检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Verma, Rajat Mishra, Sanat Kumar Shukla, Satvik Dhanda, Namrata Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India Amity University Department of Computer Science and Engineering Uttar Pradesh Lucknow India

ISBN: (纸本)9798350385199

Since the dawn of internet software growth, we have resided in a digital environment fraught with cybersecurity concerns. The sophistication in today's traditional security measures is one of the primary reasons for the increasing number of cyberattacks, thus leaking sensitive data. Encryption, which involves transforming plain text into cipher text, is important for maintaining software security and preventing the unauthorized entry of probable hackers. However, it is evident that the encryption algorithms generally have a high resource requirement in terms of memory, processing time, and electricity. This paper compares ten more common used encryption algorithms: AES, Blowfish, CAST5, Camellia, ChaCha20, DES, 3DES, RC2, SEED, and SM4. There have been evaluations under different conditions: different data types and sizes and requirements for CPU time and encryption/decryption speed. The results of these simulations have pinpointed that each of the algorithms is good enough on its own to be efficient and effective, hence helping the organizations choose the more appropriate technique for their software's security. Therefore, this work importantly contributes to the field of cybersecurity as it adds posing of extensive examination on the most represented encryption algorithms and their performances in different situations. Organizations use the stringent encryption methods to secure their software and data which lessens the odds of cyberattack and data leak. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Opting Right Cloud: The Data Security Vs Adoption Trade-off 1

Opting Right Cloud: The Data Security Vs Adoption Trade-off

引用

1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Kumar, Sanjay Negi, Alok Swami Rama Himalayan University Department of Computer Science & Engineering Dehradun India University of Petroleum and Energy Studies Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350360707

In this paper, we first explain the different cloud computing models and then elaborate on a detailed analysis of the problems associated with the cloud environment. However, due to the complexity of the cloud, it is difficult to achieve end-to-end security. This study also intends to investigate the cloud computing security concerns in public and private cloud-based environments and how they affect data security and may compromise data availability, confidentiality, and integrity. The article provides a thorough review of several cloud security concerns, such as data loss, insider threats, vendor lock-in, breaches of confidentiality, malicious assaults, and user ignorance. The security methods and solutions now in use to deal with these problems are also covered in the paper, including encryption, access control, monitoring, backup, and service level agreements (SLA). In this paper, the pros and cons of adopting a cloud are being evaluated based on the cost and security. The study examines the variables that institutions must take into account before deciding to use cloud computing from the perspective of cost and security. Most of the businesses are planning to shift to cloud computing but they are unable to even consider the possibility of having their confidential information off-site. The study does not claim that cloud computing is the best option for all situations, but whether or not to use it should be determined by a thorough investigation. Finally, in order to guarantee the security and privacy of data stored on the cloud, the study concludes by highlighting the significance of user and cloud service provider cooperation. © 2024 IEEE.

关键词： Cloud security

来源：评论

学校读者我要写书评

暂无评论

TransFuseNetS: A new Image Medical Segmentation Method based on TransFuse Architecture 24

TransFuseNetS: A new Image Medical Segmentation Method based...

引用

2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Chen, Wei Wang, Zhiheng School of Computer and Information Engineering Heilongjiang University of Science and Technology Heilongjiang China School of Computer and Information Engineering Heilongjiang University of Science and Technology Anhui China

ISBN: (纸本)9798400718199

In medical images, image segmentation is a very important method, which can accurately locate and analyze the lesions and tissues. However, due to the complexity of medical images and noise, accurate and robust segmentation is still a difficult problem. In view of the above problems, this project intends to use TransFuseNetS under the framework of Transfuse for image medical segmentation. TransFuseNetS further optimizes and strengthens the Fusion architecture. First of all, this project intends to design a more complex coding architecture and propose an overall attention strategy at the channel level to improve the feature representation and understandability of the image. Secondly, the linear fusion method of two directions is used to make the multi-level images more comprehensive and rich, so as to improve the identification and segmentation of multi-scale images. Through a large number of experiments, we find that TransFuseNetS can effectively improve the recognition rate of CT images. Compared with the traditional artificial neural network, the average error of IOU and ACC of artificial neural network is reduced by 0.1571, and the average error is reduced by 0.0029. The research results of this project will lay a foundation for the research and application in the fields of medical image processing and medical image processing. © 2024 ACM.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Green Clouds, Bright Horizons: An Analysis of the Advantages, Future Trends and Challenges of Green Cloud Computing 3

Green Clouds, Bright Horizons: An Analysis of the Advantages...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Shalvi Chauhan, Rahul Bisht, Shagun Devliyal, Swati Sunil, G. Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350381931

Green cloud computing is a recognized field of study. It grew in popularity due to the rise in demand of data storage which is the consequence of the exponential boom of data. Cloud computing has produced an impressive way to virtualize servers and data centers and to make them energy efficient, all the while maximizing different IT resources. The enormous power and energy consumption of IT resources has a negative impact on the environment, causing problems such as energy shortage and climate change. Therefore, Green Cloud Computing is required in order to provide solutions that can reduce operating costs while maintaining the energy efficiency of IT resources. Green IT is regarded as a crucial step in the journey to finding the solution to the environmental problems in the field of information technology. © 2024 IEEE.

关键词： Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Analysis on Skin Cancer Detection: A Review

Comprehensive Analysis on Skin Cancer Detection: A Review

引用

2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Verma, Shivam Sahoo, Soumya Sinha, Harsh Kumar Tripathy, Hrudaya Kumar Priyanka, Prachi C.V Raman Global University Computer Science and Engineering Bhubaneswar India Kalinga Institute of Industrial Technology Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798331508432

Skin Cancer, particularly melanoma, poses a significant threat to human health with low chances of recovery and survival. Early detection plays a crucial role in improving survival rates, with potential rates reaching as high as 95 percent. This has motivated researchers to explore technology-based solutions to contribute to this noble cause. Four primary steps are involved in diagnosing skin cancer: image processing (hair removal, noise reduction, sharpening, and resolution enhancement), which is followed by the identification of skin lesions using advanced segmentation algorithms and machine learning. InceptionV3, ResNet, and VGGNet are examples of well-known algorithms that are frequently utilized. Popular algorithms such as InceptionV3, ResNet, and VGGNet have been widely used. This research presents a combined image processing and machine learning approach for fake skin cancer detection. Specifically, methods for image super-resolution are used to improve a convolutional neural network model's accuracy. The neural network's layers are changed to train the model, which is created using the Keras backend. The publicly sourced dataset from the ISIC data archives is utilized for evaluation. Compared to previous models based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), the suggested model outperforms them both in terms of testing accuracy and loss. By leveraging advancements in technology,the research work contributes to the early detection and accurate classification of skin cancer, holding great potential to improve patient outcomes. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

Double-Click: dual-click iteration and dual-branch networks for interactive segmentation 16

Double-Click: dual-click iteration and dual-branch networks ...

引用

2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Jiang, Mingcheng Wang, Di Ren, Benzhe School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The main aim of Interactive image segmentation is to accurately segment regions of interest by utilizing simple user-provided interactive information. Currently, the most popular training approach involves using the previous segmentation result as supplementary information for the next segmentation process to establish a connection between the two interactions. This iterative training method transfers all the information carried by interactions to the global level, without considering its impact at the local level. This will affect the final segmentation results and the training efficiency. To address this problem, we propose an iterative training strategy called Double-Click to involve cropping the image based on the newly added clicks and then separately feeding the cropped image and the newly added clicks into a smaller branch network to obtain predictions for the local region. Subsequently, the locally segmented result is fused with the previous segmentation result to achieve the final outcome. Then, the refined segmentation results are used to guide the next segmentation, ensuring that more precise results guide subsequent segmentations while maintaining the guidance information of the clicks on the global image. Our training strategy improves the training effectiveness by handling two clicks in an iterative process. The accuracy of the segmentation is ensured through both local and global predictions. Additionally, we designed a dual-branch network to accommodate our proposed training strategy. The effectiveness of this strategy has been tested on four validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results demonstrate that the Double-Click method achieves better performance with fewer interactions compared to previous methods. © 2025 SPIE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Performance of Naive Bayes vs. Decision Tree in Predicting and Forecasting Diabetes 2

Evaluating the Performance of Naive Bayes vs. Decision Tree ...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Shahi, Abhishek Wahi, Amitabh Dwivedi, Anshu Kumar Bhagwant University Department of Computer Science & Engineering Rajasthan Ajmer India Bennett University Greater Department of Computer Science & Engineering Utter Pradesh Noida India

ISBN: (纸本)9798350368109

This paper investigates the comparative ability of Naive Bayes and Decision Tree algorithms in the prediction and forecasting of diabetes, a chronic condition that has an impact on millions of people worldwide. Prompt and precise prediction of diabetes is crucial for prompt action and efficient management. Machine learning algorithms, like Naive Bayes and Decision Tree, have become instrumental in enhancing the predictive accuracy of medical diagnoses. © 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

Time Complexity of Population-Based Metaheuristics

Mendel

引用

Mendel 2023年第2期29卷 255-260页

作者： Omran, Mahamed G. H. Engelbrecht, Andries Computer Science Department Gulf University for Science & Technology Kuwait Department of Industrial Engineering and Computer Science Division Stellenbosch University South Africa Centre for Applied Mathematics and Bioinformatics Gulf University for Science & Technology Kuwait

This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamen-tal concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known meta-heuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics’ time efficiency is then presented. The procedure is then used to empirically analyze the computational cost of the three aforementioned metaheuristics. The pros and cons of the two approaches, i.e. mathematical and empirical analysis, are discussed. © 2023, Brno University of Technology. All rights reserved.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

A deep learning-based compression and classification technique for whole slide histopathology images

引用

International Journal of Information Technology (Singapore) 2024年第7期16卷 4517-4526页

作者： Barsi, Agnes Nayak, Suvendu Chandan Parida, Sasmita Shukla, Raj Mani Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Department of Computer Science & amp Engineering Silicon University Bhubaneswar India Department of Computer Science & amp Engineering SoA University Bhubaneswar India

This paper presents an autoencoder-based neural network architecture to compress histopathological images while retaining the denser and more meaningful representation of the original images. Current research into improving compression algorithms is focused on methods allowing lower compression rates for Regions of Interest (ROI-based approaches). Neural networks are great at extracting meaningful semantic representations from images and, therefore can select the regions to be considered of interest for the compression process. In this work, we focus on the compression of whole slide histopathology images. The objective is to build an ensemble of neural networks that enables a compressive autoencoder in a supervised fashion to retain a denser and more meaningful representation of the input histology images. Our proposed system is a simple and novel method to supervise compressive neural networks. We test the compressed images using transfer learning-based classifiers and show that they provide promising accuracy and classification performance. © The Author(s) 2024.

关键词： Compression Deep learning Image classification Whole slide histopathology images

来源：评论

学校读者我要写书评

暂无评论

An Efficient Hybrid Model to Summarize the Text Using Transfer Learning 4th

An Efficient Hybrid Model to Summarize the Text Using Transf...

引用

4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Bajpai, Abhishek Srivastava, Vivek Shuaib, Mohammed Jaiswal, Aishwarya Bharany, Salil Alam, Shadab Ahmad, Sadaf Rajkiya Engineering College UP Kannauj India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia CET Department GNDU Amritsar India Department of Computer Science AMU Aligarh India

ISBN: (纸本)9789819700363

In today’s era, an enormous quantity of information is generated everyday by the Internet and this information need a specific tool by which we can get a more useful and important part of that information. In this study, we propose a design to generate a short and coherent version of a longer document. We can achieve this by two methods first one is extractive means it will take out the important words from the same document and the second one is abstractive means firstly it will understand the semantics of the paragraph and then it will generate a new text summary. This study work on the hybrid model which can attain this task by distilling salient words from the document and by generating completely new sentences by understanding the semantics. We used T5 which is a transfer learning model and get a better ROUGE-1 score of 45.2, ROUGE-2 score of 23.1, and ROUGE-L score of 39.7, which is better than all other models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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

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

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：