检索结果-内蒙古大学图书馆

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bharadwaj, Vivek Rakhshan, Beheshteh T. Malik, Osman Asif Rabusseau, Guillaume Electrical Engineering and Computer Science Department UC Berkeley United States Encube Technologies United States Mila & DIRO Université de Montréal Canada CIFAR AI Canada

Tensor Train (TT) decomposition is widely used in the machine learning and quantum physics communities as a popular tool to efficiently compress high-dimensional tensor data. In this paper, we propose an efficient algorithm to accelerate computing the TT decomposition with the Alternating Least Squares (ALS) algorithm relying on exact leverage scores sampling. For this purpose, we propose a data structure that allows us to efficiently sample from the tensor with time complexity logarithmic in the tensor size. Our contribution specifically leverages the canonical form of the TT decomposition. By maintaining the canonical form through each iteration of ALS, we can efficiently compute (and sample from) the leverage scores, thus achieving significant speed-up in solving each sketched least-square problem. Experiments on synthetic and real data on dense and sparse tensors demonstrate that our method outperforms SVD-based and ALS-based algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey on Production of Fabric from the Organic Extract

A Comprehensive Survey on Production of Fabric from the Orga...

引用

International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Hunar Sajjan Agrawal Mokara Praneetha Anshika Singh Abha Kiran Rajpoot Department of Computer Science and Engineering Sharda University Greater Noida India Department of CSE (AI & ML) KIET Group of Institutions Ghaziabad India

The application of varied/various fabric types is constantly on the rise over time, the process of the fabric making involves a lot of fabric offcuts. The textile industry domain consists of transformation of fiber to yarn, yarn to fabric and fabric to finished garments. During these processes, the waste is generated in a large quantity and evidences from past justify the fact that fashion industry is lacking in sustainability measures. The textile industries create environmental harm at all stages of production from raw material cultivation to disposal of finished goods. Large amount of chemical loads, high amount of water usage, high energy consumption, air emission, solid waste are major environmental challenges in textile industries. This review paper will provide a comprehensive and up-to-date overview of sustainable fabrics, offering insights into their environmental, social and economic benefits. By understanding the potential of these alternatives, we can collectively contribute to a more sustainable and responsible fashion industry, fostering a healthier planet for future generations. An comprehensive range of fabric has been explored, each renowned by unique set of qualities. Sustainable fabrics such as bamboo, banana, coffee, hemp are examined that have beneficial outcomes unlike synthetic/polyster fabrics. The transition to such sustainable fabrics is a necessity. The sustainable fabrics are decent and renewable, these can unlock a new opportunity towards a eco-friendlier alternative. Dyeing also causes negative impacts which can be resolved by using sustainable dye that provide natural color and environmental benefits. In concordant, these sustainable methods and alternatives should be employed to foster better and sustainable world.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fault Tolerant Quality of Service Provisioning in Internet of Things using One Class Support Vector Machine

Fault Tolerant Quality of Service Provisioning in Internet o...

引用

Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Mohhamied Husaein Fallaah T. Shanthi S. Balakrishna Reddy Rajavadhani K K. Sandhya Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Computer Science and Engineering (DS) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent years, the rapid development of the Internet of Things (IoT) has made fault tolerance is important for maintaining service quality and system availability. Fault tolerance Quality of Service (QoS) provisioning ensures IoT systems operate continuously, even in presence of failures or malfunctions. Traditional approaches for QoS provisioning had face several challenges which include data security risks, inconsistent updates. Therefore, this research proposes One Class Support Vector Machine (OCSVM) for fault tolerant QoS provisioning in IoT systems. Initially, data is collected from Message Queuing Telemetry Transport-IoT-Intrusion Detection System (MQTT-IoT-IDS 2020) and preprocessed by using Expectation Maximization (EM) which reduces noise in data and handles missing data. Finally, provisioning and fault detection is done by using proposed OCSVM which detected faults and communication errors. The proposed OCSVM achieved better results in Execution time (220.61), Execution Cost (793.02) and Energy Consumption (22J) when compared with existing Fault-Tolerant and Data-Oriented Scientific Workflow Management and Scheduling System (FD-SWMS).

关键词： Support vector machines Fault tolerance Costs Fault detection Computational modeling Fault tolerant systems Noise Quality of service Feature extraction Internet of Things

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Approach for Multiclass Orange Disease Classification

A Deep Learning Approach for Multiclass Orange Disease Class...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Sandeep Kumar Abhay Kumar Pandey Daksh Raghav Gaurav Gupta Varsha Srivastava Department Of Computer Science & Engineering (AI) ABES Institute of Technology Ghaziabad

This research investigates “Machine Learning (ml)” models for the multiclass classification of orange diseases, examining “K-Nearest Neighbors (KNN)”, “Convolutional Neural Network (CNN)” with MobileNetV2 architecture, “Random Forest (RF)” and “Support Vector Machine (SVM)”. The study assesses their performance across Blackspot, Canker, Fresh, and Greening disease classes —using metrics and highlighting the models' strengths. Notably, the CNN model utilizing MobileNetV2 exhibits exceptional accuracy, achieving an impressive 98.22% across all disease categories. The findings emphasize the remarkable accuracy and robustness of CNN in precisely identifying and classifying diverse orange diseases. This suggests its pivotal role in revolutionizing agricultural disease detection systems, underscoring the importance of leveraging advanced deep learning architectures for precise crop health management. The study's outcomes highlight the potential impact of employing sophisticated models in the agriculture sector for effective disease monitoring and crop preservation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging Effective Language and Speaker Conditioning In Indic TTS for Limmits 2024 Challenge

Leveraging Effective Language and Speaker Conditioning In In...

引用

Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yejin Jeon Young Jae Kim Gary Geunbae Lee Graduate School of AI POSTECH Department of Computer Science and Engineering POSTECH

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

In this paper, we explain the model that was developed by the NLP_POSTECH team for the LIMMITS 2024 Grand Challenge. Among the three tracks, we focus on Track 1, which necessitates the creation of a few-shot text-to-speech (TTS) system that generates natural speech across diverse languages. Towards this end, to realize multi-lingual capability, we incorporate a learnable language embedding. In addition, for precise imitation of target speaker voices, we leverage an inductive speaker bias conditioning methodology. Despite the simplicity of our strategy, our model is able to demonstrate remarkable efficacy in the generation of natural speech and preservation of high speaker fidelity for both mono and cross-lingual settings.

关键词： Target tracking Conferences Natural languages Signal processing Data models Acoustics Text to speech

来源：评论

学校读者我要写书评

暂无评论

Exploring Diabetic Retinopathy Diagnosis: Insights from a Comprehensive Literature Review and Initial Fundus Image Analysis

Exploring Diabetic Retinopathy Diagnosis: Insights from a Co...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar Reddy, K Uday Malleswari, M. Kumar Reddy, P Phanindra Karimullah, Shaik Shaik, Fahimuddin Hariobulesu, Peddapullaiahgari Annamacharya University Artificial Intelligence and Data Science Department Rajampet India Sri Annamacharya Institute of Technology and Sciences Computer Science and Engineering Department India Annamacharya University AI&DS Department Rajampet India Annamacharya University ECE Department Rajampet India

ISBN: (纸本)9798350378092

This article looks at recent advances in diabetic retinopathy (DR) detection with retinal fundus and optical coherence tomography angiography (OCTA) pictures. Key studies have investigated a variety of methods, including combining Multiscale Retinex with Chromaticity Preservation (MSRCP) and Bi-LSTM networks for high accuracy, integrating the Internet of Things (IoT) with deep learning for increased sensitivity, and using Multi-Stream Deep Neural Networks to classify DR severity. Innovations like the PMNPDR system for microaneurysm detection and deep learning ensembles for OCT images have also performed well. Despite these gains, issues like as data transfer latency, model complexity, and reliance on tiny datasets remain. Overall, while current models show great potential, they require further modification and validation to assure their efficacy and applicability in clinical situations. © 2024 IEEE.

关键词： Optical coherence tomography

来源：评论

学校读者我要写书评

暂无评论

An Efficient Novel Approach for Iris Recognition and Segmentation Based on the Utilization of Deep Learning

An Efficient Novel Approach for Iris Recognition and Segment...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Pasha, Syed Nawaz Anitha, D. Anbunathan, R. Gobinath, C. Madhura, G.K. Sr University School of Computer Science & Artificial Intelligence Warangal India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Sa Engineering College Department of Mathematics Chennai India Reva University School of Computing and Information Technology Bangalore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Iris recognition stands out as one of the most stable and accurate biometric identification systems such as facial, voice, fingerprint, handwriting, and iris recognition itself. Deep Learning (DL) plays a pivotal role in enhancing security by leveraging Convolutional Neural Networks (CNNs) for iris recognition. The acquisition of iris images and real-time processing have become focal points demanding heightened attention in the realm of DL. Iris recognition is prized for its high level of protection, making it challenging to reconstruct iris data easily from the database. The CASIA-Iris Dataset serves as a benchmark to verify the uniqueness of iris patterns and evaluate the performance of iris recognition. However, due to limitations in iris image access, the application of CNNs in the segmentation approach remains critical for iris localization and pupil boundary identification, ensuring both efficiency and specificity in iris images. The results obtained from this study reveal that the proposed CNN, utilizing DL models, achieves an impressive accuracy of 99.89% on the CASIA-Iris dataset, better performance of other existing models, such as U-Net and VGG-Net, and ensuring accurate classification. © 2023 IEEE.

关键词： Contrast-Limited Adaptive Histogram Equalization Convolution Neural Network Deep Learning Iris Recognition and Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

Comment Sentiment Analysis Using Bidirectional Encoder Representations from Transformers

SSRN

引用

SSRN 2024年

作者： Mittal, Harshit Garg, Neeraj Computer Science Department Maharaja Agrasen Institute of Technology Delhi India AI/ML Department Maharaja Agrasen Institute of Technology Delhi India

This research paper unravels the power of Bidirectional Encoder Representations from Transformers (BERT), a cutting-edge language representation model, in the realm of comment sentiment analysis. By focusing on two main aspects - the working principles of BERT and the methodology of comment sentiment analysis - the paper aims to captivate readers with an engaging exploration of this exciting field. In this paper, we delve into the remarkable workings of BERT, showcasing its exceptional ability to grasp context, semantics, and sentiment in natural language text. Through a comprehensive examination of its underlying architecture, intricate pre-training process, and ingenious fine-tuning techniques, readers will gain a profound understanding of how BERT transforms raw language into rich, contextualized representations. Building upon this foundation, the paper unveils the methodology employed in our comment sentiment analysis project. Here, we take readers on an enthralling journey through the data collection and pre-processing phases, carefully selecting appropriate evaluation metrics, and ingeniously designing a sentiment classification framework. Along the way, we address the challenges of word sense disambiguation and tackle the nuances of neutral sentiments. By venturing into the uncharted territory of comment sentiment analysis using BERT, this research paper becomes more than just an invaluable resource for practitioners and researchers. It serves as a seductive invitation to unlock the hidden treasures within user opinions and sentiments, enabling us to leverage this invaluable wealth of information across various domains. With insightful findings and actionable recommendations, this paper fuels the pursuit of advanced sentiment analysis techniques, propelling the field towards enhanced comprehension and optimal utilization of user sentiments in the ever-evolving digital landscape. © 2024, The Authors. All rights reserved.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Exploratory Diffusion Policy for Unsupervised Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Ying, Chengyang Chen, Huayu Zhou, Xinning Hao, Zhongkai Su, Hang Zhu, Jun Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University China

Unsupervised reinforcement learning (RL) aims to pre-train agents by exploring states or skills in reward-free environments, facilitating the adaptation to downstream tasks. However, existing methods often overlook the fitting ability of pre-trained policies and struggle to handle the heterogeneous pre-training data, which are crucial for achieving efficient exploration and fast fine-tuning. To address this gap, we propose Exploratory Diffusion Policy (EDP), which leverages the strong expressive ability of diffusion models to fit the explored data, both boosting exploration and obtaining an efficient initialization for downstream tasks. Specifically, we estimate the distribution of collected data in the replay buffer with the diffusion policy and propose a score intrinsic reward, encouraging the agent to explore unseen states. For fine-tuning the pre-trained diffusion policy on downstream tasks, we provide both theoretical analyses and practical algorithms, including an alternating method of Q function optimization and diffusion policy distillation. Extensive experiments demonstrate the effectiveness of EDP in efficient exploration during pre-training and fast adaptation during fine-tuning. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Smart Contracts for Supply Chain Management: A Strategic Analysis 3

Blockchain-Based Smart Contracts for Supply Chain Management...

引用

3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Santosh, Kathari Dash, Chinmaya Aarif, Mohd Kumar, Yadavalli Suresh Verma, Mrinal Kiranbala, B. Cmr Institute of Technology Department of Mba Bengaluru India St. Claret College Department of Computer Science Bangalore India Global Research Network Noida India Aditya College of Engineering Department of Computer Science & Engineering Andhra Pradesh India Soil School of Business Design Manesar Gurgaon India K.Ramakrishnan College of Engineering Department of Ai and Ds Trichy India

ISBN: (纸本)9798350361186

The supply chain management sector strategic examination of the blockchain relies smart contract technology employs a comprehensive approach, drawing insights from reputable secondary sources to explore performance metrics, transactional flow optimization, consensus mechanisms, and security considerations. The study reveals nuanced performance variations in average throughput and transaction duration, emphasizing the scalability and stability of the blockchain network. The depicted transactional flow showcases enhanced communication and transparency, aligning with strategic goals for supply chain optimization. The introduction of a Proof of Authority (PoA) consensus algorithm underscores the importance of efficient consensus mechanisms for resilience against attacks. By integrating PoA, the proposed blockchain-based smart contracts contribute to a strategic and reliable supply chain management system, enhancing transparency and mitigating potential risks. A robust security analysis demonstrates moderate fault tolerance, high defense mechanisms, and effective security recovery capabilities, aligning with the overarching goals of confidentiality, integrity, and availability of critical supply chain data. Ethereum's Proof of Stake (PoS) mechanism is referenced as an exemplar for improving scalability and security. The comparative analysis highlights the proposed CMBC (Consensus Mechanism with Blockchain) as a standout solution, offering middle-ground high attack diversity, fault tolerance, recovery, and a high attack cost, robust security. CMBC outperforms alternatives, demonstrating superiority in enhancing security and resilience against potential adversarial threats. The proposed CMBC demonstrates notable strengths across key performance indices when compared to Fortified-Chain and SaYoPillow. The proposed method is implemented using python software. The effective standard deviation values, ranging from 9.58 to 25.13, further underscore the system's stability and re

关键词： Smart contract

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：