检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Tommasel, Antonela Assent, Ira Isistan CONICET-UNCPBA Argentina Aarhus University Department of Computer Science Denmark Digit Aarhus University Centre for Digitalisation Big Data and Data Analytics Denmark

ISBN: (纸本)9798350362480

The widespread adoption of electronic health records has generated a vast amount of patient-related data, mostly presented in the form of unstructured text, which could be used for document retrieval. However, querying these texts in full could present challenges due to their unstructured and lengthy nature, as they may contain noise or irrelevant terms that can interfere with the retrieval process. Recently, large language models (LLMs) have revolutionized natural language processing tasks. However, despite their promising capabilities, their use in the medical domain has raised concerns due to their lack of understanding, hallucinations, and reliance on outdated knowledge. To address these concerns, we evaluate a Retrieval Augmented Generation (RAG) approach that integrates medical knowledge graphs with LLMs to support query refinement in medical document retrieval tasks. Our initial findings from experiments using two benchmark TREC datasets demonstrate that knowledge graphs can effectively ground LLMs in the medical domain. © 2024 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Real-Time Language Translation: A Server-Side Approach to Overcome Cross-Cultural Communication Challenges 4

Machine Learning for Real-Time Language Translation: A Serve...

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

The evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-The-Art results in language translation, a universal solution remains elusive. This research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. The process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). This approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Zero-shot causal learning 37

Zero-shot causal learning

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Nilforoshan, Hamed Moor, Michael Roohani, Yusuf Chen, Yining Šurina, Anja Yasunaga, Michihiro Oblak, Sara Leskovec, Jure Department of Computer Science Stanford University United States Department of Biomedical Data Science Stanford University United States School of Computer and Communication Sciences EPFL Switzerland Department of Computer Science University of Ljubljana Slovenia

ISBN: (纸本)9781713899921

Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it. However, in many settings it is important to predict the effects of novel interventions (e.g., a newly invented drug), which these methods do not address. Here, we consider zero-shot causal learning: predicting the personalized effects of a novel intervention. We propose CaML, a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. CaML trains a single meta-model across thousands of tasks, each constructed by sampling an intervention, its recipients, and its nonrecipients. By leveraging both intervention information (e.g., a drug's attributes) and individual features (e.g., a patient's history), CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training. Experimental results on real world datasets in large-scale medical claims and cell-line perturbations demonstrate the effectiveness of our approach. Most strikingly, CaML's zero-shot predictions outperform even strong baselines trained directly on data from the test interventions. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

引用

Neural Computing and Applications 2024年第18期36卷 10599-10612页

作者： Bashir, Munzir Hubiba Ahmad, Musheer Rizvi, Danish Raza El-Latif, Ahmed A. Abd Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Kom32511 Egypt

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human resource management

来源：评论

学校读者我要写书评

暂无评论

Extraction and Classification of Medicines from Handwritten Medical Prescriptions 18

Extraction and Classification of Medicines from Handwritten ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Zia, Javeria Habib, Usman Naeem, Muhammad Asif National University of Computer and Emerging Sciences Department of Ai & Data Science Islamabad Pakistan

ISBN: (纸本)9798350328172

In the modern world of rapid technological progress and digital transformation, industries are swiftly shifting from paper-based to digital systems. Document digitization, especially for image-based documents lacking machine readability, is gaining global attention. Sectors like real estate, finance, law, healthcare, and education are actively adopting digital strategies. In healthcare, where handwritten prescriptions lead to inefficiencies such as errors in medicine name by pharmacist due to doctor's hand writing lead to catastrophic failure and delays in various processes. Therefore, digital solutions are being developed to enhance patient care. This study proposes an approach to extract and categorize medicines from handwritten prescriptions, improving accuracy and communication with pharmacists. We proposed an approach that uses stroke enhancement apart from pre-processing with comprehensive multi-modal architecture, including a transformer based deep neural network and a sequence-to-sequence model such as DONUT and layoutLMv2 model. Our approach achieves a mean accuracy of about 0.85%. It surpasses models like LayoutLMv2 and DONUT by providing a balance in accuracy and consistency. Furthermore, the extracted features are passed to the various state of art machine learning classification algorithms upon which Gradient Boosting, Random forest and Ridge classifier produced the promising results for medicine type classification. © 2023 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Detection and Discrimination of Arabic Phonemes Using Long Short-Term Memory (LSTM) Model 11

Detection and Discrimination of Arabic Phonemes Using Long S...

引用

11th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Tolba, Rahma M. Fayed, Zaki T. Alsayadi, Hamzah A. Saad, Noureldin O. Elarif, Taha Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Ibb University Faculty of Sciences Computer Science Department Yemen Canadian International Collage School of Computer Science Data Science Department New Cairo Egypt

ISBN: (纸本)9798350322101

Phonetics is a crucial branch of linguistics that studies human speech sounds and is essential for language learning, speech therapy, and speech technology development. However, current Arabic speech systems cannot instantly analyze and detect mispronunciation of Arabic phonemes, only offering a recording feature for self-evaluation. So, this paper presents a state-of-the-art approach using a Long Short-Term Memory (LSTM) model for detecting and discriminating between Arabic phonemes. The model was tested on a collected dataset for the Arabic phonemes /s/ 'sin' and /s/ 'ad and demonstrated excellent performance in discriminating between them despite their close articulation. The model achieved an accuracy of 97.33% and outperformed other techniques proposed for small datasets. The model can be utilized either independently or integrated into a computer-assisted language learning (CALL) system. Furthermore, our findings suggest that there is significant room for improvement in this area, particularly in collecting large audio datasets for all the Arabic phonemes, refining the algorithms, and optimizing the data processing pipeline. © 2023 IEEE.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

An AI-driven Approach for Tailoring Java Programming Instructions 3

An AI-driven Approach for Tailoring Java Programming Instruc...

引用

3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Thiyagarajan, Gomathi Vijayalakshmi, V. Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331527549

AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but are increasingly gaining popularity in educationalsetting offering innovative approaches that transcend conventional methods. In this research we introduce ByteBuddy a virtual assistant which assists instructors on Java ***'s framework is designed in a way to integrate easily into educational context. This study reveals the architecture of ByteBuddy, the process model that captured the behavior of students in the learning environment and the observation of its application in java instructions. By applying this study, we provide profound insights on how the effective use of ByteBuddy supplements the teaching practices and measure learning outcomes. © 2025 IEEE.

关键词： Java programming language

来源：评论

学校读者我要写书评

暂无评论

A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

引用

IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1834-1844页

作者： Yanlu Gong Junhai Zhou Quanwang Wu MengChu Zhou Junhao Wen College of Computer Science Chongqing UniversityChongqing 400044China IEEE Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** computing(EC)is promising for FS owing to its powerful search ***,in traditional EC-based methods,feature subsets are represented via a length-fixed individual *** is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training *** work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional *** LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space ***,a dominance-based local search method is employed for further *** experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

关键词： Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive

来源：评论

学校读者我要写书评

暂无评论

Text Description to Facial Sketch Generation using GANs 18

Text Description to Facial Sketch Generation using GANs

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Tanveer, Mohsin Habib, Usman National University of Computer and Emerging Sciences Department of Ai & Data Science Islamabad Pakistan

ISBN: (纸本)9798350328172

Text-to-face generation is an exciting area of research focused on generating human faces based on textual descriptions, presenting unique challenges that have primarily been explored in academia. The advancement of generative adversarial networks (GANs) has played a crucial role in implementing text-to-face generation. This study aims to develop a framework that allows users to express their desired human face characteristics through textual descriptions, with the tool generating lifelike human face images in response. The approach utilizes a Deep Fusion generative adversarial network (DFGAN) architecture, incorporating deep convolutional neural networks (CNNs), trained on the CelebA dataset, which provides a diverse collection of labeled face images for training. The two model's performance are evaluated using the Fréchet Inception Distance (FID) score. DFGAN has achieved a FID score of 100 after evaluating 15k images, surpassing the results obtained by the DCGAN model. © 2023 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography

引用

Mathematical Biosciences and Engineering 2024年第3期21卷 4762-4778页

作者： Liu, Tao Vairagar, Shubhangi Adagale, Sushadevi Karthick, T. Karunya, Catherine Esther Blesswin, A. John Mary, G. Selva Tianjin Sino-German University of Applied Sciences Tianjin300350 China Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Computer Engineering KJEI's Trinity Academy of Engineering Pune411048 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Directorate of Learning and Development SRM Institute of Science and Technology Kattankulathur603203 India

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Medical applications

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：