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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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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 Procedure with MySQL Python for Developing Data Warehouse and Analytics Using Data from a Greek Soft Drinks Company

A Procedure with MySQL Python for Developing Data Warehouse ...

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2024 International Conference on Applied Mathematics and computer Science, ICAMCS 2024

作者： Tsakiridis, Sotirios Tsimpiris, Alkiviadis Angeioplastis, Athanasios Papaioannou, Nikolaos Mastorocostas, Paris Varsamis, Dimitrios International Hellenic University Dept. Computer Informatics and Telecommunications Engineering Serres Greece University of West Attica Dept. Informatics and Computer Engineering Egaleo Greece

ISBN: (纸本)9798350363807

The research focuses on the process of creating a data warehouse to meet the decision-making needs of a Greek beverage company. The data cover the period from 2018 to 2022. The developed data warehouse schema follows the star schema, with one fact table and five-dimension tables. Based on this structure, a web application was developed in PHP, which displays reports, charts, and analyses on the company's performance through interactive queries. Additionally, prediction functions tailored to the company's data were developed in Python. The process followed, the queries for creating the data warehouse, as well as the commands for creating analyses and predictions, serve as a useful guide for other companies that wish to upgrade the capabilities of their ERP systems. © 2024 IEEE.

关键词： Data warehouses

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Design and Experiment of a Four-Fingered Robotic Hand for T-FLoW 4.0 humanoid robot 25

Design and Experiment of a Four-Fingered Robotic Hand for T-...

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25th International electronics Symposium, IES 2023

作者： Apriandy, Kevin Ilham Dewantara, Bima Sena Bayu Dewanto, Raden Sanggar Pramadihanto, Dadet Ryando, Catoer Dept. of Informatics and Computer Engineering Indonesia Dept. of Electrical Engineering Indonesia

ISBN: (纸本)9798350314731

In this research study, we present a novel design of a four-fingered robotic hand for T-FLoW 4.0 humanoid robot along with its experiments to validate its grasping performance. Our proposed robotic hand designed with four fingers and five Degree-of-Freedom (DoF), with a configuration of a thumb, index, middle, and ring fingers to remain within the criteria of an anthropomorphic type robot hand. Our hand is built with a modular design on each finger and its mechanism, allowing for easy assembly and achieving a compact hand result. Main controller Arduino Mega implement the system for grasping movement, and then PCA9685 PWM controller get the hand movement instructions from it and generates the required PWM signal for the servo motors. The process of manufacturing the hand involves 3D printing technology to produce hands with a realistic appearance, and use PLA filament material achieve lightweight, affordable, and easy-To-maintain outcomes. The grasping experimental results show that our proposed robotic hand could perform basic grasping movements on household objects. With the use of Cini's taxonomy of grasp as the hand functionality standard, we achieved a success rate of 8 out of 14 tests conducted. © 2023 IEEE.

关键词： Anthropomorphic robots

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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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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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Frame-by-Frame Analysis for Assessing Chickens Flock Movement 25

Frame-by-Frame Analysis for Assessing Chickens Flock Movemen...

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25th International electronics Symposium, IES 2023

作者： Sesulihatien, Wahjoe Tjatur Barakbah, Ali Ridho Prasetyo, Piko Permata Ilham Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Assessing the behavior and welfare of chickens is crucial for successful chicken rearing and product quality. Recent research has focused on using advanced cameras and computer vision technologies to analyze chicken behavior. This camera-based approach captures high-resolution video footage, which is then processed using computer vision algorithms to extract insights on movement patterns, feeding behavior, and social interactions. While this approach has advanced our understanding, there is a need for studies employing a frame-by-frame analysis to obtain fine-grained details of chicken behavior. This method allows for precise measurements and real-Time analysis, facilitating informed decision-making in chicken farming. To expedite the assessment process, a bandpass filter 470 nm lens is incorporated into the camera setup, optimizing image segmentation and reducing processing time. The proposed system combines this specialized camera with frame-by-frame calculations to track and measure the velocity, acceleration, and position of chickens within a flock. The integration of these technologies holds the potential for improving our understanding of chicken behavior, enhancing welfare, and driving data-driven decision-making in chicken farming practices. © 2023 IEEE.

关键词： computer vision

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Sentiment Analysis on the Use of Electric Vehicles With Long Short-Term Memory

Sentiment Analysis on the Use of Electric Vehicles With Long...

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2024 International Conference on Control and Automation, electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Nalawati, Rizki Elisa Liliana, Dewi Yanti Iswara, Ratna Widya Rozana, Izzati Fauziyah, Putri Dzakiyah Politeknik Negeri Jakarta Dept. of Informatics and Computer Engineering Depok Indonesia

ISBN: (纸本)9798331511166

Electric vehicles, or EVs, have drawn a lot of attention lately as an eco-friendly way to cut carbon emissions and lessen reliance on fossil fuels. This work uses a neural network model called Long Short-Term Memory (LSTM), which is effective at processing sequential input, to analyze user attitudes toward electric vehicles. Opinion data were gathered from the social media network Twitter between March 1st and March 31st, 2023, using the keyword "electric vehicles". Based on the obtained opinion data, the LSTM approach is utilized to categorize sentiments as positive or negative. Implementation of the random oversampling technique is important to be applied in increasing the accuracy of the n recall model for the specified class. The study's conclusions show that the LSTM model performs admirably when it comes to sentiment analysis using the test dataset. The model evaluation yields a precision of 99.65% and an accuracy rating of 97.79%. The modeling process is carried out with variance splitting data where the highest accuracy is obtained from a comparison of training testing data of 90%: 10%. © 2024 IEEE.

关键词： Long short-term memory

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A Procedure for Optimization of Placement Products in Large Industrial Areas of a Greek Soft Drinks Company

A Procedure for Optimization of Placement Products in Large ...

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2024 International Conference on Applied Mathematics and computer Science, ICAMCS 2024

作者： Tsakiridis, Sotirios Tsimpiris, Alkiviadis Angeioplastis, Athanasios Papaioannou, Nikolaos Mastorocostas, Paris Papakonstantinou, Apostolos Kamilali, Angeliki Varsamis, Dimitrios International Hellenic University Dept. Computer Informatics and Telecommunications Engineering Serres Greece University of West Attica Dept. Informatics and Computer Engineering Egaleo Greece Cyprus University of Technology Dept Civil Engineering and Geomatics Lemesos Cyprus

ISBN: (纸本)9798350363807

In this paper we present the process of optimal placement of industrial products (pallets) in mapped ware-houses and specifically in a soft drink company in Greece. The optimization criterion is to minimize the cost of moving the lifting machine that places the products in specific positions. The computational method of optimal placement of industrial products receives data from the automatic product count of the mapped warehouse and from the sales predictive model. Therefore, the computational method, having knowledge of the location and number of products in the warehouse and the prediction of sales for each product, places the products produced in an optimal way. Automatic product counting is carried out using an unmanned aerial vehicle (UAV) which developed and applied in soft drink company in Greece. The sales prediction is carried out through a prediction model which developed in a soft drink company in Greece. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis 3

PhishGuard: A Convolutional Neural Network-Based Model for D...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Islam, Md Robiul Islam, Md Mahamodul Afrin, Mst. Suraiya Antara, Anika Tabassum, Nujhat Amin, Al Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Oslo Dept. of Informatics Oslo Norway Dept. of Building Engineering and Construction Management Khulna Bangladesh BRAC University Dept. of Electrical and Electronics Engineering Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Dept. of Computer Science American International Dhaka Bangladesh

ISBN: (纸本)9798350372120

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models' decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1 D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL. © 2024 IEEE.

关键词： Convolutional neural networks

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