检索结果-内蒙古大学图书馆

Deep Fake Detection Using computer Vision-Based Deep Neural Network with Pairwise Learning

Intelligent Automation & Soft Computing 2023年第2期35卷 2449-2462页

作者： R.Saravana Ram M.Vinoth Kumar Tareq M.Al-shami Mehedi Masud Hanan Aljuaid Mohamed Abouhawwash Department of Electronics and Communication Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Mathematics Faculty of ScienceSana’a UniversitySana’a 13509Yemen Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning-based approaches are applied successfully in manyﬁelds such as deepFake identiﬁcation,big data analysis,voice recognition,and image *** is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artiﬁcial intelligence for political abuse,spreading false information,and *** artiﬁcial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and *** paper has analyzed the features related to the computer vision of digital content to determine its *** method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction *** the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning *** proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

关键词： Deep fake deep belief network fuzzy clustering feature extraction pairwise learning

来源：评论

学校读者我要写书评

暂无评论

Analysis and Classification of Network Security Attacks Using Domain Generation Algorithm

Analysis and Classification of Network Security Attacks Usin...

引用

2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Gomathi, S. Samundeeswari, S. Ponmanirai, S. Nanthini, S. Vandayar Engineering College Department of Computer Science and Engineering Thanjavur India Saveetha School of Engineering Simats University Department of Computational Intelligence Thandalam India Saveetha School of Engineering Simats University Department of Department of Data Science Thandalam India

ISBN: (纸本)9798350313987

In the realm of cybersecurity, the evolution of network attacks necessitates innovative approaches for threat prediction and mitigation. Traditional methods often fall short in identifying novel attack patterns or adapting swiftly to emerging threats. This study explores the application of Domain Generative Algorithms (DGAs) in the context of network attack prediction. DGAs leverage advanced machine learning techniques to generate synthetic data samples representing various network attack scenarios. By training generative models on diverse and large-scale datasets comprising both historical attack data and benign network traffic, the algorithms learn intricate patterns intrinsic to different types of attacks. Through, systematic analysis of these synthetic attack instances, IDS and IPS algorithms can be fine-tuned to recognize subtle attack patterns that might elude conventional signature-based detection methods. This research underscores the pivotal role of DGAs in revolutionizing the landscape of network attack prediction. By harnessing the power of generative algorithms, organizations can proactively strengthen their cybersecurity posture, effectively mitigating the risks posed by an ever-evolving spectrum of network attacks. © 2023 IEEE.

关键词： data Traffic Denial of Services Domain Generation Algorithm Malware Network security Pattern Analysis

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of CNN and SVM Machine Learning Techniques for Plant Disease Detection 4th

Comparative Analysis of CNN and SVM Machine Learning Techniq...

引用

4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Adeniyi, Abidemi Emmanuel Madamidola, Olugbenga Ayomide Awotunde, Joseph Bamidele Misra, Sanjay Agrawal, Akshat Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Department of Information Technology Federal University of Technology Akure Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Science & Engineering Amity University Haryana Gurugram122412 India

ISBN: (纸本)9789819700363

The growing advancement of different jobs, skills and businesses has stimulated the spread of diseases. Most of the time, these diseases can cross countries’ borders and spread at a very rapid rate. Avoiding and detecting these infections have proved to be a major problem for our public health and one that must be treated with great importance. Crop disease has haunted, farmers have been victimized for several decades, and a remedy must be offered to halt this damage. As a result, a system should be developed to detect plant disease. Untreated plant diseases ultimately result in a loss to the farmer. Traditional methods of detecting plant disease have become less efficient and inaccurate;thus there is need to improve on the techniques of plant disease detection, as a reliable detection of plant diseases will greatly improve the quality of food crops produced and decrease the threat plant diseases pose to food security. Therefore, this study is aimed at analyzing comparatively, two machine learning language techniques convolutional neural network (CNN) and support vector machine (SVM) in terms of their respective accuracy in detecting plant diseases through their leaves in which both algorithms are used for the automatic diagnosis of plant disease. From the experimental test conducted on various plants using CNN and SVM algorithms, CNN had an accuracy of 98%, while SVM had an accuracy of 62% on various plant leaves used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

An Overview of Moving Object Detection Using YOLO Deep Learning Models 2

An Overview of Moving Object Detection Using YOLO Deep Learn...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dwivedi, Upendra Joshi, Kireet Shukla, Surendra Kumar Rajawat, Anand Singh G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India SVKM S Nmims Mpstme Department of Computer Science and Engineering Shirpur Campus Shirpur India School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9798350371055

computer vision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real time video streaming. In computer Vision and image processing detecting object from images and videos has been topic of extensive research. This is accomplished by applying various computer vision techniques to analyze the visual data and determine class of objects from image and videos files. One widespread approach to object detection is using the deep learning models. YOLO (You Only Look Once) is a convolutional neural network that offers a fast and efficient solution to object detection using deep learning. It enable computer to handle real-time detection of objects in video frames and accurately locate and classify moving objects. YOLO is able to simultaneously detect and classify objects in an efficient manner using convolutional neural networks. © 2024 IEEE.

关键词： Deep Learning Image Processing Motion Detection Moving Object Detection YOLO

来源：评论

学校读者我要写书评

暂无评论

Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

引用

1st IEEE International Conference on Computing and data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

引用

2nd International Conference computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning for Latent Variable Network Models 38

Transfer Learning for Latent Variable Network Models

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jalan, Akhil Mazumdar, Arya Mukherjee, Soumendu Sundar Sarkar, Purnamrita Department of Computer Science UT Austin United States Halıcıoğlu Data Science Institute Dept of CSE UC San Diego United States Indian Statistical Institute Kolkata India Department of Statistics and Data Sciences UT Austin United States

We study transfer learning for estimation in latent variable network models. In our setting, the conditional edge probability matrices given the latent variables are represented by P for the source and Q for the target. We wish to estimate Q given two kinds of data: (1) edge data from a subgraph induced by an o(1) fraction of the nodes of Q, and (2) edge data from all of P. If the source P has no relation to the target Q, the estimation error must be Ω(1). However, we show that if the latent variables are shared, then vanishing error is possible. We give an efficient algorithm that utilizes the ordering of a suitably defined graph distance. Our algorithm achieves o(1) error and does not assume a parametric form on the source or target networks. Next, for the specific case of Stochastic Block Models we prove a minimax lower bound and show that a simple algorithm achieves this rate. Finally, we empirically demonstrate our algorithm's use on real-world and simulated network estimation problems. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NLP-IISERB@Simpletext2022: To Explore the Performance of BM25 and Transformer Based Frameworks for Automatic Simplification of Scientific Texts

NLP-IISERB@Simpletext2022: To Explore the Performance of BM2...

引用

2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Saha, Sourav Roy, Dwaipayan Goud, B. Yuvaraj Reddy, Chethan S. Basu, Tanmay Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Computational and Data Sciences Indian Institute of Science Education and Research Kolkata India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India

CLEF SimpleText 2022 lab focuses on developing effective systems to identify relevant passages from a given set of scientific articles. The lab has organized three tasks this year. Task 1 is focused on passage retrieval from the given data for a query text. These passages can be complex and hence require further simplification to be carried out in tasks 2 and 3. The BioNLP research group at the Indian Institute of science Education and Research Bhopal (IISERB) in collaboration with two different information retrieval research groups at IISER Kolkata and ISI Kolkata participated only in Task 1 of this challenge and submitted three runs using three different retrieval models. The paper explores the performance of these retrieval models for the given task. We used a standard BM25 model as our first run to identify 1000 relevant passages for each query. Moreover, the passages for each query were ranked based on their similarity scores generated by the BM25 model. For our second run, we used a BERT (Bidirectional Encoder Representations from Transformers) based re-ranking method, called as Mono-BERT to further rank the 1000 passages retrieved by our first run for each query. A pre-trained sequence to sequence model based re-ranking method, called MonoT5 was used as our third run to reorder the 1000 passages retrieved by the Mono-BERT model for each query. As the official results of this task are not yet announced, we cannot explore the performance of our submissions. However, we have manually checked the retrieved results of many queries for each run, which indicate that the performance improved from run 1 to run 2 and further to run 3. © 2022 Copyright for this paper by its authors.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

引用

植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Challenges of E-government Models of Cloud Computing in Developing Countries 18

Machine Learning Challenges of E-government Models of Cloud ...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tamilarasi, R. Kumar, P. N. Santhosh Ghantasala, G. S. Pradeep Rao, D. Nageswara Bathla, Priyanka Gupta, Gaurav Sonia Alliance University Department of Computer Science and Engineering Bengaluru India Hyderabad India Alliance University Department of Computer Science and Engineering Bangalore India Chandigarh University Punjab India Shoolini University Solan India Shoolini University Yogananda School of AI Computer and Data Sciences Solan India

ISBN: (纸本)9789380544519

Cloud computing usage in electronic government (e-government) is developing across nations and presents a range of difficulties. Infrastructure problems, such as unreliable internet connections and insufficient devices, make it difficult to integrate cloud solutions efficiently. Concerns about data security and privacy arise when private information about citizens is stored in the cloud and could be hacked or accessed without permission. Economic limits and resource constraints could prevent cloud infrastructure installation, thereby reducing e-government service scalability and efficiency. Compliance with various regulations adds complexity to the legal and governmental aspects of managing cloud data. A scientific investigation reveals that while cloud computing-driven e-government services offer numerous advantages, factors such as IT infrastructure, internet accessibility, and confidence in emerging technologies could pose barriers to their adoption. Also, recommend trust-focused cloud computing solutions for e-government services (CCSE-GS). Finally, this study focuses on the number of obstacles that developing countries must overcome to fully realize the potential of cloud computing for e-government services. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Challenges about e-government Cloud Computing E-Government models IT infrastructure

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：