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Automatic Recognition of Driving Events based on Deep Learning 6

Automatic Recognition of Driving Events based on Deep Learni...

引用

6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Chen, Jui-Chi Lian, Zhen-You Chiang, Hsin-You Huang, Chung-Lin Chuang, Cheng-Hung Asia University Dept. of Computer Science and Information Engineering Taiwan National Chin-Yi University of Technology Dept. of Artificial Intelligence and Computer Engineering Taiwan National Tsing Hua University Dept. of Electrical Engineering Taiwan

ISBN: (纸本)9798350301953

In recent years, there has been a rapid development of intelligent driving assistance systems. Although most vehicles nowadays are equipped with driving assistance systems, the number of car accidents continues to rise. The main cause of car accidents is still largely attributed to human factors. Therefore, there has been an increasing focus on research related to accident detection and driver behavior analysis. This study used deep learning methods to automatically recognize driving events from recorded driving videos. In the training phase of deep learning, we cropped all the videos in the training data into multiple clips, and labeled driving event categories for each clip, including four categories: vehicle stopped, straight driving, turning, and collision. The proposed model references the architecture of the SlowFastNet model and the concepts of I3D. We expanded Inception-V3 to a 3D structure and replaced the bottom architecture of SlowFastNet with 3D-Inception-V3, making the network more applicable to the training data. After training, the model can recognize driving events in various driving environments. Through experimental comparisons, our network architecture achieved the highest recognition accuracy, with an accuracy rate of 93.3%. © 2023 IEEE.

关键词： Vehicles

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Energy-Based Controller Design for Decentralized AC Inverter-Base Micro-grids

Energy-Based Controller Design for Decentralized AC Inverter...

引用

2023 IEEE International Conference on Control, Electronics and computer technology, ICCECT 2023

作者： Bozorgmehrian, Mohsen Attarsharghi, Samareh Ramezani, Amin Tarbiat Modares University Dept. of Electrical and Computer Engineering Tehran Iran University of Doha for Science and Technology Dept. of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350399028

Micro-grids, which utilize photo-voltaic (PV) cells, wind turbines, and batteries, are gaining widespread adoption as a viable solution for renewable and sustainable energy infrastructures. However, ensuring the reliability and stability of these power grids is critical, and several control strategies have been developed to achieve these goals. In this study, we have modeled the decentralized behavior of microgrids using the port-Hamiltonian formulation and a PI controller to control the inverter voltage and output power. To demonstrate the effectiveness of our control method, we have conducted simulations that consider both maximum and worst-case system fluctuations. © 2023 IEEE.

关键词： Hamiltonians

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Integrated Modeling of Wireless System Performance under Beaulieu-Xie Fading and κ-μ Co-Channel Interference: A Multi-Dimensional Exploration 27

Integrated Modeling of Wireless System Performance under Bea...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Sharma, Rashmi Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Department of CSE Pune India Madhav Institute of Technology and Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350392319

This paper proposes simulation and analysis to discuss how the fading affects the wireless signal waves on different fading levels. Four types of fading: (i) no fading, (ii) a low level of fading, (iii) moderate level of fading and (iv), high level of fading was considered for simulating the signal amplitude over time. Selected fading levels were 'added' to model real world wireless scenarios in which both environmental and interference conditions are at play. The results indicated that in the absence of fading, the amplitude of the signal wave was continuous and not changed significantly;however, from low to severe fading, there could be directly proportional soaring fluctuations in its proper amplitude corresponding to lose of signal strength. Together, these findings underscore the importance of adaptive signal processing techniques and robust modulation strategies in suppressing the degradative effects of fading against the desirable characteristics of proper signal integrity and reliable performance. The findings highlight one reality in communications: understanding fading is critical for designing ultrareliable wireless systems that perform consistently across a variety of operational settings. The knowledge extracted based on this study would further add to novel way of designing robust strategies for Signal resilience and Communication reliability emerged in future wireless networks. © 2024 IEEE.

关键词： Cochannel interference

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The Role of Machine Learning in Equipping Students with Emerging Opportunities

The Role of Machine Learning in Equipping Students with Emer...

引用

2024 International Conference on Smart Devices, ICSD 2024

作者： Sengar, Alok Singh Bahadure, Nilesh Bhaskarrao Mishra, Amit Kumar Chaudhari, Vaibhav Pandey, Neeraj Kumar Dumka, Ankur Noida International University School of Sciences Department of Computer Science Greater Noida India Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Nutanix Technologies India Pvt Ltd Technical Staff-2 Karnataka Bengaluru India Graphic Era Deemed to be University Dept. of Computer Science & Engineering Dehradun India Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

The growth of the machine learning (ML) process is crucial to evaluate the basic influence on the growth of students' life. Main advantage of using ML in education is that it can be used to generate individualized learning programs that meet the desires of each learner. Large-scale information on student performance, learning preferences, and styles may be examined by ML algorithms to build personalized learning paths. This level of personalization accounts for each learner's unique learning style and pace to ensure that they receive just the appropriate amount of challenge and support. For instance, adaptive learning platforms with machine learning abilities can maximize student engagement and learning efficiency by dynamically modifying the material trouble in response to real-time student performance. Another way that machine learning is being incorporated into education is through the presence of relevant, up-to-date knowledge to the syllabus that tracks business trends. The need for expertise in fields like data analytics, cybersecurity, and artificial intelligence is rising as the labor market changes. Educational institutions may identify growing skill requirements and modify their courses accordingly by regularly analyzing labor market data with the use of ML. By ensuring that students possess the abilities that employers value, they increase their competitiveness and adaptability in the labor market. There are some different prospectuses that allows determining the basic impact of the motivated student's aspects to the improvement of their studies. In addition, maintaining appropriate ideas in the machine learning process allows for the organization of some better approaches where the student's activity is analyzed appropriately. All of the opportunities defined in the machine learning process are determined basic approach which is described the student's improvement in their growth process. There is the basic approach where the impact of the machine learni

关键词： Federated learning

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Identification of Children with ADHD from EEG Signals Based on Entropy Measures and Support Vector Machine 11

Identification of Children with ADHD from EEG Signals Based ...

引用

11th European Workshop on Visual Information Processing, EUVIP 2023

作者： Maniruzzaman, Md. Al Mehedi Hasan, Md. Suzuki, Taro Shin, Jungpil The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965-8580 Japan Rajshahi University of Engineering and Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350342185

Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders that affects 11% of children worldwide. Moreover, the prevalence of ADHD has rapidly increased over time worldwide. According to DSM-V, three types of symptoms such as inattention, hyperactivity, and combined type (inattention with hyperactivity). So, it is necessary to use a simple, non-invasive, and automatic detection system for identifying children with ADHD at an early stage. The objective of this study was to propose a machine learning (ML)based ADHD-combine type (ADHD-CT) detection system from electroencephalogram (EEG) signals. EEG signals were recorded from nineteen ADHD-CT children and fourteen healthy children. We extracted five entropy-based features such as approximate-based entropy, Shanon-based entropy, permutation-based entropy, sample-based entropy, and singular value decomposition (SVD)-based entropy from each signal. The subset of the most informative and discriminative features was selected for ADHDCT using sequential forward floating selection (SFFS). Following that, support vector machine (SVM) was implemented with leave-one-out cross-validation for the identification of ADHD-CT children and assessed its performances based on classification accuracy. Our results illustrated that SVM with polynomial kernel provided 96.87% classification accuracy to discriminate children as ADHD-CT and healthy children. Our findings showed that our proposed system can be used to detect children with ADHD-CT. © 2023 IEEE.

关键词： Support vector machines

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Channel Allocation in 5G Networks using Ant Colony Optimization Algorithm 6

Channel Allocation in 5G Networks using Ant Colony Optimizat...

引用

6th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Hussein, Areej Anbar, Mohammad Molhem, Mohammed Oudeh, Atheer Yousif Nassr, Mohammad Fardoun, Habib University of Tartous Dept. of Communication Technology Engineering Tartous Syria Al-Ayen University Thi-Qar Dept. of Computer Engineering Technology Iraq College of Education for Pure Science University of Thi-Qar Scientific Research Center Al-Ayen University Thi-Qar Dept. of Computer Science Iraq Canadian University Dubai United Arab Emirates

ISBN: (纸本)9798350382891

Efficient channel allocation is crucial for enabling concurrent calls and data sessions within a cellular network in general. When channels are inefficiently allocated, network congestion may occur leading to call blocking due to a lack of available channels. In this research, an Ant Colony Optimization (ACO) algorithm-based model has been proposed to tackle channel allocation problem in 5G cellular networks. The proposed model has been simulated using Python. The experimental study focused on minimizing Call Block Probability (CBP) by optimizing channel allocation as the number of network users increase. © 2024 IEEE.

关键词： Python

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Addressing Weight Competition in Long Short-Term Memory (LSTM) Networks for Improving Temperature Prediction 12

Addressing Weight Competition in Long Short-Term Memory (LST...

引用

12th International Conference on Awareness science and technology, iCAST 2023

作者： Lin, Kun-Wei Li, Yu-Fan Lin, Che-Yu Jiang, Zheng-Han Chen, Wei-Jian dept. Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan dept. Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This conference paper presents a study on the application of Long Short-Term Memory (LSTM) networks to predict temperature changes in the upcoming week. During our research, we encountered the issue of weight competition within the LSTM architecture, which can lead to suboptimal predictions. To overcome this challenge, three novel methods including Independent Time Series (ITS) Model, Arithmetic Segmented Time Series (ASTS) Model and Arithmetic Segmented Rolling Time Series (ASRTS) Model, respectively, were used to address weight competition and enhance the accuracy of temperature change forecasts. The effectiveness of these methods were evaluated through extensive experiments and compared against standard LSTM models. The results demonstrate the significance of mitigating weight competition to achieve more reliable and accurate temperature predictions. © 2023 IEEE.

关键词： Long short-term memory

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An Analysis of Human Factors in Malware Attacks

An Analysis of Human Factors in Malware Attacks

引用

2024 International Conference on computer and Applications, ICCA 2024

作者： Alotaibi, Jawza Al Ja'am, Jihad M. Hoda, Mohammed Oryx Universal College in Partnership with Liverpool John Moores University Dept. of Computing and Data Science Doha Qatar Kuwait College of Science and Technology Dept. of computer Science and Engineering Doha Kuwait

ISBN: (纸本)9798350367560

This research study investigates human responses to malware attacks, focusing on attacker motivations, individual reactions, and key factors influencing user vulnerability and resilience. Using a structured survey questionnaire, the study gathered data from 104 participants with diverse demographics across various occupations. The data was analyzed using SPSS statistical software and Python-based machine learning algorithms. The survey included demographic information and responses to cybersecurity-related questions about human behavior and experience. Machine learning techniques identified ten critical factors as significant predictors of vulnerability to malware attacks. The prediction model indicated that 55 out of 104 participants were vulnerable, with higher vulnerability observed among female participants and those in non-IT professions. This study contributes valuable insights into the human factors influencing cybersecurity resilience, providing a foundation for developing effective strategies to mitigate malware attack vulnerability. © 2024 IEEE.

关键词： Cyber attacks

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A Deep Learning based Bangladeshi Vehicle Classification using Fine-Tuned Multi-class Vehicle Image Network (MVINet) Model

A Deep Learning based Bangladeshi Vehicle Classification usi...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Ahmed, Tanvir Sharmin Mou, Farzana Samad, Abu Anas Ibn. Dhaka University of Engineering & Technology Dept. of Computer Science and Engineering Gazipur Dhaka Bangladesh University of Birmingham Dept. of Computer Science Birmingham United Kingdom Computer Vision Engineering Lab Sigmind.ai Limited Dhaka Bangladesh

ISBN: (纸本)9798350316001

The classification of vehicles is an important task in many applications, including traffic management, surveillance, and law enforcement. In Bangladesh, vehicle classification has become increasingly challenging due to the rapid growth in the number of vehicles on the roads. In this study, a novel deep learning-based Bangladeshi vehicle classification model using fine-tuned Multi-class Vehicle Image Network (MVINet) based on DenseNet201 with four added layers has been proposed. Our approach uses convolutional neural networks (CNNs) to extract features from images of vehicles. To make our dataset, we employ a YOLO (You Only Look Once) model for initial vehicle detection, followed by manual filtering and augmentation of the dataset to improve classification accuracy. The proposed method demonstrates superior performance compared to manual data collection methods. We evaluated our approach using standard metrics such as accuracy, precision, recall, and F1 score. Our model achieved an accuracy of 97.06%, precision of 97.17%, recall of 97.24%, and F1-score of 97.12% indicating high performance in classifying vehicles. We also compared our approach with state-of-the-art techniques and found that the proposed deep learning-based approach outperformed them significantly. The proposed approach has the potential to improve traffic management and law enforcement in Bangladesh. It can be used to monitor traffic flow, detect traffic violations, and identify stolen vehicles. Overall, this study demonstrates the effectiveness of deep learning in Bangladeshi vehicle detection and classification and highlights the importance of using advanced technologies to address the challenges of a rapidly changing transportation landscape. © 2023 IEEE.

关键词： Deep learning

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ARIA: Augmented Reality and Artificial Intelligence enabled mobile application for Yield and grade prediction of tomato crops 2

ARIA: Augmented Reality and Artificial Intelligence enabled ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Balaji Prabhu, B.V. Shashank, R. Shreyas, B. Jois Narsipura, Omkar Subbaram Malnad College of Engineering Hassan Visvesvaraya Technological University Belagavi573202 India Dept. of Computer Science Viterbi School of Engineering University of Southern California Los Angeles90007 United States Dept. of Computer Science and Engineering RNS Institute of Technology Bangalore560098 India Dept. of Aerospace Engineering Indian Institute of Science Bangalore560012 India

Agriculture is the most crucial sector of the Indian economy. Lately, there has been a surge in the usage of technologies like deep learning and computer vision to make the process of agriculture modern and consequently, lessening the mistakes related to conventional processes. This work delivers a user-friendly, accessible, and novel approach for the detection, counting, and grading of tomatoes found on a farm. An Augmented Reality (AR) based mobile application is developed to obtain the images efficiently from a tomato farm in the pre-harvest stage subjected to open situations. The proposed approach uses Faster RCNN, a convolutional neural network model for detection of tomatoes from the input image on a large scale. The proposed model is trained and tested using 2083 images. The results are then analyzed for the overall performance of detection, segmentation, and classification of tomatoes. The results have examined the efficiency of the proposed mobile application and demonstrate the robustness it exhibits for the detection, grading and yield prediction of Tomatoes. © 2024 Elsevier B.V.. All rights reserved.

关键词： Augmented reality

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