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检索条件"丛书名=Communications in Computer and Information Science"

共 43693 条记录，以下是1621-1630 订阅

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Explicit Facial Attribute Disentanglement for Hierarchical Relationships Detection 5th

Explicit Facial Attribute Disentanglement for Hierarchical R...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Sun, Pan Yu, Hong Xie, Jiang Leng, Jiaxu Wang, Ye Chongqing Univ Post & Telecommun Chongqing Key Lab Computat Intelligence Chongqing Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Face attribute editing is a popular direction in face generation. Its purpose is to modify the face attributes in a face image while keeping other attributes unchanged. However, editing multiple facial attributes often results in unintentional alterations to other unedited attributes. This primarily occurs because current generative models do not consider the hierarchical relationships among facial attributes, treating each attribute independently. Consequently, even models that achieve semantic disentanglement can still experience editing distortions. To address this issue, we propose a method that automatically discovers hierarchical attribute relationships in this paper. Specifically, We select 13 significantly varying attribute labels on the CelebA dataset and designed a hierarchical detection module that leverages prior knowledge and trained high-precision classifiers to demonstrate features affected by the interrelationships of face attributes. Easily learn the hierarchical relationship between attributes. In addition, we comprehensively verified the learned hierarchical relationship structure through quantitative experiments and qualitative experiments and achieved better division results than the baseline model.

关键词： Face attribute editing Feature disentanglement Attribute relationships

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Modeling and Analysis of Thermal Dynamics of Polyhouses for Precision Agriculture 2nd

Modeling and Analysis of Thermal Dynamics of Polyhouses for ...

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2nd International Conference on Agriculture-Centric Computation

作者： Rajput, Akash Jain, Tushar Indian Inst Technol Mandi Himachal Prades India

ISBN: (纸本)9783031744396;9783031744402

Polyhouses, designed specifically for crop cultivation, serve as complex thermodynamic systems where indoor temperature and humidity play pivotal roles in crop productivity. Recognized as multivariable input-output systems (MIMO), this study focuses on unveiling a physically validated polyhouse model capable of operating effectively under diverse seasonal conditions. The primary objective is to refine the accuracy of internal temperature modeling within polyhouse structures. Utilizing mass-energy equations, the dynamics of the polyhouse are simulated, accounting for components that either add or remove energy from the system. Specifically, we examine a forced ventilation polyhouse equipped with cooling pads and water circulation mechanisms aimed at temperature reduction. The temperature of the circulating water, serving as a crucial component, significantly influences the overall simulated dynamics of the polyhouse. By integrating these elements into our model, we aim to provide a comprehensive understanding of polyhouse dynamics, facilitating improved management practices for enhanced crop yields and sustainability.

关键词： Thermal modeling

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An Interactive Question Answer Based System on Alzheimer's Disease Using Retrieval Augmented Generation 37th

An Interactive Question Answer Based System on Alzheimer's D...

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37th International Conference on computer Applications in Industry and Engineering

作者： Sen, Sujoy Sarkar, Samay Ghosh, Partha Goto, Takaaki Sen, Soumya Univ Calcutta Kolkata India Acad Technol Adisaptagram W Bengal India Toyo Univ Saitama Japan

ISBN: (纸本)9783031762727;9783031762734

Alzheimer's Disease (AD) presents profound challenges to healthcare systems worldwide, necessitating efficient access to accurate information for optimal care delivery. Effective management of AD requires timely access to accurate information spanning disease etiology, diagnosis, treatment options, and caregiving strategies. However, the vast and constantly evolving body of AD-related literature poses a considerable barrier to efficient information retrieval, particularly for healthcare professionals operating in time-constrained environments. This paper outlines the objectives of a specialized Retrieval-Augmented Generation (RAG) system designed for answering questions related to Alzheimer's Disease (AD), employing prompt engineering and utilizing Pinecone as the vector database. With the goal of enhancing accessibility and comprehension of AD-related information, the system aims to efficiently retrieve relevant data from diverse sources and generate contextually relevant answers tailored to user queries. By leveraging advanced techniques in prompt engineering and vector similarity search, the RAG system empowers healthcare professionals, patients, and caregivers with timely access to accurate and comprehensive information, ultimately facilitating informed decision-making and improving patient outcomes in AD management.

关键词： Alzheimer's Disease Question Answering Retrieval-Augmented Generation Prompt Engineering Pinecone Vector Database

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Reduced Kernel Principal Component Analysis Approach for Microarray Spot Classification 4th

Reduced Kernel Principal Component Analysis Approach for Mic...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Roopa, C. K. Priya, M. P. Harish, B. S. JSS Sci & Technol Univ Dept Informat Sci & Engn Mysuru Karnataka India

ISBN: (纸本)9783031837890;9783031837906

Feature extraction in microarray gene expression profiling involves identifying relevant features for analysis and classification. The feature selection method outperforms gene selection methods in case of high-dimensional microarray data processing. This research presents dimensionality reduction methods, emphasizing their effectiveness in microarray feature selection. The proposed method uses three dimensionality reduction techniques, viz., Principal Component Analysis (PCA), Kernel PCA (KPCA), and Robust Kernel PCA (RKPCA). PCA and KPCA use covariance matrix to identify relationships between gene vectors with kernel function. On the other hand, RKPCA is an extension of KPCA that handles outliers and noise in microarray data. RKPCA uses kernel to map data into a higher-dimensional feature space, allowing the detection of non-linear gene relationships. RKPCA algorithm retains as much information bearing feature of spot as that of the original training data set. Hence, the microarray data classified using RKPCA algorithm has shown effective results when compared to PCA and KPCA. The accuracy of classification varies between 78% to 84% and 82% to 87% using PCA and KPCA respectively using 6 types of classifiers. Further, using RKPCA pre-processing method, classification accuracy has achieved commendable results of maximum 92.57% and 92.76% respectively using Adaptive Neuro Fuzzy Inference Systems (ANFIS).

关键词： Microarray Spots Gene expression Dimensionality Reduction Feature Selection Classification

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Comparative Evaluation of Speech-to-Text Software Based on Sociodemographic and Environmental Factors

Comparative Evaluation of Speech-to-Text Software Based on S...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Morato, Jorge Pedrero, Alejandro Sanchez-Cuadrado, Sonia Carlos III Univ Avda Univ 30 Leganes 28911 Spain Univ Complutense C Santisima Trinidad 37 Madrid 28010 Spain

ISBN: (纸本)9783031834318;9783031834325

The proliferation of voice assistants for information retrieval is propelled by technological advancements and seamless integration across multiple devices. Nevertheless, these systems face persistent limitations in accuracy and comprehension, particularly with accents, dialects, and uncommon terminology. Additional challenges include the cost of these technologies and their reliance on internet connectivity. This study conducts a comprehensive evaluation of various low-cost speech-to-text transcription software, including Windows10, Google Docs, GBoard Android, Speech-Texter, and SpeechNotes. The analysis focuses on high-error criteria in text retrieval, such as proper names, homophones, neologisms, and multilingual usage. Key variables examined include user age, message duration, and ambient noise levels. Transcription quality is meticulously assessed to determine the efficacy of voice retrieval. Results reveal significant disparities among the software, with GBoard Android demonstrating superior accuracy and the lowest error rates.

关键词： audio information retrieval speech recognition speech-to-text transcription voice assistants Spanish transcription quality

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Image Classification of Peach Leaves Using a Siamese Neural Network 12th

Image Classification of Peach Leaves Using a Siamese Neural ...

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12th Ecuadorian Conference on information and Communication Technologies

作者： Coello-Andrade, Mateo David Reyes-Chacon, Ivan Vizcaino-Imacana, Paulina Morocho-Cayamcela, Manuel Eugenio Yachay Tech Univ Sch Math & Computat Sci DeepARC Res Grp Hda San Jose S-N & Proyecto Yachay Urcuqui 100119 Ecuador Univ Internac Ecuador Fac Tech Sci Sch Comp Sci Quito 170411 Ecuador

ISBN: (纸本)9783031754302;9783031754319

The growing global population and the increasing demand for food have made food production a critical concern. To meet this challenge, techniques like vertical farming and aquaponics have been proposed to maximize output while conserving resources and space. However, there is still room for improvement in crop care processes. Deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a leading approach for addressing various agricultural issues. This paper explores the use of Siamese CNNs to classify the front and back faces of six peach leaf varieties, a crucial step in detecting bacteriosis, a common disease in peach farming. Building on prior work in Siamese CNNs for peach leaf classification, this study examines several enhancements, including increased convolutional layers, three activation functions, and the application of pre-trained CNN models. The proposed architecture, combined with the ReLU activation function, improved the accuracy of the reference model by 3.48%. Among the pre-trained models, ResNet performed best, reaching an accuracy of 0.9841, which is 7.83% higher than the benchmark model's top result. These findings indicate that the benchmark model had significant room for improvement, which was effectively addressed through the experiments conducted.

关键词： computer Vision Image Classification Peach Leaves Classification Siamese Convolutional Neural Networks

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Using Deep Learning to Translate Algerian Sign Language to Spoken and Written Words 4th

Using Deep Learning to Translate Algerian Sign Language to S...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Abderrazak, Benarous Leila, Benarous Salim, Bitam Abdelhamid, Mellouk Univ Amar Telidji Dept Comp Sci Fac Sci Laghouat Algeria Univ Amar Telidji Dept Comp Sci LIM Lab Fac Sci Laghouat Algeria Univ Biskra Dept Comp Sci LESIA Lab Biskra Algeria Univ Paris Est Creteil Dept Networks & Telecom LISSI Lab Creteil France

ISBN: (纸本)9783031821493;9783031821509

Having the ability to hear and talk are blessings that allow us to converse and communicate with each other with ease. However, due to some illnesses or disabilities, not all human beings are able to talk and hear. Some are born mute or deaf, and some lose these abilities because of illnesses or accidents. Luckily, to enable them to converse, sign language was created, where they rely on their kinetic and visual abilities to interpret what they want to say or to understand what is said to them. Yet, in this case, instead of spoken words, signs are used. Although sign language is the bridge connecting the deaf and mute communities together and with normal people, it is still only learned by those who need to use it frequently. Limiting the chances of people from these communities to converse with ease with a wider range of normal people. In this paper, we explain the development process of Talk2Me which is a real-time sign language translator mobile app using computer vision and deep learning techniques. Talk2Me was developed to extend the bridge connecting the deaf and mute communities to a wider range of normal people without the need to learn sign language. In its initial version, we used transfer learning on the YOLOV5S model to recognize the Algerian sign alphabets and numbers. The Algerian sign alphabets and digits dataset images were self-collected, labeled, and preprocessed. Talk2me now provides real-time translation of Algerian signs in textual and oral formats.

关键词： computer vision deep learning YOLOV5S Talk2me Android mobile app training inference sign language translation

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Secure Intelligence Development Lifecycle (SIDL) Model for Vulnerability Detection 4th

Secure Intelligence Development Lifecycle (SIDL) Model for V...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Dhanush, V. Chandra, T. Sarath Divakarla, Usha Chandrasekaran, K. Natl Inst Technol Karnataka Surathkal CSE Dept Mangalore India NITTE NMAMIT ISE Dept Mangalore India

ISBN: (纸本)9783031837890;9783031837906

This paper presents the Secure Intelligence Development Life-Cycle, a Software Development Life-Cycle created to address the need for advanced security and intelligent technologies, such as artificial intelligence and machine learning, at the development level. It differs from standard software development models that usually involve security measures only in the later stages of development;this one integrates security measures right from the early stage of requirements gathering through design, development, and deployment. The embedding of security from the onset allows real-time vulnerability detection and response with adaptability to emerging threats. It has also improved threat detection, made better decisions, and enhanced system resilience with intelligent processes. It is most effective with sectors like healthcare, finance, and critical infrastructure for secure and complex software systems.

关键词： Secure Intelligence Development Lifecycle (SIDL) SDLC Intelligent Systems AI Security Vulnerability Detection

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SINR Analysis of Cell-Free Massive MIMO Systems Assisted by Intelligent Reflecting Surface 5th

SINR Analysis of Cell-Free Massive MIMO Systems Assisted by ...

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5th China Intelligent Robotics Academic Conference

作者： Jin, Si-Nian Zhao, Shihang Song, Yu Ma, Jitong Ju, Moran Dalian Maritime Univ Dalian 116026 Peoples R China China Southern Power Grid Co Ltd Guangzhou Bur Extra High Voltage Transmiss Co Guangzhou 510663 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

In 6th generation (6G) mobile networks, cell-free massive multiple-input multiple-output (MIMO) and intelligent reflecting surfaces (IRS) play a crucial role. Integrating these technologies can significantly enhance communication network performance. This paper explores cell-free massive MIMO systems augmented by IRS, where M multi-antenna base stations (BSs) communicate with K single-antenna users, aided by N IRSs with many passive reflecting elements located within the BSs' line of sight. Using the maximum ratio combined under perfect channel state information, we begin by examining the probability density function (PDF) of both the desired signal and the interference generated by other users. From these analyses, we then derive the PDF of the signal-to-interference-plus-noise ratio (SINR) for each individual user. Numerical results confirm the accuracy of the SINR PDF.

关键词： Intelligent reflecting surface cell-free massive multiple-input multiple-output SINR

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Vaccine Sentiment Analysis: A Twitter Study Using NLP and ML Approach 4th

Vaccine Sentiment Analysis: A Twitter Study Using NLP and ML...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Jaganathan, Subash Chandra Bose Sreeram, M. V. Praharaj, Badrikanath Sanjay, R. Roy, Soumik Kastode, Yashraj Jyoti Sanjay Priyadharshini, A. VIT Bhopal Univ Sch Comp Sci & Artificial Intelligence Bhopal Indore Highway Sehore 466114 Madhya Pradesh India VIT Bhopal Univ Sch Comp Sci & Engn Bhopal Indore Highway Sehore 466114 Madhya Pradesh India VIT Bhopal Univ Sch Elect & Elect Engn Bhopal Indore Highway Sehore 466114 Madhya Pradesh India Jain Deemed Univ Comp Sci Engn Bengaluru India

ISBN: (纸本)9783031837890;9783031837906

This study explores the vast realm of Twitter, leveraging its real-time data on public opinions and sentiments about COVID-19 vaccinations, where diverse voices converge in succinct tweets. By applying sentiment analysis and natural language processing (NLP) techniques, we unveil public perceptions of COVID-19 vaccinations, analysing a dataset of over ten thousand tweets from January to April 2021. Our approach identifies prevailing themes within these tweets, translating them into digestible narratives, providing healthcare experts with unique insights into public reactions to health interventions as they unfold. The analytical pipeline begins with accessing Twitter's API and authentication tokens, followed by NLP techniques to process raw tweets. Sentiment analysis then categorizes tweets as positive, negative, or neutral, allowing us to gauge the collective emotional pulse. From these categorized tweets, key themes are extracted, giving a high-level view of the main conversations, like crafting a masterpiece where each algorithmic step unveils insights from vast data. Ultimately, this study offers a deep dive into the world of tweets, transforming them into actionable insights and providing a real-time radar for the health community to navigate the evolving landscape of public opinion.

关键词： COVID-19 Tweets Dataset NLP Vaccine Sentiment Analysis Public Health India The United States of America

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