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3rd international Workshop on Applications of Medical Artificial intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

3rd International Workshop on Applications of Medical Artifi...

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3rd international Workshop on Applications of Medical Artificial intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031820069

The proceedings contain 24 papers. The special focus in this conference is on Applications of Medical Artificial intelligence. The topics include: SP-NAS: Surgical Phase recognition-Based Navigation Adjustment System for Distal Gastrectomy;transforming Multimodal Models into Action Models for Radiotherapy;enhanced Interpretability in Histopathological Images via Combined Tissue and Cell-Level Graph Analysis;targeted Visual Prompting for Medical Visual Question Answering;deep Learning for Resolving 3D Microstructural Changes in the Fibrotic Liver;predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework;Optimizing ICU Readmission Prediction: A Comparative Evaluation of AI Tools;source Matters: Source Dataset Impact on Model Robustness in Medical Imaging;evaluating Perceived Workload, Usability and Usefulness of Artificial intelligence Systems in Low-Resource Settings: Semi-automated Classification and Detection of Community Acquired Pneumonia;incremental Augmentation Strategies for Personalised Continual Learning in Digital Pathology Contexts;assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears;automated Feedback System for Surgical Skill Improvement in Endoscopic Sinus Surgery;quantifying Knee Cartilage Shape and Lesion: From Image to Metrics;RadImageGAN – A Multi-modal Dataset-Scale Generative AI for Medical Imaging;Ensemble-KAN: Leveraging Kolmogorov Arnold Networks to Discriminate Individuals with Psychiatric Disorders from Controls;SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury;EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models;evaluating the Impact of Pulse Oximetry Bias in machine Learning Under Counterfactual Thinking;normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification;one-Shot Medical

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Natural Language Processing Technology Based on Artificial intelligence in Software Testing 3

Natural Language Processing Technology Based on Artificial I...

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3rd international conference on Artificial intelligence and Computer Information Technology, AICIT 2024

作者： Liu, Yanyan Shandong Vocational College of Science and Technology Shandong Weifang China

ISBN: (纸本)9798350365443

The application of Natural Language Processing (NLP) technology based on artificial intelligence technology in the field of software testing is becoming increasingly widespread. This article explores how NLP technology based on BERT (Bidirectional Encoder Representations from Transformers) can be deeply integrated into various aspects of software testing, achieving automation in requirement analysis, test case generation, defect report processing, and test result description. Through a series of experiments, the significant advantages of BERT technology in improving testing efficiency, accuracy, and reducing manual intervention have been verified. This article first introduces the theoretical foundation and key technologies of NLP technology in software testing, then describes in detail the integration framework based on BERT algorithm, and demonstrates its application effects in test data preparation, preprocessing, automated defect recognition, and report generation through experimental results. Experimental data shows that BERT technology has significant advantages in test case coverage, defect detection accuracy, and test regression cycle, with a maximum regression cycle of only 599ms. Despite challenges such as model generalization ability, data quality dependency, and computational resource consumption, the integrated framework of BERT technology provides new possibilities for the automation and intelligence of software testing. © 2024 IEEE.

关键词： Application programs

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2023 3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

2023 3rd International Conference on Communication, Computin...

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3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

ISBN: (纸本)9798350332391

The proceedings contain 31 papers. The topics discussed include: efficient virtual network embedding with hierarchical and cooperative multi-agent reinforcement learning;testing neurofeedback system for studying cognitive tests;a survey on human factors in cyberspace: a new dimension of privacy threats;user recognition based on gait pattern via smartwatch accelerometer in unrestricted environment;performing walk-through energy audits in smart homes;exploration of machine learning algorithms for development of intelligent intrusion detection systems;chaos-based secured modified strong recovery conditions for least support orthogonal matching pursuit in the noisy case;machine learning technique to monitor heartbeat using amalgamated data of multi-sensor stream;a metadata approach to classify domain-specific documents for event-based surveillance systems;a novel font color and compression text steganography technique;hybrid deep learning approach for nonfunctional software requirements classifications;and consensus MPC-based hierarchical control of islanded AC microgrid.

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The Scientific Analysis on Effective Yoga Posture recognition Techniques 3rd

The Scientific Analysis on Effective Yoga Posture Recognitio...

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3rd international conference on machine Vision and Augmented intelligence, MAI 2023

作者： Rajgure, E.G. Patidar, Raghavendra School of Engineering ECE University of Technology Jaipur India

ISBN: (纸本)9789819743582

Today’s globalized scenario of competition has increased beyond expectations and to fulfill it, everyone has to face many competitive challenges and stressful work culture. In addition to prescribing the proper medication, many medical professionals advise optimal food, pranayama, and yoga to maintain this environment. Due to this physical health awareness number of professional yoga practitioners has been increased. Yoga exercises with proper postures are very important to get the benefits of increasing flexibility, muscle strength and also improve the functionality of the respiratory system. Along with spirituality, yoga has a number of impacts on the human body which ultimately integrates the coordination between mind and body. Due to which regular practitioners can maintain self-discipline and self-awareness. Due to improper methods of practice, maximum beginners do not get benefited from the routine yoga practice. Our main goal is to review the literature on yoga posture recognition systems based on scientific analysis. It also presents our methodical approach to posture recognition and classification based on AI and machine learning techniques, which we hope will help validate the accuracy of the postures performed during yoga practice. In order to collect time series data from sensors for various postures, our ongoing work will create an embedded wearable sensor-based system. Along with this, we have also enlisted measurable benefits of practicing yoga in the improvements of human health parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence machine learning Posture recognition Yoga

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An Effective machine Learning Algorithm for Forecasting the Market Value of a House 3

An Effective Machine Learning Algorithm for Forecasting the ...

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

作者： Ancy, S.G. Praveenchandar, J. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Karunya Nagar Coimbatore641114 India

ISBN: (纸本)9798350375190

machine learning plays a virtual role in everyday speech commands, product recommendation, and even medical fields. But instead of providing better customer service, it provides safer autonomous vehicle systems. House price prediction using machine learning is a hot topic in data science and AI. With the increasing availability of data and the advancement of machine learning algorithms, accurately predicting house prices has never been easier. This study will not only assist housing developers and researchers to determine the most significant factors affecting housing prices but will also help determine the best machine learning model to use in this research. This model will allow people to allocate resources to a request without relying on a broker. According to the results of this study, the random forest regression is the most accurate. Real estate market analysis is one of the most important tools for predicting changes in house prices and rental income. Several techniques have been developed in recent years to solve this problem, including statistical models and data mining methodologies and machine learning algorithms. However, a few issues, including data quality and availability, needed to be fixed. Missing numbers, outliers, and inconsistent formats are common in housing databases. These issues could impair the performance of the prediction models. To solve these issues, thorough data preprocessing is required. Collaboration with data providers and governmental agencies is also required to ensure data coverage and accuracy. © 2024 IEEE.

关键词： Forecasting

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A Deep Learning Approach for Herbal Plant Detection and recognition 3

A Deep Learning Approach for Herbal Plant Detection and Reco...

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

作者： Giridharan, N. Aaffrin Sulthana, R. Mohanraj, R. Sanjay Murugan, M. K.S.Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350375190

In this research, TensorFlow, a powerful deep learning toolkit, is utilized in conjunction with the Python programming language to conduct a comprehensive analysis of plant names and specifications. The research involves compiling a diverse dataset containing various plant species and their corresponding characteristics. Prior to processing, the dataset is meticulously cleaned and standardized to ensure consistency. By leveraging TensorFlow and natural language processing techniques, features are extracted from the plant names, enabling the identification of patterns and relationships. Deep learning models based on TensorFlow are then trained to recognize these patterns and associate them with specific plant attributes. The inclusion of a user-friendly interface facilitates seamless communication and allows users to input plant names for analysis. The research aims to enhance the understanding of plant taxonomy by providing valuable insights with implications for fields such as botany, agriculture, and pharmaceutical research. The project focuses on creating a robust herbal plant detection system using Convolutional Neural Networks (CNNs) with a target accuracy of 90%. CNNs enable precise herbal plant classification through automatic feature extraction from input. © 2024 IEEE.

关键词： Feature extraction

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Artificial intelligence in Innovation Research A Bibliometric Perspective 3

Artificial Intelligence in Innovation Research A Bibliometri...

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3rd international conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Triana, Ika Arief, M.T.S. Alamsjah, Firdaus Elidjen, Elidjen Bina Nusantara University Bbs Research in Management Jakarta Indonesia Bina Nusantara University Industrial Engineering Department Jakarta Indonesia Bina Nusantara University Business Creation Program Bbs in Management Jakarta Indonesia

ISBN: (纸本)9798350367492

This study explores the intersection of artificial intelligence (AI) and innovation research through bibliometric analysis, aiming to identify key trends, influential authors and pivotal publications within this rapidly evolving field. By systematically examining a large dataset of scholarly articles, we map the intellectual structure and development of AI-driven innovation research over the past decade. The analysis reveals a significant growth in publications, with an emphasis on the application of AI technologies to enhance innovation processes in various industries. Key findings highlight the predominant research themes, such as machine learning, data analytics, and their impact on product development and organizational efficiency. The study also uncovers the geographic distribution of research activities, with leading contributions from North America, Europe, and Asia. Through network analysis, we identify core clusters of collaboration and citation patterns, illustrating the interconnected nature of the research community. This bibliometric perspective provides a comprehensive overview of the state of AI in innovation research, offering valuable insights for scholars, policymakers, and practitioners aiming to leverage AI for future advancements in innovation. © 2024 IEEE.

关键词： Artificial intelligence Data Analytics Innovation Research machine Learning Research Trends

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User recognition based on Gait pattern via Smartwatch Accelerometer in Unrestricted Environment 3

User Recognition based on Gait Pattern via Smartwatch Accele...

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3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Khan, Sheharyar Shah, Syed M Adnan Arsalan, Aamir Ahmed, Wakeel University of Engineering and Technology Taxila Department of Computer Science Taxila Pakistan Fatima Jinnah Women University Rawalpindi Department of Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350332391

Wearable gadgets are becoming a major constituent of our society due to a wide range of applications like financial transactions, unlocking automobiles, tracking health and fitness, and many more. Personal data is usually required to manage these services, however, most commercially available wearables devices either lack a user authentication mechanism or employ knowledge-based authentication like passwords, PINs, or pattern locks. In this growing digital age, it is really difficult for an individual to memorize such a huge number of passwords. In this research, we put forward a technique for recognizing users from their walking patterns recorded in real-life settings using a smartwatch accelerometer sensor. Ensemble classification techniques (bagging and boosting) are used for the purpose of user identification using the selected time domain features. Our proposed method attained the highest classification accuracy of 88.35% which transcends the existing user recognition schemes available in the literature. © 2023 IEEE.

关键词： machine learning

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A Hybrid Nut Sorting System: Utilizing machine Vision and Augmented Information for Efficient Human-Robot Collaboration 3

A Hybrid Nut Sorting System: Utilizing Machine Vision and Au...

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3rd international conference on Mechatronics, Control and Robotics, ICMCR 2025

作者： Sangchai, Kanisorn Adirekkittikun, Wongsakorn Vivatanaprasert, Kevin Boonpun, Methasit Chokthitirath, Wongpakorn Thipyophat, Nutdanai International School of Engineering Chulalongkorn University Robotics and Artificial Intelligence Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9798331544546

Efficient sorting and classification of nuts is a critical task in various industries, yet current methods often rely on machine vision systems that inspect each nut individually, making the process time-consuming and inefficient. Additionally, most processes are fully automated, lacking the option for human monitoring or intervention. The novelty of this paper lies in the development of a hybrid nut sorting system that combines machine vision with augmented information to facilitate efficient human-robot collaboration. Unlike traditional fully automated systems that exclude human involvement, our approach integrates a projector-based visual aid to provide real-time, easily interpretable information for manual or semi-automated sorting. Additionally, the system incorporates a CoreXY robot path and mechanism, enabling collaboration between humans and robots. The detection system consistently identifies nuts of different sizes with reliable accuracy, particularly for simultaneous multi-object detection compared to singular detection methods. As a hybrid sorting system, our solution also allows humans to manually sort nuts at a faster rate than the machine while utilizing the system's assistance. © 2025 IEEE.

关键词： Collaborative robots

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Research on Learning Terminal System for English Teaching Practice in Artificial intelligence 3

Research on Learning Terminal System for English Teaching Pr...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Cao, Yu Xi'an Eurasia University Global Language Center Xi'an710065 China

ISBN: (纸本)9798350380989

This paper is mainly for the English test terminal research and development. It has realized the functions of question bank, examination, examination statistics and so on. The main feature of the system is that the whole process of using cryptography and digital signature technology, the paper generation process and the content of the examination questions are more fully processed, and also provides a safe and flexible authorized access control mechanism, a complete function of dynamic query and statistical analysis, intelligent paper design. It converts speech recognition and English into Chinese intonation, and designs and develops English translation system at the same time. The deviation between the basic frequency curve of the established tone system and the original sound is only 0.02, and the basic frequency curve is very close. This method can obtain good speech production results, and the generated sounds are diversified and accurate. The CPU usage of this system is 27.8%. Compared with the mobile terminal system and the WEB system, the CPU usage decreased by 19.0%. © 2024 IEEE.

关键词： Artificial intelligence English teaching English test learning terminal system spoken language

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