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Survey on Online Learning applications in Multi-UAV Networks 15

Survey on Online Learning Applications in Multi-UAV Networks

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kim, Jaemin Kim, Hyosu Paek, Jeongyeup Son, Yongseok Lee, Hyung Tae Cho, Sungrae Chung-Ang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

This paper provides a comprehensive review of the application of online learning techniques in multi-UAV systems, highlighting their role in enhancing autonomy and efficiency in dynamic environments. Key areas of focus include adaptive path planning, collision avoidance, and swarm behavior control, along with the associated technical challenges and future research directions. © 2024 IEEE.

关键词： Federated learning

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Transformer-based Self-Supervised Imputation and Attention GANs Oversampling for Medical Data Processing 31

Transformer-based Self-Supervised Imputation and Attention G...

引用

31st Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Singh, Aryan Kumar Saikia, Arpit Baro, Pranita Borah, Malaya Dutta Dept. of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798331509095

Dealing with missing data and class imbalance is a necessity for the development of a solid predictive model in medical research. This work presents a novel hybrid dynamic method devoted to addressing these dual challenges, specifically focusing on medical datasets. In this work, we investigate the incompleteness and imbalance of the dataset using two sophisticated measures: the Incompleteness Factor and the Imbalance Ratio. These metrics are used to guide customized approaches. A transformer-based imputation method is employed to learn complex data dependencies to impute missing data. Second, the oversampling method of Self-Attention Enhanced Conditional Generative Adversarial Network is implemented. This technique generates better model training results by accommodating self-attention mechanisms that create fake samples that follow the actual data distributions as well as class distributions. This paper introduces one potential solution to obtain model-level accuracy increase for tasks like disease prediction, patient prognosis, etc., over medical datasets. The proposed method aims to remove the measurement biases caused by incompleteness and class imbalance and preprocess the data to enhance the credibility and clinical relevance of the experimental results. © 2024 IEEE.

关键词： Generative adversarial networks

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Machine Learning-based Decision Making for Charging/Discharging Cost Optimization of PREV in Smart City 1

Machine Learning-based Decision Making for Charging/Discharg...

引用

1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Neelakantam, Gone Ecole Centrale School of Engineering Mahindra University Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350349658

Smart cities, equipped with smart grid infrastructure, establish advanced communication networks that facilitate interactions among multiple entities. It is well-known that smart grid technology ensures the reliable transmission of electricity. Additionally, Plug-in Hybrid Electric Vehicles (PHEVs) significantly contribute to the efficient utilization of energy in mobility-aware environments. The Intelligent Transportation System (ITS) enables Vehicle-to-Infrastructure (V2I) communication, which is crucial for providing transportation services and managing PHEV recharging at user-preferred locations. This capability is a key element of smart city infrastructure. To optimize the use of PHEV services in a smart city, addressing the cost minimization of charging and discharging is essential. Therefore, this paper proposes a decision tree machine learning-based algorithm within a fog computing platform aimed at minimizing charging and discharging costs. Performance evaluation demonstrates that the model outperforms existing algorithms in terms of accuracy. These results indicate that our model can accurately predict the costs associated with charging and discharging PHEVs in smart city environments. © 2024 IEEE.

关键词： Adversarial machine learning

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A novel approach incorporating feature extraction followed by YOLOv7 for quality enhancement of mangoes in Bangladesh 14

A novel approach incorporating feature extraction followed b...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kundu, Debashis Rahaman, Md. Mushfiqur Sakib, Md. Nazmus Abdullah Al Sohan, Md. Faruk Fardaush Mithu, Jannatul Hossain, Afzal Aktar, Nishita American International University- Bangladesh Dept. Management Information Systems Dhaka Bangladesh Daffodil Institute of It Dept. of Computer Science and Engineering Dhaka Bangladesh World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh American International University- Bangladesh Dept. of Computer Science Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Savar Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation's agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the export market. This is particularly important for mangoes, which are quite well-liked inBangladesh. On the other hand, the physical grading of mangoes is a procedure that is labor-intensive, prone to error, and very subjective. In this paper, we proposed a YOLOv7 integrated Discrete wave transformation computer vision system. The proposed model includes support vector machine (SVM) and decision tree for the classification of high-quality mangoes. The results of the experiments show that the proposed solution obtained 96.25% accuracy when the system was trained and tested using a publicly accessible mango database. © 2023 IEEE.

关键词： Support vector machines

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A Survey on Machine and Deep Learning Approaches in Sign Language Recognition: Techniques and Future Trends

A Survey on Machine and Deep Learning Approaches in Sign Lan...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Harshitha, C. Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Puducherry Dept. of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Sign language is an essential communication medium for individuals with hearing impairments. It enables them to convey messages, disseminate knowledge, and transfer ideas within the deaf community. However, not everyone understands sign language, making it challenging for individuals with hearing impairments to communicate effectively with other common people. Sign Language Recognition (SLR) is vital for bridging the communication gap between persons with hearing loss and non-sign language speakers. This survey paper aims to explore Machine Learning (ML) and Deep Learning (DL) techniques for recognition of sign languages. Investigate further research scope in this field. © 2024 IEEE.

关键词： Adversarial machine learning

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Empirical Investigation of Adversarial Attacks for Semi-Supervised Object Detection 6

Empirical Investigation of Adversarial Attacks for Semi-Supe...

引用

6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Jo, Junhyung Kim, Joongsu Suh, Young-Joo Pohang Korea Republic of Dept. of Computer Science and Engineering Pohang Korea Republic of

ISBN: (纸本)9798350344349

Semi-supervised learning (SSL) techniques have been rapidly developed and adopted in various vision tasks because of their advantage of leveraging unlabeled data. However, existing works have neglected the vulnerability of SSL because most of the existing adversarial attacks are mainly discussed in a supervised learning manner. In this paper, we study the effects of adversarial examples on Semi-Supervised Object Detection (SS-OD) which is the mainstream of SSL techniques. We build our hypothesis that attacks on the supervised learning model are also effective on SSL models. Since the state-of-The-Art SS-OD methods borrow the teacher-student network, we prepared two pseudo-label based SS-OD networks to validate our hypothesis. We attempt to attack the inference model with adversarial examples crafted by using pretrained auxiliary model and found that SS-OD networks are more vulnerable to adversarial attacks. In addition, we found that selecting different loss components of SS-OD networks to generate perturbations determines the effect and performance of the attack such as misclassification or mislocalization. Visual examples are provided for a clearer understanding. To the best of our knowledge, this is the first effort to investigate the vulnerability of SS-OD. © 2024 IEEE.

关键词： Object detection

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Smart Traffic Management System for Urban Mobility Enhancement Using RF Sensors, Cameras, and Machine Learning 4

Smart Traffic Management System for Urban Mobility Enhanceme...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Vivekanandan, G. Pathmanaban, J. James, Prabahar Godwin Barani Sree, S. Priyanka, V. Samvardhini, V.M. Sri Sai Ram Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331508845

This paper introduces a Smart Traffic Management System (STMS)employing RF sensors, cameras, and machine learning algorithms to monitor and optimize urban traffic. The system dynamically adjusts traffic signal timings, offers real-time route recommendations based on GPS data, and incorporates adaptive control mechanisms to reduce congestion and improve overall mobility. Simulation studies and real-world testing demonstrate the effectiveness of the STMS in enhancing traffic flow, minimizing waittimes, and contributing to sustainable urbandevelopment. © 2024 IEEE.

关键词： Traffic signals

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Autonomous Drone Control for Wireless Charging Using Power Lines 18

Autonomous Drone Control for Wireless Charging Using Power L...

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Wang, Pengkai Kim, Jonghoek Mutahira, Husna Muhammad, Mannan Saeed Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Sejong University Dept. of Defense System Engineering Seoul Korea Republic of Sogang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350331011

The limited autonomy of flight has long been considered a significant constraint in drone systems. In the context of drone inspections of power lines, this study focuses on a drone equipped with a coil designed for autonomous battery charging. Positioned atop the drone is a charging coil, and on the upper surface, there is an upward-facing camera with a restricted field-of-view (FOV), serving the crucial role of aligning the drone with power lines. This research introduces a wireless power transfer strategy to facilitate the self-charging process of the drone while maintaining its position beneath power lines. To enable the drone's autonomous approach to power lines, a method is devised to guide the drone from below. As the drone approaches the power lines, it monitors the current induced by these lines and gradually adjusts its altitude until a sufficiently high induced current is achieved. The proposed approach ensures safety, as the drone avoids direct contact with high-voltage power lines, setting it apart from other existing methods that require such contact. To the best of our knowledge, this study offers a novel approach to precisely controlling drone movements, and establishing reliable wireless power transfer from power lines. The effectiveness of the developed wireless transfer strategy has been verified through MATLAB simulations. © 2024 IEEE.

关键词： Drones

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Detection and Treatment of Epilepsy Disease using EEG Signals

Detection and Treatment of Epilepsy Disease using EEG Signal...

引用

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Dhondiyal, Shiv Ashish Dimri, Sushil Chandra Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

Epilepsy Seizures is a neurological illness that causes repeated seizures and affects millions of people throughout the world. From the studies it has been detected that 70% peoples in the world suffer from epilepsy disease. The prognosis and medical care of epileptic seizure are critical for improving the quality of life of affected individual. Electroencephalography (EEG) is a valuable technique for diagnosing and managing epilepsy because it allows for real-time monitoring of brain electrical activity. Recent advancements in signal processing techniques, such as the Discrete Wavelet Transform (DWT), have shown significant promise in enhancing the diagnosis and treatment of epilepsy. This paper purposed an optimize Random Forest (RF) algorithm by using the DWT application in the analysis of EEG signals for the prognosis of epilepsy disease along with its treatment like medicines, therapies and other treatments methods to cure epilepsy disease. The suggested approach provides clinicians with a promising tool for early and precise identification of epileptic seizures, which is critical for making improvements in patient outcomes and quality of life. The simulation results show that the purposed optimize support vector machine algorithm gives better results with an accuracy of 98% along with the sensitivity, specificity and f1-score of 97%, 96% and 98% respectively. © 2024 IEEE.

关键词： Electroencephalography

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Plant Disease Detection using Convolutional Neural Network

Plant Disease Detection using Convolutional Neural Network

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Agarwal, Jyoti Naithani, Shivansh Singh, Utkarsh Raj, Harsh Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Pest-infested plants and crops have a negative effect on the nation's agricultural output. To detect and identify diseases in plants, farmers or other experts must keep a careful watch on plants. However, this method is frequently expensive, time-consuming, and inaccurate. Finding a spot on the leaves of the infected plant is one method of detecting plant diseases. This research aims to develop a disease detection model based on the classification of leaf images. To detect plant illnesses the proposed model utilizes image processing with a convolution neural network (CNN). This research work focus on three different plant diseases: tomato, corn and potato. Experimental results show high accuracy rate of 98.8%. Proposed work is also compared with some existing work working o same plants diseases. © 2024 IEEE.

关键词： Support vector regression

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