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检索条件"任意字段=16th IEEE International Conference and Workshop on the Engineering of Computer-Based Systems"

共 4953 条记录，以下是31-40 订阅

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Enhancing Wireless Network Performance Using Zone-based Reinforcement Learning: the ZoneWise-RL Approach 16

Enhancing Wireless Network Performance Using Zone-Based Rein...

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16th ieee international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Bhagoriya, Swati Rizvi, M.A. Saxena, Kanak Nitttr Department of Computer Science and Engineering M.P. Bhopal India Nitttr Department of Computer Science and Engineering Bhopal India Sati Department of Computer Science and Information Technology M.P. Vidisha India

ISBN: (纸本)9798331505264

Wireless devices are essential for communication in networks but face limitations in memory and power, affecting their ability to manage computational tasks. Routing, though crucial, consumes significant energy. To address this, we propose ZoneWise-RL, an energy-efficient routing protocol based on reinforcement learning (RL). RL enables devices to adapt to dynamic network conditions, optimizing routing decisions based on mobility and energy fluctuations. Compared to existing protocols, ZoneWise-RL outperforms in energy efficiency, network lifetime, and scalability, making it a robust solution for sustainable routing in wireless networks. © 2024 ieee.

关键词： Dynamic Networks Energy-Efficient Routing Reinforcement Learning RL-based Decision Mechanism Routing Efficiency Zone Classification Zone-based Routing

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A Standing Support Arm Design for Robotic Wheelchairs Using PPO-based RL Strategy 16

A Standing Support Arm Design for Robotic Wheelchairs Using ...

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16th international conference on computer and Automation engineering (ICCAE)

作者： Wang, Daifeng Cao, Wenjing Takanishi, Atsuo Waseda Univ Fac Sci & Engn Tokyo Japan Sophia Univ Dept Engn & Appl Sci Tokyo Japan Waseda Univ Humanoid Robot Inst Tokyo Japan

ISBN: (纸本)9798350370058;9798350370164

the aging of the population poses significant challenges, particularly in the mobility of the elderly. Robotic wheelchairs have emerged as a promising solution. However, the natural decline in the physical strength of the elderly makes independent sitting and standing movements challenging for wheelchair users. this study addresses the challenges of independent sit-to-stand movements for elderly wheelchair users by employing proximal policy optimization (PPO), a reinforcement learning (RL) method, to design a standing support arm. the research explores the potential and effectiveness of the standing support arm in aiding sit-to-stand motions. through RL training in a simulated environment, the study demonstrates the support arm's capability to assist elderly individuals. Moreover, the comparison results show that the human model cannot complete independent standing without our standing support arm under the same training method. this further demonstrates that our approach provides a promising solution to the mobility challenges faced by an aging population, showing its potential in enhancing independence and reducing caregiver workload.

关键词： sit-to-stand motion standing support arm reinforcement learning elderly independence

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Design of a CNN based Autonomous Sitting Posture Recognition System 16

Design of a CNN based Autonomous Sitting Posture Recognition...

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16th international conference on computer and Automation engineering (ICCAE)

作者： Gupta, Yash Goyal, Yogesh Chauhan, Siddharth Rao, Madhav IIIT Bangalore Bangalore Karnataka India

ISBN: (纸本)9798350370058;9798350370164

the identification and characterization of sitting postures are crucial for various applications, particularly in addressing issues related to postural abnormalities, and musculoskeletal symptoms. this study presents a novel seat-mounted sitting posture detection system comprising 15 Pressure Sensitive Conductive (PSC) sensors. these sensors are strategically positioned within a seat, that interfaces with an edge computing system to run a convolutional neural network (CNN) model and predict one among 8 different sitting postures. A dataset consisting of all 15 sensory time-series signals was captured for each of the 8 postures, for training the model. the trained CNN model demonstrated superior accuracy compared to SVM, KNN, ANN, and CNN+LSTM models, leveraging spatio-temporal features for precise posture identification. High classification accuracy was achieved when applied to an unseen dataset. this cameraless system offers real-time sitting posture prediction without compromising user privacy. Its adaptability and scalability make it an innovative solution for medical, domestic, and clinical applications, aiding in posture-related concerns. the dataset, CNN model, and source-codes are made freely available for easy adoption and further usage by the designers and scientific community.

关键词： Sensory system Sitting Posture CNN Sensors Pressure Sensitive Sensors Edge Computing Real-Time Estimation

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Detecting Alcohol-Induced Drowsiness in Four Wheeled Vehicles: An Eye Tracking and MQ3 Sensor based Approach with Arduino Uno and Raspberry Pi, Integrated with SMS Notification System and GPS 16

Detecting Alcohol-Induced Drowsiness in Four Wheeled Vehicle...

引用

16th international conference on computer and Automation engineering (ICCAE)

作者： Valiente, Flordeliza L. Parel, Earl Levi P. Miranda, Gerzon Jil Y. Vergara, Ernesto, Jr. Valiente, Leonardo D., Jr. Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

ISBN: (纸本)9798350370058;9798350370164

Alcohol and drowsiness are common reasons for vehicle accidents. For this research, the researchers created an Alcohol and drowsiness detection system with a key ignition access cover and a drowsiness alert system using a speaker to ensure the driver's safety. A notification system was also included to provide additional help to the driver when the subject is either under the influence of alcohol or drowsy during the driving session. A correlation value 0.924 was observed on the MQ3 sensor for maximum values observed on different alcohol concentrations, respectively. A 6-point EAR ratio system was used for the drowsiness detection of the device. It produced an accuracy of 0.7903 and an F-score of 0.8088. GSM Module, the average response time was found to be 4.737 seconds. the average latitude recorded was 14.535411, and the average longitude was 121.000553. the percentage error for longitude is 0.0078, and for latitude is 0.0372, which is close to the actual value of the reference trial.

关键词： alcohol drowsiness safety EAR ratio system MQ3 sensor

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Reputation-based Federated Learning Defense to Mitigate threats in EEG Signal Classification 16

Reputation-Based Federated Learning Defense to Mitigate Thre...

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16th international conference on computer and Automation engineering (ICCAE)

作者： Zhang, Zhibo Li, Pengfei Al Hammadi, Ahmed Y. Guo, Fusen Damiani, Ernesto Yeun, Chan Yeob Khalifa Univ Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Khalifa Univ Dept Elect Engn & Comp Sci C2PS Abu Dhabi U Arab Emirates Deakin Univ Sch Informat Technol Geelong Vic Australia

ISBN: (纸本)9798350370058;9798350370164

this paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning. While EEG signal analysis has attracted attention because of the emergence of brain-computer interface (BCI) technology, it is difficult to create efficient learning models for EEG analysis because of the distributed nature of EEG data and related privacy and security concerns. To address these challenges, the proposed defending framework leverages the Federated Learning paradigm to preserve privacy by collaborative model training with localized data from dispersed sources and introduces a reputation-based mechanism to mitigate the influence of data poisoning attacks and identify compromised participants. To assess the efficiency of the proposed reputation-based federated learning defense framework, data poisoning attacks based on the risk level of training data derived by Explainable Artificial Intelligence (XAI) techniques are conducted on both publicly available EEG signal datasets and the self-established EEG signal dataset. Experimental results on the poisoned datasets show that the proposed defense methodology performs well in EEG signal classification while reducing the risks associated with security threats.

关键词： EEG signal Explainable Artificial Intelligence (XAI) Federated Learning label flipping reputation management

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An Improved Graph Convolutional Neural Network based on Graph Auto-encoder 16

An Improved Graph Convolutional Neural Network based on Grap...

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16th international conference on computer and Automation engineering (ICCAE)

作者： Wang, Dongqi Du, Tianqi Liu, Zhongwu Chen, Dongming Ren, Tao Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9798350370058;9798350370164

Graph Convolutional Neural Networks (GCN) is a rapidly advancing deep learning algorithm for learning graph representations. One limitation of GCN is that it cannot guarantee optimal low-pass characteristics, thus struggling to effectively filter out high-frequency noises. In this study, we propose a GCN -based autoencoder that addresses this issue by incorporating Laplace-based filters for high-frequency noise reduction. To address potential overfitting caused by the mean-square error loss function used for reconstructing feature matrices, which is sensitive to the number of vector paradigms and dimensions, we introduce a cosine error loss function to mitigate this impact. Additionally, we employ a feature enhancement strategy during training. through experiments conducted on three real-world datasets, we demonstrate that our proposed autoencoder clustering algorithm outperforms baseline graph representation learning algorithms in node clustering tasks. Furthermore, we assess the parameter sensitivity of our algorithm through extensive experiments.

关键词： Complex Network Graph Autoencoder Graph Clustering Graph Convolutional Neural Network

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Machine Learning-based Trustworthy Diabetes Prediction Model 16

Machine Learning-Based Trustworthy Diabetes Prediction Model

引用

16th international conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Aruna Devi, B. Karthik, N. National Institute of Technology Puducherry Department of Computer Science and Engineering Puducherry Karaikal India

ISBN: (纸本)9798350383119

Diabetes is a condition that develops when the blood sugar is above average level. Diabetes shoots up the risk of eye, kidney, nerve, and heart problems. By adopting suitable measures to prevent or manage blood sugar level may reduce the chances of getting diabetes-related health issues. Medical data collection is not done on a regular basis but is determined by the patient's condition and other clinical or administrative factors. A trustworthy model is free from missing values, faulty data and makes reliable predictions. Factors like data quality, anomalies, and model selection play a significant role in building a trustworthy model. Hence, this work concentrates on the prediction of diabetes by considering the following issues: 1) missing values in the dataset, 2) an imbalanced dataset and the presence of anomalies, and 3) diabetes prediction using Machine learning (ML) algorithm. the main objective of this work is to propose a trustworthy diabetes prediction model for an incomplete and imbalanced dataset using ML-based imputation, techniques for balancing datasets, and anomaly detection. © 2024 ieee.

关键词： anomalies diabetes imbalanced dataset missing values

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VR Applications and Vision-based Recognition Techniques for Sign Language: A Survey 16

VR Applications and Vision-Based Recognition Techniques for ...

引用

16th ieee international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Seher, Sumayyah Abedi, Kulsoom Fatima, Kulsum Badiwalla, Sadiya Houda, Taha Prince Mohammad Bin Fahd University Computer Engineering and Science Khobar Saudi Arabia

ISBN: (纸本)9798331505264

Virtual Reality (VR) has emerged as a transformative tool in education. Its immersive nature provides interactive and engaging environments that make learning, including sign language acquisition, more accessible and memorable. this paper explores the integration of vision-based sign language recognition systems into virtual reality applications, emphasizing technological developments and tools that enable the creation of immersive and interactive learning environments. It explores how these systems could enhance learning and improve the communication experience for sign language users such as the deaf and non-verbal. Furthermore, the study explores visual recognition systems, dataset acquisition methods, comprehensive static and dynamic sign language datasets and various approaches to sign recognition. the results illustrate the transformative potential of VR-based sign language recognition to promote accessible, inclusive education, while highlighting avenues for future research and development. © 2024 ieee.

关键词： deep learning dynamic sign language Sign Language Recognition (SLR) static sign language VR

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Deep Learning-based Face Mask Recognition System with YOLOv8 16

Deep Learning-Based Face Mask Recognition System with YOLOv8

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16th international conference on computer and Automation engineering (ICCAE)

作者： Dewi, Christine Manongga, Danny Hendry Mailoa, Evangs Satya Wacana Christian Univ Dept Informat Technol Salatiga Indonesia

ISBN: (纸本)9798350370058;9798350370164

A system for recognizing face masks is a technological solution that employs computer vision and machine learning methodologies to detect and ascertain the presence or absence of a face mask on an individual. the utilization of face masks has garnered substantial significance and prevalence in contemporary times, primarily attributable to the COVID-19 pandemic, wherein it has emerged as a pivotal measure to impede the transmission of the virus. the facial mask recognition system is commonly comprised of two primary stages, namely face detection and mask classification. During the face detection stage, the system can detect and localize the existence of a human face within an image or video frame. the categorization of masks can be executed through a range of machine learning methodologies, including deep learning algorithms. the algorithms are trained using a substantial dataset of labeled facial images, which includes both masked and unmasked faces. this training enables the algorithms to discern the distinctive characteristics between the two categories. this work utilizes the You Only Look Once (YOLO) v8 algorithm to discern the presence or absence of a mask on a given subject, subsequently classifying them into two distinct categories based on the extent to which they are wearing a mask, namely "no_mask" and "mask". the present investigation utilized the Face Mask Dataset as the primary data source for our empirical analysis. the assessments and appraisals of these models incorporate essential criteria. based on the available data, it can be inferred that YOLOv8s has attained the maximum mean average precision (96.1%) in the "mask" category.

关键词： YOLOv8 Face Mask Identification CNN COVID-19

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Comparative Study of Video Summarization Using Keyframes with State-of-the-Art Techniques 16

Comparative Study of Video Summarization Using Keyframes wit...

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16th ieee international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Yeola, Manjusha Barve, Sunita Dr D y Patil Institute of Technology Department of Computer Engineering Pimpri Maharashtra Pune India School of Computer Engineering Mit Academy of Engineering Alandi Maharashtra Pune India

ISBN: (纸本)9798331505264

Multimedia data is growing exponentially and a huge quantity of video footage is being posted to the internet. the users waste a lot of time watching the complete film just to find the pertinent content. this concept brought up the need to take important information out of the video. these major instances are combined to give a concise overview. When a video is being summarized, keyframes-the significant moments that are recorded and preserved in their overall context-are recognized. the process of video summarization (VS) necessitates the extraction of keyframes, as they contain a variety of important and varied elements. In the literature, different approaches to keyframe-based video summarization are examined. the research work comprises a thorough analysis, evaluation, challenges encountered during the process of summarizing the video contents, as well as the implementation of primary keyframe selection strategies and a comparison of the outcomes achieved with current methodologies. © 2024 ieee.

关键词： Feature extraction

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