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A Review of Artificial intelligence in Medical Prescription Analysis 3

A Review of Artificial Intelligence in Medical Prescription ...

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Agrawal, Mayur Mishra, Vishwajeet Jogani, Hanesh Sunil Kashyap, Prerna Mishra, Raj Gaurav School of Engineering Ajeenkya Dy Patil University Pune India

ISBN: (纸本)9798350322842

Accurate reading and comprehension of medical prescriptions are crucial for healthcare providers to ensure appropriate treatment for the patients. However, with the growing volume of prescriptions and increasingly complex medication regimens, errors can occur, which can result in severe consequences. To mitigate this issue, Artificial intelligence (AI) can automate tasks such as medication identification, dosage calculation, and drug interaction checks, potentially improving the accuracy and efficiency of prescription analysis. This research study intends to examine the latest AI-based approaches for analyzing medical prescriptions in the healthcare industry, which can aid in the development of diagnostic systems capable of extracting patients' medical history, identifying incorrect medications and conducting statistical analyses on prescribed drugs. By analyzing the current state-of-the-art techniques in this field, one can identify areas of improvement to enhance prescription analysis accuracy and efficiency, ultimately benefiting patient care and medication management. © 2023 IEEE.

关键词： machine learning

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Cooperative Search and Tracking Framework for Multi-UAV Systems in Urban Environments 3rd

Cooperative Search and Tracking Framework for Multi-UAV Sy...

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3rd international conference on machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wang, Zhenzhao Ma, Yunchang Li, Jinxu Li, Linxuan Liu, Zhihong College of Intelligence and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819624676

Cooperative search and tracking of multiple Unmanned Aerial Vehicle(UAV) systems is becoming a key application paradigm for realizing efficient coordination missions. This paper proposes a multiple UAV cooperative search and tracking framework. This framework includes four components: task assignment, motion planning, target detection and target tracking. Among them, task assignment uses auction algorithm to solve the distributed task assignment problem;motion planning uses deep reinforcement learning algorithm;target detection uses YOLOv8 algorithm and ByteTrack algorithm to achieve target recognition and tracking on the image plane;and target tracking utilizes position-based visual servo control to achieve target tracking control of UAV in space. We experimentally validate our proposed framework through XTDrone simulator. The experimental results show that each functional module in our framework plays its corresponding role and can effectively improve the search and tracking efficiency in complex environments of UAVs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion planning

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Integrated Optimization of Underwater Acoustic Ship-Radiated Noise recognition Using Relative GMM-MAP-UBM Approach 3

Integrated Optimization of Underwater Acoustic Ship-Radiated...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Mamatha, K. Joshua Oyebode, Oluwadare Rexy V, Arul Mary Kaliappan, S. Suganthi, D. Hemamalini, U. Department of Mathematics Telangana Hyderabad India Afe Babalola University Department of Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria Saveetha College of Liberal Arts and Sciences Simats Chennai Thandalam India Lovely Professional University Division of Research and Development Jalandhar - Delhi Punjab Phagwara India Saveetha College of Liberal Arts and Sciences Simats Department of Computational Intelligence Chennai Thandalam India Department of Information Technology Chennai India

ISBN: (纸本)9798350381931

Due to the wealth of information it contains, ship radiated noise plays a crucial role in underwater acoustic signal processing for ship identification. The traditional approach to ship radiated noise detection is extremely inefficient and inaccurate since it relies excessively on the operator's prior knowledge and experience. While doing preprocessing, feature extraction, and training the model, make sure to keep the order of significance. The application of frequency component decomposition drives data preparation. Researchers examine a wide variety of feature extraction approaches for application in ship-radiated noise detection, classifying the resulting features accordingly. Following feature extraction, with unified GMM-MAP-UBM models must be trained. Both GMM and UBM, which are considered state-of-the-art approaches, are surpassed by the proposed method. An improvement of 98.54% in accuracy was achieved after implementing the technique. © 2024 IEEE.

关键词： Feature extraction

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Sign Language to Regional Language Converter 3

Sign Language to Regional Language Converter

引用

3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Mohanrja, S. Babu, C Ganesh Jayendraprabhu, S. Lokeshkumar, V. Trishala, Ag KGiSL Institute of Technology Department of Artificial Intelligence & Data Science Coimbatore India Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Coimbatore India

ISBN: (纸本)9798350370867

In recent days most people who are disabled are not able to make proper communication with others. Deaf people frequently communicate through sign language, a kind of manual communication. There are many different sign languages used by deaf people around the world;sign language is not a global language. Recognizing activities from sign languages is a computer job known as sign language recognition, or SLR for short. The main motive of the project is to translate their sign language to regional language through which we can able to full fil their needs. By providing input data through the form of speech, text, or sign language in the translation process, it is possible to change the translation for them better. © 2023 IEEE.

关键词： Translation (languages)

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No Regularization Is Needed: Efficient and Effective Incomplete Label Distribution Learning 33

No Regularization Is Needed: Efficient and Effective Incompl...

引用

33rd international Joint conference on Artificial intelligence (IJCAI)

作者： Li, Xiang Chen, Songcan Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (纸本)9781956792041

In reality, it is laborious to obtain complete label degrees, giving birth to Incomplete Label Distribution Learning (InLDL), where some degrees are missing. Existing InLDL methods often assume that degrees are uniformly random missing. However, it is often not the case in practice, which arises the first issue. Besides, they often adopt explicit regularization to compensate the incompleteness, leading to burdensome parameter tuning and extra computation, causing the second issue. To address the first issue, we adopt a more practical setting, i.e., small degrees are more prone to be missing, since large degrees are likely to catch more attention. To tackle the second issue, we argue that label distribution itself already contains abundant knowledge, such as label correlation and ranking order, thus it may have provided enough prior for learning. It is precisely because existing methods overlook such a prior that leads to the forced adoption of explicit regularization. By directly utilizing the label degrees prior, we design a properly weighted objective function, exempting the need from explicit regularization. Moreover, we provide rigorous theoretical analysis, revealing in principle that the weighting plays an implicit regularization role. To sum up, our method has four advantages, it is 1) model selection free;2) with closed-form solution (sub-problem) and easy-to-implement (a few lines of codes);3) with linear computational complexity in the number of samples, thus scalable to large datasets;4) competitive with state-of-the-arts in both random and non-random missing scenarios.

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Intelligent Detection Model for Power Grids Based on Graph Neural Networks 3

Intelligent Detection Model for Power Grids Based on Graph N...

引用

3rd international conference on Big Data, Artificial intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Zhang, Guoliang Zhang, Yi Du, Zexu Zhang, Xiangquan Wang, Liping He, Zhouqiang Artificial Intelligence on Electric Power System State Grid Beijing China State Grid Gansu Electric Power Company Lanzhou China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

With the rapid development of artificial intelligence and machine vision technology, power grid inspection system based on vision is widely used. However, the power grid intelligent inspection system has the problem of unsatisfactory accuracy of small target detection. To address this problem, this paper proposes an intelligent detection model for power grids based on graph neural networks. Firstly, the VGG16 model is used to construct the trunk feature extraction network. Secondly, the graph relation network between target and background is constructed. Finally, the defect detection model based on graph neural network is constructed. The model proposed in this article is compared with the existing object recognition model in the simulation of the different datasets. The average recognition accuracy of the proposed model is 0.858, which is improved by9.71%. © 2022 IEEE.

关键词： Object recognition

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Research on Traffic Classification Algorithm of Power Communication Network based on Cluster Analysis 3

Research on Traffic Classification Algorithm of Power Commun...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Wang, Lijun Lu, Yi Zhu, Guopeng Wang, Qi Ma, Kai Information Telecommunications Company State Grid Shandong Electric Power Company Jinan250001 China

ISBN: (纸本)9798350319125

Power communication network is an important infrastructure in power system, and its security and stability are very important to ensure the normal operation of power system. With the development and application of power system, the traffic of power communication network is increasing, and the classification of traffic is of great significance for network management and optimization. In this paper, a classification algorithm based on clustering analysis is proposed to solve the problem of traffic classification in power communication network. The algorithm can effectively classify the traffic data and provide powerful support for the management and optimization of power communication network © 2023 IEEE.

关键词： clustering algorithms pattern recognition theory power communication networks Traffic classification

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A Graph-based Multi-view Clustering Approach for Building Group patterns recognition 3

A Graph-based Multi-view Clustering Approach for Building Gr...

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3rd international conference on Internet of Things and Smart City, IoTSC 2023

作者： Yang, Ruolin Yang, Lin Zuo, Zejun Wei, Jing National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan China School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9781510666375

Building pattern informs urban spatial structure understanding and modeling. However, previous studies showed limitations on the identification of building groups which has complex spatial distribution. Specifically, they usually use original spatial or non-spatial characteristics but omit certain complementary among multi-faceted features of buildings. In this paper, we give a novel method based on a multi-view clustering framework, which establishes a centroid distance view and a building attribute view to recognize building patterns accurately. The two views are based on both the spatial structure and non-spatial attributes. The similarity graphs on these views, which are obtained by the Gaussian kernel function, are executed graph diffusion and fusion process to obtain a unified graph. Then, the clustering results are obtained via the Normalized cut algorithm. We conduct experiments to recognize building patterns by four real-world community building footprint datasets of two cities in China: Wuhan and Chengdu. The experimental results show that our proposed method identifies building groups with grid, grid-like and unstructured patterns more effectively. Our method performs better in seven evaluation indexes compared with the baseline model. © 2023 SPIE.

关键词： pattern recognition

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Detection of Cyberbullying in Social Media Texts Using Explainable Artificial intelligence 3rd

Detection of Cyberbullying in Social Media Texts Using Expla...

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3rd international conference on Ubiquitous Security (UbiSec)

作者： Islam, Mohammad Rafsun Bataineh, Ahmed Saleh Zulkernine, Mohammad Queens Univ Sch Comp Kingston K7L 2N8 ON Canada

ISBN: (纸本)9789819712731;9789819712748

The widespread use of social media has opened the door to new forms of harassment and abuse, such as cyberbullying, that have a serious impact on individuals' psychological health. Therefore, research communities have recently developed detection approaches using Natural Language Processing (NLP) combined with machine learning algorithms to identify instances of cyberbullying in social media texts. However, they are unable to determine the type of cyberbullying and the reasons why victims may be targeted. This paper develops a novel detection approach that can identify the type of cyberbullying based on characteristics such as gender, religion, age, and ethnicity, even if the original records in the training dataset do not include such information or features. This paper has accomplished this objective by utilizing Explainable Artificial intelligence (XAI) technology alongside machine learning models to justify and explain the classification of text as cyberbullying. Technically speaking, XAI technology enables machine learning models to capture and highlight the most influential words that affect the decision to classify a text as cyberbullying. Those influential words are utilized to re-label and update the training data. The machine learning models are then re-trained using the updated data. To evaluate the performance of the proposed approach, a simulation experiment has been conducted on a large dataset containing texts from Twitter. Simulation results show that XAI technology provides convincing explanations for classifying a text as cyberbullying. It also enables machine learning models to identify various types of cyberbullying and enhances their performance in terms of classification accuracy.

关键词： Cyberbullying Explainable Artificial intelligence (XAI) Local Interpretable Model-Agnostic Explanations (LIME) Natural Language Processing (NLP)

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Artificial intelligence in Wearables - Challenges and Opportunities in Physical Therapy and Sports Training 3

Artificial Intelligence in Wearables - Challenges and Opport...

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3rd international conference on Hybrid Human-Artificial intelligence, HHAI 2024

作者： Sorysz, Joanna RPTU Kaiserslautern-Landau Kaiserslautern Germany

ISBN: (纸本)9781643685229

Adherence to procedures and rules is essential in order to obtain the best results in medicine and sports. However, traditional clinical setups can induce stress in patients, hindering recovery. Meanwhile, advancements in activity recognition and monitoring technology have revolutionised the sports industry, yet systems suggesting exercises for performance improvement are sparse. At the same time, children training supervision is lacking comprehensive research altogether. In my research, I propose a project that aims to unify wearables solutions in physical therapy, sport training and children development. The key aspects of the research include the exploration of sensor modality fusion in order to obtain better results, body motion tracking, and physiological parameters recording. Planned experiments will focus on joint and torso movement mapping, integration with vital signs in order to perform real-life evaluations in cooperation with athletes, patients, coaches, and therapists. © 2024 The Authors.

关键词： Sports

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