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A Study on Learning of Ensemble Classification Model Based on Radial Basis Function Neural Networks

Transactions of the Korean Institute of Electrical Engineers 2024年第8期73卷 1429-1436页

作者： Roh, Seok-Beom Oh, Sung-Kwun Dept. of Electrical and Electronic Engineering University of Suwon Korea Republic of Dept. of Electronics and Computer Engineering Seokyeong University Korea Republic of

In this study, we introduce an ensemble model that leverages a radial basis function neural network as the base model to enhance the classification performance of a pattern classifier. While existing ensemble models use the majority vote technique for aggregation, we proposes a new aggregation method that combines a simple linear function and a softmax function, using the outputs of the base model as input variables. This aggregation approach linearly combines the class prediction probabilities from each base model to calculate the class-specific probability for the ensemble model. To evaluate the performance of the proposed pattern classifier, we used 14 datasets to optimize the parameters of the linear function and compared the performance with existing pattern classifiers. Our method achieved better results on 10 out of the 14 datasets compared to other pattern classifiers. Additionally, when compared with a recently reported ensemble model, our method also demonstrated superior performance. Copyright © The Korean Institute of Electrical Engineers.

关键词： Radial basis function networks

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MOPO-HBT: A movie poster dataset for title extraction and recognition

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Multimedia Tools and Applications 2024年第18期83卷 54545-54568页

作者： Ghosh, Mridul Roy, Sayan Saha Banik, Bivan Mukherjee, Himadri Obaidullah, Sk Md Roy, Kaushik Dept. of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah India Dept. of Computer Science & Engineering Aliah University Kolkata India Dept. of Electronics & Electrical Communication Engineering IIT Kharagpur Kharagpur India TISA Lab Dept. of Computer Science West Bengal State University Barasat India

Real-world images often encompass embedded texts that adhere to disparate disciplines like business, education, and amusement, to name a few. Such images are graphically rich in terms of font attributes, color distribution, foreground-background similarity, and component organization. This aggravates the difficulty of recognizing texts from these images. Such characteristics are very prominent in the case of movie posters. One of the first pieces of information on movie posters is the title. Automatic recognition of movie titles from images can aid in efficient indexing as well as information conveyance. However, it is accompanied by other texts like the names of actors, producers, taglines, dates, etc. Though the organization of components is somewhat similar across different film industries like Tollywood (West Bengal), Bollywood (Mumbai), and Hollywood (Los Angeles), the graffiti patterns differ in multifarious instances. To address the problem of movie title understanding, we propose a dataset named MOvie POsters-Hollywood Bollywood Tollywood (MOPO-HBT) that encompasses movie posters from the aforementioned industries. The entire dataset is publicly available (http://***/11564) for research purposes. The baseline results of title identification and recognition were obtained with a CNN-based (Convolutional Neural Network) approach, wherein the titles were extracted using the M-EAST (Modified-Efficient and Accurate Scene Text) detector model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Character recognition

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Remote Multi-Robot Control and Management Based on a Kubernetes Cluster

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Journal of Institute of Control, Robotics and Systems 2023年第2期29卷 161-167页

作者： Gu, Sewan Yoon, Dongweon Dept. of Electronics and Computer Engineering Hanyang University Korea Republic of Dept. of Electronic Engineering Hanyang University Korea Republic of

This paper proposes a method to remotely control and manage a Kubernetes-based multi-robot swarm efficiently while ensuring high availability of the robots’ internal controller modules. We evaluate the effectiveness of the proposed approach by experimenting with a self-balancing robot and robot arm. Each robot is configured as a worker node in a Kubernetes cluster. A master node is configured to remotely monitor their statuses and manage the cluster by running, stopping, and updating the controller module. We also develop a labeling module to automate the process of imprinting labels on all the robots in the cluster according to their hardware, software, and functions. A control module required for each robot is automatically deployed remotely, and the appropriate module for robot is selected when the control module is deployed. Furthermore, we experimentally evaluate a control feature designed to ensure high resource availability and fault tolerance. The Kubernetes system is configured to detect problems with the controllers of the self-balancing robots and to create new pods to solve these problems automatically. Our results demonstrate that multi-robots can be controlled simultaneously using an existing cloud-computing container-orchestration system. © ICROS 2023.

关键词： Containers

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A Dual-Action Approach to Mosquito Control: Harnessing Mosquitoes Affinity for Light with Metofluthrin's Lethality 18

A Dual-Action Approach to Mosquito Control: Harnessing Mosqu...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Mittal, Manisha Singh, Mahender Dewan, Prachi Singh, Prabhmeet Dept. of Electronics and Communication Engineering New Delhi India Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9789380544519

Amidst rapid urbanization and fluctuating climatic patterns, the world witnessed a surge in mosquito-borne diseases, escalating global health concerns. Mosquitoes, often dismissed as mere irritants, are carriers for a spectrum of conditions, including malaria, dengue, and the Zika virus, with WHO reporting large numbers of dengue cases annually. This review delves deep into the observation of mosquito behavior and the challenges in eradicating these pests, emphasizing the inefficacy of singular sensory targeting repellents. This paper introduces a pioneering device, combining the mosquito's affinity towards light with the suffocating effects of metofluthrin, offering a holistic solution. By the combination of advanced engineering with a comprehensive understanding of mosquito behavioral dynamics, this device promises not only to repel but to substantially reduce these vectors, fostering safer indoor spaces. Through a detailed exploration of current methodologies and the introduction of this integrated strategy, this paper provides insights into creating a healthier, mosquito-free environment. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： fluorescent light metofluthrin mosquito control mosquito-borne diseases multifaceted sensory system

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Small Object Detection for UAVs Using Deep Learning Models on Edge Computing: A Comparative Analysis 5

Small Object Detection for UAVs Using Deep Learning Models o...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Vasishta, M S Saathvik Reddy Amireddy, Abhinava Teja Shrivastava, Pranjal Raghuram, S. Talasila, Viswanath Shastry, Prasad N Ramaiah Institute of Technology Dept. of Electronics and Telecommunication Engineering Bangalore India Ramaiah Institue of Technology Dept. of Electronics and Communication Engineering Bangalore India Ramaiah Institute of Technology Dept. of Computer Science Engineering Bangalore India Ramaiah Institute of Technology Dept. of Electronics and Communication Engineering Bangalore India Bradley University Dept. of Electrical and Computer Engineering PeoriaIL United States

ISBN: (纸本)9798331528539

This paper presents a comparative analysis of DNN-models, including TensorFlow Lite, SSD-MobileNet, Mask R-CNN and YOLOv8, for the high-precision detection and classification of small objects in aerial search and rescue scenarios. The study features a proprietary dataset collected through drone flights over an extended period. The model performance was evaluated on compact computing platforms like Jetson Nano and Raspberry Pi, emphasizing detection accuracy and computational efficiency. Results reveal trade-offs between model complexity and real-time processing capabilities. The findings guide the selection of on-board computer models, thereby enhancing drone capabilities in critical operations © 2024 IEEE.

关键词： Drones

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Investigation of VGG 16 Transfer Learning and CNN Integrated Brain Tumour Detection

Investigation of VGG 16 Transfer Learning and CNN Integrated...

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2024 International Conference on computer, electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Dept. of Electronics and Engineering Dehradun India

ISBN: (纸本)9798350388534

This study had conducted an intensive exploration of the possibilities of VGG-16 and VGG-19 Convolutional Neural Network models for picture categorisation. Using a thorough method comprising substantial training and severe testing, we may observe both steady loss decrease and significant model accuracy improvements. It is assumed that the models may appropriately keep track of subtle variations in data to which they are adversarial while identifying regular patterns they may generalize. The VGG models' architecture is quite sophisticated, but as seen above, they give strong classification accuracy, with performance in nearly ideal 99 percent in many trials. This integration proves that extremely advanced deep learning instruments are competent to handle and complete even very sophisticated picture recognition assignments. Additionally, our experiments identify a few essential aspects of model adjustment and tuning identifying important techniques considerably enhancing CNN outcomes in application scenarios. This study is pioneering for the automatic picture identification discipline since it provided a strong understanding of the generality and model quicker than many real-world models. Additionally, it exhibits that the CNN structures enhance artificial intelligence's power to recognize visual input and could improve many sectors like the surveillance and health systems. The findings defined here may now open the door to more progress in deep learning and picture process analyses while reiterated the importance of CNN in modern technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Non-intrusive Emotional Development Monitoring of Pre-Primary Children Using Image Processing and Machine Learning Model

Non-intrusive Emotional Development Monitoring of Pre-Primar...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Frank, Anilloy Raj, Mrinal Presidency University Dept. of Electronics and Communication Engineering Bangalore India Presidency University Dept. of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350309140

The development of emotions begins in a child from infancy and continues into adulthood. The pre-primary experiences are unique to each child and the learning environments in early life often serve as the source for behavioral outcomes as they grow. The study is mainly based on detecting the emotions of children to have an insight of their behavior with respect to the external environment. The approach aligns with the Goal-3 Good health and well-being of the United Nations Sustainable Development Goals. It provides with the behavior pattern in better understanding of the child emotions both by parents and teachers of the student. Emotion recognition model using image processing enable understand how the emotions of children change and affects their lives with the human interaction. The model uses Transfer Learning model. Many students sit in front of their computers and can easily analyze their interaction with the system. The technology is evolving and using this model, can analyze human interaction and their behavior patterns in a given particular situation. © 2024 IEEE.

关键词： Child Emotional development face recognition Image processing Machine Learning in education

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Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Establishing Dataset and Clustering by using Sequence and Image Similarity Comparisons 24

Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Es...

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7th International Conference on Computational Intelligence and intelligent Systems, CIIS 2024

作者： Wang, Tzung-Wei Li, Ming-Kuang Lou, Der-Chyuan Lin, Chun-Yuan Dept. of Electronics Engineering Chang Gung University Taoyuan Taiwan Dept. of Computer Science and Information Engineering Asia University Taiwan Taichung Taiwan Dept. of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9798400717437

Based on the MPXV Clade IIb cases in Taiwan, 175 segments were identified from OPG001 to OPG210. The first focus of this paper is on sequence analysis of the MPXV cases in Taiwan, detailing the procedures, including semi-automated segmentation and dataset creation from a large number of MPXV Clade IIb nucleotide sequences found in the NCBI and GISAID databases. The second feature highlights the pioneering establishment of an MPXV Clade IIb RNA 2nd structure image dataset in Taiwan, as such a dataset does not yet exist globally. The third characteristic involves the use of unsupervised learning methods for the rapid clustering of MPXV Clade IIb RNA 2nd structure images. This approach allows not only for the quick detection and clustering of unknown MPXV Clade IIb cases but also for the rapid identification of new MPXV mutations and structural changes. Compared to traditional sequence analysis, this new hybrid systematic method significantly enhances overall efficiency, improving processing speed by at least 24 hours when handling the alignment of 10 MPXV sequences from Taiwan. Future plans include using this prototype analytical model and method to accelerate the analysis of critical lethal factors in MPXV Clade IIb, such as D14L, ultimately helping biologists reduce the time required for analysis and antiviral drug development. © 2024 Copyright held by the owner/author(s).

关键词： Nucleotides

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Contextual Spelling Correction with Language Model for Low-Resource Setting 7

Contextual Spelling Correction with Language Model for Low-R...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Luitel, Nishant Bekoju, Nirajan Sah, Anand Kumar Shakya, Subarna Tribhuwan University Dept. of Electronics And Computer Engineering Pulchowk Campus Lalitpur Nepal

ISBN: (纸本)9798350359299

The task of Spell Correction(SC) in low-resource languages presents a significant challenge due to the availability of only a limited corpus of data and no annotated spelling correction datasets. To tackle these challenges a small-scale word-based transformer LM is trained to provide the SC model with contextual understanding. Further, the probabilistic error rules are extracted from the corpus in an unsupervised way to model the tendency of error happening(error model). Then the combination of LM and error model is used to develop the SC model through the well-known noisy channel framework. The effectiveness of this approach is demonstrated through experiments on the Nepali language where there is access to just an unprocessed corpus of textual data. © 2024 IEEE.

关键词： Errors

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Analysis of Historical Weather of Karnataka, India for Insights in Climate Trends

Analysis of Historical Weather of Karnataka, India for Insig...

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2024 IEEE International Conference on Modeling, Simulation and intelligent Computing, MoSICom 2024

作者： Sanjana, B.K. Frank, Anilloy Presidency University Dept. of Computer Science Engineering Bangalore India Presidency University Dept. of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331533311

Climate change in simpler words means the change in the long-term average weather parameters. Climate change is an important issue because its causing imbalance in the environment and affects the lives of all flora and fauna. The climate change is due to many factors, but the major factor is the human activities which are leading to global warming over the past decades. Numerous studies consider analysis of continental or global data, providing the overall insights in climatic changes continentally or globally respectively. The approach in this paper considers regional analysis to provide better insights to the local people needs so they get adapted to the changes that are taking place. This provides a warning to the people to take preventive and precautionary measures. The dataset of Karnataka region from 1951 to 2020 is used to study the regional climate change pattern. The main objective of the paper is to analyze the climate change and extending in the future. The results show that there is a gradual increase of average mean surface air temperature and gradual decrease of the precipitation over each decade between the years 1951 to 2020. The trend line extending to 2040 by analyzing the historical data gives insights about the climate change in the near future. Understanding the historical data and the future predictions are very important to the policy makers, people and researchers so that effective planning can be done for adapting and decreasing the climate change patterns. © 2024 IEEE.

关键词： Agriculture Environment impact Regional Climate trends

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