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Prediction of Cyclone with Metrological Factors using Gradient Boosting Algorithm 4

Prediction of Cyclone with Metrological Factors using Gradie...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Durai Arumugam, S.S.L. Praveenkumar, R. Shreya, P. Selvakumar, J. Easwari Engineering College Dept. of Information Technology Chennai India Easwari Engineering College Dept. of Electronics and Communication Engineering Chennai India Sri Ramakrishna Engineering College Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300093

Innovations in computer vision and satellite imaging technologies have made fully automatic weather forecasting a reality. The cyclone prediction system is developed with the goal of reducing both natural disasters and human casualties. Machine Learning object recognition system is created to pinpoint the cyclone, which contains more information about the storm's activity. This is crucial work because it pinpoints the exact spot where the cyclone's formation. The accuracy of cyclone forecasts from many machine learning and deep learning models is 86%-95% or higher, while the accuracy of cyclone eye detection is 87% or higher from the same models. The impressive findings show that the experiment can surpass both established methods and previous attempts. © 2023 IEEE.

关键词： Weather forecasting

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Real-time Human Movement Recognition and Interaction in Virtual Fitness using Image Recognition and Motion Analysis 12

Real-time Human Movement Recognition and Interaction in Virt...

引用

12th International Conference on Awareness science and Technology, iCAST 2023

作者： Yeh, ChunHsiu Shen, Wei-Cheng Ma, Chi-Wei Yeh, Qiu-Tong Kuo, Chung-Wei Chen, Jong-Shin Dept. of Information Engineering Computer Science Feng Chia University Taichung Taiwan CollegeofComputerScience Jimei University Xiamen China Dept. of Information Communication Engineering ChaoYang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This research focuses on the application of image recognition, motion detection, and artificial intelligence techniques to achieve real-time identification and analysis of human movements in a dynamic manner. The primary goal is to establish a seamless association between captured human movements and corresponding actions performed by virtual avatars. The implementation involves the utilization of image processing methodologies from python's OpenCV and pygame libraries. MediaPipe technology is employed to capture users' images and facilitate accurate detection of specific movement patterns. By calculating the angles between users' limbs and torso, the correctness of certain movements can be assessed. The research is practically developed and applied to a simulated fitness system, where an interactive exercise game platform is devised. The system is characterized by its engaging user interface, enhancing the ease and enjoyment of fitness activities. The seamless integration of interactive virtual avatars and real users within physical activities serves to stimulate greater participation, particularly among the elderly. The amalgamation of image recognition and motion detection technologies presents opportunities for combining fitness and entertainment in diverse life scenarios, offering an enhanced experience of physical exercise and enjoyment. © 2023 IEEE.

关键词： Image recognition

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Data Pre-Processing Framework for Kannada Vachana Sahitya

Data Pre-Processing Framework for Kannada Vachana Sahitya

引用

2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Lavanya, C.B. Swamy, H.S. Nagendra Pavan Kumar, M.P. University Of Mysore Dept. Of Studies In Computer Science Mysore India Dr. Ait Bangalore India J.N.N College Of Engineering Dept. Of Information Science & Engg. Shimoga India

ISBN: (纸本)9798350371567

Advancements in Natural Language Processing (NLP) driven by machine learning, deep learning, and artificial intelligence have significantly broadened its scope and improved interactions between humans and computers. Despite these advancements, NLP systems encounter challenges arising from incomplete and error-prone data, which can result in biased model outputs. Technical domains present further hurdles, necessitating domain-specific fine-tuning and the development of custom lexicons. Additionally, many languages lack robust NLP support, limiting accessibility. In this context, innovative NLP data pre-processing and tokenization methods tailored for Kannada Vachana Sahitya texts are investigated. The majority of documents or text utilized in any language processing applications consists of raw text, in which some of the words are not represented in the standard form. There is a necessity of pre-processing the input text before building the translation model. In this paper, the main goal is to pre-process the source text with essential steps like Cleaning, Parsing, Tokenization, Padding, Stemming and Lemmatization. During this process, Kannada source text - Vachana Sahitya is tested on bilingual corpus and approximately 35% of non-standard input text is considered for experimental analysis. © 2024 IEEE.

关键词： Natural language processing systems

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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Explicit Feature Pyramids Are Enhanced Object Detectors 7

Explicit Feature Pyramids Are Enhanced Object Detectors

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Wang, Wenlong Hua, Pinyan HangZhou DianZi University School of Computer Science Hangzhou China Gongshang University School of Information and Electronic Engineering Dept. ZheJiang Hangzhou China

ISBN: (纸本)9798350352009

Object detection has seen new advancements with the introduction of DETR, leading to notable performance improvements. However, DETR-based object detectors, such as AdaMixer and Sparse-RCNN, suffer from long training times and defective accuracy for small objects due to their complex heads. Previous attempts to address the issues by using dense priors for initialization and single-decoder layer detectors have resulted in lower accuracy compared to those with complex decoder layers. To enhance multi-scale information capture, the traditional object detection methods use Feature Pyramid Networks (FPN). In this study, we present an Optimal Transport Decoder (OTD), a pioneering effort in employing patches as its fundamental units to investigate the feature pyramid structure for the research-hot DETR, deviating from the traditional grid prediction method. OTD proposes an explicit feature pyramid between the backbone and decoder, which can enhance multi-scale image representation without increasing computational complexity. A box distribution alignment method is designed to leverage Optimal Transport (OT) for calculating the model's loss so as to converge towards the global optimum instead of the local optima, even when dealing with intricate scenes and small target detection. The experimental results verify our model OTD over-performing the state-of-the-art related models. © 2024 IEEE.

关键词： Decoding

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Fingerprint definition for song recognition using Machine Learning Algorithm

Fingerprint definition for song recognition using Machine Le...

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2023 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2023

作者： Arunakumari, B.N. Shashidhar, R. Sahana, B. Jagadamba, G. Manjunath, A.S. Roopa, M. Bms Institute of Technology and Management Dept. of Computer Science and Engineering Bengaluru India Jss Science and Technology University Dept. of Electronics & Communication Engineering Mysuru India Rv College of Engineering Dept. of Electronics & Communication Engineering Bengaluru India Siddaganga Institte of Technology Dept. of Information Science and Engineering Tumkuru India Jss Science and Technology University Dept. of Master of Computer Applications Mysuru India Dayananda Sagar College of Engineering Dept. of Electronics & Communication Bengaluru India

ISBN: (纸本)9798350347296

A condensed content-based signature that summarises an audio recording is known as an audio fingerprint. Audio The capability to recognize music regardless of design and without the use of meta-data or watermark embedding has made fingerprinting technologies popular. In. tegrity checking, watermark support, and content-based aural retrieval are additional applications for fingerprinting. The various methods of fingerprinting have been discussed using various justifications and terminologies, such as pattern matching, multimedia information retrieval, or cryptography. It is a distinct and condensed digest created using perceptually important elements of a video. Audio material can be monitored using fingerprinting technologies without requiring embedding of metadata or watermarks. However, audio fingerprinting has extra applications The algorithm is trained using thousands of mini audio speech commands. In order to improve the training efficiency, audio features are extracted during a pre-processing. The accuracy of the developed model is 83.7%. © 2023 IEEE.

关键词： Cryptography

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Enhancing OCR Accuracy Using Convolutional Neural Networks: A Study on Spelling Correction in Handwritten Numerals 1

Enhancing OCR Accuracy Using Convolutional Neural Networks: ...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Dewal, Inder Singh Singh Rathore, Sajjan Singh, Vinay Kumar, Sani Bhavani, P.M. Sindhu Chandigarh University Dept. of Computer Science Engineering Chandigarh India Madhav University Dept. of Humanities & S.Science Pindwara India Usha Martin University Dept. of Com. & Information Technology Jharkhand India Mangalayatan University Dept. of Computer Eng. & Applications Aligarh India Meenakshi A.H.Edu. & Research Dept. of Humanities & Science Chennai India

ISBN: (纸本)9798350356816

Artificial neural networks have transformed machine learning and had a significant impact on a wide range of industries, including robotics, deep learning, healthcare, sports, and surveillance. Convolutional Neural Networks (CNNs) are unique among these developments because they combine cutting-edge deep learning methods with conventional Artificial Neural Networks (ANNs). CNNs are essential for tasks like data analysis, text classification, speech and face recognition, and pattern recognition in a variety of fields. In order to improve spelling correction, this article uses a dataset that was upgraded from the National Institute of Standards & Technology (NIST) and includes 70,000 grayscale pictures of handwritten numbers. © 2024 IEEE.

关键词： Convolutional neural networks

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IDP: Image Denoising Using PoolFormer 6

IDP: Image Denoising Using PoolFormer

引用

6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Yin, Shou-Kai Chen, Jenhui Chang Gung University Guishan Dist. Dept. Computer Science & Information Engineering Taoyuan33302 Taiwan

ISBN: (纸本)9798350301953

Recently, transformer-based models have achieved significant success in various computer vision tasks, with the attention-based token mixer module commonly believed to be the key factor. However, further research has shown that the attention-based token mixer module in transformers can be replaced by other methods, such as spatial multilayer perceptrons (MLPs) or Fourier transforms, to mix information between different tokens without sacrificing performance. Therefore, some have raised whether the success of transformers and its variants is not solely due to the attention-based token mixer module but rather to other factors. In a recent paper titled 'PoolFormer' the authors demonstrated that using a simple spatial pooling operation instead of the attention module in transformers can achieve competitive performance in object detection vision tasks. Based on this finding, we propose a low-computation model for image denoising based on the PoolFormer and an MLP + CNN Transformer decoder for image restoration. By reducing the computational complexity brought by the token mixer, the model still achieves a good peak signal-to-noise ratio (PSNR) in grayscale as well as in color image denoising. This suggests that, in low-level vision tasks such as denoising, simple attention modules can also achieve good results, particularly in grayscale image denoising. © 2023 IEEE.

关键词： Mixers (machinery)

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Estimation of Lung Surface Roughness by Means of an Ultrasound Multifrequency Approach, in silico and in vitro results

Estimation of Lung Surface Roughness by Means of an Ultrasou...

引用

2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Mento, Federico Perini, Matteo Malacarne, Ciro Demi, Libertario University of Trento Ultrasound Laboratory Trento Dept. of Information Engineering and Computer Science Trento Italy Rovereto Italy

ISBN: (纸本)9798350346459

Lung ultrasound (LUS) is an important tool to evaluate the state of the lung surface, but is limited to the visual evaluation of imaging artifacts. Specifically, it is mainly focused on the vertical artifacts, which correlate with pathologies characterized by a reduction of air-spaces' dimensions. In contrast, there exist pathologies, e.g., chronic obstructive pulmonary disease (COPD), characterized by an enlargement of the air-spaces. This enlargement causes the lung surface to behave essentially as a perfect reflector, which does not allow the ultrasound penetration. As air-spaces of different sizes may generate a different roughness level at the lung surface, the roughness estimation could provide a way to characterize the state of lung surface. In this study, we show how a multifrequency quantitative approach has the potential to estimate the roughness of the lung surface by measuring image intensity variations along the lung surface as a function of frequency. © 2023 IEEE.

关键词： Surface roughness

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On-Demand Recharge Scheduling Algorithm in Wireless Sensor Networks

On-Demand Recharge Scheduling Algorithm in Wireless Sensor N...

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2022 International Conference on Electronic Systems and Intelligent Computing, ICESIC 2022

作者： Dande, Bhargavi Chang, Chih-Yung Su, Ming-Yang Fan, Cheng-De Tamkang University Dept. of Computer Science and Information Engineering New Taipei City Taiwan Ming Chuan University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9781665483858

In recent years, energy transfer technology provides a new opportunity for recharging the sensors using the mobile charger. Most of the studies proposed recharging algorithms using a single charger but overlooked the consideration of multiple chargers and cooperation between them. Therefore, this paper proposed an On-Demand Recharge Scheduling (ODRS) algorithm for multiple mobile chargers, aiming to improve the network surveillance quality (NSQ). In this paper, the network is partitioned into many subregions. One mobile charger is employed in each subregion to recharge the sensors based on the benefit of coverage quality (BCQ) obtained. In addition, there is cooperation between neighboring mobile chargers. Experiments are evaluated to show the achieved NSQ of the proposed ODRS algorithm. © 2022 IEEE.

关键词： Wireless sensor networks

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