检索结果-内蒙古大学图书馆

A Two Stream Fusion Assisted Deep Learning Framework for Stomach Diseases Classification

computers, Materials & Continua 2022年第11期73卷 4423-4439页

作者： Muhammad Shahid Amin Jamal Hussain Shah Mussarat Yasmin Ghulam Jillani Ansari Muhamamd Attique Khan Usman Tariq Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Department of Information Sciences University of Education Lahore(Multan Campus)Pakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary *** technique is sensitive to these ***,fake samples cause AI and DL model to produce diverse *** attacks that successfully implemented in real world scenarios highlight their applicability even *** this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks ***,such attacks and defensive strategies are gaining lot of attention by the machine learning and security *** use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding *** a result,it took weeks to generate patients test report,which is tiring and strenuous for ***,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such *** far as the classification is concern,the adversarial attacks generate problems in classified *** days,to handle this issue machine learning is mainstream defensive approach against adversarial ***,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid *** obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.

关键词： WCE images adversarial attacks FGSM noise salt and pepper noise feature fusion deep learning

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Image Processing Framework for Early Diagnosis of Diabetic Retinopathy

引用

computers, Materials & Continua 2024年第11期81卷 2665-2683页

作者： Kusum Yadav Yasser Alharbi Eissa Jaber Alreshidi Abdulrahman Alreshidi Anuj Kumar Jain Anurag Jain Kamal Kumar Sachin Sharma Brij BGupta College of Computer Science and Engineering University of Ha’ilHa’il81481Saudi Arabia Department of CSE Chandigarh UniversityMohaliPunjab140413India Department of Electronics and Communication Engineering Amity UniversityNoida201301India Department of Information Technology Indira Gandhi Delhi Technical University for WomenNew Delhi110006India Chief Manager State Bank of IndiaPanchkulaHaryana134109India Department of Computer Science and Information Engineering Asia UniversityTaichung City413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

In today’s world,image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical *** analysis of medical images is essential for doctors,as manual investigation often leads to inter-observer *** research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing *** proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization(MIWPSO)and Fuzzy C-Means clustering(FCM)*** FCM does not incorporate spatial neighborhood features,making it highly sensitive to noise,which significantly affects segmentation *** method incorporates a modified FCM that includes spatial functions in the fuzzy membership matrix to eliminate *** results demonstrate that the proposed FCM-MIWPSO method achieves highly precise and accurate medical image ***,segmented images are classified as benign or malignant using the Decision Tree-Based Temporal Association Rule(DT-TAR)*** analysis with existing state-of-the-art models indicates that the proposed FCM-MIWPSO segmentation technique achieves a remarkable accuracy of 98.42%on the dataset,highlighting its significant impact on improving diagnostic capabilities in medical imaging.

关键词： Image processing biological data PSO Fuzzy C-Means(FCM)

来源：评论

学校读者我要写书评

暂无评论

Optimization of Data Encryption Algorithms in information Construction

Optimization of Data Encryption Algorithms in Information Co...

引用

2024 International Conference on Distributed Systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Zhu, Shuai You, Yi Zhou, Bo Chen, Chuanting Yi, Xueqin Hankou University Network Information Center Wuhan China Hankou University School of Computer Science and Technology Wuhan China Wuhan University of Engineering Science Library and Information Center Wuhan China Hankou University School of Music Wuhan China Hankou University Board Office Wuhan China

ISBN: (纸本)9798350375442

Data encryption algorithms play a crucial role in information construction, effectively protecting data security. The problems in traditional research include a lack of targeting, a balance between efficiency and safety, and a lack of comprehensive consideration. Therefore, this article proposed targeted optimization strategies and methods by in-depth analyzing the needs and scenarios of information construction, combined with the characteristics and problems of data encryption algorithms. Meanwhile, this article also comprehensively considered the integration, deployment, management, and maintenance of algorithms to achieve optimal matching and collaborative work between algorithms and information systems. Through research, this article aimed to promote the application and development of data encryption algorithms in information construction, and improve the efficiency, security, and availability of data encryption algorithms in protecting data security. The results showed that the efficiency of the data encryption algorithm based on RSA (Rivest-Shamir-Adleman) reached the highest of 89.6%. This article provides strong support for the application and development of data encryption algorithms, promoting the sustainable development of information construction and the protection of data security. © 2024 IEEE.

关键词： advanced encryption standard data encryption algorithm information technology construction RSA algorithm

来源：评论

学校读者我要写书评

暂无评论

TWBias: A Benchmark for Assessing Social Bias in Traditional Chinese Large Language Models through a Taiwan Cultural Lens

TWBias: A Benchmark for Assessing Social Bias in Traditional...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hsieh, Hsin-Yi Huang, Shih-Cheng Tsai, Richard Tzong-Han Department of Computer Science and Information Engineering National Central University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Center for GIS RCHSS Academia Sinica Taiwan

ISBN: (纸本)9798891761681

Large Language Models (LLMs) have shown remarkable capabilities in natural language processing, but concerns about social bias amplification have *** research on social bias in LLMs is extensive, studies on non-English, particularly Traditional Chinese models, remain *** study introduces TWBias, a social bias evaluation benchmark for Traditional Chinese *** methodology incorporates chat templates and diverse prompts for comprehensive bias assessment, focusing on Taiwan's cultural context and prioritizing gender and ethnicity bias *** main contributions of this research include: (1) establishing the first social bias evaluation benchmark for Traditional Chinese;(2) integrating chat templates and diverse prompts into bias assessment;and (3) extending bias evaluation methods beyond traditionally recognized disadvantaged groups, while incorporating nuanced categorizations of stereotypes specific to Taiwanese *** this study, we aim to contribute to the advancement of fairness and inclusiveness in *** dataset and code are available on our GitHub repository. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Pyneapple-L: Scalable Expressive Learning-based Spatial Analysis 24

Pyneapple-L: Scalable Expressive Learning-based Spatial Anal...

引用

32nd ACM SIGSPATIAL International Conference on Advances in Geographic information Systems, ACM SIGSPATIAL 2024

作者： Liu, Yongyi Lee, Nicolas Kang, Yunfan Shahneh, Mohammad Reza Mahmood, Ahmed Chinnam, Vishal Rohith Sarawadekar, Aparna Vivek Oymak, Samet Sabek, Ibrahim Magdy, Amr Department of Computer Science and Engineering University of California Riverside United States Center for Geospatial Sciences University of California Riverside United States Google LLC United States Thomas Lord Department of Computer Science University of Southern California United States Department of Geography & Geographic Information Science University of Illinois Urbana-Champaign United States Electrical Engineering and Computer Science Department University of Michigan-Ann Arbor United States

ISBN: (纸本)9798400711077

This paper demonstrates Pyneapple-L, an open-source library designed to enhance scalable spatial analysis through learning-based techniques. Through collaboration with social scientists and domain experts, we identify scalability challenges inherent in conventional spatial analysis methods, particularly as the data size increases. Pyneapple-L addresses these challenges by leveraging learning-based models to offer scalable solutions. We demonstrate two modules: scalable learning of spatial hotspots along spatial networks and augmented geographically weighted regression. To showcase Pyneapple-L, we have developed a user-friendly frontend web application to interact with different datasets, algorithms, model configurations, and visualize outcomes on interactive maps that support both broad and analytical views. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

CervixNet: A Reward-Based Weighted Ensemble Framework for Cervical Cancer Classification 4th

CervixNet: A Reward-Based Weighted Ensemble Framework for Ce...

引用

4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Roy, Kaushiki Bhattacharjee, Deboparna Banik, Debapriya Krejcar, Ondrej Sarkar, Ram Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata700015 India Department of Computer Science and Engineering ICFAI University Tripura Agartala799210 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

ISBN: (纸本)9789819726103

Cervical cancer is one of the most common causes of death among women worldwide. However, this fatal disease can be treated, and the mortality rate can be decreased if detected at an early stage. The Papanicolaou test is the gold standard for screening cervical cancer patients. However, the process of manual inspection is tedious and subject to manual errors. Thus, computer-based automatic screening is considered a viable alternative. To this end, in the present work, we have developed a novel system for classifying cervical cancer images as either normal or malignant. We have used the Herlev dataset in this study, which is the standard Pap smear image benchmark dataset. The proposed framework uses multiple base deep learning frameworks, namely Vision Transformer, Xception, VGG-19, VGG-16, and ResNet-101, for classification. Further, we have introduced a novel reward-based weighing technique to decide the weights of individual classifiers, which are, in turn, used to make decisions about the final class label of an input image using the weighted average technique. The proposed framework achieves an overall accuracy, precision, recall, F1-score, and AUC of 96%, 94%, 92%, 93%, and 94%, respectively, for the binary classification task that is normal vs. malignant. An elaborate study of the performance achieved by our proposed framework on the Herlev dataset shows it to be both robust and effective for the Pap smear image classification task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Data Augmentation via Face Morphing for Recognizing Intensities of Facial Emotions

引用

IEEE Transactions on Affective Computing 2023年第2期14卷 1228-1235页

作者： Huang, Tsung-Ren Hsu, Shin-Min Fu, Li-Chen National Taiwan University Department of Psychology Center for Artificial Intelligence & Advanced Robotics Taipei106319 Taiwan National Taiwan University Center for Artificial Intelligence & Advanced Robotics Department of Electrical Engineering Department of Computer Science and Information Engineering Taipei106319 Taiwan

Being able to recognize emotional intensity is a desirable feature for a facial emotional recognition (FER) system. However, the development of such a feature is hindered by the paucity of intensity-labeled data for model training. To ameliorate the situation, the present study proposes using face morphing as a novel way of data augmentation to synthesize faces that express different degrees of a designated emotion. Such an approach has been successfully validated on humans and machines. Specifically, humans indeed perceived different levels of intensified emotions in these parametrically synthesized faces, and FER systems based on neural networks indeed showed improved sensitivities to intensities of different emotions when additionally trained on the synthesized faces. Overall, the proposed data augmentation method is not only simple and effective but also useful for building FER systems that recognize facial expressions of mixed emotions. © 2010-2012 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

A nano-scale design of a multiply-accumulate unit for digital signal processing based on quantum computing

引用

Optical and Quantum Electronics 2024年第1期56卷 1-25页

作者： Ahmadpour, Seyed-Sajad Navimipour, Nima Jafari Yalcin, Senay Bakhshayeshi Avval, Danial Ain, Noor Ul Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou 64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul Turkey Department of Information Systems Engineering Sakarya University Sakarya Turkey Department of Business Administration Kadir Has University Istanbul Turkey

Digital signal processing (DSP) is used in computer processing to conduct different signal-processing tasks. The DSPs are used in the series numbers representing a continuous variable in a domain such as time, area, or frequency. The multiply-accumulate (MAC) unit is crucial in various DSP applications, including convolution, discrete cosine transform (DCT), Fourier Transform, etc. Thus, all DSPs contain a critical MAC unit in signal processing. The MAC unit conducts multiplication and accumulation operations for continuous and complicated DSP application processes. On the other hand, in the MAC structure, the stability of the circuit and the occupied area pose some significant challenges. However, high-performance quantum technology can easily overcome all the previous shortcomings. Hence, this paper suggests an efficient MAC for DSP applications using a Vedic multiplier, half adder, and accumulator based on quantum technology. All the proposed structures have used a single-layer layout without rotated cells. The suggested architecture is designed and validated based on the QCADesigner 2.0.3 tool. The findings revealed that all the developed circuits have a simple architecture with fewer quantum cells, optimal area, and low latency. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Multiply-accumulate (MAC) Nano-design Quantum dot cellular automata (QCA) Signal processing

来源：评论

学校读者我要写书评

暂无评论

Ensemble Multifeatured Deep Learning Models and Applications: A Survey

引用

IEEE Access 2023年 11卷 107194-107217页

作者： Abimannan, Satheesh El-Alfy, El-Sayed M. Chang, Yue-Shan Hussain, Shahid Shukla, Saurabh Satheesh, Dhivyadharsini Mumbai410206 India King Fahd University of Petroleum and Minerals SDAIA-KFUPM Joint Research Center for Artificial Intelligence Information and Computer Science Department Dhahran34464 Saudi Arabia King Fahd University of Petroleum and Minerals Interdisciplinary Research Center of Intelligent Secure Systems Information and Computer Science Department Dhahran34464 Saudi Arabia National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan School of Business MaynoothW23 F2H6 Ireland Lucknow226002 India Vellore632014 India

Ensemble multifeatured deep learning methodology has emerged as a powerful approach to overcome the limitations of single deep learning models in terms of generalization, robustness, and performance. This survey provides an extended review of ensemble multifeatured deep learning models, and their applications, challenges, and future directions. We explore potential applications of these models across various domains, including computer vision, medical imaging, natural language processing, and speech recognition. By combining the strengths of multiple models and features, ensemble multifeatured deep learning models have demonstrated improved performance and adaptability in diverse problem settings. We also discuss the challenges associated with these models, such as model interpretability, computational complexity, ensemble model selection, adversarial robustness, and personalized and federated learning. This survey highlights recent advancements in addressing these challenges and emphasizes the importance of continued research in tackling these issues to enable widespread adoption of ensemble multifeatured deep learning models. It provides an outlook on future research directions, focusing on the development of new algorithms, frameworks, and hardware architectures that can efficiently handle the large-scale computations required by these models. Moreover, it underlines the need for a better understanding of the trade-offs between model complexity, accuracy, and computational resources to optimize the design and deployment of ensemble multifeatured deep learning models. © 2013 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Classification of Unbalanced Data Based on Bayesian Optimal Neural network Model

Journal of Network Intelligence

引用

Journal of network Intelligence 2024年第3期9卷 1427-1440页

作者： Ye, Li-Zhu Alkawaz, Mohammed Hazim Johar, Md Gapar Md School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou511363 China School of Graduate Studies Management and Science University 40100 Shah Alam Selangor Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Mosul41002 Iraq Software Engineering and Digital Innovation Center Management and Science University Shah Alam40100 Malaysia

Aiming at the unbalanced problem of big data classification, this paper proposes a classification method of unbalanced data on the ground of Bayesian optimal neural network model. The method firstly uses Optimized Graph Convolutional network (OGCN) network to carry out message passing mechanism in data graph, designs neural network model with self-attention pooling and mean maximization pooling, and then uses Bayes optimization to update the posterior probability of optimization function by constantly updating the probability model and evaluating the objective function. Effectively solve the problem of speed and optimal solution of hyperparameter optimization in the neural network model. Secondly, the model is used for data set training to obtain the weight of the entire network. Secondly, the unbalanced data is mixed sampling to reduce the number of negative samples. Finally, the capability of the model was verified on the CiteSeer dataset. Experimental results showed that the accuracy rate, accuracy rate and recall rate of the proposed algorithm were 0.974, 0.982 and 0.973, respectively, which effectively improved the accuracy rate, accuracy rate and recall rate of unbalanced data classification compared with other algorithms. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Neural network models

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：