检索结果-内蒙古大学图书馆

Student Trend Analysis for Foreign Education Employing Machine Learning: A Case Study from ‘Disha Consultants’, Gujarat, India

引用

Annals of Data science 2024年第2期11卷 571-588页

作者： Shah, Manan Kshirsagar, Ameya Sushra, Tulasi Department of Chemical Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Information Technology Symbiosis Institute of Technology Maharashtra Pune India Department of Business Administration School of Liberal Administration Pandit Deendayal Energy University Gujarat Gandhinagar382426 India

For many years, there has been literature on study abroad, student mobility, and international student exchange;however, the scope & depth of this work has expanded dramatically in the recent two decades. Most of this research in comparative education studies is rarely published in its primary publications. This study report aims to give a complete overview of the trends and difficulties surrounding international student recruiting and assist institutional leaders & administrators in making informed choices and effectively setting priorities. We have performed EDA testing, a thorough analysis that helps discover data distribution. It is essential for all domains because it exposes trends, patterns, and relationships that are not immediately apparent. EDA is the most effective approach to finding outliers, but it might lead us wrong if not done correctly. We demonstrated that our research gives a suitable pragmatic answer for future International study patterns among students by conducting thorough trend analysis of varying detailed data obtained from various sources. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Localize the Copy-Move Forged Region of an Image Using Improved SIFT

引用

SN computer science 2024年第1期5卷 71页

作者： Dhanaraj, Rachel Selva Sridevi, M. Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

With the advancement in technology, digital images have become a very popular source of information. But this source of information is also being used to mislead people and society by altering the original image. One of the ways through which digital images are tampered with is the copy-move forgery method, wherein a part of an image is copied and pasted into another area inside the same image. To avoid and prevent any kind of anomalies, this paper focuses on ways to detect this copy and move forgery in an image using the keypoint-based approach in an efficient manner. The methodology involves detecting key points using Improved Scale Invariant Feature Transform (SIFT) based on which features are identified. Within the isolated descriptors, the deployed K-Nearest Neighbor (K-NN) classifier determines the closest match. Now in order to remove the outliers present in the features, Hierarchical Clustering is used to cluster similar descriptors. Experimental result has proved this methodology to be effective in detecting the forgery in an image for different kinds of related transformations. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Copy-move forgery Hierarchical clustering K-NN classifier Scale invariant feature transform

来源：评论

学校读者我要写书评

暂无评论

Mathematical Model and AI Integration for COVID-19: Improving Forecasting and Policy-Making

引用

SN computer science 2024年第2期5卷 246页

作者： Panda, Dev Sourav Dixit, Rahul Dixit, Anuja Dwaracherla, Harshitha Shukla, Anupam Bosch Digital Department Bosch Global Software Technologies Private Limited Karnataka Bangalore India Department of Artificial Intelligence Sardar Vallabhbhai National Institute of Technology Gujarat Surat India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Pune India Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India

In this work, a new susceptible–exposed–infectious–recovered (SEIR) compartmental model is proposed which has additional media influence for precise quantization of the coronavirus disease 2019 (COVID-19). In the proposed model, first-order ordinary differential equations (ODEs) are used for the formulation of basic reproduction number, whereas genetic algorithm (GA) is used for its estimation. The inclusion of climatic parameters, governmental impact, and human behavioral response toward the disease provides an upper hand in determining the dynamics of its transmissibility, thereby indicating their significance in precising the outcomes. In addition, the future trends for the new normalized confirmed cases of COVID-19 are predicted using the long short-term memory (LSTM) model which helps in evaluating and modifying the current preventive actions taken to improve the situation. The robustness of the proposed model is measured by five different error functions which are tested in five different countries. According to the experimental results, this is observed that the proposed model has a smaller prediction deviation as well and the proposed scheme outperforms state-of-the-art models of COVID-19. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithm LSTM model Reproduction number SEIR-type compartmental model Social media Transmission dynamics

来源：评论

学校读者我要写书评

暂无评论

Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions 2

Enhancing UAV Path Planning Efficiency through Adam-Optimize...

引用

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

作者： Akshya, J. Neelamegam, G. Sureshkumar, C. Nithya, V. Kadry, Seifedine SRM Institute of Science and Technology Department of Computational Intelligence Chennai603203 India Karpagam College of Engineering Department of Computer Science and Technology Coimbatore641032 India KGiSL Institute of Technology Department of Information Technology Coimbatore641032 India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore641032 India Noroff University College Department of Applied Data Science Kristiansand4612 Norway

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications. © 2024 Elsevier B.V.. All rights reserved.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

Anomalous Sound Detection Using Time-Frequency Feature and Mixbatch

引用

Journal of Shanghai Jiaotong University (science) 2025年 1-8页

作者： Huang, Shun Zhang, Yunxiang Fang, Zhihua Tang, Minrui Xu, Ruifeng He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The sound emitted by machines under abnormal working conditions exhibits various frequency patterns. Currently, the most advanced anomalous sound detection (ASD) approach is to apply a multi-head self-attention mechanism to the Log-Mel spectrogram for automatic frequency pattern analysis. However, the Log-Mel spectrogram may filter out high-frequency components of abnormal sounds;thus the use of self-attention mechanisms on Log-Mel spectrogram seems to have certain limitations. In this paper, we construct a simple convolutional neural network to extract comprehensive frequency features from raw audio to complement spectral-temporal information fusion. The parameters of this neural network are continuously updated during the training process to extract better frequency features for downstream classification neural networks in ASD. Additionally, a method for data augmentation in the batch dimension was developed to help the classification model learn both types of comprehensive features simultaneously. The proposed method achieved an AUC of 94.13% and a pAUC of 89.09% on the DCASE 2020 Challenge Task 2 dataset. Even when using only the features proposed in this paper, an AUC of 84.62% was achieved. (Figure presented.). © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

来源：评论

学校读者我要写书评

暂无评论

Plant Leaf Disease Detection using Machine Learning 15

Plant Leaf Disease Detection using Machine Learning

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rathod, Jayant Kumar Nandan, M.R. Raju, Mohan V. Department of Information Science and Engineering Alva's Institute of Engineering and Technology Mangalore India

ISBN: (纸本)9798331300579

The field of agriculture greatly influences our *** most significant economic sector in our country is agriculture.A profit in agricultural products is the result of proper *** produce less because they lack knowledge about leaf *** plant leaf diseases is significant because manufacturing determines profit and *** method for classifying and detecting leaf diseases is *** primary goal of this study is to identify leaf diseases in tomato, potato, grape, apple, and corn *** leaf diseases are tracked throughout wide agricultural fields with the purpose of detecting crop *** a result, certain disease features are automatically identified and treated *** suggested Deep CNN model has been contrasted with well-known transfer learning techniques like *** disease detection has several uses in a variety of industries, including agriculture institutes and biological *** of the necessary study topics is plant leaf disease detection, since it has the potential to be useful in monitoring vast agricultural fields and automatically identify disease symptoms as soon as they manifest on plant leaves. © Grenze Scientific Society, 2024.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber Sensor: A Machine Learning Approach

Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber S...

引用

2024 International Conference on Innovations in science, engineering and technology, ICISET 2024

作者： Mahmud, Mahmudul Hoque Hoque Maruf, Ariful Uddin, Mohammad Nasir Department of Computer Science Dhaka Bangladesh Institute of Information Technology University of Dhaka Dhaka Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

This study introduces a novel approach to improving Photonic Crystal Fiber (PCF) Surface Plasmon Resonance (SPR) sensors by incorporating machine learning (ML) models for predicting core mode index. Traditionally, identifying core phenomena in PCF SPR sensors required manual visual inspection by experts, a process that is both time-consuming and prone to errors. To address this, a dual-core PCF model is developed and used Finite Element Method (FEM) simulations to create extensive training data for ML models. The proposed PCF model shows exceptional sensitivity to wavelength changes, with a recorded sensitivity of 6 0 0 0 nm/RIU, making it highly effective for accurate mode prediction. The study evaluated nine ML models, including XGBoost, Random Forest, and Decision Tree, achieving accuracies of 0.9742,0.9645, and 0.9667, respectively. By integrating ML models, the core mode identification process is significantly faster and more efficient, offering major benefits for PCF SPR research. This work not only advances PCF sensor technology but also enhances efficiency and accuracy in various SPR-based applications. © 2024 IEEE.

关键词： Surface plasmon resonance

来源：评论

学校读者我要写书评

暂无评论

Advances in Thermal Imaging: A Convolutional Neural Network Approach for Improved Breast Cancer Diagnosis 2

Advances in Thermal Imaging: A Convolutional Neural Network ...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Agughasi, Victor Ikechukwu Bhimshetty, Sampoorna Deepu, R. Mala, M.V. Maharaja Institute of Technology Mysore Department of Computer Science & Engineering Karnataka India University of Visvesvaraya College of Engineering Department of Computer Science & Engineering Karnataka India Pes University Department of Computer Science & Engineering Karnataka Bangalore India

ISBN: (纸本)9798350382952

This study explores the application of thermal imaging in breast cancer diagnostics, presenting a novel methodology that integrates pre-processing techniques and Convolutional Neural Networks (CNN) for the classification of thermal breast images. A comprehensive dataset from the DMR Database is used, comprising balanced samples of normal and cancerous images. The images are augmented, standardized, and enhanced through data augmentation, resizing, and filtering, with crucial features extracted via the Histogram of Oriented Gradients (HOG). A CNN model, specifically constructed for this study, is then trained and tested on these processed images. The approach's effectiveness is benchmarked against other classifiers, displaying promising results with accuracy rates ranging from 95.7% to 98.5%. Future work suggests exploring the fusion of traditional and advanced imaging modalities, expanding datasets, and utilizing pre-trained models for improved diagnostic precision. This research signifies a step toward the development of real-time, efficient diagnostic tools for breast cancer, highlighting the potential impact on patient outcomes and the broader medical field. © 2024 IEEE.

关键词： Infrared imaging

来源：评论

学校读者我要写书评

暂无评论

Mining skyline frequent-utility patterns from big data environment based on MapReduce framework

引用

Intelligent Data Analysis 2023年第5期27卷 1359-1377页

作者： Wu, Jimmy Ming-Tai Li, Ranran Wu, Mu-En Lin, Jerry Chun-Wei Department of Information Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters. © 2023 - IOS Press. All rights reserved.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

Securing Electronics Gadgets using Blockchain based Framework 2

Securing Electronics Gadgets using Blockchain based Framewor...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Prakash, P. N. Senthil Lanitha, B. Sundaram, Karthic Vellore Institute of Technology School of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331534950

The usage of electronic gadgets has grown dramatically in the current digital era. Devices like mobile phones and smart watches are unavoidable and has ingrained themselves into every person's daily routine. Almost everyone owns at least one portable electronic device and these devices are usually expensive. Due to this widespread use, there is also an increase in the number of lost or stolen cases reported. In a recent study, it has been revealed that the number of devices lost is mostly because of misplacing in public places like home, office, public transport. Such incidents will not only cause financial loss but it may also lead to data breaches stored in the portable devices. Hence there is a need for a secure eco-system which helps to identify the genuine owners of electronic devices. A blockchain-based framework is deployed on an Ethereum-based network and adopts the Proof of Stake (PoS) mechanism to protecting gadgets. Further, it is designed using the smart contract defined using solidity programming language. The system enables the owner's details to be recorded upon purchase of a device. The complete specification details of the devices along with owner details is contained in a block and then appended to network. Later on, if the owner wants to resale the device, then the transfer of ownership information is stored in a block and added to the network. The proposed system provides an appropriate interface to check the genuine owner. The proposed framework has been implemented using solidity language, Remix IDE and Sepolia testnet. Obtained results highlights the suitability of the method and its performance in terms of gas consumption and execution latency. © 2024 IEEE.

关键词： Ethereum

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：