检索结果-内蒙古大学图书馆

14th Annual Undergraduate Research Conference on "ICT for Resilient and Sustainable Infrastructure", URC 2022

作者： Balobid, Alhanof Mabruk Binamro, Jawaher Saleh Yohannes, Sewit Tewoldemedhin Kaddoura, Sanaa Zayed University College of Technological Innovation Computing and Applied Technology Department Abu Dhabi United Arab Emirates Zayed University College of Technological Innovation Network and Security Department Abu Dhabi United Arab Emirates Zayed University College of Technological Innovation Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350346800

Credit card fraud is one of the most common attacks affecting millions annually. Detecting fraud transactions using traditional software is not suitable anymore. The attackers are becoming more intelligent in their way of attacking. The investment of machine learning techniques in detecting suspicious activities is the trend nowadays. It helps stop fraud transactions without preventing benign transactions from being completed. This paper applies different supervised machine learning algorithms to a given dataset, such as random forest, gradient boosted trees, logistic regression, k-nearest neighbors, artificial neural network, and others. Consequently, the artificial neural network and the logistic regression algorithms show the highest performance according to the ROC-AUC performance measure. However, the k-nearest neighbor's algorithm performs better according to the F1-score performance measure. © 2022 IEEE.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Review to Build a Smart Guide for the Government E-services in Oman

Comprehensive Review to Build a Smart Guide for the Governme...

引用

IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Sana Fathima Amal Al Abri Fatma Al Shamli Sara Shukaili Moza Al Balushi Mastan Vali Shaik Nooritawati Md Tahir College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman College of Engineering Universiti Teknologi MARA (UiTM) Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Malaysia Applied College Princess Nourah Bint Abdul Rahman University (PNU) Riyadh Saudi Arabia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

In the modern age, the idea of the E-government has attained a lot of attention due to its impactful benefits and advantages in achieving the significant goals of the country. The E-government systems play a vital role in facilitating the government’s duties and responsibilities along with the citizen’s interaction and participation. Despite the availabilities of the various studies on the E government, there is still a need to review the previous related literature and combine the issues emerging out of it such that an appropriate system is built incorporating the right blend of AI, social media, and public dissemination of the services. This type of guidance is required in many countries including Oman. The aim of this review is to provide the recent studies that have been done in similar areas focusing on providing the approach and the framework deployed to help build a model for Oman’s e-government systems endorsing social media in its delivery.

关键词： Deep learning Reviews Social networking (online) Buildings Focusing Control systems Rendering (computer graphics) Market research Artificial intelligence Electronic government

来源：评论

学校读者我要写书评

暂无评论

IMPROVED DEEP LEARNING OF CHAOTIC DYNAMICAL SYSTEMS WITH MULTISTEP PENALTY LOSSES

arXiv

引用

arXiv 2024年

作者： Chakraborty, Dibyajyoti Chung, Seung Whan Chattopadhyay, Ashesh Maulik, Romit School of Information Sciences and Technology Pennsylvania State University University ParkPA16802 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States

Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of traditional data-driven modeling approaches. This paper introduces a novel framework that addresses these challenges by leveraging the recently proposed multi-step penalty (MP) optimization technique. Our approach extends the applicability of MP optimization to a wide range of deep learning architectures, including Fourier Neural Operators and UNETs. By introducing penalized local discontinuities in the forecast trajectory, we effectively handle the non-convexity of loss landscapes commonly encountered in training neural networks for chaotic systems. We demonstrate the effectiveness of our method through its application to two challenging use-cases: the prediction of flow velocity evolution in two-dimensional turbulence and ocean dynamics using reanalysis data. Our results highlight the potential of this approach for accurate and stable long-term prediction of chaotic dynamics, paving the way for new advancements in data-driven modeling of complex natural phenomena. © 2024, CC BY-NC-SA.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Development of Interpretable Machine Learning Models for COVID-19 Drug Target Docking Scores Prediction

Development of Interpretable Machine Learning Models for COV...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenqi Shi Mio Murakoso Xiaoyan Guo May D. Wang Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Applied Life Science Kyoto University Kyoto Japan College of Computing Georgia Institute of Technology Atlanta USA Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta USA

With the extensive time and financial requirements incumbent on drug discovery, computational approaches, such as protein-ligand docking predictions, are increasingly crucial for accelerating the process of drug repurposing. However, the proliferation of identified protein targets has exposed a critical knowledge gap in developing robust models that offer both generalizability and interpretability for docking score prediction. Addressing this, our study presents a machine learning-based surrogate model, employing interpretable artificial intelligence techniques for accurate docking score prediction for SARS-CoV-2 protein targets. We demonstrate the model generalization on its expansion to accommodate unseen protein targets by integrating protein target information through feature concatenation. Moreover, we leverage the SHapley Additive exPlanations (SHAP) method to identify the data-driven feature importance of molecular substructures for knowledge-based validation. Our experiments reveal that the combination of data-driven prediction and knowledge-driven validation could provide biomedical insights into the interactions between drugs and SARS-CoV-2 proteins, elucidating their consequent effects on docking scores.

关键词：

来源：评论

学校读者我要写书评

暂无评论

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wilfred Blessing N. R Hemalatha Gunasekaran Hariharan B. Jaber Saleh Salim Al Jabri Kavitha S. J Sutherlin Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy.

关键词： Accuracy Magnetic resonance imaging Redundancy Medical services Predictive models Brain modeling Real-time systems Alzheimer's disease Medical diagnostic imaging Overfitting

来源：评论

学校读者我要写书评

暂无评论

GAPPY AE: A NONLINEAR APPROACH FOR GAPPY DATA RECONSTRUCTION USING AUTO-ENCODER

arXiv

引用

arXiv 2023年

作者： Kim, Youngkyu Choi, Youngsoo Yoo, Byounghyun Center for Artificial Intelligence Korea Institute of Science and Technology Seongbuk-gu Seoul02792 Korea Republic of Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States

We introduce a novel data reconstruction algorithm known as Gappy auto-encoder (Gappy AE) to address the limitations associated with Gappy proper orthogonal decomposition (Gappy POD), a widely used method for data reconstruction when dealing with sparse measurements or missing data. Gappy POD has inherent constraints in accurately representing solutions characterized by slowly decaying Kolmogorov N-widths, primarily due to its reliance on linear subspaces for data prediction. In contrast, Gappy AE leverages the power of nonlinear manifold representations to address data reconstruction challenges of conventional Gappy POD. It excels at real-time state prediction in scenarios where only sparsely measured data is available, filling in the gaps effectively. This capability makes Gappy AE particularly valuable, such as for digital twin and image correction applications. To demonstrate the superior data reconstruction performance of Gappy AE with sparse measurements, we provide several numerical examples, including scenarios like 2D diffusion, 2D radial advection, and 2D wave equation problems. Additionally, we assess the impact of four distinct sampling algorithms - discrete empirical interpolation method, the S-OPT algorithm, Latin hypercube sampling, and uniformly distributed sampling - on data reconstruction accuracy. Our findings conclusively show that Gappy AE outperforms Gappy POD in data reconstruction when sparse measurements are given. © 2023, CC BY-NC-ND.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Forecasting the load flow of Engine Driven Pump based on Light Gradient Boosting Machine Model

Forecasting the load flow of Engine Driven Pump based on Lig...

引用

2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2021

作者： Niu, Wei Song, Xiaogang Wang, Hao Chen, Juan Xi'an Aeronautics Computing Technique Research Institute AVIC Xi'an China Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an China Xi'an Institute of Applied Optics Xi'an China

ISBN: (纸本)9780738146492

The flow rate of aircraft hydraulic system is one of the important parameters to measure the performance of aircraft. The stability of flow rate of hydraulic pipeline is related to the safety of hydraulic system and even aircraft flight. Aiming at the problem that it is difficult to measure the load flow of aircraft hydraulic system, a prediction method based on ensemble learning for the load flow of Engine Driven Pump (EDP) is proposed. According to the different working conditions of EDP, the Light Gradient Boosting Machine (LightGBM) model is established. Through Particle Swarm Optimization algorithm optimizes the super parameters of LightGBM algorithm, and realizes the regression prediction mode. Finally, compared with the multiple linear regression, random forest, experimental results indicate that the proposed model obviously can improve the prediction accuracy. © 2021 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

引用

Computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

来源：评论

学校读者我要写书评

暂无评论

Computational Protein Science in the Era of Large Language Models (LLMs)

arXiv

引用

arXiv 2025年

作者： Fan, Wenqi Zhou, Yi Wang, Shijie Yan, Yuyao Liu, Hui Zhao, Qian Song, Le Li, Qing Department of Computing Department of Management and Marketing The Hong Kong Polytechnic University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Michigan State University United States Department of Applied Biology and Chemical Technology The Hong Kong Polytechnic University Hong Kong GenBio AI and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Proteins are macromolecules that play essential roles in almost all essential life activities, such as immunity, digestion, disease regulation, etc. Considering the significance of proteins, computational protein science has always been a critical field of scientific research, dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm. In the last few decades, Artificial Intelligence (AI) has made a significant impact in computational protein science, leading to notable, even Nobel-Prize-level successes in various specific protein modeling tasks. However, those previous AI models still meet limitations, such as the difficulty in comprehending the grammar and semantics contained in protein sequences, and the inability to generalize across a wide range of protein modeling tasks. Recently, Large Language Models (LLMs) have emerged as a milestone in AI advances due to their remarkable language processing capability and unprecedented generalization capability. They are capable of promoting comprehensive progress in fields, rather than merely solving individual tasks. As a result, researchers have actively introduced powerful LLM techniques in promoting computational protein science, developing protein Language Models (pLMs) that skillfully grasp the foundational knowledge of proteins and can be effectively generalized to solve a diversity of sequence-structure-function reasoning problems. While witnessing prosperous developments, it’s necessary to present a systematic overview of computational protein science empowered by LLM techniques. First, we summarize existing pLMs into categories based on their mastered protein knowledge, i.e., underlying sequence patterns, explicit structural and functional information, and external scientific languages. Second, we introduce the utilization and adaptation of pLMs, highlighting their remarkable achievements in promoting protein structure prediction, protein function predict

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization 1st

A Comparative Study of the Coulomb’s and Franklin’s Laws I...

引用

1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Hemmati, Rasul Abualigah, Laith Forestiero, Agostino Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Electrical Engineering Faculty of Engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Marquette University MilwaukeeWI United States Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town11800 Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Institute for High Performance Computing and Networking National Research Council of Italy Rende Italy

ISBN: (纸本)9783031314681

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://***/projects/cfa, https://***/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Charged particles

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：