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Slow Well-Being Gardening: Creating a Sensor Network for Radiation Therapy Patients via Horticultural Therapeutic Activity

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Sensors 2024年第12期24卷 3771页

作者： Chang, Teng-Wen Tsai, Shih-Ting Huang, Hsin-Yi Wu, Yi-Sin Chang, Ching-Chih Datta, Sambit School of Design National Yunlin University of Science and Technology Douliou64002 Taiwan School of Electrical Engineering Computing and Mathematical Sciences Curtin University Bentley6102 Australia

Well-being can reflect people’s psychological conditions and be used alongside physiological parameters to evaluate patients’ physical and mental health. The modern medical environment increasingly incorporates digital carriers, human–computer interaction devices, sensible spaces, and the execution of suitable algorithms. Slow design in healthy human–computer interaction is often used to reflect people’s dependence on or support from behaviors or objects, promoting the stability of behaviors as well as meaningful and positive changes. Therefore, in this study, we propose a slow sensing model, develop a Slow Well-Being Gardening system, and use it to evaluate behavioral data from radiation therapy patients during treatment sessions and horticultural therapy. This study is based on SENS and slow design, setting the hospital lounge as a sensible space and establishing a sensor system. After a 10-day inspection, the process was evaluated and verified. Ultimately, data from facial detection (smile) and HRV showed that the patients in the experimental group experienced a significant improvement in their well-being, feeling better than those in the control group who maintained the most common state in normal treatment. Therefore, it can be inferred that the Slow Well-Being Gardening model is indeed valid and can be further developed. © 2024 by the authors.

关键词： Wearable sensors

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Long-Term Dissolved Organic Carbon Changes in Woronora Drinking Water System in Australia

SSRN

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SSRN 2024年

作者： Sathasivan, Arumugam Ramanathan, Thusyanthini Ollivier, Quinn Rahman, Ataur Hamilton, Lisa School of Computing Engineering and Mathematics School of Engineering Design and Built Environment Western Sydney University NSW2751 Australia WaterNSW ParramattaNSW2150 Australia

Assessing historical records of DOC concentrations (DOC) in drinking water sources is important for water utilities to understand long-term planning for infrastructure needs. This study investigates 15 to 20 years of historical data of the Woronora water supply catchment in Australia inclusive of the water filtration plant (WFP), the lake from where the water was drawn for WFP supply, and the two primary river inputs. The mean DOC at each site ranged from 3.54 ± 2.54 mg L-1 to 5.71 ± 2.67 mg L-1, with higher concentrations within the lake and WFP. The DOC in the lake and WFP significantly (p0.05). UV254 also showed increasing trends at Woronora River, Lake Woronora, and Woronora WFP, indicating an increase in aromatic DOC compounds in all three sites. While Waratah Rivulet showed no trend (p>0.05) in either DOC or UV254, it transported >60% of the total DOC load into Lake Woronora due to high flow volumes (>65% of total annual system flow). Annual DOC load to the lake is positively correlated with annual rainfall (R2>0.92;p73%) of the frequency distribution of SUVA254 > 2 L mg -1 m-1 in all four sites indicates a dominance of hydrophobic DOC. The terrestrial plant-derived DOC has increased in Lake Woronora, predominantly influenced by historical rainfall magnitude. Our results underscore the importance of considering the impact of increased DOC at the treatment plant intake for the planning and operation of the Woronora water supply system. © 2024, The Authors. All rights reserved.

关键词： Lakes

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Machine Learning Algorithms for the Detection of Threats in IoT Healthcare 1

Machine Learning Algorithms for the Detection of Threats in ...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Arun, V. Shenbagavalli, P. Sridhar, T. Manivannan, B. Mahesh, T.R. Anitha, K. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur India Karunya Institute of Technology and Sciences Division of Digital Sciences India Alliance University Alliance College of Engineering and Design Bangalore India Computer Science and Engineering Namakkal India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Csi College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798350359756

IoT (Internet of Things) technology enables the connectivity and communication of healthcare-related equipment, medical devices, and sensors. This connectivity enables better monitoring, data collecting, and analysis, which leads to better patient outcomes and more effective healthcare delivery. However, with the rising use of linked devices, it is critical to detect and prevent Cyberattacks in order to secure sensitive patient information. In order to tackle this problem, the article delves into the application of machine learning methods like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB), These algorithms were evaluated using the WUSTL EHMS2020 dataset and demonstrated the highest level of accuracy. © 2023 IEEE.

关键词： Internet of things

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Tensile specimen design for LB-PBF Inconel truss-based lattice structures: Manufacturing and experimental validation 23

Tensile specimen design for LB-PBF Inconel truss-based latti...

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23rd International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2023

作者： Tawfik, Ahmed Meyer, Guillaume Sperling, Philip Bills, Paul Mittelstedt, Christian Blunt, Liam University of Huddersfield Department of Engineering and Technology School of Computing and Engineering Huddersfield United Kingdom Lightweight Construction and Design Department of Mechanical Engineering Technical University of Darmstadt Darmstadt Germany Additive Manufacturing Center Technical University of Darmstadt Darmstadt Germany Volume Graphics Heidelberg Germany

ISBN: (纸本)9781998999132

The aerospace industry strives for an optimum between sustainability, cost and resource efficiency. In this regard, additive manufacturing (AM) has established itself as a disruptive technology by offering complex part design, geometrical flexibility and hence a high degree of lightweight grade. An improved competitiveness advantage for AM in an aerospace context, can be achieved using cellular structures such as honeycombs or lattices. However, in engineering practice, lattice structures are still not yet implemented for load-bearing structures. In order to use their lightweight potential and exploit lattice potential, a reliable and reproducible assessment of the mechanical properties of lattice structures for different loading scenarios is essential. On the one hand, test specimens used for material characterization have to be designed so that the required mechanical properties are not falsified by the test specimen geometry or the experiment itself. On the other hand, the small-scale manufacturing of lattice structures and their implementation into bulk parts pose challenges. Any deviation from the initial lattice design geometry may impact the mechanical performance. It is therefore of utmost importance to establish reliable and robust method for design and manufacturing and quality assessment. © 2023 Euspen Headquarters.

关键词： Aerospace industry

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SC-LSTM Based Human Action Recognition

SC-LSTM Based Human Action Recognition

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Naixin Kang Pengfei Yi Qingshu Geng Jing Dong Rui Liu Ling Wang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Human Action Recognition is one of the most applied research directions in the field of Computer Vision, which is widely used in human-computer interaction, Augmented Reality (AR) technology, security monitoring, and other scenarios. However, due to the complexity of human action gestures, existing Human Action Recognition methods have certain deficiencies in dealing with variable human gestures and action information, and the accuracy needs to be improved. To improve the accuracy, We propose a multi-dimensional network model based on SC-LSTM(Skip-Connection + LSTM). First, a Temporal Feature Extraction Module is designed based on SC-LSTM, and a Spatial Feature Extraction Module is designed based on CNN and Multi-Attention Mechanism to extract potential human action features from both temporal and spatial dimensions, respectively. Then, a separate SC-LSTM classification network is utilized to process these spatio-temporal features to obtain the final HAR results. The experimental results show that compared to other algorithms, the present model can more fully utilize the information in the temporal dimension, and thus performs better in terms of HAR accuracy.

关键词： Human computer interaction Computer vision Accuracy Image recognition Feature extraction Complexity theory Human activity recognition

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An Active Visual Perception Methodology for the Safety of Human-Robot Collaborations

An Active Visual Perception Methodology for the Safety of Hu...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Siyuan Cheng Pengfei Yi Mengmeng Chen Jing Dong Rui Liu Ling Wang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In industrial settings with multiple robotic arms, ensuring human-robot interaction safety through visual feedback is essential. However, existing methods use multiple pre-set cameras to avoid occlusion, making implementation challenging in compact spaces. We propose an adaptive active visual perception method for safety in human-robot collaboration by equipping a single RGBD camera on an idle robotic arm. This method eliminates the need to add additional brackets, is low cost, and is suitable for compact environments compared to current methods. Firstly, to capture comprehensive and accurate global information as much as possible, an active visual perception strategy integrating static visual perception and proprioceptive pose optimization is designed; then, an optimization-based robot position and pose decision method is proposed by using the real-time robots state and obstacles state, which enables the camera at the end of the robot to acquire more effective views; finally, a dual-robot following strategy is proposed to ensure that it can work in tandem. The validity was proved by experimentation. The video are available at https://***/03lwb.

关键词： Visualization Service robots Robot vision systems Human-robot interaction Streaming media Cameras Manipulators

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design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

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2024 International Conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Deepan, S. Rajendran, Dineshkumar Dhaliwal, Navdeep Praveena, K. Lourens, Melanie Anandhi, R.J. School Of Cs & Ai Sr University Telangana Warangal India School Of Computing And Innovative Technologies British University Computer Games Design And Programming Viet Nam Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Dundigal Hyderabad India Durban University Of Technology Faculty Of Management Sciences South Africa New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India

ISBN: (纸本)9798350366846

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes - Low Performance Student, Average Student, and Smart Student - were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments. © 2024 IEEE.

关键词： Decision support systems

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ResTransUnet: An effective network combined with Transformer and U-Net for liver segmentation in CT scans

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Computers in Biology and Medicine 2024年 177卷 108625-108625页

作者： Ou, Jiajie Jiang, Linfeng Bai, Ting Zhan, Peidong Liu, Ruihua Xiao, Hanguang School of Artificial Intelligence Chongqing University of Technology Chongqing China School of Computing and College of Design and Engineering National University of Singapore Singapore School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Liver segmentation is a fundamental prerequisite for the diagnosis and surgical planning of hepatocellular carcinoma. Traditionally, the liver contour is drawn manually by radiologists using a slice-by-slice method. However, this process is time-consuming and error-prone, depending on the radiologist's experience. In this paper, we propose a new end-to-end automatic liver segmentation framework, named ResTransUNet, which exploits the transformer's ability to capture global context for remote interactions and spatial relationships, as well as the excellent performance of the original U-Net architecture. The main contribution of this paper lies in proposing a novel fusion network that combines Unet and Transformer architectures. In the encoding structure, a dual-path approach is utilized, where features are extracted separately using both convolutional neural networks (CNNs) and Transformer networks. Additionally, an effective feature enhancement unit is designed to transfer the global features extracted by the Transformer network to the CNN for feature enhancement. This model aims to address the drawbacks of traditional Unet-based methods, such as feature loss during encoding and poor capture of global features. Moreover, it avoids the disadvantages of pure Transformer models, which suffer from large parameter sizes and high computational complexity. The experimental results on the LiTS2017 dataset demonstrate remarkable performance for our proposed model, with Dice coefficients, volumetric overlap error (VOE), and relative volume difference (RVD) values for liver segmentation reaching 0.9535, 0.0804, and −0.0007, respectively. Furthermore, to further validate the model's generalization capability, we conducted tests on the 3Dircadb, Chaos, and Sliver07 datasets. The experimental results demonstrate that the proposed method outperforms other closely related models with higher liver segmentation accuracy. In addition, significant improvements can be achieved by applyi

关键词： Computerized tomography

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Implementation of Digital Logic Gate Families Using Low Operating Flexible Organic TFT Based on Innovative Compact Modeling Approach 3

Implementation of Digital Logic Gate Families Using Low Oper...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Mehrolia, Mukuljeet Singh Mishra, Ashwini Kumar Singh, Rasveen Yadav, Surendra Devarakonda, Vijayakumar Singh, Abhishek Kumar Rajiv Gandhi Institute of Petroleum Technology Department of Electrical and Electronics Engineering Amethi India GLA University Department of Electtonics and Commuication Engineering Mathura India G.D. Goenka University Department of Electronics and Communication Engineering Gurugram India School of Computing MIT Vishwaprayag University Solapur India Indian Institute of Information Technology Design and Manufacturing Department of Electronics and Commuication Engineering Kurnool India

ISBN: (纸本)9798331533205

The article demonstrates the brief fabrication method of the flexible organic thin film transistor (OTFT) developed earlier and its utilization for the development of various basic logic gates (NAND, NOR, AND, NOT, and OR Gates). The compact modeling of the fabricated device and the implementation of the logic gates has been carried out using Silvaco-Techmodeler and Silvaco-Gateway tools, respectively. The device can operate at -2 V supply with good electrical characteristics in terms of -2μ A drain current, ION/IOFF ∼ 103 ' and μp∼ 0.1cm2/V-sec respectively. The Silvaco-Gateway tool implements the logic gates with excellent characteristics, demonstrating logic swing of 1.18 V and a delay of 3.7 ns. The logic gates also possess a good transient characteristic with all four possible logic inputs (00, 01, 10, and 11). The Silvaco-Techmodeler tool models the device with an accuracy of approximately 100%, with a 0.07% and 0.08% error between the experimental and modeled data, respectively. The cost-efficiently fabricated solution-processed transistor in this article can be further utilized in the fields of medical and sensing applications. © 2025 IEEE.

关键词： Logic gates

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