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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

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Theoretical Foundations of Corpus-based Uzbek-English machine Translation 3

Theoretical Foundations of Corpus-based Uzbek-English Machin...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Zaynobiddin Qizi, Nilufar Abdurakhmonova Asliddin Qizi, Shamsiyeva Gulshoda National University of Uzbekistan The Department of Computational and Applied Linguistics Tashkent Uzbekistan National University of Uzbekistan Tashkent Uzbekistan

ISBN: (纸本)9798331516321

This article explores the linguistic, cultural, and historical significance of Turkic languages, with a particular focus on their evolution and impact on regional and global communication. The study delves into the roots of Turkic languages, tracing their influence across different civilizations and highlighting their role in shaping both spoken and written communication in Central Asia and beyond. It also examines the structural and grammatical intricacies of the language family, shedding light on how these unique features contribute to the richness and diversity of linguistic studies. By analyzing various aspects of language development, the article provides a comprehensive understanding of how Turkic languages have preserved their identity while adapting to modern trends. This research contributes to broader discussions in linguistics by offering insights into how language evolution and cultural exchange are intertwined. The findings emphasize the relevance of Turkic languages in the context of globalization, where the preservation of linguistic heritage remains crucial for maintaining cultural identity. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comparative Analysis of machine learning and Deep learning Algorithms for Skin Cancer Detection and Classification: Exploring Performance Across Multiple Parameters 3

A Comparative Analysis of Machine Learning and Deep Learning...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Goindi, Summi Thakur, Khushal Singh Kapoor, Divneet Chandigarh University Electronics and Communication Engineering Punjab India

ISBN: (纸本)9798350396157

Skin cancer is a substantial health concern, with a yearly incidence of 123,000 new cases and melanoma being the most prevalent variant. Identifying skin diseases is becoming increasingly challenging due to visual similarities. Automated skin cancer screening devices have the potential to enhance accuracy in identifying skin cancers. Advancements in technology and processing capacities have enabled the use of machine and deep learning algorithms for skin lesion examination. However, there is a pressing need for expedited diagnostic systems for life-threatening illnesses. This study provides an overview of various methodologies used in diagnosing skin cancer using image analysis and examines lessons and insights from previous studies. Future directions and scope will help address issues in autonomous detection of skin cancer. In a comparative analysis of various machine learning models, different performance metrics were observed. The Support Vector machines (SVM) yielded an accuracy of 75.23%, with an AUC of 90.22%. While effective in high-dimensional spaces, SVMs may face limitations with non-linear or noisy data. Random Forest, an ensemble method, demonstrated superior performance with an accuracy of 80.12% and an AUC of 93.22%, possibly due to its proficiency in handling non-linear data structures. Neural Networks, with their ability to model intricate relationships, achieved an accuracy of 81.34%. Multi-Layer Perceptron (MLP), a subset of neural networks, slightly varied in performance, indicating potential differences in architecture or training. Convolutional Neural Network (CNN), renowned for image recognition tasks, showcased an impressive accuracy of 93.23%, hinting at the dataset's spatial structures. The Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) emerged as the top performer with an accuracy of 94.344%, suggesting the dataset's potential temporal or sequential patterns. This comparative study underscores the importance of selecting the app

关键词： Multilayer neural networks

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Towards the Integration of Mixed Reality and machine learning for Older Adults Personalized Smart Home and Health Monitoring 3

Towards the Integration of Mixed Reality and Machine Learnin...

引用

3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Mahroo, Atieh Sacco, Marco Lecco Italy University of Milano-Bicocca Department of Informatics Systems and Communication Milan Italy

ISBN: (纸本)9798350378009

This contribution proposes a multi-modal system for older adults' health monitoring within the smart home framework that integrates Mixed Reality, a network of IoT sensors, wearable health sensors, and users' feedback to collect data and monitor older adults' health. It provides a comprehensive framework for older adults' health monitoring and safe exercise sessions to enhance their physical and cognitive well-being. The proposed system allows older adults to control the smart home features and benefit from assistive services via a mixed reality application. The system explores exploiting machine learning models to provide personalized physical and cognitive training routines to enhance older adults' quality of life, autonomy, and general well-being. It leverages historical data coming from wearable sensors, smart mixed reality glasses, IoT sensors, and users' feedback to predict the users' health status and activity level and suggest the proper exercise routine accordingly. © 2024 IEEE.

关键词： Electronic health record

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Enhancing Sign Language Classification through Reservoir Computing and Depth Camera Technology 3

Enhancing Sign Language Classification through Reservoir Com...

引用

3rd international conference on Image Processing and Robotics, ICIPRoB 2024

作者： Thongking, Witchuda Premachandra, Chinthaka Wiranata, Ardi Maeda, Shingo Shibaura Institute of Technology Department of Engineering and Science Tokyo Japan Shibaura Institute of Technology Department of Electronic Engineering Tokyo Japan University of Gadjah Mada Department of Mechanical and Industrial Engineering Yogyakarta Indonesia Tokyo Institute of Technology Department of Mechanical Engineering Tokyo Japan Research Group Tokyo Japan

ISBN: (纸本)9798350374766

The depth camera has emerged as an efficacious tool for facilitating interactions between individuals and machines. Its advantageous features, including a lightweight design, high durability, completeness, superior image quality, and versatility, have prompted researchers to explore its integration into advanced applications. Currently, the prevailing methods for classifying sign language exhibit certain limitations, such as suboptimal accuracy and inefficiency in gesture recognition technologies. In this study, we conduct a comprehensive examination of hand language characteristics rooted in American Sign Language (ASL), utilizing gesture-based interactions and augmenting sensing performance through preprocessing and the application of reservoir computing in the training model. We conduct a comprehensive evaluation of the gesture characteristics captured by the depth camera, elucidating a methodology encompassing preprocessing, machine learning, and the technical prediction of salient gesture features derived from conventional depth camera video footage. This endeavor aims to establish a robust signal model for enhanced understanding and representation of the analyzed gestures. Our machine-learning models adeptly predict American Sign Language signs. Correlation values derived from the depth camera data demonstrate a pronounced alignment with naturally occurring variations in metrics observed within specific gestures. Our methodologies for quantifying gestures with depth cameras contribute to heightened accessibility in quantitative motion analysis. The outcomes of the reservoir computing implementation exhibit the successful classification of three hand language signs, thereby manifesting high precision and recall. The robust performance of the classification is pivotal for practical applications and underscores the efficacy of our approach. © 2024 IEEE.

关键词： machine learning

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AI Techniques for Anomaly Detection in Video Surveillance Using Deep learning Method 3

AI Techniques for Anomaly Detection in Video Surveillance Us...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Jain, Swati Choudhary, Naveen College of Technology and Engineering Department of Computer Science and Engineering Udaipur India

ISBN: (纸本)9798350372120

Researchers are becoming more interested in the topic of anomaly detection in video surveillance, which has made substantial advancements. In many domains, including industrial safety, surveillance, and healthcare monitoring, video anomaly detection is essential. This work suggests a novel approach for applying deep learning algorithms to identify abnormalities in films. The complexity and variety of real-world data sometimes pose challenges for traditional approaches, which results in a lack of accuracy and resilience. Deep learning methods, on the other hand, have demonstrated incredible promise in identifying spatiotemporal patterns in video data. This research investigates and identifies outliers in video surveillance by examining different kinds of video *** application scenario, which depends on the types of data and anomalous types of behavior with different circumstances, is the emphasis of the various methodologies. Large datasets are used to train the Convolutional Neural Network architecture, which enhances its capacity to learn general visual representations. Our work gives futuristic performance in terms of efficiency and accuracy as demonstrated by the experimental findings. © 2024 IEEE.

关键词： Recurrent neural networks

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Power System Fault Treatment Using Deep learning 3

Power System Fault Treatment Using Deep Learning

引用

3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Sahoo, Anjan Kumar Samal, Sudhansu Kumar Panigrahi, Girija Sankar Centurion University of Technology & Management Department of Electrical Engineering Odisha Bhubaneswar India Iter Siksha 'o' Anusandhan Deemed to Be University Deptt. of Ee Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

A novel complete protection scheme is proposed in this article that detects and classifies the transmission line faults. Also it estimates the location of fault. The proposed method is implemented in Kundur two area 4 machine 11 bus system over a distance of 220 km. The data is extracted automatically by the LSTM (Long Short Term Memory) networks of deep learning model. Two different LSTM network modules like LSTM (FL) and LSTM (FD) are used for this purpose. LSTM (FD) module is responsible for accurate fault detection and classification. The LSTM (FL) module is responsible for the estimation of exact location of the fault. Both symmetrical and unsymmetrical fault cases are tested under both LSTM fault modules. The protection modules are also tested under different fault locations, fault resistances and noisy signals etc. The features taken into consideration for the operation of the protection modules are different bus voltages, bus currents, zero sequence voltage, zero sequence current etc. The advantages of this method are it works at very low sampling frequency and it also does not require any communication link. © 2024 IEEE.

关键词： Long short-term memory

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Analysis of machine learning Models for Wearable Devices Centric Human Activity Recognition 3

Analysis of Machine Learning Models for Wearable Devices Cen...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Zara, Namia Jalal Rifat, Rashiqur Rahman Tasnim, Jarin Baker, Md. Khan, Riasat North South University Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350375190

This work presents a comprehensive study on applying machine learning models for human activity recognition. The increasing number of wearable devices and the growing interest in health monitoring and intelligent environments have fueled the demand for robust and accurate systems that automatically recognize human activities. This study explores and compares the performance of different machine learning (ML) models in human activity recognition. This study intends to identify the most effective ML model to classify and predict human activities accurately. In this study, various models are evaluated, including ensemble-based classifiers such as Decision Tree, Random Forest, K-Nearest Neighbor, Logistic Regression, AdaBoost algorithm, and XGBoost algorithm. All models have been evaluated using the publicly available dataset 'Human Activity Recognition with Smartphones,' which captures six daily activities: Lying, Standing, Sitting, Walking, Walking Upstairs, and Walking Downstairs. Among all the applied models, XGBoost accomplished the highest accuracy of 99.52% with 100% precision, 100% recall (100%), and 1.0 F1 score. Hyperparameter tuning on the ML models is implemented to attain the best accuracy. © 2024 IEEE.

关键词： Adaptive boosting

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Gear Misalignment Optimization for Electric Drive Unit Using machine learning Based Prescriptive Analytics

Gear Misalignment Optimization for Electric Drive Unit Using...

引用

2025 international conference on Advances in Design, Materials, Manufacturing and Surface engineering for Mobility, ADMMS 2025

作者： Penumatsa, Venkata Ramana Raju Thomas, Benson Black, Derrick Jain, Sachin Tata Consultancy Services Ltd. India General Motors LLC United States

In Electric vehicle Drive Unit Gears, high mesh misalignments result in shift in load distribution of a gear pair that can increase contact and bending stresses. It can move the peak bending and contact stresses to the edge of the face width and increase gear noise as well. Lower misalignment value is often required to reduce the peak bending and contact stresses and have a balanced load distribution along the gear flank, which in turn helps in reducing noise and improving durability of drive unit. This paper delineates Prescriptive Analytics method that combines virtual simulations, machine learning (ML) and optimization techniques to minimize different gear misalignments for the electric vehicle drive units. Generally, the manual optimization process is carried out by sequential modifications of stiffness of individual components. However, this process is time consuming and does not account for interactions between the components. In this study, firstly, machine learning models are developed based on design of experiments (DOE) simulations. These ML models are used as surrogates for actual simulations in generic algorithms (Differential Evolution) based optimization techniques. It finally prescribes changes in stiffness of different components to get optimum misalignment value. © 2025 SAE international and SAEINDIA;General Motors.

关键词： Electric load distribution

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Natural Language Processing for Sentiment Analysis with Deep learning 3

Natural Language Processing for Sentiment Analysis with Deep...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Sruthi, Srigiri Trinath, V.G. Jayanth, V. Balaji, V. Pavan Singh, Tripty Mandal, Adhirath Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India Pai Chai University Korea Republic of

ISBN: (纸本)9798350381931

In order to identify and analyse the thoughts, feelings, and sentiments indicated in textual data, the field of sentiment analysis is crucial. Deep learning models have become effective tools for sentiment analysis thanks to their capacity to recognise semantic links and understand complicated patterns. In-depth analysis of the use of deep learning algorithms for sentiment analysis tasks is presented in this research report. We investigate different deep learning architectures, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models, to assess how well they are able to capture nuances in sentiment and contextual information. On benchmark datasets, numerous trials are run, and the models are assessed using important performance indicators as accuracy, precision, recall, and F1 score. applications that are in this field. we also investigate methods like pre- trained word embeddings and transfer learning. The study also investigates the effects of regularisation and hyperparameter tuning methods on the accuracy and applicability of models. In addition, we discuss issues with sentiment analysis's class imbalance, model interpretability, and data pre-processing. The findings show that deep learning models regularly perform better than conventional machine learning techniques, attaining cutting-edge performance in sentiment classification tasks. The results of this study open the door for more advanced applications in this field by giving important new insights into the capabilities and constraints of deep learning models for sentiment analysis. © 2024 IEEE.

关键词： Sentiment analysis

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Zero-shot data-to-text generation via dual learning 3

Zero-shot Data-to-text generation via dual learning

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3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Liao, Weiming Chen, Bo Zhao, Xiaobing School of Information Engineering Minzu University of China China

ISBN: (纸本)9781510680531

data-to-text task aims to convert structured data into natural language text. However, the lack of large parallel data is a major practical problem for many domains in data-to-text generation. To address these issues, this paper proposes a method based on dual learning. Dual learning simultaneously learns two mutually dual generator and extractor, where the generator is responsible for text generation and the extractor for information extraction. Through dual learning, we can effectively utilize the interrelationships between unaligned data, thereby improving the performance of the generation model. We conduct experiments on an advertising datasets, and compare with traditional generation models. Experimental results demonstrate that the dual learning-based method achieves nearly the same performance as fully supervised approaches for the data-to-text generation task validating its effectiveness and feasibility in data-to-text generation tasks. © 2024 SPIE.

关键词： Zero-shot learning

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