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Статті в журналах з теми "Stress classification"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Kaeding, Christopher C., and Robert G. Najarian. "Stress Fractures: Classification and Management." Physician and Sportsmedicine 38, no. 3 (October 2010): 45–54. http://dx.doi.org/10.3810/psm.2010.10.1807.

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2

Roche, Roland L. "Practical procedure for stress classification." International Journal of Pressure Vessels and Piping 37, no. 1-4 (January 1989): 27–44. http://dx.doi.org/10.1016/0308-0161(89)90138-5.

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3

Kalejaiye, Odunayo, Monika Vij, and Marcus John Drake. "Classification of stress urinary incontinence." World Journal of Urology 33, no. 9 (June 25, 2015): 1215–20. http://dx.doi.org/10.1007/s00345-015-1617-1.

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4

Miller, Timothy L., and Christopher Kaeding. "Stress Fractures: A New Classification System." Medicine & Science in Sports & Exercise 41 (May 2009): 84. http://dx.doi.org/10.1249/01.mss.0000353531.90405.97.

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5

Blaivas, Jerry G., and Carl A. Olsson. "Stress Incontinence: Classification and Surgical Approach." Journal of Urology 139, no. 4 (April 1988): 727–31. http://dx.doi.org/10.1016/s0022-5347(17)42611-5.

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6

Dhalla, A. K. "Stress Classification for Elevated Temperature Service." Journal of Pressure Vessel Technology 113, no. 4 (November 1, 1991): 488–96. http://dx.doi.org/10.1115/1.2928785.

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Анотація:

The paper presents two procedures which can be used to classify the stresses originating at the structural discontinuities of pressure vessel and piping components designed for elevated temperature service. The stress classification is based upon the definitions of primary and secondary stress intensities which are provided in the ASME Boiler and Pressure Vessel Code, Sections III and VIII. The intent is not to reclassify primary stresses, but to provide a designer with practical guidance in evaluating the primary characteristics of thermal and structural discontinuity pressure stresses. Two objectives for an appropriate classification of stresses are: (a) to reduce undue conservatism in elevated temperature Code Case N-47 design rules when primary stress is only a small fraction of secondary stress, and (b) to provide a sufficient margin of safety for extreme (Level D) loadings when the primary local and general membrane stress levels are close to the yield point of the material at structural discontinuities.

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7

Cipresso, Pietro, Andrea Gaggioli, Silvia Serino, and Giuseppe Riva. "Stress Diffusion through Complex Networks." International Journal of Adaptive, Resilient and Autonomic Systems 3, no. 1 (January 2012): 46–64. http://dx.doi.org/10.4018/jaras.2012010103.

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Research has proven that stress reduces quality of life and causes many diseases. It is not clear how stress spreads among the population and how its diffusion in a society can be estimated. From a complex system perspective, this paper defines the rules of stress transmission, including input and output factors. Stress transmission flow is defined to describe an entropy-derived measure of stress between two interconnected individuals, and the analysis is extended to networked individuals to analyze stress diffusion in a theoretical setting that includes the modeling of complex networks and the use of agent-based models in a simulated framework. These approaches endow artificial, interacting agents with behavioral rules, allowing the authors to determine the important components that must be considered as the nature of the equilibrium that exists between two distinctly different classifications of individuals. The first classification is “isolated individuals” who experience self-induced stress. The second classification consists of “too connected individuals” who have a high perception of social pressure, have a higher probability of being stressed, and who are surrounded by a higher number of stressed people.

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8

Liu, Xinyu, Yuhao Shan, Min Peng, Huanyu Chen, and Tong Chen. "Human Stress and StO2: Database, Features, and Classification of Emotional and Physical Stress." Entropy 22, no. 9 (August 31, 2020): 962. http://dx.doi.org/10.3390/e22090962.

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Emotional and physical stress can cause various health problems. In this paper, we used tissue blood oxygen saturation (StO2), a newly proposed physiological signal, to classify the human stress. We firstly constructed a public StO2 database including 42 volunteers subjected to two types of stress. During the physical stress experiment, we observed that the facial StO2 right after the stress can be either increased or decreased comparing to the baseline. We investigated the StO2 feature combinations for the classification and found that the average StO2 values from left cheek, chin, and the middle of the eyebrow can provide the highest classification rate of 95.56%. Comparison with other stress classification method shows that StO2 based method can provide best classification performance with lowest feature dimension. These results suggest that facial StO2 can be used as a promising features to identify stress states, including emotional and physical stress.

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9

Dhole, N. P., and S. N. Kale. "Multilayer Perceptron Classification in Stress Speech Identification." International Journal of Computer Sciences and Engineering 6, no. 4 (April 30, 2018): 471–75. http://dx.doi.org/10.26438/ijcse/v6i4.471475.

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10

Kang, Jun-Su, Giljin Jang, and Minho Lee. "Stress status classification based on EEG signals." Journal of the Institute of Internet Broadcasting and Communication 16, no. 3 (June 30, 2016): 103–8. http://dx.doi.org/10.7236/jiibc.2016.16.3.103.

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11

Amira, Tlija, Istrate Dan, Badii Atta, Gattoufi Said, Bennani Az-eddine, and Wegrzyn-Wolska Katarzyna. "Stress Level Classification Using Heart Rate Variability." Advances in Science, Technology and Engineering Systems Journal 4, no. 3 (2019): 38–46. http://dx.doi.org/10.25046/aj040306.

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12

Pedrotti, Marco, Mohammad Ali Mirzaei, Adrien Tedesco, Jean-Rémy Chardonnet, Frédéric Mérienne, Simone Benedetto, and Thierry Baccino. "Automatic Stress Classification With Pupil Diameter Analysis." International Journal of Human-Computer Interaction 30, no. 3 (January 31, 2014): 220–36. http://dx.doi.org/10.1080/10447318.2013.848320.

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13

Fanous, Ihab F. Z., and R. Seshadri. "Stress Classification Using the r-Node Method." Journal of Pressure Vessel Technology 129, no. 4 (June 28, 2006): 676–82. http://dx.doi.org/10.1115/1.2767357.

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Анотація:

The ASME Code Secs. III and VIII (Division 2) provide stress-classification guidelines to interpret the results of a linear elastic finite element analysis. These guidelines enable the splitting of the generated stresses into primary, secondary, and peak. The code gives some examples to explain the suggested procedures. Although these examples may reflect a wide range of applications in the field of pressure vessel and piping, the guidelines are difficult to use with complex geometries. In this paper, the r-node method is used to investigate the primary stresses and their locations in both simple and complex geometries. The method is verified using the plane beam and axisymmetric torispherical head. Also, the method is applied to analyze 3D straight and oblique nozzles modeled using both solid and shell elements. The results of the analysis of the oblique nozzle are compared with recently published experimental data.

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14

Anusuya, M. "An Efficient Human Stress Classification Using Fuzzy." Journal of Computer and Mathematical Sciences 9, no. 10 (October 30, 2018): 1536–44. http://dx.doi.org/10.29055/jcms/912.

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15

YASTREMSKYI, Oleksandr. "Fiscal stress test: genesis, classification, application, development." Fìnansi Ukraïni 2018, no. 275 (October 27, 2018): 54–69. http://dx.doi.org/10.33763/finukr2018.10.054.

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16

Lakshmi Priya, Chaithra B, Kavana R, Manjulamma A, and Meghana S. "Prediction and Classification of Stress in Humans." ACS Journal for Science and Engineering 2, no. 2 (September 1, 2022): 1–10. http://dx.doi.org/10.34293/acsjse.v2i2.30.

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Анотація:

To avoid serious health problems, researchers are working difficult to come up with effective methods for detecting and coping with excessive amounts of stress. Facial gestures are an essential part of human speech. Researchers in psychology, computer science, neuroscience, and other fields are increasingly interested in a human-computer interface device for automated face recognition or facial expression recognition. as well as similar areas. The machine identifies frontal faces in photographs and codes each frame according to a series of dimensions. A system for acquiring the corresponding information is inside this case of sentiment classification utilizing a biologic output or a thermodynamic picture, which was being used, it is necessarily researched extensively. To develop efficient strategies for identifying and controlling elevated levels of stress in order to reduce severe health effects. Facial gestures are an essential part of human speech. Researchers are particularly involved in a human-computer interface device for automated face recognition or facial expression recognition. In the photographs, the machine senses frontal facesand codes each frame according to certain dimensions. Experimentation indicate that the proposed algorithm can detect more efficient action

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17

Hong, Kan. "Classification of emotional stress and physical stress using facial imaging features." Journal of Optical Technology 83, no. 8 (August 1, 2016): 508. http://dx.doi.org/10.1364/jot.83.000508.

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18

K.V., Suma, Niranjana Murthy H.S., Umesharaddy Radder, and Suma P. "The IoT based PPG Signal Classification System for Acute Audio-Visual Stimulus Induced Stress." Webology 19, no. 1 (January 20, 2022): 5547–62. http://dx.doi.org/10.14704/web/v19i1/web19373.

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Mental stress causes a great impact on our autonomic nervous system. Pulse rate variability (PRV) is a method which measures the changes in the autonomic nervous system of an individual. This study aims to acquire PPG signals in real time from a single spot Pulse sensor and then PRV analysis is performed on Pulse signal to determine perceived stress from the subject caused due to nerve-wracking audio-visual stimulus. PPG signal is then transferred wirelessly over an Android app. Also this work incorporates several Machine Learning models to organize the stress level of the subjects as average stress or high stress. Non-linear model gives best average classification precision, sensitivity and specificity of 90%, 100% and 82% respectively. With the advancement of portable PPG monitoring device acts as a substitute to Heart rate variability (HRV) even during the moving conditions. Also PPG signal is compared with ECG signal and a close precision is obtained with average percentage error of 8% for BPM and 3% for RR interval. PPG sensors offer more comfortness to the users which can be positioned on fingertip and wrist. By means of the improvement of android app provides feasibility to monitor stress by providing an alert to the mobile users whenever the stress exceeds the normal limits.

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19

Aderibigbe, Aderonke, Clotilde Chen Valdes, and Zoya Heidari. "Integrated rock classification in the Wolfcamp Shale based on reservoir quality and anisotropic stress profile estimated from well logs." Interpretation 4, no. 2 (May 1, 2016): SF1—SF18. http://dx.doi.org/10.1190/int-2015-0138.1.

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Анотація:

Reliable rock classification is the key to identify target zones for successful hydraulic fracturing stimulation treatment in unconventional reservoirs such as organic-rich mudrocks. Such a rock classification scheme should take into account geologic attributes, petrophysical, and geomechanical properties (i.e., in situ stress gradient and elastic properties) to improve the likelihood of successful fracture treatment. However, conventional rock classification methods do not take into account stress gradients in the formation. We have developed a new rock classification technique that integrates four rock classification schemes based on the (1) geologic facies, (2) reservoir quality, (3) stress profile, and (4) completion quality. The techniques applied in these classification schemes include core description and thin section analysis, well-log-based depth-by-depth petrophysical and compositional characterization, and analysis of geomechanical measurements. Geomechanical analysis of core measurements and well logs provide a depth-by-depth assessment of minimum horizontal stress assuming vertical transverse isotropy in the formation. We have performed the geologic facies and reservoir quality classifications using an artificial neural network analysis, in which well logs and well-log-based estimates of the petrophysical and compositional properties were inputs to the network. Our technique was applied to a well located in the Wolfcamp Shale in the Delaware Basin. Based on the integrated rock classification results, we recommend the middle of the upper Wolfcamp and the bottom of the lower Wolfcamp depth intervals as the best candidates for fracture initiation and fracture containment zones, respectively. The selection of these zones was based on the reservoir quality and average minimum horizontal stress gradient calculated in these intervals. Our integrated rock classification technique can improve the planning and execution of completions design for hydraulic fracture treatments.

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20

Zhang, Ying, Kewei Tian, Xianghao Ma, Leilei Zhang, Ruibo Sun, Huichao Wang, Youwen Liu, and Guangquan Zhou. "Analysis of damage in relation to different classifications of pre-collapse osteonecrosis of the femoral head." Journal of International Medical Research 46, no. 2 (September 6, 2017): 693–98. http://dx.doi.org/10.1177/0300060517719625.

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Анотація:

Objective This study aimed to investigate the damage pattern of the stress transfer path (STP) for the Japanese Investigation Committee (JIC) classification of pre-collapse osteonecrosis of the femoral head. We aimed to provide a specific biomechanical basis for treatment decisions of each subtype. Methods Five computational models were used in the experiment. Different necrotic classifications were simulated based on the JIC classification system. Damage patterns of the STP were used for qualitative assessment and average stresses were used for quantitative analysis. Results The STP of type A showed a strong similarity to the healthy level, which was consistent with the bone density distribution in X-rays and previous simulations results. The damaged area of principal stress of type B was approximately 25% of the healthy level. The STPs of types C1 and C2 were broken and the damaged areas of principal stress were more than 50% of the healthy level. The efficiency of stress transfer was reduced. Conclusions These results indicate that the damage patterns and stress transfer efficiency of the femoral head are associated with necrotic classifications.

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21

Feng, W., S. V. Hoa, and Q. Huang. "Classification of stress modes in assumed stress fields of hybrid finite elements." International Journal for Numerical Methods in Engineering 40, no. 23 (December 15, 1997): 4313–39. http://dx.doi.org/10.1002/(sici)1097-0207(19971215)40:23<4313::aid-nme259>3.0.co;2-n.

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22

Kang, Mingu, Siho Shin, Gengjia Zhang, Jaehyo Jung, and Youn Tae Kim. "Mental Stress Classification Based on a Support Vector Machine and Naive Bayes Using Electrocardiogram Signals." Sensors 21, no. 23 (November 27, 2021): 7916. http://dx.doi.org/10.3390/s21237916.

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Examining mental health is crucial for preventing mental illnesses such as depression. This study presents a method for classifying electrocardiogram (ECG) data into four emotional states according to the stress levels using one-against-all and naive Bayes algorithms of a support vector machine. The stress classification criteria were determined by calculating the average values of the R-S peak, R-R interval, and Q-T interval of the ECG data to improve the stress classification accuracy. For the performance evaluation of the stress classification model, confusion matrix, receiver operating characteristic (ROC) curve, and minimum classification error were used. The average accuracy of the stress classification was 97.6%. The proposed model improved the accuracy by 8.7% compared to the previous stress classification algorithm. Quantifying the stress signals experienced by people can facilitate a more effective management of their mental state.

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23

Gioia, Federica, Alberto Greco, Alejandro Luis Callara, and Enzo Pasquale Scilingo. "Towards a Contactless Stress Classification Using Thermal Imaging." Sensors 22, no. 3 (January 27, 2022): 976. http://dx.doi.org/10.3390/s22030976.

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Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates the contribution given by thermal imaging for an effective automatic stress detection with the perspective of a contactless stress recognition system. To this aim, we recorded both ANS correlates (cardiac, electrodermal, and respiratory activity) and thermal images from 25 volunteers under acute stress induced by the Stroop test. We conducted a statistical analysis on the features extracted from each signal, and we implemented subject-independent classifications based on the support vector machine model with an embedded recursive feature elimination algorithm. Particularly, we trained three classifiers using different feature sets: the full set of features, only those derived from the peripheral autonomic correlates, and only those derived from the thermal images. Classification accuracy and feature selection results confirmed the relevant contribution provided by the thermal features in the acute stress detection task. Indeed, a combination of ANS correlates and thermal features achieved 97.37% of accuracy. Moreover, using only thermal features we could still successfully detect stress with an accuracy of 86.84% in a contact-free manner.

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24

Devi, S. Siamala, Elamparithi, V. Gowtham, and B. Julian Sharon. "Daily stress classification using functional near infrared spectroscopy." Journal of Physics: Conference Series 1916, no. 1 (May 1, 2021): 012161. http://dx.doi.org/10.1088/1742-6596/1916/1/012161.

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25

Tengku Zainul Akmal, Tengku ‘Afiah Mardhiah, Abd Hafiz Qayyum Abd Talib, Siti Zura A. Jalil, and Siti Armiza Mohd Aris. "Stress classification based on human electromagnetic radiation analysis." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (May 1, 2021): 826. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp826-834.

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Анотація:

<p>Stress is a feeling of emotional or physical tension due to any events that makes one feel frustrated, angry or nervous. It a situation that trigger particular biological response when encounter a threat or challenge. This paper discussed stress classification based on human electromagnetic radiation (EMR). At first, the collected radiation frequency data is analyzed using multivariate analysis of variance (MANOVA) to identify the significance points for the classification. Then, the data is classified using locally weighted learning (LWL) algorithm. The results show stress classification using EMR based on third eye and throat chakra points obtained accuracy of more than 60%.</p>

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26

Aversano, Lerina, Mario Luca Bernardi, and Marta Cimitile. "Water stress classification using Convolutional Deep Neural Networks." JUCS - Journal of Universal Computer Science 28, no. 3 (March 28, 2022): 311–28. http://dx.doi.org/10.3897/jucs.80733.

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In agriculture, given the global water scarcity, optimizing the irrigation system have become a key requisite of any semi-automatic irrigation scheduling system. Using efficient assessment methods for crop water stress allows reduced water consumption as well as improved quality and quantity of the production. The adoption of Neural Network can support the automatic in situ continuous monitoring and irrigation through the real-time classification of the plant water stress. This study proposes an end-to-end automatic irrigation system based on the adoption of Deep Neural Networks for the multinomial classification of tomato plants’ water stress based on thermal and optical aerial images. This paper proposes a novel approach that cover three important aspects: (i) joint usage of optical and thermal camera, captured by un-manned aerial vehicles (UAVs); (ii) strategies of image segmentation in both thermal imaging used to obtain images that can remove noise and parts not useful for classifying water stress; (iii) the adoption of deep pre-trained neural ensembles to perform effective classification of field water stress. Firstly, we used a multi-channel approach based on both thermal and optical images gathered by a drone to obtain a more robust and broad image extraction. Moreover, looking at the image processing, a segmentation and background removal step is performed to improve the image quality. Then, the proposed VGG-based architecture is designed as a combination of two different VGG instances (one for each channel). To validate the proposed approach a large real dataset is built. It is com- posed of 6000 images covering all the lifecycle of the tomato crops captured with a drone thermal and optical photocamera. Specifically, our approach, looking mainly at leafs and fruits status and patterns, is designed to be applied after the plants has been transplanted and have reached, at least, early growth stage (covering vegetative, flowering, friut-formation and mature fruiting stages).

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27

Moridani, M. K., Z. Mahabadi, and N. Javadi. "Heart rate variability features for different stress classification." Bratislava Medical Journal 121, no. 09 (2020): 619–27. http://dx.doi.org/10.4149/bll_2020_107.

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28

Park, Soyeon, and Suh-Yeon Dong. "Effects of Daily Stress in Mental State Classification." IEEE Access 8 (2020): 201360–70. http://dx.doi.org/10.1109/access.2020.3035799.

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29

Arciuli, Joanne, and Linda Cupples. "Effects of Stress Typicality During Speeded Grammatical Classification." Language and Speech 46, no. 4 (December 2003): 353–74. http://dx.doi.org/10.1177/00238309030460040101.

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30

Furushima, Kozo, Yoshiyasu Itoh, Shohei Iwabu, Yuzuru Yamamoto, Ryuji Koga, and Masaki Shimizu. "Classification of Olecranon Stress Fractures in Baseball Players." American Journal of Sports Medicine 42, no. 6 (April 2014): 1343–51. http://dx.doi.org/10.1177/0363546514528099.

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31

CAO, An-ye, Lin-ming DOU, Ru-ling YAN, Heng JIANG, Cai-ping LU, Tao-tao DU, and Zhen-yu LU. "Classification of microseismic events in high stress zone." Mining Science and Technology (China) 19, no. 6 (November 2009): 718–23. http://dx.doi.org/10.1016/s1674-5264(09)60131-9.

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32

Yao, Xiao, Takatoshi Jitsuhiro, Chiyomi Miyajima, Norihide Kitaoka, and Kazuya Takeda. "Classification of speech under stress by physical modeling." Acoustical Science and Technology 34, no. 5 (2013): 311–21. http://dx.doi.org/10.1250/ast.34.311.

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33

Zhou, G., J. H. L. Hansen, and J. F. Kaiser. "Nonlinear feature based classification of speech under stress." IEEE Transactions on Speech and Audio Processing 9, no. 3 (March 2001): 201–16. http://dx.doi.org/10.1109/89.905995.

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34

Nickolaenko, S. A., and S. V. Pukhno. "APPROACHES TO CLASSIFICATION OF STRATEGIES FOR OVERCOMING PSYCHOLOGICAL STRESS." BULLETIN Series Psychology 64, no. 3 (September 20, 2020): 5–10. http://dx.doi.org/10.51889/2020-3.1728-7847.01.

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Анотація:

The article reveals the concept of psychological stress, reveals the psychological mechanism of the occurrence of psychological stress, defines the concept of a strategy for overcoming psychological stress, distinguishes autogenic and heterogeneous strategies for overcoming psychological stress, describes the main approaches to the classification of strategies to overcome psychological stress, defines the main typologies within the framework of the main approaches to classification stress management strategies

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35

Li, Yue Feng, Xu Dong Pan, and Guang Lin Wang. "The Classification of Disposable Mechanical Elements." Applied Mechanics and Materials 163 (April 2012): 86–90. http://dx.doi.org/10.4028/www.scientific.net/amm.163.86.

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Анотація:

Disposable mechanical elements with extremely short lives are widely used in the aerospace and defense fields. To reliably evaluate the life of disposable mechanical elements, many attentions were concentrated in the fatigue properties of disposable mechanical elements. According to the different meanings of static strength for metals, disposable mechanical elements are divided into two groups with different fatigue properties: extremely low cycle fatigue module for Type I with ultimate strength as design stress and low cycle fatigue module for Type II with yield strength as design stress. The Kuroda model and a cumulative damage model consisting of the Miners rule and the sequential law are used in the fatigue design process of the Type I. To the Type II, the Manson-Coffin model is suitable for conventional applications but more attempts are still conducted to further improve stress levels. The Type II with increasing load sequences are specially treated, since the cyclic yield strength of certain materials under pulsating stress closing to the yield strength increase with the deepening of fatigue damage. Consequently, under the increasing pulsating cyclic loading, the later load whose amplitude is higher than the initial yield strength will be permitted.

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36

Gupta, Megha V., Shubhangi Vaikole, Ankit D. Oza, Amisha Patel, Diana Petronela Burduhos-Nergis, and Dumitru Doru Burduhos-Nergis. "Audio-Visual Stress Classification Using Cascaded RNN-LSTM Networks." Bioengineering 9, no. 10 (September 27, 2022): 510. http://dx.doi.org/10.3390/bioengineering9100510.

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Анотація:

The purpose of this research is to emphasize the importance of mental health and contribute to the overall well-being of humankind by detecting stress. Stress is a state of strain, whether it be mental or physical. It can result from anything that frustrates, incenses, or unnerves you in an event or thinking. Your body’s response to a demand or challenge is stress. Stress affects people on a daily basis. Stress can be regarded as a hidden pandemic. Long-term (chronic) stress results in ongoing activation of the stress response, which wears down the body over time. Symptoms manifest as behavioral, emotional, and physical effects. The most common method involves administering brief self-report questionnaires such as the Perceived Stress Scale. However, self-report questionnaires frequently lack item specificity and validity, and interview-based measures can be time- and money-consuming. In this research, a novel method used to detect human mental stress by processing audio-visual data is proposed. In this paper, the focus is on understanding the use of audio-visual stress identification. Using the cascaded RNN-LSTM strategy, we achieved 91% accuracy on the RAVDESS dataset, classifying eight emotions and eventually stressed and unstressed states.

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37

Phutela, Nishtha, Devanjali Relan, Goldie Gabrani, Ponnurangam Kumaraguru, and Mesay Samuel. "Stress Classification Using Brain Signals Based on LSTM Network." Computational Intelligence and Neuroscience 2022 (April 28, 2022): 1–13. http://dx.doi.org/10.1155/2022/7607592.

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The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals are frequently employed in stress detection research and are both inexpensive and noninvasive modality. This paper proposes a stress classification system by utilizing an EEG signal. EEG signals from thirty-five volunteers were analysed which were acquired using four EEG sensors using a commercially available 4-electrode Muse EEG headband. Four movie clips were chosen as stress elicitation material. Two clips were selected to induce stress as it contains emotionally inductive scenes. The other two clips were chosen that do not induce stress as it has many comedy scenes. The recorded signals were then used to build the stress classification model. We compared the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) for classifying stress and nonstress group. The maximum classification accuracy of 93.17% was achieved using two-layer LSTM architecture.

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38

Daei alhag, Davood, Varahram Rashidi, Saeed Aharizad, Farhad Farahvash, and Bahram Mirshekari. "Classification of advanced spring wheat genotypes under non-stress and drought stress conditions." Journal of Crop Breeding 12, no. 34 (June 1, 2020): 115–29. http://dx.doi.org/10.29252/jcb.12.34.115.

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39

Song, Se-Hui, and Dong Keun Kim. "Development of a Stress Classification Model Using Deep Belief Networks for Stress Monitoring." Healthcare Informatics Research 23, no. 4 (2017): 285. http://dx.doi.org/10.4258/hir.2017.23.4.285.

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40

Seshadri, R., and D. L. Marriott. "On relating the reference stress, limit load and the ASME stress classification concepts." International Journal of Pressure Vessels and Piping 56, no. 3 (January 1993): 387–408. http://dx.doi.org/10.1016/0308-0161(93)90007-g.

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41

Elzeiny, Sami, and Marwa Qaraqe. "Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images." Sensors 20, no. 18 (September 17, 2020): 5312. http://dx.doi.org/10.3390/s20185312.

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Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify stress status is crude. Building a person-specific model leads to a reliable classification, but it requires the collection of new data to train a new model for every individual and needs periodic upgrades because stress is dynamic. In this paper, a new binary classification (called stressed and non-stressed) approach is proposed for a subject’s stress state in which the inter-beat intervals extracted from a photoplethysomogram (PPG) were transferred to spatial images and then to frequency domain images according to the number of consecutive. Then, the convolution neural network (CNN) was used to train and validate the classification accuracy of the person’s stress state. Three types of classification models were built: person-specific models, generic classification models, and calibrated-generic classification models. The average classification accuracies achieved by person-specific models using spatial images and frequency domain images were 99.9%, 100%, and 99.8%, and 99.68%, 98.97%, and 96.4% for the training, validation, and test, respectively. By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models’ accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models.

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42

Zhang, Ji, Zhong Xian Zhang, and Cai Ping Huang. "Representation Based Classification of Strength Theories of Concrete." Advanced Materials Research 168-170 (December 2010): 74–77. http://dx.doi.org/10.4028/www.scientific.net/amr.168-170.74.

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This paper proposes a new classification of strength theories of concrete based on mathematical representation. First, stress invariants in strength criteria are classified according to their geometrical meanings in the stress space. Then, strength criteria of concrete are classified according to the categories of stress invariants employed in their representations and subclassified according to the nonlinear terms involved. By this formal classification, the mathematical properties of strength theories of concrete can be analyzed more clearly for constitutive formulation.

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43

Arsalan, Aamir, and Muhammad Majid. "Human stress classification during public speaking using physiological signals." Computers in Biology and Medicine 133 (June 2021): 104377. http://dx.doi.org/10.1016/j.compbiomed.2021.104377.

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44

Kumar, M. G. Sharavana, and V. R. Sarma Dhulipala. "Fuzzy Logic Based Stress Level Classification using Physiological Parameters." Asian Journal of Research in Social Sciences and Humanities 6, cs1 (2016): 697. http://dx.doi.org/10.5958/2249-7315.2016.00990.4.

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45

Kijowski, Richard, James Choi, Kazuhiko Shinki, Alejandro Munoz Del Rio, and Arthur De Smet. "Validation of MRI Classification System for Tibial Stress Injuries." American Journal of Roentgenology 198, no. 4 (April 2012): 878–84. http://dx.doi.org/10.2214/ajr.11.6826.

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46

Oh, Jiyoung, and Heykyung Park. "Evaluation of Environmental Color Emotions by Stress Group Classification." Journal of Korea Society of Color Studies 34, no. 1 (February 29, 2020): 25–33. http://dx.doi.org/10.17289/jkscs.34.1.202002.25.

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47

Blanco, Justin, Ann Vanleer, Taylor Calibo, and Samara Firebaugh. "Single-Trial Cognitive Stress Classification Using Portable Wireless Electroencephalography." Sensors 19, no. 3 (January 25, 2019): 499. http://dx.doi.org/10.3390/s19030499.

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This work used a low-cost wireless electroencephalography (EEG) headset to quantify the human response to different cognitive stress states on a single-trial basis. We used a Stroop-type color–word interference test to elicit mild stress responses in 18 subjects while recording scalp EEG. Signals recorded from thirteen scalp locations were analyzed using an algorithm that computes the root mean square voltages in the theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands immediately following the initiation of Stroop stimuli; the mean of the Teager energy in each of these three bands; and the wideband EEG signal line-length and number of peaks. These computational features were extracted from the EEG signals on thirteen electrodes during each stimulus presentation and used as inputs to logistic regression, quadratic discriminant analysis, and k-nearest neighbor classifiers. Two complementary analysis methodologies indicated classification accuracies over subjects of around 80% on a balanced dataset for the logistic regression classifier when information from all electrodes was taken into account simultaneously. Additionally, we found evidence that stress responses were preferentially time-locked to stimulus presentation, and that certain electrode–feature combinations worked broadly well across subjects to distinguish stress states.

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48

Marcelino, Aline D. A. de L., Pedro D. Fernandes, Jean P. C. Ramos, Wellison F. Dutra, José J. V. Cavalcanti, and Roseane C. dos Santos. "Multivariate classification of cotton cultivars tolerant to salt stress." Revista Brasileira de Engenharia Agrícola e Ambiental 26, no. 4 (April 2022): 266–73. http://dx.doi.org/10.1590/1807-1929/agriambi.v26n4p266-273.

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ABSTRACT Two multivariate methods were adopted to classify salt-tolerant cotton genotypes based on their growth and physiological traits. The genotypes were cultivated in a greenhouse and subjected to 45 days of irrigation with saline water from the V4 phase onwards. Irrigation was performed with saline water with electrical conductivity (ECw) of 6.0 dS m-1. A factorial-randomized block design was adopted with nine cultivars, two treatments of ECw (0.6 as the control, and 6.0 dS m-1), and four replicates. Plants were evaluated for growth, gas exchange, and photosynthesis. The data were statistically analyzed using univariate and multivariate methods. For the latter, non-hierarchical (principal component, PC) and hierarchical (UPGMA) models were used for the classification of cultivars. Significant differences were found between cultivars based on univariate analyses, and the traits that differed statistically were used for multivariate analyses. Four groups were identified with the same composition in both the PC and UPGMA methods. Among them, one contained the cultivars BRS Seridó, BRS 286, FMT 705, and BRS Rubi, which were tolerant to salt stress imposed on the plants. Photosynthesis, transpiration, and stomatal conductance data were the main contributors to the classification of cultivars using the principal component method.

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49

Arquilla, Katya, Andrea K. Webb, and Allison P. Anderson. "Utility of the Full ECG Waveform for Stress Classification." Sensors 22, no. 18 (September 17, 2022): 7034. http://dx.doi.org/10.3390/s22187034.

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The detection of psychological stress using the electrocardiogram (ECG) signal is most commonly based on the detection of the R peak—the most prominent part of the ECG waveform—and the heart rate variability (HRV) measurements derived from it. For stress detection algorithms focused on short-duration time windows, there is potential benefit in including HRV features derived from the detection of smaller peaks within the ECG waveform: the P, Q, S, and T waves. However, the potential drawback of using these small peaks is their smaller magnitude and subsequent susceptibility to noise, making them more difficult to reliably detect. In this work, we demonstrate the potential benefits of including smaller waves within binary stress classification using a pre-existing data set of ECG recordings from 57 participants (aged 18–40) with a self-reported fear of spiders during exposure to videos of spiders. We also present an analysis of the performance of an automated peak detection algorithm and the reliability of detection for each of the smaller parts of the ECG waveform. We compared two models, one with only R peak features and one with small peak features. They were similar in precision, recall, F1, area under ROC curve (AUC), and accuracy, with the greatest differences less than the standard deviations of each metric. There was a significant difference in the Akaike Information Criterion (AIC), which represented the information loss of the model. The inclusion of novel small peak features made the model 4.29×1028 times more probable to minimize the information loss, and the small peak features showed higher regression coefficients than the R peak features, indicating a stronger relationship with acute psychological stress. This difference and further analysis of the novel features suggest that small peak intervals could be indicative of independent processes within the heart, reflecting a psychophysiological response to stress that has not yet been leveraged in stress detection algorithms.

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50

Miller, Timothy, Christopher C. Kaeding, and David Flanigan. "The Classification Systems of Stress Fractures: A Systematic Review." Physician and Sportsmedicine 39, no. 1 (February 2011): 93–100. http://dx.doi.org/10.3810/psm.2011.02.1866.

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