检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Iqbal, Muhammad Ijaz, Muhammad Mazhar, Tehseen Shahzad, Tariq Abbas, Qamar Ghadi, Yazeed Yasin Ahmed, Waseem Hamam, Habib Institute of Computing and Information Technology Gomal University Dera Ismail Khan KP Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science FCIT International Islamic University Islamabad Pakistan Department of computer science and software engineering Al Ain University Abu Dhabi15322 United Arab Emirates Department of Computer Science and Information Technology University of Malakand Pakistan Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada Commune d'Akanda B.P. Libreville1989 Gabon School of Electrical Engineering Department of Electrical and Electronic Engineering. Science University of Johannesburg Johannesburg2006 South Africa Spectrum of Knowledge Production & Skills Development Sfax3027 Tunisia

Objctives This research delves into the domain of user story-based effort estimation in agile software development, aiming to explore the issues arising from inaccurate estimations. The primary goal is to uncover these issues comprehensively and propose potential solutions, thus enhancing the efficacy. Methods To achieve the research objectives, a systematic literature review (SLR) is conducted, surveying a wide range of sources to gather insights into issues surrounding user story-based effort estimation. Results The SLR reveals a spectrum of issues undermining the accuracy of user story-based effort estimation. It identifies internal factors like communication, team expertise, and composition. © 2023, The Authors. All rights reserved.

关键词： software design

来源：评论

学校读者我要写书评

暂无评论

Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks 19th

Breast Cancer Classification from Histopathological Images U...

引用

19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Aloyayri, Abdulrahman Krzyżak, Adam Department of Computer Science and Software Engineering Concordia University MontrealH3G 1M8 Canada Faculty of Computer Science Department Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783030614003

Early diagnosis of breast cancer is the most reliable and practical approach to mitigate cancer. computer-aided detection or computer-aided diagnosis is one of the software technologies designed to assist doctors in detecting or diagnosing cancer and to reduce mortality using medical image analysis. Recently, Convolution Neural Networks became very popular in medical image analysis helping to process vast amount of data to detect and classify cancer in a fast and efficient manner. In this paper, we implemented deep neural networks ResNet18, InceptionV3 and ShuffleNet for binary classification of breast cancer in histopathological images. We have used networks pre-trained by the transfer learning on the ImageNet database and with fine-tuned output layers trained on histopathological images from the public dataset BreakHis. The highest average accuracy achieved for binary classification of benign or malignant cases was 98.73% for ResNet18, followed by 97.65% for ShuffleNet and 97.44% for Inception-V3Net. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

RETRACTED ARTICLE: Intrusion detection based on machine learning in the internet of things, attacks and counter measures

引用

The Journal of Supercomputing 2022年第6期78卷 8890-8924页

作者： Rehman, Eid Haseeb-ud-Din, Muhammad Malik, Arif Jamal Khan, Tehmina Karmat Abbasi, Aaqif Afzaal Kadry, Seifedine Khan, Muhammad Attique Rho, Seungmin Department of Software Engineering Foundation University Islamabad Islamabad Pakistan Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Computer Science HITEC University Taxila Taxila Pakistan Chung-Ang University - Seoul Campus: Chung-Ang University Seoul Korea

Globally, data security and privacy over the Internet of Things (IoT) are necessary due to its emergence in daily life. As the IoT will soon invade each part of our lives, attention to IoT security is significant. The nature of attacks is dynamic, and addressing this requires designing dynamic methods and a self-adaptable scheme to discover security attacks from malicious use of IoT equipment. The best detection mechanism against attacks from compromised IoT devices includes machine learning techniques. This study emphasizes the latest literature on attack types and uses a scheme based on machine learning for network support in IoT and intrusion detection. Therefore, the current work includes a thorough analysis of multiple intelligence methods and their deployed architectures of network intrusion detection, focusing on IoT attacks and machine learning-based intrusion detection schemes. Moreover, it explores methods based on machine learning appropriate for identifying IoT devices associated with cyber attacks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Merging synthetic and real embryo data for advanced AI predictions

arXiv

引用

arXiv 2024年

作者： Presacan, Oriana Dorobanţiu, Alexandru Thambawita, Vajira Riegler, Michael A. Stensen, Mette H. Iliceto, Mario Aldea, Alexandru C. Sharma, Akriti Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest Bucharest061071 Romania Department of Computer Science and Electrical Engineering Lucian Blaga University of Sibiu Sibiu550024 Romania Department of Holistic Systems SimulaMet Stensberggata 27 Oslo0170 Norway Department of Computer Science Oslo Metropolitan University Oslo0167 Norway Volvat Spiren Oslo0176 Norway Faculty of Biotechnologies University of Agronomic Sciences and Veterinary Medicine Bucharest011464 Romania Department of Validation Intelligence for Autonomous Software Systems Simula Research Laboratory Oslo0164 Norway Cyber Security Simula Research Laboratory Oslo0164 Norway

Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trained two generative models using two datasets—one we created and made publicly available, and one existing public dataset—to generate synthetic embryo images at various cell stages, including 2-cell, 4-cell, 8-cell, morula, and blastocyst. These were combined with real images to train classification models for embryo cell stage prediction. Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 94.5% when trained solely on real data. This trend remained consistent when tested on an external Blastocyst dataset from a different clinic. Notably, even when trained exclusively on synthetic data and tested on real data, the model achieved a high accuracy of 92%. Furthermore, combining synthetic data from both generative models yielded better classification results than using data from a single generative model. Four embryologists evaluated the fidelity of the synthetic images through a Turing test, during which they annotated inaccuracies and offered feedback. The analysis showed the diffusion model outperformed the generative adversarial network, deceiving embryologists 66.6% versus 25.3% and achieving lower Fréchet inception distance scores. © 2024, CC BY-SA.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Local-photon model of the momentum of light

引用

Physical Review A 2025年第2期111卷 023703-023703页

作者： Gabriel Waite Daniel Hodgson Ben Lang Varghese Alapatt Almut Beige The School of Physics and Astronomy University of Leeds Leeds LS2 9JT United Kingdom Centre for Quantum Computation and Communication Technology and Centre for Quantum Software and Information School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology Sydney NSW 2007 Australia The School of Physics and Astronomy University of Nottingham Nottingham NG7 2RD United Kingdom Instituto de Ciencia Molecular (ICMol) Universidad de Valencia E-46980 Paterna Valencia Spain

Recently we introduced a local photon approach for modeling the quantized electromagnetic field in position space. Using this approach, we define the momentum of light in this paper as in quantum mechanics as the generator for spatial translation. Afterward, we analyze the momentum dynamics of photonic wave packets which transition from air into a denser dielectric medium. Our analysis shines new light onto the Abraham-Minkowski controversy, which highlights the intricacies involved in the characterization of the momentum of the electromagnetic field. Although our results align with Minkowski's theory and with the definition of the canonical momentum of light in quantum electrodynamics, there are also some crucial differences.

关键词： Momentum

来源：评论

学校读者我要写书评

暂无评论

Effect of geometric heterogeneity using an auxiliary device on the accuracy of complete arch implant scanning: An in vitro study of different clinical simulations

引用

Journal of Prosthetic Dentistry 2023年

作者： Abdelrehim, Aly Sulaiman, Eshamsul Sofian, Hazrina Salleh, Nosizana Mohd Graduate student Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Associate Professor Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Senior Lecturer Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya (UM) Kuala Lumpur Malaysia

Statement of problem: Intraoral scanning of implants supporting complete arch prostheses is limited because of the lack of geometric heterogeneity and unique reference points, creating inherent errors in the image stitching process by the scanner software program. Purpose: The purpose of this in vitro study was to evaluate the significance of geometric heterogeneity on complete arch implant scanning by using a novel auxiliary geometric device. Three different clinical simulations were tested to assess its significance. The study also assessed whether scans produced using the auxiliary device would meet a clinically acceptable threshold. Material and methods: A total of 60 scans (n=20) were performed using an intraoral scanner in 3 different clinical simulations: 2 parallel implants, 4 parallel implants, and 4 implants with a 30-degree posterior angulation of the distal implants. Scanning alternated between using the auxiliary geometric scanning device (test groups;4IP+, 4IA+, 2IP+) and not using the device (control groups;4IP-, 4IA-, 2IP-). A reference scan for each model was prepared from a high precision laboratory scanner. The scans were analyzed for accuracy in 3-dimensional deviation, interimplant distance deviation, and angular deviation by using an inspection software program. The effect of the auxiliary device was statistically analyzed by comparing scans of the same group using the paired t test for normally distributed data and the Wilcoxon Signed Rank test when data were not normally distributed (α=.05). Results: Significant effects of the auxiliary geometric device were found in 3-dimensional, distance and angular deviations (P<.05). Scans performed using the device were significantly more accurate in most implant positions (P<.05). Linear and angular deviations were clinically acceptable for all test groups. However, the deviations were above the clinically acceptable threshold for the control groups. Conclusions: Using an auxiliary geometric device sig

关键词：

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Machine Learning Approaches in Stroke Prediction 4

Performance Analysis of Machine Learning Approaches in Strok...

引用

4th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2020

作者： Emon, Minhaz Uddin Keya, Maria Sultana Meghla, Tamara Islam Rahman, Md. Mahfujur Mamun, M Shamim Al Kaiser, M Shamim Daffodil International University Dept of Computer Science and Engineering Dhaka1207 Bangladesh Tampere University Software Web Cloud Faculty of Information Technology and Communications Finland

ISBN: (纸本)9781728163871

Most of strokes will occur due to an unexpected obstruction of courses by prompting both the brain and heart. Early awareness for different warning signs of stroke can minimize the stroke. This research work proposes an early prediction of stroke diseases by using different machine learning approaches with the occurrence of hypertension, body mass index level, heart disease, average glucose level, smoking status, previous stroke and age. Using these high features attributes, ten different classifiers have been trained, they are Logistics Regression, Stochastic Gradient Descent, Decision Tree Classifier, AdaBoost Classifier, Gaussian Classifier, Quadratic Discriminant Analysis, Multi layer Perceptron Classifier, KNeighbors Classifier, Gradient Boosting Classifier, and XGBoost Classifier for predicting the stroke. Afterwards, results of the base classifiers are aggregated by using the weighted voting approach to reach highest accuracy. Moreover, the proposed study has achieved an accuracy of 97%, where the weighted voting classifier performs better than the base classifiers. This model gives the best accuracy for the stroke prediction. The area under curve value of weighted voting classifier is also high. False positive rate and false negative rate of weighted classifier is lowest compared with others. As a result, weighted voting is almost the perfect classifier for predicting the stroke that can be used by physicians and patients to prescribe and early detect a potential stroke. © 2020 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

A graph-based methodology for tracking covid-19 in time series datasets 2

A graph-based methodology for tracking covid-19 in time seri...

引用

2nd IEEE International Conference on Electronics, Control, Optimization and computer science, ICECOCS 2020

作者： El Mouden, Zakariyaa Ait Jakimi, Abdeslam Taj, Rachida Moulay Hajar, Moha Moulay Ismail University Software Engineering Information Systems Engineering Faculty of Sciences and Techniques Errachidia Meknes Morocco Moulay Ismail University Operational Research Computer Science Faculty of Sciences and Techniques Errachidia Meknes Morocco

ISBN: (纸本)9781728169217

Since its first appearance in December 2019, Covid-19 has become a wide field of scientific research. Starting from biology to bioinformatic solutions, Artificial Intelligence has contributed in turn as a powerful tool for tracking and predicting the outbreak of Covid-19 using different types of datasets. Chest X-ray images are widely used in computer vision applications and Time series datasets are used for predicting the spread of the novel coronavirus. Graph analytics is a recent field of study that links the mathematical definition and operations of a graph to its application in computer science as a complex data structure, this combination has played a critical role in making graph-based applications present in different fields. One of the most powerful graph analytics is community detection which is an intelligent and unsupervised grouping of a set of graph structured data using the similarity between them. The aim of this work is to highlight the importance of graph-based algorithms in tracking Covid-19 using time series datasets, our work will also focus on Spectral Clustering (SC) as a community detection approach to extract clusters from the input datasets. Further applications are needed in order to validate the proposed theoretical approach. © 2020 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Are Halal Food Supply Chains Sustainable: A Review And Bibliometric Analysis

引用

Journal of Foodservice Business Research 2021年第5期24卷 554-595页

作者： Rejeb, Abderahman Rejeb, Karim Zailani, Suhaiza Department of Logistics and Forwarding Doctoral School of Regional Sciences and Business Administration Széchenyi István University Györ Hungary Department of Software Engineering Higher Institute of Computer Science El Manar Ariana Tunisia Department of Operations Management and Information System Faculty of Business and Accountancy University Malaya Kuala Lumpur Malaysia

Halal food supply chains (HFSCs) are increasingly characterized by fragmentation, complexity, and uncertainty. Halal food consumers are becoming more demanding for food quality and compliance with Islamic dietary law. As sustainability is a multidimensional concept that entails several aspects and interpretations, the objectives of HFSCs significantly overlap with the sustainable dimensions of the food ecosystem. The purpose of this study is to examine the inherent sustainable aspects and characteristics of HFSCs. Therefore, a systematic literature review and a bibliometric analysis were conducted to redress the lack of academic attention being given to the relationships of HFSCs with sustainability. Selected 74 papers were coded and analyzed according to the dimensions of sustainability, namely, the economic, social, and environmental dimensions. The review primarily attempts to provide answers to the following research question: What are the sustainable aspects of HFSCs? We provide a comprehensive analysis of the three critical components of sustainability, offering a broad and comprehensive picture of many critical issues in HFSCs. Finally, the contribution of the study, the scope of future research, and the limitations are highlighted in detail. © 2021 Taylor & Francis.

关键词： economic Halal Food Supply Chains social sustainability

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics Model Based on Multiclass Classification for Asthma Severity by Using Random Forest Algorithm 2

Predictive Analytics Model Based on Multiclass Classificatio...

引用

2nd International Conference on Electrical, Communication and computer engineering, ICECCE 2020

作者： Akbar, Wasif Wu, Wei-Ping Faheem, Muhammad Saleem, Sehrish Javed, Arslan Saleem, Muhammad Asim School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China National College of Business Administration Economics Department of Computer Science Lahore Pakistan MNS University of Engineering and Technology Multan Department of Computer Science Multan Pakistan Faculty of Electronic Information and Electrical Engineering School of Computer Science Dalian University of Technology Liaoning China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781728171166

In modern life, health status prediction has become very crucial. Big data analysis plays a vital role to predict this perfectly. Asthma is a severe chronic disease with severe symptoms. Asthma disease is a chronic disease that leads to death. Researchers have focused on this for better decision making to predict the disease timely use of predictive analysis. This study proposes a Predictive Analytic Model for Asthma prediction using Random Forest (PAM-RF). Data of patients suffering from Asthma has been trained by a random forest approach which predicts to classify the data. Experiments are performed on Hadoop-spark which predicts the future health state of patients. The proposed approach has attained an accuracy of 98.80 percent to predict the asthma disease. © 2020 IEEE.

关键词： Predictive analytics

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：