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Developing the 3D Model of the Human Lower Body using the Skeleton System Method 11th

Developing the 3D Model of the Human Lower Body Using the Sk...

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11th international conference on E-Health and Bioengineering (EHB)

作者： Branescu, Andrei-Horia Rusu, Dan-Mihai Cofaru, Nicolae-Florin Petrescu, Raul-Mihai Lucian Blaga Univ Sibiu Fac Engn Dept Ind Engn & Management 4 Emil Cioran St Sibiu 550025 Romania Tech Univ Cluj Napoca Mechatron & Machine Dynam Dept Cluj Napoca 400114 Romania

ISBN: (纸本)9783031625220;9783031625237

the current study approaches a thematic of very high interest, an interdisciplinary one, regarding the modelling of the whole human lower body. the main goal consists in developing a fully parametric and generalized 3D assembly;this product has been created through the Skeleton System method, by modelling the hip, knee, ankle and metatarsophalangeal joints, constraining the subassemblies through parameters. the development of the user interface has been designed to be as user-friendly as it can be, consisting in a spreadsheet file. the input data can be extracted from X-rays or computer tomographs and the assembly will be automatically reconfigured with the given values. Diverse 3D models of the lower body with pathological conditions can also be generated by inserting offsets between the reference systems linking the elements within the assembly. the final product can be further used for a wide range of applications, pre-operatory and post-operatory studies, gait cycle or finite element analysis.

关键词： Lower body 3D modeling Hip Joint knee ankle

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Big Five: What User Posts Say? 7th

Big Five: What User Posts Say?

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7th international Scientific conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Oliseenko, Valerii D. Hastiev, Shamil R. Tulupyeva, Tatiana V. St. Petersburg Federal Research Center of the Russian Academy of Sciences Laboratory of Theoretical and Interdisciplinary Problems of Informatics 14-th Linia VI No 39 St. Petersburg199178 Russia St. Petersburg State University Mathematics and Mechanics Faculty Universitetskaya Emb. 7-9 St. Petersburg199034 Russia North-West Institute of management branch of the Russian Presidential Academy of National Economy and Public Administration Faculty of State and Municipal Administration Sredny prospect VI 57/43 St. Petersburg199178 Russia

ISBN: (纸本)9783031437885

this paper considers the question of automating the prediction of the users’ expression of psychological traits, as assessed by the Big Five test. CatBoost is used as a model to automate this process, which as an input receives traits extracted analytically from user posts on personal pages (the average number of words in a sentence, the ratio of the number of emoticons per number of words, the metric of ease of reading text, emotional coloring of text, etc.) and the result of additional training model RuBERT, and as the output label (expression of a factor of the Big Five). the final dataset is 1018 test results and 67052 posts from the pages of users who passed the test. the result of the work showed that the use of the pre-trained RuBERT model greatly improves the predictive model, except it was possible to identify the need to increase the initial data set. the theoretical significance lies in the creation of an approach for the analysis of users’ posts in the context of the possibility to predict their scores in Big Five test. the practical significance determines the creation of an automated system to predict psychological trait severity scores from posts on their personal social media page. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Forecasting

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7th international conference on Machine Learning, Optimization, and Data Science, LOD 2021

7th International Conference on Machine Learning, Optimizati...

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7th international conference on Machine Learning, Optimization, and Data Science, LOD 2021

ISBN: (纸本)9783030954666

the proceedings contain 85 papers. the special focus in this conference is on Machine Learning, Optimization, and Data Science. the topics include: Utilizing Predictive Models to Identify the Influence of Full Prior Distribution in Hypothesis Testing Problems;optimally Weighted Ensembles for Efficient Multi-objective Optimization;Anomaly Detection in Smart Grid Network using FC-Based Blockchain Model and Linear SVM;unsupervised PulseNet: Automated Pruning of Convolutional Neural Networks by K-Means Clustering;a Noisy-Labels Approach to Detecting Uncompetitive Auctions;deep Autonomous Agents Comparison for Self-driving Cars;method for Generating Explainable Deep Learning Models in the context of Air Traffic Management;shufText: A Simple Black Box Approach to Evaluate the Fragility of Text Classification Models;a Framework for Imbalanced Time-Series Forecasting;training Convolutional Neural Networks with Competitive Hebbian Learning Approaches;pH-RL: A Personalization Architecture to Bring Reinforcement Learning to Health Practice;predicting Worst-Case Execution Times During Multi-criterial Function Inlining;Small Geodetic Datasets and Deep Networks: Attention-Based Residual LSTM Autoencoder Stacking for Geodetic Time Series;Forecasting the IBEX-35 Stock Index using Deep Learning and News Emotions;action-Conditioned Frame Prediction Without Discriminator;inference and De-noising of Non-gaussian Particle Distribution Functions: A Generative modeling Approach;convolutional Neural Network for Classification of Aerial Survey Images in the Recognition System;Can You Tell? SSNet - A Biologically-Inspired Neural Network Framework for Sentiment Classifiers;ODIN: Pluggable Meta-annotations and Metrics for the Diagnosis of Classification and Localization;predictable Features Elimination: An Unsupervised Approach to Feature Selection;towards Understanding Neuroscience of Realisation of Information Need in Light of Relevance and Satisfaction Judgement;Fast ABC with Joint Generati

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7th international conference on Machine Learning, Optimization, and Data Science, LOD 2021

7th International Conference on Machine Learning, Optimizati...

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7th international conference on Machine Learning, Optimization, and Data Science, LOD 2021

ISBN: (纸本)9783030954697

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Dandu Srivamsi Ommi Mohan Deepak M.D. Anto Praveena A. Christy Department of CSE Sathyabama Institute of Science and Technology Chennai

the conversion process of words to vectors involves mapping each word or term in the biomedical data to a unique numerical vector, called a word embedding. One of the key advantages of using word embeddings is that they capture the meaning and context of words in a numerical format, which can be used as input for various machine learning and data analysis tasks. the Milvus vector engine is an open source software that allows for the efficient and accurate representation of large-scale data in vector space. It is particularly useful for high-dimensional data such as biomedical data that contains a large number of unique words and terms. the use of the Milvus vector engine is to convert biomedical data into numerical vectors for machine learning and data analysis tasks. In the context of biomedical data, the use of word embeddings can enable new discoveries and insights by allowing for more accurate and efficient analysis of large datasets. this word embeddings can be used to identify patterns and relationships in biomedical literature, or to classify medical articles based on their content.

关键词： Support vector machines Data analysis Biological system modeling Machine learning Market research Data models Bioinformatics

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Triple Band Antenna using Superstate with Double Substrate for Wireless Applications 26

Triple Band Antenna Using Superstate with Double Substrate f...

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26th international conference on Computer and Information Technology, ICCIT 2023

作者： Uddin, Md. Kutub Hossen, Shawon Alam, Touhidul Islam, Mohammad Tariqul Alam, Mohammed Shamsul Hakim, Mohammad Lutful Institut Perubahan Iklim Selangor Bangi Malaysia International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong4318 Bangladesh Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Selangor Bangi Malaysia International Islamic University Chittagong Department of Computer Science and Engineering Chittagong4318 Bangladesh

ISBN: (纸本)9798350359015

A multiband antenna is a good choice to keep the device as compact as possible. this study introduces a tripleband antenna that is well-suited for 5G New Radio applications, satellite communications, and WiMAX. the antenna design has a two-layer substrate, including a ring-slotted rectangular superstate. To meet the design requirements, a rectangular superstate was used to provide the antenna with its triple-band capabilities. the antenna's triband capabilities come from its rectangular superstate, constructed using a ring slot at every antenna's four patch corners. In addition, it is advised and used to employ two FR-4 substrates that are 1.5 millimeters each to improve the antenna's efficacy substantially. the three frequencies shown by the results of the antenna modeling cover the frequency range used by 5G NR service operations: 4.780 GHz, 5.50 GHz, and 10.75 GHz. In each operational range, the measured reflection coefficient has a value below -10 dB at the frequency center, while the peak gains surpass 7 dBi. Nevertheless, the antennas provided in this context exhibit a broad spectrum of applications within the realm of wireless communication. © 2023 IEEE.

关键词： 5G Double substrate Multiband superstate WiMAX

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Methods for Creating contextual Models of Subject Areas

Methods for Creating Contextual Models of Subject Areas

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Nikita G. Vorobyov Anna Yu. Philippovich Moscow Polytechnic University Moscow Russian Federation Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

the aim of the study is to evaluate the methods of autonomous collection and use of local contexts for selecting an answer option by an intelligent system in the dialog interaction process. the following research methods were used: analysis of technical documentation, scientific papers, conference materials and instructional manuals. the current methods of collecting local contexts used in the most successful projects are analyzed. the methods of coding text words for processing by software are analyzed. the methods of measuring the quality of data collected in this way are analyzed. the result of the work is the presentation of the studied methods of collecting, storing and evaluating local contexts. A method for modeling subcategory contexts by a subject area is proposed in order to improve the quality of the decision-making system. A new method of graph modeling of logical connections of natural language data is proposed. A comparison of the effectiveness of various approaches to modeling local contexts is carried out and the corresponding conclusions are made.

关键词： Training Analytical models Adaptation models Accuracy Computational modeling Machine learning Probabilistic logic Vectors Data models context modeling

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Evaluation of Generative AI in E-commerce Product Description Generation: An Experimental Study

Evaluation of Generative AI in E-commerce Product Descriptio...

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Software Engineering and Computer Science (CSECS), international conference on

作者： Aditi M Jain Ayush Jain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Generative AI has emerged as a powerful tool for content generation in e-commerce, enabling the automated creation of product descriptions, reviews, and marketing copy. this paper presents a systematic evaluation of state-of-the-art generative AI models for e-commerce content generation using the WANDS dataset. We conduct a series of experiments to compare commonly used AI models based on their BLEU and ROUGE scores, assessing their effectiveness in generating high-quality, coherent, and contextually relevant content. Our analysis reveals distinct strengths for each model: GPT-4 excels in producing high-conversion SEO-driven sales copy, Claude generates engaging, storytelling-rich product descriptions, DeepSeek provides precise, factual descriptions for technical products, LLaMA offers simple, cost-effective product descriptions, and OpenHermes-2.5-Mistral-7B creates engaging and informative product descriptions suitable for e-commerce platforms. these findings highlight the importance of aligning AI model selection with specific e-commerce use cases and content objectives. By providing a comprehensive benchmark and targeted recommendations, this study offers valuable insights for businesses and researchers seeking to optimize AI-driven content generation strategies in online retail, enabling more effective and tailored implementation of generative AI in e-commerce contexts.

关键词： Training Adaptation models Systematics Generative AI Computational modeling Semantics Benchmark testing Electronic commerce context modeling Software engineering

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Application of Artificial Intelligence Methods for Generating Mathematical Problems in Primary Education: Analysis of Effectiveness and Prospects for Automated Assessment

Application of Artificial Intelligence Methods for Generatin...

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Machine Learning and Natural Language Processing (MLNLP), international conference on

作者： Nazipov Rustam Salavatovich Avksentieva Elena Yurievna Faculty of Software Engineering and Computer System ITMO University Saint Petersburg Russia Faculty of Software Engineering and Computer System ITMO University Saint-Petersburg Russia

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

the present study aims to evaluate the effectiveness of using modern neural network models (ChatGPT 4o, Claude 3.5 Sonnet, GigaChat 3.0, YandexGPT 3) for generating mathematical problems for elementary school students. the research conducts a comparative analysis of the quality of generated problems, assesses the possibility of their direct use without expert review, and develops recommendations for automating the quality control process. the methodology includes the generation of various types of mathematical problems, their expert evaluation according to developed criteria, and statistical analysis of the results. the study identifies the strengths and weaknesses of each model, determines the types of problems in which the models demonstrate the best results, and suggests directions for further improvement of AI systems in the educational context.

关键词： Analytical models Statistical analysis Reviews Neural networks Process control Quality control Machine learning Chatbots Mathematical models context modeling

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DLSC-Net: Double Level Spatio-temporal context-aware Network for Continuous Sign Language Recognition

DLSC-Net: Double Level Spatio-temporal Context-aware Network...

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IEEE international conference on Information Communication and Signal Processing (ICICSP)

作者： Ziyang Deng Weidong Min School of Mathematics and Computer Science Nanchang University Nanchang China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Continuous sign language recognition based on the deep learning is a hot topic in computer vision. Existing methods struggle to accurately capture fine-grained local features and effectively model the long-term contextual information. these limitations often lead to suboptimal performance in continuous sign language recognition. To address these issues, we propose a novel network named DLSC-Net: Double Level Spatio-temporal context-aware Network for Sign Language recognition. the network employs a double stream structure, consisting of the Time-sequence Learning Module and the Temporal context-aware Module. the Time-sequence Learning Module leverages Convolutional Neural Networks (CNNs) to capture short-term local feature variations, ensuring precise recognition of detailed hand gestures. In parallel, the Temporal context-aware Module employs dilated convolutions to handle long-term dependencies, expanding the receptive field without increasing computational load, thus effectively modeling the temporal dynamics in sign language sequences. We validated the effectiveness of the DLSC-Net on the large-scale continuous sign language sentence dataset. Our results show that DLSC-Net enhances recognition accuracy, making it a promising approach for continuous sign language recognition.

关键词： Hands Deep learning Sign language Convolution Computational modeling Video sequences Feature extraction Reliability context modeling Load modeling

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