The main characteristics of the dataset.
\r\n\tMethadone maintenance treatment (MMT) has become the main pharmacological option for the treatment of opioid dependence. Methadone remains the gold standard in the substitution treatment, which is a harm reduction intervention, because the patient does not become abstinent, but there are a series of positive changes. Currently, the surveillance of methadone substitution treatment is considered an ongoing challenge, given the need for the individualization and the increasing of the therapy efficiency. Methadone has been also studied as an analgesic for the management of cancer pain and other chronic pain conditions.
\r\n\r\n\tThe complexity of methadone pharmacology, the high inter-individual variability in methadone pharmacokinetics, the risk of opioid diversion, the overdose and other adverse events pose many challenges to clinicians.
\r\n\tThe aim of the proposed book is to update and summarize the scientific knowledge on the opioid dependence, including the mechanism of opioid dependence, the misuse of prescription opioids and the substitution therapy of opioid dependence.
Recently, online systems in education have increased, and student digital data has come to big data size. This makes possible to draw rules and predictions about the students by processing educational data with data mining techniques. All kinds of information about the student’s socioeconomic environment, learning environment, or course notes can be used for prediction, which affect the success or failure of a student.
In this study, the successes of the students at the end of the semester are estimated by using the student data obtained from secondary education of two Portuguese schools. The aim of this study is to predict the students’ final grades to support the educators to take precautions for the children at risk. A number of data preprocessing processes were applied to increase the accuracy rate of the prediction model. A wrapper method for feature subset selection was applied to find the optimal subset of features. After that, three popular data mining algorithms (decision tree, random forest, and naive Bayes) were used and compared in terms of classification accuracy rate. In addition, this study also investigates the effects of two different grade categorizations on data mining: five-level grade categorization and binary grade categorization.
The remainder of this paper is organized as follows. In Section 2, the previous studies in this field are mentioned. In Section 3, the methods used in this study are briefly explained to provide a comprehensive understanding of the research concepts. In Section 4, experimental studies are presented with dataset description, data preprocessing, and experimental result subtitles. Finally, conclusion and the direction for future research are given in Section 5.
Predicting students’ academic performance is one of the main topics of educational data mining [1, 2]. With the advancement of technology, technological investments in the field of education have increased. Along with technological developments, e-Learning platforms such as web-based online learning and multimedia technologies have evolved, and both learning costs have decreased, and time and space limitations have been eliminated [3]. The increase of online course trainings and the increase of online transactions and interactive transactions in schools led to the increase of digital data in this field. Costa (2017) emphasized the data about the failure rate of the students; the educators were concerned and raised important questions about the failure prediction [4].
Estimating students’ performances becomes more difficult because of the large volume of data in training databases [5]. Descriptive statistical analysis can be effectively used to provide the basic descriptive information of a given set of data [6]. However, this alone is not always enough. To inform the instructors and students early, students may be able to identify early, using estimated modeling methods [7]. It is useful to classify university students according to their potential academic performance in order to increase success rates and to manage the resources well [8]. The large growth of electronic data from universities leads to an increase in the need to obtain meaningful information from these large amounts of data [9]. By using data mining techniques on education data, it is possible to improve the quality of education processes [10].
Until now, data mining algorithms have been applied on various different educational fields such as engineering education [11], physical education [12], and English language education [13]. Some studies have focused on high school students [14], while some of them have interested in higher education [15]. Whereas some data mining studies have focused on the prediction of student performance [16], some studies have investigated the instructor performance [17].
The increase in digitalization caused us to have plenty of data in every field. Having too much data is getting worth if we know how to use it. Data mining aims to access knowledge from data using various machine learning techniques. With data mining, it becomes possible to establish the relationships between the data and make accurate predictions for the future. One of the application areas of data mining is education. Data mining in education is the field that allows us to make predictions about the future by examining the data obtained so far in the field of education by using machine learning techniques. There are basically three data mining methods: classification, clustering, and association rule mining. In this study, we focus on the classification task.
The methods to be used in data mining may differ depending on the field of study and the nature of the data we have. In this study, three well-known classification algorithms (decision tree, random forest, and naive Bayes) were employed on the educational datasets to predict the final grades of students.
Naive Bayes classifiers are a family of algorithms. These classifiers are based on Bayes’ Theorem, which finds the possibility of a new event based on previously occurring events. Each classification is independent of one another but has a common principle.
A decision tree uses a tree like graph. Decision trees are like flowchart but not noncyclic. The tree consists of nodes and branches. Nodes and branches are arranged in a row. Root node is on the top of a tree and represents the entire dataset. Entropy is calculated when determining nodes in a tree. It models decisions with efficacy, results, and resource costs. In this study, decision tree technique is preferred because it is easy to understand and interpret.
Random forest is an ensemble learning algorithm. It is a supervised classification method. It consists of randomly generated many decision trees. The established forest is formed by the decision trees community trained by the bagging method, which is one of the ensemble methods. Random forest creates multiple decision trees and combines them to achieve a more accuracy rates and stable prediction.
Figure 1 illustrates the workflow of data mining model for classification. In the first step, feature selection algorithms are applied on the educational data. Next, classification algorithms are used to build a good model which can accurately map inputs to desired outputs. The model evaluation phase provides feedback to the feature selection and learning phases for adjustment to improve classification performance. Once a model is built, then, in the second phase, it is used to predict label of new student data.
Flowchart of the data mining model.
In this study, the feature subset selection and classification operations were conducted by using WEKA open-source data mining software [18]. In each experiment, 10-fold cross-validation was performed to evaluate the classification models. The classification accuracy of the algorithm for the test dataset was measured as given in Eq. 1:
where T is a test set that consists of a set of data items to be classified; c is the actual class of the item t, where t є T; and classify(t) returns the classification output of t by the algorithm.
In this study, two publically available datasets [19] were used to predict student performances. Both datasets were collected from secondary education of two Portuguese schools. Dataset attributes are about student grades and social, demographic, and school-related features. All data were obtained from school reports and questionnaires. The first dataset has information regarding the performances of students in Mathematics lesson, and the other one has student data taken from Portuguese language lesson. Both datasets have 33 attributes as shown in Table 1.
Feature | Description | Type | Values |
---|---|---|---|
Sex | The gender of the student | Binary | Female or male |
Age | The age of the student | Numeric | From 15 to 22 |
School | The school of the student | Binary | GP (Gabriel Pereira) or MS (Mousinho da Silveira) |
Address | Home address type of student | Binary | Urban or rural |
Pstatus | Cohabitation status of student’s parent | Binary | Living together or apart |
Medu | Education of student’s mother | Numeric | From 0 to 4 |
Mjob | Job of student’s mother | Nominal | Teacher, health, services, at home, others |
Fedu | Education of student’s father | Numeric | From 0 to 4 |
Fjob | Job of student’s father | Nominal | Teacher, health, services, at home, others |
Guardian | Guardian of student | Nominal | Mother, father, or otherd |
Famsize | Size of family | Binary | “LE3” (less or equal to 3) or “GT3” (greater than 3) |
Famrel | Quality of family relationships | Numeric | From 1 very bad to 5 excellent |
Reason | Reason of choosing this school | Nominal | Close to home, school reputation, course preference, or others |
Travel time | Travel time of home to school | Numeric | 1–<15 min., 2–15 to 30 min., 3–30 min. to 1 hour, or 4–>1 hour |
Study time | Study time of a week | Numeric | –< 2 hours, 2–2 to 5 hours, 3–5 to 10 hours or 4− > 10 hours |
Failures | Number of past class failures | Numeric | n if 1 < =n < 3, else 4 |
Schoolsup | Extra educational school support | Binary | Yes or no |
Famsup | Family educational support | Binary | Yes or no |
Activities | Extracurricular activities | Binary | Yes or no |
Paid class | Extra paid classes | Binary | Yes or no |
Internet | Internet access at home | Binary | Yes or no |
Nursery | Attended nursery school | Binary | Yes or no |
Higher | Wants to take higher education | Binary | Yes or no |
Romantic | With a romantic relationship | Binary | Yes or no |
Free time | Free time after school | Numeric | From 1 (very low) to 5 (very high) |
Go out | Going out with friends | Numeric | From 1 (very low) to 5 (very high) |
Walc | Alcohol consumption of weekend | Numeric | From 1 (very low) to 5 (very high) |
Dalc | Alcohol consumption of workday | Numeric | From 1 (very low) to 5 (very high) |
Health | Status of current health | Numeric | From 1 (very low) to 5 (very high) |
Absences | Number of school absences | Numeric | From 0 to 93 |
G1 | Grade of first period | Numeric | From 0 to 20 |
G2 | Grade of second period | Numeric | From 0 to 20 |
G3 | Grade of final period | Numeric | From 0 to 20 |
The main characteristics of the dataset.
In the raw dataset, the final grade is in the range of 0–20 as with many European countries, where 0 is the worst grade and 20 is the best score. Since the final grade of the students is in the form of integer, the predicted class should be in the form of categorical values, the data needed to be transformed to categories according to a grading policy. In the study, we used and compared two different grading systems: five-level grading and binary grading systems.
We first categorized the final grade in five groups. These ranges are defined based on the Erasmus system. As shown in Table 2, the range 0–9 refers to grade F, which is the worst grade and corresponds to “fail” label. The others (10–11, 12–13, 14–15, and 16–20) correspond to D (sufficient), C (satisfactory), B (good), and A (excellent/very good) class labels, respectively.
1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|
Excellent/very good | Good | Satisfactory | Sufficient | Fail |
16–20 | 14–15 | 12–13 | 10–11 | 0–9 |
A | B | C | D | F |
Five-level grading categories.
To compare the results, we also categorized the final grade as “passed” and “fail.” As shown in Table 3, the range of 0–9 corresponds to F, and it means “fail”; the range of 10–20 refers to A, B, C, and D, and it means “pass.”
Pass | Fail |
---|---|
10–20 | 0–9 |
A, B, C, D | F |
Binary fail/pass category.
As a preprocessing operation, the final grade attribute was categorized according to two different grading systems, before classification. As a result, we have created two versions of both datasets. Both math and Portuguese datasets were available in both five-level and binary grading versions. Hence, we have the chance to compare the results of these versions.
In the first experiment, three algorithms [decision tree (J48), random forest, and naive Bayes] were compared on the five-level grading version and binary version of the Portuguese dataset. As shown in Table 4, the best performance for the five-level grading version for this dataset was obtained with an accuracy rate of 73.50% with the random forest algorithm. However, this accuracy rate was increased with binary grading version of this dataset. In the dataset, where the final grade is categorized in binary form (passing or failing), the accuracy rate was increased to 93.07%.
Algorithm | Five-level grading | Binary grading (P/F) |
---|---|---|
Decision tree (J48) | 67.80% | 91.37% |
Random forest | 73.50% | 93.07% |
Naive Bayes | 68.26% | 88.44% |
Classification accuracy rates for the Portuguese lesson dataset.
(accuracy values, bold – best model).
The performances of three classification algorithms on mathematics datasets (five-level and binary label dataset versions) are shown in Table 5. The best results for five-level grading version were obtained with the decision tree (J48) algorithm with an accuracy rate of 73.42%. The best accuracy rate 91.39% for binary dataset version was obtained with the random forest ensemble method.
Mathematics | Five-level grading | Binary grading (P/F) |
---|---|---|
Decision tree (J48) | 73.42% | 89.11% |
Random forest | 71.14% | 91.39% |
Naive Bayes | 70.38% | 86.33% |
Classification accuracy rates for the mathematics lesson dataset.
(accuracy values, bold – best model).
As a second experiment, we made all comparisons after dataset preprocessing, in other terms, after feature subset selection. Hence, the most appropriate attributes were selected by using wrapper subset method to increase the accuracy rates.
One of the important steps to create a good model is attribute selection. This operation can be done in two ways: first, select relevant attributes, and second, remove redundant or irrelevant attributes. Attribute selection is made to create a simple model, to create a model that is easier to interpret, and to find out which features are more important for the results. Attribute selection can be done using filters and wrapper methods. In this study, we use the wrapper method, because it generally produces better results. This method has a recursive structure. The process starts with selecting a subset and induces the algorithm on that subset. Then evaluation is made according to the success of the model. There are two options in this assessment. The first option returns to the top to select a new subset, the second option uses the currently selected subset.
In Table 6, the accuracy rates were compared before and after the attribute selection process for the Portuguese dataset for five-level grade version. Thanks to the wrapper subset method, the accuracy rate of the J48 algorithm has increased from 67.80 to 74.88% with the selected attributes. This accuracy rate increased from 68.26 to 72.57% for naive Bayes algorithm. For the random forest method where we get the best accuracy results, the accuracy rate has increased from 73.50 to 77.20%.
Feature selection | Wrapper subset (J48) | Wrapper subset (naive Bayes) | Wrapper subset (random forest) |
---|---|---|---|
Before | 67.80% | 68.26% | 73.50% |
After | 74.88% | 72.57% | 77.20% |
Selected features | Search backward: age, famsize, Mjob, schoolsup, paid, internet, go out, health, G1, G2 | Search backward: travel time, romantic, free time, health, G1, G2 | School, Travel time, G2 |
Before and after feature selection with five-level grading system.
The obtained classification accuracy rates for the Portuguese lesson dataset with five-level grading system.
(accuracy values, bold – best model).
In Table 7, the accuracy rates were compared before and after the attribute selection process for the mathematics dataset for five-level grading version. In this dataset, attribute selection significantly increased our accuracy. Here, unlike Portuguese language dataset, the best jump was obtained with J48 algorithm and search forward technique in wrapper method. In this way, the accuracy rate increased from 73.42 to 79.49%. A close result was obtained with the search backward technique and accuracy increased from 73.42 to 78.23%. Through this way, naive Bayes and random forest methods also increased significantly. This method increased the accuracy rate of naive Bayes method from 70.38 to 74.18%. Random forest result is increased from 71.14 to 78.99%. These results show that attribute selection with this wrapper subset method also works in this dataset.
Feature selection | Wrapper subset (J48) | Wrapper subset (J48) | Wrapper subset (naive Bayes) | Wrapper subset (random forest) |
---|---|---|---|---|
Before | 73.42% | 73.42% | 70.38% | 71.14% |
After | 78.23% | 79.49% | 74.18% | 78.99% |
Selectedfeatures | Search backward: age, pstatus, Medu, Fedu, Fjob, failures, schoolsup, paid, activities, famrel, Dalc, Walc, G2 | Search forward: sex, Mjob, Walc, G2 | Famsize, Medu, Fjob, activities, higher, romantic, free time, G2 | Famsize, Fedu, schoolsup, paid, activities, higher, romantic, Walc, absences, G1, G2 |
Before and after feature selection with binary grading system.
The obtained classification accuracy rates for the mathematics lesson dataset with five-level grading system.
(accuracy values, bold – best model).
In Table 8, the results of the wrapper attribute selection method before and after the application to the Portuguese binary version are compared. There was no significant increase in accuracy. The best results were obtained with random forest. The best jump was experienced by the naive Bayes method but did not reach the random forest value. Naive Bayes result has risen from 88.44 to 89.68%. Random forest maintained the high accuracy achieved before the attribute selection and increased from 93.07 to 93.22%.
Feature selection | Wrapper subset (J48) | Wrapper subset (naive Bayes) | Wrapper subset (random forest) |
---|---|---|---|
Before | 91.37% | 88.44% | 93.07% |
After | 91.99% | 89.68% | 93.22% |
Selected Features | School, age, address, Medu, Fjob, travel time, study time, schoolsup, nursery, higher, famrel, free time, G1, G2 | Sex, age, Pstatus, Fedu, Mjob, Fjob, reason, failures, famsup, paid, higher, Internet, romantic, go out, health, absences, G1, G2 | School, sex, age, address, famsize, Pstatus, Medu, Mjob, Fjob, reason, guardian, travel time, study time, failures, schoolsup, famsup, paid, activities, higher, Internet, romantic, famrel, free time, go out, Dalc, Walc, health, absences, G1, G2 |
Before and after feature selection.
The obtained classification accuracy rates for the Portuguese lesson dataset with binary grading system.
(accuracy values, bold – best model).
After successful results in five-level grade versions, we tried the same attribute selection method in binary label version dataset. Table 9 shows the accuracy values before and after the wrapper attribute selection for the mathematical binary dataset version. Because the accuracy of the binary version is already high, the jump is less than the five-level grades. But again, there is a nice increase in accuracy. The accuracy rate of the J48 algorithm was increased from 89.11 to 90.89%, while the naive Bayes result was increased from 86.33 to 89.11%. As with the mathematics five-level grade dataset, the best results were obtained with random forest in binary label dataset. Accuracy rate increased from 91.39 to 93.67%.
Feature selection | Wrapper subset (J48) | Wrapper subset (naive Bayes) | Wrapper subset (random forest) |
---|---|---|---|
Before | 89.11% | 86.33% | 91.39% |
After | 90.89% | 89.11% | 93.67% |
Selected features | School, age, address, Medu, Fedu, guardian, failures, schoolsup, famsup, Internet, romantic, famrel, free time, G1, G2 | Sex, age, Pstatus, Fedu, Mjob, Fjob, reason, failures, famsup, paid, higher, Internet, romantic, go out, health, absences, G1, G2 | Address, famsize, Fedu, Mjob, Fjob, reason, guardian, study time, schoolsup, higher, famrel, go out, absences, G2 |
Before and after feature selection.
The obtained classification accuracy rates for the mathematics lesson dataset with binary grading system.
(accuracy values, bold – best model).
As a result, it can be possible to say that accuracy rates have changed positively in all trials using wrapper subset attribute selection method.
This paper proposes the application of data mining techniques to predict the final grades of students based on their historical data. Three well-known classification techniques (decision tree, random forest, and naive Bayes) were compared in terms of accuracy rates. Wrapper feature subset selection method was used to improve the classification performance. Preprocessing operations on the dataset, categorizing the final grade field into five and two groups, increased the percentage of accurate estimates in the classification. The wrapper attribute selection method in all algorithms has led to a noticeable increase in accuracy rate. Overall, better accuracy rates were achieved with the binary class method for both mathematics and Portuguese dataset.
In the future, different feature selection methods can be used. In addition, different classification algorithms can also be utilized on the datasets.
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\\n\\n7.7 No partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Corresponding Author or any Co-Author, nor authorize any party to make or enter into any commitments for or on behalf of any other party.
\\n\\n7.8 Governing law: This Publication Agreement and any dispute or claim (including non-contractual disputes or claims) arising out of or in connection with it or its subject matter or formation shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of or in connection with this Publication Agreement (including any non-contractual disputes or claims).
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The Corresponding Author (acting on behalf of all Authors) and INTECHOPEN LIMITED, incorporated and registered in England and Wales with company number 11086078 and a registered office at 5 Princes Gate Court, London, United Kingdom, SW7 2QJ conclude the following Agreement regarding the publication of a Book Chapter:
\n\n1. DEFINITIONS
\n\nCorresponding Author: The Author of the Chapter who serves as a Signatory to this Agreement. The Corresponding Author acts on behalf of any other Co-Author.
\n\nCo-Author: All other Authors of the Chapter besides the Corresponding Author.
\n\nIntechOpen: IntechOpen Ltd., the Publisher of the Book.
\n\nBook: The publication as a collection of chapters compiled by IntechOpen including the Chapter. Chapter: The original literary work created by Corresponding Author and any Co-Author that is the subject of this Agreement.
\n\n2. CORRESPONDING AUTHOR'S GRANT OF RIGHTS
\n\n2.1 Subject to the following Article, the Corresponding Author grants and shall ensure that each Co-Author grants, to IntechOpen, during the full term of copyright and any extensions or renewals of that term the following:
\n\nThe aforementioned licenses shall survive the expiry or termination of this Agreement for any reason.
\n\n2.2 The Corresponding Author (on their own behalf and on behalf of any Co-Author) reserves the following rights to the Chapter but agrees not to exercise them in such a way as to adversely affect IntechOpen's ability to utilize the full benefit of this Publication Agreement: (i) reprographic rights worldwide, other than those which subsist in the typographical arrangement of the Chapter as published by IntechOpen; and (ii) public lending rights arising under the Public Lending Right Act 1979, as amended from time to time, and any similar rights arising in any part of the world.
\n\nThe Corresponding Author confirms that they (and any Co-Author) are and will remain a member of any applicable licensing and collecting society and any successor to that body responsible for administering royalties for the reprographic reproduction of copyright works.
\n\nSubject to the license granted above, copyright in the Chapter and all versions of it created during IntechOpen's editing process (including the published version) is retained by the Corresponding Author and any Co-Author.
\n\nSubject to the license granted above, the Corresponding Author and any Co-Author retains patent, trademark and other intellectual property rights to the Chapter.
\n\n2.3 All rights granted to IntechOpen in this Article are assignable, sublicensable or otherwise transferrable to third parties without the Corresponding Author's or any Co-Author’s specific approval.
\n\n2.4 The Corresponding Author (on their own behalf and on behalf of each Co-Author) will not assert any rights under the Copyright, Designs and Patents Act 1988 to object to derogatory treatment of the Chapter as a consequence of IntechOpen's changes to the Chapter arising from translation of it, corrections and edits for house style, removal of problematic material and other reasonable edits.
\n\n3. CORRESPONDING AUTHOR'S DUTIES
\n\n3.1 When distributing or re-publishing the Chapter, the Corresponding Author agrees to credit the Book in which the Chapter has been published as the source of first publication, as well as IntechOpen. The Corresponding Author warrants that each Co-Author will also credit the Book in which the Chapter has been published as the source of first publication, as well as IntechOpen, when they are distributing or re-publishing the Chapter.
\n\n3.2 When submitting the Chapter, the Corresponding Author agrees to:
\n\nThe Corresponding Author will be held responsible for the payment of the Open Access Publishing Fees.
\n\nAll payments shall be due 30 days from the date of the issued invoice. The Corresponding Author or the payer on the Corresponding Author's and Co-Authors' behalf will bear all banking and similar charges incurred.
\n\n3.3 The Corresponding Author shall obtain in writing all consents necessary for the reproduction of any material in which a third-party right exists, including quotations, photographs and illustrations, in all editions of the Chapter worldwide for the full term of the above licenses, and shall provide to IntechOpen upon request the original copies of such consents for inspection (at IntechOpen's option) or photocopies of such consents.
\n\nThe Corresponding Author shall obtain written informed consent for publication from people who might recognize themselves or be identified by others (e.g. from case reports or photographs).
\n\n3.4 The Corresponding Author and any Co-Author shall respect confidentiality rights during and after the termination of this Agreement. The information contained in all correspondence and documents as part of the publishing activity between IntechOpen and the Corresponding Author and any Co-Author are confidential and are intended only for the recipient. The contents may not be disclosed publicly and are not intended for unauthorized use or distribution. Any use, disclosure, copying, or distribution is prohibited and may be unlawful.
\n\n4. CORRESPONDING AUTHOR'S WARRANTY
\n\n4.1 The Corresponding Author represents and warrants that the Chapter does not and will not breach any applicable law or the rights of any third party and, specifically, that the Chapter contains no matter that is defamatory or that infringes any literary or proprietary rights, intellectual property rights, or any rights of privacy. The Corresponding Author warrants and represents that: (i) the Chapter is the original work of themselves and any Co-Author and is not copied wholly or substantially from any other work or material or any other source; (ii) the Chapter has not been formally published in any other peer-reviewed journal or in a book or edited collection, and is not under consideration for any such publication; (iii) they themselves and any Co-Author are qualifying persons under section 154 of the Copyright, Designs and Patents Act 1988; (iv) they themselves and any Co-Author have not assigned and will not during the term of this Publication Agreement purport to assign any of the rights granted to IntechOpen under this Publication Agreement; and (v) the rights granted by this Publication Agreement are free from any security interest, option, mortgage, charge or lien.
\n\nThe Corresponding Author also warrants and represents that: (i) they have the full power to enter into this Publication Agreement on their own behalf and on behalf of each Co-Author; and (ii) they have the necessary rights and/or title in and to the Chapter to grant IntechOpen, on behalf of themselves and any Co-Author, the rights and licenses expressed to be granted in this Publication Agreement. If the Chapter was prepared jointly by the Corresponding Author and any Co-Author, the Corresponding Author warrants and represents that: (i) each Co-Author agrees to the submission, license and publication of the Chapter on the terms of this Publication Agreement; and (ii) they have the authority to enter into this Publication Agreement on behalf of and bind each Co-Author. The Corresponding Author shall: (i) ensure each Co-Author complies with all relevant provisions of this Publication Agreement, including those relating to confidentiality, performance and standards, as if a party to this Publication Agreement; and (ii) remain primarily liable for all acts and/or omissions of each such Co-Author.
\n\nThe Corresponding Author agrees to indemnify and hold IntechOpen harmless against all liabilities, costs, expenses, damages and losses and all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of or in connection with any breach of the aforementioned representations and warranties. This indemnity shall not cover IntechOpen to the extent that a claim under it results from IntechOpen's negligence or willful misconduct.
\n\n4.2 Nothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\n5. TERMINATION
\n\n5.1 IntechOpen has a right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Corresponding Author or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Corresponding Author or any Co-Author (being an individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Corresponding Author or any Co-Author (being a company) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for or enters into any compromise or arrangement with any of its creditors.
\n\nIn case of termination, IntechOpen will notify the Corresponding Author, in writing, of the decision.
\n\n6. INTECHOPEN’S DUTIES AND RIGHTS
\n\n6.1 Unless prevented from doing so by events outside its reasonable control, IntechOpen, in its discretion, agrees to publish the Chapter attributing it to the Corresponding Author and any Co-Author.
\n\n6.2 IntechOpen has the right to use the Corresponding Author’s and any Co-Author’s names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Chapter and has the right to contact the Corresponding Author and any Co-Author until the Chapter is publicly available on any platform owned and/or operated by IntechOpen.
\n\n6.3 IntechOpen is granted the authority to enforce the rights from this Publication Agreement, on behalf of the Corresponding Author and any Co-Author, against third parties (for example in cases of plagiarism or copyright infringements). In respect of any such infringement or suspected infringement of the copyright in the Chapter, IntechOpen shall have absolute discretion in addressing any such infringement which is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\n\n7. MISCELLANEOUS
\n\n7.1 Further Assurance: The Corresponding Author shall and will ensure that any relevant third party (including any Co-Author) shall, execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\n\n7.2 Third Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\n\n7.3 Entire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces and extinguishes all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by or on behalf of the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (together "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of its pre-contract fraudulent misrepresentation or fraudulent concealment.
\n\n7.4 Waiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\n\n7.5 Variation: No variation of this Publication Agreement shall be effective unless it is in writing and signed by the parties (or their duly authorized representatives).
\n\n7.6 Severance: If any provision or part-provision of this Publication Agreement is or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted.
\n\nAny modification to or deletion of a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\n\n7.7 No partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Corresponding Author or any Co-Author, nor authorize any party to make or enter into any commitments for or on behalf of any other party.
\n\n7.8 Governing law: This Publication Agreement and any dispute or claim (including non-contractual disputes or claims) arising out of or in connection with it or its subject matter or formation shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of or in connection with this Publication Agreement (including any non-contractual disputes or claims).
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