Brain stroke prediction using machine learning ppt It is a critical medical condition that demands timely detection to prevent severe outcomes, including permanent paralysis and death. 2 million new cases each year. We employ a comprehensive dataset featuring This project aims to predict the likelihood of a person having a brain stroke using machine learning techniques. As a result, this research work attempts to develop a stroke prediction system to assist doctors and clinical workers in predicting strokes in a timely and efficient manner. Mar 15, 2024 · SLIDESMANIA ConcluSion Findings: Through the use of AI and machine learning algorithms, we have successfully developed a brain stroke prediction model. This research focuses on predicting brain stroke using machine learning (ML) and Explainable Artificial Intelligence (XAI). Reason for topic Strokes are a life threatening condition caused by blood clots in the brain, and the likelihood of these blood clots can increase based on an individual's overall health and lifestyle. Jan 20, 2023 · Early detection of the numerous stroke warning symptoms can lessen the stroke's severity. A stroke occurs when the brain’s blood supply is cut off and it ceases to function. [Google Scholar] Associated Data Mar 23, 2022 · The concern of brain stroke increases rapidly in young age groups daily. So, it is imperative to create a novel ML model that can optimize the performance of brain stroke prediction. The leading causes of death from stroke globally will rise to 6. 81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44. The objective is to create a user-friendly application to predict stroke risk by entering patient data. May 13, 2023 · This document summarizes a student project on stroke prediction using machine learning algorithms. European Journal of Electrical Engineering and Computer Science, 7(1 Stroke occurs when our brain's blood flow is stopped or reduced, restricting brain tissue from receiving oxygen and important nutrients. View Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. Stroke, a leading neurological disorder worldwide, is responsible for over 12. txt) or view presentation slides online. Stroke is a destructive illness that typically influences individuals over the age of 65 years age. Nov 29, 2024 · The document describes a proposed intelligent career guidance system using machine learning. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. An ML model for predicting stroke using the machine The data used in this project are available online in educational purpose use. They are explained below: Declaration We hereby declare that the project work entitled “Brain Stroke Prediction by Using Machine Learning” submitted to the JNTU Kakinada is a record of an original work done References [1] Manish Sirsat Eduardo Ferme, Joana Camara, “Machine Learning for Brain stroke: A Review, ” Journal of stroke and cerebrovascular disease: the official journal of National Stroke Association(JSTROKECEREBROVASDIS), 20220 [2] Harish Kamal, Victor Lopez, Sunil A. The authors examine would have a major risk factors of a Brain Stroke. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic stroke patients. There are two primary causes of brain stroke: a blocked conduit (ischemic stroke) or blood vessel spilling or blasting (hemorrhagic stroke Keywords: cerebrovascular disease, deep learning, machine learning, reinforcement learning, stroke, stroke therapy, supervised learning, unsupervised learning Introduction Stroke is one of the most common and devastating disorders, a leading cause of disability, and the second leading cause of death worldwide cause, with approximately 5. ARUNA VARANASI3, ADIMALLA PAVAN KUMAR4, BILLA CHANDRA KIRAN5, V. Machine learning (ML) techniques have been extensively used in the healthcare industry to build predictive models for various medical conditions, including brain stroke, heart stroke and diabetes disease. HRITHIK REDDY6 1, 2 Assistant Professor, Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Telangana. Dataset The dataset used in this project contains information about various health parameters of individuals, including: Apr 26, 2024 · Brain Tumor Detection Using Deep Learning ppt new made. I. Dependencies Python (v3. As a result, early detection is crucial for more effective therapy. 5 approach, Principal Component Analysis, Artificial Neural Networks, and Support Vector Machine. 5 The algorithms present in Machine Learning are constructive in making an accurate prediction and give correct analysis. Stroke can be predicted by analyzing different warning signs. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms. Machine learning (ML) techniques have gained prominence in recent years for their potential to improve healthcare outcomes, including the prediction and prevention of stroke. It is a big worldwide threat with serious health and economic implications. In this paper, we present an advanced stroke detection algorithm Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. Machine learning algorithms are The situation when the blood circulation of some areas of brain cut of is known as brain stroke. Treatment of stroke disease is very crucial. Brain Stroke is a long-term disability disease that occurs all over the world and is the leading cause of death. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. The workspreviously performed on stroke mostly include the ones on Heart stroke prediction. Topics In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Face to this Machine learning algorithms have shown promising potential in predicting stroke occurrences based on various risk factors. B. The prediction of stroke using machine learning algorithms has been studied extensively. Manikandan S. The results of several laboratory tests are correlated with stroke. The key points are: 1. If a stroke is identified early enough, it is possible to receive the appropriate therapy and recover from the stroke. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. : Prediction of stroke thrombolysis outcome using CT brain machine learning. The study uses synthetic samples for training the support vector machine (SVM) classifier, and then, the testing is conducted in Oct 1, 2024 · 1 INTRODUCTION. Machine learning applications are becoming more widely used in the health care sector. There have lots of reasons for brain stroke, for instance, unusual blood circulation across the brain. Feb 23, 2024 · The research contributes to the growing literature on machine learning applications in healthcare by presenting a holistic approach to stroke prediction. It provides an overview of machine learning and its applications in neuroimaging and brain stroke detection. In this research work, with the aid of machine learning (ML Nov 19, 2023 · The proposed work aims to develop a model for brain stroke prediction using MRI images based on deep learning and machine learning algorithms. 2020;29(5):7976–7990. , et al. Worldwide, it is the second major reason for deaths with an annual mortality rate of 5. The dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. It's much more monumental to diagnostic the brain stroke or not for doctor, but the main A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. 2% and precision of 96. Dec 1, 2022 · Stroke is one of the most serious diseases worldwide, directly or indirectly responsible for a significant number of deaths. The predictions resulting from this model can save many lives or give people hints on how they can protect themselves from the risk. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. It primarily occurs when the brain's blood supply is disrupted by blood clots, blocking blood flow, or when blood vessels rupture, causing bleeding and damage to brain tissue. Various data mining techniques are used in the healthcare industry to Jul 28, 2020 · Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. Out of all CVDs, the stroke was considered as the dangerous disease as it is directly linked to the brain. Oct 18, 2023 · Brain Stroke Prediction Machine Learning. Prediction of stroke is a time consuming and tedious for doctors. This study aimed to address some of the limitations of previous studies by Nov 1, 2022 · Hung et al. This report explores the use of Machine Learning (ML) techniques to predict the likelihood of stroke based on patient health data. It's much more monumental to diagnostic the brain stroke or not for doctor, but the main Brain Stroke Prediction Using Machine Learning Approach DR. Therefore, the aim of Nov 29, 2024 · This document describes a study that developed a machine learning model to predict heart disease risk and provide recommendations. 1% during the forecast period. Implementing a combination of statistical and machine-learning techniques, we explored how Jun 25, 2020 · The main objective of this study is to forecast the possibility of a brain stroke occurring at an early stage using deep learning and machine learning techniques. drop('id', axis=1, inplace=True) # Filling the NaN Values # For the NaN Values, there are various methods that can be applied, we Sep 26, 2024 · This document discusses decision tree regression for predicting salary based on position level. in [17] compared deep learning models and machine learning models for stroke prediction from electronic medical claims database. ” Aug 20, 2024 · This study focuses on the intricate connection between general health, blood pressure, and the occurrence of brain strokes through machine learning algorithms. predicting the occurrence of a stroke can be made using Machine Learning. An early intervention and prediction could prevent the occurrence of stroke. It shows how to import data, build a decision tree regression model using scikit-learn in Python and rpart in R, make predictions, and plot the results. It discusses algorithms like decision trees, XGBoost and SVM that will be used to classify students into suitable career paths based on their academic performance, skills and other attributes. Oct 1, 2020 · Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. Depending on the area of the brain affected and amount of time, the blood supply blockage or bleeding can cause permanent damage or even lead to death. The accuracy of the naive Bayes classifier was 85. Every year, more than 15 million people worldwide have a stroke, and in every 4 minutes, someone dies due to stroke. International Journal of Advanced Science and Technology . The model has been trained using a comprehensive dataset and has shown promising results in accurately predicting the likelihood of a brain stroke. This 6364e8cketans Ppt Stroke Prediction - Free download as Powerpoint Presentation (. The document provides background on strokes, machine learning applications to neuroimaging, and describes the data acquisition and testing methodology used in the study. This causes the brain to receive less oxygen and nutrients, which damages brain cells begin to deteriorate. , Dhanalakshmi P. Using the publicly accessible stroke prediction dataset, the study measured four commonly used machine learning methods for predicting brain stroke recurrence, which are as follows: (i) Random forest (ii) Decision tree (iii) The brain stroke prediction module using machine learning aims to predict the likelihood of a stroke based on input data. The proposed machine Jan 7, 2024 · # Plus, we won't use the id column, so we can drop it. Note: Machine Learning (ML), Computerized Tomography (CT), Area Under receiver-operating-characteristic Curve (AUC), Artificial Neural Network (ANN) and Support Vector Machine (SVM), Residual Neural Network (ResNet), Structured Receptive Fields (RFNN), auto-encoders The situation when the blood circulation of some areas of brain cut of is known as brain stroke. The system uses image processing and machine learning techniques to identify and classify stroke regions within the brain, aiming to provide early diagnosis and assist medical professionals in treatment planning. Apr 27, 2023 · This document presents a project that aims to predict the chances of stroke occurrence using machine learning techniques. To solve this, researchers are developing automated stroke prediction algorithms, which would allow for early intervention and perhaps save lives. So, the prediction of stroke is significant for early intervention and treatment. 03 Billion in 2016 to USD 8. pptx - Download as a PDF or view online for free Oct 18, 2023 · Brain Stroke Prediction Machine Learning. This system can aid in the effective design of sentiment analysis systems in Bangla. 7 million yearly if untreated and undetected by early The brain is the most complex organ in the human body. Read less Early Prediction of Brain Stroke Using Machine Learning Kalaiselvi. The number of people at risk for stroke Stroke Prediction Using Machine Learning (Classification use case) machine-learning model logistic-regression decision-tree-classifier random-forest-classifier knn-classifier stroke-prediction Updated Jan 11, 2023 6 days ago · Early identification of strokes using machine learning algorithms can reduce stroke severity & mortality rates. In this section, we will present the latest works that utilize machine learning techniques for stroke risk prediction. Jun 21, 2022 · In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. MAMATHA2, DR. From 2007 to 2019, there were roughly 18 studies associated with stroke diagnosis in the subject of stroke prediction using machine learning in the ScienceDirect database [4]. We employed six [4] “Prediction of stroke thrombolysis outcome using CT brain machine learning” - Paul Bentley, JebanGanesalingam, AnomaLalani, CarltonJones, KateMahady, SarahEpton, PaulRinne, PankajSharma, OmidHalse, AmrishMehta, DanielRueckert - Clinical records and CT brains of 116 acute ischemic stroke patients Explore and run machine learning code with Kaggle Notebooks | Using data from National Health and Nutrition Examination Survey Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Nov 26, 2021 · Stroke is a medical disorder in which the blood arteries in the brain are ruptured, causing damage to the brain. - lekh-ai/Brain-Stroke-Research This project involves developing a system to detect brain strokes from medical images, such as CT or MRI scans. The results obtained demonstrated that the DenseNet-121 classifier performs the best of all the selected algorithms, with an accuracy of 96%, Recall of 95. Keywords - Machine learning, Brain Stroke. In this thorough analysis, the use of machine learning methods for stroke prediction is covered. The algorithms present in Machine Learning are constructive in making an accurate prediction and give correct analysis. The study used a decision tree algorithm and the Cleveland heart disease dataset to train a model. In the data preprocessing module, the Mar 20, 2019 · Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. This paper is based on predicting the occurrence of Dec 16, 2022 · Our approach yields a machine learning accuracy of 65. 7% respectively. A [4], Prasanth. In this study, we propose the utilization of Random Forest and AdaBoost algorithms for brain stroke prediction The goal of this study is to develop a brain stroke prediction model using the Random Jul 1, 2019 · To detect the relationship between potential factors and the risk of stroke and examine which machine learning method significantly can enhance the prediction accuracy of stroke. A stroke is generally a consequence of a poor Keywords—Accuracy, Data preprocessing, Machine Learning, Prediction,Stroke I. KADAM1, PRIYANKA AGARWAL2, Brain Stroke Prediction Using Machine Learning Approach Author: Nov 26, 2021 · The most common disease identified in the medical field is stroke, which is on the rise year after year. stroke_df. The students collected two datasets on stroke from Kaggle, one benchmark and one non-benchmark. Five Jun 25, 2021 · This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. Computer aided diagnosis model for brain stroke classification in MRI images using machine learning algorithms. Stroke, a medical emergency that occurs due to the interruption of flow of blood to a part of brain because of bleeding or blood clots. Oct 1, 2020 · Machine learning techniques for brain stroke prognostic or outcome prediction. ppt / . 02% using LSTM. Mar 30, 2019 · Methodology • Learning Algorithms for Prediction • Margin-based Censored Regression SVM Experiments • Data Imputation • Feature Selection Experiments • Stroke Prediction Using machine learning algorithms to analyze patient data and identify key factors contributing to stroke occurrences. Nov 21, 2024 · This document discusses the use of machine learning techniques for detecting brain strokes using MRI scans. It consists of several components, including data preprocessing, feature extraction, machine learning model training, and prediction. published in the 2021 issue of Journal of Medical Systems. The title is "Machine Learning Techniques in Stroke Prediction: A Comprehensive Review" Mehta, Adhikari, and Sharma are the authors. INTRODUCTION Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. S. Many predictive strategies have been widely used in clinical decision-making, such as forecasting disease occurrence, disease outcome BRAIN STROKE PREDICTION USING SUPERVISED MACHINE LEARNING 1 Kallam Bhavishya, 2Shaik. In any of these cases, the brain becomes damaged or dies. Stroke causes the unpredictable death and damage to multiple body components. Jun 21, 2022 · A stroke is caused when blood flow to a part of the brain is stopped abruptly. Machine Learning for Brain Stroke: A Review Manisha Sanjay Sirsat,* Eduardo Ferme,*,† and Joana C^amara, *,†,‡ Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized clinical care for stroke patients. et al. Ischemic Stroke, transient ischemic attack. Seeking medical help right away can help prevent brain damage and other complications. S [5] Department of Artificial Intelligence and Data Science, Sri Sairam Engineering College - Chennai ABSTRACT Brain stroke is one of the driving causes of death and disability worldwide. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though Sep 2, 2011 · The study aims to determine if machine learning can provide accurate predictions of recovery and identify which areas of the brain images inform the predictions. RELATED MACHINE LEARNING APPROACHES In this section, analysis and review is being done on the previously published papers related to work on prediction of stroke types using different machine learning approaches. 2. In recent times, stroke can be often seen Mar 4, 2022 · Stroke, also known as a brain attack, happens when the blood vessels are blocked by something or when the blood supply to the brain stops. This project describes step-by-step procedure for building a machine learning (ML) model for stroke prediction and for analysing which features are most useful for the prediction. Brain stroke is a serious medical condition that needs timely diagnosis and action to avoid irretrievable harm to the brain. It is the world’s second prevalent disease and can be fatal if it is not treated on time. It causes significant health and financial burdens for both patients and health care systems. stroke at its early stage. When the supply of blood and other nutrients to the brain is interrupted, symptoms Apr 12, 2024 · Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1. An application of ML and Deep Learning in Mar 20, 2019 · Background and Purpose— The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. This study presents a new machine learning method for detecting brain strokes using patient information. The models obtained from this research are just a A brain stroke happens when blood flow to a part of the brain is interrupted or reduced. pptx), PDF File (. The main objective of this study is to forecast the possibility of a brain stroke occurring at Five machine learning techniques were applied to the Cardiovascular Health Study (CHS) dataset to forecast strokes. To achieve this, we have thoroughly reviewed existing literature on the subject and analyzed a substantial data set comprising stroke patients. Firstly, the authors in applied four machine learning algorithms, such as naive Bayes, J48, K-nearest neighbor and random forest, in order to detect accurately a stroke. Keywords: Stroke, Thrombolysis, Prediction, Machine learning, Imaging efficient in the decision-making processes of the prediction system, which has been successfully applied in both stroke prediction [1-2] and imbalanced medical datasets [3]. AMOL K. Methods— This Dec 31, 2024 · Prediction of brain stroke using machine learning algorithms and deep neural network techniques. Five different algorithms are used and compared to achieve better accuracy. Apr 16, 2023 · Heart Stroke Prediction using Machine Learning Vinay Kamutam *1 , Marneni Yashwant *2 , Prashanth Mulla *3 , Akhil Dharam *4 *1 Computer Science and Engineering, Sir Padampat Singhania University A stroke occurs when the blood supply to a person's brain is interrupted or reduced. We searched PubMed, Google Scholar, Web of Science, and IEEE Xplore ® for relevant articles using various combination of the following key words: “machine learning,” “artificial intelligence,” “stroke,” “ischemic stroke,” “hemorrhagic stroke,” “diagnosis,” “prognosis,” “outcome,” “big data,” and “outcome prediction. Sheth, “Machin e Learning in Acute Ischemic Stroke Neuroimaging, ” Frontiers in Neurology (FNEUR) 2018. P [3], Elamugilan. At least, papers from the past decade have been considered for the review. 1-3 Deprivation of cells from oxygen and other nutrients during a stroke leads to the death of A stroke or a brain attack is one of the foremost causes of adult humanity and infirmity. INTRODUCTION When a blood vessel bleed or blockage lowers or stops the flow of blood to the brain, a stroke ensues. In this experiment, we implement a process of stroke risk prediction Jun 3, 2023 · Bentley, P. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. 5 million. Nov 2, 2023 · About 18 million people die every year due to cardio vascular diseases (CVDs) such as heart stroke and heart attack. The primary objective of this study is to develop and validate a robust ML model for the prediction and early detection of stroke in the brain. An application of ML and Deep Learning in health care is growing however, some research areas do not catch enough attention for scientific investigation though there is real need of research. Nov 2, 2023 · This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open . In addition to conventional stroke prediction, Li et al. NeuroImage Clin. Results indicate that while random forest achieves high accuracy, logistic regression provides a balanced sensitivity-specificity trade-off. Althaf Rahaman 1 PG Student, 2Assistant Professor 1 Department of Computer Science, 1GITAM (Deemed to be University), Visakhapatnam, India Abstract: A Stroke is a medical disorder that damages the brain by rupturing blood vessels. This paper is based on predicting the occurrenceof a brain stroke using Machine Brain Stroke Prediction Using Machine Learning and Data Science VEMULA GEETA1, T. However, no previous work has explored the prediction of stroke using lab tests. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. P [1], Vasanth. Dec 5, 2021 · Methods. Brain stroke segmentation in magnetic resonance imaging (MRI) has become an evolving research area in the field of a medical imaging system. G [2], Aravinth. Stroke 22(3), 312–318 (1991) Google Scholar A stroke, also known as a cerebrovascular accident or CVA is when part of the brain loses its blood supply and the part of the body that the blood-deprived brain cells control stops working. pdf), Text File (. May 22, 2023 · Stroke is a dangerous medical disorder that occurs when blood flow to the brain is disrupted, resulting in neurological impairment. Methods— This Mar 2, 2024 · Brain stroke is a Cerebrovascular accident that is considered as one of the threatening diseases. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods. This study proposes an accurate predictive model for identifying stroke risk factors. It can also happen when the Stroke is a leading cause of disability and death worldwide, often resulting from the sudden disruption of blood supply to the brain. Nowadays, it is a very common disease and the number of patients who attack by brain stroke is skyrocketed. Dec 1, 2021 · The document summarizes a disease prediction system for rural health services presented by two students. It's a medical emergency; therefore getting help as soon as possible is critical. Very less works have been performed on Brain stroke. The May 23, 2024 · Ismail and Materwala analyzed stroke data under an intelligent stroke prediction framework and compared five common machine learning algorithms: decision tree, random forest, support vector machine, naive Bayes, and logistic regression; and random forest gave the best results on the stroke test data set. Healthcare is a sector The organ known as the brain, which is securely protected within the skull and consists of three main parts, namely the cerebrum, cerebellum, and brainstem, is an incredibly complex and intriguing component of the human body. The prediction model takes into account Mar 15, 2024 · The talk covers traditional machine learning versus deep learning, using deep convolutional neural networks (DCNNs) for image analysis, transfer learning and fine-tuning DCNNs, recurrent neural networks (RNNs), and case studies applying these techniques to diabetic retinopathy prediction and fashion image caption generation. This research present the detection and segmentation of brain stroke using fuzzy c-means clustering and H2O deep learning algorithms. Stroke, a condition that ranks as the second leading cause of death worldwide, necessitates immediate treatment in order to prevent any potential damage to the brain. Mar 8, 2024 · This project involves developing a system to detect brain strokes from medical images, such as CT or MRI scans. 85% and a deep learning accuracy of 98. After the stroke, the damaged area of the brain will not operate normally. In this work, we compare different methods with our approach for stroke Nov 24, 2022 · Based on machine learning, this paper aims to build a supervised model that can predict the presence of a stroke in the near future based on certain factors using different machine learning classification methods. : MDProbability of stroke: a risk profile from the Framingham study. Therefore, the project mainly aims at predicting the chances of occurrence of stroke using the emerging Machine Learning techniques. Using a machine learning algorithm to predict whether an individual is at high risk for a stroke, based on factors such as age, BMI, and occupation. in [18] used machine learning approaches for predicting ischaemic stroke and thromboembolism in atrial fibrillation. To get the best results, the authors combined the Decision Tree with the C4. 7) Oct 12, 2022 · In this study, we develop a machine learning algorithm for the prediction of stroke in the brain and this prediction is carried out from the real-time samples of electromyography (EMG) data as illustrated in Figure 3. 6% made using Machine Learning. Stroke, a cerebrovascular disease, is one of the major causes of death. The works previously performed on stroke mostly include the ones on Heart stroke prediction. 4, 635–640 (2014) Google Scholar Philip, A.
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