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共 3244 条记录，以下是431-440 订阅

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Exploring ASR Models in Low-Resource Languages: Use-Case the Macedonian Language 1

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4th international conference on Deep learning Theory and Applications (DeLTA)

作者： Bogdanoski, Konstantin Mishev, Kostadin Simjanoska, Monika Trajanov, Dimitar Ss Cyril & Methodius Univ Fac Comp Sci & Engn Rugjer Boshkovikj 16 Skopje North Macedonia iReason LLC 3rd Macedonian Brigade 37 Skopje North Macedonia

ISBN: (数字)9783031390593

ISBN: (纸本)9783031390586;9783031390593

We explore the use of Wav2Vec 2.0, NeMo, and ESPNet models trained on a dataset in Macedonian language for the development of Automatic Speech recognition (ASR) models for low-resource languages. The study aims to evaluate the performance of recent state-of-the-art models for speech recognition in low-resource languages, such as Macedonian, where there are limited resources available for training or fine-tuning. The paper presents a methodology used for data collection and preprocessing, as well as the details of the three architectures used in the study. The study evaluates the performance of each model using WER and CER metrics and provides a comparative analysis of the results. The findings of the research showed that Wav2Vec 2.0 outperformed the other models for the Macedonian language with a WER of 0.21, and CER of 0.09, however, NeMo and ESPNet models are still good candidates for creating ASR tools for low-resource languages such as Macedonian. The research presented provides insights into the effectiveness of different models for ASR in low-resource languages and highlights the potentials for using these models to develop ASR tools for other languages in the future. These findings have significant implications for the development of ASR tools for other low-resource languages in the future, and can potentially improve accessibility to speech recognition technology for individuals and communities who speak these languages.

关键词： machine learning Deep learning Automatic speech recognition Inference metric ASR pipeline Speech-to-text Wav2Vec2 ESPNet NeMo Natural language processing Macedonian language Low-resource languages Word error rate WER Character error rate CER

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Exploring machine learning Applications for Additive Manufacturing 3rd

Exploring Machine Learning Applications for Additive Manufac...

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

作者： Chouhan, Kshitij Gupta, Sumit Chaudhary, Vijay Gupta, Pallav Kumar, Sundeep Department of Mechanical Engineering ASET Amity University Uttar Pradesh Noida201313 India Management Studies Engineering College Ajmer Rajasthan305025 India

ISBN: (纸本)9789819918935

The use of data and algorithms to open the ways that human learn, which gradually increase the accuracy, is all considered under a branch of computer science and machine learning known as machine learning (ML). This is a rapidly growing field of data science majorly includes the use of trained algorithms and statistical methods to make classifications, which uncovering basic insights of data mining projects. AM is a relatively new technology that has the potential to drastically alter the future of digital manufacturing. In recent years, machine learning (ML) has attracted a lot of attention in AM because of its amazing performance in data tasks including classification, regression, and clustering. One of the primary justifications is this very fact. The complexity of production systems leads to a variety of problems with regard to design principles, standardization, and quality control. There is a growing need, however, for more sophisticated and superior products. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D printing

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FairdR: Ensuring Fairness in Mixed data of Fairly and Unfairly Treated Instances 1

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3rd CAAI international conference on Artificial Intelligence (CICAI)

作者： Liu, Yuxuan Kuang, Kun Zhang, Fengda Wu, Fei Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

Fairness has emerged as a crucial topic in data mining and machine learning applications, driven by ethical and legal considerations. It is important to recognize that not all samples are treated unfairly, resulting in data heterogeneity in fairmachine learning. Existing fair models primarily focus on achieving fairness across all heterogeneous data, yet they often fall short in ensuring fairness within specific subgroups, such as fairly treated and unfairly treated data. This paper presents a novel problem of training a fair model on heterogeneous data, aiming to achieve fairness for both types of data, with a particular emphasis on the unfairly treated subset. To address this challenge, an effective approach is to recover the distribution of both fairly and unfairly treated data. In this study, we adopt the Structural Causal Model (SCM) to model the heterogeneous data as a mixture of causal structures. Leveraging the perspective of SCM, we propose a framework called FairdR, which utilizes the Hirschfeld-Gebelein-R ' enyi (HGR) correlation to accurately recover the distribution of both fairly and unfairly treated data. FairdR can serve as a pre-processing method for other fair machine learning models, providing protection for the unfairly treated members. Through empirical evaluation on synthetic and real-world datasets, we demonstrate that the presence of heterogeneous data can introduce unfairness in previous algorithms. However, FairdR successfully recovers the distribution of fairly and unfairly treated data, thus improving the fairness of downstream algorithms when dealing with heterogeneous data.

关键词： Fairness Causality Heterogeneity

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From Wrist to World: Harnessing Wearable IMU Sensors and TinyML to Enable Smart Environment Interactions 3

From Wrist to World: Harnessing Wearable IMU Sensors and Tin...

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3rd international conference on AI-ML Systems, AIMLSystems 2023

作者： Saha, Bidyut Samanta, Riya Ghosh, Soumya Kanti Roy, Ram Babu Indian Institute Of Technology Kharagpur India

ISBN: (纸本)9798400716492

In this demonstration paper, we introduce a pioneering method to elevate smart environment interactions using IMU sensor data from smartwatches or fitness trackers. Central to our approach is the classification of hand gestures, facilitating a seamless synergy between human gestures and device interactions. Through the integration of Tiny machine learning (TinyML) techniques, our system expands wearables' functionalities beyond traditional fitness monitoring to include gesture-based controls. The process comprises data collection, model training, and subsequent deployment to the wearable's microcontroller (MCU) for on-device machine learning inference. Such on-device computing ensures data privacy, as no data is transmitted externally, and guarantees low latency for real-time responses. Preliminary evaluations showcase the system's effectiveness in tasks like controlling PowerPoint presentations, managing table lamp operations, and interpreting specific hand gestures for device commands. Notably, our system achieves commendable gesture recognition accuracy while optimizing resource usage, underscoring its vast potential in diverse smart environment settings. © 2023 Owner/Author.

关键词： machine learning

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Privacy-Preserving data mining Methods for Sensitive Information 3

Privacy-Preserving Data Mining Methods for Sensitive Informa...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Singh, Sukhvinder Jasim, L.H. Dari Dhote, Vijay Suseela, D. Venkatasubramanian, R. Pune India College of Technical Engineering The Islamic University Najaf Iraq Ies College of Technology Department of Computer Science & Engineering Bhopal India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science & Engineering India

ISBN: (纸本)9798350342338

This study explores innovative Privacy-Preserving data mining (PPDM) methods for handling sensitive information, addressing the critical balance between data utility and privacy. We identify prevalent issues arising from traditional data mining techniques and evaluate advanced PPDM approaches like differential privacy, homomorphic encryption, and federated learning using real-world datasets. Through a comprehensive analysis, our results indicate that hybrid models, which combine multiple PPDM techniques, are particularly effective in enhancing privacy without compromising data utility. The findings underscore the need for adaptable and robust PPDM strategies in the era of Big data, contributing valuable insights to both the academic and practical domains. Our research paves the path for future studies and developments in creating a harmonious ecosystem where data mining efficiency and privacy coexist, promoting an insightful yet private data-centric world. © 2023 IEEE.

关键词： Analytically Valuable patterns data Privacy data Repositories Homomorphic Encryption and Utility Privacy-Preserving data mining Sensitive Information

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3rd international conference on Artificial Intelligence and Knowledge Processing, AIKP 2023

3rd International Conference on Artificial Intelligence and ...

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3rd international conference on Artificial Intelligence and Knowledge Processing, AIKP 2023

ISBN: (纸本)9783031686160

The proceedings contain 28 papers. The special focus in this conference is on Artificial Intelligence and Knowledge Processing. The topics include: How Do Senior Secondary Level Students and Their Teachers Perceive Artificial Intelligence and Its Implementation? An Exploratory Study;anomalous Sound pattern Detection for machine Health Monitoring;performance Evaluation of Various machine learning Algorithms for Lung Cancer Prediction Using Demographic data;enhancing Stock Portfolio Optimization Based on a Hybrid Approach Using Artificial Bee Colony Optimization and Firefly Optimization;Enhancing Yarn Quality in the Cotton Industry: AI- Based Nep Detection for Improved Manufacturing Processes;breast Cancer Diagnosis from Ultrasonic Image and Histopathology Image Using Deep learning Approach;Advancing Time Series Forecasting: LSTM Networks with Multiple Attention Mechanisms;trajectory Tracking and Navigation Model for Autonomous Vehicles Using Reinforcement learning;quantum Graph Neural Networks Based Protein-Ligand Classification;comparative Analysis on Speech Driven Gesture Generation;enhancing Deep learning: Leveraging Skip Connections and Memory Efficiency;quality-Based Decision-Making Using Image Processing for Supply Chain Management;enhancing Endometrial Tumor Detection: Early Diagnosis with Advanced Vision Transformer Architecture;sweetSight: A Deep Convolutional Neural Network Approach for Automatic Categorization of Bengal Sweets;a Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth;automatic Conversion of Broadcasted Football Match Recordings to Its 2D Top View;measuring the Vehicle-in-Motion, Density and Allocation of Traffic Signal Using Transfer learning;Ensemble Model of VGG16, ResNet50, and DenseNet121 for Human Identification Through Gait Features;Performance of Sentiment Analysis APIs on Political Opinion Polling;summarization of Telugu Text Discourses;crowd-Sourced Supervisors for the Automatic Invigilation of Online

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Predicting Coronary Heart Disease Through machine learning Algorithms 3rd

Predicting Coronary Heart Disease Through Machine Learning A...

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3rd international conference on Innovations in Computing Research, ICR 2024

作者： Mariettou, Savina Koutsojannis, Constantinos Triantafillou, Vassilios Electrical and Computer Engineering Department University of Peloponnese Patras Greece Health Physics and Computational Intelligence Lab Physiotherapy Department School of Health Rehabilitation Sciences University of Patras Patras Greece Network Technologies and Digital Transformation Lab Electrical and Computer Engineering Department University of Peloponnese Patras Greece

ISBN: (纸本)9783031655210

machine learning has gained popularity in medical fields due to the increasing availability of health data and the improvement of machine learning algorithms. It can be used to create predictive models that diagnose diseases, predict disease progression, tailor treatment to individual patient needs, and improve the functioning of medical systems. The right use of data can have a positive impact on improving the quality of patient care, reducing healthcare costs, and creating tailored and effective medical approaches. The healthcare sector benefits greatly from the accurate interpretation of medical data as it contributes to the early prediction of diseases in patients. Early detection of a disease can help control the symptoms and provide the correct treatment. In our work, we analyzed actual measurements from the Framingham Heart Study and we created a medical database with 78001 records. Our ultimate goal is to develop an expert Artificial Intelligence system and an Artificial Neural Network that can predict the development of coronary heart disease by employing intelligent knowledge-mining algorithms. We have created two intelligent systems that predict the progression of coronary heart disease using machine learning algorithms such as Random Forest, Decision Trees and Neural Networks. In our experimental analysis, the Decision Tree and Neural Network achieved an accuracy of 90.08% and 84.56% respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neural networks

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Research on Traffic sign recognition based on CNN Deep learning Network 3

Research on Traffic sign recognition based on CNN Deep Learn...

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3rd international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Qiao, Xiaoxuan Beijing University of Technology Beijing China

With the continuous development of social economy, the road traffic system as one of the basis of national economy is also increasingly developed, which is followed by the traffic safety problems are increasingly prominent. Among them, driving violations such as not obeying traffic signs are the main cause of traffic accidents. In this research, Traffic Sign recognition (TSR) has become a hot research topic. The research status and application field of traffic sign detection and recognition technology are introduced and analyzed in detail, and the improvement of traffic sign database is introduced. After that, the application of CNN in image processing and traffic sign recognition is introduced, and the existing problems in traffic sign recognition are analyzed. The CNN deep learning network used in this paper can conduct training while identifying targets, effectively improve the recognition accuracy of traffic signs under complex natural conditions and reduce the time consumption. © 2023 Elsevier B.V.. All rights reserved.

关键词： Traffic signs

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A data-Driven Retrospective Analysis of Cyberbullying Using machine learning Algorithms 3

A Data-Driven Retrospective Analysis of Cyberbullying Using ...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Tripathi, Ashirwad R. Tyagi, Palki Sivaraman, Himani Kumar, Anuj Jain, Sachin Jigyasa University Department of Cs&it Dhradun India

ISBN: (纸本)9798331542375

Cyberbullying has become one of the main concerns in today's digital world, posing a great risk to everyone being online and effecting their mental health and their online safety. This research explores the application of machine learning using the machine learning algorithm to detect and reduce the cyberbullying behaviours. Here we utilize the data from the dataset of cyberbullying done by people online, preprocessing the data to clean and structure it for analysis. Here we use various machine learning models like Naive Bayes, Logistic Regression and SVM, which are then tested on dataset for accuracy, precision, recall, and overall effectiveness in detecting the harmful words and pattern. Our result highlight that which of the model that we used performed best contributing to detection of Cyberbullying online. Additionally, the research underscores the importance of integrating NPL(Natural Language Processing) for delicate text analysis and sentiment detection. The insights from this research aim to support the development of machine learning models for monitoring and addressing online harassment, making a safer digital environment. © 2025 IEEE.

关键词： Sentiment analysis

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Exploring the Potential Benefits of machine learning for Optimizing Real-Time data Analysis 3

Exploring the Potential Benefits of Machine Learning for Opt...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Ravindra Sakhare, Nitin N. Thangarasu, N. Senthilkumar, K.K. Wawage, Pawan S. Rengarajan, A. Vivekananda Global University Department of Civil Engineering Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms. © 2023 IEEE.

关键词： machine learning

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