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Multi-sensor Data Fusion and Deep Machine Learning Models-Based Mental Stress Detection System 1

引用

2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Gedam, Shruti Paul, Sanchita Jharkhand Mesra Ranchi India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

Stress is a concern in today’s paced society impacting individuals in various aspects of their lives including educational environments. It is crucial to identify and examine students’ stress levels as it offers insights into their well-being academic performance and excellence of life. The goal of this study is to develop a mental stress detection system that utilizes a combination of multiple sensor’s data fusion and deep machine learning (ML) models with the help of three physiological signals named electrocardiogram (ECG), galvanic skin response (GSR) and skin temperature (ST). Data is gathered from 200 students with the help of some stressors using a novel Internet of Medical Things (IoMT) device developed using low-cost sensors. Then the data went through pre-processed methods before being analyzed using some techniques of deep ML models like multi-layer perceptron neural networks (MLPNN), recurrent neural networks (RNN), and long short-term memory (LSTM) networks to identify stress. The results show that the RNN and LSTM models regularly outperform the MLPNN model in recognizing mental stress. These models adequately depicted the dynamic character of stress reactions while efficiently capturing temporal dependencies. The accuracy of the RNN and LSTM models was 92.34% and 93.20%, respectively. These models’ improved performance demonstrates their potential in stress detection applications. The combination of multi-sensor data fusion with deep ML algorithms allows for the precise detection of stress-related physiological changes. This has major consequences for student well-being and academic success since it enables personalized stress management tactics and interventions. Student’s mental health and academic experience can be improved by proactive efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Facial Emotion Recognition Using Machine Learning Algorithms: Methods and Techniques 1

引用

5th International Conference on Data and Information Sciences, ICDIS 2023

作者： Gupta, Akshat Department of CSE National Institute of Technology Goa India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

The interpretation of a person’s state of mind relies heavily on their facial expressions. The identification of facial expressions requires the categorization of a number of different feelings, such as neutral, happy, angry. This use is becoming increasingly significant in day-to-day living. Identifying emotions can be accomplished through a variety of strategies, such as those based on machine learning and artificial intelligence (AI) techniques. Methods such as deep learning and image classification are utilised in order to recognise facial expressions and categorise them in accordance with the corresponding photographs. For the purpose of training expression recognition models, a variety of datasets are utilised. This article provides a comprehensive analysis of the facial recognition systems and techniques that have been utilised in the past, as well as those that have been developed more recently, together with the methodology behind them and some examples of their applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Emotion Recognition

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System Modification Process to Reduce Calculation Complexity in Backward Forward Sweep Algorithm 1

引用

5th International Conference on Computer and Communication Technologies, IC3T 2023

作者： Sarada Devi, T.S.N.G. Pavan Phani Kumar, T.V.L.N. Hyderabad India Tata Advanced Systems Limited Bangalore India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

In backward forward sweep algorithm, even though forward sweep (FW) to compute bus voltages, backward sweep (BW) is to compute branch currents, junction bus voltage calculation is required as mandatory additional step in backward sweep process. This calculation complexity increases with increase in number of junction buses particularly when loads are also connected to junction buses and consequently, execution time also increases. This paper concentrates on this problem and contributes with System Modification Process (SMP) to reduce Backward Forward (BW-FW) sweep algorithm calculations complexity. In SMP, transfer buses are connected to a given electrical distribution system to avoid calculating junction bus voltage, which is a mandatory step in backward sweep process, thus decoupling the voltage and current calculations. Hence in this present work, conventional backward forward sweep algorithm is modified as Decoupled Backward Forward Sweep (DBFS) algorithm. The system modification process is demonstrated with a 3-node lateral as numerical example and implemented on practical 55 bus three-phase unbalanced distribution system. In Matlab simulation, the efficiency of the DBFS algorithm has been tested with unbalanced radial distribution systems (URDS). The simulation outcomes in terms of number of repetition, execution time obtained, bus voltages per phase, and total computed power per phase with DBFS are compared to conventional BW-FW sweep algorithm results to demonstrate the proposed method efficiency. From the results, the proposed DBFS algorithm not only reduces conventional BW-FW sweep algorithm calculations complexity but also requires less amount of execution time (including SMP) to achieve same results without degrading the efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computational complexity

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Ontology-Based AI Mentor for Training Future "Digital Railway" Engineers 1

引用

International Scientific Conference on Fundamental and Applied Scientific Research in the Development of Agriculture in the Far East, AFE 2022

作者： Khabarov, Valeriy Volegzhanina, Irina Volegzhanina, Eugeniya Siberian Transport University 191 Dusi Kovalchuk Street Novosibirsk630049 Russia Novosibirsk State Technical University 20 K. Marksa Pr. Novosibirsk630073 Russia

ISBN: (数字)9783031379789

ISBN: (纸本)9783031379772

There has been a lively discussion in the pedagogical community about the effects from applying information technology to the education process. Educators’ activities are often reduced to attempts at bringing some modern format to conventional education, yet without challenging its cognitive side. The digital transformation of industries requires a revision of existing education practices in industry-oriented universities. This is particularly important for railway universities oriented towards the industry that is in demand for engineers to operate on digital railways. A potential effect is the formation of a digital ecosystem "railway industry—railway-oriented university", to be implemented in a service-based format. The focus for choosing implementation tools is a service multi-agent architecture, with its core element being the Artificial Intelligence Mentor in a mobile or web application format. The AI mentor structure consists of three models which are the educator behaviour model, the subject domain/training course model, and the student identification model. These models are at the core of a didactic framework called "Industry-oriented University is a Knowledge Factory". They enable the development of theoretical statements of this framework to a certain working hypothesis, which could be implemented through integrative education solutions for training future "digital railway" engineers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ontology

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Pipeline Leakage Detection via Extreme Seeking Entropy 1

引用

7th Computational Methods in systems and Software, CoMeSySo 2023

作者： Steinbach, Jakub Seiner, Jakub Vrba, Jan Department of Computing and Control Engineering University of Chemistry and Technology in Prague Technická 1905/5 Praha 6 Czech Republic

ISBN: (数字)9783031535499

ISBN: (纸本)9783031535482

In this article, we present a case study of pipeline leakage detection via an adaptive novelty detection algorithm. The detection is based on an affine projection filter applied to a sound recording of the pipeline outlet. Adaptive weight increments of the filter are evaluated by the Extreme Seeking Entropy algorithm to estimate the amount of novelty in data. The suitability of the proposed approach is presented in three case studies where the leakage was simulated in the degassing process model by increasing the discharge flow. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adaptive filtering

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Yoga Pose Identification Using Deep Learning 1

引用

5th International Conference on Data and Information Sciences, ICDIS 2023

作者： Verma, Ashutosh Kumar Sharma, Divyanshu Aggarwal, Himanshu Chauhan, Naveen Department of Computer Science and Engineering KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

Yoga is an ancient practice that has gained popularity all over the world. It is a holistic approach to maintaining physical and mental health. Identifying yoga poses can be a challenging task for beginners and even experienced practitioners. In this research paper, we present a deep learning-based approach for identifying yoga poses from images. We put up a convolutional neural network (CNN) architecture that can appropriately classify 20 distinct yoga poses. Using a dataset of 5000 images, our suggested method had an accuracy of 97.3%. The results show that deep learning techniques can be used to accurately identify yoga poses from images, which can be used to develop an intelligent yoga training system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Convolutional neural networks

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Road Signage and Road Obstacle Detection Using Deep Learning Method 1

引用

International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

作者： Juen, Lee Cheng Khairuddin, Ismail Mohd Majeed, Anwar P. P. Abdul Abdullah, Muhammad Amirul Nasir, Ahmad Fakhri Ab Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang26600 Malaysia School of Robotics Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia

ISBN: (数字)9789819984985

ISBN: (纸本)9789819984978

This study presents a deep learning approach for road signage and road obstacle detection. The purpose of this research was to train a robust and efficient method for detecting road signs and obstacles in real time. This study aims to address the challenges and feasibility of deep learning on road signage and obstacles. A model is trained on YOLOv5 using transfer learning method and the performance of the proposed model was evaluated on a test set. The results showed the YOLOv5 achieved 93.5% mean average precision (mAP). The study concludes that deep learning is a promising method for road signage and road obstacle detection and has potential applications in the field of autonomous vehicles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Irregular Frame Rate Synchronization of Multi-camera Videos for Data-Driven Animal Behavior Detection 2nd

Irregular Frame Rate Synchronization of Multi-camera Videos ...

引用

2nd International Conference on Advances in Computing Research-ACR

作者： Dovdon, Enkhzol Agarwal, Manu Dajsuren, Yanja de Vlieg, Jakob Eindhoven Univ Technol Math & Comp Sci Dept Eindhoven Netherlands

ISBN: (纸本)9783031569494;9783031569500

Deep learning and camera-based monitoring play a pivotal role in effective farm management. However, reliable data availability remains essential for successful deep-learning applications. Cameras are the primary data sources for computer vision deep learning models. For effective farm management, a multi-camera setup is often used. In a multi-camera farm setup, the input dataset for deep learning is prepared by combining the records of the cameras installed on many sides of the farm. However, an irregular frame rate of various cameras in a multi-camera setup can cause issues such as drift. Therefore, the data from different cameras must be in sync before feeding it to a deep learning model. In this work, we present a method for frame rate synchronization that leverages the timestamp information on the video and achieves high accuracy. Our method addresses a critical use case where the frame rate synchronization is performed post-video recording. Its effectiveness is demonstrated in real-world animal behavior detection scenarios, where precise synchronization is vital. Via this work, we contribute to robust deep-learning models for farm management and livestock analysis by addressing frame rate irregularities.

关键词： Frame rate synchronization Multi-camera video synchronization Optical character recognition Feature extraction Precision livestock farming

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Sunflower Optimization with Elite Learning Strategy (SFO-ELS) for Antenna Selection in Massive MIMO Subarray Switching Architecture 1

引用

2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Gaikwad, Snehal Malathi, P. D. Y. Patil College of Engineering Pune India

ISBN: (数字)9789819995219

ISBN: (纸本)9789819995202

Massive MIMO is a promising technology used by fifth generation of wireless technology to increase the channel capacity significantly. But the use of RF transceivers for every antenna at the base station increases hardware complexity and implementation cost of the system making it very challenging for deployment. This paper focuses on addressing the hardware complexity and cost challenges associated with Massive Multiple-Input Multiple-Output systems. To mitigate these challenges, an efficient antenna selection algorithm is essential for identifying a subset of antennas that contribute maximum to the channel capacity. By employing advanced antenna selection schemes, this study aims to identify the most effective approach for optimizing antenna selection in Massive MIMO technology. The Sunflower Optimization algorithm, combined with the elite learning strategy, offers a novel approach to antenna selection in Massive MIMO subarray switching architecture. It leverages the benefits of both the SFO algorithm and the elite learning strategy to improve the selection of antennas, thereby optimizing system performance. Sunflower Optimization with elite learning strategy has been proposed and evaluated the effectiveness of the simulated SFO algorithm by comparing it with traditional approaches. The result shows that the proposed method for antenna selection significantly improves upon other methods offering a more efficient and effective approach for enhanced channel capacity in a Massive MIMO system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Signal to noise ratio

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A Theoretical framework for Harnessing Machine Learning for Digital Forensics in Online Social networks 1

引用

International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Wakili, Abubakar Bakkali, Sara School of Digital Engineering and Artificial Intelligence Euromed Research Center Euromed University of Fez Fez Morocco

ISBN: (数字)9783031523885

ISBN: (纸本)9783031523878

Online social networks have become a popular platform for communication and information exchange, but also a target for various cybercriminal activities. Digital forensics is essential for investigating and combating these crimes, but it faces challenges in handling the vast amount of data generated on social network platforms. This paper proposes a machine learning-based digital forensic framework for online social networks, consisting of five stages: data identification, preparation, acquisition, examination, and reporting. The framework employs machine learning algorithms such as Artificial Neural networks, Decision Trees, and Support Vector Machines for data analysis and evidence extraction in the examination stage. The framework aims to automate data processing and analysis, enabling investigators to focus on understanding crime dynamics and reporting. The paper presents the theoretical analysis of the framework, addressing challenges in digital forensics for online social networks. It also discusses theoretical limitations and future research directions to enhance the framework’s capabilities. A proof-of-concept implementation using a real-world dataset is planned to validate its practicality in solving actual digital forensic investigations. The paper contributes to the advancement of digital forensics for a safer online environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Digital forensics

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