DEVELOPMENT OF PREDICTION MODEL USING DEEP BELIEF NETWORK FOR STRESS BASED COMPLICATIONS PROJECT REPORT PHASE-I Submitted by MADHUSRI V Register No 17MCO001 in partial fulfillment for the requirement of award of the degree of MASTER OF ENGINEERING in COMMUNICATION SYSTEMS Department of Electronics and Communication Engineering KUMARAGURU COLLEGEOF TECHNOLOGY

DEVELOPMENT OF PREDICTION MODEL USING DEEP BELIEF NETWORK FOR STRESS BASED COMPLICATIONS PROJECT REPORT PHASE-I Submitted by MADHUSRI V Register No 17MCO001 in partial fulfillment for the requirement of award of the degree of MASTER OF ENGINEERING in COMMUNICATION SYSTEMS Department of Electronics and Communication Engineering KUMARAGURU COLLEGEOF TECHNOLOGY (An autonomous institution affiliated to Anna University, Chennai) COIMBATORE-641049 ANNA UNIVERSITY CHENNAI 600 025 OCTOBER 2018 BONAFIDE CERTIFICATE Certified that this project report titled DEVELOPMENT OF PREDICTION MODEL USING DEEP BELIEF NETWORK FOR STRESS BASED COMPLICATIONS is the bonafide work of MADHUSRI.V Reg.No.17MCO001 who carried out the research under my supervision. Certified further that, to the best of my knowledge the work reported herein does not form part of any other project or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. SIGNATURE SIGNATURE Dr.RAMALATHA MARIMUTHU Prof. S.A.PASUPATHY PROJECT SUPERVISOR PROFESSOR AND HEAD Department of ECE Department of ECE Kumaraguru College of Technology Kumaraguru College of Technology Coimbatore – 641049 Coimbatore – 641049 ACKNOWLEDGEMENT First, I would like to express my praise and gratitude to the Lord, who has showered his grace and blessings enabling me to complete this project in an excellent manner. I express my sincere thanks to the management of Kumaraguru College of Technology and Joint Correspondent Shri. Shankar Vanavarayar, for his kind support and for providing necessary facilities to carry out the work. I would like to express my sincere thanks to our beloved Principal Dr .Senthil Jayavel, Ph.D., Kumaraguru College of Technology, who encouraged me with his valuable thoughts. I would like to thank Prof. Dr.S.A.Pasupathy,Ph.D., Head of PG Programme, Electronics and Communication Engineering, for his kind support and for providing necessary facilities to carry out the project work. I wish to thank everlasting gratitude to the project coordinator Dr.S.N.Shivappriya Ph.D., Assistant Professor II, Department of Electronics and Communication Engineering, throughout the course of this project work. I am greatly privileged to express my heartfelt thanks to my project guide Dr.Ramalatha Marimuthu,Ph.D., Professor, Department of Electronics and Communication Engineering, for her expert counseling and guidance to make this project to a great deal of success and I wish to convey my deep sense of gratitude to all teaching and non-teaching staffs of ECE Department for their help and cooperation. Finally, I thank my parents and my family members for giving me the moral support and abundant blessings in all of my activities and my dear friends who helped me to endure my difficult times with their unfailing support and warm wishes. ii ABSTRACT Stress management is related to public healthcare and quality of life this method is necessary for the design of stress monitoring systems. Therefore, the goal of this study is to analyze the existing paper and extracting the reasons behind the complications in pregnancy using Deep Belief Networks (DBN), among which the stress is one of the major reasons behind the complications in pregnancy. This study also involves analyzing the data which is obtained from a survey conducted by several organizations. This also concludes that there is a huge gap in the collection of data (monitoring) the pregnant women after delivery. In next phase, a survey will be conducted in such a way to bridge the gap between the causes and the outcomes and to design and develop a prediction model using deep belief network for stress based complications using a deep learning method. LIST OF FIGURES FIGURETITLEPAGENONO1Block Diagram For Speaker Recognition System132Project Flow Chart 213Deep Learning Architecture 224Deep Belief Networks 23 5 Mothers experiencing stressful events 32 LIST OF ABBREVIATIONS ABBREVATIONS NOMENCLATURE DL DBN ML IoT SVM DR CNN MTL DA DNN EER RVS ASQ BCR PSTD TL RDM Deep Learning Deep Belief Networks Machine Learning Internet of Things Support Vector Machine Dimensionality Reduction Convolutional Neural Networks Multitask Learning Domain Adaptation Deep Neural Networks Equal Error Rate Respiration Variability Spectrogram Ages and Stages Questionnaire Benefit- Cost Ratios Post-traumatic stress disorder Transfer Learning HYPERLINK https//en.wikipedia.org/wiki/Restricted_Boltzmann_machine o Restricted Boltzmann machine Restricted Boltzmann Machines CHAPTER 1 INTRODUCTION INTRODUCTION Stress is a vital part of everyones life. Stress is the sentiment of being under a lot of mental or enthusiastic weight. People have different ways of reacting tostress, so a situation that feelsstressfulto one person may, in fact, be motivating to another. Stress is your bodys method for reacting to any sort of interest. It can be caused by both good and bad experiences. At the point when individuals feel worried by something going ahead around them, their bodies respond by discharging synthetic substances into the blood. Stresssymptomscan affect yourbody,yourthoughts and feelings, and yourbehavior. Being able to recognize commonstresssymptomscangive you a jump on managing them. Stress that is left unchecked can add to numerous medical issues, such as high blood pressure, heart disease, obesity, and diabetes. Though it is both men and women who deal with stress, it is women who tend to be its most common victims and it is particularly the working women who find themselves struggling with stress more than others. In recent times, working ladies went about as a home producer as well as an expert and contribute 46 of the aggregate workforce in India. DEFINITION Stressis a physical, mental, or emotional factor that causes bodily or mental tension. Stress can be outside (from nature, mental, or social circumstances) or inward (illness, or from a medical procedure). Stressis notitselfassociatehealth problembutitcancause serioushealth problemif not tackled. It is important to recognize thesymptoms of stress early. Spotting the earlysigns of stress will also helpprevent it worsening and potentially causing serious complications, such as high blood pressure, anxiety, and depression. There are many things you can do to manage stress more effectively, like learning the way to relax, taking regular exercise and adopting experience management techniques. 1.2 BASIC TERMS Stress Stress is the bodys response to a change that requires a physical, mental or emotional adjustment or reaction. It can originate from any situation that makes you feel frustrated, angry, nervous etc. Stress at Work Place Work-related stress is the response people may have when presented with work demands and pressures that are not matched to their knowledge and abilities and which challenge their ability to cope. Working women Women who work for her family and she has to do work for her family and face lot of problems, she faces all difficulties just to help her family is called working women. Stress during Pregnancy Feeling stressed is common during a pregnancy. But too much stress can make you uncomfortable. Stress can make you trouble sleeping, have headaches, lose your appetite or overeat. High levels of stress that continue for a long time may cause health problems, like HYPERLINK https//www.marchofdimes.org/complications/high-blood-pressure-during-pregnancy.aspx high blood pressureand heart disease. During a pregnancy, this type of stress can increase the chances of having a HYPERLINK https//www.marchofdimes.org/complications/premature-babies.aspx premature baby,or a HYPERLINK https//www.marchofdimes.org/complications/low-birthweight.aspx low-birth-weight baby.Babies born too soon or too small are at increased risk for health problems. The detailed explanation of this project is described in the following chapter. 1.3 COMPLICATIONS DUE TO STRESS Depression feelings of extreme sadness, despair or inadequacy that last for a long time Anxiety constant feelings of unease, such as worry or fear, that affect your daily life Insomnia difficulty getting to sleep or staying asleep High blood pressure(hypertension) Stomach ulcers (peptic ulcer) Cardiovascular disease High blood pressure can cause many different types of cardiovascular disease (conditions that affect your heart and circulation), including Stroke a serious condition where the blood supply to the brain is interrupted Heart attack a serious condition where the blood supply to the heart is blocked An aneurysm a serious condition that is caused by a weakness in the blood vessel wall, which forms a bulge in the blood vessel. Inflammation Inflammation is the bodys response to infection, irritation or injury, which causes redness, swelling, pain and sometimes a feeling of heat in the affected area. 1.4 PROJECT OBJECTIVE Stress management is related to public healthcare and quality of life. The goal of this study to design and develop a prediction model for stress based complications using a deep learning method, based on the Physical, Social, Environmental and Biological factors. CHAPTER 2 LITERATURE REVIEW Papers Regarding Deep Learning Mr.Purnendu Shekhar Pandey ., Machine Learning and IOT for Prediction and Detection of Stress, July (2017). As indicated by a cardiovascular specialist, it is hard to foresee age from pulse as it is nonlinear, however utilize a mans heart beat to anticipate whether that individual is fit, unfit and over prepared or not, gave that individuals age. In view of heart beat it can foresee whether a man is in Stress or not. Stress is one of the principle factors that are influencing a large number of lives. Along these lines, it is essential to educate the individual about his undesirable way of life and even alert him/her before any intense condition happens. To distinguish the pressure previously it can utilized heart beat rate as one of the parameters. The created model distinguishes whether a man is in pressure utilizing inconstancy in his/her pulse. It can likewise help in identifying an example of changes in a mans pulse when he/she is working out at the exercise center. Every gadget is singular particular and should be aligned for it to work appropriately. This setup used to decide if that individual is in pressure/apprehensive, over prepared or as of now preparing. The heartbeat readings are pushed to the server where they are separated utilizing a clients system id to monitor readings for a specific person. Internet of Things (IoT) alongside Machine Learning (ML) is utilized to alert the circumstance when the individual is in genuine hazard. ML is utilized to foresee the state of the patient and IoT is utilized to convey the patient about his/her intense pressure condition. Utilizing Logistic Regression and SVM we get a precision of 66 and 68 separately, which demonstrates a change in exactness in the wake of utilizing SVM. To show signs of improvement results, utilizing his pulse and furthermore his every day action example to decide at what time he is exercising and what time he is stressed. A standout amongst the most encouraging future parts of this work can be to utilize a mans profile and his day by day pulse estimations alongside his galvanic skin response to determine the mood of a person. Micael Carvalho, Matthieu Cord, Deep Neural Network Under Stress, HYPERLINK http//refhub.elsevier.com/S1434-8411(15)00203-4/sbref0090 IEEE 19 August 2016. In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets. The properties of their features remain, however, largely unstudied under the transfer perspective. Our main objective is to explore the VGG M deep convolutional model, which was originally trained on ImageNet, in a transfer scheme for the classification task of the Pascal VOC 2007 dataset. In this work, two important aspects of deep architectures are dimensionality and numerical precision of their representations. It present an extensive analysis of the resiliency of feature vectors extracted from deep models, with special focus on the trade-off between performance and compression rate. By introducing perturbations to image descriptions extracted from a deep convolutional neural network, it change their precision and number of dimensions, measuring how it affects the final score, and observed that despite being more compact, deep architectures are also more robust to perturbations, when compared to approaches based on Bags of Visual Words. The results for our dimensionality reduction (DR) experiments in the Pascal VOC 2007 dataset. Strong redundancy on the representations is detected since, across runs, small variations in the score were observed. Those findings are specially useful for image retrieval and metric learning, in which the size of the feature vector is crucial to achieving fast response times, and for applications involving portable devices or remote classification, in which data must be efficiently transferred over the network. Our findings show that there is a high level of redundancy in deep representations, and thus, they may be heavily compressed. It show that deep features are more robust to these disturbances when compared to classical approaches, achieving a compression rate of 98.4, while losing only 0.88 of their original score for Pascal VOC 2007. Huijie Lin, Jia Jia, Jiezhong Qiu.,Detecting Stress Based on Social Interactions in Social Networks, JOURNAL OF LATEX CLASS FILES,VOL.13, NO.9, SEPTEMBER 2014. Psychological stress is threatening peoples health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, find that users stress state is closely related to that of his/her friends in social media, and it employ a large-scale dataset from real-world social platforms to systematically study the correlation of users stress states and social interactions. It first define a set of stress-related textual, visual, and social attributes from various aspects, and then propose a novel hybrid model – a factor graph model combined with Convolutional Neural Network to leverage tweet content and social interaction information for stress detection. Experimental results show that the proposed model can improve the detection performance by 6-9 in F1-score. The experimental results and analyses are 1) users stress states are not only revealed in their own tweets, but also affected by the contents of their social interactions, including commenting on and retweeting others tweets and 2) users stress states are revealed by the structure of their social interactions, including structural diversity, social influence, and strong/weak ties. These insights quantitatively prove the necessity and effectiveness of combining social interactions for stress detection. By further analyzing the social interaction data, it also discover several intriguing phenomena, i.e. the number of social structures of sparse connections (i.e. with no delta connections) of stressed users is around 14 higher than that of non-stressed users, indicating that the social structure of stressed users friends tend to be less connected and less complicated than that of non-stressed users. Apeksha Bhansali, Apoorva Inamdar, Harsha Mahajan, Stress Detection and Sentiment Prediction A Survey, International Journal of Engineering Applied Sciences and Technology, 2016. Self-expression is a vital usage of social media, in the form of daily life updates, experience description, information and comments sharing, etc. Social media is slowly replacing the practice of face-to-face communication. In a way, virtual communication is taking over the real-life communication. Active participation of Teen-age users on social media is one of the reasons behind this changing dynamics. Twitter is a popular micro blogging site amongst youth today. The humongous volume of tweeting content, along with various tweeting, retweeting and comments statistics, presents an opportunity for human sentiment analysis and behavioral study. Twitter users behavior and their mood transition are an in demand study domain with gigantic scope and many unexplored aspects. One such area is Stress Detection. It uses history of mood information to predict future moods. The paper is divided into two sections Systems for Stress Detection and systems for Sentiment. It implements regression analysis techniques on Tweets. It defines mood labels and describes mood swings based on it. It implements a sentiment analysis method using Hadoop. It focuses on analysis speed, rather than analysis accuracy. It converts unstructured Twitter data into structured data. It implements Map Reduce Algorithm. There are some studies which have implemented stress identifying and prediction techniques. There are many systems for stress detection, very few systems particularly emphasize stress prediction. Also, the systems above work on word analysis, rather than content analysis. The sentiment polarities explored are clichd, conventional and inadequate. Still, the ongoing work presents a great hope for human sentiment analysis and prediction. This paper presents an analytical survey of such recent published studies. Sajjad Basharpoor, Hadis Heydarirad, Seyed Javad Daryadel, The Role of Perceived Stress and Social Support Among Predicting anxiety in Pregnant Women,. Pregnancy anxiety is a common disorder occurred due to various factors. It has a significant impact on the outcome of pregnancy. It is associated with undesirable pregnancy outcomes, such as preterm delivery, low birth weight, fetal distress and some anomalies of the infant, including cleft palate and pyloric stenosis , nausea and vomiting, poor system, increased episiotomy, and neonatal infections and postpartum mental disorder . Severe anxiety during pregnancy damages mother-infant relationship and reduces the mothers ability to play the maternal role. This study aimed at determining the role of perceived stress and social support to tame anxiety among pregnant women. This is a descriptive-analytical study with correlational design. The studys population included all pregnant women who were referred to Ardabil health centers in the second half of 2013 to receive prenatal care. A total of 110 subjects were selected using random sampling method and responded to demographic, perceived stress and social support questionnaires. It should also be noted that a total of 30 samples are suggested per predictor variable in correlation studies. The data were collected was analyzed using Pearsons correlation coefficient and multiple regression analysis. The results showed that anxiety during pregnancy had a negative correlation with negative perception of stress (r0.56 P0.001) and positive correlation with positive perception of stress (r-0.36 P0.001). It also has support of friends (r0.42 P0.001) and family (r-0.52 P0.001) and total score of social protection (r0.52 P0.001). Results of regression analysis also revealed that 37 of the total variance of pregnancy anxiety is justified by perceived stress and social support. In this paper, data obtained from 101 women were used in the final analysis. The results showed that the perception of stress and social support received by pregnant women play a role in anxiety during pregnancy. In the future, it is recommended to boost stress management skills by modifying the negative perception of stress to improve pregnancy outcomes and encourage families to give social support to pregnant women. Se-Hui Song, Dong Keun Kim, Development of a Stress Classification Model using Deep Belief Networks for Stress Monitoring, health inform Res 2017,October.Res. 2017 October Stress management is related to public healthcare and quality of life an accurate stress classification method is necessary for the design of stress monitoring systems. Therefore, the goal of this study was to design a novel stress classification model using a deep learning method. In this paper, it present a stress classification model using the dataset from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze stress-related health data. Statistical analysis was performed to identify the nine features of stress detection, and it evaluated the performance of the proposed stress classification by comparison with several stress detection models. The proposed model was also evaluated using Deep Belief Networks (DBN). Among machine learning methods, the Deep Belief Network (DBN) is an advanced learning method using artificial neural networks that involves a high level of technology and performs well. It designed profiles depending on the number of hidden layers, nodes, and hyper-parameters according to the loss function results. It selected stress-related physical activity and lifestyle data, such as sleep time, systolic blood pressure, body mass index, drinking, and smoking. Clustering, DBN modeling, and model performance evaluation were performed on the extracted variables, evaluate the performance of the DBN model, it compared the stress classification results obtained using the statistical analysis data by existing models and the DBN model. The selected data were statistically analyzed to extract the variables that were considered significant for stress assessment. The experimental results showed that the proposed model achieved accuracy and a specificity of 66.23 and 75.32, respectively. The proposed DBN model performed better than other classification models, such as support vector machine, naive Bayesian classifier, and random forest. The accuracy values of the SVM and DBN model are similar the performance of DBN model is better considering human labeling time. The proposed model in this study was demonstrated to be effective in classifying stress detection, and in particular, it is expected to be applicable for stress prediction in stress monitoring systems. Natasha Jaques, Sara Taylor, Ehimwenma Nosakhare, Multitask Learning for Predicting Health,Stress and Happiness, August 2017. Multi-task Learning (MTL) is applied to the problem of predicting next-day health, stress, and happiness using data from wearable sensors and Smartphone logs. Three formulations of MTL are compared i) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types, ii) a Hierarchical Bayes model in which tasks share a common Dirichlet prior, and iii) Deep Neural Networks, which share several hidden layers but have final layers unique to each task. MTL was applied to this data in two ways 1) It treat predicting each wellbeing label (health, stress, and happiness) as one task, and 2) It treat predicting the wellbeing of a single user as one task. Since happiness and stress are more strongly correlated with each other than either is with health, the NN likely learned representations that were more related to these than health. The performance of HBDPP and MTMKL generally suffered for the wellbeing-as-tasks problem. One explanation is that the multi-tasking constraints are causing these models to under fit the data. If the model is not sufficiently complex, the regularization effect of MTL may reduce the power of the models to fit the optimal decision boundary. The training accuracy for these models was low, lending support to this theory. It show that by using MTL to leverage data from across the population while still customizing a model for each person, it can account for individual differences, and obtain state-of-the-art performance on this dataset. This work has demonstrated that accounting for individual differences through MTL can dramatically improve wellbeing prediction performance. The improvement is not simply due to the application of MTL, but rather to the ability of MTL to allow each user to have a model trained specifically for them, which also benefits from the data of other users. The three formulations of MTL, have explored offer different strengths, including the ability to learn how the importance of feature types differs across tasks and the ability to learn implicit groupings of users. A major contribution of this research is enabling data-driven insights about behaviors that may contribute to poor wellbeing and declines in mental health, or to a happy, calm, and healthy state. By examining the models weights and clusters, it can identify behaviors that may be significant predictors for each person, while accommodating different personalities and lifestyles. Hypotheses related to these behaviors can subsequently be tested via causal inference techniques. Traditional methods that derive results from models used to fit groups may or may not apply to individuals. In contrast, the methods shown here have the potential to leverage group data, while also identifying behaviors that may truly influence each individuals path to better health and wellbeing. Natasha Jaques, Ognjen Rudovic, Sara Taylor, Predicting Tomorrows Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation , August 2017. Predicting a persons mood tomorrow, from data collected unobtrusively using wearable sensors and smartphones, could have a number of beneficial clinical applications however, this prediction is an extremely challenging problem. Past approaches often lack accurate and reliable performance necessary for real-world applications. It posit that this is due to the inability of traditional, one-sizefits- all machine learning models to account for individual differences. To overcome this, it treat predicting tomorrows mood for a single person as one task, or problem domain. It adopt Multitask Learning (MTL) and Domain Adaptation (DA) approaches to learn a model which is customized for each person, while still being able to benefit from data across the population. the advantage that they can be trained even when there is not enough data per-person to train many individual person-specific models, in our case we still require at least 15 days of training data per person. Empirical results on real-world, continuous monitoring data show that the new personalized models an MTL deep neural network, and a Gaussian Process with DA both significantly outperform their generic counterparts, providing substantial performance enhancements in automatic prediction of continuous levels of tomorrows reported mood, stress, and physical health based on data through today. These performance improvements may have important clinical benefits, such as enabling a model to better distinguish between participants who are severely unhappy or stressed, which then can enable more relevant and targeted treatments for improving wellbeing. Sri Harsha Dumpala, Sunil Kumar Kopparapu, Improved Speaker Recognition System for Stressed Speech using Deep Neural Networks, IEEE Xplore 03 July 2017. Good speaker recognition systems should identify the speaker irrespective of what is spoken, including non-speech sounds that are often produced during natural conversations. In this work, the inclusion of breath sounds in the training phase of the speaker recognition is analyzed using the popular Gaussian mixture model-universal background model (GMM-UBM) and deep neural network (DNN) based systems. It is shown that the DNN-based systems have a better learning capability to perform well even on unseen data compared to GMM-UBM based systems. Specifically, enhancement in speaker recognition Performance is obtained on unseen stressed speech data by training systems with both breath sounds and modal speech. Experimental results show that the inclusion of breath sounds in Training data reduces the equal error rate (EER) of the speaker recognition system on a stressed speech by 40 to 50 in absolute terms. It is also shown that increasing the number of hidden layers help DNNs to improve the performance even on unseen data. Increasing the number of hidden layers help DNNs to better learn and capture the characteristics of the training data which allows it to work robustly even when there are large variations in the test data. Figure 1 Block Diagram for Speaker Recognition System Papers Regarding Child Birth and Maternal care Youngjun Cho, Nadia Bianchi-Berthouze, Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings, August 20, 2017 A deep learning model which automatically recognizes peoples psychological stress level (mental overload) from their breathing patterns. Using a low-cost thermal camera, it track a persons breathing patterns as temperature changes around his/her nostril. The papers technical contribution is threefold. First of all, instead of creating handcrafted features to capture aspects of the breathing patterns, it transform the uni-dimensional breathing signals into two- dimensional respiration variability spectrogram (RVS) sequences. The spectrograms easily capture the complexity of the breathing dynamics. Second, a spatial pattern analysis based on a deep Convolutional Neural Network (CNN) is directly applied to the spectrogram sequences without the need of hand-crafting features. Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e.g., up to a few hours). The model is trained and tested with data collected from people exposed to two types of cognitive tasks (Stroop Colour Word Test, Mental Computation test) with sessions of different difficulty levels. Using normalized self-report as ground truth, the CNN reaches 84.59 accuracy in discriminating between two levels of stress and 56.52 in discriminating between three levels. In addition, the CNN outperformed powerful shallow learning methods based on a single layer neural network. Deep Breath is a novel stress recognition system based on deep learning and low-cost thermal imaging. The results show high performances in automatic stress detection under unconstrained settings. Hence expect that the combination of breathing variability with other signals may lead to further improvement in the recognition rate. Finally, the dataset of labeled thermal images will be open to the community. Dilip Chandrasekhar, Aswathi Shaji, Effectiveness of Pharmacist Intervention (SAHELI) on Family Planning, Maternal Care and Child Care Among Rural Women, J Young Pharm, 2018. Due to low literacy rates and poor economic status rural women are either completely or partially unaware about modern Family Planning techniques and proper scientific maternal care and child care. SAHELI- Scheme to Aware Help and Empower Ladies in India was introduced in this context. It offers rural women, a complete assistance in learning modern FP techniques and practicing proper reproductive or sexual health care. Maternal health refers to the health of women during pregnancy, childbirth and postpartum period. The major direct cause of maternal morbidity and mortality include hemorrhage, infection, high blood pressure, unsafe abortion, and obstructed labor. To assess the knowledge regarding Family Planning, Maternal Care and Child Care among rural women and to educate, aware and empower them regarding Family Planning, Maternal Care and Child Care. A quasi experimental study with 6-month duration was conducted among 140 women in the age group 15-45, in a rural area of Kerala state, India to evaluate the effectiveness of pharmacist intervention in Family Planning, Maternal Care and Child Care among rural women. Before the intervention, 53.8 of the subject did not have knowledge about any methods of contraception, but after intervention all the subjects had knowledge about two or more methods. There was a significant improvement in family planning, maternal care, and child care knowledge after intervention with a p0.05. The Paired t-test shows the mean total score for knowledge for FP, maternal care and child care. The study clearly conveyed that majority of rural women were unaware about the Family Planning methods, Maternal Care and Child Care practices. The scheme has got a great acceptance by the rural women and they showed great enthusiasm and interest in acquiring more knowledge. From the feedback received, it was clear that SAHELI was able to change the attitude and knowledge of rural women regarding the modern family planning techniques and the importance of maternal care and child care. This can be attributed to the care of pharmacist/community services in India and the perceived role of a pharmacist in the health care set up. This can help in creating collaboration between the general public, pharmacists with the help of local health centers. Abram L.Wagner, Lu Xia, Priyamvada Pandey3, Risk Factors During Pregnancy and Early Childhood in Rural West Bengal, India A Feasibility Study Implemented via Trained Community Health Workers Using Mobile Data Collection Devices, part of Springer Nature 2018. This study measures the prevalence of risk factors among pregnant women and young children aged 1224 months in a rural community in West Bengal, India. Community health workers (CHWs) enrolled women and children into this 2015 cross-sectional study. Pregnant women were notified if they had a risk factor associated with pregnancy, and parents of the young children were notified if the child showed an indication of a developmental delay, this study included body weight, hemoglobin, blood pressure and presence of protein or sugar in urine. Pregnant women were evaluated for underweight, anemia, and abnormal blood pressure. Children were evaluated for underweight, abnormal head and upper arm circumferences, and low scores from the Ages and Stages Questionnaire (ASQ). Data were collected on smartphones and tablets or by paper. More than half of the 279 women (59.9) had a risk factor during pregnancy 48.7 were anemic, 35.1 had low blood pressure, and 7.5 were underweight. Among the 366 children, 59.3 had a risk factor, including 24.0 with low ASQ scores and 49.7 who had abnormal anthropometric measures, We used standard descriptive statistics, such as proportions and Pearsons Chi-square test, to compare indications for healthcare need between different sociodemographic groups. For Practice, Vulnerable populations, such as pregnant women and young children, needed a greater connection to doctors in this rural community. This study used recent mobile device technologies to collect data and check for pregnancy complications and developmental delays. This study demonstrated the feasibility of CHWs to listen to health concerns and connect underserved populations with health care services. Future research can detail whether our findings on the prevalence of healthcare need reflect the situation in other parts of India, it could either substantiate this finding or reveal it to be a chance finding. Soo Downe, Kenneth Finlayson, What matters to women during childbirth A systematic qualitative review, PLOS ONE April 17, 2018. Design and provision of good quality maternity care should incorporate what matters to childbearing women. This qualitative systematic review was undertaken to inform WHO intrapartum guidelines. Using a pre-determined search strategy, it searched Medline, CINAHL, PsycINFO, AMED, EMBASE, LILACS, AJOL, and reference lists of eligible studies published 1996- August 2016 (updated to January 2018), reporting qualitative data on women childbirth beliefs, expectations, and values. Studies including specific interventions or health conditions were excluded. PRISMA guidelines were followed. Authors findings were extracted, logged on a study-specific data form, and synthesized using meta-ethnographic techniques. Confidence in the quality, coherence, relevance and adequacy of data underpinning the resulting themes was assessed using GRADE-CERQual. A line of argument synthesis was developed. 35 studies (19 countries) were included in the primary search, and 2 in the update. Confidence in most results was moderate to high. What mattered to most women was a positive experience that fulfilled or exceeded their prior personal and socio-cultural beliefs and expectations. This included giving birth to a healthy baby in a clinically and psychologically safe environment with practical and emotional support from birth companions, and competent, reassuring, kind clinical staff. Most wanted a physiological labor and birth while acknowledging that birth can be unpredictable and frightening, and that they may need to go with the flow. If the intervention was needed or wanted, women wanted to retain a sense of personal achievement and control through active decision-making. These values and expectations were mediated through women embodied (physical and psychosocial) experience of pregnancy and birth local familial and socio cultural norms and encounters with local maternity services and staff. Most healthy childbearing women want a positive birth experience. Safety and psychosocial wellbeing are equally valued. Maternity care should be designed to fulfill or exceed women personal and socio-cultural beliefs and expectations. Abusaleh Shariff, Amit Sharma Strategic Initiatives to Improve Maternal and Child Health in Andhra Pradesh A Cost-Benefit Analysis, April 12, 2018. The objective of this paper is to address the issue of maternal and child health in India specifically in Andhra Pradesh. Despite a significant decrease in infant and maternal mortality rates in the country, India continues to demonstrate among the highest prevalence of neonatal mortality in the world, with about 0.75 million neonates dying every year. Andhra Pradesh has improved significantly on its maternal and child survival indicators in the last 10 years. Between 2005-06 and 2015-16, Andhra Pradeshs infant mortality fell from 54 to 35 deaths per 1000 live births (NFHS-3, NFHS-4). The government of Andhra Pradesh has taken several steps to ensure the improvement of maternal and child health. Maternal mortality rate fell from 134 per 100,000 live births in 2008, to 92 in 2012 (Ministry of Health and Family Welfare, NFHS-4). Nevertheless, there is still room for improvement, particularly on several indicators related to access and use of child and maternal health services. In Andhra Pradesh, 70 percent of women exclusively breastfeed, 76 percent of women had at least 4 antenatal care (ANC) visits during pregnancy, and only 65 percent of children are fully vaccinated (NFHS-4). A cost-benefit analysis of three policy interventions – breastfeeding promotion, promotion and incentivization of immunizations in lagging districts and conditional cash transfers for accessing ante natal services shows significantly positive benefit-cost ratios (BCRs). The paper argues that the most effective (highest benefits-to-costs) intervention to improve maternal and child health is immunization promotion, while promotion of exclusive breastfeeding to new mothers has the largest net benefits. Debrup Banerjee, Kazi Islam, A Deep Transfer Learning Approach for Improved Post-Traumatic Stress Disorder Diagnosis, 2017 IEEE International Conference on Data Mining. Post-traumatic stress disorder (PTSD) is a traumatic-stressor related disorder developed by exposure to a traumatic or adverse environmental event that caused serious harm or injury. Two challenges exist for the current speech based PTSD diagnostic systems 1) The capabilities of different speech feature categories and advanced machine learning techniques have not been fully explored 2) PTSD speech corpora are limited that presents difficulties in training complex diagnostic models. PTSD patient data is usually difficult to collect and therefore data needed for complex model training is usually not available. A typical speech based PTSD diagnostic system consists of three components including data acquisition, feature extraction and classification structured interview is the only widely accepted clinical practice for PTSD diagnosis but suffers from several limitations including the stigma associated with the disease. Diagnosis of PTSD patients by analyzing speech signals has been investigated as an alternative since recent years, where speech signals are processed to extract frequency features and these features are then fed into a classification model for PTSD diagnosis. Other modalities such as EEG, fMRI and MRI were also studied for PTSD diagnosis the data collection process for these modalities is expensive and cannot meet the growing need. Speech is non-invasive and the interview can be conducted remotely via telephone or recording media so that privacy of the patient is strictly protected, making the speech based method an ideal diagnostic tool for diagnosis and treatment monitoring. In this paper, it developed a deep belief network (DBN) model combined with a transfer learning (TL) strategy for PTSD diagnosis. It computed three categories of speech features and utilized the DBN model to fuse these features. The TL strategy was utilized to transfer knowledge learned from a large speech recognition database, TIMIT, for PTSD detection where PTSD patient data is difficult to collect. It evaluated the proposed methods on two PTSD speech databases, each of which consists of audio recordings from 26 patients. It compared the proposed methods with other popular methods and showed that the state-of-the-art support vector machine (SVM) classifier only achieved an accuracy of 57.68, and TL strategy boosted the performance of the DBN from 61.53 to 74.99. Altogether, our method provides a pragmatic and promising tool for PTSD diagnosis. CHAPTER 3 PROPOSED METHODOLOGY 3.1 INTRODUCTION After a hypothetical preparation, the main part of the paper is presented in this chapter. Stress is a predominant hazard factor for various ailments therefore, an accurate and efficient prediction of stress levels could provide a means for targeted prevention and intervention in the personal healthcare domain. Therefore, to prevent the occurrence of stress-related diseases, stress should be detected and managed early. In general, stress detection is assessed subjectively through surveys, and health conditions tend to be evaluated higher than the actual personal health status of individuals. Surveys are used to evaluate an individuals stress condition with ease of measurement and requiring little time. There have been several domestic studies on the relationship between stress, physical activity, and lifestyle most studies have been limited to specific groups and variables and propose a DBN-based stress prediction model that uses stress-related physical activity and lifestyle. First, analyzed whether stress evaluation is possible by comparing the stress-related physical activity and lifestyle information of individuals. Second, the DBN-based stress prediction model is implemented as a feature of the physical activity and lifestyle data that were judged to be meaningful in assessing stress. The remainder of this paper is organized as follows. Section II presents the proposed system and data processing method. Section III describes the design and implementation of the proposed stress prediction by a deep learning model using the dataset. Section IV discusses the experimental results and provides conclusions. The objectives of this observation include the main psychosocial risks factors, especially stress factors. 3.2 PROJECT FLOW Figure 2 Project flow chart 3.3 DEEP LEARNING Deep learning(also known asdeep structured learningorhierarchical learning) is part of a broader family of HYPERLINK https//en.wikipedia.org/wiki/Machine_learning o Machine learning machine learningmethods based on HYPERLINK https//en.wikipedia.org/wiki/Learning_representation o Learning representation learning data representations, as opposed to task-specific algorithms. Learning can be HYPERLINK https//en.wikipedia.org/wiki/Supervised_learning o Supervised learning supervised, HYPERLINK https//en.wikipedia.org/wiki/Semi-supervised_learning o Semi-supervised learning semi-supervisedor HYPERLINK https//en.wikipedia.org/wiki/Unsupervised_learning o Unsupervised learning unsupervised. Deep learning architectures such as HYPERLINK https//en.wikipedia.org/wiki/Deep_learning l Deep_neural_networks deep neural networks, HYPERLINK https//en.wikipedia.org/wiki/Deep_belief_network o Deep belief network deep belief networksand HYPERLINK https//en.wikipedia.org/wiki/Recurrent_neural_networks o Recurrent neural networks recurrent neural networkshave been applied to fields including HYPERLINK https//en.wikipedia.org/wiki/Computer_vision o Computer vision computer vision, HYPERLINK https//en.wikipedia.org/wiki/Automatic_speech_recognition o Automatic speech recognition speech recognition, HYPERLINK https//en.wikipedia.org/wiki/Natural_language_processing o Natural language processing natural language processing, audio recognition, social network filtering, HYPERLINK https//en.wikipedia.org/wiki/Machine_translation o Machine translation machine translation, HYPERLINK https//en.wikipedia.org/wiki/Bioinformatics o Bioinformatics bioinformatics, HYPERLINK https//en.wikipedia.org/wiki/Drug_design o Drug design drug designand HYPERLINK https//en.wikipedia.org/wiki/Board_game o Board game board gameprograms, where they have produced results comparable to and in some cases superior to human experts. INCLUDEPICTURE https//cdn-images-1.medium.com/max/1600/1dnvGC-PORSoCo7VXT3PV_A.png MERGEFORMATINET Figure 3 Deep Learning Architecture 3.4 DEEP BELIEF NETWORKS A Deep Belief Network(DBN) is a HYPERLINK https//en.wikipedia.org/wiki/Generative_model o Generative model generative HYPERLINK https//en.wikipedia.org/wiki/Graphical_model o Graphical model graphical model, or alternatively a class of HYPERLINK https//en.wikipedia.org/wiki/Deep_learning o Deep learning deep HYPERLINK https//en.wikipedia.org/wiki/Artificial_neural_network o Artificial neural network neural network, composed of multiple layers of HYPERLINK https//en.wikipedia.org/wiki/Latent_variables o Latent variables latent variables(hidden units), with connections between the layers but not between units within each layer. A deep belief network (DBN) is a sophisticated type of generative neural network that uses an unsupervised machine learning model to produce results. This type of network illustrates some of the work that has been done recently in using relatively unlabeled data to build unsupervised models. When trained on a HYPERLINK https//en.wikipedia.org/wiki/Training_set o Training set set of examples HYPERLINK https//en.wikipedia.org/wiki/Unsupervised_learning o Unsupervised learning without supervision, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as HYPERLINK https//en.wikipedia.org/wiki/Feature_learning o Feature learning feature detectors.After this learning step, a DBN can be further trained with HYPERLINK https//en.wikipedia.org/wiki/Supervised_learning o Supervised learning supervisionto perform HYPERLINK https//en.wikipedia.org/wiki/Statistical_classification o Statistical classification classification. DBNs can be viewed as a composition of simple, unsupervised networks such as HYPERLINK https//en.wikipedia.org/wiki/Restricted_Boltzmann_machine o Restricted Boltzmann machine restricted Boltzmann machines(RBMs)or HYPERLINK https//en.wikipedia.org/wiki/Autoencoder o Autoencoder auto encoders, where each sub-networks hidden layer serves as the visible layer for the next. An RBM is an HYPERLINK https//en.wikipedia.org/wiki/Undirected_graph o Undirected graph undirected, generative energy-based model with a visible input layer and a hidden layer and connections between but not within layers. This composition leads to a fast, layer-by-layer unsupervised training procedure, where HYPERLINK https//en.wikipedia.org/wiki/Contrastive_divergence o Contrastive divergence contrastive divergenceis applied to each sub-network in turn, starting from the lowest pair of layers (the lowest visible layer is a HYPERLINK https//en.wikipedia.org/wiki/Training_set o Training set training set). INCLUDEPICTURE https//upload.wikimedia.org/wikipedia/commons/thumb/f/fa/Deep_belief_net.svg/220px-Deep_belief_net.svg.png MERGEFORMATINET Figure 4 Deep Belief Network 3.5 SIMULATION TOOL MATLAB MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include Math and computation Algorithm development Modeling, simulation, and prototyping Data analysis, exploration, and visualization Scientific and engineering graphics Application development, including Graphical User Interface building MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar non-interactive language such as C or FORTRAN. Application Development Tools. Image Processing Signal Processing Control System Communications Neural Network Instrument Control Aerospace Fuzzy Logic Embedded Matlab CHAPTER 4 RESULTS AND DISCUSSIONS As per the study of centers for disease control and prevention, they conducted the survey (2009-2010) to find the number of stressful events in women during pregnancy. They define stressful events based on moved to a new address, argued more than usual with husband/partner, serious illness and hospitalization of a family member, unable to pay lots of bills, death of someone close to her, husband/partner lost job, drug use by someone close to her, lost job, was divorced or separated, husband/partner did not want job, experience homeless, husband/partner went to jail, was in a fight. The graph below shows that relation between the race and stressful events (separately). On the whole 75 of the women during Pregnancy undergo stress and it directly affects the pregnancy complications. But a clear survey is not there, to depict the conclusion. There is a gap between the surveys that it does not cover the post-pregnancy lives of women. Thus the second phase a survey will be conducted based on the outcomes of Phase 1. Figure 5 Mothers Experiencing Stressful Events In India, the pregnant women suffer from various complications such as Gestational Diabetes which has a direct dependency on stress. The study conducted says that about 50 of women in India have the probability to be affected with Gestational diabetes. This also says that one of the major reasons behind the Gestational diabetes is stress. Thus stress and its reason is an emerging problem and its eradication is an essential study. CHAPTER 5 CONCLUSION AND FUTURE WORK 5.1 CONCLUSION Various papers were studied and referred to the overall literature survey about the stress classification and detection methods .Theories and previous research have been the basic reference in order to define the stress level of an individual. The existing methods uses different methodology such as support vector machine, naive Bayesian classifier, and random forest to detect, extract and classify the Stress level. These methods are effective in classifying stress detection .The proposed methodology uses deep learning method, so that the stress can be predicted and their complications are identified using Deep Belief Networks (DBN) than traditional methods. FUTURE WORK The proposed work can be evaluating the stress level for a majority of people using various variables and it can be further enhanced to pregnancy women. REFERENCES Mr.Purnendu Shekhar Pandey ., Machine Learning and IOT for Prediction and Detection of Stress, July (2017). Micael Carvalho, Matthieu Cord, Deep Neural Network Under Stress, HYPERLINK http//refhub.elsevier.com/S1434-8411(15)00203-4/sbref0090 IEEE 19 August 2016. Huijie Lin, Jia Jia, Jiezhong Qiu.,Detecting Stress Based on Social Interactions in Social Networks, JOURNAL OF LATEX CLASS FILES,VOL.13,NO.9, SEPTEMBER 2014. Apeksha Bhansali, Apoorva Inamdar, Harsha Mahajan, Stress Detection and Sentiment Prediction A Survey, International Journal of Engineering Applied Sciences and Technology, 2016. Sajjad Basharpoor, Hadis Heydarirad, Seyed Javad Daryadel, The Role of Perceived Stress and Social Support Among Predicting anxiety in Pregnant Women, . Se-Hui Song, Dong Keun Kim, Development of a Stress Classification Model using Deep Belief Networks for Stress Monitoring, healthc inform Res 2017,October. Natasha Jaques, Sara Taylor, Ehimwenma Nosakhare, Multitask Learning for Predicting Health,Stress and Happiness , August 2017. Natasha Jaques, Ognjen Rudovic, Sara Taylor, Predicting Tomorrows Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation , August 2017. Sri Harsha Dumpala, Sunil Kumar Kopparapu , Improved Speaker Recognition System for Stressed Speech using Deep Neural Networks, IEEE Xplore 03 July 2017. Youngjun Cho, Nadia Bianchi-Berthouze, Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings, August 20, 2017. Dilip Chandrasekhar, Aswathi Shaji, Effectiveness of Pharmacist Intervention (SAHELI) on Family Planning, Maternal Care and Child Care Among Rural Women, J Young Pharm, 2018. Abram L.Wagner, Lu Xia, Priyamvada Pandey3, Risk Factors During Pregnancy and Early Childhood in Rural West Bengal, India A Feasibility Study Implemented via Trained Community Health Workers Using Mobile Data Collection Devices, part of Springer Nature 2018. Soo Downe, Kenneth Finlayson, What matters to women during childbirth A systematic qualitative review, PLOS ONE April 17, 2018. Abusaleh Shariff, Amit Sharma Strategic Initiatives to Improve Maternal and Child Health in Andhra Pradesh A Cost-Benefit Analysis, April 12, 2018 Debrup Banerjee, Kazi Islam, A Deep Transfer Learning Approach for Improved Post-Traumatic Stress Disorder Diagnosis, 2017 IEEE International Conference on Data Mining. PAGE MERGEFORMAT 31 Project Problem Statement Background Research (Research on Published Papers) Comparing the Results between the Published Papers Generating a Study on Published papers identifying the reasons behind the complications in Pregnancy Comparing Reasons showing which reason affects the most in Pregnancy through Graphical Structure representation. Finalizing the result and filtering the top 5 reasons for the complications in women Stop Start xFtYijE80Py3iiaDd9cFiwMu (B2HV4njfe7/zwzL tazWd 0 9u55tHcg 8i vjT 6xukwR 5OoPIf-L(ggWE6 s(j7EiKIn oaWkpKFj6
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