Stress is an escalated psycho-physiological state of the human body emerging in response to a challenging event or a demanding condition. Environmental factors that trigger stress are called ...stressors. In case of prolonged exposure to multiple stressors impacting simultaneously, a person's mental and physical health can be adversely affected which can further lead to chronic health issues. To prevent stress-related issues, it is necessary to detect them in the nascent stages which are possible only by continuous monitoring of stress. Wearable devices promise real-time and continuous data collection, which helps in personal stress monitoring. In this paper, a comprehensive review has been presented, which focuses on stress detection using wearable sensors and applied machine learning techniques. This paper investigates the stress detection approaches adopted in accordance with the sensory devices such as wearable sensors, Electrocardiogram (ECG), Electroencephalography (EEG), and Photoplethysmography (PPG), and also depending on various environments like during driving, studying, and working. The stressors, techniques, results, advantages, limitations, and issues for each study are highlighted and expected to provide a path for future research studies. Also, a multimodal stress detection system using a wearable sensor-based deep learning technique has been proposed at the end.
Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. ...The diagnosis of depression is questionnaire based and is not based on any objective criteria. In this paper, feature extracted from EEG signal are used for the diagnosis of depression. Alpha, alpha1, alpha2, beta, delta and theta power and theta asymmetry was used as feature. Alpha1, alpha2 along with theta asymmetry was also used as a feature. Multi-Cluster Feature Selection (MCFS) was used for feature selection when feature combination was used. The classifiers used were Support Vector Machine (SVM), Logistic Regression (LR), Naïve-Bayesian (NB) and Decision Tree (DT). Alpha2 showed higher classification accuracy than alpha1 and alpha power in all applied classifier. From t-test it was found that there was a significant difference in the theta power of left and right hemisphere of normal subjects, but there was no significant difference in depression patients. Average theta asymmetry in normal subjects is higher than MDD patients but the difference in theta asymmetry in normal subjects and MDD patients is not significant. The combination of alpha2 and theta asymmetry showed the highest classification accuracy of 88.33% in SVM.
AbstractMax-point-tolerance graphs (MPTG) were introduced by Catanzaro et al. in 2017 as a generalization of interval graphs. This graph class has many practical applications in the study of the ...human genome as well as in signal processing for networks. The same class of graphs was also studied by Soto and Caro in 2015 with a different name, p -BOX(1) graphs. In our article, we consider a natural subclass of max-point-tolerance graphs, namely, proper max-point-tolerance graphs (proper MPTG), where intervals associated with the vertices are not contained in each other properly. We present the first characterization theorem of this graph class by defining certain linear ordering on the vertex set. In the course of this study, we prove proper max-point-tolerance graphs are asteroidal triple-free, and perfect. We also find that proper max-point-tolerance graphs are equivalent to unit max-point-tolerance graphs. Further, we show that MPTG (proper MPTG) and max-tolerance graphs (proper max-tolerance graphs) are incomparable. In conclusion, we demonstrate relations between proper MPTG with other variants of MPTG and max-tolerance graphs.
On central max-point-tolerance graphs Paul, Sanchita
AKCE international journal of graphs and combinatorics,
09/2020, Letnik:
ahead-of-print, Številka:
ahead-of-print
Journal Article
Recenzirano
Odprti dostop
Max-point-tolerance graphs (MPTG) were studied by Catanzaro et al. in 2017 and the same class of graphs were introduced in the name of p-BOX(1) graphs by Soto and Caro in 2015. This class has a wide ...application in genome studies as well as in telecommunication networks. In our article, we consider central max-point-tolerance graphs (central MPTG) by taking the points of MPTG as center points of their corresponding intervals. In the course of study on this class of graphs, we show that the class of central MPTG is same as the class of unit max-tolerance graphs. We also prove that the class of unit central MPTG is same as that of proper central MPTG and both of them are equivalent to the class of proper interval graphs.
Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong diagnosis of depression is absence of any ...laboratory test for detection as well as severity scaling of depression. Any degradation in the working of the brain can be identified through change in the electroencephalogram (EEG) signal. Thus detection as well as severity scaling of depression is done in this study using EEG signal. In this study, features are extracted from the temporal region of the brain using six (FT7, FT8, T7, T8, TP7, TP8) channels. The linear features used are delta, theta, alpha, beta, gamma1 and gamma2 band power and their corresponding asymmetry as well as paired asymmetry. The non-linear features used are Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA). The classifiers used are: Bagging along with three different kernel functions (Polynomial, Gaussian and Sigmoidal) of Support Vector Machine (SVM). Feature selection technique used is ReliefF. Highest classification accuracy of 96.02% and 79.19% was achieved for detection and severity scaling of depression using SVM (Gaussian Kernel Function) and ReliefF as feature selection. From the analysis, it was found that depression affects the temporal region of the brain (temporo-parietal region).It was also found that depression affects the higher frequency band features more and it affects each hemisphere differently. It can also be analysed that out of all the kernel of SVM, Gaussian kernel is more efficient to other kernels. Of all the features, combination of all paired asymmetry and asymmetry showed high classification accuracy (accuracy of 90.26% for detection of depression and accuracy of 75.31% for severity scaling).
This work details the synthesis, characterization, and catalytic activity of reactive low‐coordinate organozinc complexes. The complexes activate hydrogen and they appear to be more active in ...hydrogenation of ketones and imines than their tridentate pincer analogs. This is thought, in part, to be due to the lack of trailing third phosphorus arm present in previous work. DFT computations reveal a sigma‐bond metathesis mechanism is comparable to an alternative aromatization/dearomatization metal‐ligand cooperative mechanism.
Catalytic activity of bidentate three‐coordinate Zn(II) complexes towards hydrogenation of model ketone and imine substrates is enhanced compared to the analogous tridentate PNP system. DFT calculations and experimental evidence point towards a sigma bond metathesis mechanism, instead of aromatization/dearomatization metal‐ligand cooperativity, with the lack of trailing P‐arm enabling the enhancement.
Negation handling is an important sub-task in Sentiment Analysis. Negation plays a significant role in written text. Negation terms in sentence often changes the polarity of entire sentence from ...positive to negative or vice versa, resulting in the opposite meaning of the sentence than what is observed by the machine learning based linguistic model. As automatic opinion mining has become very important in this digital era, proper handling of negation term is the need of the hour. In any natural language negations can be formulated both explicitly or implicitly while their use is very much domain-specific. Existing negation handling techniques follow rule-based approach and mainly used in medical domain. Due to the complex syntactic structure of negation, it is hard to build general purpose machine learning based negation handling model on user review or conversational text data. In this paper, we investigate negation components i.e., cue and scope in a sentence which determine the polarity shift in sentence. We propose LSTM based deep neural network model for negation handling task where the model automatically learns the negation features from labeled input training dataset. We used ConanDoyle story corpus for model training and testing, which is pre-annotated with negation information. The proposed model first identify negation cues in each sentence and then using bidirectional LSTM extracts the relationship between cue and other words to identify scope of the cue in sentences. We derived word level features for model training to determine correct polarity of the sentence. Result shows that the LSTM based nonlinear language models perform comparatively better than the traditional state of the art SVM, HMM or CRF based models. BiLSTM achieved best result, F1 measures 93.34%, outperform traditional rule based model in negation handling task.
Neutral three‐coordinate iron alkylidenes of the form PN−Fe=CHR have been proposed as viable candidates for alkene metathesis. Indeed, during the final stages of preparing this current study, a ...separate report disclosed that dearomatized PN−Fe‐alkyl complexes are active precatalysts for ring‐opening metathesis polymerization (ROMP) of norbornene implicating PN−Fe=CHR species as possible intermediates. In yet another separate report, we prepared Zn analogues of PN−Fe‐alkyl complexes and herein provide an account for the synthesis, characterization, and reactivity of some new iron complexes with the same tBu substituted PN platform.
Little is known about the reactivities of PN‘−Fe−CH2R complexes. Until recently, there was little reason to suspect anything interesting about them. However, recent ROMP precatalysts of the PN‘−Fe−CH2R formulation from the Milstein group justify fundamental coordination and reactivity studies and herein detail our own efforts.
Terrestrial planets and their moons have impact craters, contributing significantly to the complex geomorphology of planetary bodies in our Solar System. Traditional crater identification methods ...struggle with accuracy because of the diverse forms, locations, and sizes of the craters. Our main aim is to locate lunar craters using images from Terrain Mapping Camera-2 (TMC-2) onboard the Chandrayaan-II satellite. The crater-based U-Net model, a convolutional neural network frequently used in image segmentation tasks, is a deep learning method presented in this study. The task of crater detection was accomplished with the proposed model in two steps: initially, it was trained using Resnet18 as the backbone and U-Net based on Image Net as weights. Secondly, TMC-2 images from Chandrayaan-2 were used to detect craters based on the trained model. The model proposed in this study comprises a neural network, feature extractor, and optimization technique for lunar crater detection. The model achieves 80.95% accuracy using unannotated data and precision and recall are much better with annotated data with an accuracy of 86.91% in object detection with TMC-2 ortho images. 2000 images have been considered for the present work as manual annotation is a time-consuming process and the inclusion of more images can enhance the performance score of the model proposed.
Prediction of incidences of thunderstorms using different techniques is quite well established. To add to this knowledge, in this paper, a heuristic equation is proposed which relates the correlation ...coefficient of atmospheric parameters with the number of hourly incidences of thunderstorms. There are four ways to compute the indices values from the proposed heuristic equation. These indices values are used in the classification of the hourly incidences of thunderstorms. The proposed equation and indices work well, as tested on pre-monsoon hourly atmospheric data from 2018 and validated with hourly data from April 2019 and 2020. From the four indices, the first index value is computed with normalized average values of parameters of only hourly incidence of thunderstorms data of 2016-2017 in the month-wise method. The other three indices use optimization techniques namely, the Teaching Learning Based Optimization (TLBO) technique, Differential Evaluation (DE), and Simulated Annealing (SA). TLBO shows better classification of hourly incidences of thunderstorms for 2018 atmospheric data. TLBO is also precisely validating the hourly incidences of thunderstorms for April 2019 and April 2020 hourly atmospheric data. It performed better with 2020 data by 88%. The variations of atmospheric parameters before, after or, during the incidences of thunderstorms are also depicted.
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