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共 67328 条记录，以下是421-430 订阅

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Handcrafted Feature Assisted Light-Weight Encoder Decoder Based Classifier for Yoga Posture Recognition 2

Handcrafted Feature Assisted Light-Weight Encoder Decoder Ba...

引用

2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Sarma, Prasiddha Ibotombi Singh, S. Tezpur University Dept. of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350372847

The trend for the need for at-home workout regimens and the potential for technology to support the trend is rising as remote employment becomes more and more common. One such activity that has benefits like increased flexibility and stress alleviation is yoga. Presently, numerous self-guided yoga courses in the form of pictures and videos are available online. Consequently, there is an urgent requirement for a system that can recognize and evaluate the precision of yoga posture execution in these instructional materials. In the current scenario lightweight prediction models are very much essential because devices like smartphones, edge devices, and embedded systems that have limited processing power or battery life, lightweight models are the best option as they require less resources to train and operate. One particular approach of making the recognition system lightweight is the use of keypoint-based pose/action classifiers. In this paper, a lightweight classifier for yoga postures based on neural network architecture using MoveNet pose estimation technique is proposed. Our model is able to classify the yoga postures by learning the human joints information in a efficient manner. © 2024 IEEE.

关键词： Classification (of information)

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Proposal for a Virtual Reality System to Practice Knife Handling in Cooking 13

Proposal for a Virtual Reality System to Practice Knife Hand...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Nakazato, Yukitoshi Kano, Toru Ibaraki Univercity Dept. Computer Science and Engineering Ibaraki Japan

ISBN: (纸本)9798350355079

Knife handling is one of the most important skills in cooking. However, it is difficult to practice the handling of a kitchen knife repeatedly owing to limitations in resources. To address this, we propose a highly realistic system using a haptic device and CT (Computed Tomography) data. This system allows users to practice knife handling in a virtual environment, providing force feedback that reproduces real-life situations. Our goal is to enhance the efficiency and effectiveness of cooking training by enabling unlimited practice without the constraints of ingredients. © 2024 IEEE.

关键词： Virtual environments

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Machine Learning Aided Spectrum Sensing

Machine Learning Aided Spectrum Sensing

引用

2023 IEEE International Conference on electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Jabbal, Raunaq Singh Srivastava, Vishwas Prasad, Binod Dept. of Computer Science and Engineering MP Gwalior India Dept. of Electrical and Electronics Engineering MP Gwalior India

ISBN: (纸本)9798350305111

Spectrum sensing (SS) is a key concept that determines the effectiveness of cognitive radio (CR). Although multiple sensing practices have been proposed in the literature, out of them energy detector (ED) have been widely adopted. However, the performance of ED relies on pre-existing knowledge of noise distribution and threshold. Therefore, efficient spectrum sensing remains a challenge in wireless communication technologies. This paper aims to analyze the sensing performance of various machine learning (ML) models in different fading scenarios. We carefully model a spectrum sensing environment and generate a large-scale comprehensive dataset. We then train and test several well-established classical and ML techniques for spectrum sensing. The performance of classical spectrum sensing and ML-based spectrum sensing are compared under various parameters like the number of secondary user (SU), the distance between primary user (PU) and SU;and different fading environments such as Rayleigh, Rician, and Nakagami. The Simulation result shows that machine learning models outperform classical sensing techniques. Furthermore, it is found that support vector machine (SVM) outperformed other ML techniques in predicting channel availability. © 2023 IEEE.

关键词： Cognitive radio

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DERM-AI: A Convolutional Neural Network Based Approach For Skin Disease Detection

DERM-AI: A Convolutional Neural Network Based Approach For S...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Juneja, Sonam Tiwari, Parth Devi, Rajni Atwal, Shikha Chandigarh University Dept. of Computer Science and Engineering Gharuan India

ISBN: (纸本)9798350375237

This paper shows how convolutional neural networks (CNNs) are useful for accurately and quickly diagnosing skin issues, underscoring the need of utilizing state-of-the-art machine learning approaches for accurate and fast diagnosis. A CNN architecture including convolutional and pooling layers, fully linked layers, and a softmax classifier was developed to handle the problem of diagnosing skin diseases. The CNN model was trained using stochastic gradient descent, and its hyperparameters were adjusted using grid search and random search. Future research in this area may benefit from the insights the study offers on CNN model construction and application for skin disease diagnosis. © 2024 IEEE.

关键词： Stochastic systems

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A Bridgless Stacked Switches-Based AC-DC Converter with Reduced Storage Capacitances and Low-Frequency Voltage Ripple for HV EV Systems

A Bridgless Stacked Switches-Based AC-DC Converter with Redu...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Derakhshan, Siamak Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350316445

This paper presents a fully soft-switched single-stage stacked switches-based AC-DC converter with significantly reduced storage capacitances and low-frequency voltage ripple for High Voltage (HV) Electric Vehicle (EV) application. In the proposed converter, a front-end duty ratio closed-loop control system based on the input current equation provides a high-quality sinusoidal input current. Moreover, a dual feedback control loops in both the primary and secondary side of the converter are responsible to minimize the low-frequency DC-link and output voltage oscillations, eliminating the need for bulky DC-link and output storage capacitors and enhancing the reliability, lifetime, and cost-effectiveness of the converter. Moreover, the output voltage of the converter is regulated through a Variable Frequency (VF) control algorithm. The performance of the proposed converter is verified through a 1kW, 120Vrms/800Vdc, 150kHz proof-of-concept hardware prototype. © 2023 IEEE.

关键词： closed-loop control low capacitance size minimum low-frequency ripple Soft-switching

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FOOTBALL DATA ANALYSIS: THE PREDICTIVE POWER OF EXPECTED GOALS 25

FOOTBALL DATA ANALYSIS: THE PREDICTIVE POWER OF EXPECTED GOA...

引用

25th International Conference on Intelligent Games and Simulation, GAME-ON 2024

作者： Roccetti, Marco Berveglieri, Filippo Cappiello, Giuseppe Dept. of Computer Science and Engineering University of Bologna Italy Dept. of Management University of Bologna Italy

ISBN: (纸本)9789492859327

Major football events, such as the World Cup, have popularized the European football (i.e., soccer) beyond its traditional geographical borders, for example with a growing number of Americans who have begun to incorporate European football into their daily sport consumption. Many video game series based on this sport have further contributed to this increasing development. Not only that, but many performance indicators inspired by football computer games have become metrics that are now part of the normal conversation about real football, being used to analyse tactics, teams and players. Among these indicators, of prominent importance is the role of expected goals (xG) that measure the quality of a shot by calculating the likelihood that it will be scored from a particular position during a particular phase of play. Often, someone talks about xG leading to imprecise conclusions and to a distorded point of view on this performance indicator. In this paper, using a non-parametrical Kolmogorov Smirnov hypothesis testing method, we show that there is no statistically significant difference if, in a given football tournament, the final scores are awarded to the participating teams either with real goals or based on xGs, thus demystifying some wrong assumptions about this indicator. Data for this study come from all the matches played during the 2020 UEFA European Football Championship. © 2024 25th International Conference on Intelligent Games and Simulation, GAME-ON 2024. All rights reserved.

关键词： Predictive analytics

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Enhanced Text-Summarization and Language Simplification:End-to-End Approach for Content Comprehension 1

Enhanced Text-Summarization and Language Simplification:End-...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Riza Ayisha, P. Mathew, George Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350307795

Text summarization is the process of shortening a large body of text in a document to form a shorter while preserving its most important information. With the exponential growth of digital data, there is a need for automated text summarization to help users quickly comprehend the content. The use of machine learning techniques, particularly deep learning models, has shown great promise in generating high-quality summaries. Among these models, BERT (Bidirectional Encoder Representations from Transformers) has emerged as a state-of-the-art method for various natural language processing (NLP) tasks, including text summarization. In this paper, we propose to implement a text summarization system using the BERT model. Apart from the normal text summarization, we will be implementing a Language Simplification model that simplifies complex sentence structures or use simpler vocabulary while preserving the original meaning. This model transforms convoluted or verbose sentences into shorter and more straight-forward constructions The paper aims to develop an end-to- end summarization system that takes a long document as input and generates a summary of the most important information contained in the document. For example, like taking the entire news from an online portal and generates it summary. © 2024 IEEE.

关键词： Signal encoding

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An In-dept. Exploration of Machine Learning Algorithms and Performance Evaluation Approaches for Personalized Diabetes Prediction

An In-Depth Exploration of Machine Learning Algorithms and P...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Kaur, Inderdeep Ali, Aleem Chandigarh University Dept. of Computer Science and Engineering Kharar India

ISBN: (纸本)9798350376470

Diabetes is now recognised as a major worldwide health problem, demanding effective early detection and prediction measures to limit its repercussions. Machine learning algorithms have shown to be excellent tools in healthcare for creating prediction models. This paper delves into an extensive investigation of machine learning (ML) algorithms and methodologies for assessing their performance in the context of personalized diabetes prediction. Different machine learning models are rigorously explored and evaluated for how well they can customise forecasts to the unique characteristics of individual patients. The study also critically assesses several performance evaluation techniques in an effort to improve the accuracy and reliability of diabetes prediction algorithms. Furthermore, interpretability problems and class imbalance are addressed as challenges and limitations to the application of machine learning in the prediction of diabetes. This study aims to motivate researchers, facilitating a deeper understanding of disease prediction algorithms and conduct related research. © 2024 IEEE.

关键词： Prediction models

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Spatial Data Discovery Using Small Language Model 5

Spatial Data Discovery Using Small Language Model

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thakur, Sujit Kumar Tyagi, Neha Amity University Dept. of Computer Science & Engineering Noida India

ISBN: (纸本)9798350383522

The ubiquitous use of data in a plethora of use cases and different organizational dependence on it for various insight has made data a valuable resource across business and scientific domains. Data has attained much more significance than oil itself, unlike the British mathematician Clive Humby's quote in 2006 'data is the new oil'. Every business and processes generate data and with the advent of AI and machine learning, all types of data have valuable use case attached. This also ensued a series of initiatives to clean, augment and provide data as data-products. This has given rise to businesses and solutions using inhouse data or data purchased from a dedicated data provider. Data consumers often face the challenge of inefficient data product discovery. More frequently than not they require active support from data product experts to point them to right dataset. The semantic meaning of the attributes and keywords of a data product is also heavily domain oriented. Data providers find it easy to update and augment metadata of a data set with details more feasible solution than modifying the dataset itself with attribute rename or adding composite attribute. The expected next step is that the metadata be self-explanatory enough to help in data cataloguing, but often we find consuming data scientist looking for help from data experts to make sense of the data in the context of the consumer's use case. This research seeks to analyse if the role of a data expert can be reduced and a finetuned small language model can help a data consuming data scientist in discovering the right data product, based on the metadata of the data product. Overall, this research contributes to the advancement of data discovery techniques and highlights the potential of leveraging language models to streamline and democratize access to valuable data resources, thereby empowering organizations to make more informed and data-driven decisions. © 2024 IEEE.

关键词： data discovery finetuning llama2 small llm spatial data

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Automail AI: Intelligent Voice-Controlled Email Generator 3

Automail AI: Intelligent Voice-Controlled Email Generator

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3rd International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, IITCEE 2025

作者： Jayaram, Kavitha Mandagere, Samarth Gopisetti, Sampriti Sahit, S. Bnm Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the form of an email generation tool that automatically sends an email according to end-user-specified keywords. Automail AI utilizes NLP, ML, and Speech Recognition to differentiate formal emails from informal ones and draft appropriate responses as the situation demands. It has been tuned with Naive Bayes Classifier that yields a better level of accuracy as compared to models such as Decision Tree, SVM, and Logistic Regression. Setting the benchmark for Automated Communication tools, Automail AI combines diverse technologies like SMTP to guarantee secure Email delivery, Google Text-to-Speech or GTTS for auditory feedback, and a module providing an interface for speech recognition that offers the possibility to use the tool with no hands. © 2025 IEEE.

关键词： Automail AI email generation GTTS machine learning Naive Bayes natural language processing SMTP speech recognition

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