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Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

Intelligent Automation & Soft Computing 2023年第11期38卷 139-151页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road *** a result,reckless driving behaviour can cause congestion and *** vision and multimodal sensors have been used to study driving behaviour categorization to lessen this *** research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s *** the other hand,driving a car is a complicated action that requires a wide range of body *** this work,we proposed a ResNet-SE model,an efﬁcient deep learning classiﬁer for driving activity clas-siﬁcation based on signal data obtained in real-world trafﬁc conditions using smart ***-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning *** data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this *** performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its *** to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.

关键词： Smart glasses human activity recognition deep learning wearable sensors driving activity

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On Supervised Class-Imbalanced Learning: An Updated Perspective and Some Key Challenges

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第6期3卷 973-993页

作者： Das, Swagatam Mullick, Sankha Subhra Zelinka, Ivan Indian Statistical Institute Electronics and Communication Sciences Unit Kolkata700108 India VŠB-TUO Department of Computer Science Faculty of Electrical Engineering and Computer Science Czech Republic IT4Innovations National Supercomputing Centre Ostrava70800 Czech Republic

The problem of class imbalance has always been considered as a significant challenge to traditional machine learning and the emerging deep learning research communities. A classification problem can be considered as class imbalanced if the training set does not contain an equal number of labeled examples from all the classes. A classifier trained on such an imbalanced training set is likely to favor those classes containing a larger number of training examples than the others. Unfortunately, the classes that contain a small number of labelled instances usually correspond to rare and significant events. Thus, poor classification accuracy on these classes may lead to severe consequences. In this article, we aim to provide a comprehensive summary of the rich pool of research works attempting to combat the adversarial effects of class imbalance efficiently. Specifically, following a formal definition of the problem of class imbalance, we explore the plethora of traditional machine learning approaches aiming to mitigate its adversarial effects. We further discuss the state-of-the-art deep-learning-based approaches for improving a classifier's resilience against class imbalance and highlight the need for techniques tailored for such a paradigm. Moreover, we look at the emerging applications where class imbalance can be a major concern. Finally, we outline a few open problems along with the various challenges emerging with the advent of modern applications, deep learning paradigm, and new sources of data. © 2020 IEEE.

关键词： Object detection

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Improving Performance and Explainability of Sensor-Based Human Activity Recognition 9

Improving Performance and Explainability of Sensor-Based Hum...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350318241

Significant progress has been made in developing systems that can automatically recognize human activities, largely thanks to deep learning models and sensor data. However, high performance and comprehensibility are often at odds in these systems. The aim of this research is to enhance both the accuracy and understandability of models that use sensors for human activity recognition (HAR). We present a new convolutional neural network design which takes input from accelerometers and gyroscopes across multiple data streams. This allows efficient capture of time and frequency characteristics. Additionally, we put forward an attention module that identifies informative patterns in the sensor signals to shed light on the model's predictions. Experiments were conducted on a benchmark dataset of common daily movements gathered from ubiquitous sensors. Our model achieves state-of-the-art accuracy exceeding prior deep learning techniques. The attention visuals provide transparency into which sensor segments are most relevant for accurate activity classification. This validates improved model interpretability. Our multi-channel architecture pushes sensor-based HAR forward through heightened predictive performance. We offer original perspectives to create reliable, precise systems for HAR. © 2024 IEEE.

关键词： Wearable sensors

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Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

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Intelligent Automation & Soft Computing 2023年第9期37卷 2669-2686页

作者： Sakorn Mekruksavanich Narit Hnoohom Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok10800Thailand

Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical *** evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor ***,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many *** study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection *** proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body *** experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor ***,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and ***,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.

关键词： Human activity recognition deep learning wearable sensors indoor and outdoor activity deep pyramidal residual network

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IoT Enabled Driver Compatible Cost-Effective system for Drowsiness Detection with Optimized Response Time 3rd

IoT Enabled Driver Compatible Cost-Effective System for Drow...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Sarkar, Argha Kundu, Mayuri Pareek, Prakash Gupta, Nishu Reis, Manuel J. Cabral S. School of Computer Science and Engineering REVA University Karnataka Bangalore560064 India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Electronic Systems Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Gjøvik2815 Norway Engineering Department UTAD/IEETA Vila Real5001-801 Portugal

ISBN: (纸本)9783031289743

The present work researches driver drowsiness, which constitutes a huge problem, and can turn into fatal incidents potentially involving the losing of lives, being it while driving in a highway (car, bus, truck, etc.), in the railway, or any other transportation mean (ship, airplane, etc.). Recent technology has been involved in the study of the fatigue behaviour. The purpose of this work is to identify the in-driver’s drowsiness, contributing to get rid of the accidents, mainly during the night, and to ensure better safety in train and on the highways. A camera is used to capture images from the driver, and face detection is executed in real-time to find out the exact position of driver’s face. Here, the blinking of the driver’s eye is under consideration. The fundamental concept lies in the closing of the driver’s eye for a specific duration of time. If the closing time exceeds that certain duration, drowsiness is detected and an alarm is sounded for awareness. Python and OpenCV (using Haarcascade library) are the fundamental programming platforms for sensing the facial attributes. In this work, a cost-effective system for the driver’s drowsiness detection is aimed. Further, the result also indicates the reduction of response and processing time, which is ideal for real time detection. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Python

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Analysis of the Energy Efficiency of Smart City Buildings Based on Deep Learning Algorithms Using AI

Analysis of the Energy Efficiency of Smart City Buildings Ba...

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2024 International Conference on Smart systems for Electrical, Electronics, communication and computer engineering, ICSSEEC 2024

作者： Abid, Yaser M. Jalil, Mustafa A. Abd, Sura Khalil Sabah, Hawraa Ali Bakri, Bilal Ibrahim Jaleel Maktoof, Mohammed Abdul Mohammed, Aymen Adile, Mohaned Mustafa Ahmed, Mohammed Sulaiman Mohammed, Mohammed Abdulamer Alzubaidi, Laith H. University of Information Technology and Communication Baghdad Iraq Mustansiriyah University Faculty of Engineering Electrical Engineering Department Baghdad Iraq Ministry of Higher Education and Scientific Research Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Hilla University College Babil Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

Energy represents a promising innovation notion concerning enhancing smart city structures. A 'smart city' is an urban area wired to receive data from various electrical technology and gadgets. The typical problems with energy efficiency include less rain due to pollution, higher energy use, and water shortages if people aren't prepared to reduce their energy use. With the proper use of AI, improving technological operations' efficiency and reducing energy consumption may be possible. In particular, artificial intelligence may shorten the time it takes businesses to realize their full potential for energy savings by improving the effectiveness of their technological infrastructure. The suggested method of deep learning-based energy-efficient management on a convolutional network (EEMCN-DL) is made to withstand energy efficiency problems in smart cities. The system employs sensor modules to engage with environmental issues. For detecting the pollution factor and forecasting rainfall, EEMCN-DL employs the convolutional networking technique (CNS) on the hardware units of sensors. By grouping the parts to integrate into cloud unit storage, the useful statistic methodology may identify energy usage and water shortage and then present the data as a percentage. Hence, EEMCN-methodologies DLs are incorporated to improve energy efficiency for future generations. Energy efficiency is improved by 98.9 percent thanks to the emergent system's study in simulation. © 2024 IEEE.

关键词： Sustainable city

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Network Time Synchronization as a Quantum Physical Layer Service 28

Network Time Synchronization as a Quantum Physical Layer Ser...

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28th European Wireless Conference, EW 2023

作者： Nunavath, Nikhitha Garbugli, Andrea Nande, Swaraj Shekhar Foschini, Luca Bassoli, Riccardo Fitzek, Frank H.P. The Deutsche Telekom Institute of Communication Technology Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden Germany Cluster of Excellence Dresden Germany research group Technische Universität Dresden Dresden Germany The Department of Computer Science and Engineering University of Bologna Italy

ISBN: (纸本)9783800762262

In the context of 6G architecture development, the concept of a softwarized (orchestration) continuum is a key pillar. Nevertheless, achieving complete softwarization of network functionalities, tasks, and operations presents inherent challenges, leading to critical trade-offs and limitations. This article explores a novel approach to address these issues by integrating quantum technologies and the Physical Layer Service Integration (PLSI) paradigm. Specifically, we propose the formulation and analysis of network synchronization as a quantum PLSI problem. Our study evaluates synchronization time offset in both conventional Precision Time Protocol (PTP) and quantum-based approaches within the network. We investigate the impact of various network conditions on the precision of PTP synchronization, ranging from nanoseconds under ideal circumstances to microseconds when utilizing virtual network devices. Further, we perform a simulation to generate frequency-entangled photon pairs to access nonlocal temporal correlations and calculate the time offsets. Our findings reveal that entanglement-based PLSI for network synchronization achieves precision at the picosecond level. These results emphasises the high precision achievable by interpreting the network synchronisation problem in the perspective of PLSI and not as a service of the softwarized continuum. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Quantum communication

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Flexible Localization Method with Motion Estimation for Underwater Wireless Sensor Networks 26

Flexible Localization Method with Motion Estimation for Unde...

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26th International Conference on Advanced communications Technology, ICACT 2024

作者： Ismail, A.S. Hawbani, Ammar Wang, Xingfu Aziz, Samah Abdel Zhao, Liang Saeed, Nasir School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China Zagazig University Faculty of Science Zagazig44519 Egypt School of Computer Science Shenyang Aerospace University Shenyang110136 China United Arab Emirates University Department of Electrical and Communication Engineering Al Ain15551 United Arab Emirates

ISBN: (纸本)9791188428120

Due to the challenging conditions of underwater environments, such as node mobility and large-scale networks, achieving localization in large-scale mobile underwater sensor networks (UWSN) is a difficult task. This paper introduces a scheme known as the Flexible Localization Method with Mobility Estimation (FLMME) for UWSNs by utilizing the expected mobility patterns of underwater objects. FLMME performs localization hierarchically by splitting the process into anchor and ordinary node localization. Each node estimates its next mobility pattern based on previous location information, enabling estimates about its next location. Anchor nodes, holding known locations, manage the localization process to balance accuracy and error trade-offs. Extensive simulations demonstrate that FLMME reduces localization errors and hence increases localization accuracy. © 2024 Global IT Research Institute - GIRI.

关键词： Location

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Optimizing Urban Heat Response with IoT-Driven Manhole Heat Detection using Recurrent Neural Networks 8

Optimizing Urban Heat Response with IoT-Driven Manhole Heat ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Shelke, Atmaram Fakirba Raman, Ramakrishnan Vekariya, Vipul Sivakumar, V.G. Ganeshbabu, T.R. Srinivasan, C. Pune Maharashtra Pune India Symbiosis Institute of Business Management Pune Maharashtra Pune India Parul University Faculty of Engineering and Technology Parul Institute of Engineering and Technology Gujarat Vadodara India Vidya Jyothi Institute of Technology Department of Electronics Communication Enginnering Telangana Hyderabad India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

With the urban heat island effect amplifying the effects of warming temperatures, cities are having a harder and harder time keeping up. This research offers a novel solution to this critical problem by combining Recurrent Neural Networks (RNNs) with Internet of Things (IoT)-driven manhole heat monitoring devices. IoT sensors strategically placed across urban infrastructure can measure subsurface heat dynamics in real-time. After that, RNN models are used to forecast heat trends and make proactive treatments possible;these models may learn temporal connections. The effectiveness of the proposed method in improving heat response methods is shown using a thorough case study executed in a highly crowded metropolitan setting. The findings highlight the possibility of combining IoT technology with machine learning approaches to improve urban resilience and successfully deal with the problems caused by urban heat. More sustainable and suitable cities may result from creative solutions to the problems caused by urban heat and the existing body of understanding in that direction. © 2024 IEEE.

关键词： Environmental monitoring

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Classification of Medicinal Wild Plants Using Radial Basis Function Neural Network with Least Mean Square 2

Classification of Medicinal Wild Plants Using Radial Basis F...

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2nd International Conference on Electronic and Electrical engineering and Intelligent system, ICE3IS 2022

作者： Borman, Rohmat Indra Rossi, Farli Alamsyah, Dedy Nuraini, Rini Jusman, Yessi Fauziyah Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Muhammadiyah Tangerang Faculty of Engineering Tangerang Indonesia Universitas Nasional Jakarta Faculty of Communication Technology and Informatics Selatan Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia University of Science and Technology Beijing Faculty of Engineering Beijing China

ISBN: (纸本)9781665465410

Wild plants are considered as plants whose growth interferes with other plants. However, in its development it turns out that wild plants contain ingredients for medicines. However, information and knowledge about wild plant species, especially plants with herbaceous vegetation, needs to be carried out with various technologies, including through digital image processing. This study aims to develop a classification model for medicinal plant species using color and texture extraction using the Radial Basis Function Neural Network (RBFNN) algorithm with a combination of the Least Mean Square (LMS) algorithm. Color feature extraction is obtained by calculating the RGB average value and texture feature extraction using a Gabor filter obtained from the mean, entropy, and variance parameters. The result of feature extraction becomes input for RBFNN with LMS. RBFNN has three layers that have feedforward properties that can assist in solving classification or pattern recognition problems. The LMS algorithm is used for learning or updating neural network weights. Based on the test results by calculating precision, recall, and accuracy, it gets a precision value of 92.50%, recall gets a value of 91.74, accuracy gets a value of 92.08. These results indicate that the developed model can classify wild plant species with medicinal properties well. © 2022 IEEE.

关键词： Gabor filters

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