Applied AI letters最新文献_第7页

Building Text-to-Speech Models for Low-Resourced Languages From Crowdsourced Data 从众包数据中构建低资源语言的文本到语音模型

Applied AI letters Pub Date : 2025-04-28 DOI: 10.1002/ail2.117

Andrew Katumba, Sulaiman Kagumire, Joyce Nakatumba-Nabende, John Quinn, Sudi Murindanyi

{"title":"Building Text-to-Speech Models for Low-Resourced Languages From Crowdsourced Data","authors":"Andrew Katumba, Sulaiman Kagumire, Joyce Nakatumba-Nabende, John Quinn, Sudi Murindanyi","doi":"10.1002/ail2.117","DOIUrl":"https://doi.org/10.1002/ail2.117","url":null,"abstract":"Text-to-speech (TTS) models have expanded the scope of digital inclusivity by becoming a basis for assistive communication technologies for visually impaired people, facilitating language learning, and allowing for digital textual content consumption in audio form across various sectors. Despite these benefits, the full potential of TTS models is often not realized for the majority of low-resourced African languages because they have traditionally required large amounts of high-quality single-speaker recordings, which are financially costly and time-consuming to obtain. In this paper, we demonstrate that crowdsourced recordings can help overcome the lack of single-speaker data by compensating with data from other speakers of similar intonation (how the voice rises and falls in speech). We fine-tuned an English variational inference with adversarial learning for an end-to-end text-to-speech (VITS) model on over 10 h of speech from six female common voice (CV) speech data speakers for Luganda and Kiswahili. A human mean opinion score evaluation on 100 test sentences shows that the model trained on six speakers sounds more natural than the benchmark models trained on two speakers and a single speaker for both languages. In addition to careful data curation, this approach shows promise for advancing speech synthesis in the context of low-resourced African languages. Our final models for Luganda and Kiswahili are available at https://huggingface.co/marconilab/VITS-commonvoice-females.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879734","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

Empowering Public Health: AI-Powered Security Solutions for AI-Driven Challenges 增强公共卫生能力：人工智能驱动的安全解决方案，应对人工智能驱动的挑战

Applied AI letters Pub Date : 2025-04-20 DOI: 10.1002/ail2.119

Shahrukh Mushtaq, Qurra-Tul-Ain Hameeda

{"title":"Empowering Public Health: AI-Powered Security Solutions for AI-Driven Challenges","authors":"Shahrukh Mushtaq, Qurra-Tul-Ain Hameeda","doi":"10.1002/ail2.119","DOIUrl":"https://doi.org/10.1002/ail2.119","url":null,"abstract":"The escalating integration of artificial intelligence (AI) in public healthcare has raised a critical concern: the vast amounts of data being generated and utilised by AI language models are not adequately connected to privacy and security considerations. This study addresses the problem by exploring how AI language models can be used to enhance digital security in public healthcare while addressing challenges related to privacy and ethics. The research adopts a three-phase methodology: a bibliometric analysis of literature from the Scopus database to identify research trends, the generation of AI-driven scenarios refined by healthcare professionals and analysing AI responses using grounded theory. Two scenarios, focused on AI-driven clinical decision support systems and AI-powered telemedicine platforms, were validated by healthcare experts and tested using ChatGPT-4 and Gemini, two prominent AI models. While ChatGPT-4 produced contextually specific and diverse responses, Gemini's outputs were inconsistent and repetitive, highlighting discrepancies in their performance. These discrepancies are linked to the data used to train these models, implying that incorporating more specialised healthcare data could enhance performance; however, such data usage must align with ethical guidelines. The analysis found that human, organizational, and technological dimensions are critical for addressing security issues and promoting trust in healthcare systems utilising AI. While AI-generated scenarios are a valuable starting point, they must be mediated by medical professionals to ensure practical applicability. The findings provide a theoretical framework for handling AI-generated issues related to privacy and security concerns, which can be used for future empirical research to enhance digital security in public healthcare.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852631","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

Evaluating Adversarial Attacks Against Artificial Intelligence Systems in Application Deployments 评估应用程序部署中针对人工智能系统的对抗性攻击

Applied AI letters Pub Date : 2025-04-20 DOI: 10.1002/ail2.121

Lera Leonteva

{"title":"Evaluating Adversarial Attacks Against Artificial Intelligence Systems in Application Deployments","authors":"Lera Leonteva","doi":"10.1002/ail2.121","DOIUrl":"https://doi.org/10.1002/ail2.121","url":null,"abstract":"Businesses have invested billions into artificial intelligence (AI) applications, leading to a sharp rise in the number of AI applications being released to customers. Taking into account previous approaches to attacking machine learning models, we conduct a comparative analysis of adversarial attacks, contrasting large language models (LLMs) being deployed through application programming interfaces (APIs) with the same attacks against locally deployed models to evaluate the significance of security controls in production deployments on attack success in black-box environments. The article puts forward adversarial attacks that are adapted for remote model endpoints in order to create a threat model that can be used by security organizations to prioritize controls when deploying AI systems through APIs. This paper contributes: (1) a public repository of adversarial attacks adapted to handle remote models on https://github.com/l3ra/adversarial-ai, (2) benchmarking results of remote attacks comparing the effectiveness of attacks on remote models with those on local models, and (3) a framework for assessing future AI system deployment controls. By providing a practical framework for benchmarking the security of remote AI systems, this study contributes to the understanding of adversarial attacks in the context of natural language processing models deployed by production applications.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852874","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

Improving Machine Learning Workflows Using the “Normative-Descriptive-Prescriptive” Decision Framework 使用“规范-描述-规范”决策框架改进机器学习工作流程

Applied AI letters Pub Date : 2025-04-10 DOI: 10.1002/ail2.118

Naveen Gudigantala, Manaranjan Pradhan, Naga Vemprala

{"title":"Improving Machine Learning Workflows Using the “Normative-Descriptive-Prescriptive” Decision Framework","authors":"Naveen Gudigantala, Manaranjan Pradhan, Naga Vemprala","doi":"10.1002/ail2.118","DOIUrl":"https://doi.org/10.1002/ail2.118","url":null,"abstract":"To maximize business value from artificial intelligence and machine learning (ML) systems, understanding what leads to the effective development and deployment of ML systems is crucial. While prior research primarily focused on technical aspects, important issues related to improving decision-making across ML workflows have been overlooked. This paper introduces a “normative-descriptive-prescriptive” decision framework to address this gap. Normative guidelines outline best practices, descriptive dimensions describe actual decision-making, and prescriptive elements provide recommendations to bridge gaps. The three-step framework analyzes decision-making in key ML pipeline phases, identifying gaps and offering prescriptions for improved model building. Key descriptive findings include rushed problem-solving with convenient data, use of inaccurate success metrics, underestimation of downstream impacts, limited roles of subject matter experts, use of non-representative data samples, prioritization of prediction over explanation, lack of formal verification processes, and challenges in monitoring production models. The paper highlights biases, incentive issues, and systematic disconnects in decision-making across the ML pipeline as contributors to descriptive shortcomings. Practitioners can use the framework to pinpoint gaps, develop prescriptive interventions, and build higher quality, ethical, and legally compliant ML systems.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809827","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 Deep Learning Paradigm for Robust Feature Extraction and Classification for Cataracts 一种用于白内障鲁棒特征提取和分类的混合深度学习范式

Applied AI letters Pub Date : 2025-03-28 DOI: 10.1002/ail2.113

Akshay Bhuvaneswari Ramakrishnan, Mukunth Madavan, R. Manikandan, Amir H. Gandomi

{"title":"A Hybrid Deep Learning Paradigm for Robust Feature Extraction and Classification for Cataracts","authors":"Akshay Bhuvaneswari Ramakrishnan, Mukunth Madavan, R. Manikandan, Amir H. Gandomi","doi":"10.1002/ail2.113","DOIUrl":"https://doi.org/10.1002/ail2.113","url":null,"abstract":"The study suggests using a hybrid convolutional neural networks-support vector machines architecture to extract reliable characteristics from medical images and classify them as an ensemble using four different models. Manual processing of fundus images for the automated identification of ocular disorders is laborious, error-prone, and time-consuming. This necessitates computer-assisted technologies that can automatically identify different ocular illnesses from fundus images. The interpretation of the photos also plays a massive role in the diagnosis. Automating the diagnosing procedure reduces human mistakes and helps with early cataract detection. The oneDNN library available in the oneAPI Environment provided by Intel has been used to optimize all transfer learning models for better performance. The suggested approach is verified through a range of metrics in experiments using the openly accessible Ocular Disease Intelligent Recognition dataset. The MobileNet Model outperformed other transfer learning techniques with an accuracy of 0.9836.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717423","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

Technical Language Processing for Telecommunications Specifications 电信规范的技术语言处理

Applied AI letters Pub Date : 2025-03-24 DOI: 10.1002/ail2.111

Felipe A. Rodriguez Y

引用次数: 0

On Training Spiking Neural Networks by Means of a Novel Quantum Inspired Machine Learning Method 用一种新的量子启发机器学习方法训练尖峰神经网络

Applied AI letters Pub Date : 2025-03-06 DOI: 10.1002/ail2.114

Jean Michel Sellier, Alexandre Martini

引用次数: 0

Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques 基于机器学习技术的光伏板系统故障检测与分类

Applied AI letters Pub Date : 2025-03-05 DOI: 10.1002/ail2.115

Ghalia Nassreddine, Amal El Arid, Mohamad Nassereddine, Obada Al Khatib

{"title":"Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques","authors":"Ghalia Nassreddine, Amal El Arid, Mohamad Nassereddine, Obada Al Khatib","doi":"10.1002/ail2.115","DOIUrl":"https://doi.org/10.1002/ail2.115","url":null,"abstract":"The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is imperative to implement efficient methods for the accurate detection and diagnosis of PV system faults to prevent unexpected power disruptions. This paper introduces a potential strategy for fault identification and classification through the utilization of machine learning (ML) techniques. The study aimed to use ML algorithms to identify and classify normal operations, seven different types of faults, in two operational modes (maximum power point tracking and intermediate power point tracking). Four machine learning algorithms and ensemble methods (decision trees, k-nearest neighbors, random forest, and extreme gradient boosting) were employed, followed by hyperparameter tuning and cross-validation to determine the best configuration. The results indicated that ensemble methods, particularly XGBoost, excelled in detecting and classifying faults in PV systems, achieving a 99% accuracy rate after hyperparameter adjustments. The TPR values show a high sensitivity of 0.999, with some achieving a perfect score of 1.000. The FPR shows very low values, with the majority of metrics indicating FPRs at or close to 0%. This performance is crucial in the solar energy context, as failing to detect faults can result in significant energy loss and increased maintenance costs.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554350","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

Earnings Call Scripts Generation With Large Language Models Using Few-Shot Learning Prompt Engineering and Fine-Tuning Methods 使用少量学习提示工程和微调方法的大型语言模型生成收益电话脚本

Applied AI letters Pub Date : 2025-02-13 DOI: 10.1002/ail2.110

Sovik Kumar Nath, Yanyan Zhang, Jia Vivian Li

引用次数: 0

Scaling Laws for Discriminative Classification in Large Language Models 大型语言模型判别分类的标度规律

Applied AI letters Pub Date : 2025-02-13 DOI: 10.1002/ail2.109

Dean Wyatte, Fatemeh Tahmasbi, Ming Li, Thomas Markovich

{"title":"Scaling Laws for Discriminative Classification in Large Language Models","authors":"Dean Wyatte, Fatemeh Tahmasbi, Ming Li, Thomas Markovich","doi":"10.1002/ail2.109","DOIUrl":"https://doi.org/10.1002/ail2.109","url":null,"abstract":"Modern large language models (LLMs) represent a paradigm shift in what can plausibly be expected of machine learning models. The fact that LLMs can effectively generate sensible answers to a diverse range of queries suggests that they would be useful in customer support applications. While powerful, LLMs have been observed to be prone to hallucination which unfortunately makes their near-term use in customer support applications challenging. To address this issue, we present a system that allows us to use an LLM to augment our customer support advocates by re-framing the language modeling task as a discriminative classification task. In this framing, we seek to present the Top-K best template responses for a customer support advocate to use when responding to a customer. We present the result of both offline and online experiments where we observed offline gains and statistically significant online lifts for our experimental system. Along the way, we present observed scaling curves for validation loss and Top-K accuracy, resulted from model parameter ablation studies. We close by discussing the space of trade-offs with respect to model size, latency, and accuracy as well as and suggesting future applications to explore.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143404303","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