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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Kobayashi, Manabu Kumoi, Gendo Yagi, Hideki Hirasawa, Shigeichi Center for Data Science Waseda University Tokyo Japan Nagaoka University of Technology Department of Information and Management Systems Engineering Niigata Japan University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9781665410205

In machine learning, one of the methods to solve multiclass classification problems is a framework called Error-Correcting Output Codes (ECOC), which constructs a multiclass classifier by combining a lot of binary classifiers. ECOC assigns binary codewords to each category, and the multiclass classification performance varies depending on the code. In this study, we treat each element of the codeword as a random variable and evaluate the average performance of ECOC. As a result, for M class classification if the number of binary classifiers is O(M), then the average error probability of various codes approaches that of MAP estimation. We show that the important points are the ratio between the number of binary classifiers and M and the difference between the maximum posterior probability and the second highest posterior probability for the categories. © 2024 IEEE.

关键词： Coding errors

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Real-Time Services Robot with an Intelligent System Based on a Radio Frequency Identification Unit 17

Real-Time Services Robot with an Intelligent System Based on...

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17th International Conference on Developments in esystems engineering, DeSE 2024

作者： Al Mashhadany, Yousif Al-Askari, Mohanad A. Al-Ani, Abdullah Alani, Taisir Ahmed Yaseen Dawod Algburi, Sameer Department of Electrical Engineering University of Anbar Anbar Iraq Information Systems Department University of Anbar Baghdad Iraq Computer Network Department University of Anbar Anbar Iraq College of Engineering Al-Kitab University Kirkuk Iraq

ISBN: (纸本)9798350368697

Technological developments have come a long way in developing service robots and employing them to serve human needs to the fullest extent. Communication between the robot and its user is the most important aspect, and developing a system to train robots and provide them with the necessary capabilities to accomplish specific tasks is important for creating a suitable service robot. This work provides solutions to most of the challenges in developing such systems using smart systems that can implement multiple and complex commands in a short and appropriate time, such as matching data outputs with what is examined by these systems. For this reason, (RFID) was used to compare data and give commands so that the robot can move in complex environments. Another challenge to consider is keeping the robot on the specified path without deviating from its specified path, so a special sensor was developed to track the robot's deviation and correct the path. Another challenge to consider is if the robot might collide with an object on the path, this problem has been solved by a wireless sensor, all of which is processed and controlled by the microprocessor and motor control unit. Several practical tasks were implemented for the proposed model, and these tasks were in complex and different environments and under difficult conditions. The proposed service robot model was able to implement them with very high accuracy, so it can be relied upon as an advanced service robot model. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

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computers, Materials & Continua 2022年第9期72卷 5939-5954页

作者： Shaher Alshammrei Sahbi Boubaker Lioua Kolsi Department of Mechanical Engineering College of EngineeringUniversity of Ha’ilHa’il2440Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Laboratory of Metrology and Energy Systems National Engineering SchoolEnergy Engineering DepartmentUniversity of MonastirMonastir5000Tunisia

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and *** this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra *** achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and ***,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target *** its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following *** this aim,the MR was equipped with an ultrasonic sensor used as obstacle *** an obstacle is found,the MR updates its diagraph by excluding the corresponding ***,Dijkstra algorithm runs on the modified *** procedure is repeated until reaching the target *** verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 *** obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking *** study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

关键词： Mobile robot(MR) STEAM path planning obstacle avoidance improved dijkstra algorithm

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Predicting the Air Pollution Level and Creating an Awareness Platform 5

Predicting the Air Pollution Level and Creating an Awareness...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Nuha, M.N. Wishvajith, G.K.N.P. Caldera, Amitha Weerasinghe, N.T. Weeratunga, G.S. Lokuliyana, Shashika Sri Lanka Institute of Infromation Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Infromation Technology Department of Computer Systems & Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study proposes a comprehensive method to reduce the problems posed by global air pollution. To measure pollution levels and raise public awareness, the research uses a comprehensive strategy that incorporates image processing, time series analysis, chatbot, and facemask recognition. Time series analysis is used to build prediction models by recognizing historical patterns, and image processing techniques are used to examine cloud pictures for precise pollution predictions. Chatbots provide real time communication and may educate the public on environmental facts, health risks, and safety procedures. Additionally, it has a facemask identification system that monitors and promotes the use of masks. Importantly this not only encourages users to take action to reduce pollution but also utilizes predictive models to help mitigate pollution's consequences. These components result in improved public health, environmental conservation, and sustainability. The outcomes reflect significant advantages, with evidence of accuracy and its positive impact on both awareness and proactive measures to combat air pollution. © 2023 IEEE.

关键词： Time series analysis

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IoT-Based Smart Beehive Monitoring System 6

IoT-Based Smart Beehive Monitoring System

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Liyanage, Nisal Attanayaka, Chamindra Perera, Tarinya Neilkumara, Daniru Bandara, Isuru Sri Chandrasiri, Lakshman NSBM Green University Town Department of Computer Science and Data Science Homagama Sri Lanka NSBM Green University Town Department of Computer Security and Network Systems Homagama Sri Lanka NSBM Green University Town Department of Electrical Electronic and Systems Engineering Homagama Sri Lanka

ISBN: (纸本)9798331517878

In recent years, there has been an alarming decline in bee populations both globally and in Sri Lanka, severely impacting ecosystems and agriculture worldwide. Traditional methods in Sri Lankan beekeeping are often time-consuming and less efficient, making hive management and health assessment challenging. This paper introduces a smart IoT-based beehive monitoring system tailored to the Sri Lankan beekeeping industry. The system integrates various sensors to measure key hive parameters, such as temperature, humidity, and weight, offering beekeepers a user-friendly web interface for real-time monitoring of hive conditions. It also provides insights into hive management aspects, including feeding needs, optimal harvesting times, and abnormal temperature detection within the hive. This paper highlights the initial steps towards implementing a robust IoT-based beehive monitoring system, acknowledging its current limitations. By addressing these limitations in the future, the authors aim to develop a more effective and efficient monitoring method for Sri Lankan beekeepers, saving time and enhancing the productivity and health of bee colonies. © 2024 IEEE.

关键词： Electronic health record

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Outbreak prediction of COVID-19 using Recurrent neural network with Gated Recurrent Units

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Materials Today: Proceedings 2023年 80卷 3433-3437页

作者： Natarajan, Sathish Kumar, Mohit Gadde, Sai Kiran Kumar Venugopal, Vijay Cognizant Technology Solutions Chennai India Department of Computer Science and Engineering University Institute of Engineering Chandigarh University Punjab Mohali India Senior Data Developer Flagstar Bank Troy Michigan United States Network Consultant Cisco Systems China

Respiratory infections corona virus 2-caused inflammatory disorders are CORONAVIRUS DISEASE 2019 (COVID-19) (SARS-CoV-2). A serious corona virus acute disease arose in 2019. Wuhan, China, was the first location to find the virus in December 2019, which has now been spreading all over the world. Recurrent neural networks, together with the use of LSTMs, fail to provide solutions to numerous issues (RNNs). So this paper has proposed RNN with Gated Recurrent Units for the COVID-19 prediction. This paper utilizes system, which was developed to assist nations (the Czech Republic, the United States, India, and Russia) combat the early stages of a newly emerging infection. For instance, the system tracks confirmed and reported cases, and monitors cures and deaths on a daily basis. This was done to allow the relevant parties to have an early grasp of the disastrous damage the lethal virus will bring. The implemented is an ensemble approach of RNN and GRU that work has computed the RMSE value for the different cases such as infected, cure and death across the four different countries. © 2021

关键词： Viruses

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Effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images)

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Multimedia Tools and Applications 2024年 1-25页

作者： Ramadass, Sudhir Narayanan, Sreekumar Kumar, Rajiv K, Thilagavathi Data Analytics Sterck Systems Private Limited Chennai Nungambakkam India Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

For clinical analysis or medical intrusion, imaging technology is a potent tool for producing visual images of a body's inside. However, several existing methods include restricted access to high-quality and diverse datasets, overfitting, a lack of interpretability, high computational costs, varying medical imaging data, and ethical issues concerning patient privacy, consent, and bias. To overcome these issues, the effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images) is proposed. The input CT images are first gathered and preprocessed to reduce undesired noise using Altered Phase Preserving Dynamic Range Compression (APPDRC). The General Adversarial network (GAN) is then given these preprocessed images to cope with the noisy areas of CT/MRI while also complying with maintaining image structures such as boundary details while preventing over-smoothing, and the weight parameters for the GAN are optimized using the Golden Eagle Optimization (GEO) technique. General Adversarial network (GAN) has piqued the interest of academics studying medical image-denoising mechanisms and developing sophisticated medical image-denoising algorithms based on deep learning. In light of reducing the difficulty in feature learning and improving final diagnosis accuracy, data preprocessing is necessary and crucial in gan-based medical image denoising methods. This work focuses on GAN-based effective denoising approaches in CT/MRI. The potential challenges and research objectives are in the context of data preprocessing in medical image denoising. Because of how CT/MRI images are acquired, it was observed that noise in CT/MRI data follows the distribution. Generative Adversarial networks (GANs) for denoising medical imaging, mainly CT and MRI images, have yielded promising results of 99% accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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High Performance of Smart Refrigerator System Based on IoT Technique 16

High Performance of Smart Refrigerator System Based on IoT T...

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16th International Conference on Developments in esystems engineering, DeSE 2023

作者： Alsanad, Hamid R. Al-Askari, Mohanad A. Omar, Khaldoon A. Almashhadany, Yousif Algburi, Sameer Yaseen, Taisir A. University of Anbar Department of Electrical Engineering Anbar Iraq University of Anbar Information Systems Department Anbar Iraq Al-Kitab University College of Engineering Kirkuk Iraq University of Anbar Computer Network Department Anbar Iraq

ISBN: (纸本)9798350381344

The Internet of Things (IoT) has made a significant impact on improving human life in the modern era, offering convenience and ease. Smart appliances, which are connected to the Internet, have become increasingly popular due to their improved efficiency, robustness, power consumption, and connectivity. The main objective of this paper is to convert a conventional refrigerator into an intelligent appliance by integrating computational intelligence using a microcontroller and a range of sensors. The proposed approach aims to improve food management capabilities by introducing automated techniques and cost-effective intelligence to the refrigerator. The resulting smart refrigerator is designed to be user-friendly and improve the overall quality of life. The system includes a mobile or web application that acts as a bridge between the refrigerator and the user, allowing for remote control and monitoring. The smart refrigerator employs sensors to detect shortages of items and can automatically place orders online or send messages to grocery stores for item delivery. Additionally, the smart module continuously monitors temperature and humidity levels, uploading real-time updates to a cloud platform for user access. The integration of IoT technology into refrigerators offers numerous features and benefits, ultimately enhancing convenience and efficiency in managing food storage and consumption. © 2023 IEEE.

关键词： Internet of things

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Water Quality Assessment of Reservoirs in Sri Lanka 5

Water Quality Assessment of Reservoirs in Sri Lanka

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Mudunkotuwa, Kavindi Senadeera, Chamath Gunawardana, Beuni Pathiraja, Onara Wijendra, Dinuka Fernando, Harinda Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems and Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study presents a comprehensive assessment of water quality in Sri Lankan reservoirs using available data sources. The study focuses on the identification of microorganisms through image analysis, watercolor analysis in satellite-based images detection of water quality, and the inference of water quality index using relevant physical parameters. By analyzing images, the study quantifies microorganisms, providing insights into microbial diversity and potential risks. Image processing techniques enable the identification of contaminants based on watercolor analysis. Utilizing satellite data, it examines variations in water quality parameters, providing a comprehensive assessment of overall water quality. The derived water quality index, using relevant physical parameters, quantitatively assesses and compares water quality across different reservoirs. The findings contribute to understanding water quality in Sri Lankan reservoirs, supporting informed decision-making and sustainable water resource management. © 2023 IEEE.

关键词： Water quality

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