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共 1872 条记录，以下是151-160 订阅

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Design of Bird Repellent System Based on Machine Vision Detection and Localization 4

Design of Bird Repellent System Based on Machine Vision Dete...

引用

4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Jiang, Zhizhe Zhang, Yuechong Liu, Mingyang Qu, Zenghui Liu, Haiying Deng, Lixia Liu, Lida Li, Shuang Qilu University of Technology Shandong Academy of Sciences School of Information and Automation Engineering Jinan250353 China Shandong Runyi Intelligent Technology Co. Ltd. Jinan250002 China

ISBN: (纸本)9798350361643

Bird strike phenomenon generally refers to incidents where aircraft structural damage due to collisions with birds during low-altitude flight. Such incidents can lead to accidents, casualties, and economic losses. the civil aviation industry has attached great importance to bird strike prevention, devising regulations and standards to mitigate bird strike risks through scientific management and measures. Machine vision bird repellent systems, an environmentally friendly and non-contact method, align with current environmental trends. this paper comprehensively employed and fused traditional YOLOv5, KCF, Haarcascade, and Adaboost for target recognition and tracking. It proposes constructing a visual gimbal using a Raspberry Pi, servos, camera, etc. Python programming language is utilized to implement multi-mode bird identification within the field of view and issue voice alerts to ground control personnel promptly upon bird detection. the tracking device is activated according to predetermined conditions to efficiently and accurately handle bird situations. the designed system can accurately identify and track birds, thereby helping to reduce the probability of bird strike incidents and enhance flight safety. © 2024 IEEE.

关键词： Computer vision

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Improving Biomedical Claim Detection using Prompt Learning Approaches 4

Improving Biomedical Claim Detection using Prompt Learning A...

引用

4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Chen, Tong Stefanidis, Angelos Jiang, Zhengyong Su, Jionglong Jiangsu Suzhou215123 China

ISBN: (纸本)9798350324303

Biomedical claim detection is an effective method to uncover negative effects arising from the treatment of disease and detect misinformation about medications from online platforms. Due to the power of pre-trained language models (PLMs), such as BERT, RoBERTa and T5, fine-tuned PLMs perform exceptionally well in biomedical claim detection. However, a gap exists in the text classification task between objective forms used in pre-training and fine-tuning for PLMs methods, preventing these models from taking full advantage of the information for biomedical claim detection. Motivated by the prompt learning approach, we propose a method, in which the classification task is transformed into a masked language modeling task that fully utilizes the mask learning capability of PLMs for better prediction of biomedical claim detection. In our method, a template with a mask representing the label is first constructed, and the mask is then filled and mapped to the corresponding label. We use three PLMs as backbone models, i.e., BERT, RoBERTa, and T5, with both hard and mixed templates which are fully and partially predefined templates. Experimental results using the BioClaim dataset demonstrate the superiority of the prompt learning methods over the BERT and RoBERTa classification baselines. Furthermore, the T5 model with mixed template consistently outperforms the rest of experimented models and achieves state-of-the-art performance with an increase of 5.3% on F1-score compared to previous research on this dataset. © 2023 IEEE.

关键词： Learning systems

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intelligent Household Waste Classification System Based on Machine Learning 4

Intelligent Household Waste Classification System Based on M...

引用

4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Linqiang, You Sindiramutty, Siva Raja A L Ashraf, Humaira Muzammal, Syeda Mariam Balakrishnan, Sumathi A P Gupta, Sarita Kavita School of Computer Science Taylor's University Subang Jaya Malaysia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Department of Computer Science and Engineering PunjabJhanjeri Mohali140307 India

ISBN: (纸本)9798350353266

In today's world, rapid global development has improved living standards but also led to a significant rise in waste generation, posing challenges for waste management systems. To address this issue, integrating smart technologies into waste classification has emerged as a promising solution. Countries like Japan, Singapore, Stockholm, and China have explored diverse models using IoT and big data to promote recycling and minimize waste. this research is dedicated to the design and development of an intelligent household waste classification system, leveraging the power of machine learning techniques. the primary objective of this system is to achieve high-precision waste detection and recognition, enabling automatic identification and categorization of diverse types of waste. By harnessing the potential of deep learning algorithms, the system aims to enhance the efficiency of waste management, mitigate environmental pollution, and foster sustainable development. the study delves into existing image recognition methodologies, explores the applicability of these techniques to waste detection and recognition, and proposes a comprehensive approach for implementing waste detection and classification. the research utilizes data from open-source repositories and supplements it with a broad collection of waste images to construct a diverse and representative dataset. the performance of two primary models, VGG13 and YOLO, is assessed in the context of waste classification. the outcomes of this research could potentially offer a practical and efficient solution for waste classification, contributing to improved waste management practices and environmental preservation. © 2024 IEEE.

关键词： component formatting insert style styling

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Development and Clinical Evaluation of a Five-Fingered Myoelectric Prosthetic Hand with pattern recognition 4

Development and Clinical Evaluation of a Five-Fingered Myoel...

引用

4th IEEE Global conference on Life Sciences and Technologies, LifeTech 2022

作者： Kuroda, Yuki Tsujimoto, Tatsuki Shimada, Takayoshi Yabuki, Yoshiko Bai, Dianchun Yamanoi, Yusuke Jiang, Yinlai Zhu, Jinying Yokoi, Hiroshi The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan Shenyang University of Technology School of Electrical Engineering Shenyang China The University of Electro-Communications Center for Neuroscience and Biomedical Engineering Tokyo Japan Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China

ISBN: (纸本)9781665419048

this study reports the development of a five-fingered myoelectric prosthetic hand with a pattern recognition function. this hand is lightweight (330 gw) and has a mechanism by which the fingers absorb external force, preventing damage and making it safe to use. In addition, it includes electromyography sensors for robust measurements and a recognition stabilization filter to deal with the computational performance limitation of the controller. We applied this prosthetic hand to subjects and evaluated its discrimination and task performance. © 2022 IEEE.

关键词： Palmprint recognition

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Integrating Optimized CNN and Deep CNN Model for Enhanced Maize Plant Leaf Disease Classification and Prediction systems 4th

Integrating Optimized CNN and Deep CNN Model for Enhanced Ma...

引用

4th international conference on Advanced Network Technologies and intelligent Computing-ANTIC-Annual

作者： Deepan, P. Vidya, R. Kalaivani, K. Ganesh, J. Dhiravidaselvi, S. St Peters Engn Coll Hyderabad 500043 India Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India SASTRA Deemed Univ Dept CSE Srinivasa Ramanujan Ctr Kumbakonam India Roever Engn Coll Perambalur 621212 India

ISBN: (纸本)9783031837890;9783031837906

India's economy is supported by agriculture crops, which is one of the reasons why it is considered to be one of the developing nations in the globe. the detection of crop diseases is still a challenging process, despite the fact that crop diseases pose a significant threat to the nation's food supply. ANN, SVM, k-NN, and CNN are only some of the classic classification methods that can be utilized in this context. Both Machine Learning and Deep Learning are also available for application. As a consequence of this, the accuracy of such systems has reached its maximum level because they are dependent on a feature extraction process that is built by hand. For the purpose of improving the accuracy of our classification method, it is essential to merge more than one deep learning model for the classification of maize leaf disease. therefore, in order to effectively address this matter, we have developed a hybrid plant disease recognition model in order to enhance the learning outcomes of Deep CNN. this was accomplished by combining two models, namely VGG-16 and optimized CNN model, into a single model. For the goal of evaluating the effectiveness of the maize crop in the experiments, 4,400 images of maize crop leaves were taken from the Village Net dataset. these images included Common_rust, Gray_leaf_spot, Healthy and Northern_leaf_blight. According to the findings of our investigation, we have shown that a hybrid maize disease recognition model (VGG-16 Net model and Optimized CNN model) performs better than separate deep learning models (VGG-16 Net model and Optimized CNN model).

关键词： Maize leaf leaf disease disease prediction Deep CNN model hybrid model and transfer learning

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Resumate: A Prototype to Enhance Recruitment Process with NLP based Resume Parsing 4

Resumate: A Prototype to Enhance Recruitment Process with NL...

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4th international conference on intelligent Engineering and Management, ICIEM 2023

作者： Mohanty, Saswat Behera, Anshuman Mishra, Sushruta Alkhayyat, Ahmed Gupta, Deepak Sharma, Vandana Kalinga Institute of Industrial Technology Deemed to be University India College of technical engineering The Islamic University Najaf Iraq Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India Amity Institute of Information Technology Amity University Uttar PradeshCampus Noida India

ISBN: (纸本)9798350341126

the process of reviewing resumes takes time. the unstructured written language can be understood and parsed by natural language processing and machine learning, which can then extract the required information. the goal is to teach the computer to analyze written papers similarly to a human. For assessing and analyzing the unstructured data, various methods including named entity recognition, tokenization, text classification, and approaches of other natural language processing techniques have been explored. A user uploads a resume into the program, which takes it and converts it into a standard text format before parsing it for the necessary information and organizing the extracted data in a standard defined format. After extraction and preprocessing, a predictive model is trained is better and categorize resumes for use in various applications. the k-fold cross validation with k=5 outcome in this case reveals that SVM(0.58 - 0.65) and XG Boost(0.65 - 0.73) have higher ranges of training performance than all other models. Here, the XG Boost outperforms previous trained models, accurately classifying the newly created resume into the target category. the proposed work is novel and presents one of its kind methodology. © 2023 IEEE.

关键词： Natural language processing systems

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Behavior recognition of Teachers and Students in the Smart Classroom Based on Deep Learning 4

Behavior Recognition of Teachers and Students in the Smart C...

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4th international conference on Information Science and Education, ICISE-IE 2023

作者： Shen, Yang Zhang, Jian Li, Yu Collaborative Innovation Center of Assessment for Basic Education Quality Beijing Normal University Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Beijing Normal University Faculty of Psychology Beijing China

ISBN: (纸本)9798350394610

With the development of the concept of education evaluation, it is a key issue to effectively and accurately detect and analyze the behavior categories of teachers and students in the classroom. Firstly, this paper investigates the common behavior categories of students and teachers in the classroom. And on this basis, we sorts out 7 major behavior categories that students are usually involved in the classroom. then, computer vision and artificial intelligence techniques are introduced to design an intelligent classroom behavior recognition system for teachers and students. Finally, the system is applied to various types of datasets for testing to verify the effectiveness of the system. Based on computer vision and artificial intelligence technology, the detection and analysis of students learning behavior in classroom can provide timely data support for teachers to grasp students' classroom learning status, optimize teaching design and implementation. © 2023 IEEE.

关键词： Students

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intelligent Driving pattern recognition 1

引用

8th international conference on Innovative Technologies in intelligent systems and Industrial Applications, CITISIA 2023

作者： Noorshah, Sara Senanayake, S. M. N. Arosha University of Brunei GadongBB1410 Brunei Multimedia University Melaka75450 Malaysia

ISBN: (数字)9783031717734

ISBN: (纸本)9783031717727

Analysis of foot movements during driving has been made possible with the recent advancement of studies in the fields concerning smart driving assistance. the outcomes of previous analysis are proven to be beneficial in different areas such as in energy consumption, safety and in autonomous car feedback systems. An experiment involving embedded IMU sensors in smart shoes, combined with 4G internet connectivity and cloud storage, called Motion-core IoT has been implemented to achieve the goal in building a case library containing safe cases of drivers during driving. Approaches used in attempt to reach the goal are by conducting/implementing data preprocessing on raw data acquired during the driving experiment, clustering using K-Means algorithm, feedforward neural network and recurrent neural network, as well as suitability analysis using case-based reasoning. the goal was reached such that the system managed to successfully obtain safe cases for a test driver with an average suitability of 0.45 which is within the safe cases. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

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4th international Symposium on intelligent Computing systems, ISICS 2022

4th International Symposium on Intelligent Computing Systems...

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4th international Symposium on intelligent Computing systems, ISICS 2022

ISBN: (纸本)9783030984564

the proceedings contain 11 papers. the special focus in this conference is on intelligent Computing systems. the topics include: On the Performance of Preconditioned Methods to Solve Lp -Norm Phase Unwrapping;Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption;DDoS Attack Preventing and Detection with the Artificial Intelligence Approach;iris recognition Using Supervised Learning Based on Matching Features;convolutional Neural Network for Segmentation of Single Cell Gel Electrophoresis Assay;Evaluation of Human SCD Test by Digital Image Analysis;machine Vision-Based Expert System for Automated Skin Cancer Detection;AI Used to Identify Car Accident Injury and Mortality Risk Factors in Dubai UAE;toward Automatic Water Pollution Analysis: A Machine Learning Approach for Water-Quality Monitoring through pattern Classification of Water Crystallization.

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Text to 3D,2D scene generation systems, Frameworks and approaches: a survey 4

Text to 3D,2D scene generation systems, Frameworks and appro...

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4th international conference on pattern Analysis and intelligent systems, PAIS 2022

作者： Ghorab, Mostafa Anouar Lakhfif, Abdelaziz University of Ferhat ABBAS-SETIF 1 Faculty of Sciences Setif Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

Generating 3D scene and animation plays an important role in many domain of our daily life. However, the difficulty of its creating manually and its consumption of effort and time which pushed many and many researchers to shift their interest into the field of automatic 3D scene generation and precisely from natural language description. the results obtained during the last few years revived hopes for the widespread availability of 3D scenes in our daily activities. In this survey, we will give an overview of the most popular 'Text' to '3D scenes' converting systems and their architectures highlighting their strengths and weaknesses. © 2022 IEEE.

关键词： Surveys

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