检索结果-内蒙古大学图书馆

A Survey on Evolutionary Computation Based Drug Discovery

IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

来源：评论

学校读者我要写书评

暂无评论

Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student’s Tweets during COVID-19

引用

computers, Materials & Continua 2023年第6期75卷 5355-5377页

作者： Shabir Hussain Muhammad Ayoub Yang Yu Junaid Abdul Wahid Akmal Khan Dietmar P.F.Moller Hou Weiyan School of Information Engineering Zhengzhou UniversityZhengzhouChina School of Computer Science and Engineering Central South UniversityChangshaChina Department of Data Science The Islamia University of BahawalpurBahawalpurPakistan Clausthal University of Technology Clausthal-ZellerfeldGermany

As the COVID-19 pandemic swept the globe,social media plat-forms became an essential source of information and communication for *** students,particularly,turned to Twitter to express their struggles and hardships during this difficult *** better understand the sentiments and experiences of these international students,we developed the Situational Aspect-Based Annotation and Classification(SABAC)text mining *** framework uses a three-layer approach,combining baseline Deep Learning(DL)models with Machine Learning(ML)models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 *** the pro-posed aspect2class annotation algorithm,we labeled bulk unlabeled tweets according to their contained aspect ***,we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled *** address this issue,we proposed the Volatile Stopwords Filtering(VSF)technique to reduce sparsity and enhance classifier *** resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21%when using the random forest as a *** testing on three benchmark datasets,we found that the SABAC ensemble framework performed exceptionally *** findings showed that international students during the pandemic faced various issues,including stress,uncertainty,health concerns,financial stress,and difficulties with online classes and returning to *** analyzing and summarizing these annotated tweets,decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.

关键词： COVID-19 pandemic situational awareness ensemble learning aspect-based text classification deep learning models international students topic modeling

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Exploration of Real-Time 3-D View Reconstruction Methods

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5915-5927页

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati781039 India Indian Institute of Technology Kanpur Department of Electrical Engineering 208016 India

Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This article thoroughly examines and tests contemporary methodologies addressing challenges in 3-D view reconstruction. The methods being explored in this article are categorized into volumetric and mesh, generative adversarial network based, and open source library based methods. The exploration of these methods undergoes detailed discussions, encompassing methods, advantages, limitations, and empirical results. The real-time testing of each method is done on benchmarked datasets, including ShapeNet, Pascal 3D+, Pix3D, etc. The narrative highlights the crucial role of 3-D view reconstruction in domains such as robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The article also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms. © 2020 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A structural variation genotyping algorithm enhanced by CNV quantitative transfer

引用

Frontiers of computer Science 2022年第6期16卷 159-165页

作者： Tian ZHENG Xinyang QIAN Jiayin WANG Department of Computer Science and Technology School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China Institute of Data Science and Information Quality Shaanxi Engineering Research Center of Medical and Health Big DataXi’an Jiaotong UniversityXi’an710049China

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging ***,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of *** characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream *** the allelic copy number increases,the error rate of genotyping increases *** instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the *** by these,we propose a transfer learning-based method to genotype structural variations accurately considering *** method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype *** maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct *** adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be *** applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and *** experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and *** source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

关键词： genotyping copy number variations transfer earning

来源：评论

学校读者我要写书评

暂无评论

Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in semi-automated dataset for rice crop

Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in ...

引用

2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024

作者： Dey, Anamika Sarkar, Somrita Kumar, Debajyoti Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre For Computational And Data Sciences Kharagpur India Indian Institute of Technology Department of Computer Science And Engineering Patna Bihta India Indian Institute of Technology Kharagpur Department of Computer Science And Engineering Kharagpur India

ISBN: (纸本)9798350389678

Accurate Normalized Difference Vegetation Index (NDVI) forecasting is crucial for effective agricultural planning. However, a good prediction of the same requires sufficient data, but structured data is not available in the public domain or open-source community. Also, most of the existing methods do not consider spatial information. This study presents a novel semi-automated dataset generation framework that utilizes Sentinel-2, POWER data Access Viewer, and Google Earth Engine to create a comprehensive time-series dataset. We propose a smoothed long short-term memory (LSTM) model considering time series, historical meteorological, and spatial information. The proposed Smoothed-LSTM-based model outperforms Traditional-LSTM models, demonstrating its effectiveness in NDVI prediction for agricultural applications. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Adaptive Learning in Multiagent Systems Using Brain-computer Interfaces 2

Adaptive Learning in Multiagent Systems Using Brain-Computer...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dhatterwal, Jagjit Singh Kaswan, Kuldeep Singh Thapliyal, Nitin Malik, Kiran Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Galgotias University Department of Computer Science and Engineering UP Greater Noida India Graphic Era Hills University Department of Computer Science and Engineering Uttarakhand Dehradun India Matu Ram Institute of Engineering and Management Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350371055

Adaptive learning in multiagent systems has emerged as a promising approach to enhance agents' capabilities to adapt to dynamic environments and optimize their performance. In this research paper, we investigate the integration of brain-computer interfaces (BCIs) as a novel means to facilitate adaptive learning in multiagent systems. BCIs establish direct communication channels between the human brain and external devices, enabling real-time monitoring and interpretation of neural activity. By leveraging BCIs, agents can capture valuable cognitive signals from human operators and use this information to adapt their decision-making processes and behaviors. We present a theoretical framework that outlines the incorporation of BCIs into multiagent systems and the mechanisms for adaptive learning. Additionally, we propose a comprehensive methodology for evaluating the effectiveness of this approach. The results of our experiments demonstrate the potential of adaptive learningin multiagent systems using BCIs, paving the way for new applications in various domains, including human-machine col-laboration, assistive technologies, and interactive gaming systems. © 2024 IEEE.

关键词： Adaptive Learning Brain-computer Interfaces Human-Agent Interaction Human-Machine Collaboration Multiagent Systems

来源：评论

学校读者我要写书评

暂无评论

CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

引用

computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

HAM: Hybrid Associations Models for Sequential Recommendation (Extended abstract) 39

HAM: Hybrid Associations Models for Sequential Recommendatio...

引用

39th IEEE International Conference on data engineering, ICDE 2023

作者： Peng, Bo Ren, Zhiyun Parthasarathy, Srinivasan Ning, Xia The Ohio State University Department of Computer Science and Engineering United States The Ohio State University Department of Biomedical Informatics United States The Ohio State University Translational Data Analytics Institute Department of Computer Science and Engineering United States The Ohio State University Translational Data Analytics Institute Department of Biomedical Informatics Department of Computer Science and Engineering United States

ISBN: (纸本)9798350322279

Sequential recommendation aims to identify and recommend the next few items of users' interest. It becomes an effective tool to help users select their favorite items from a variety of options. A key challenge in sequential recommendation is to learn the patterns and dynamics, which are most pertinent to inform future interactions of users. With the prosperity of deep learning, many deep models, particularly based on recurrent neural networks [1] and with attention mechanisms [2] , [3] , have been developed for sequential recommendation purposes. However, our analysis demonstrates that, these deep models, particularly those with attention mechanisms, may not always learn meaningful attention weights from the extremely sparse recommendation data, and thus, could degrade the recommendation performance. Therefore, in this study, instead of deep models, we develop novel, effective and efficient hybrid associations models (HAM) to better learn from the sparse and limited recommendation data. This study has been published in IEEE Transactions on Knowledge and data engineering. Please refer to the full manuscript [4] for more details. © 2023 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on Semantic Matching of Thesis Themes Based on Comparative Learning and Adversarial Training 7

Research on Semantic Matching of Thesis Themes Based on Comp...

引用

7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Zhang, Xu Shao, Zengzhen Li, Zhuangzhuang Han, Shuai Sun, Yajing Shandong Normal University Department Of Information Science And Engineering Jinan China Shandong Women's College Department Of Data Science And Computer Science Jinan China

ISBN: (纸本)9798350361445

At the doctoral level in the field of computer science, the domain of literature plagiarism detection encompasses numerous plagiarism detection algorithms, but relatively fewer have focused on methods specifically designed for topic-based plagiarism detection. To address this research gap, this paper introduces a text semantic matching algorithm based on contrastive learning and adversarial training, which we have named 'Comparative Adversarial Training (CAT).' Specifically, during the fine-tuning process, we generate affirmative and negative sentences using paper titles and keywords and optimize the model's performance through contrastive learning. Simultaneously, we generate adversarial samples by perturbing the word embedding matrix of the model and then employ these adversarial samples for adversarial training. Through experiments conducted on our proprietary internal dataset, the fine-tuned BERT model exhibited an average performance improvement of 3.71% compared to the baseline BERT model. Similarly, the fine-tuned RoBERTa model demonstrated a performance enhancement of 3.18% relative to the RoBERTa baseline model. Based on these experimental results, our algorithm's effectiveness has been thoroughly validated. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

An Overview of Different Types of Recommendations Systems-A Survey 4

An Overview of Different Types of Recommendations Systems-A ...

引用

4th International Conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Duraisamy, Premkumar Yuvaraj, S. Natarajan, Yuvaraj Niranjani, V. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665494144

In recent years the boom of internet and social media usage everyone spend their invaluable time in social media app and looking for the solution for all kind of their problems. This work analysis deeply on how recommendation system works and its types in different platforms. Most of the modern recommendation system use machine learning algorithms like linear regression, random forest regression and support vector model with collaborative filtering method. Recommendation is nothing but an choice making system. It is vary from person to person based on their interest, culture, locality, education background, interpersonal skills etc., The huge item can be filtered from one by one based on each parameter and finally it will reach the right recommendation item. The research community has worked tremendous way in the field of recommendation system and produced huge variety of result. This survey enlightening the ideas about variety of recommendation system and techniques used by the research community. © 2023 IEEE.

关键词： Recommender systems

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：