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Classifying Human Activities Using Machine learning and Deep learning Techniques 1

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11th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2023

作者： Sanku, Satya Uday Satti, thanuja Pavani Lakshmi, T. Jaya Nandini, Y.V. School of Engineering and Sciences SRM University Andhra Pradesh Amaravati India

ISBN: (数字)9789819967063

ISBN: (纸本)9789819967056

the ability of machines to recognize and categorize human activities is known as human activity recognition (HAR). Most individuals today are health aware;thus, they use smartphones or smartwatches to track their daily activities to stay healthy. Kaggle held a challenge to classify six human activities using smartphone inertial signals from 30 participants. HAR’s key difficulty is distinguishing human activities using data so they do not overlap. Expert-generated features are visualized using t-SNE, then logistic regression, linear SVM, kernel SVM, and decision trees are used to categorize the six human activities. Deep learning algorithms of LSTM, bidirectional LSTM, RNN, and GRU are also trained using raw time series data. these models are assessed using accuracy, confusion matrix, precision, and recall. Empirical findings demonstrated that the linear support vector machine (SVM) in the realm of machine learning, as well as the gated recurrent unit (GRU) in deep learning, obtained higher accuracy for human activity recognition. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smartphones

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data-Driven Shielding of Online Reinforcement learning: A Stormwater Pond Case Study 11th

Data-Driven Shielding of Online Reinforcement Learning: A ...

引用

11th IFIP WG 2.2 international conference on Fundamentals of Software engineering, FSEN 2025

作者： Kim, Esther Hahyeon Goorden, Martijn Angelo Larsen, Kim Guldstrand Nielsen, thomas Dyhre Department of Computer Science Aalborg University Aalborg East9220 Denmark Department of Mechanical Engineering Eindhoven University of Technology Eindhoven5612 AZ Netherlands

ISBN: (纸本)9783031870538

To synthesize a safe and optimal controller for switched hybrid systems, one can first synthesize a shield that ensures safety, and then apply reinforcement learning within the constraints of the shield to obtain the desired controller. However, developing such a shield for switched hybrid systems typically requires a full model of the environment, which is not always available. Instead, historical data of the environment might be available. In this paper, we introduce a method for the construction of safety shields based on different scenarios captured in historical data. We show how individual shields for different scenarios can be combined to obtain a single shield that is provably safe within the bounds of the observed scenarios. We demonstrate the method using an industrial case study of a stormwater detention pond, which includes ten years of historical data of different rain events/scenarios. Our experimental results show that the shielded optimal controller ensures safety across all individual historical rain scenarios compared to the unshielded optimal controller. Additionally, we empirically show that the shield may also generalize for scenarios not covered by the historical data. © IFIP international Federation for Information Processing 2025.

关键词： Storm sewers

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Distribution Network Topology Identification Based on CNN and Improved Conformal Prediction 11

Distribution Network Topology Identification Based on CNN an...

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Bian, Ziyue Long, Huan Zhao, Jingtao Zheng, Shu Zhang, Xiaoyan School of Electrical Engineering Southeast University Nanjing China Nari Technology Co. Ltd Nanjing China

ISBN: (数字)9798350368345

ISBN: (纸本)9798350368345

New power system state changes frequently, which needs identifying the possible operating state of the distribution network accurately and quickly. However, the accuracy of the topology recognition using machine learning method is difficult to reach 100%. therefore, a method of distribution network topology identification based on CNN and improved conformal prediction is proposed in this paper. It gives a variety of possible alternative topologies and their probabilities. Based on multi-mapping relationship between the switching state of the distribution network and measured voltage data, this paper uses CNN to build a multi-output classifier. Improved conformal prediction method is used to integrate and output the possible operating topologies. the IEEE-33-node distribution network is used for verify the proposed method. the simulation experimental results indicate that this model would achieve a high accuracy of topology identification when the average number of outputs is small. © 2024 IEEE.

关键词： Network topology

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A Robust Ensemble Machine learning Model with Advanced Voting Techniques for Comment Classification 11th

A Robust Ensemble Machine Learning Model with Advanced Votin...

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11th international conference on Big data Analytics in Astronomy, Science, and engineering (BDA)

作者： Shiplu, Ariful Islam Rahman, Md Mostafizer Watanobe, Yutaka Dhaka Univ Engn & Technol Gazipur Bangladesh Univ Aizu Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9783031585012;9783031585029

In the modern era, we find ourselves immersed in an ever-expanding flow of data where data is increasing exponentially. data is generated from different platforms like Education, Business, E-commerce, and predominantly, social media platforms such as Twitter, YouTube, Facebook, and Instagram. Amidst this proliferation of content, user comments have emerged as a crucial element, serving as a platform for expressions of opinions, commendations, and critiques. However, within the abundance of user feedback lies a persistent issue: the presence of undesirable comments that elicit negative emotional responses and prove to be tedious and irrelevant. Effectively identifying and removing such comments poses a major challenge. this research addresses the imperative need for a robust comment classification model. To tackle this issue, a comprehensive investigation is conducted, employing a variety of machine learning models, including Decision Trees, Random Forests (RF), Naive Bayes, K-Nearest Neighbors, Gradient Boosting, AdaBoost, Logistic Regression, and Support Vector Machines (SVM) for comment classification. Furthermore, fundamental voting techniques such as Hard-Voting, Averaging, and Soft-Voting are incorporated with machine learning models to improve the classification performance. the objective is to discern the characteristics of text comments, classifying them, with the aim of achieving superior accuracy compared to prior research. In this paper, we propose a robust ensemble model, RF+AdaBoost+SVM+Soft-Voting, specifically designed for comment classification. the results obtained indicate that the proposed ensemble model achieved an impressive accuracy of approximately 98% for comment classification on YouTube dataset.

关键词： Ensemble Model Machine learning Voting Techniques Comment Classification Soft-Voting Hard-Voting Averaging

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Crop Type Classification Network for Hyperspectral Images Coupled with Agricultural Vegetation Indices 11

Crop Type Classification Network for Hyperspectral Images Co...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Zhang, Shuai Jiang, Yonghua Wang, Chengjun Liu, Weiling Li, Da Wu, Kaiwen Wuhan University School of Remote Sensing and Information Engineering Wuhan China Xiangtan University School of Computer Science School of Cyberspace Science Xiangtan China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China China University of Geosciences Cugb School of Earth Sciences and Resources Beijing China

ISBN: (纸本)9798350303513

Hyperspectral remote sensing images, due to their unique characteristics, can provide rich spectral features and spatial information. Crops have different absorption and reflection characteristics in different bands, which gives hyperspectral images a natural advantage in crop classification. the academic community has also made progress in this area through a series of studies. this is of great significance for accurately obtaining the spatial distribution information of crops and realizing precision and intelligent management in agricultural production. However, hyperspectral data have low spatial resolution and suffers from mixed pixel interference and lack of spatial information description for crops. Meanwhile, influenced by the phenological characteristics of crops, different crops respond differently to the same spectrum during the same phenological period, and similarly, the spectral response of the same crop varies at different phenological stages. this phenomenon leads to typical intra-class and inter-class feature differences, which increase the complexity of hyperspectral crop classification. To address the issues of missing spatial information and inconsistent spectral responses, We propose a prior-guided and data-driven deep learning method for crop classification in hyperspectral remote sensing imagery, named Cross-scale Enhancement Network (CSENet). this method couples index features and incorporates them as prior knowledge to the network. Firstly, we utilize the phenological differences of different crops to select the spectral bands with significant responses and perform feature dimension reduction;Secondly, we utilize the high reflectance characteristics of vegetation in the near-infrared band and introduce multiple classic vegetation indices to construct a spectral feature library as prior knowledge to enrich the network features. Finally, a cross-scale enhancement attention mechanism is designed to obtain multi-scale crop features and compensate

关键词： Crops

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Fog and Edge Computing-Based Smart Health Care System Using Machine learning 7th

Fog and Edge Computing-Based Smart Health Care System Using ...

引用

7th international conference on Advanced Computing and intelligent engineering, ICACIE 2022

作者： Tripathy, Subhranshu Sekhar Tripathy, Niva Rath, Mamata Hu, Yu-Chen Pattanaik, Swarupa Samantaray, Swarnakanti Department of Computer Science and Engineering DRIEMS Autonomous College Odisha Cuttack India Department of Computer Science and Information Management Providence University Taichung City Taiwan GIET Khurdha Odisha Baniatangi India Nalanda Institute of Technology Odisha Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of Computer Science and Engineering SOET Centurion University of Technology and Management Bhubaneswar India

ISBN: (纸本)9789819950140

data in the medical field include varying degrees of ambiguity and imprecision, which makes it difficult for doctors to improve the health of patients with a group of related suggestive disorders. As a result, doctors use data stored in networks and computing systems to identify a variety of illnesses in patients. the proposed strategy boosts the system’s effectiveness and makes accurate diagnoses and suggestions for treating life-threatening diseases easier. the adaptive prediction systems are assessed, and a machine learning-based model for heart disease detection is trained and compared to the relevant traditional approaches. When compared to other sophisticated classification methods, the cloud-fog-fog architecture was shown to be the most accurate. As a result, the suggested strategy is more accurate in identifying different cardiac issues and suggesting effective treatment options. the performance of the proposed system warrants its application in automated cardiac patient diagnostic and recommendation systems. the Heart Failure Clinical Records repository at UCI provided the dataset for this study, which has 13 characteristics. the experimental findings showed that the CNN model performs better than other deep learning and machine learning models which are proposed by eminent researchers. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Research data management of structural health monitoring projects and subsequent applications of artificial intelligence methods 11th

Research data management of structural health monitoring pro...

引用

11th international conference on Bridge Maintenance, Safety and Management, IABMAS 2022

作者： Simon, P. Herrmann, R. Schneider, R. Hille, F. Baeßler, M. El-Athman, R. Berlin Germany Berlin Germany

ISBN: (纸本)9781032345314

Structural health monitoring (SHM) intends to improve the management of engineering structures. the number of successful SHM projects - especially SHM research projects - is ever growing, yielding added value and more scientific insight into the management of infrastructure asset. With the advent of the data age, the value of accessible data becomes increasingly evident. In SHM, many new data-centric methods are currently being developed at a high pace. A consequent application of research data management (RDM) concepts in SHM projects enables a systematic management of raw and processed data, and thus facilitates the development and application of artificial intelligence (AI) and machine learning (ML) methods to the SHM data. In this contribution, a case study based on an institutional RDM framework is presented. data and metadata from monitoring the structural health of the Maintalbrucke Gemunden for a period of 16 months are managed with the RDM system BAM data Store, which makes use of the openBIS data management software. An ML procedure is used to classify the data. Feature engineering, feature training and resulting data are performed and modelled in the RDM system. © 2022 the Author(s).

关键词： Structural health monitoring

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Machine learning for Enhanced Exercise Performance and Planning 11th

Machine Learning for Enhanced Exercise Performance and Plann...

引用

11th international conference on Future data and Security engineering, FDSE 2024

作者： thien, Nguyen Phuoc Dang, Chau N. B. Trinh, Truc T. T. Nhut, Nguyen Minh thuan, Nguyen Dinh University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819604364

this study proposes a machine learning-based approach to improve exercise evaluation and schedule planning. Key point detection via MoveNet assesses user movements, compares them to standard exercises, and analyzes body metrics. In the absence of body data, BodyPix estimates BMI through body part segmentation. the application, built on React Expo Go, provides real-time feedback to help users correct their form during workouts. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive learning

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intelligent Position Control of Magnetically Actuated Catheter Equipped with External Permanent Magnet 11

Intelligent Position Control of Magnetically Actuated Cathet...

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11th RSI international conference on Robotics and Mechatronics, ICRoM 2023

作者： Sinaki, Mahbod Gholamali Pishkenari, Hossein Nejat Ghafari, Ali Selk Sharif University of Technology School of Mechanical Engineering Tehran Iran Sharif University of Technology School of Science and Engineering Kish Island Iran

ISBN: (纸本)9798350308105

the precise manipulation of the magnetic catheters is of great importance for different applications, ensuring efficacy while minimizing potential complications. this research delves into the position control of a magnetically actuated catheter guided with an external permanent magnet. Due to the complexity of governing equations describing the system's behavior, a neural network approach was deemed suitable for modeling. Using a five degrees of freedom manipulator carrying an external permanent magnet as a payload, data was gathered from real-world positionings, tracking the coordination of the magnetic catheter's end point. these data points served to train the neural network, subsequently allowing for an effective simulation of the system's complex response, the proposed model's accuracy was validated, revealing a Total Percent Error of 0.973%, underscoring the neural network model's high precision in simulating real system behavior. In controlling the simulated plant, two methods were assessed: A traditional PID controller and an adaptive neural network controller. Comparative analysis revealed the adaptive neural network controller's superiority in accuracy and responsiveness. the adaptive neural network controller outperformed the PID with a 0% steadystate error compared to 7%, reduced overshoot to 20% from 150%, and had faster rise and settling times of under 5 seconds and 4.5 seconds, respectively. Furthermore, when system is subjected to eight Gaussian disturbances, the adaptive controller maintained its commendable performance, underscoring its potential for successful practical applications. this study demonstrates the efficacy of intelligent controllers in the realm of magnetic catheter positioning and the advantages of neural network modeling for intricate biomedical systems. © 2023 IEEE.

关键词： Catheters

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Optimizing MEG-EEG Mapping in Resource-Constrained Non-Intrusive Bio-Magnetic Sensing Systems: A data-Driven Approach 11

Optimizing MEG-EEG Mapping in Resource-Constrained Non-Intru...

引用

11th international conference on Information and Communication Technology, ICoICT 2023

作者： Elshafei, Mohamed Fadlullah, Zubair Md Fouda, Mostafa M. Lakehead University Department of Computer Science Thunder BayON Canada Western University Department of Computer Science LondonON Canada Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9798350321982

While Magnetoencephalography (MEG) and electroencephalography (EEG) are well-known neuroimaging techniques to capture a myriad of brain activities and stimulations, accessing conventional M/EEG devices is challenging. this is because of the bulky nature of the MEG machines, the need for magnetic shielding and cooling system, intrusive EEG electrodes, and various other complications involving preparing these devices to guarantee a clinical-grade signal acquisition. To address these issues, in this paper, we consider bio-magnetic sensing with emerging Magnetic Tunnel Junction (MTJ) sensors operating at room temperature that can map the sensed MEG to EEG signals, which can be helpful in this domain. However, such ultra-sensitive sensors are resource-constrained, and incorporating such MEG-EEG mapping needs to be optimized to balance the accuracy and computational/energy trade-off. therefore, we adopt a data-centric approach to address this optimization problem. Furthermore, we conduct rigorous comparative analytics on prominent machine/deep learning models on a publicly available dataset to establish a baseline proof-of-concept that can be seamlessly integrated with the considered bio-magnetic sensing systems. Our research unlocks the possibility for real-time monitoring of brain activities and abnormality detection away from the clinical environment and complex hospital settings. Moreover, the compact size and low-power requirements for the considered MTJ sensor make it compatible with IoT and wearable devices. © 2023 IEEE.

关键词： Electroencephalography

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