检索结果-内蒙古大学图书馆

4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Roy, Sarthak Ghosh, Partha Goto, Takaaki Sen, Mainak Techno India University Computer Sc. & Engg. Dept. West Bengal Kolkata India Academy of Technology Adisaptagram Dept. of Computer Science & Engg. India Toyo University Dept. of Information Sciences and Arts Saitama Japan

ISBN: (纸本)9798350389128

Underwater object classification is a critical task in domains such as marine research, underwater exploration, maintenance of underwater structures and deep-sea oil industries etc. Accurate detection of underwater objects is a task of vital importance for autonomous underwater vehicles (AUVs). Tasks such as obstacle avoidance and autonomous navigation become easier when underwater obstacles can be recognized. A learningbased, robust object detection model capable of processing images at a high rate is beneficial for real-time object detection. This project focuses on developing a robust underwater object classification system utilizing machine learning techniques to classify objects present in underwater imagery. In this paper, we explore the efficacy of transfer learning using VGG16. VGG16 offers valuable pre-trained weights that encapsulate knowledge about fundamental image features. Here, we have trained a convolutional neural network and a VGG-16 based neural network classifier on three datasets and received 84.89% of accuracy with VGG-16 based classifier using transfer learning. © 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

Early Forest-Fire Detection by Linear Regression, Ridge Regression and Lasso Regression 1

Early Forest-Fire Detection by Linear Regression, Ridge Regr...

引用

1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Mandre, Ananya. Hebbar, Deeksha.R. Rao, J. Shreya Keshav, Ananya Kamal, Shoaib Rao, Trupthi Global Academy of Technology Department of Computer Science Engineering Bangalore India Global Academy of Technology Department of Electronics and Communication Engineering Bangalore India Global Academy of Technology Department of Information Science Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Science Bangalore India

ISBN: (纸本)9798350339727

Forest fires are the wildfires that grow uncontrollably, burning plants, animals, grassland and bushlands. For reducing the losses caused by the disaster an effective fire detection device is required. The main agenda of This research will provide a model for early detection. of forest fire. Previously many methods were proposed for the detection such as Neural network model, Three dimension otsu method, NMDI methodology and many more, As we can see there are certain limitations for all these methods. We have implemented certain methodology such as Ridge regression, Lasso regression and Linear regression, equations and the methodology used in this system is studied and implemented during this study. Thus all these three systems demonstrate the required solution for the problem with a good percentage of accuracy, © 2023 IEEE.

关键词： Fires

来源：评论

学校读者我要写书评

暂无评论

Optimized Crop Prediction and Monitoring Using Ensemble Machine Learning Algorithms 2

Optimized Crop Prediction and Monitoring Using Ensemble Mach...

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Kavitha, H.S. Usha, S.M. Sheela, S.N. Anu, H. Sharmila, N. Mallikarjunaswamy, S. Basavaraju, N.M. Ravi, P. Pavithra, G.S. Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Dept of Computer Science and Engineering Mysuru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

This paper addresses the urgent need for enhanced agricultural practices by pinpointing the significant limitations of existing crop prediction and monitoring systems. Traditional methods, often characterized by low accuracy and high resource consumption, severely limit effective farming decision-making and sustainability. In response to these challenges. A proposed method is introducing the Optimized Crop Prediction and Monitoring Model (OCPMM), a groundbreaking approach that harnesses the power of advanced Machine Learning Algorithms (MLAs) to elevate accuracy and efficiency in crop management. The OCPMM distinguishes itself through innovative features such as real-time data analysis and predictive analytics, establishing a new benchmark for precision agriculture. Our comparative analysis reveals that the OCPMM markedly surpasses conventional methods, such as the Standard Crop Yield Estimation Technique (SCYET), across key metrics, including prediction accuracy, resource utilization, and time efficiency. These enhancements are instrumental in optimizing crop yields, minimizing waste, and advancing sustainable agricultural practices. By rigorously addressing the deficiencies of traditional systems and highlighting the superiority of the OCPMM with empirical evidence, this study illuminates the transformative potential of machine learning technologies in agriculture. It presents a scalable, reliable, and efficient framework poised to revolutionize agricultural practices, paving the way for a future where farming decisions are informed by precision and foresight. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Algorithms in Mobile Edge with Real-Time Abnormal Event Detection for 5G-IoT Devices

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2023年第17期17卷 59-71页

作者： Praveenchandar, J. Kumar, S. Vinoth Paul, A. Christopher Mukunthan, M.A. Maharajan, K. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi India Department of Information Technology Karpagam Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India

IoT is becoming increasingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-RAN with IoT networks to provide an experimental 5G testbed. In a 5G IoT environment, this experience is utilized to enhance both perpendicular and flat localization (3D localization). DRCaG, an acronym for the proposed model, stands for a deep, complicated network with a gated layer on top. The performance of the proposed model has been demonstrated through extensive simulations in terms of learning reduction, accuracy, and matrix disorientation, with a variable signal-to-noise ratio (SNR) spanning from 20 dB to + 20 dB, which illustrates the superiority of DRCaG compared to others. An online, end-to-end solution based on deep learning techniques is presented in this study for the fast, precise, reliable, and automatic detection of diverse petty crime types. By detecting tiny crimes like hostility, bag snatching, and vandalism, the suggested system may not only identify unusual passenger behavior like vandalism and accidents but also improve passenger security. The solution performs admirably in a variety of use cases and environmental settings. © 2023 by the authors of this article. Published under CC-BY.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

Assessment of ridge regression-based machine learning model for the prediction of automotive sales based on the customer requirements

引用

Interactions 2024年第1期245卷 1-13页

作者： Akash, C. Renga Vivekanandhan, P.K. Adam Khan, M. Ebenezer, G. Vinoth, K. Prithivirajan, J. Kishan, V. J. Pranesh Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626126 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626126 India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu 626126 India

The primary objective of the paper is to predict the price of the "Toyota Cars" based on the requirement of the customer. The primary need is to identify the minimum price of the "Toyota Cars" based on the 37 key attributes/factors. The levels of each factor in the data set varies from 2 to 312, moreover the data sets used for the analysis, contain both feature as well as numerical based data. The data sets are taken from the "KAGGLE" an open-source real time data provider. The total number of data available for the analysis is 1442, since the data are large and the factors are more with different levels, the prediction done using the Machine Learning (ML) approach. Ridge regression, a supervised machine learning algorithm is used to predict the sales price of the "Toyota Cars", thereby identifying the minimum price based on different combinations of attributes. The prime novelty lies on the usage of the Ridge regression, a linear regression algorithm for the prediction process. The ridge regression model was developed by means of the Python programming and Google Colab, an open-source online compiler for the ML was used for the code compilation process. Based on the result, the minimum price was found to be 6520.8 dollars, with the basic customer requirements such as Model: Corolla 1.6-16v, Type: Sedan, 4 Doors, CNG, 69hp, 1854 cc, with air conditioner, centre lock and power steering. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Design of Chest Visual Based Image Reclamation Method Using Dual Tree Complex Wavelet Transform and Edge Preservation Smoothing Algorithm

引用

SN computer science 2024年第4期5卷 352页

作者： Kumar, B. P. Pradeep Darshan, S. L. Shiva Naresh, E. Srinidhi, N.N. Shreyas, J. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The proposed article put forward a new scheme for image reclamation using second phase discrete symlet transform for medical images. The current medical image reclamation approaches have limitations in providing accurate reclamation fallouts with high visual insight and low computational complexity. To address these issues, this model presents a methodology for creating a medical image database using Image Reclamation using DT-CWT and EPS filter suited for the Resolution Enhancement of query chest image sample well utilized to get better retrieval rate where DWT algorithm is utilized for feature extraction of query input images. Flat and perpendicular prognoses of summation of pixels are analyzed to extract BC quantities, which are then used to compute the matching score of similarity for the images present in the database. The system selects the samples that are most pertinent to the given query sample image based on the matching score. The system’s untrained database is used to obtain the photographs with the highest BC value. The projected method aims to improve the enhancement of sampled image by DT-CWT EPS algorithm to leads to increase the accuracy and efficiency of medical image reclamation for various research applications. © The Author(s) 2024.

关键词： B quantities DWT Healthcare systems Medical image Visual BIR

来源：评论

学校读者我要写书评

暂无评论

TwinNet: Twin Structured Knowledge Transfer Network for Weakly Supervised Action Localization

引用

Machine Intelligence Research 2022年第3期19卷 227-246页

作者： Xiao-Yu Zhang Hai-Chao Shi Chang-Sheng Li Li-Xin Duan Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Computer Science Beijing Institute of TechnologyBeijing 100081China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Action recognition and localization in untrimmed videos is important for many applications and have attracted a lot of attention. Since full supervision with frame-level annotation places an overwhelming burden on manual labeling effort, learning with weak video-level supervision becomes a potential solution. In this paper, we propose a novel weakly supervised framework to recognize actions and locate the corresponding frames in untrimmed videos simultaneously. Considering that there are abundant trimmed videos publicly available and well-segmented with semantic descriptions, the instructive knowledge learned on trimmed videos can be fully leveraged to analyze untrimmed videos. We present an effective knowledge transfer strategy based on inter-class semantic relevance. We also take advantage of the self-attention mechanism to obtain a compact video representation, such that the influence of background frames can be effectively eliminated. A learning architecture is designed with twin networks for trimmed and untrimmed videos, to facilitate transferable self-attentive representation learning. Extensive experiments are conducted on three untrimmed benchmark datasets (i.e., THUMOS14, ActivityNet1.3, and MEXaction2), and the experimental results clearly corroborate the efficacy of our method. It is especially encouraging to see that the proposed weakly supervised method even achieves comparable results to some fully supervised methods.

关键词： Knowledge transfer weakly supervised learning self-attention mechanism representation learning action localization

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

来源：评论

学校读者我要写书评

暂无评论

Design of Cost Efficient VBIR Technique Using ICA and IVCA

引用

SN computer science 2024年第5期5卷 560页

作者： Pradeep Kumar, B.P. Srinidhi, N.N. Shiva Darshan, S.L. Naresh, E. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

With an emphasis on early-stage contrast agent transit through tumour vasculature, this study presents Adaptive Complex Independent Components Analysis (ACICA) as a unique method for evaluating intravascular responsiveness in prostatic tissue. Furthermore, a new SVM clustering method is introduced that outperforms the conventional k-means clustering for image retrieval based on vision. The study emphasises how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be improved in conjunction with quantitative analysis. Within the field of DCE-MRI, ACICA stands out as a unique intravascular attention measurer. Utilising the spatial independence of extravascular and intravascular magnetic resonance imaging (MR) data, ACICA offers a strong foundation for DCE-MRI image analysis. It incorporates pharmacokinetic modelling to optimise the time lag, especially useful for arterial curves, and a reference region (RR)-based technique to adjust the intravascular concentration curve. The model's evaluation yields outstanding results, with recall and accuracy ranging from 83 to 99% and 82.8% to 99.6%, respectively. The average recall and precision across datasets are 92.86% and 92.82%).All things considered, this study demonstrates the effectiveness of ACICA in evaluating intravascular responsiveness and presents viable paths for enhancing clinical results and diagnostic precision in the treatment of prostate cancer. © The Author(s) 2024.

关键词： Adaptive complex independent components analysis Computed tomography Dynamic contrast enhancement Magnetic resonance imaging Visual-based image retrieval

来源：评论

学校读者我要写书评

暂无评论

Fruit Ripeness Detection using RipeNet 3

Fruit Ripeness Detection using RipeNet

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 IEEE.

关键词： Fruits

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：