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Showing papers in "Journal of Social and Clinical Psychology in 2021"


Journal ArticleDOI
TL;DR: Experimental analysis indicates that the proposed work is more successful in terms of attack resistance, scalability, execution speed and accuracy when compared with other algorithms that are used for privacy preservation.
Abstract: The advancement and introduction of computing technologies has proven to be highly effective and has resulted in the production of large amount of data that is to be analyzed. However, there is much concern on the privacy protection of the gathered data which suffers from the possibility of being exploited or exposed to the public. Hence, there are many methods of preserving this information they are not completely scalable or efficient and also have issues with privacy or data utility. Hence this proposed work provides a solution for such issues with an effective perturbation algorithm that uses big data by means of optimal geometric transformation. The proposed work has been examined and tested for accuracy, attack resistance, scalability and efficiency with the help of 5 classification algorithms and 9 datasets. Experimental analysis indicates that the proposed work is more successful in terms of attack resistance, scalability, execution speed and accuracy when compared with other algorithms that are used for privacy preservation.

103 citations


Journal ArticleDOI
TL;DR: The proposed efficient deep learning framework is a combination of CNN and SVM architecture for the detection of abnormal conditions at video surveillance with the help of an image classification procedure and it is used to reduce the time complexity.
Abstract: Performing dimensionality reduction in the camera captured images without any loss is remaining as a big challenge in image processing domain. Generally, camera surveillance system is consuming more volume to store video files in the memory. The normally used video stream will not be sufficient for all the sectors. The abnormal conditions should be analyzed carefully for identifying any crime or mistakes in any type of industries, companies, shops, etc. In order to make it comfortable to analyze the video surveillance within a short time period, the storage of abnormal conditions of the video pictures plays a very significant role. Searching unusual events in a day can be incorporated into the existing model, which will be considered as a supreme benefit of the proposed model. The massive video stream is compressed in preprocessing the proposed learning method is the key of our proposed algorithm. The proposed efficient deep learning framework is based on intelligent anomaly detection in video surveillance in a continuous manner and it is used to reduce the time complexity. The dimensionality reduction of the video captured images has been done by preprocessing the learning process. The proposed pre-trained model is used to reduce the dimension of the extracted image features in a sequence of video frames that remain as the valuable and anomalous events in the frame. The selection of special features from each frame of the video and background subtraction process can reduce the dimension in the framework. The proposed method is a combination of CNN and SVM architecture for the detection of abnormal conditions at video surveillance with the help of an image classification procedure. This research article compares various methods such as Journal of Soft Computing Paradigm (JSCP) (2021) Vol.03/ No.02 Pages: 55-69 http://irojournals.com/jscp/ DOI: https://doi.org/10.36548/jscp.2021.2.001 56 ISSN: 2582-2640 (online) Submitted:6.03.2021 Revised: 30.03.2021 Accepted: 21.04.2021 Published: 10.05.2021 background subtraction (BS), temporal feature extraction (TFE), and single classifier classification methods.

99 citations


Journal ArticleDOI
TL;DR: In this article, a simple capsule network algorithm was used for hierarchical multi-label text classification. And the encoded latent data was combined with the algorithm while handling structurally diverse categories and rare events in hierarchical multilabel text applications.
Abstract: In regions of visual inference, optimized performance is demonstrated by capsule networks on structured data. Classification of hierarchical multi-label text is performed with a simple capsule network algorithm in this paper. It is further compared to support vector machine (SVM), Long Short Term Memory (LSTM), artificial neural network (ANN), convolutional Neural Network (CNN) and other neural and non-neural network architectures to demonstrate its superior performance. The Blurb Genre Collection (BGC) and Web of Science (WOS) datasets are used for experimental purpose. The encoded latent data is combined with the algorithm while handling structurally diverse categories and rare events in hierarchical multi-label text applications.

77 citations


Journal ArticleDOI
TL;DR: This research work has evaluated different machine learning methods and found that the suggested work delivers better results, and is more focused on leveraging the accurate forecast of bitcoin prices via the normalization of a particular dataset.
Abstract: In recent years, there has been an increase in demand for machine learning and AI-assisted trading. To extract abnormal profits from the bitcoin market, the machine learning and artificial intelligence (AI) assisted trading process has been used. Each day, the data gets saved for the specified amount of time. These approaches produce great results when integrated with cutting-edge algorithms. The results of algorithms and architectural structures drive the development of cryptocurrency market. The unprecedented increase in market capitalization has enabled the cryptocurrency to flourish in 2017. Currently, the market accommodates totally 1500 cryptocurrencies, all of which are actively trading. It is always possible to mine the cryptocurrency and use it to pay for online purchases. The proposed research study is more focused on leveraging the accurate forecast of bitcoin prices via the normalization of a particular dataset. With the use of LSTM machine learning, this dataset has been trained to deploy a more accurate forecast of the bitcoin price. Furthermore, this research work has evaluated different machine learning methods and found that the suggested work delivers better results. Based on the resultant findings, the accuracy, recall, precision, and sensitivity of the test has been calculated.

77 citations


Journal ArticleDOI
TL;DR: The modified version of ELM has been developed to obtain better accuracy and minimize the classification error, and solves the slow training speed and over-fitting problems present in the existing learning approach.
Abstract: Extreme Learning Machine (ELM) is one of the latest trends in learning algorithm, which can provide a good recognition rate within less computation time. Therefore, the algorithm can sustain for a faster response application by utilizing a feed-forward neural network. In this research article, the ELM method has been designed with the presence of sigmoidal function of biases in the hidden nodes to perform the classification task. The classification task is very challenging with the existing learning algorithm and increased computation time due to the huge amount of dataset. While handling of the stochastic matrix for hidden layer, output provides the lower performance for learning rate and robustness in the determination. To address these issues, the modified version of ELM has been developed to obtain better accuracy and minimize the classification error. This research article includes the mathematical proof of sigmoidal activation function with biases of the hidden nodes present in the networks. The output matrix maintains the column rank in order to improve the speed of the training output weights (β). The proposed improved version of ELM leverages better accuracy and efficacy in classification and regression problems as well. Due to the inclusion of matrix Journal of Soft Computing Paradigm (JSCP) (2021) Vol.03/ No.02 Pages: 70-82 http://irojournals.com/jscp/ DOI: https://doi.org/10.36548/jscp.2021.2.002 71 ISSN: 2582-2640 (online) Submitted: 26.03.2021 Revised: 15.04.2021 Accepted: 4.05.2021 Published: 25.05.2021 column ranking in mathematical proof, the proposed improved version of ELM solves the slow training speed and over-fitting problems present in the existing learning approach.

64 citations


Journal ArticleDOI
TL;DR: A novel CNN is introduced that can be used to increase the performance of detectors built that use Local Binary Patterns and Histogram of Oriented Gradients and it has been identified that the proposed CNN proves to be an improvement over the others.
Abstract: In the recent years, there has been a high surge in the use of convolutional neural networks (CNNs) because of the state-of-the art performance in a number of areas like text, audio and video processing. The field of remote sensing applications is however a field that has not fully incorporated the use of CNN. To address this issue, we introduced a novel CNN that can be used to increase the performance of detectors built that use Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). Moreover, in this paper, we have also increased the accuracy of the CNN using two improvements. The first improvement involves feature vector transformation with Euler methodology and combining normalized and raw features. Based on the results observed, we have also performed a comparative study using similar methods and it has been identified that the proposed CNN proves to be an improvement over the others.

51 citations





Journal ArticleDOI
TL;DR: The fuzzy logic, chaos theory, whale optimization algorithm (WOA) and BAT algorithm (FCW-BAT) is combined to create a novel model for parameter estimation to enable the WWTP system operators to make appropriate control decisions.
Abstract: Biological and social issues rise with faults that occur in waste water treatment plant (WWTP). Nature as well as humans are negatively impacted by the dangerous effects of poorly treated wastewater. This paper combines the fuzzy logic, chaos theory, whale optimization algorithm (WOA) and BAT algorithm (FCW-BAT) to create a novel model for parameter estimation. The WWTP applications are exposed to FCW-BAT algorithm for identifying nonwell-structured domain, validating decision rules, cost reduction and estimation of several relevant attributes from the complete dataset. The significant data is retained while reducing the complete feature set using FCW-BAT prior to the classification process. Estimation of data uncertainty and fuzzification is performed with the cost function fast fuzzy c-means. The WOA parameters are estimated and tuned with the help of several chaos sequence maps. Complex real-time datasets consisting of missing values and several uncertainty features are tested and experimented. Shorter execution time, higher convergence speed, lower error and improved performance are obtained with the sine chaos map embedded in the proposed algorithm. Additionally, the WWTP sensor process faults may also be detected by the proposed model with great levels of accuracy enabling the system operators to make appropriate control decisions. Journal of Soft Computing Paradigm (JSCP) (2021) Vol.03/ No.01 Pages: 10-18 http://irojournals.com/jscp/ DOI: https://doi.org/10.36548/jscp.2021.1.002 11 ISSN: 2582-2640 (online) Submitted:10.02.2021 Revised: 05.03.2021 Accepted: 28.03.2021 Published: 15.04.2021

32 citations


Journal ArticleDOI
TL;DR: This paper is highlighting hybrid DL techniques with Reinforcement Learning (RL) for the better performance during attack and compared with existing one to identify the higher accuracy algorithm for security solutions.
Abstract: In the history of device computing, Internet of Things (IoT) is one of the fastest growing field that facing many security challenges. The effective efforts should have been made to address the security and privacy issues in IoT networks. The IoT devices are basically resource control device which provide routine attract impression for cyber attackers. The IoT participation nodes are increasing rapidly with more resource constrained that creating more challenging conditions in the real time. The existing methods provide an ineffective response to the tasks for effective IoT device. Also, it is an insufficient to involve the complete security and safety spectrum of the IoT networks. Because of the existing algorithms are not enriched to secure IoT bionetwork in the real time environment. The existing system is not enough to detect the proxy to the authorized person in the embedding devices. Also, those methods are believed in single model domain. Therefore, the effectiveness is dropping for further multimodal domain such as combination of behavioral and physiological features. The embedding intelligent technique will be securitizing for the IoT devices and networks by deep learning (DL) techniques. The DL method is addressing different security and safety problems arise in real time environment. This paper is highlighting hybrid DL techniques with Reinforcement Learning (RL) for the better performance during attack and compared with existing one. Also, here we discussed about DL combined with RL of several techniques and identify the higher accuracy algorithm for security solutions. Finally, we discuss the future direction of decision making of DL based IoT security system.


Journal ArticleDOI
TL;DR: This work uses integrated deep learning approach to overcome workload and time series issue in cloud computing and accurate prediction of workload and resource allocation with time series enhances the performance of the network.
Abstract: In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

Journal ArticleDOI
TL;DR: A Random Forest model is constructed to predict the failure of the various machine in manufacturing industry and compares the prediction result with Decision Tree (DT) algorithm and proves its superiority in accuracy and precision.
Abstract: Predictive maintenance is the way to improve asset management in every manufacturing industry. While handling advance costlier machinery in the industry, the predictive maintenance knowledge will be essential to protect the machinery before gets degradation performance. Recently, the emergence of business in manufacturing industry deals with good systems, regular intervals maintenance process, predictive maintenance (PdM), machine learning (ML) approaches are extensively applied for handling the health standing of business instrumentation. Now the digital transformation towards I4.0, data techniques, processed management and communication networks; it’s doable to gather huge amounts of operational and processes conditions information generated type many items of kit and harvest information for creating an automatic fault detection and diagnosing with the aim to attenuate period of time and increase utilization rate of the parts and increase their remaining helpful lives. The predictive maintenance is inevitable for property good producing in I40. This paper aims to provide a comprehensive review of the recent advancements of metric capacity unit techniques wide applied to PdM for good producing in I4.0 by classifying the analysis consistent with metric capacity unit algorithms, ML class, machinery and instrumentation used device employed in information acquisition, classification of knowledge size and kind, and highlight the key contributions of the researchers and so offers pointers and foundation for additional analysis. In this research paper we constructed a Random Forest model to predict the failure of the various machine in manufacturing industry. It compares the prediction result with Decision Tree (DT) algorithm and proves its superiority in accuracy and precision.

Journal ArticleDOI
TL;DR: For example, this paper found that following more friends was negatively correlated with loneliness whereas following more strangers was positively correlated with depression, and that moderately active engagement may be the most adaptive.
Abstract: Introduction: Given previous experimental research on the benefits of limiting time spent on social media and correlational research linking active use to better well-being, we designed an experimental study to investigate the effects of limiting time and increasing active use on social media on well-being. Method: After completing a survey on who they followed on social media and undergoing a week of baseline monitoring, 88 undergraduates at a private university were randomly assigned to either limit Facebook, Instagram, Twitter, and Snapchat use to 30 minutes in total per day and increase their active use, just limit their use to 30 minutes per day, or continue to use social media as usual for three weeks. Results: The initial survey revealed that following more friends was negatively correlated with loneliness whereas following more strangers was positively correlated with depression. Highly depressed participants in the limited use group showed significant reductions in depression compared to the control group, but the active group did not show similar benefits. Highly active participants in the active group reported greater loneliness and anxiety and lower self-esteem at week 4 compared to less active participants in the active group, whereas the most passive participants in the control group showed the worst depression overall. Discussion: Our findings suggest that following friends rather than strangers and limiting time spent on social media may lead to significant improvements in well-being, and that moderately active engagement may be the most adaptive.

Journal ArticleDOI
TL;DR: The Perfectionism Social Disconnection Model (PSDM) as mentioned in this paper posits that perfectionism confers risk for depression by promoting social disconnection, however, the specific indirect effect of social disconnections on the prospective relation of perfectionism dimensions with depression severity is not well understood.
Abstract: Introduction: The Perfectionism Social Disconnection Model (PSDM) posits that perfectionism confers risk for depression by promoting social disconnection. However, the specific indirect effect of social disconnection on the prospective relation of perfectionism dimensions with depression severity is not well understood. The aim of the current study was to provide the first comprehensive examination of the PSDM. Methods: A diverse community sample of 447 completed measures of trait perfectionism, perfectionistic self-presentation styles, and depressive symptoms at baseline. Six months later, participants completed measures of perfectionistic self-presentation styles, social disconnection, and depressive symptoms. Indirect effects models were analyzed to examine the impact of each facet of perfectionism on social disconnection and subsequent depression severity. Results: Consistent with the PSDM, all perfectionism traits and self-presentation styles resulted in greater depression severity via one or more facets of social disconnection, with social hopelessness and loneliness demonstrating the most widespread effects. Furthermore, perfectionistic self-presentation styles and social disconnection demonstrated sequential indirect effects on the relation of self-oriented and socially prescribed perfectionism with depressive symptoms at follow-up. Discussion: This study is the first to demonstrate the depressogenic effects of all perfectionism dimensions. Findings delineate the interpersonal mechanisms underlying the perfectionism-depression link.

Journal ArticleDOI
TL;DR: In this article, the authors tested the associations of multiple domains of social support (family, friends, significant other) with measures of adjustment and adversity and found that support from friends was incrementally associated with nearly every outcome and support from family was associated with a majority of outcomes.
Abstract: Introduction: We were interested in building on previous studies showing the promotive and buffering roles of social support for emerging adults We tested the associations of multiple domains of social support (i e , family, friends) with measures of adjustment and adversity Methods: Across four studies, U S college adults reported on domains of social support (family, friends, significant other), psychological adjustment (i e , life satisfaction, flourishing), and psychological adversity (i e , recent stress, depressive symptoms) Studies 1 and 4 were cross-sectional, whereas Studies 2 and 3 involved two, monthly survey reports Study 4 was completed against the backdrop of early COVID-19 disruptions for college adults in the Spring of 2020 Results: In each study, each domain of social support was positively correlated with measures of adjustment and negatively correlated with measures of adversity Partial correlations indicated that support from friends was incrementally associated with nearly every outcome, whereas support from family was incrementally associated with a majority of outcomes Multiphase studies supported unidirectional, but not bidirectional, effects from earlier adjustment onto later social support Discussion: Overall, findings reinforce the importance of social support for young adults and highlight the distinct importance of family and friends Findings also suggest that a lack of perceived social support may contribute to risks fitting views such as the stress generation theory among emerging adults [ABSTRACT FROM AUTHOR] Copyright of Journal of Social & Clinical Psychology is the property of Guilford Publications Inc and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

Journal ArticleDOI
TL;DR: The authors examined self-stigma at the intersection of two identities: mental illness and gender and found significant effects for mental illness (participants with mental illness reported greater perceptions of being different from the population and disdain themselves because of that) but not for gender or the interaction.
Abstract: Introduction: This study sought to examine self-stigma at the intersection of two identities: mental illness and gender. Methods: Using an MTurk panel, 100 self-identified men and women with and without mental illness (total N = 400) completed the Difference and Disdain Self-Stigma Scale. Results: Significant effects were found for mental illness (participants with mental illness reported greater perceptions of being different from the population and disdain themselves because of that) but not for gender or the interaction. Discussion: Failure to find intersectionality may reflect classic findings from social psychological research that suggests people do not necessarily diminish self-esteem because of socio-demographic identity (I am a woman or African American). Future studies need to test for an intersection effect for public stigma.

Journal ArticleDOI
TL;DR: The authors examined prospective associations between negative emotionality (NE) and implicit beliefs about emotions, as well as the indirect effect of NE on depressive symptoms through implicit beliefs of emotions about emotions.
Abstract: Introduction: Individual differences in beliefs about the controllability of emotions are associated with a range of psychosocial outcomes, including depressive symptoms. Less is known, however, about factors contributing to individual differences in these beliefs. The current study examined prospective associations between negative emotionality (NE) and implicit beliefs about emotions, as well as the indirect effect of NE on depressive symptoms through implicit beliefs about emotions. Methods: In a sample of children and adolescents, NE was assessed at baseline, implicit beliefs about emotions were assessed 18 months later, and depressive symptoms were assessed at baseline and 36 months later. Results: NE was associated with implicit beliefs about emotions, and an indirect effect of NE on depressive symptoms through implicit beliefs about emotions was observed. Discussion: NE represents a salient dispositional vulnerability factor contributing to individual differences in implicit beliefs about emotions, with implications for the development of depressive symptoms in youth.

Journal ArticleDOI
TL;DR: In this paper, the authors evaluated measurement reactivity in EMA of body comparisons and found that participants reported fewer occasions of loss of control while eating and less pleasant social company at time of assessment.
Abstract: Introduction: Body comparisons have been implicated in body dissatisfaction and eating disorder development. Ecological momentary assessment (EMA) has been used to capture body comparisons in everyday life; however, the potential for measurement reactivity this approach has on EMA responses has yet to be examined. The present study systematically evaluated measurement reactivity in EMA of body comparisons. Methods: Undergraduate women (N = 75) completed four surveys daily for 11 days; Days 1–4 did not include comparison assessments and Days 5–11 did. Changes were examined in related EMA measures between these time periods and individual difference measures pre- and post-EMA. Results: A within-person MANOVA showed no significant change in the individual difference measures. Multilevel analyses revealed that participants reported fewer comparisons as day of study increased. When they completed EMA with versus without comparison items, they reported fewer occasions of loss of control while eating and less pleasant social company at time of assessment. Discussion: Findings suggest limited concern for reactivity in this approach, though researchers examining body comparisons, loss of control eating, and perceptions of social company should be cautious when interpreting their findings and consider examining reactivity directly. Future research on body comparisons should also consider using run-in periods to improve data quality.


Journal ArticleDOI
TL;DR: In this paper, the authors investigated the relationship between the different components of received social support (RSS) and post-traumatic stress symptoms (PTSS) in emergency responders in New Zealand and the Philippines.
Abstract: Introduction: Emergency responders are at risk of post-traumatic stress symptoms (PTSS). This study investigates the relationship between the different components of received social support (RSS) and PTSS in these occupations. Method: This cross-sectional study involved emergency responders from New Zealand and the Philippines (n=223). Participants answered an online questionnaire measuring social support and PTSS. Results: High amount of total received support was significantly associated with lower levels of PTSS. Analyses of RSS components revealed that co-worker, emotional, and tangible support exhibited salutary main effects on PTSS. Furthermore, high amounts of RSS from supervisors were found to be associated with low levels of PTSS only among participants who reported low trauma exposure. Discussion: Findings were congruent with the main effects model of RSS. Results also underscored the importance of investigating varied manifestations of social support as they may differentially influence the strength of effects of receiving social support by emergency responders.

Journal ArticleDOI
TL;DR: Findings suggest that time with in-person friends is associated with increased PA overall and decreased NA only for those lower in depressive symptoms, but these effects do not generalize to virtual interactions.
Abstract: Introduction Depression is associated with increased negative affect (NA) and low positive affect (PA), as well as interpersonal difficulties. Although most studies examine symptoms and affect at only one time point, ecological momentary assessment (EMA) captures data on affect and activity in real time and across contexts. The present study used EMA to explore the links between in-person and virtual social interactions, depressive symptoms, and momentary affect. Methods Emerging adults (N=86) completed a self-report measure of both general depression and dysphoria symptoms, followed by EMA surveys 8 times daily for one week, reporting momentary affect (PA and NA) and social context (in-person and virtual interactions with friends). Results In-person, but not virtual, presence of friends was associated with increased PA overall. Depressive symptoms were linked to less time with in-person friends and elevated NA. There was also a significant interaction between in-person presence of friends and dysphoria in predicting NA, such that presence of in-person friends was associated with lower NA only for those low in dysphoria. Discussion Findings suggest that time with in-person friends is associated with increased PA overall and decreased NA only for those lower in depressive symptoms, but these effects do not generalize to virtual interactions. Those with greater depressive symptoms may be less responsive to positive stimuli and experience less of an effect of friends' presence on affect. Alternatively, maladaptive social behaviors characteristic of depression may influence the effect of social interactions on affect.

Journal ArticleDOI
TL;DR: In this article, the authors examined the impact of police exchanges and perceived discrimination on changes in adolescent depression symptoms and found that African American adolescents had significantly higher levels of initial depression than other racial/ethnic identity groups.
Abstract: Introduction: We examined police exchanges' and feelings of discrimination's impact on changes in adolescent depression symptoms. Relative to other races, police speak more disrespectfully to African Americans and often exert unnecessary force. We investigated the impact of these exchanges on depression. Methods: Adolescent Health Study data were analyzed. Latent growth curve modeling with mediation illustrated relationships between police exchanges, perceived discrimination, and depression changes. Results: African American adolescents had significantly higher levels of initial depression than other racial/ethnic identity groups. For African Americans, police exchanges predicted depression changes. Perceived respect predicted levels and changes of depression for both groups, but mediated the relationship between police exchanges and depression changes only in the “other” racial/ethnic identity group. Discussion: Police stoppings impacted depression changes for African Americans independent of perceived respect. Findings highlight a potentially unique relationship between depression and police exchanges among African Americans. Future studies may investigate roles of individual differences.

Journal ArticleDOI
TL;DR: In this paper, the authors examined whether inequities in dyadic coping covary with depressive symptoms in mixed-gender couples with one partner diagnosed with major depression (60% female).
Abstract: Introduction: For couples, depression can position diagnosed partners to receive dyadic coping and mates to primarily provide support. We examine whether inequities in dyadic coping covary with depressive symptoms. Methods: Using data from 62 mixed-gender couples with one partner diagnosed with major depression (60% female), we computed differences between provided and received dyadic coping reported by both partners. With Response Surface Analyses we examined the associations with depressive symptoms. Results: In patients, lower equity of dyadic coping was associated with more depressive symptoms, regardless of whether the patient felt underbenefitted or overbenefitted. In partners, dyadic coping was negatively associated with depressive symptoms while equity of dyadic coping showed no significant associations. Patients and partners both reported providing more dyadic coping than they received. Discussion: Inequities in dyadic coping covary with depressive symptoms in patients, beyond main effects of dyadic coping, justifying the inclusion of couples in treatment for depression.

Journal ArticleDOI
TL;DR: In this paper, a randomized, controlled trial tested a mobile-based intervention, derived from social psychological and relationship science, for improving relational well-being and decreasing depression and loneliness.
Abstract: Introduction: Effective, scalable interventions to address depression and loneliness and improve the quality of social relationships are needed for public health in pandemic and non-pandemic contexts. Towards this end, a randomized, controlled trial tested a mobile-based intervention, derived from social psychological and relationship science, for improving relational well-being and decreasing depression and loneliness. Methods: Participants were randomly assigned to either intervention (n = 719) or assessment only (n = 701) conditions and completed daily diary surveys for 28 days in the midst of the first wave of the Covid-19 pandemic. In the middle 14 days of the study, intervention participants received daily text-message suggestions for improving relational and mental well-being. Results: Results indicated that the intervention decreased depression and loneliness and improved relationships during the intervention period but these changes were not sustained when the intervention ceased. Discussion: Results are encouraging in that evidence-based suggestions can be scaled effectively but additional efforts are required to sustain improvements over time.


Journal ArticleDOI
TL;DR: This paper investigated how processing of self-referential praise and criticism varies with personal attributes related to anxiety and depression, and found that more highly anxious and depressed individuals may be subject to negatively-distorted self-representations in response to selfreferential evaluations, thus exhibiting attenuated rejection for criticism or reduced acceptance for praise.
Abstract: Introduction: While praise is generally pleasant and criticism unpleasant, individual differences in response to social evaluations arise from distinct personal traits and states. Here, we investigate how processing of self-referential praise and criticism varies with personal attributes related to anxiety and depression, two highly prevalent and often chronic affective conditions. Methods: Ninety-three healthy participants first completed questionnaires for anxiety- and depression-related traits and states, and then they were scheduled to perform an evaluation task to rate praise and criticism for pleasantness and truthfulness. Results: Fear of negative evaluation positively correlated with unpleasantness of criticism. Trait- and state-anxiety and depression were positively associated with the truthfulness of criticism but negatively associated with that of praise. We further divided participants into high- and low-scoring groups based on the medians of their scores of each scale that displayed significant correlations with comment ratings and found group differences in their responses to praise and criticism. Discussion: The findings suggest that more highly anxious and depressed individuals may be subject to negatively-distorted self-representations in response to self-referential evaluations, thus exhibiting attenuated rejection for criticism or reduced acceptance for praise, which may have important implications not only for facilitating daily social interactions but also for subclinical and clinical diagnosis and treatment given that affective and cognitive processing of self-referential evaluations serves as a critical process exhibiting the sense of the self.

Journal ArticleDOI
TL;DR: The suggested denoising technique has delivered minimal effect on the MSE value and the dual-tree complex wavelet transform incorporates an increase in transform data density to reduce the effects of sparse data.
Abstract: In recent years, there has been an increasing research interest in image de-noising due to an emphasis on sparse representation. When sparse representation theory is compared to transform domain-based image de-noising, the former indicates that the images have more information. It contains structural characteristics that are quite similar to the structure of dictionary-based atoms. This structure and the dictionary-based method is highly unsuccessful. However, image representation assumes that the noise lack such a feature. The dual-tree complex wavelet transform incorporates an increase in transform data density to reduce the effects of sparse data. This technique has been developed to decrease the image noise by selecting the best-predicted threshold value derived from wavelet coefficients. For our experiment, Discrete Cosine Transform (DCT) and Complex Wavelet Transform (CWT) are used to examine how the suggested technique compares the conventional DCT and CWT on sets of realistic images. As for image quality measures, DT-CWT has leveraged superior results. In terms of processing time, DT-CWT gave better results with a wider PSNR range. Further, the proposed model is tested with a standard digital image named Lena and multimedia sensor images for the denoising algorithm. The suggested denoising technique has delivered minimal effect on the MSE value.

Journal ArticleDOI
TL;DR: A hybrid electric vehicle that uses intelligent energy management strategy to decrease the energy consumption and improve the energy efficiency of the vehicle is proposed and an intelligent controller is proposed in this work to execute its ability to increase the total amount of energy consumed and improve its energy efficiency.
Abstract: This paper has proposed a hybrid electric vehicle that uses intelligent energy management strategy to decrease the energy consumption of the vehicle. Here, the total energy consumption of the vehicle is initially modelled and further investigated to reduce the amount of energy used to be identified as a sum of electrical energy provided by consumed fuels and on-board batteries. In particular, an intelligent controller is proposed in this work to execute its ability to decrease the total amount of energy consumed and improve the energy efficiency of the vehicle. A fuzzy system is utilized in an account supervisory controller to decide the appropriate mode of operation for the system. The part of the proposed work involves development of optimal control strategies by using neuro-fuzzy logic. In order to obtain optimal performance, the controllers are used to regulate vehicle subsystems and set points. The biggest advantage of this work is the reduction in energy consumption and their ability to execute the operation online. Simulink/MATLAB is used to simulate and validate the performance of the proposed work under various conditions and under several dataset values.