computer Science and Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350325188

Ocean wave prediction is crucial for many marine applications, including navigation and coastal engineering. Accurate wave height prediction can help reduce the risks. In this paper, we have implemented an interface that can predict wave height based on Neural Network, Optimization models and compared the performances of these models on the Mooloolaba Beach Dataset with regular and irregular waves. The models we experimented with are single-shot LSTM, multi-shot LSTM, CNN-LSTM Hybrid model, Bidirectional LSTM, Artificial Neural Networks(ANN) with different kernels like Laplacian, Polynomial, ANOVA Radial basis, and Multi Additive Regression Trees(MART). These models are designed to capture the Nonlinear and Temporal characteristics of ocean waves. We have also experimented with different optimization techniques like Symbiotic Organisms Search (SOS), Hybrid Symbiotic Organisms Search (HSOS), and Moth Flame Optimization (MFO). The Dataset covers a period of One year and has a sampling Frequency of 1.28Hz. We evaluate the models using six metrics: Accuracy, Precision, F1 score, Mean Square Error, Mean Absolute Error, and Root mean square error. The results show that the ANN model with ANOVA kernel outperformed other models in terms of Accuracy of 94.5%. We have also developed a Web Interface. It helps Fishermen to make informed decisions, reduce the risks associated with uncertain wave conditions, and contribute to a safer fishing experience. © 2023 IEEE.

关键词： ANN HSOS kernel methods LSTM Machine Learning MFO Ocean Wave Prediction SOS

来源：评论

学校读者我要写书评

暂无评论

Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

引用

Journal of Electronic science and Technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

来源：评论

学校读者我要写书评

暂无评论

Impact of Pollutants on Temperature Change and Forecasting Temperature of US Cities 1

引用

3rd International Conference on Sustainable Expert Systems, ICSES 2022

作者： Kankaria, Tanmaay Krishna, Bandla Vaibhav Teja, Duppanapudi Surya Dinesh Chandra Gupta Kolipakula, D.V.S. Sujee, R. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789811978746

ISBN: (纸本)9789811978739

Planet Earth has endured extensive climatic changes throughout history. According to the 2018 IPCC report, human activities have accounted for 0.8 °C to 1.2 °C of global warming above pre-industrial levels. Since 1901, the global average temperature has surged by an average rate of 255 K per decade. With the rise in levels of air pollutants, temperatures have altered the chemical composition of the atmosphere. However, current reports and theories only point out the relations between this temperature change and greenhouse gases. By exploiting pollution and temperature datasets in a machine learning model, we will scrutinize the correlations amongst the radical elements causing a surge in temperature. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

PHASE2VEC: DYNAMICAL SYSTEMS EMBEDDING WITH A PHYSICS-INFORMED CONVOLUTIONAL NETWORK 11

PHASE2VEC: DYNAMICAL SYSTEMS EMBEDDING WITH A PHYSICS-INFORM...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Ricci, Matthew Moriel, Noa Piran, Zoe Nitzan, Mor School of Computer Science and Engineering The Hebrew University Jerusalem Israel School of Computer Science and Engineering Racah Institute of Physics Faculty of Medicine The Hebrew University Jerusalem Israel

Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible. Predicting these classes from data remains an essential open challenge in computational physics on which existing time-series classification methods struggle. Here, we propose, phase2vec, an embedding method that learns high-quality, physically-meaningful representations of low-dimensional dynamical systems without supervision. Our embeddings are produced by a convolutional backbone that extracts geometric features from flow data and minimizes a physically-informed vector field reconstruction loss. The trained architecture can not only predict the equations of unseen data, but also produces embeddings that encode meaningful physical properties of input data (e.g. stability of fixed points, conservation of energy, and the incompressibility of flows) more faithfully than standard blackbox classifiers and state-of-the-art time series classification techniques. We additionally apply our embeddings to the analysis of meteorological data, showing we can detect climatically meaningful features. Collectively, our results demonstrate the viability of embedding approaches for the discovery of dynamical features in physical systems. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Skin Lesion Decode A CNN-SVM Convergence for Enhanced Diagnostic Accuracy

Skin Lesion Decode A CNN-SVM Convergence for Enhanced Diagno...

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Rana, Samir Manwal, Manika Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

This research study investigates the development and evaluation of a merged CNN and SVM model for detecting and classifying skin lesions. The research systematically divides skin lesions into five categories that allow a thorough analysis of the model's diagnostic skills. The CNN-SVM model is developed and tested using a high-quality dataset, and its accuracy, recall, and F1 scores provide insights into specific performance aspects of the model. The results show a high level of precision for all the classes that vary from 92.95% for Class 1 to 95.95% for Class 5, which suggests that the model is good at making optimistic predictions. The recall measure, a sensitivity measure of the model, varies from 91.44% for Class 3 to 98.47% for Class 1, indicating the model's capability to recognize true positives correctly. The F1-score, which balances precision and recall, is always high, fluctuating from 93.00% for Class 3 to 96.28% for Class 5, reflecting the model's well-balanced categorization. Several types of averages are computed to assess the model's performance thoroughly. The macro average indicates accuracy of 94.70%, recall of 94.81%, and F1-score of 94.73%, with equal weight given to each class. With class distribution, the weighted average is as follows: accuracy - 94.74%, recall - 94.72%, F1-score - 94.71%. The micro average indicating the sum of contributions from all classes is 94.72% for all three parameters across the board. The model is entirely accurate, with an overall accuracy of 94.72397, showing the model's strong ability to generalize across different skin lesion types. The statistical measures confirm the CNN-SVM model as a practical approach to accurate and reliable classification of skin lesions, revealing high clinical potential. The paper's impressive experimental and statistical analyses demonstrate the power of the model and its ability to improve dermatological diagnostic procedures. © 2024 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Delhi Air Pollution Prediction: A Comparative Analysis using Time Series Forecasting

Delhi Air Pollution Prediction: A Comparative Analysis using...

引用

2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Singh, Sparsh Kumar, Vidit Ahmed, Zaid Mittal, Kajol Sharda University School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350323887

Recent years have seen a substantial increase in study on air pollution as a result of its negative ramifications It is also acknowledged as one in the current atmosphere. one of the main risk elements. Accurate air quality assessment is the first stage in the implementation of air pollution control systems, which helps in the growth of developed nations' economies and societies. the two methodical Accurate air quality predictions are essential for emissions control, public health, and wellbeing. The metropolis of India, Delhi, has been the most polluted metropolis in the world for the past two years. In this study, the results have been compared using SARIMAX, Prophet, and LSTM are three distinct machine learning algorithms that were tested against one another. The same quantity of parameter calibration was applied to all the models, and SARIMAX seems to be more reliable out of the three. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Cluster-Head Selection Protocol for Improving the Network Lifetime of Wireless Sensor Network 9

Cluster-Head Selection Protocol for Improving the Network Li...

引用

9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Sharan, Bhagwati Manjula, R. Department of Computer Science and Engineering School of Engineering and Sciences SRM University-AP Guntur India

ISBN: (纸本)9798350383201

Prolonged network operations are crucial for any Wireless Sensor Network (WSN) based applications such as health care, military, industrial, etc. The fixed energy and the restrained communication range of the sensor nodes (SNs) make it challenging to achieve a longer life of network operations. Generally, the data collection and direct message transmissions to the base station (BS) consume most of the SNs' energy resulting in a shorter network lifetime. Reduction in the energy expended on these data transmissions leads to an improvement in the WSNs' network lifetime. Clustering approaches are employed to mitigate this issue. The cluster head (CH) in each cluster collects the data from the SNs and forwards it to the BS. Proper CH node selection is vital in achieving enhanced network lifetime. Therefore, in this work, a new clustering technique is proposed that probabilistically selects the best CHs for each cluster by considering the residual energy of each SN. The suggested technique has been compared with the existing LEACH scheme and the experimental outcomes show the suggested technique has improved the network lifetime by an average of 8.32% and 14.23% in terms of the first and the last node dead respectively. © 2023 IEEE.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

U-Net based Pancreas Segmentation from Computed Tomography Images 3

U-Net based Pancreas Segmentation from Computed Tomography I...

引用

3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Devendhar, Tirumalasetti Lakshmipriya, B. Jayalakshmy, S. Vellore Institute of Technology Chennai Campus School of Computer Science & Engineering Chennai India Jawaharlal Nehru University School of Engineering New Delhi India IFET College of Engineering Electronics & Communication Engineering India

ISBN: (纸本)9798350391565

Delineation of pancreas from computed tomography (CT) scan images is burdensome owing to its anatomic variation in size, shape and its presence with respect to adjacent organs. This work explores the U-Net architecture to delineate pancreas from the CT volume of abdomen. U-Net was trained for variable number of epochs to identify the optimal learning environment for better segmentation performance. U-Net when trained for pancreas segmentation using CT dataset taken from The Cancer Imaging Archive (TCIA) has resulted in a segmentation performance of dice similarity coefficient of 0.8138, intersection over union of 0.7962 and boundary F1 score of 0.8036. © 2024 IEEE.

关键词： Image segmentation

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：