检索结果-内蒙古大学图书馆

Computer, Big data and artificial intelligence (ICCBD+AI), International Conference on

作者： Tariq Mahmood Tanzila Saba Amjad Rehman Xiang Zhang Muhammad Yaqub Artificial Intelligence and Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Kingdom of Saudi Arabia Chinese Academy of Sciences Institute of Biophysics Beijing China School of Biomedical Sciences Hunan University Changsha China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Breast cancer poses a significant threat to women’s health, and early screening and diagnosis are crucial for improving treatment outcomes and reducing mortality. Computer-aided diagnosis (CAD) systems have been developed to help physicians classify benign and malignant lesions in mammograms. The paper introduces a new pre-processing technique for mammographic datasets, addressing issues like tissue overlap and image noise. The technique includes denoising, segmentation, contrast adjustment, and data augmentation algorithms, enhancing CNN input and generalization. The ECA-Net152 model, which combines channel attention and focal loss function, is introduced to classify breast lesions. The method achieves an AUC of 0.960 and performs better through experimental comparisons and GradCAM visualization. This study contributes to AI-assisted diagnosis and early screening of breast cancer.

关键词： Solid modeling Transfer learning Noise reduction Noise data visualization Mortality Medical services Breast cancer Mammography Lesions

来源：评论

学校读者我要写书评

暂无评论

Combining stochastic tendency and distribution overlap towards improved nonparametric effect measures and inference

引用

Scandinavian Journal of Statistics 2025年第0期

作者： Beck, Jonas Langthaler, Patrick B. Bathke, Arne C. Intelligent Data Analytics (IDA) Lab Department of Artificial Intelligence and Human Interfaces (AIHI) Paris Lodron University of Salzburg Salzburg Austria Department of Neurology Paracelsus Medical University of Salzburg Salzburg Austria

A fundamental functional in nonparametric statistics is the Mann-Whitney functional (Formula presented.), which constitutes the basis for the most popular nonparametric procedures. The functional (Formula presented.) measures a location or stochastic tendency effect between two distributions. A limitation of (Formula presented.) is its inability to capture scale differences. If differences of this nature are to be detected, specific tests for scale or omnibus tests need to be employed. However, the latter often suffer from low power, and they do not yield interpretable effect measures. In this article, we extend (Formula presented.) by additionally incorporating the recently introduced distribution overlap index (nonparametric dispersion measure) (Formula presented.) that can be expressed in terms of the quantile process. We derive the joint asymptotic distribution of the respective estimators of (Formula presented.) and (Formula presented.) and construct confidence regions. Extending the Wilcoxon–Mann–Whitney test, we introduce a new test based on the joint use of these functionals. It results in much larger consistency regions while maintaining competitive power to the rank sum test for situations in which (Formula presented.) alone would suffice. Compared with classical omnibus tests, the simulated power is much improved. Additionally, the newly proposed inference method yields effect measures whose interpretation is surprisingly straightforward. © 2025 The Author(s). Scandinavian Journal of Statistics published by John Wiley & Sons Ltd on behalf of The Board of the Foundation of the Scandinavian Journal of Statistics.

关键词： confidence region nonparametric test resampling simultaneous inference

来源：评论

学校读者我要写书评

暂无评论

Deep Learning and Shape-Driven Combined Approach for Breast Cancer Tumor Segmentation

Deep Learning and Shape-Driven Combined Approach for Breast ...

引用

International Conference on Advancements in Computational Sciences (ICACS)

作者： Mudassar Ali Haoji Hu Tufail Muhammad Muhammad Ahsan Qureshi Tariq Mahmood College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science Air University Islamabad Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan University Riyadh Kingdom of Saudi Arabia.

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Our aim is to enhance the performance of segmenting breast cancer from medical images by overcoming major challenges, including data insufficiency and complexity when training a model. Using the INbreast dataset, it proposes a hybrid method for improving the segmentation accuracy and computational efficiency by combining M3D-Neural Cellular Automata with Shape-Guided Segmentation. This work aims at optimal performance on complex tasks of tumor segmentation by leveraging shape priors and reducing the need for extensive training data. Results confirm that significant improvements in both accuracy and robustness are achieved, which reduces computation time and improves tumor boundary delineation. The hybrid approach provides a time- and cost-effective method for precise detection in breast cancer diagnosis and treatment.

关键词： Deep learning Training Image segmentation Accuracy Shape Training data Breast cancer data models Robustness Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Trust Management Frameworks In Multi-Cloud Environment: A Review

Trust Management Frameworks In Multi-Cloud Environment: A Re...

引用

Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Arwa Alajroush Rabia Latif Tanzila Saba College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Artificial Intelligence and Data Analytics Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

The term "cloud computing" refers to a method of managing and using resources remotely. Networks, servers, applications, machines, and other resources and storage are all part of the shared and virtualized resource pool that is offered. In addition to providing the flexibility, stability, and scalability many organizations need the services of cloud computing, this reduces the need for setting up the private cloud and deploy separate hardware resources. However, multi-could settings presented several security and trust issues. This paper primary contribution is an examination of contemporary, serious trust challenges that arise in a multi-cloud context. And to put out a survey comparing trust management frameworks for Multi-Cloud environment to address the challenges identified. The suggested solution is given to have a more robust and secure trust framework based on data compliance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

引用

Proceedings of the 6th International Conference on Advances in artificial intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization artificial Bee Colony Ethanol Flux Balance Analysis

来源：评论

学校读者我要写书评

暂无评论

Early Stage Kidney Chronic Diseases Diagnosis using Feature Fusion and Deep Learning Algorithms

Early Stage Kidney Chronic Diseases Diagnosis using Feature ...

引用

International Conference on Developments in eSystems Engineering (DESE)

作者： Tanzila Saba Muhamamd Mujahid Noor Ayesha Mahyar Kolivand Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh Saudi Arabia School of Clinical Medicine Zhengzhou University Zhengzhou Henan RP China Department of Medicine University of Liverpool Liverpool UK School of Computing and Digital Technologies Staffordshire University Staffordshire UK

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

Chronic Kidney Disease (CKD) is a global health issue due to pre-existing conditions like anemia, diabetes, and hypertension. Early warning signals are commonly neglected, complicating diagnosis and treatment. This study uses high-performance data mining techniques to provide self-analysis and prognostic tools for medical professionals. Clinical decision support using domain experts and specialists aids CKD prognosis and rehabilitation. We use advanced data mining to evaluate demographic characteristics, test findings, and patient medical information to uncover insights and patterns. We used a modified artificial neural network to extract characteristics from a dataset and long-term and short-term memory to classify chronic kidney disease stages. LSTM algorithms improve the accuracy of CKD diagnosis and stage prediction. Initial validation tests demonstrate a 92.23% accuracy gain over traditional methods. The binary classification AUC was 99.95% and the multi-class classification was 94.13% with the proposed technique. LSTM algorithms use temporal dynamics in patient data to enhance diagnostic accuracy, early diagnosis, therapy customization, and healthcare costs. We proposed a novel CKD diagnostic and prognosis paradigm and test multiple classification algorithms for disease diagnosis and staging. Multiple methods improve reliability and account for CKD diagnostic peculiarities.

关键词： Support vector machines Accuracy Heuristic algorithms Prediction algorithms Chronic kidney disease Classification algorithms data mining Medical diagnosis Prognostics and health management Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients

引用

Computers, Materials & Continua 2021年第8期68卷 2363-2376页

作者： Muhammad Usman Ghani Khan Zubaira Naz Javeria Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Usman Tariq Al-khawarizmi Institute of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Artificial Intelligence and Data Analytics Lab CCIS Prince Sultan UniversityRiyadhSaudi Arabia College of Computer Engineering and Science Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),*** treatment methods for ADHD are either psycho-stimulant medications or cognitive *** traditional methods,namely therapy,need a large number of visits to hospitals and include *** could be used for the effective treatment of *** could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain *** paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial *** proposed system consists of a puzzle game where its complexity increases with each *** signals were acquired using the Neurosky headset;then sent the signals to the designed gaming *** neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation *** results show that this game improves player overall performance from 74%to 98%by playing each game level.

关键词： Neurogaming brain-computer interface attention deficit hyperactivity disorder electroencephalography spatial memory cognitive skills attention level

来源：评论

学校读者我要写书评

暂无评论

Human Brain Stroke Prediction using Machine Learning Methods with Synthetic Minority Oversampling Approach

Human Brain Stroke Prediction using Machine Learning Methods...

引用

Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Muhammad Mujahid Noor Ayesha Ahmad Taher Azar Tanzila Saba Zeeshan Haider Artificial Intelligence and Data Analytics (AIDA) Lab CCIS Prince Sultan University Riyadh School of Clinical Medicine Zhengzhou University Zhengzhou China College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

The brain is considered to be the principal anatomical component of the human body. A stroke is a medical condition defined by cerebral injury that occurs as a consequence of blood vessel rupture within the brain. Symptoms might become apparent when there is an interruption in the brain circulation of blood and other vital chemicals. Stroke is widely acknowledged as a significant medical disorder because to its elevated mortality rates, enduring cognitive impairments, and concomitant comorbidities. The timely recognition of the numerous prodromal manifestations linked to a stroke has the capacity to attenuate its magnitude. The primary objective of this study is to utilize machine learning methodologies in order to predict the likelihood of an early-stage occurrence of a cerebral stroke. In order to assess the efficacy of the algorithm, a dataset for stroke prediction was acquired via the Kaggle platform. Various preprocessing techniques are applied to normalize and clean the dataset for stroke prediction. This research utilized many classification models including Random Forest, Ada Boost, LightGBM,, Decision Tree (DT), Logistic Regression (LR),and Support vector machine (SVM) to perform classification tasks. The Random Forest classifier has a higher level of classification accuracy in comparison to alternative machine learning classifiers, obtaining an impressive accuracy rate of 99.45%. The results of the investigation revealed that machine learning approaches demonstrated a higher level of performance in comparison to alternative methods.

关键词： Support vector machines Deep learning Accuracy Machine learning algorithms Computed tomography Stroke (medical condition) Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

引用

International Conference on data Engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks labeling Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Suhail the Guiding Star: A Sustainable Chatbot for Saudi Arabia Culture

Suhail the Guiding Star: A Sustainable Chatbot for Saudi Ara...

引用

Women in data Science at Prince Sultan University (WiDS PSU), International Conference of

作者： Wahaj Saidam Lina Alnasi Sarah Ayman Rasha Alnaji Alqahtani Heyfa Ammar Anis Koubaa CCIS Prince Sultan University Riyadh Saudi Arabia College of Comp. and Info. Sciences Prince Sultan University Riyadh Saudi Arabia Artificial Intelligence and Data Analytics Lab. CCIS Prince Sultan University Riyadh Saudi Arabia University of Tunis ELManar Tunisia Alfaisal University Riyadh Saudi Arabia

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Protecting the cultural heritage is essential to ensure its survival for future generations while fostering cultural diversity and global understanding. It is then crucial to develop engaging, interactive and innovative approaches to inform and educate people about the significance of cultural heritage, making it accessible and appealing to diverse audiences. Particularly in the Kingdom of Saudi Arabia, various solutions have been proposed to assist visitors during their journey in the kingdom. However, the existing solutions lack localized content or fail to provide immersive dynamic user experiences, making them less effective in achieving cultural education goals. In this regard, the present work proposes Suhail: a novel AI-powered chatbot that allows for interactive communication, and improves understanding of Saudi cultural heritage through relevant responses. Suhail's reliability is underpinned by two novel datasets that we propose in this work, derived from trusted and verified sources. Our evaluation indicates an impressive accuracy score of 93.3%. Suhail's potential to advance Saudi cultural preservation and dissemination is underscored through expert interviews and survey responses in the direction of Saudi Vision 2030 and UN Sustainable Development Goals, specifically Quality Education (SDG 4) and Sustainable Cities (SDG 11).

关键词： Surveys Accuracy Education Urban areas Stars Chatbots Cultural differences Global communication Reliability Sustainable development

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：