检索结果-内蒙古大学图书馆

2024 IEEE Conference on Engineering Informatics, ICEI 2024

作者： Naik, Aravind Bhat, Sandeep Naik, Daya Srinivas Institute of Technology Department of Computer Science and Engg Mangaluru India Srinivas Institute of Technology Department of Artificial Intelligence and Machine Learning Mangaluru India

ISBN: (纸本)9798331505776

Open wells are a major hazard in India's rural and urban settings, especially for youngsters and unwary adults. The absence of safety barriers surrounding wells frequently causes mishaps, which can occasionally be fatal. This study suggests a brand-new, reasonably priced, vision-based solution that uses a CCTV camera to identify people approaching a well and sound an alarm when they get too close. The system draws boundary boxes and determines the distance between the well and the person using object detection methods to precisely detect and classify both. Accidents may be avoided if an automated alert is set off if the distance between the person and the well drops below a certain level. This method is trained using custom YOLOv8 models for well detection and a pre trained model for person detection. This paper emphasizes the system's importance in the context of countries like India, where safety infrastructure around wells is often insufficient, and accidental falls are a common hazard. © 2024 IEEE.

关键词： Accidents

来源：评论

学校读者我要写书评

暂无评论

WHOLE-SONG HIERARCHICAL GENERATION OF SYMBOLIC MUSIC USING CASCADED DIFFUSION MODELS 12

WHOLE-SONG HIERARCHICAL GENERATION OF SYMBOLIC MUSIC USING C...

引用

12th International Conference on learning Representations, ICLR 2024

作者： Wang, Ziyu Min, Lejun Xia, Gus Computer Science Department NYU Shanghai China Machine Learning Department MBZUAI United Arab Emirates

Recent deep music generation studies have put much emphasis on long-term generation with structures. However, we are yet to see high-quality, well-structured whole-song generation. In this paper, we make the first attempt to model a full music piece under the realization of compositional hierarchy. With a focus on symbolic representations of pop songs, we define a hierarchical language, in which each level of hierarchy focuses on the semantics and context dependency at a certain music scope. The high-level languages reveal whole-song form, phrase, and cadence, whereas the low-level languages focus on notes, chords, and their local patterns. A cascaded diffusion model is trained to model the hierarchical language, where each level is conditioned on its upper levels. Experiments and analysis show that our model is capable of generating full-piece music with recognizable global verse-chorus structure and cadences, and the music quality is higher than the baselines. Additionally, we show that the proposed model is controllable in a flexible way. By sampling from the interpretable hierarchical languages or adjusting pre-trained external representations, users can control the music flow via various features such as phrase harmonic structures, rhythmic patterns, and accompaniment texture. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

Story Generation Using GAN, RNN and LSTM 13th

Story Generation Using GAN, RNN and LSTM

引用

13th International Advanced Computing Conference, IACC 2023

作者： Shrouti, Devika Bayas, Ameysingh Joshi, Nirgoon Misal, Mrinank Mahajan, Smita Gite, Shilpa Computer Science and Engineering Department Symbiosis Institute of Technology Pune India Artificial Intelligence and Machine Learning Department Symbiosis Institute of Technology Pune India

ISBN: (纸本)9783031566998

This paper explores the domain of story generation and presents a novel approach that uses Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks. The objective is to generate realistic and engaging stories for children. The traditional language models are proficient in maintaining grammatical consistency but often fail to establish long-term coherence. This study addresses and explores the individual performances and capabilities of 3 distinct text generation models based on GAN, RNN & LSTM respectively. The project employed three individual models and trained them on the same dataset and the evaluation was conducted using METEOR scores, accuracy, and loss metrics. To address this, our study introduces the use of GANs to enhance the quality of synthetic text. The MaskGAN model gave the highest accuracy and decent output on the trained dataset followed by the RNN and LSTM models. This paper is a significant step forward for story generation, highlights the unique contributions of GANs, RNNs, and LSTMs, amplifies the consistency and quality of independently generated narratives, and provides a foundation for future comparative analyses. MaskGAN achieved the highest accuracy and excelled in generating realistic and high-quality narratives followed by RNNs which exhibited decent accuracy but faced challenges with longer narratives, while the beam search-enhanced LSTM improved narrative quality offering a promising solution for coherent story generation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Optimizing Crop Yields with Multilayer Perceptrons: A Data-Driven Approach 8

Optimizing Crop Yields with Multilayer Perceptrons: A Data-D...

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Nivetha, N. Usharani, S. Department of Computer Science and Engineering Villupuram India Department of Artificial Intelligence and Machine Learning Villupuram India

ISBN: (纸本)9798350386578

Precision agriculture has become a major change in crop farming. It utilises cutting-edge technologies to maximise field-level management. Precision agriculture has completely transformed crop production by leveraging the latest developments to maximize field-level management. Predicting crop yields with accuracy helps farmers reduce their environmental impact, increase productivity, and make well-informed decisions. Accurate and timely insights are frequently lacking in traditional agricultural yield prediction approaches. The study offers a deep learning method for precisely predicting agricultural yields. Accurate crop yield forecasts assist farmers in minimizing their negative environmental effects, boosting productivity, and making educated choices. However, there are many obstacles because conventional agricultural yield prediction methods frequently need more timely and precise insights. Despite their success, several challenges still exist. These include handling heterogeneous data, dealing with missing values, and the complexity of capturing non-linear relationships in the data. To determine whether decision trees or Multi-Layer Perceptrons (MLP) are ideal in crop yield prediction, these models are compared with each other. Multi-layer perceptrons (MLP) are prominent among these techniques. Even though the MLP model was more accurate, decision trees also are relevant to the prediction process. This means have the capability of understanding multi-layer intra-data intricacies through their structure whereas decision trees may overfit on noisy data or grow too deep hence leading to many splits also known as being bushy unless they are pruned to reduce this bushiness. The study suggests a novel method for predicting agricultural productivity using a machine learning model Decision Tree and Multi-Layer Perceptrons (MLP). A web interface is also created to enable smooth communication with the prediction model. Through the usage of this interface, farmers and agr

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Decoding Bias in #Climatechange Tweets: A Neural Language Style Transfer Approach

Decoding Bias in #Climatechange Tweets: A Neural Language St...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： George, Elizabeth Leah Parthasarathy, Subashini Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India Avinashilingam Institute for Home Science and Higher Education for Women Centre for Machine Learning and Intelligence Department of Computer Science Coimbatore India

ISBN: (纸本)9798331517953

In response to the increase of biased and misleading opinions on social media regarding climate change, we present the Neural Language Style Transfer Bias (NLST Bias) framework - an AI-driven solution to identify and mitigate bias in climate-related tweets. Our approach analyses a curated dataset of climate change tweets collected from Twitter, leveraging spaCy for text pre-processing and BERT-based sentence embeddings to extract semantic features. Evaluation metrics with classifier models showed significant improvements, with CNN-LSTM achieving the highest F1 score of 78%. Most classifiers show marked improvements across accuracy, precision, recall, and F1 score with NLST, highlighting its effectiveness on this dataset. NLST Bias employs DistilBERT to classify tweets as biased or unbiased, with the Named Entity Recognition (NER) model trained on RobERTa transformers to identify word-level bias indicators. The framework's core component is a transformer-based style transfer model (e.g., GPT-2) that modifies the stylistic tone of biased text while preserving its semantic content. This transfer uses a style-specific decoder within the transformer architecture, ensuring the generated text maintains grammatical accuracy and contextual relevance. The resulting output exhibits tone, phrasing, and word choice adjustments to foster a more neutral style. Results indicate a significant reduction in bias without compromising text fluency or naturalness, verified through statistical metrics and human evaluations. Furthermore, Explainable AI techniques are integrated to enhance transparency in model decisions. Our findings underscore NLST Bias's efficacy in countering climate misinformation and promoting balanced discussions on social media, highlighting its potential as a tool for responsible AI in digital discourse. © 2024 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

learning Low-Rank Tensor Approximation for GPU-Based Tractography 15th

Learning Low-Rank Tensor Approximation for GPU-Based Tracto...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Gruen, Johannes Schultz, Thomas B-IT and Institute for Computer Science University of Bonn Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany

ISBN: (纸本)9783031869198

Fast algorithms for diffusion MRI tractography are required due to the increasing amounts of diffusion MRI data, and the increasing popularity of whole-brain tractography. Representing fiber orientation density functions (fODFs) as higher-order tensors and extracting main fiber directions from them via low-rank tensor approximation is a state-of-the-art variant of streamline tractography, but involves a computationally costly nonlinear optimization in each integration step. In this work, we demonstrate that unsupervised training of a neural network to map fODF coefficients to the corresponding fiber contributions directly is not only faster, but also achieves lower approximation residuals. This is due to the fact that training the network amounts to a joint optimization of all fiber contributions, while traditional algorithms follow an alternating optimization strategy. However, we observe that the traditional approach implicitly favors sparse solutions, and that a corresponding explicit regularization is required to obtain useful results with a joint optimization strategy. Building on those insights, we create the first GPU-based implementation of low-rank tractography, which achieves a speedup by a factor of 68, compared to traditional tractography on a single CPU core, while at the same time improving the median dice. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Empirical techniques for effort estimation in designing effective ML models

引用

International Journal of computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

machine learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the Standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

来源：评论

学校读者我要写书评

暂无评论

Advanced Energy Consumption Forecasting in Smart Grids Using Quantum Boltzmann machines 2

Advanced Energy Consumption Forecasting in Smart Grids Using...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Velayutham, P. Balaji, V.S. Krissh Shankaran, N. Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

Smart grid systems are seen as the next big thing to energy distribution and management with more efficiency, stability, and sustainability in power distribution and energy management systems. It is challenging to predict the amount of energy consumption in smart grid system because of its randomness resulting from various factors such as consumer's behavior, weather conditions. This is mainly because traditional forecasting techniques are usually inadequate in capturing these complexities and as a result resources are poorly allocated and organizational processes become inefficient. The work provides the energy consumption forecasting in smart grids through the application of Quantum Boltzmann machine(QBM) in the smart grid utilizing historical data. Some of the variables incorporated in the data are reaction time, power balance, and price elasticity coefficients customized through smart meters. Once raw data is preprocessed, QBMs are developed to establish an empirical probability model that will predict actual future energy utilization characteristics. The QBM is tested against the actual consumption data and focuses on the visualization of energy profile and identification of points like local extremes. © 2024 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions

Advancing Social Intelligence in AI Agents: Technical Challe...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mathur, Leena Liang, Paul Pu Morency, Louis-Philippe Language Technologies Institute School of Computer Science Carnegie Mellon University United States Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9798891761643

Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial).Progress towards Social-AI has accelerated in the past decade across several computing communities, including natural language processing, machine learning, robotics, human-machine interaction, computer vision, and *** language processing, in particular, has been prominent in Social-AI research, as language plays a key role in constructing the social *** this position paper, we identify a set of underlying technical challenges and open questions for researchers across computing communities to advance *** anchor our discussion in the context of social intelligence concepts and prior progress in Social-AI research. © 2024 Association for Computational Linguistics.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Privacy Preserving Data Imputation via Multi-party Computation for Medical Applications

Privacy Preserving Data Imputation via Multi-party Computati...

引用

2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Jentsch, Julia Ünal, Ali Burak Mağara, Şeyma Selcan Akgün, Mete Department of Computer Science Medical Data Privacy and Privacy Preserving Machine Learning Tübingen Germany

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350548

Handling missing data is crucial in machine learning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are simple but inadequate, the literature offers more sophisticated and effective methods, thereby improving sample size and accuracy. However, these methods require accessing the whole dataset, which contradicts the privacy regulations when the data is distributed among multiple sources. Especially in the medical and healthcare domain, such access reveals sensitive information about patients. This study addresses privacy-preserving imputation methods for sensitive data using secure multi-party computation, enabling secure computations without revealing any party’s sensitive information. In this study, we realized the mean, median, regression, and kNN imputation methods in a privacy-preserving way. We specifically target the medical and healthcare domains considering the significance of protection of the patient data, showcasing our methods on a diabetes dataset. Experiments on the diabetes dataset validated the correctness of our privacy-preserving imputation methods, yielding the largest error around 3 × 10-3, closely matching plaintext methods. We also analyzed the scalability of our methods to varying numbers of samples, showing their applicability to real-world healthcare problems. Our analysis demonstrated that all our methods scale linearly with the number of samples. Except for kNN, the runtime of all our methods indicates that they can be utilized for large datasets. © 2024 IEEE.

关键词： Differential privacy

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：