检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,218 篇 会议
37 篇 期刊文献

馆藏范围

1,255 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

642 篇 工学
- 551 篇 计算机科学与技术...
- 456 篇 软件工程
- 225 篇 信息与通信工程
- 126 篇 控制科学与工程
- 96 篇 电气工程
- 94 篇 生物工程
- 90 篇 生物医学工程（可授...
- 73 篇 电子科学与技术（可...
- 69 篇 网络空间安全
- 53 篇 光学工程
- 46 篇 安全科学与工程
- 34 篇 仪器科学与技术
- 32 篇 机械工程
- 30 篇 动力工程及工程热...
- 29 篇 化学工程与技术
- 27 篇 交通运输工程
368 篇 理学
- 226 篇 数学
- 114 篇 统计学（可授理学、...
- 102 篇 生物学
- 54 篇 物理学
- 33 篇 化学
- 29 篇 系统科学
242 篇 管理学
- 167 篇 管理科学与工程(可...
- 97 篇 图书情报与档案管...
- 55 篇 工商管理
85 篇 医学
- 80 篇 基础医学(可授医学...
- 79 篇 临床医学
- 43 篇 公共卫生与预防医...
- 43 篇 药学(可授医学、理...
39 篇 法学
- 36 篇 社会学
25 篇 经济学
21 篇 农学
7 篇 教育学
3 篇 军事学

主题

177 篇 feature extracti...
165 篇 accuracy
113 篇 convolutional ne...
79 篇 deep learning
60 篇 optimization
55 篇 support vector m...
54 篇 computational mo...
51 篇 internet of thin...
51 篇 predictive model...
38 篇 real-time system...
38 篇 data models
36 篇 long short term ...
35 篇 training
33 篇 machine learning
31 篇 classification a...
26 篇 visualization
26 篇 security
25 篇 prediction algor...
25 篇 measurement
25 篇 adaptation model...

机构

211 篇 department of co...
139 篇 department of el...
117 篇 the islamic univ...
98 篇 nitte meenakshi ...
87 篇 school of comput...
82 篇 the islamic univ...
58 篇 sr university sc...
53 篇 college of techn...
44 篇 school of comput...
40 篇 department of co...
36 篇 department of ec...
33 篇 nitte meenakshi ...
33 篇 nitte meenakshi ...
31 篇 nitte meenakshi ...
29 篇 department of ar...
28 篇 school of comput...
26 篇 east west instit...
26 篇 department of co...
26 篇 sr university sc...
25 篇 department of co...

作者

74 篇 piyush kumar par...
73 篇 pareek piyush ku...
24 篇 ramachandra a.c.
23 篇 muntather almusa...
23 篇 almusawi muntath...
22 篇 ramachandra a c
21 篇 parameshachari b...
20 篇 myasar mundher a...
20 篇 srinivas aluvala
20 篇 aluvala srinivas
19 篇 al-jawahry hassa...
18 篇 alabdeli haider
18 篇 hassan m. al-jaw...
18 篇 adnan myasar mun...
18 篇 haider alabdeli
17 篇 mohammed i. habe...
16 篇 mohammed al-faro...
16 篇 nalini n.
15 篇 sundaram s. meen...
15 篇 alzubaidi laith ...

语言

902 篇 英文
352 篇 其他
2 篇 中文

检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

共 1255 条记录，以下是621-630 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Empirical Analysis of Energy Harvesting and Clustering Methods for D2D Communication Systems

Empirical Analysis of Energy Harvesting and Clustering Metho...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Bura Vijay Vijayarangan, R. Thirumurugan, V. Sugumaran, S. Raghunathareddy, M.V. School of Computer Science and Artificial Intelligence Sr University Warangal India Cse Rvt Research Associates Chennai India Cse Vistas Chennai India Vishnu Institute of Technology Ece Department Bhimavaram India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Unmanned Aerial vehicle (UAV)-based post-disaster communications face significant problems due to their energy consumption and operational range. Energy harvesting is used to overcome these problems by providing power for communication during disasters and extending the lifespan of wireless communication for networks. In order to expand the network's overall coverage and offer continuous connection during the disaster and post-disaster periods, Device-to-Device (D2D) communication and clustering methods were used. And Emergency Communication System (ECS) that uses the best Cluster Head (CH) technology to increase transfer energy effectiveness for long-term connectivity of the network. The clustering method and reinforcement learning in D2D connections are able to collect energy to extend the network lifespan with the help of UAV model. These strategies are predicted to increase the network's constancy during the emergency situations. The enhanced features with the use of the best CHs, and the path loss was significantly decreased. Hence, it reduces the computing complexity of network architecture to recover from natural disasters and also save numerous lives. © 2023 IEEE.

关键词： cluster head Emergency communication system reinforcement learning unmanned aerial vehicle

来源：评论

学校读者我要写书评

暂无评论

Multi-Path Attention Block-Loss Function for Non-Uniform Blind Image Deblurring 3

Multi-Path Attention Block-Loss Function for Non-Uniform Bli...

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Yamsani, Nagendar Pradeep Kumar, S. Al-Attabi, Kassem Alassedi, Zainab Hussein, Abbas Hameed Abdul School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Blind image deblurring is a significant and difficult task in the computer vision field. It is mainly focused on decreasing the blur caused by motion or camera shake. However, image deblurring has made great progress, but there is a need for enhancement in the visual effect and image information to enhance the overall image quality. In this research, the Multi-Path Attention Block-Loss Function (MPAB-LF) is proposed for non-uniform blind image deblurring to enhance image quality. The MPAB approach is employed to provide the foundation for blind image deblurring. To combine numerous MPABs instead of employing associated feature aggregation, the Improved One-Shot Aggregation (IOSA) is utilized. Finally, the loss functions are employed to enhance the generator and discriminator of the framework. Existing approaches like Generative Adversarial Network (GAN), MPAB, Two Convolutional Neural Networks (CNN), and Dense Dilated Block and Improved Attention Module (DDBIAM) are used to compare with the proposed MPAB-FL approach. The proposed MPAB-FL achieves a better Peak-to-Signal Noise Ratio (PSNR) of 35.65 dB and Structural Similarity Index (SSIM) of 0.9957 compared to the existing approaches like GNN, MPAB, Two-stage CNN, and DDBIAM respectively. © 2023 IEEE.

关键词： Blind image deblurring Generative Adversarial Network Improved One-Shot Aggregation Loss Function Multi-Path Attention Network

来源：评论

学校读者我要写书评

暂无评论

Hybrid Optimization Based Long Short-Term Memory for Android Malware Detection

Hybrid Optimization Based Long Short-Term Memory for Android...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Lande, Jayapal Maheswari, M. Kamali, S.M. Dineshkumar, R. Priyanka, M.S. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Institue of Science and Technology School of Computing Department of Computational Intelligence Chennai India Annapoorana Engineering College Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Department of Ece Sriperumbudur Thandalam India Nitte Meenakshi Institute of Technology Department of Ai and Ml Yelahanka Bengaluru India

ISBN: (纸本)9798350313413

Malware detection is process of detecting the suspicious files and activities, that can classify as benign and malicious depends on different characters like capability of infection, structure of malware and speed rate. In this paper, proposed a Particle Swarm Optimization and Ant Colony Optimization (HPSALO) based Long Short-Term Memory (LSTM) model for malware detection and classification. The combination of these two algorithms that leads to the balance between exploration and exploitation. The dataset utilized for the research is DREBIN dataset and process of data cleaning is performed by NumPy library. The feature selection is performed by combination of hybrid PSALO algorithms and selected features are given to Long Short-Term Memory (LSTM) classifier that classifies as benign and malware files. The performance of proposed method is estimated by performance metrics of accuracy, precision, recall and f1-score. The proposed method attained accuracy of 99.38%, precision of 99.30%, recall of 99.08% and f1-score of 99.42% which is comparatively higher than other existing methods like Whale Optimization Algorithm-LSTM (WOA-LSTM), Cat Swarm Optimization-LSTM (CSO-LSTM), Particle Swarm Optimization-LSTM and Ant Lion Optimization-LSTM. © 2023 IEEE.

关键词： Ant Lion Optimization Long Short-Term Memory Malware Detection NumPy and Particle Swarm Optimization

来源：评论

学校读者我要写书评

暂无评论

Automated Identity Document Recognition and Classification (AIDRAC)-A Review

Automated Identity Document Recognition and Classification (...

引用

Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Vijaya Shetty S Madhumitha R Mekala Meghana Reddy Shreya Shettar Tejashree Krishna Murthy Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9781665489638

In this technological era, official documents like Government approved IDs, Certificates, and other documents are paper-based or image based. The problem arises when an organization has to go through each of these documents manually and extract, enter or search for information. Automated Identity Document Recognition and Classification (AIDRAC) is one of the techniques to tackle this problem by decreasing manual effort. This kind of model automatically classifies government-issued identity documents into predefined categories such as AADHAR card, PAN card, and Driving License. It also recognizes and extracts details from the documents uploaded, providing the users the option to autofill their details upon scanning. This can be achieved through the application of Image Processing algorithms using Machine Learning. The extracted information can be stored succeeding relevant access and permissions. The data can be viewed by authenticated consumers when required. Due to the automation of manual work, the implementation of this model saves time, thus saving resources. This paper presents an overview of the exhaustive survey of the techniques employed to build such models and, a comparative analysis of these techniques. It has been found from the survey that there are no models existing that completely accomplish the said automation. Our future research is to build an automation model that can be used in various organizations like Banks, Education Institutions, Law firms, Travel Agencies etc. to autofill the details extracted from the scanned images of Identity proofs. The Sequence diagram of the proposed system is also presented in this paper. The proposed system is expected to outperform the existing models.

关键词： Automation Machine learning algorithms Image processing Neural networks Manuals Machine learning Licenses

来源：评论

学校读者我要写书评

暂无评论

Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image 3

Transmission Line Fault Detection and Classification Using F...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Almusawi, Muntather Babu, D. Raja Mazumder, Debarshi Bala Dhandayuthapani, V. Saranya, N. Naga College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Computing And Information Sciences University Of Technology And Applied Sciences Department Of It Shinas campus Oman Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value. © 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

Signal Quality Evaluation and Processing for QRS Detection in ECG based Smart Healthcare Systems

Signal Quality Evaluation and Processing for QRS Detection i...

引用

2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Singh, Devendra Akram, ShaikVaseem Sivakumar, T. Prakash, U. Loyola Jasmine, J. Chaithra, K.N. Uttaranchal University Dehradun India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665456371

The next-generation wearable electrocardiogram (ECG) equipment requires signal processing with low battery usage in order to transmit signals when harmful rhythms are recognized and to capture signals when anomalous rhythms are discovered. The visual deflections that are frequently visible on an ECG make up the QRS complex. This research suggests a real-time QRS recognition and R point detection system that is extremely accurate and simple. The recommended ECG signal modification eliminates baseline wandering while also enhancing QRS intervals and controlling P and T waves. In this work, the peaks and valleys of the converted signal were used to calculate the fiducial point for the QR. The R point could then be determined using four QRS waveform templates, and the initial categorization of cardiac rhythms could be completed simultaneously. On two benchmark datasets, the proposed method's effectiveness is shown. Positive prediction (+P) and detected sensitivity (Se) values for QRS are 99.82 and 99.81 percent, respectively, according to the standard. The outcome demonstrates that the method has a low processing complexity and that real-time software may be successfully executed on both an embedded system and a mobile device. © 2022 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

A Heterogeneous Network Model Based Analysis and Detection of RNA Enabling Convolutional Neural Network 4

A Heterogeneous Network Model Based Analysis and Detection o...

引用

4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Priyatharsini, G. Soniya Aruna, M. Myneni, Madhu Bala Kumar, P. Kiran Anil Kumar, D.B. Singh, Vijay Dr. M.G.R. Educational and Research Institute Department of Computer Science and Engineering Tamil Nadu Chennai600095 India Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bangalore India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Cvr College of Engineering Department of Computer Science and Engineering Telangana Ibrahimpatnam India School of Electrical and Electronic Engineering Reva University Karnataka Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350300888

RNA is an important target in the research of genetic diseases and disorders since it is essential for the process of gene expression and control. Convolutional neural networks (CNNs) and other machine learning-based methods for analyzing RNA sequences have recently attracted more attention. But reliable analysis and detection are significantly hampered by the enormous variability of RNA sequences. In this research, a brand-new heterogeneous network model for CNN-based RNA analysis and detection is provided. This model accounts for the wide range of RNA sequence properties, such as length fluctuation, secondary structure, and chemical changes. A unified model that incorporates these features is created by combining 1D and 2D convolutional layers. Multiple datasets, including synthetic and real-world datasets, were used to assess proposed model's performance. The outcomes show that the model performs better in terms of accuracy, sensitivity, and specificity than current state-of-the-art approaches. Furthermore, the model's performance is greatly enhanced with the addition of 2D convolutional layers and attention processes, according to rigorous studies to assess the contributions of each component of the model. Overall, the heterogeneous network model offers a reliable and precise method for analyzing and identifying RNA sequences, which has significant ramifications for comprehending hereditary illnesses and creating targeted therapies. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Novel Ultrasonic Image Reconstruction and Deep Learning Model-Based Categorization for Accurate Liver Disease Diagnosis

Novel Ultrasonic Image Reconstruction and Deep Learning Mode...

引用

2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Nijaguna, G.S. Sureddy, Sneha Bindumadhavi Mohapatra, Dayanidhi Parameshachari, B.D. S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Gitam University Bengaluru Campus Gst Dept. of Cse Bengaluru India Hyderabad Institute of Technology and Management Department of Ece Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

The diagnosis of liver illnesses is critical for efficient treatment and management of the condition. By combining deep learning model-based classification with ultrasonic image reconstruction, the developed method aims to surpass standard deep learning techniques. This method's nine primary processes include pre-processing approaches, image reconstruction, liver area segmentation, feature extraction, deep learning model training, testing, and comparing model performance. The suggested approach was tested on patients with fatty liver disease by processing huge sets of ultrasound pictures and RF data frames. The InceptionResNetV2 CNN was used to detect fatty liver disease in these persons. The proposed approach has a sensitivity of 95.2 percent, an accuracy of 92.8 percent, and a specificity of 94.1 percent. By combining deep learning model-based classification with ultrasonic image reconstruction, the technology overcomes some of the limitations of classic deep learning methods. Medical ultrasound imaging is commonly used because it provides for the non-invasive, risk-free identification of liver disorders. Ultrasonic imaging is a sort of medical image diagnostic that assists doctors in diagnosing and treating a variety of ailments. This suggests that the suggested technique might considerably improve the precision and consistency with which liver disease is detected, resulting in better patient outcomes. © 2023 IEEE.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Optimizing Resource Allocation in Mobile Edge Computing for IIoT through Reinforcement Learning and Multiobjective Particle Swarm Optimization 2

Optimizing Resource Allocation in Mobile Edge Computing for ...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Avv, Sudhakar Mohan, M. Maharajan, K. Habelalmateen, Mohammed I. Naga Saranya, N. SR University Department of Electrical and Electronics Engineering Telangana India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Bangalore India Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Mobile networks are flexible enough to support a range of resource allocation models and service-based choices in computing domains, which affects both virtual reality and the Industrial Internet of Things (IIOT). Virtual resource management is made easier by the Mobile Edge Computing (MEC) paradigm, which also makes edge connectivity between data terminals and execution in the core network under heavy load possible. Meeting customer requirements efficiently is achieved through thoughtful planning, aided by cognitive agents. User data, incorporating behavioral patterns, is amalgamated to cater to IIOT service types. Neural caching for memory during task execution is made easier by the use of swarm intelligence and reinforcement learning techniques. Prediction strategies optimize business operations and caching to reduce execution delays. Predictive models evaluate performance by taking into account variables like nearby user equipment and mobile edge computing resources. This method's efficacy is demonstrated by a cognitive agent model that manages resource distribution and creates networks of communication to improve service quality. For accurate resource distribution among end users, reinforcement learning techniques - more especially, Multi-Objective Particle Swarm Optimization (MOPSO) algorithms - are used. This includes building cost mapping tables and optimizing allocation in MEC. The proposed method outperforms existing algorithms like Task-Offloading and Resource Allocation Strategy and achieves a better throughput value of 785.32. © 2024 IEEE.

关键词： Communication Industrial Internet of Things Mobile Edge Computing Optimization Quality of Service Reinforcement Learning

来源：评论

学校读者我要写书评

暂无评论

Image Manipulation Detection and Classification Using SIFT and SURF with Support Vector Machine Classifier 3

Image Manipulation Detection and Classification Using SIFT a...

引用

3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Ramadan, Ghazi Mohamad Naser, Zamen Latef Jagadish, Sripelli Phadke, Rekha Almusawi, Muntather College of MLT Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science & Artificial Intelligence SR University Telangana Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9798350317022

Enhance digital trust and integrity by mastering image forgery detection in machine learning. Uncover the hidden truth behind visuals, ensuring authenticity in a world inundated with digital content. Various image manipulation detection machines have been innovated in the past, but these models face limitations like demanding domain expertise, hyperparameter tuning, time-consuming processes, and overlapping issues. Initially employ the histogram equalization to enhance dataset images, reducing unwanted noise and generating synthetic data for class balance. The pre-processed images are then fed into the model for ongoing analysis. In this paper, propose a Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF)-based SVM for image manipulation detection and classification, achieving outstanding performance metrics on the CASIA 1.0 dataset: 0.96 accuracy, 0.8120 IOU score, 0.7710 MCC, 0.8850 F1 score, 0.98 precision, and 0.92 sensitivity. This model surpasses the capabilities of existing models, including the Two-Stream Boundary-aware Network, Discrete Cosine Transformation (DCT)-based multitask learning network, and Multilevel Local Binary Pattern Texture Descriptor. © 2023 IEEE.

关键词： & Splicing Copy-move Image Manipulation Scale-Invariant Feature Transform Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共126页 << < 59 60 61 62 63 64 65 66 67 68 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：