Applied AI letters最新文献_第8页

Fingerprinting-Based Indoor Localization in a 3 × 3 Meter Grid Using OFDM Signals at Sub-6 GHz 基于指纹识别的3 × 3米栅格室内定位，使用sub - 6ghz OFDM信号

Applied AI letters Pub Date : 2024-11-11 DOI: 10.1002/ail2.104

Jaspreet Kaur, Kang Tan, Muhammad Z. Khan, Olaoluwa R. Popoola, Muhammad A. Imran, Qammer H. Abbasi, Hasan T. Abbas

引用次数: 0

Towards Predictive Pollution Control Through Traffic Flux Forecasting With Deep Learning: A Case Study in the City of Valencia 基于深度学习的交通流量预测对污染的预测控制——以巴伦西亚市为例

Applied AI letters Pub Date : 2024-10-29 DOI: 10.1002/ail2.106

Miguel G. Folgado, Verónica Sanz, Johannes Hirn, Edgar Lorenzo-Sáez, Javier F. Urchueguía

{"title":"Towards Predictive Pollution Control Through Traffic Flux Forecasting With Deep Learning: A Case Study in the City of Valencia","authors":"Miguel G. Folgado, Verónica Sanz, Johannes Hirn, Edgar Lorenzo-Sáez, Javier F. Urchueguía","doi":"10.1002/ail2.106","DOIUrl":"https://doi.org/10.1002/ail2.106","url":null,"abstract":"Traffic congestion represents a significant urban challenge, with notable implications for public health and environmental well-being. Consequently, urban decision-makers prioritize the mitigation of congestion. This study delves into the efficacy of harnessing extensive data on urban traffic dynamics, coupled with comprehensive knowledge of road networks, to enable Artificial Intelligence (AI) in forecasting traffic flux well in advance. Such forecasts hold promise for informing emission reduction measures, particularly those aligned with Low Emission Zone policies. The investigation centers on Valencia, leveraging its robust traffic sensor infrastructure, one of the most densely deployed worldwide, encompassing approximately 3500 sensors strategically positioned across the city. Employing historical data spanning 2016 and 2017, we undertake the task of training and characterizing a Long Short-Term Memory (LSTM) Neural Network for the prediction of temporal traffic patterns. Our findings demonstrate the LSTM's efficacy in real-time forecasting of traffic flow evolution, facilitated by its ability to discern salient patterns within the dataset.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mechanical Ventilator Settings Estimation From an AI Model 基于AI模型的机械呼吸机设置估计

Applied AI letters Pub Date : 2024-10-24 DOI: 10.1002/ail2.103

Ali Moghadam, Ramana M. Pidaparti

引用次数: 0

Deep Learning-Driven Modeling of Dynamic Acoustic Sensing in Biomimetic Soft-Robotic Pinnae 仿生软体机器人Pinnae动态声传感的深度学习驱动建模

Applied AI letters Pub Date : 2024-10-22 DOI: 10.1002/ail2.107

Sounak Chakrabarti, Rolf Müller

{"title":"Deep Learning-Driven Modeling of Dynamic Acoustic Sensing in Biomimetic Soft-Robotic Pinnae","authors":"Sounak Chakrabarti, Rolf Müller","doi":"10.1002/ail2.107","DOIUrl":"https://doi.org/10.1002/ail2.107","url":null,"abstract":"Biological function often depends on complex mechanisms of a dynamic, time-variant nature. An example is certain bat species (horseshoe bats—Rhinolophidae) that use intricate pinna musculatures to execute a variety of pinna deformations. While prior work has indicated the potential significance of these motions for sensory information encoding, it remains unclear how the complex time-variant pinna geometries could be controlled to enhance sensory performance. To address this issue, this work has investigated deep neural network models as digital twins for biomimetic pinnae. The networks were trained to predict the acoustic impacts of the deformed pinna geometries. A total of three network architectures have been evaluated for this purpose using physical numerical simulations (boundary element method) as ground truth. The networks predicted the acoustic beampattern function from pinna shape or even directly from the states of actuators that were used to deform the pinna shapes in simulation. Inserting prior knowledge in the form of beam-shaped basis functions did not improve network performance. The ability of the networks to produce beampattern predictions with low computational effort (in about three milliseconds each) should lend itself readily to supporting learning methods such as deep reinforcement learning that require many such functional evaluations.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Fuzzy Deep Belief Network Extreme Learning Machine Framework With Hyperbolic Secant Activation Function for Robust Semi-Supervised Sentiment Classification 基于双曲割线激活函数的混合模糊深度信念网络极限学习机框架用于鲁棒半监督情感分类

Applied AI letters Pub Date : 2024-10-13 DOI: 10.1002/ail2.102

Maryam Mozafari, Mohammad Hossein Moattar

引用次数: 0

An Application of 3D Vision Transformers and Explainable AI in Prosthetic Dentistry 三维视觉变形与可解释人工智能在牙科修复中的应用

Applied AI letters Pub Date : 2024-09-03 DOI: 10.1002/ail2.101

Faisal Ahmed Sifat, Md Sahadul Hasan Arian, Saif Ahmed, Taseef Hasan Farook, Nabeel Mohammed, James Dudley

{"title":"An Application of 3D Vision Transformers and Explainable AI in Prosthetic Dentistry","authors":"Faisal Ahmed Sifat, Md Sahadul Hasan Arian, Saif Ahmed, Taseef Hasan Farook, Nabeel Mohammed, James Dudley","doi":"10.1002/ail2.101","DOIUrl":"https://doi.org/10.1002/ail2.101","url":null,"abstract":"To create and validate a transformer-based deep neural network architecture for classifying 3D scans of teeth for computer-assisted manufacturing and dental prosthetic rehabilitation surpassing previously reported validation accuracies obtained with convolutional neural networks (CNNs). Voxel-based representation and encoding input data in a high-dimensional space forms of preprocessing were investigated using 34 3D models of teeth obtained from intraoral scanning. Independent CNNs and vision transformers (ViTs), and their combination (CNN and ViT hybrid model) were implemented to classify the 3D scans directly from standard tessellation language (.stl) files and an Explainable AI (ExAI) model was generated to qualitatively explore the deterministic patterns that influenced the outcomes of the automation process. The results demonstrate that the CNN and ViT hybrid model architecture surpasses conventional supervised CNN, achieving a consistent validation accuracy of 90% through three-fold cross-validation. This process validated our initial findings, where each instance had the opportunity to be part of the validation set, ensuring it remained unseen during training. Furthermore, employing high-dimensional encoding of input data solely with 3DCNN yields a validation accuracy of 80%. When voxel data preprocessing is utilized, ViT outperforms CNN, achieving validation accuracies of 80% and 50%, respectively. The study also highlighted the saliency map's ability to identify areas of tooth cavity preparation of restorative importance, that can theoretically enable more accurate 3D printed prosthetic outputs. The investigation introduced a CNN and ViT hybrid model for classification of 3D tooth models in digital dentistry, and it was the first to employ ExAI in the efforts to automate the process of dental computer-assisted manufacturing.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

History Matching Reservoir Models With Many Objective Bayesian Optimization 用多目标贝叶斯优化法匹配历史储层模型

Applied AI letters Pub Date : 2024-08-27 DOI: 10.1002/ail2.99

Steven Samoil, Clyde Fare, Kirk E. Jordan, Zhangxin Chen

{"title":"History Matching Reservoir Models With Many Objective Bayesian Optimization","authors":"Steven Samoil, Clyde Fare, Kirk E. Jordan, Zhangxin Chen","doi":"10.1002/ail2.99","DOIUrl":"https://doi.org/10.1002/ail2.99","url":null,"abstract":"Reservoir models for predicting subsurface fluid and rock behaviors can now include upwards of billions (and potentially trillions) of grid cells and are pushing the limits of computational resources. History matching, where models are updated to match existing historical data more closely, is conducted to reduce the number of simulation runs and is one of the primary time-consuming tasks. As models get larger the number of parameters to match increases, and the number of objective functions increases, and traditional methods start to reach their limitations. To solve this, we propose the use of Bayesian optimization (BO) in a hybrid cloud framework. BO iteratively searches for an optimal solution in the simulations campaign through the refinement of a set of priors initialized with a set of simulation results. The current simulation platform implements grid management and a suite of linear solvers to perform the simulation on large scale distributed-memory systems. Our early results using the hybrid cloud implementation shown here are encouraging on tasks requiring over 100 objective functions, and we propose integrating BO as a built-in module to efficiently iterate to find an optimal history match of production data in a single package platform. This paper reports on the development of the hybrid cloud BO based history matching framework and the initial results of the application to reservoir history matching.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.99","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Developing and Deploying End-to-End Machine Learning Systems for Social Impact: A Rubric and Practical Artificial Intelligence Case Studies From African Contexts 开发和部署端到端机器学习系统以产生社会影响：来自非洲的评分标准和实用人工智能案例研究

Applied AI letters Pub Date : 2024-08-27 DOI: 10.1002/ail2.100

Engineer Bainomugisha, Joyce Nakatumba-Nabende

{"title":"Developing and Deploying End-to-End Machine Learning Systems for Social Impact: A Rubric and Practical Artificial Intelligence Case Studies From African Contexts","authors":"Engineer Bainomugisha, Joyce Nakatumba-Nabende","doi":"10.1002/ail2.100","DOIUrl":"https://doi.org/10.1002/ail2.100","url":null,"abstract":"Artificial intelligence (AI) and machine learning have demonstrated the potential to provide solutions to societal challenges, for example, automated crop diagnostics for smallholder farmers, environmental pollution modelling and prediction for cities and machine translation systems for languages that enable information access and communication for segments of the population who are unable to speak or write official languages, among others. Despite the potential of AI, the practical and technical issues related to its development and deployment in the African context are the least documented and understood. The development and deployment of AI for social impact systems in the developing world present new intricacies and requirements emanating from the unique technology and social ecosystems in these settings. This paper provides a rubric for developing and deploying AI systems for social impact with a focus on the African context. The rubric is derived from the analysis of a series of selected real-world case studies of AI applications in Africa. We assessed the selected AI case studies against the proposed rubric. The rubric and examples of AI applications presented in this paper are expected to contribute to the development and application of AI systems in other African contexts.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fine-Tuned Pretrained Transformer for Amharic News Headline Generation 针对阿姆哈拉语新闻标题生成的微调预训练变换器

Applied AI letters Pub Date : 2024-07-19 DOI: 10.1002/ail2.98

Mizanu Zelalem Degu, Million Meshesha

{"title":"Fine-Tuned Pretrained Transformer for Amharic News Headline Generation","authors":"Mizanu Zelalem Degu, Million Meshesha","doi":"10.1002/ail2.98","DOIUrl":"10.1002/ail2.98","url":null,"abstract":"Amharic is one of the under-resourced languages, making news headline generation particularly challenging due to the scarcity of high-quality linguistic datasets necessary for training effective natural language processing models. In this study, we fine-tuned the small check point of the T5v1.1 model (t5-small) to perform Amharic news headline generation with an Amharic dataset that is comprised of over 70k news articles along with their headline. Fine-tuning the model involves dataset collection from Amharic news websites, text cleaning, news article size optimization using the TF-IDF algorithm, and tokenization. In addition, a tokenizer model is developed using the byte pair encoding (BPE) algorithm prior to feeding the dataset for feature extraction and summarization. Metrics including Rouge-L, BLEU, and Meteor were used to evaluate the performance of the model and a score of 0.5, 0.24, and 0.71, respectively, was achieved on the test partition of the dataset that contains 7230 instances. The results were good relative to result of the t5 model without fine-tuning, which are 0.1, 0.03, and 0.14, respectively. A postprocessing technique using a rule-based approach was used for further improving summaries generated by the model. The addition of the postprocessing helped the system to achieve Rouge-L, BLEU, and Meteor scores of 0.72, 0.52, and 0.81, respectively. The result value is relatively better than the result achieved by the nonfine-tuned T5v1.1 model and the result of previous studies report on abstractive-based text summarization for Amharic language, which had a 0.27 Rouge-L score. This contributes a valuable insight for practical application and further improvement of the model in the future by increasing the article length, using more training data, using machine learning–based adaptive postprocessing techniques, and fine-tuning other available pretrained models for text summarization.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.98","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TL-GNN: Android Malware Detection Using Transfer Learning TL-GNN：利用迁移学习检测安卓恶意软件

Applied AI letters Pub Date : 2024-05-10 DOI: 10.1002/ail2.94

Ali Raza, Zahid Hussain Qaisar, Naeem Aslam, Muhammad Faheem, Muhammad Waqar Ashraf, Muhammad Naman Chaudhry

{"title":"TL-GNN: Android Malware Detection Using Transfer Learning","authors":"Ali Raza, Zahid Hussain Qaisar, Naeem Aslam, Muhammad Faheem, Muhammad Waqar Ashraf, Muhammad Naman Chaudhry","doi":"10.1002/ail2.94","DOIUrl":"10.1002/ail2.94","url":null,"abstract":"Malware growth has accelerated due to the widespread use of Android applications. Android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect Android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million Android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state-of-the-art Android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1-measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.94","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140990897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0