Prevalent mental disorders, such as depression and anxiety, commonly manifest in students throughout the transition to early adulthood. Mental illnesses can significantly impact students’ academic and social activities. An automatic or semiautomatic health monitoring approach is very effective for diagnosing depression and anxiety. This study aims to implement and scrutinize a data-driven optimization method for identifying and providing therapy to students with symptoms of depression and anxiety. The proposed method starts with data preprocessing and operating sentiment analysis to identify mentally disordered students. An ensemble learning classifier later divides students with symptoms into three categories based on their health condition: severe, moderate, and mild. A hyperparameter optimization approach is further adopted to improve the model’s performance. Finally, a rule-based dispatching system is implemented for scheduling therapy sessions. The proposed novel data-driven method is a post-disaster intelligent and reliable method that integrates three welladopted techniques to address students’ depression and anxiety. The findings indicate that the conventional approach to monitoring depression among students previously detected only 7 to 15% of cases. However, the performance of the offered strategy revealed a confirmed rate of 44% of depressed and anxious students. give me summary of the above paragraph and that summary should be of 10 lines can you do it?

Machine learning is a powerful tool that can be used to solve a wide range of problems in various applications and industries. The healthcare sector has faced specific challenges that have kept machine learning algorithms from becoming as widely and quickly adopted as in other industries. Data access and management challenges, ethical considerations, safety, and physician and patient perception present bigger barriers to implementation than model performance. In this paper, we propose adapting and customizing the concept of preconditions and postconditions from software engineering to develop a framework based on required clinical parameters and expected clinical output that will help bridge identified gaps in the implementation of machine learning tools in health care. give me summary of the above paragraph and that summary should be of 10 lines can you do it?

can you give e the summary for the paper title :accurate classification of depression through optimized machine learning models on high dimensional noisy data. and the summary should be of 10 lines can you do it?

Background: We aimed to examine age and gender differences in the relationship between depression and quality of life among United States adults. Methods: Medical Expenditure Panel Survey data for 2008 to 2016 on 227,663 adults were analyzed. The dependent variable, quality of life, included physical component summary scores and mental component summary scores from the Short Form Health Survey. The key independent variable, depression, was measured using the two-item Patient Health Questionnaire. General linear regression models examined the relationship between quality of life and depression. Models were adjusted for individual and environmental characteristics, symptom status, functional and biological status, and health perceptions and were stratified by gender and age. Results: In adjusted models, mental component summary scores were significantly lower among those with depression compared with those without depression (b ¼ –0.39; 95% confidence interval [CI], 0.38 to –1.16) and lower among women compared with men (b ¼ –0.10; 95% CI, 0.10 to –1.31). Models stratified by gender and age found women with depression ages 40 to 64 (b ¼ –0.07; 95% CI, 0.07 to –0.20) and 65 or older (b ¼ –0.08; 95% CI, 0.08 to –0.24) had significantly lower physical component summary scores compared with those without depression. Among men with depression, those ages 18 to 39 (b ¼ –0.03; 95% CI, 0.03 to –0.10) and 40 to 64 (b ¼ –0.09, 95% CI, 0.08 to –0.26) had lower physical component summary scores compared with those without depression. Women and men of all ages with depression had significantly lower mental component summary scores compared with those without depression. Conclusions: Public health interventions and clinical approaches to address depression in women and men should target functional status in men and perceptions of health in women. give me summary of the above paragraph and that summary should be of 10 lines can you do it?

it exceeds more than 10 lines shorten it into 10 lines not leaving any valuable information

Among all the forms of psychological and mental disorders, depression is the most common form. Nowadays a large number of youths and adults around the world suffer from depression. Depression can cause severe problems in case of failing to detect it at an early stage or failing to ensure the timely counseling of a depressed person. It is one of the major reasons to raise suicidal cases. But ironically, our society still does not want to acknowledge depression as a mental disorder causing a significant number of depressed persons to remain unidentified and untreated. This study has investigated six different machine learning classifiers using various socio-demographic and psychosocial information to detect whether a person is depressed or not. Besides, three different feature selection methods, such as Select K-Best Features (SelectKBest), Minimum Redundancy and Maximum Relevance (mRMR), and Boruta feature selection algorithm have been used for extracting the most relevant features from the dataset. To achieve better accuracy in predicting depression, Synthetic Minority Oversampling Technique (SMOTE) has been used that reduces the class imbalance of the training data. The AdaBoost classifier with the SelectKBest feature selection technique has outperformed all other approaches with an accuracy of 92.56%. Moreover, other evaluation metrics, namely sensitivity, specificity, precision, F1-score, and area under the curve (AUC) of different models have been calculated to identify the most efficient model for predicting depression. give me the summary of above paragraph and that summary should be only of 10 lines.

Purpose We report on prevalence of anxiety, depression, and concentration difculties and their associations in survivors of cancer in a nationally representative sample up to 25 years after diagnosis. Methods Using the National Health and Nutrition Examination Survey (NHANES) data from 2015 to 2018, participants between the ages of 18 and 79 self-reported on cancer history, symptoms of anxiety, depression, and difculties with concentration. Results Of 10,337 participants, 691 (6.7%) reported a previous diagnosis of cancer; the median time since diagnosis was 8 years. Prevalence was similar between those with and without cancer for anxiety (45.8% versus 46.9%) and depression (19.7% versus 20.0%). Concentration difculties were more common (11.3% versus 9.0%) for those with a history of cancer compared to those without (adjusted OR=1.38, 95% CI: 1.00–1.90). Prevalence of mental health symptoms was not related to time since diagnosis. Anxiety and depression were highly correlated (r=0.81, 95% CI: 0.74–0.86) and moderately correlated with difculty with concentration (r=0.52, 95%CI: 0.40–0.64 and r=0.64, 95% CI: 0.53–0.74 respectively). Conclusions Difculty with concentration was more commonly reported by participants with than without a cancer history. Report of anxiety and depression was no diferent between participants with and without a history of cancer. Anxiety, depression, and difculties with concentration were strongly related. Further research is needed to explore if there is a causal association, and if so, the direction of these correlations, so that interventions may be appropriately targeted. give me the summary of above paragraph and that summary should be only of 10 lines.

Background: Social jetlag in nurses is a long-standing challenge for nursing management. It has been attributed to the effects of shift work that disrupts nurses’ circadian rhythms, may be detrimental to their health, and can lead to rapid turnover. Aim: We aimed to identify the association between social jetlag, affect, depression, and quality of life of early career nurses. Methods: In this cross-sectional study, 201 early career nurses at three tertiary hospitals in South Korea were included. Data were collected from May to July 2018. Social jetlag, affect, depression, and quality of life were measured using the Munich Chronotype Questionnaire (shift version), the Positive and Negative Affect Scale, the Centre for Epidemiological Studies Depression Scale (Korean version), and the Korean World Health Organization Quality of Life Scale (abbreviated version), respectively. Findings: Participants’ mean overall social jetlag and quality of life scores were 4 hours 28 minutes and 80.21, respectively. Multiple regression analyses identified overall social jetlag, positive and negative affect, and depression as factors influencing the nurses’ quality of life. Discussion: Understanding the implications of factors affecting early career nurses’ quality of life, including social jetlag, is vital to ensure staff retention. Nursing management should consider the individual social jetlag of nurses when scheduling shifts and accordingly create institutional human resources management strategies to reduce negative affect and depression while promoting positive affect in early career nurses. Conclusion: Social jetlag, negative affect, and depression negatively impact early career nurses’ quality of life, whereas positive affect positively impacts their quality of life. give me the summary of above paragraph and that summary should be only of 10 lines.

Background: The acceleration of aging and the increase in life expectancy have resulted in an increasing number of older adults developing physical and mental comorbidities. We examined the association between depression and quality of life (QoL) using cognitive impairment (COI) as a mediator and sleep quality (SQ) as a moderator among older adults with type 2 diabetes. Methods: A total of 2646 participants from Weifang, Shandong, China completed the survey. Their depression, cognitive function, SQ, and QoL were assessed. PROCESS was used to investigate mediating and moderating effects. Results: COI mediated the association between depression and QoL (indirect effect = − 0.1058, bootstrapped 95 % CI [− 0.1417, − 0.0725]). Moderated mediation analyses indicated that SQ moderated the first half of the pathway of depression’s impact on QoL through COI (moderating effect = − 0.1128, bootstrapped 95 % CI [− 0.1981, − 0.0348]). Depression negatively impacted cognitive function in participants with poor (vs. better) SQ. Limitations: First, multiple assessment tools should be considered to increase objective assessment. Second, the cross-sectional design limited our ability to make causal inferences. Third, additional diabetes-related variables should be included to explore this relationship. Finally, the pathways of influence and mechanisms of action of COI in older adults should be explored further. Conclusion: Depression could impair the QoL of older adults by aggravating their COI. Fortunately, improving patients’ SQ may undermine this negative effect. These findings may play an integral role in promoting the psychiatric health of older adults with type 2 diabetes. give me the summary of above paragraph and that summary should be only of 10 lines.

During the coronavirus pandemic, the number of depression cases has dramatically increased. Several depression suferers disclose their actual feeling via social media. Thus, big data analytics on social networks for real-time depression detection is proposed. This research work detected the depression by analyzing both demographic characteristics and opinions of Twitter users during a two-month period after having answered the Patient Health Questionnaire-9 used as an outcome measure. Machine learning techniques were applied as the detection model construction. There are fve machine learning techniques explored in this research which are Support Vector Machine, Decision Tree, Naïve Bayes, Random Forest, and Deep Learning. The experimental results revealed that the Random Forest technique achieved higher accuracy than other techniques to detect the depression. This research contributes to the literature by introducing a novel model based on analyzing demographic characteristics and text sentiment of Twitter users. The model can capture depressive moods of depression suferers. Thus, this work is a step towards reducing depression-induced suicide rates. give me the summary of above paragraph and that summary should be only of 10 lines.

Estimating and classifying depression status are critical in the clinical and psychological domains to map the course of treatment. Prior researchers used biosignal time-series data to reflect the variation in factors associated with depression. In addition, machine learning algorithms were applied to determine the underlying relationships between depressive symptoms and these factors. In this study, we introduce a classification framework for depression levels using actigraphy data based on machine learning algorithms. Fourteen circadian rhythm features (minimum, amplitude, alpha, beta, acrotime, upmesor, downmesor, mesor, f_pseudo, interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), M10, and L5) extracted from accelerometer-based actigraphy data were used to model depression status with survey variables. Six evaluation metrics (accuracy, precision, recall, F1-score, receiver operating characteristic curve, and area under the curve) were applied to validate the performance of the proposed framework. Among the four candidate classifiers (XGBoost classifier, support vector classifier, multilayer perceptron, and logistic regression), the XGBoost classifier was the best at classifying depression levels. Moreover, we confirmed that the actigraphy data of two days were optimal for feature extraction and classification. The results of this study provide novel insights into the relationship between depression and physical activity in terms of both identification of depression and application of actigraphy data. give me the summary of above paragraph and that summary should be only of 10 lines.

Purpose Lifestyle changes associated with end-stage renal disease may be a factor in depression and quality of life (QOL) for patients receiving hemodialysis. This cross-sectional study examined the relationship between nutritional status, QOL, and depression in 124 hemodialysis patients. Methods Nutritional markers included serum albumin, normalized protein catabolic rate (nPCR), body mass index (BMI), body fat percentage, and daily protein intake. Physical and Mental dimension scores of the Kidney Disease QOL-Short Form (KDQOL-SF), and the Center for Epidemiological Studies of Depression (CESD) survey were used to measure QOL and depression, respectively. Data were analyzed using regression analyses. Measures of efect size were used for interpretation. Results Nutritional status indicators explained a moderate amount of the variability of the Physical dimension of QOL (crude R2=.14, covariate-adjusted ΔR2=.06) but had weak explanatory ability for the Mental dimension of QOL (crude R2=.05, covariate-adjusted ΔR2=.02) and CESD (crude R2=.02, covariate-adjusted ΔR2=.005). Additional fndings suggested the presence of non-linear relationships between protein intake and both the Physical and Mental QOL dimension scores. Longer dialysis vintage was also correlated with lower psychosocial patient outcomes. Conclusion While nutritional status is an important element in predicting hemodialysis patient outcomes, its relationship to depression and QOL, in this sample, demonstrated only moderate explanatory ability. However, dialysis vintage and level of education had a signifcant relationship with depression and QOL. These fndings suggest that patients with longer dialysis vintage and limited health literacy require unique plans of care. Future studies aimed at understanding the interrelationships between non-modifable patient characteristics and psychosocial outcomes are imperative. give me the summary of above paragraph and that summary should be only of 10 lines.

Approximately 300 million people worldwide suffer from depression, and more than 60% of psychiatric patients do not have access to mental health services due to the shortage of psychiatrists and the high costs associated with clinical diagnosis and treatment. Correct and efficient diagnosis of depression can help overcome these straits. Automatic detection of depressive symptoms can help improve the accuracy and availability of diagnosis. In this paper, a fusion feature for Bispectral Features and Bicoherent Features by using higher-order spectral analysis. Experiments were performed on the Depression Sub-Challenge Dataset of the Audio/Visual Emotion Challenge 2017. The fusion feature fuses higher-order spectral features and traditional speech features with classification weights greater than 100 extracted by using A Collaborative Voice Analysis Repository. The support vector machine and k-nearest neighbor classification algorithms were used as the traditional machine learning models, and the convolutional neural network was used as the deep learning model to verify the proposed features. The experimental results show that under the support vector machine algorithm, the accuracies of extraction of speech-related features by using a collaborative voice analysis repository, The higher-order spectral analysis, and their fusion features were 63.15%, 68.42%, and 73.68%, respectively. Under the k-nearest neighbor classification algorithms model algorithm, the corresponding accuracies were 68.18%, 72.73%, and 77.27%, respectively. For the convolutional neural network model, the corresponding accuracies were 70%, 77%, and 85%, respectively. The results demonstrate that the fusion feature recognition accuracy is high and can be employed to improve the accuracy of depression identification by using traditional machine learning and deep learning models. give me the summary of above paragraph and that summary should be only of 10 lines.

Given the prevalence of depression worldwide and its major impact on society, several studies employed artificial intelligence modelling to automatically detect and assess depression. However, interpretation of these models and cues are rarely discussed in detail in the AI community, but have received increased attention lately. In this study, we aim to analyse the commonly selected features using a proposed framework of several feature selection methods and their effect on the classification results, which will provide an interpretation of the depression detection model. The developed framework aggregates and selects the most promising features for modelling depression detection from 38 feature selection algorithms of different categories. Using three real-world depression datasets, 902 behavioural cues were extracted from speech behaviour, speech prosody, eye movement and head pose. To verify the generalisability of the proposed framework, we applied the entire process to depression datasets individually and when combined. The results from the proposed framework showed that speech behaviour features (e.g. pauses) are the most distinctive features of the depression detection model. From the speech prosody modality, the strongest feature groups were F0, HNR, formants, and MFCC, while for the eye activity modality they were left-right eye movement and gaze direction, and for the head modality it was yaw head movement. Modelling depression detection using the selected features (even though there are only 9 features) outperformed using all features in all the individual and combined datasets. Our feature selection framework did not only provide an interpretation of the model, but was also able to produce a higher accuracy of depression detection with a small number of features in varied datasets. This could help to reduce the processing time needed to extract features and creating the model. give me the summary of above paragraph and that summary should be only of 10 lines.

In this digital world, data is an asset, and enormous data was generating in all the felds. Data in the healthcare industry consists of patient information and disease-related information. This medical data and machine learning techniques will help us to analyse a large amount of data to fnd out the hidden patterns in the disease, to provide personalised treatment for the patient and also used to predict the disease. In this work, a general architecture has proposed for predicting the disease in the healthcare industry. This system was experimented using with reduced set features of Chronic Kidney Disease, Diabetes and Heart Disease dataset using improved SVM-Radial bias kernel method, and also this system has compared with other machine learning techniques such as SVM-Linear, SVM-Polynomial, Random forest and Decision tree in R studio. The performance of all these machine learning algorithms has evaluated with accuracy, misclassifcation rate, precision, sensitivity and specifcity. From the experiment results, improved SVM-Radial bias kernel technique produces accuracy as 98.3%, 98.7% and 89.9% in Chronic Kidney Disease, Diabetes and Heart Disease dataset respectively. give me the summary of above paragraph and that summary should be only of 10 lines.

In this paper we introduce the idea of two-stage learning for multiple kernel SVM (MKSVM) and present a new MKSVM algorithm based on two-stage learning (MKSVM-TSL). The first stage is the pre-learning and its aim is to obtain the information of data such that the ‘‘important’’ samples for classification can be generated in the formal learning stage and these samples are uniformly ergodic Markov chain (u.e.M.c.). To study comprehensively the proposed MKSVM-TSL algorithm, we estimate the generalization bound of MKSVM based on u.e.M.c. samples and obtain its fast learning rate. And in order to show the performance of the proposed MKSVM-TSL algorithm for better, we also perform the numerical experiments on various publicly available datasets. From the experimental results, we can find that compared to three classical multiple kernel learning (MKL) algorithms, the proposed MKSVM-TSL algorithm has better performance in three aspects of the total time of sampling and training, the accuracy and the sparsity of classifiers, respectively. give me the summary of above paragraph and that summary should be only of 10 lines.

among the masses. In this paper, predictions of anxiety, depression and stress were made using machine learning algorithms. In order to apply these algorithms, data were collected from employed and unemployed individuals across different cultures and communities through the Depression, Anxiety and Stress Scale questionnaire (DASS 21). Anxiety, depression and stress were predicted as occurring on five levels of severity by five different machine learning algorithms – because these are highly accurate, they are particularly suited to predicting psychological problems. After applying the different methods, it was found that classes were imbalanced in the confusion matrix. Thus, the f1 score measure was added, which helped identify the best accuracy model among the five applied algorithms as the Random Forest classifier. Furthermore, the specificity parameter revealed that the algorithms were also especially sensitive to negative results. give me the summary of above paragraph and that summary should be only of 10 lines.

In cloud-based health monitoring services, support vector machine (SVM) classification techniques are often utilized by medical institutes to build medical decision models, which can be outsourced to a cloud server for producing medical decisions based on medical features from remote clients. In this paper, we propose a verifiable and secure SVM classification scheme (VSSVMC) for cloud-based health monitoring services in a malicious setting where the cloud server may return invalid decisions. By constructing verifiable indexes, VSSVMC ensures the verifiability of medical decisions, which enables clients to detect whether the cloud server returns incorrect or incomplete medical decisions. Symmetric key encryption is leveraged to ensure the confidentiality of the medical decision model and medical data with computational efficiency. We give security and verifiability definitions, and provide formal security and verifiability proofs for VSSVMC. Performance analyses show that VSSVMC is extremely efficient in terms of computation, communication, and storage. Experimental evaluations demonstrate that VSSVMC achieves microsecond-level execution time with kilobyte-level communication and storage overheads on the tested dataset. give me the summary of above paragraph and that summary should be only of 10 lines.

Since an increasing amount of data is generated and collected in real life, clustering is more frequently applied to process these unlabeled data in practical problems. Due to the simple similarity measure of conventional clustering methods, they are unable to achieve good performance on current big data. With the popularity of deep learning, deep clustering has been developed in recent years and obtained remarkable results. However, they have complex architecture and consume numerous computational resources, which goes against the migration to edge devices. Therefore, methods with low cost are required to satisfy edge computing, which is the trend of development. In this paper, we propose GAN–SOM as a new architecture for clustering based on deep learning. A SOM-similar network is designed to simultaneously implement encoding and clustering purposes on data samples, which is jointly trained with a GAN to optimizes a new defned clustering loss. We also utilize selfattention mechanism and spectral normalization in the GAN architecture to enhance efects of generated data, which aims to achieve better clustering results. The experimental results compared with other clustering baselines with deep learning verify that our method maintains high clustering metrics while saving computational cost signifcantly. give me the summary of above paragraph and that summary should be only of 10 lines.

can you identify limitations of the paper by just giving you a title of the paper

then tell me what specific details do you need to identify the limitation so that i can provide you with those you need

how about giving you abstract of each paper?

Prevalent mental disorders, such as depression and anxiety, commonly manifest in students throughout the transition to early adulthood. Mental illnesses can significantly impact students’ academic and social activities. An automatic or semiautomatic health monitoring approach is very effective for diagnosing depression and anxiety. This study aims to implement and scrutinize a data-driven optimization method for identifying and providing therapy to students with symptoms of depression and anxiety. The proposed method starts with data preprocessing and operating sentiment analysis to identify mentally disordered students. An ensemble learning classifier later divides students with symptoms into three categories based on their health condition: severe, moderate, and mild. A hyperparameter optimization approach is further adopted to improve the model’s performance. Finally, a rule-based dispatching system is implemented for scheduling therapy sessions. The proposed novel data-driven method is a post-disaster intelligent and reliable method that integrates three welladopted techniques to address students’ depression and anxiety. The findings indicate that the conventional approach to monitoring depression among students previously detected only 7 to 15% of cases. However, the performance of the offered strategy revealed a confirmed rate of 44% of depressed and anxious students. the above paragraph is the abstract i need you to help me identify the limitations of the paper

can you give me in point wise manner

Machine learning is a powerful tool that can be used to solve a wide range of problems in various applications and industries. The healthcare sector has faced specific challenges that have kept machine learning algorithms from becoming as widely and quickly adopted as in other industries. Data access and management challenges, ethical considerations, safety, and physician and patient perception present bigger barriers to implementation than model performance. In this paper, we propose adapting and customizing the concept of preconditions and postconditions from software engineering to develop a framework based on required clinical parameters and expected clinical output that will help bridge identified gaps in the implementation of machine learning tools in health care. the above paragraph is the abstract i need you to help me identify the limitations of the paper in a point wise manner