July 2021 International Journal of Innovative Technology and Creative Engineering (ISSN:2045-8711)

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.7 JULY 2021

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UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK

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IJITCE PUBLICATION

International Journal of Innovative Technology & Creative Engineering Vol.11 No.7 July 2021

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.7 JULY 2021

Dear Researcher, Greetings!

Articles in this issue discusses about Overview of Handling Imbalanced Datasets in Machine Learning, Measurement of gastric motility by using infrared light for the assessment and classification of chronic stress.

We look forward many more new technologies in the next month.

Thanks, Editorial Team IJITCE

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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia.

Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Technology Architect, Healthcare and Insurance Industry, Chicago, USA Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA. Dr. S.Prasath Ph.D Assistant Professor, Department of Computer Science, Nandha Arts & Science College, Erode , Tamil Nadu, India Dr. P.Periyasamy, M.C.A.,M.Phil.,Ph.D. Associate Professor, Department of Computer Science and Applications, SRM Trichy Arts and Science College, SRM Nagar, Trichy - Chennai Highway, Near Samayapuram, Trichy - 621 105, Mr. V N Prem Anand Secretary, Cyber Society of India

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Review Board Members Dr. Rajaram Venkataraman Chief Executive Officer, Vel Tech TBI || Convener, FICCI TN State Technology Panel || Founder, Navya Insights || President, SPIN Chennai Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE. Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore.

Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India.

Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India.

Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE Professor, JSPM's Rajarshi Shahu College of Engineering, MCA Dept., Pune, India.

Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21

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Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21

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Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ.

Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America

Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.7 JULY 2021 Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688

Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.

Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.

Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India.

Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India.

Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India.

Mr. Md. Musfique Anwar B.Sc(Engg.)

Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh.

Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom.

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Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India.

Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.7 JULY 2021 Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 558123042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India.

MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran.

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Contents Overview of Handling Imbalanced Datasets in Machine Learning,……………………………...….…. [ 996 ] Measurement of gastric motility by using infrared light for the assessment and classification of chronic stress …..……….……………. [ 1002 ]

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OVERVIEW OF HANDLING IMBALANCED DATASETS IN MACHINE LEARNING D. Kavitha1 and Dr. R. Ramkumar2 1. Research Scholar, Computer Science, Nandha Arts and Science College, Erode.

E-mail: kavitha.d@nandhaarts.org 2. School of Computer Science, VET Institute of Arts and Science (Co-education) College, Erode

Abstract

Abstract— Data mining and Machine learning are the most important research areas which attracts researchers. Class imbalance problem plays a vital role in data mining. In real world, technology evolution makes increase in data in various problems.

This is termed as data velocity and volume.

In machine learning, the required accuracy level of imbalance data classification is not produced by various techniques. The number of observations differs for the classes in a classification dataset termed as imbalanced datasets which leads to inaccurate results.

Machine learning algorithms are powered by data and it’s important to have balanced datasets in a machine learning workflow. This paper describes the issues of imbalance datasets, differences between balanced and imbalanced datasets and various techniques for handling imbalance dataset problems. Of course, a single article cannot be a complete review of all the methods and algorithms, however hope that the references cited will cover the major theoretical issues which guiding the researcher in interesting research directions and suggesting conceivable combinations that have yet to be explored.

Keywords: balanced and imbalanced dataset, undersampling, oversampling and ensemble learning.

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Why is imbalance an issue?

In current years, imbalance problems have got even more prominent. In many practical domains, imbalanced datasets occur, such as spotting unreliable telecommunication customers, detection of oil spills in satellite radar images, learning word pronunciations, text classification, information retrieval, detection of fraudulent telephone calls and filtering tasks, and so on. Machine learning algorithms are feed by data. Evan the best of the algorithms struggle to produce good results in the absence of a good quality dataset. The major differences in the distribution of the classes in the dataset is used to define an imbalanced dataset which means that a dataset is biased towards a class in the dataset. An algorithm trained on the same data will be biased towards the same class, if the dataset is biased towards one class. The model learns more from biased examples as opposed to the examples in the minority class. There may be circumstances where the model assumes any data fueled to it belongs to the minority class. As a result, the model may seem unmatured in its predictions, regardless of achieving high accuracy scores[2].

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Balanced and imbalanced datasets A dataset whose distribution of labels is roughly alike is referred as a balanced dataset. Labels in this context refer to a class associated with each data point. For instance, consider a dataset with two classes as female and male. If around half the distribution signifies the male class and the supplementary half represents the female class, then the dataset is balanced.

Fig.1. Example of balanced and imbalanced data The distribution of an imbalanced dataset is pigeon-holed by very high differences amongst the classes involved. an imbalanced dataset may have a very high difference between the two classes, for example, the dataset of male and female classes mentioned above.

Techniques There are methods to rectify the mentioned imbalance for to prevent the impact of imbalanced datasets. some are listed below.

Oversampling Technique to amend unequal classes of data to create balanced datasets is called oversampling. This technique shot to rise the size of erratic samples to create a balance

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when the data is not sufficient. For example, study a classification problem with two classes and 100k data points. Positive class has 20k data points and the negative class has 80k data points. The minority class i.e., the positive class needs to be oversampled.

For this the 20k data points are replicated four times to yield 80k. Hence, equal number of both positive and negative classes are created. 160K would be the size of the dataset as a result of oversampling. In the above cited example balance is accomplished no new or extra data is supplemented to the model. Oversampling is carried out by a technique Synthetic Minority Over-Sampling Technique (SMOTE) [1]. The approach is increased in the technique where the balance is obtained by duplicating the examples in a minority class. SMOTE synthesizes new examples as conflicting to duplicating examples. SMOTE opts for examples that happen to be in juxtaposition in a feature space. It then receipts a new example at a point along the segment, joining the adjacent examples.

A classification approach where the likelihood of a data point belongs to one group or another depending on the data points in closest proximity to the data point is referred to as k-NN. SMOTE picks an instance of the minority class at random and computes its k-NN. A neighbour to it is then preferred aimlessly. After that, a synthetic example is formed at a point selected at random amongst the two examples. For a minority class to create balance, this process can generate as many synthetic examples as needed. A merit of oversampling is that there is no data loss from the original training dataset. All the data from both minority and majority classes are used. Hitherto the demerit of oversampling is that it causes overfitting

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. Fig.2. Generation of Synthetic Instances with the help of SMOTE Undersampling When a class that exists is in copiousness, to balance the dataset undersampling focuses to reduce the size of copious class. For instance, study a similar situation of the undersampling technique where there is a classification problem with two classes and 100K data points. The positive class is of 20K data points, the negative class is of 80K data points.

To balance the dataset undersampling the majority class is required.

This results in choosing 20K data points aimlessly from the 80K available. Hence 20K positive and 20K negative data points are obtained, adding up to the total dataset size of 40K data points. Tomek links is an existing method for classifications problems which shots to improve the accuracy of data classification. Removal of as much as possible class label noise is done. Class noise is a type of label noise that changes the instances of labels assigned. By considering an example of assigning a positive label for a negative instance when a higher probability of being incorrect instances could be removed by tomex links even borderline examples referred as tomex link removal.

Tomex link are points of different class labels which are closest neighbors to each other.

Subsequently this technique makes it

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conceivable to identify points near different class labels, it is easy to get rid of them until none are left. In terms of undersampling, tomex link removal technique gets rid of unwanted overlap between classes.

Neighbors of the same class in close proximity is considered as the result. The dispute of support vector machine algorithm can function similarly to the Tomek links method. SVM algorithm is very effective at classification tasks since it finds a hyperplane decision boundary that separates examples into two categories. For both classes the hyperplane is placed equidistantly. To maximizing the distance between the boundary and nearest examples from each class by which the hyperplane margin is defined. However, a distinguishing aspect between Tomek links and SVMs is that SVMs are ineffective at imbalanced classification also very sensitive in imbalanced datasets and produce a less than optimal performance[6].

Ensemble learning An ensemble-based method plays a vital role to deal imbalanced datasets. The acceptance is that multiple learning methods are more operative than a single one. It’s an approach that combines the performance or results of many classifiers to better the performance of a single classifier. Let’s briefly define few of ensemble methods: Bagging– This method attempts to apply similar learners on tiny sample populations which takes the mean of all predictions made [3][4].

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.11 NO.7 JULY 2021

Stacking It is an attractive way of combining models which works on the basis of a learner to combine output from different learners and it can lead to decrease in either bias or variance error depending on the combining learner could be used.

Fig. 3. Ensemble with Bagging Boosting A technique that adjusts an observation’s weight based on the most recent classification. Boosting attempts to rise the weight of an observation that has been incorrectly classified. This is an iterative process, since new models are then trained on the inefficiencies of prior models. This makes the newer models better and stronger than the previous ones. The resulting ensemble has many machine learning models. These models boast different accuracies and can provide better accuracies when used together. Boosting also reduces bias error of the models[3][4].

Fig.5. Ensemble with Stacking The right evaluation metrics Whenever there is an imbalance in class labels, that means that the classification accuracy metric is not ideal for model performance[5]. What do paper mean by this classification accuracy is a good metric when the samples belonging to each class are equal in number. Consider a scenario with 93% of samples from class C and 7% from class F in a training set. A model can simply achieve 93% training accuracy by predicting each training sample in class C. This is even assuming that it fails to predict any samples in class F correctly. Therefore, when dealing with imbalanced datasets, it’s wise to use the correct evaluation metrics. Accordingly, it’s not advisable to use accuracy as a measure of performance and may consider the metrics such as F1-score, precision, and recall.

Fig.4. Ensemble with Boosting

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Precision is the number of true positives against the total positive results predicted by a classifier.

TruePositives Precision= ____________________ TruePositives+FalsePositives The recall is the number of true positives divided by all the samples that should have been positive.

TruePositives Recall = ______________________ TruePositives+FalseNegatives F1-score shows us how accurate a model is by showing how many correct classifications are made. F1-score has a range between 0 and 1. The greater the score, the better the performance of the model.

Conclusion Literally advisable for the researchers to collect huge data with low distribution ratio to deal an imbalanced dataset on any.

However, data collection is often an expensive, tedious, and time-consuming process. Imbalanced datasets can deceive both human beings and the model itself into believing that it generalizes well. To avoid such a scenario, it is important to understand how to correct the datasets imbalance. Paper has explored a few of the possible techniques to carry out this correction. However, the choice of technique is dependent on the nature of the problem which should be taken into consideration.

2∗Precision∗Recall F1−score=

______________________ Precision+Recall

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REFERENCES

[1] Jason Brownlee “SMOTE for Imbalanced Classification with Python”, machine learning mastery, January 17, 2020 [2] Rushi Longadge et.al “Class Imbalance Problem in Data Mining: Review”, International Journal of Computer Science and Network (IJCSN) Volume 2, Issue 1, February 2013 [3] Graczyk M, Lasota T, Trawinski B, Trawinski K. “Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal”, Asian conference on intelligent information and database systems. 2010. pp. 340–50.

[4] Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F ” A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches”, IEEE Trans Syst Man Cybern C Appl Rev. 2012;42(4):463–84 [5] Learning from imbalanced data. Retrieved from https://www.jeremyjordan.me/imbalanceddata/ [6] Benoît Frénay et al , “Classification in the Presence of Label Noise: A Survey”, IEEE Transactions on Neural Networks and Learning Systems 25(5):845-869, 2014.

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MEASUREMENT OF GASTRIC MOTILITY BY USING INFRARED LIGHT FOR THE ASSESSMENT AND CLASSIFICATION OF CHRONIC STRESS Menakaa P #1, Muhammadu Sathik Raja M S *2, Thiyagarajan N #3 #

Department of medical electronics, Sengunthar College of Engineering India 1 pmymmenaka@gmail.com.

3 thiyaguec@gmail.com. * Department

of medical electronics, Sengunthar College of Engineering, India 2 scewsadik@gmail.com

Abstract—This study is regarding the assessment of chronic stress by finding the gastric motility with the assistance of infrared rays. Measure of stress will be notice by numerous strategies like ele ctrogastrography and echogram.

However these aren't appropriate for home use. In order that the most issue is to develop a non-invasive and compact measure system of gastric motility by infrared.

This method consists of IR LEDs as supply and avalanche photodiode (APD) as detector.

APD receives that transmission from IR LEDs and reflective on the stomach wall. AI is that the projected technique of classifying humans stress as a result of it's supported existing information.

This thesis use NN for classifying the stress by the comparison of results with these computer science techniques.

Keywords: Artificial intelligence, Avalanche photodiode, chronic stress, Gastric motility, Infrared rays I.

INTRODUCTION

Stress could be a natural physical and mental reaction to life experiences.

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Everybody expresses stress from time to time. For immediate, short things, stress will be useful to your health. However if your stress response doesn’t stop firing, and these stress-level keep magnified way longer than is critical for survival it will cause severe health problems. Chronic stress will cause a spread of symptoms and have an effect on your overall well-being. For locating this we tend to measure the gastric motility.

A. Gastric Motility Gastric motility refers to the movement of food from the mouth through the throat, esophagus, stomach and intestine.

Here we tend to area unit finding the motility within the space of pyloric region.

The movement of food within the space between the small intestine and small intestine.

B. Case based Reasoning Case-based reasoning (CBR) is one in all the main elements of the present project.

In most literatures case based reasoning is outlined as a problem solving paradigm wherever past experiences guide

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problem determination. At the best level of generality, four processes: Retrieve, Reuse, Revise and Retain C. Neural Network Artificial neural network (ANN) could be a biologically inspired structure of process parts referred to as neurons. The network practicality is decided by the design and also the kind of neurons utilized.

II. RELATED WORK In this chapter some fascinating comes are going to be shown that area unit addressing stress detection from SC signals. The term SC applies to the phenomena of exosomatic activity of the skin once external voltage is applied. Once measurement SC ,two principals are vital. Skin Conductance level (SCL) which is associated with the specific amount of continuity over time (tonic value) and Skin Conductance Response (SCR) stands for the changes in SC. Studies have shown that SCR could be sensible of stress, anxiety and anger. Another development is that the non specific SCR fluctuations (NS.SCR) occur with no external stimulations. NS.SCR has an equivalent or a really similar form as specific SCR. SCR are often characterized by four parameters: Amplitude (SCR amp), Latency of response onset (SCR lat), Rise time of the response peak (SCR ris.t), and also the half time of the recovery time (SCR rec.tc) as shown in Fig. 1

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Fig 1. Schematic Skin Conductance Response (SCR) to a hypothetical stimulus

Possible conductor sides area unit the feet and also the palms. once hands used, typically the non- dominant is most well-liked. doable sites on the palm area unit the medial phalanx or the thenar and hypothenar eminences as shown in Fig. 2

Fig 2. Palmar electrode For the signal recording the projected manner is by applying constant dc voltage of zero,5V to the 2 electrodes and sixteen bit resolution (or higher) for the ADC conversion. Off line strategies will later be accustomed split the signal into the SCR and SCL values.

Ideally, associate conductor with double sided adhesive collar is needed alongside associate conductor paste associated at an surroundings of 24-26 astronomer and five hundredth humidness.

Conjointly movements ought to be avoided. A crucial analysis has been done at that shows the signal behavior once the device isn’t tight enough, or once some disturbance occur, if the topic is doing exercise or once device isn’t properly connected see Fig. 3

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Fig 3. Change of signal due to factors other than stress For gathering the signals the topic was having the device connected as a clock watch. It’s obvious that sensors embodied in car’s steering or maybe on the mechanical device can manufacture signals troublesome to decipher. Doable resolution for this, is sweet preprocessing and also the use of a second or additional external stress device signals to participate to the systems output.

Finally the SCR form in information, as shown in figure 1, pushed researchers to use the corresponding detection technique.

III. EXPERIMENT A.

Problem Formulation and Analysis

In a fashionable society, mental stress is inevitable major problem and mental care become necessary. From now of several studies are dedicated to the quantitative observance of mental stress. The mental stress influences the gastric motility. The aim of this study is to develop a mensuration system of human gastric motility for quantification of mental stress and to classify them with the assistance of neural network.

A mental stress mensuration system ought to be used simply in lifestyle. However, general measurements systems as CT, supersonic echogram and medical instrument aren’t appropriate for home use. We’ve developed a non invasive and compact gastric motility mensuration system victimization near infrared light rays. Near infrared particularly wavelength of 700~1200nm incorporates a feature of comparatively low scattering and coefficient to skin and internal organs.

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Therefore, it simply transmits into deep internals.

Additionally, NIR makes mensuration system comparatively compact . Hence, we have a tendency to used NIR during this study. Traditional contracted activity of human abdomen happens at concerning 3c/m. Particularly once ingestion, this contracted activity will increase and additionally its amplitude will increase. This activity continues for 3 to 5 hours. Gastric motility is believed to be simply detected.

Here we have a tendency to area unit aiming to use a ray having 900nm and it transmit for 10µs with 50 Hz.

B.

L293D

L293D could be a typical motor driver or motor driver IC that permits DC motor to drive on either direction. L293D could be a 16 pin IC which may manage a group of 2 DC motors at the same time in any direction.

Dual H- bridge motor driver integrated circuit (IC). It works on the thought of H-bridge. Hbridge could be a circuit that permits the voltage to be flown in either direction. As you recognize voltage got to amendment its direction for having the ability to rotate the motor in right-handed or anticlockwise direction, thus H-bridge IC area unit ideal for driving a DC motor.

During a single L293D chip there area unit 2 h-Bridge circuit within the IC which may rotate 2 dc motor severally. Due its size it's considerably employed in robotic application for dominant DC motors. Given below is that the pin diagram of a L293D motor controller. There are unit 2 modify pins on l293d. Pin one and pin nine, for having the ability to drive the motor, the pin one and nine got to be high. For driving the motor with left H-bridge you would like to modify pin one to high. And for right

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H-Bridge you would like to create the pin nine to high. If anyone of the either pin1 or pin9 goes low then the motor within the corresponding section can suspend operating. It’s sort of a switch.

NodeMCU NodeMCU is open supply computer code for open supply supply prototyping board styles area unit out there. The name “NodeMCU” combines “node” and “MCU” properly speaking refers to the computer code instead of the associated development kits. Each the computer code and prototyping board styles area unit are open supply. The computer code uses the Lua scripting language. The computer code is predicated on the eLua project, and designed on the Espressif Non-Os SDK for ESP8266.it uses several open supply comes like lua-cjson and SPIFFS. Support for the 32-bit ESP32 has additionally been enforced. The prototyping hardware usually used could be a card functioning as a twin inline package (DIP) that integrates a USB controller with a smaller surface mounted board containing the MCU and antenna.

C.

IR LED IR semiconductor diode (infrared light emitting diode) could be a state lighting (SSL) device that emits light within the infrared varies of electromagnetic wave spectrum.IR LEDs leave low cost, economical production of infrared ray, that is electromagnetic wave within the 700nm to 1mm vary. IR LEDs area unit helpful during a range of varieties of physical science, as well as many sorts of remote controls for televisions and different physical science.

Used with infrared cameras, IR LEDs will act sort of a spot light whereas remaining invisible to the eye. Due to IR LEDs is employed in conjunction with variety of various varieties of sensors, they're D.

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turning into common in machine-to-machine (M2M) environments and net of Things (IoT) applications.

Arduino software package Arduino is ASCII text file physical science platform supported easy-to-use hardware and software package. Arduino boards area unit ready to browse inputs-light on a detector, a finger on a button, or a twitter message – and switch it on outputactivating a motor, turning on semiconductor diode, business one thing online. The Arduino Integrated Development surroundings – or Arduino software package (IDE) - contain a text editor for writing code, a message space, a text console, a toolbar with buttons for common functions and a series of menus. It connects to the Arduino and Genuino hardware to transfer programs and communicate with them. Programs written victimization Arduino software package (IDE) area unit referred to as sketches. These sketches area unit written within the text editor and area unit saved with the file extension .ino. The editor has options for cutting/pasting and for searching/replacing text.

E.

The message space offers feedback whereas saving and commerce and additionally displays errors. The console displays text output by the Arduino software package (IDE), as well as complete error messages and different data. Rock bottom paw corner of the window displays the organized board and port. The toolbar buttons permit you to verify and transfer programs, create, open, and save sketches, and open the serial monitor.

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Sc slope

Feature CBR platform

My Cbr

” 1” correct,” 0” false classification 1st similar

Case

” 1” correct,” 0” false classification 2nd similar

case

” 1” correct,” 0” false classification

Fuzzy cbr

case

For each case a classification from the knowledgeable is shown (column 1) and is compared to the expert’s classification of the similar case (column three, 5, 7, 9) that the CMB platform produces.

“1” is inserted once the cases have similar knowledgeable classification and “0” once totally different.

The knowledgeable similarity row shows the proportion similarity of all the cases from the first and therefore the second similar case output of the CBR platforms.

The knowledgeable similarity total row is that the total proportion with zero, half dozen weights for the first similar case and

TABLE I SC SLOPE FEATURES ON CBR PLATFORMS AGAINST EXPERT’S CLASSIFICATION

” 1” correct,” 0” false classification 2nd similar

The Features on CBR platforms against expert classification In this section the results are shown from the output of the 2 CBR platforms and are compared to expert’s identification.

The comparison is restricted to stressed and traditional while not the subcategories terribly stressed and relaxed.

Table one and a pair of shows the comparison of SC and foot slope options on each CBR platforms whereas table three shows SCR on MyCBR solely as a result of the Fuzzy CBR platform is devoted to slope options. The result's a helpful metric of the potency of the ways beneath take a look at.

A.

What is shown in that the SC slope provides quite promising results with eighty three similarity to the knowledgeable classification on each fuzzy and Mycbr platforms. The foot slope conjointly provides sensible results that mean that SCR feature extraction algorithmic rule would like reexamination with the interpolation and 0 assignment techniques.

Stressed

1

5

1

6

0

5

1

11

1

Normal

2

13

16

5

10

18

1 1

15

3

0 1

13

Stressed

1 1

10

1 1

Normal

4

13

1

3

0

18

0

13

1

Stressed

5

1

1

3

1

1

1

11

1

Normal

6

1

1

11

0 1

7

11

0 1

1

7

0 1

7

Stressed

1

0 1

Stressed

8

7

1

11

1

11

1

2

0

Normal 9 Expert similarity (%)

10

0

6

1

10

0

18

0

Total (%)

Volvo case No 1st similar case

In this experiment we tend to performed a chronic stress assessment by finding the gastric motility. The experiment are conducted when a meal. In order that we will simply notice the gastric motility in patients.

Gastric motility activity is done once the patients are in rest position. From that we will classify the sort of stress by the CBR.

with zero, four weight of the second similar case. The 0, 6 and 0, four weights were arbitrary chosen for the creation of one overall score.

Expert classification

IV. RESULT

0,83 0,70

0,50

0,72

0,83

0,77

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TABLE II FT SLOPE FEATURE ON CBR PLATFORMS AGAINST EXPERT’S CLASSIFICATION

fuzzy cbr

classification

2nd similar case

” 1” correct,” 0” false

classification

Stressed

1

8

1

7

1

8

1

18

0

Normal

2

3

0

6

1

6

1

4

1

Stressed

3

2

0

6

0

15

0

14

1

Normal

4

2

1

3

0

15

1

10

0

Stressed

5

9

0

13

0

9

0

13

0

Normal

6

17

1

15

1

17

1

16

0

Stressed

7

3

1

2

0

15

0

4

0

Stressed

8

12

1

1

1

1

1

18

1

Normal 9 Expert similarity (%)

5

0

13

1

13

1

15

1

Total (%)

false

1st similar Case

” 1” correct,” 0” false

Mycbr

classification

Volvo case 1st similar No case ” 1” correct,” 0”

platform

” 1” correct,” 0” false

CBR

Expert classification

B. Neural Network Pattern and feature comparison

FT slope

classification 2nd similar case

Feature

0,61

0,56

0,59

0,83

Recognition

Here the pattern recognition tool of matlab was to classify the 3 totally different options for stress detection of the nine Volvo cases. In pattern recognition confusion perform calculates the true/false results from examination network output classification with target categories i.e practician knowledgeable. The Matlab code and therefore the network used are shown and therefore the confusion results are within the following figures 4, 5, and 6.

The results are as follows: foot slope sixty one.1%, SC slope thirty 8.9% and SCR thirty 8.9%. The results aren’t thus promising within the experiment section each pattern recognition and fitting tool manufacture sensing the results. The most reason is that solely nine cases were used whereas within the experiment. Also, NN acts as a black box manufacturing totally different end in every iteration.

0,39

0,66

TABLE III SCR FEATURES ON CBR PLATFORM AGAINST EXPERT’S CLASSIFICATION

CBR

Mycb platform r Features SCR Vol 1st Expert vo classificatio simila case n r case No Stressed

1

Fig 4. FT slope confusion ”1” correct,

”1” correct, 2nd similar ”0” false ”0” false classification case classification

9

0

6

1

13

0

Normal

2

15

0

Stressed

3

4

0

6

0

Normal

4

3

0

14

0

18

1

Stressed

5

1

1

Normal

6

3

0

1

0

1

1

Stressed

7

16

1

Stressed

8

11

1

1

1

Normal 9 Expert similarity (%)

4

1

1

0

Total (%)

0,56

0,61

0,58

Fig 5. SC slope confusion

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REFERENCES [1]

[2]

[3] [4] Fig 6. SCR confusion

[5] [6] [7] [8]

V. CONCLUSION

Begum S., Ahmed M.U., Funk P., Xiong N., Folke M, (2011), Case-Based Reasoning Systems in the Health Sciences: A Survey on Recent Trends and Developments, IEEE Trans on Systems, Man, and Cybernetics, Part C: Applications an Reviews, 41(4), 421-434.

Begum, S., Ahmed, M.U., Funk, P., Xiong, N., and Schéele, B.V, (2009), Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching, The Journal of Computational Intelligence (CI), vol 25, nr 3, p180-195(16),Blackwell Publishing.

Cheng M.-Y, H.-C. Tsai, Y.-H. Chiu, Journal (2009), Fuzzy case-based reasoning for copingwith construction disputes, Expert Systems with Applications.

Bakker Jorn, Mykola Pechenizkiy, Natalia Sidorova,(2011), ICDM Workshop , What’s yourcurrent stress level? Detection of stress patterns from GSR sensor data.

Farooq Azam, Journal (2000), Biologically Inspired Modular Neural Networks, BlacksburgVirginia.

Haley A. Jennifer, Journal (2000), Wearable And Automotive Systems for AffectRecognition from Physiology.

Rigas George, Yorgos Goletsis, and Dimitrios I. Fotiadis, Journal (2012), Real-Time Driver’sStress Event Detection.

Stefan Schmidt and Harald Walach, (2000), Electro Dermal Activity (EDA) Research – State of the art measurement and techniques for Para psychological purposes.

From this experiment we will classify the stress by finding the gastric motility with the assistance of neural network and CBR. In future we will add the communication devices that alert the relatives of the patient and a doctor to send word the standing of their patient. And that we will embrace the music system that helps to scale back the stress sort of a music medical care.

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