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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), ieee international conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling Image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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Analysis and Prediction of Computer Science Students' Performance Based on Core Foundational Courses using Ensemble Machine Learning Methods

Analysis and Prediction of Computer Science Students' Perfor...

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international ieee conference on Intelligent Systems, IS

作者： Betty Mayeku Juma Kilwake Ambrose Osala Mamai Eve Khatundi Wamasebu School of Computing and Informatics Kibabii University Bungoma Kenya

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

As the surge toward a technology-driven and dependent society increases, the demand for technology oriented workforce particularly computer science continues to rise. However, the concern that continues to persist in a number of higher institutions of learning is the low graduation rates and poor performance in computer science programs. This study, carried out in a Kenyan public university attempted to extend the understanding on the performance of computer science students The study aimed at examining whether performance in early foundational core courses had any influence on the final graduation score (through final accumulative score) among computer science students using ensemble machine learning methods. The study specifically employed Random Forest and XGBoost ensemble methods. The results showed the Random forest regression model having a prediction power of 83.20% while XGBoost had a slightly improved prediction power of 84.6%. The results also revealed better model performance when using only years 1 and 2 core courses as compared to using years 1 to 3 core courses. This is an indicator that foundational core courses are good predictors of student academic performance in computer science programs. This work in progress is part of a preliminary study aimed at addressing the challenge of low graduation rates and poor performance in computer science programs.

关键词： Computer science Power measurement Computational modeling Predictive models Ensemble learning Surges Intelligent systems Random forests

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Smart Corridor+: A Testbed-As-A-Service for Intelligent Transportation Research

Smart Corridor+: A Testbed-As-A-Service for Intelligent Tran...

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ieee international conference on Big Data

作者： Yasir Hassan Austin Harris Mina Sartipi Center for Urban Informatics and Progress University of Tennessee at Chattanooga Chattanooga TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Urbanization over the next decade will present many complex challenges to cities. Transportation plays an important role in a city’s ability to address urbanization and create resilient, sustainable, inclusive, and safer cities for all. However, challenges introduced by urbanization will decrease the effectiveness of current transportation methodologies. By utilizing innovations in sensing, computing, and communication along with machine learning and real-time data, intelligent transportation systems (ITS) can advance transportation efficiency and effectiveness. Integrating these technologies into intelligent transportation systems can potentially reduce congestion, pollution, energy consumption, and traffic incidents. This integration is at the forefront of academic and industry research. Although testbeds have been developed for the development, testing, and validation of ITS solutions, existing testbeds focus on narrow aspects of the ITS domain and are unable to support large-scale experimentation and integrations. Additionally, access to resources and data is limited and not remotely accessible to potential researchers. In this paper, we propose a smart city and ITS Testbed-As-A-Service. The testbed is located in downtown Chattanooga, TN, and offers a unified platform for a wide range of ITS applications including connected and autonomous vehicles, wireless communication, cooperative transportation, internet-of-things (IoT), and edge-based AI.

关键词： Wireless communication Technological innovation Connected vehicles Smart cities Transportation Traffic control Real-time systems Sensors Autonomous vehicles Testing

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Robust Optical Flow Estimation for Wilted Plant Leaves Motion: Predicting Water-Stressed and Well-Watered Conditions

Robust Optical Flow Estimation for Wilted Plant Leaves Motio...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Sana Rao Rao Kashif Department of Computer Science Virtual University of Pakistan Multan Pakistan Department of Computing National University of Modern Languages Pakistan

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Optical flow estimation is essential in computer vision for analyzing and interpreting motion in image sequences. For agriculture, optical flow can be a powerful tool in predicting plant health, particularly in identifying water-stressed versus well-watered conditions in wilted plant leaves. While traditional methods for optical flow estimation have made significant progress, challenges such as ill-defined edges and blurred motion boundaries remain, particularly when dealing with complex motion patterns like those found in natural leaf movements. This paper investigates the robustness of non-local total variation methods in optical flow estimation for wilted plant leaves. By leveraging the strengths of non-local total variation-based approaches, we aim to improve motion boundary preservation in wilted leaf motion sequences, offering a way to differentiate between water-stressed and well-watered leaves. We review existing non-local total variation methods, analyze their performance on wilted plant datasets, and present a new approach integrating weighted guided filtering to handle edge preservation. The findings suggest that non-local total variation with weighted guided filter achieves robust optical flow estimations, offering potential for precision agriculture applications, particularly in detecting early signs of drought stress.

关键词： Precision agriculture Optical filters Filtering Reviews Plants (biology) Image edge detection Estimation Robustness Optical flow Stress

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Latent Dirichlet Allocation for Medical Records Topic Modeling: Systematic Literature Review 6

Latent Dirichlet Allocation for Medical Records Topic Modeli...

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6th international conference on informatics and computing, ICIC 2021

作者： Mustakim, M. Wardoyo, Retantyo Mustofa, Khabib Rahayu, Gandes Retno Rosyidah, Ida Sunan Kalijaga State Islamic University Department of Informatics Yogyakarta Indonesia Universitas Gajah Mada Department of Computer Science and Electronics Indonesia Universitas Gajah Mada School of Medicine Public Health and Nursing Yogyakarta Indonesia UIN Syarif Hidayatullah Faculty of Social and Political Sciences Jakarta Indonesia

ISBN: (纸本)9781665421553

The fast growth of Electronic Medical Records (EMR) has improved its functionalities and increase its use in secondary functions. EMRs can be used to improve the quality and capacity of physicians and medical students. It is done by using EMRs as a data source for researches and learning. A lot of studies have been done in this area. Various methods are also proposed. One of the methods used in medical records retrieval is Latent Dirichlet Allocation (LDA). Thus, research on LDA for medical records has also been carried out quite a lot. Unfortunately, those researches are still scattered and make it difficult to know the state of the arts of utilizing LDA in the medical records field. Our research intends to explore the studies of the LDA method progress in the medical field. To better present responsible literature study, our research used Systematic Literature Review as a research methodology. The literature review was conducted on studies from 2015 to 2021. The result of the literature review showed that the LDA method had been used in several research topics. And most of the literature used private datasets in their experiments. Some researchers also added a modification in the LDA method. Even though the LDA method proved a great method in medical fields, it has several limitations that need to be overcome. © 2021 ieee.

关键词： Statistics

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A Dual-Path Transformer Network for Scene Text Detection

A Dual-Path Transformer Network for Scene Text Detection

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingyu Lin Yan Yan Hanzi Wang Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China

The prosperity of deep learning contributes to the rapid progress of scene text detection. Among all the methods, segmentation-based methods have drawn extensive attention due to their superiority in detecting text instances of arbitrary shapes and extreme aspect ratios. However, the bottom-up methods are limited to the performance of their segmentation models. In this paper, we propose DPTNet (Dual-Path Transformer Network), a simple yet effective network to utilize both global and local information for the scene text detection task. Moreover, we propose a parallel design that integrates the convolutional network with a powerful self-attention mechanism to provide complementary clues. In addition, a bi-directional interaction module across two paths is developed to provide complementary clues along the channel and spatial dimensions. Our DPTNet achieves state-of-the-art results on several standard benchmarks in terms of both detection accuracy and speed.

关键词： Deep learning Shape Benchmark testing Signal processing Transformers Robustness Convolutional neural networks

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Gene Expression Analysis on Cancer Dataset 5

Gene Expression Analysis on Cancer Dataset

引用

5th international conference on Trends in Electronics and informatics, ICOEI 2021

作者： Vignesh, R. Deepa, D. Mana, Suja Cherukullapurath Samhitha, B.Keerthi Anandhi, T. School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9781665415712

Genes are the basis of tumor formations around the body, which is better known as cancer. They inhibit basic processes such as cell death (apoptosis) and promote cell division to an unhealthy extent. The expression of every gene provides a baseline to know the progress of cancer from the organ or tissue it originated from along with its approximated course of action. The analysis of such gene expression values using traditional machine learning methods provide a higher efficiency and accuracy in finding relationships between genes and also it may serve as a future for diagnosing the cancer by using these values. The main challenge is to use the bases that are created to efficiently compute the highly effective genes to treat specific types of cancer by using their expression values and thus, raise the question of a potential relationship between them for each type. A Random Forest Model has been used to perform Feature Selection over the dataset in order to extract the important features (i.e.) the most influential genes. They are then visualized by using traditional packages in Python (i.e. Scikit-plot, Matplotlib, Seaborn) and using a data visualization tool called Tableau to project the result of the analysis. © 2021 ieee.

关键词： Gene expression

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Interactive Multimodal Learning: Towards Using Pedagogical Agents for Inclusive Education

Interactive Multimodal Learning: Towards Using Pedagogical A...

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ieee Canada international Humanitarian Technology conference (IHTC)

作者： Muhammad H. Al Omoush Sumaia E. Salih Sameer Kishore Tracey Mehigan Faculty of Engineering and Computing Dublin City University Dublin Ireland Dublin Ireland Department of Computer Engineering and Informatics Middlesex University Dubai UAE

A pedagogical agent is an animated interface in an interactive online learning environment. Its role involves guiding users through instructions and participating in direct discussions. Numerous research studies underline the advantages of pedagogical agents in delivering instruction, tailoring learning experiences, and promoting inclusivity. The authors utilize machine learning techniques to develop an interactive multimodal application (combining visuals and audio) designed as a pedagogical agent. This application is intended to assist in teaching primary school students (aged 6 to 8) the recognition of colors and letters. Additionally, it incorporates voice interaction to assess their learning progress. The overarching objective of this pedagogical agent is to seamlessly integrate artificial intelligence (AI) into educational settings, thereby amplifying student engagement and motivation during the learning process. This paper will explore the journey of creating a machine learning-based application and its potential utility in supporting educators. Furthermore, the authors will investigate potential augmentations that could render the application more adept at aiding students with dyslexia. The paper will emphasize the significance of human-computer interaction (HCI), as it significantly shapes pedagogical agents' design and operational aspects.

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Machine Learning Innovations in Code Generation: A Systematic Literature Review of Methods, Challenges and Directions

Machine Learning Innovations in Code Generation: A Systemati...

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Information Technology and computing (ICITCOM), international conference on

作者： Mardi Siswo Utomo Ema Utami Kusrini Arief Setyanto Dept. of Information Technology and Industry Universitas Stikubank Semarang Indonesia Dept. of Informatics Doctorate Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

This paper provides a comprehensive review of recent advances in machine learning (ML) technologies applied to code generation, with a focus on literature published between 2022 and 2024. We collect and analyze 36 studies from four major publication databases: ACM, ieee Xplore, Springer, and Scopus, highlighting the transformative impact of transformer-based models in this domain. Significant findings indicate that transformer-based models and graph neural networks (GNNs) are the best ways to understand and generate code. Exact match accuracy, BLEU, ROUGE, execution verification, and human studies are some of the evaluation methods that now provide comprehensive performance assessments. Major challenges identified include a lack of high-quality training data, high computational resource requirements, and security concerns. Recent trends emphasize the use of diverse neural network architectures, industrial project datasets, and novel evaluation methods to improve model robustness and applicability. Applications cover a wide range of paradigms, including code-to-description, description-to-code, and code-to-code, with tasks such as automated documentation and code optimization. Dataset innovations include the use of open-source repositories, competition datasets, and synthetic data to improve code structural and contextual understanding. This paper seeks to guide future research and foster additional innovations in the field by providing a thorough examination of both progress and challenges in ML-powered code generation.

关键词： Technological innovation Codes Accuracy Computational modeling Training data Transformers Data models Security Optimization Context modeling

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Constructing Multi-granular Knowledge Graph with Structure Similarity and Hierarchical Clustering

Constructing Multi-granular Knowledge Graph with Structure S...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Ke Yang Jingyun Feng Zhiyu Xi Qiuke Zheng Huan Zhang Wei Deng China Railway Eryuan Engineering Group Co. Ltd. Chengdu China Dept. of Statistics Southern University of Science and Technology Shenzhen China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computer Science and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China School of Statistics Southwestern University of Finance and Economics Chengdu China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

The structure of knowledge graph is basically flat and lack of hierarchies to make knowledge graph too general to deal with concrete reasoning problems. There are few work about hierarchical knowledge graph. In this paper, the entity and relation combined method is proposed to divide the entities into groups based on structure similarity. The hierarchical cluster method is proposed with the entity and relation combined embedding representation to construct a multi-granular knowledge graph. The experimental results show that the hierarchical division relied on combined embedding are significantly better than those based on the existing entity embedding methods in three evaluation indexes. The combined embedding can capture the structure information which is helpful to hierarchical knowledge graph accurately and reasonably.

关键词： Accuracy Large language models Clustering algorithms Knowledge graphs Cognition Classification algorithms Indexes informatics

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