检索结果-内蒙古大学图书馆

Machine learning-assisted retrosynthesis planning:Current status and future prospects

Chinese Journal of Chemical engineering 2025年第1期77卷 273-292页

作者： Yixin Wei Leyu Shan Tong Qiu Diannan Lu Zheng Liu Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Beijing 100084China

Machine learning-assisted retrosynthesis planning aims to utilize machine learning(ML)algorithms to find synthetic pathways for target *** recent years,with the development of artificial intelligence(AI),especially ML,researchers’interest in ML-assisted retrosynthesis planning has rapidly increased,bringing development and opportunities to the *** this review,we aim to provide a comprehensive understanding of ML-assisted retrosynthesis *** first discuss the formal definition and the objective of retrosynthesis planning,and organize a modular framework which includes four modules:data preparation,data preprocessing,pathway generation and evaluation,and pathway ***,we sequentially review the current status of the first three modules(except pathway verification)in the ML-assisted retrosynthesis planning framework,including ideas,methods,and latest *** that,we specifically discuss large language models in retrosynthesis ***,we summarize the extant challenges that are faced by current ML-assisted retrosynthesis planning research and offer a perspective on future research directions and development.

关键词： Retrosynthesis planning Machine learning Artificial intelligence Synthetic pathway Chemoinformatics

来源：评论

学校读者我要写书评

暂无评论

A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process

引用

Chinese Journal of Chemical engineering 2023年第3期55卷 266-276页

作者： Yiming Bai Shuaiyu Xiang Feifan Cheng Jinsong Zhao Department of Chemical Engineering Tsinghua UniversityBeijing 100084China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua UniversityBeijing 100084China Sinopec Engineering Incorporation Beijing 100084China

With the increase in the complexity of industrial system, simply detecting and diagnosing a fault may be insufficient in some cases, and prognosing the fault ahead of time could have a certain necessity. Accurate prediction of key alarm variables in chemical process can indicate the possible change to reduce the probability of abnormal conditions. According to the characteristics of chemical process data, this work proposed a key alarm variables prediction model in chemical process based on dynamic-inner principal component analysis(DiPCA) and long short-term memory(LSTM). DiPCA is used to extract the most dynamic components for prediction. While LSTM is used to learn the relationship and predict the key alarm variables. This work used a simulation data set and a real hydrogenation process data set for applications and explained the model validity from the essential characteristics. Comparison of results with different models shows that our model has better prediction accuracy and performance, which can provide the basis for fault prognosis and health management.

关键词： Fault prognosis Process systems Safety Prediction Principal component analysis Long short term memory

来源：评论

学校读者我要写书评

暂无评论

A hybrid memory architecture supporting fine-grained data migration

引用

Frontiers of Computer Science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

来源：评论

学校读者我要写书评

暂无评论

SAND: semi-automated adaptive network defense via programmable rule generation and deployment

引用

Science China(Information Sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

来源：评论

学校读者我要写书评

暂无评论

A Novel Convolutional-Transformer Neural Network Architecture for Diagnosis of Pneumothorax 28

A Novel Convolutional-Transformer Neural Network Architectur...

引用

28th International Computer Conference, Computer Society of Iran, CSICC 2023

作者： Sanati, Amir Dashtestani, Mansoureh A. Rostami, Habib Azad, Saeed Talatian Persian Gulf University Faculty of Intelligent System Engineering and Data Science Computer Engineering Department Bushehr Iran

ISBN: (纸本)9798350338195

Pneumothorax is a life-threatening and urgent chest disease than can be detected using Chest X-Ray (CXR) image. CXR images are low resolution and diagnosis of pneumothorax based on them is error prone. Deep learning-based computer aided diagnosis systems can improve diagnosis performance of pneumothorax. Convolutional Neural Networks (CNNs) are default networks in deep learning-based medical image process. However, CNNs fail to capture long range features. On the other side, Transformer are proposed to exploit long range feature, but they cannot capture local features. In this paper, we propose a general method with a convolution and a transformer module which can classify CXR images to diagnose pneumothorax by extracting local features, global features and global features attended by local ones using a novel architecture. Results show that the proposed method outperforms base architectures and the other previous works. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

DiaSet: An Annotated dataset of Arabic Conversations 30

DiaSet: An Annotated Dataset of Arabic Conversations

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Israeli, Abraham Naaman, Aviv Makhoul, Rawaa Maduel, Guy Ejmail, Amir Jubran, Julian Karain, Dana Lisnyansky, Dina Fine, Shai Bar, Kfir The Data Science Institute Reichman University Israel Department of Software and Information System Engineering Ben-Gurion University of the Negev Israel

ISBN: (纸本)9782493814104

We introduce DiaSet, a novel dataset of dialectical Arabic speech, manually transcribed and annotated for two specific downstream tasks: sentiment analysis and named entity recognition. The dataset encapsulates the Palestine dialect, predominantly spoken in Palestine, Israel, and Jordan. Our dataset incorporates authentic conversations between YouTube influencers and their respective guests. Furthermore, we have enriched the dataset with simulated conversations initiated by inviting participants from various locales within the said regions. The participants were encouraged to engage in dialogues with our interviewer. Overall, DiaSet consists of 644.8K tokens and 23.2K annotated instances. Uniform writing standards were upheld during the transcription process. Additionally, we established baseline models by leveraging some of the pre-existing Arabic BERT language models, showcasing the potential applications and efficiencies of our dataset. We make DiaSet publicly available for further research. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

P3DC:Reducing DRAM Cache Hit Latency by Hybrid Mappings

引用

Journal of Computer Science & Technology 2024年第6期39卷 1341-1360页

作者： Ye Chi Ren-Tong Guo Xiao-Fei Liao Hai-Kun Liu Jianhui Yue National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Laboratory Wuhan 430074China Cluster and Grid Computing Laboratory Wuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Department of Computer Science Michigan Technological UniversityHoughton 49931-1295U.S.A.

Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.

关键词： die-stacked dynamic random access memory(DRAM) cache set-associative direct-mapped hit latency

来源：评论

学校读者我要写书评

暂无评论

Classification and Signal Detection in Shared Spectrum Using a Deep Learning Method 2

Classification and Signal Detection in Shared Spectrum Using...

引用

2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Mandala, Vishwanadham Surabhi, Srinivas Naveen R Dolu Selvan, R. Senthamil Indiana University Department of Analytics Data Engineering Lead IN United States General Motors R & D Department of Control System IN United States Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350364699

In shared spectrum networks, proper signal type identification is essential for equitable coexistence and effective resource allocation. To increase system throughput and prevent accidents, particularly in situations when the environment is changing quickly, it may be utilised for scheduling gearbox chances. To train deep neural networks, or DNNs, to distinguish coexisting signal kinds without encoding them, researchers use the In-phase/Quadrature (I/Q) sample as input. Two neural networks, one convolutional and one recurrent, are trained using input segments taken from the received signal samples utilising categorical cross-entropy (CE) optimisation. Results for the 5-6 GHz unlicensed band, where LTE LAA, Wi-Fi, and 5G NR-U signals coexist, demonstrate that the suggested architecture is quite accurate. After that, use spectral analysis on the I/Q sequence to boost classification precision even further. More data in the frequency domain may be shown as a spectrogram by using a Short-time Fourier Transform (STFT). Increasing the size of the DNN's input accordingly. Researchers use USRP radios to carry out over-the-air (OTA) tests that validate the efficacy of the suggested detection system. In both software and hardware trials, the suggested method achieves precise categorization. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Early Detection and Classification of Tomato Leaf Blight Disease Using Deep Neural Networks 2

Early Detection and Classification of Tomato Leaf Blight Dis...

引用

2nd IEEE International Conference on data Science and Network Security, ICDSNS 2024

作者： Halder, Anangsha Guha Neogi, Subhrendu Dutta, Shibdas Khamaru, Koustov Bhattacharya, Arghya Roy, Shovan The Neotia University Department of Computer Science and Engineering Sarisha India Dcg Data-Core System India Department of Information Technology Kolkata India University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350373110

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. Computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Optimizing Hate Speech Detection in the Indonesian Language using FastText and LSTM Algorithms 2

Optimizing Hate Speech Detection in the Indonesian Language ...

引用

2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Wenando, Febby Apri Yusoff, Nooraini Izrin, Nurul Universitas Andalas Department of Information System Padang Indonesia Universiti Malaysia Kelantan Department of Data Science Kelantan Malaysia Universiti Teknikal Malaysia Department of Software Engineering Malaysia

ISBN: (纸本)9798350389241

The term hate speech encompasses communication that conveys hostility or prejudice toward individuals or groups based on specific attributes, including race, ethnicity, gender, or religion. Given the prevalence of such harmful content on major social media platforms like Twitter, it is imperative to prioritize ongoing research into automated detection methods. This study employs advanced artificial intelligence and machine learning techniques, involving pivotal stages such as data collection, preprocessing, oversampling, fastText word embedding, and classification using Long Short-Term Memory (LSTM) networks to achieve the objective. The amalgamation of fastText embeddings with LSTM classification yielded noteworthy results, with the model achieving 94% accuracy, 92% precision, 94% recall, and a 93% F1 score. These outcomes underscore the effectiveness of sophisticated machine learning methodologies in accurately discerning and addressing the proliferation of hate speech online, particularly in the context of Indonesian-language content. © 2024 IEEE.

关键词： Speech recognition

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：