This document discusses using data mining techniques to analyze student performance data from an undergraduate program in India. Specifically, it aims to identify related subjects and classify student performance. The methodology involves first using association rule mining to identify pairs of subjects that students often pass together. It then uses Pearson correlation coefficients to determine the strength of relationships between subject pairs. Finally, it clusters students into categories like good, average, poor based on their marks and builds a decision tree to predict student performance and help weaker students.
Evidential reasoning based decision system to select health care locationIJAAS Team
The general public’s demand of Bangladesh for safe health is rising promptly with the improvement of the living standard. However, the allocation of limited and unbalanced medical resources is deteriorating the assurance of safe health of the people. Therefore, the new hospital construction with rational allocation of resources is imminent and significant. The site selection for establishing a hospital is one of the crucial policy-related decisions taken by planners and policy makers. The process of hospital site selection is inherently complicated because of this involves many factors to be measured and evaluated. These factors are expressed both in objective and subjective ways where as a hierarchical relationship exists among the factors. In addition, it is difficult to measure qualitative factors in a quantitative way, resulting incompleteness in data and hence, uncertainty. Besides it is essential to address the subject of uncertainty by using apt methodology; otherwise, the decision to choose a suitable site will become inapt. Therefore, this paper demonstrates the application of a novel method named belief rulebased inference methodology-RIMER base intelligent decision system(IDS), which is capable of addressing suitable site for hospital by taking account of large number of criteria, where there exist factors of both subjective and objective nature.
Classification: Basic Concepts and Decision Treessathish sak
Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Find a model for class attribute as a function of the values of other attributes.
Goal: previously unseen records should be assigned a class as accurately as possible.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Survey on the Classification Techniques In Educational Data MiningEditor IJCATR
Due to increasing interest in data mining and educational system, educational data mining is the emerging topic for research
community. educational data mining means to extract the hidden knowledge from large repositories of data with the use of technique
and tools. educational data mining develops new methods to discover knowledge from educational database and used for decision
making in educational system. The various techniques of data mining like classification. clustering can be applied to bring out hidden
knowledge from the educational data.
In this paper, we focus on the educational data mining and classification techniques. In this study we analyze attributes for the
prediction of student's behavior and academic performance by using WEKA open source data mining tool and various classification
methods like decision trees, C4.5 algorithm, ID3 algorithm etc.
Evidential reasoning based decision system to select health care locationIJAAS Team
The general public’s demand of Bangladesh for safe health is rising promptly with the improvement of the living standard. However, the allocation of limited and unbalanced medical resources is deteriorating the assurance of safe health of the people. Therefore, the new hospital construction with rational allocation of resources is imminent and significant. The site selection for establishing a hospital is one of the crucial policy-related decisions taken by planners and policy makers. The process of hospital site selection is inherently complicated because of this involves many factors to be measured and evaluated. These factors are expressed both in objective and subjective ways where as a hierarchical relationship exists among the factors. In addition, it is difficult to measure qualitative factors in a quantitative way, resulting incompleteness in data and hence, uncertainty. Besides it is essential to address the subject of uncertainty by using apt methodology; otherwise, the decision to choose a suitable site will become inapt. Therefore, this paper demonstrates the application of a novel method named belief rulebased inference methodology-RIMER base intelligent decision system(IDS), which is capable of addressing suitable site for hospital by taking account of large number of criteria, where there exist factors of both subjective and objective nature.
Classification: Basic Concepts and Decision Treessathish sak
Given a collection of records (training set )
Each record contains a set of attributes, one of the attributes is the class.
Find a model for class attribute as a function of the values of other attributes.
Goal: previously unseen records should be assigned a class as accurately as possible.
A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Survey on the Classification Techniques In Educational Data MiningEditor IJCATR
Due to increasing interest in data mining and educational system, educational data mining is the emerging topic for research
community. educational data mining means to extract the hidden knowledge from large repositories of data with the use of technique
and tools. educational data mining develops new methods to discover knowledge from educational database and used for decision
making in educational system. The various techniques of data mining like classification. clustering can be applied to bring out hidden
knowledge from the educational data.
In this paper, we focus on the educational data mining and classification techniques. In this study we analyze attributes for the
prediction of student's behavior and academic performance by using WEKA open source data mining tool and various classification
methods like decision trees, C4.5 algorithm, ID3 algorithm etc.
The main objective of this paper is to develop a basic prototype model which can determine and extract
unknown knowledge (patterns, concepts and relations) related with multiple factors from past database records of
specific students. Data mining is science and engineering study of extracting previously undiscovered patterns
from a huge set of data. Data mining techniques are helpful for decision making as well as for discovering patterns
of data. In this paper students eligibility prediction system using Rule based classification is proposed to predict
the eligibility of students based on their details with high prediction accuracy. In Educational Institutes, a
tremendous amount of data is generated. This paper outlines the idea of predicting a particular student’s placement
eligibility by performing operations on the data stored. In this paper an efficient algorithm with the technique
Fuzzy for prediction is proposed.
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
With the growth of voluminous amount of data in educational institutes’, the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes’ which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
Organizational Identification of Millennial employees working remotely: Quali...HennaAnsari
The problem of practice for this study is to understand how Millennial employees identify with their organizations when working in a remote role. Understanding the employee experience could help us consider OID which is linked to range of positive employee outcomes, such as low turnover intention and higher engagement, as well as improved employee satisfaction, well-being, and employee performance (Ashforth, 2008 ). Actively disengaged employees manifest discontent by undermining more engaged employees’ efforts, and these workers can actively seek to harm the organization (Carrillo, 2017; Kompaso, 2010; Walden, 2017).
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
Combining forecast from different models has shown to perform better than single forecast in most time series. To improve the quality of forecast we can go for combining forecast. We study the effect of decomposing a series into multiple components and performing forecasts on each component separately... The original series is decomposed into trend, seasonality and an irregular component for each series. The statistical methods such as ARIMA, Holt-Winter have been used to forecast these components. In this paper we focus on how the best models of one series can be applied to similar frequency pattern series for forecasting using association mining. The proposed method forecasted value has been compared with Holt Winter method and shown that the results are better than Holt Winter method
Independent Component Analysis for Filtering Airwaves in Seabed Logging Appli...IJASCSE
Marine controlled source electromagnetic (CSEM) sensing method used for the detection of hydrocarbons based reservoirs in seabed logging application does not perform well due to the presence of the airwaves (or sea-surface). These airwaves interfere with the signal from the subsurface, as a result masks the receiver response at larger offsets. The task is to identify these air waves and the way they interact, and to filter them out. In this paper, a popular method for counteracting with the above stated problem scenario is Independent Component Analysis (ICA). Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional or multivariate dataset into its constituent components (sources) that are statistically as independent from each other as possible. ICA-type de-convolution algorithm that is FASTICA is considered for mixed signals de-convolution and considered convenient depending upon the nature of the source and noise model. The results from the FASTICA algorithm are shown and evaluated. In this paper, we present the FASTICA algorithm for the seabed logging application.
The main objective of this paper is to develop a basic prototype model which can determine and extract
unknown knowledge (patterns, concepts and relations) related with multiple factors from past database records of
specific students. Data mining is science and engineering study of extracting previously undiscovered patterns
from a huge set of data. Data mining techniques are helpful for decision making as well as for discovering patterns
of data. In this paper students eligibility prediction system using Rule based classification is proposed to predict
the eligibility of students based on their details with high prediction accuracy. In Educational Institutes, a
tremendous amount of data is generated. This paper outlines the idea of predicting a particular student’s placement
eligibility by performing operations on the data stored. In this paper an efficient algorithm with the technique
Fuzzy for prediction is proposed.
The development of data mining is inseparable from the recent developments in information technology that enables the accumulation of large amounts of data. For example, a shopping mall that records every sales transaction of goods using various POS (point of sales). Database data from these sales could reach a large storage capacity, even more being added each day, especially when the shopping center will develop into a nationwide network. The development of the internet at the moment also has a share large enough in the accumulation of data occurs. But the rapid growth of data accumulation it has created conditions that are often referred to as "data rich but information poor" because the data collected can not be used optimally for useful applications. Not infrequently the data set was left just seemed to be a "grave data". There are several techniques used in data mining which includes association, classification, and clustering. In this paper, the author will do a comparison between the performance of the technical classification methods naïve Bayes and C4.5 algorithms.
Slides presenting preliminary overview of thesis work presented at the International Conference on Electronic Learning in the Workplace at Columbia University on June 11, 2010.
With the growth of voluminous amount of data in educational institutes’, the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes’ which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.
STUDENTS’ PERFORMANCE PREDICTION SYSTEM USING MULTI AGENT DATA MINING TECHNIQUEIJDKP
A high prediction accuracy of the students’ performance is more helpful to identify the low performance students at the beginning of the learning process. Data mining is used to attain this objective. Data mining techniques are used to discover models or patterns of data, and it is much helpful in the decision-making.Boosting technique is the most popular techniques for constructing ensembles of classifier to improve the classification accuracy. Adaptive Boosting (AdaBoost) is a generation of boosting algorithm. It is used for
the binary classification and not applicable to multiclass classification directly. SAMME boosting
technique extends AdaBoost to a multiclass classification without reduce it to a set of sub-binaryclassification.In this paper, students’ performance prediction system usingMulti Agent Data Mining is proposed to predict the performance of the students based on their data with high prediction accuracy and provide helpto the low students by optimization rules.The proposed system has been implemented and evaluated by investigate the prediction accuracy ofAdaboost.M1 and LogitBoost ensemble classifiers methods and with C4.5 single classifier method. The results show that using SAMME Boosting technique improves the prediction accuracy and outperformed
C4.5 single classifier and LogitBoost.
Organizational Identification of Millennial employees working remotely: Quali...HennaAnsari
The problem of practice for this study is to understand how Millennial employees identify with their organizations when working in a remote role. Understanding the employee experience could help us consider OID which is linked to range of positive employee outcomes, such as low turnover intention and higher engagement, as well as improved employee satisfaction, well-being, and employee performance (Ashforth, 2008 ). Actively disengaged employees manifest discontent by undermining more engaged employees’ efforts, and these workers can actively seek to harm the organization (Carrillo, 2017; Kompaso, 2010; Walden, 2017).
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Predictionijtsrd
Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed student data classification using ID3 Iterative Dichotomiser 3 Decision Tree Algorithm Khin Khin Lay | San San Nwe "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26545.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26545/using-id3-decision-tree-algorithm-to-the-student-grade-analysis-and-prediction/khin-khin-lay
Combining forecast from different models has shown to perform better than single forecast in most time series. To improve the quality of forecast we can go for combining forecast. We study the effect of decomposing a series into multiple components and performing forecasts on each component separately... The original series is decomposed into trend, seasonality and an irregular component for each series. The statistical methods such as ARIMA, Holt-Winter have been used to forecast these components. In this paper we focus on how the best models of one series can be applied to similar frequency pattern series for forecasting using association mining. The proposed method forecasted value has been compared with Holt Winter method and shown that the results are better than Holt Winter method
Independent Component Analysis for Filtering Airwaves in Seabed Logging Appli...IJASCSE
Marine controlled source electromagnetic (CSEM) sensing method used for the detection of hydrocarbons based reservoirs in seabed logging application does not perform well due to the presence of the airwaves (or sea-surface). These airwaves interfere with the signal from the subsurface, as a result masks the receiver response at larger offsets. The task is to identify these air waves and the way they interact, and to filter them out. In this paper, a popular method for counteracting with the above stated problem scenario is Independent Component Analysis (ICA). Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional or multivariate dataset into its constituent components (sources) that are statistically as independent from each other as possible. ICA-type de-convolution algorithm that is FASTICA is considered for mixed signals de-convolution and considered convenient depending upon the nature of the source and noise model. The results from the FASTICA algorithm are shown and evaluated. In this paper, we present the FASTICA algorithm for the seabed logging application.
Enhanced Performance of Search Engine with Multitype Feature Co-Selection of ...IJASCSE
Information world meet many confronts nowadays and one such, is data retrieval from a multidimensional and heterogeneous data set. Han & et al carried out a trail for the mentioned challenge. A novel feature co-selection for web document clustering is proposed by them, which is called Multitype Features Co-selection for Clustering (MFCC). MFCC uses intermediate clustering results in one type of feature space to help the selection in other types of feature spaces. It reduces effectively of the noise introduced by “pseudoclass” and further improves clustering performance. This efficiency also can be used in data retrieval, by implementing the MFCC algorithm in ranking algorithm of Search Engine technique. The proposed work is to apply the MFCC algorithm in search engine architecture. Such that the information retrieves from the dataset is retrieved effectively and shows the relevant retrieval.
Improving Utilization of Infrastructure CloudIJASCSE
A key advantage of Infrastructure-as-a-Service (IaaS) cloud is providing users on-demand access to resources. However, to provide on-demand access, cloud providers must either significantly overprovision their infrastructure (or pay a high price for operating resources with low utilization) or reject a large proportion of user requests (in which case the access is no longer on-demand). At the same time, not all users require truly on-demand access to resources. Many applications and workflows are designed for recoverable systems where interruptions in service are expected. For instance, many scientists utilize High Throughput Computing (HTC)-enabled resources, such as Condor, where jobs are dispatched to available resources and terminated when the resource is no longer available. We propose a cloud infrastructure that combines on-demand allocation of resources with opportunistic provisioning of cycles from idle cloud nodes to other processes by deploying backfill Virtual Machines (VMs).
Four Side Distance: A New Fourier Shape SignatureIJASCSE
Shape is one of the main features in content based image retrieval (CBIR). This paper proposes a new shape signature. In this technique, features of each shape are extracted based on four sides of the rectangle that covers the shape. The proposed technique is Fourier based and it is invariant to translation, scaling and rotation. The retrieval performance between some commonly used Fourier based signatures and the proposed four sides distance (FSD) signature has been tested using MPEG-7 database. Experimental results are shown that the FSD signature has better performance compared with those signatures.
Theoretical study of axially compressed Cold Formed Steel SectionsIJASCSE
Conceptual and finite element analysis oriented design of cold formed steel columns are presented in this paper. Total four 1 meter channel lipped section with thickness of 1, 1.2, 1.5 and 1.9 are tested. All columns were tested under a pure axial load, Conceptual design results are compared with finite element analysis for axially loaded compression members. The results provide useful information regarding allowable load estimation of cold formed steel column section. Based on the results, design recommendations were proposed. The proposed design approach is recommended for the design of complex shape of cold formed steel section, where design rules are not available in standards.
Improved Performance of Unsupervised Method by Renovated K-MeansIJASCSE
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.
A Study on the Effectiveness of Computer Games in Teaching and LearningIJASCSE
Games, especially computer games are becoming one of the tools of education. Nowadays, the usage of computer games as an educational tool has become a worldwide trend. An early assumption suggests that since the appeal of computer games can engage interest and motivation, thus it is a wise step to use computer games for the purpose of educating. This is because students often get bored with the learning process; therefore we need to find creative ways to teach them. Instead of the usual, dull lesson in class, educators are trying out new ways to attract the interest of students to focus the lessons and thus increase their understanding, with one of it using computer games. A lot of papers supported the idea of computer games being effective as an aid for students. Educators alike also agreed that it is one of the ways to gain students interest in their lessons. Before coming to the ultimate conclusion that computer games are a good choice, first of all we need to study carefully the effectiveness of using computer games as an educational medium. This paper aims to study the effectiveness of computer games in learning among students. Issues on the integration of computer games in formal education are and the current status of educational gaming in learning were reviewed in this paper. We focused on higher learning context which is for university students.
Clustering Based Lifetime Maximizing Aggregation Tree for Wireless Sensor Net...IJASCSE
Energy efficiency is the most important issue in all facets of wireless sensor networks (WSNs) operations because of the limited and non-replenish able energy supply. The data aggregation mechanism is one of the possible solutions to prolong the lifetime of sensor nodes and on the other hand it also helps in eliminating the data redundancy and improving the accuracy of information gathering, is essential for WSNs. In this paper we propose a Clustering based lifetime maximizing aggregation tree (CLMAT) in which we create aggregation tree which aim to reduce energy consumption.
Design Equation for CFRP strengthened Cold Formed Steel Channel Column SectionsIJASCSE
Carbon fiber reinforce polymer (CFRP) strengthened steel structural members such as beams, columns and bridge decks have become progressively popular as a result of extensive studies in this field. This paper presents the recent developments in CFRP strengthened steel channel sections and proposed conceptual model for prediction of column strength under pure axial loads per Indian standards-IS801-1975 and Euro code 3(EC 3)standards . Eight cold-formed steel circular lipped channel section columns with externally bonded CFRP were tested under pure axial compression. IS801/EC3 proposed methods were compared with experimental results. The results show that the proposed method gives around 11 percent increase in strength due to CFRP.
A Study on Thermal behavior of Nano film as thermal interface layerIJASCSE
Increase in thermal design power and re-duce in manufacturing cost of the processor chip has pushes the need for high performance and durable test fixture design in future. Test fixture with efficient themal management has lowest resistance possible to maintain the accuracy of the device temperature when it makes contact with processor chip’s silicon during test. High thermal conductivity and mechanical relia-bility of text fixures are desired for high volume test environment. Nano film materials such as Aluminum Titanium Nitride (AlTiN), Titanium carbide (TiC), Ti-tanium on Titanium nitride (Ti on TiN), Titanium ni-tride on Titanium (TiN on Ti) and Aluminum(III) Ox-ide (Al2O3) are coated over copper substrates by Fil-tered Cathodic Vacuum Arc (FCVA) deposition method and tested for their thermal conductivity behavior for high volume test (HVM) environment. Thermal con-ductivity of the prepared films is tested by using the ASTM 5470 Thermal Interface Material (TIM) Tester. Titanium on Titanium nitride (Ti on TiN) and Alumi-num (III) Oxide (Al2O3) observed with highest thermal conductivity of 117.68 W/mk and 128.34 W/mk respec-tively among the prepared nano thin films. Thickness of the film and stack configuration influenced the thermal conductivity of the prepared film.
Investigation of Integrated Rectangular SIW Filter and Rectangular Microstrip...IJASCSE
This paper presents an investigation based on the resonant circuit approach to characterize an integrated microwave filter and antenna from a lumped element prototype. This approach is used to design an integrated filter and antenna to reduce the overall size of the physical dimensions of the RF/microwave front-end subsystem. This study focuses on the integration of a rectangular Substrate Integrated Waveguide (SIW) filter with a rectangular microstrip patch antenna to produce a filtering and radiating element in a single device. The physical layouts of the SIW filter and rectangular microstrip patch antenna based on single- and dual-mode will be developed. To prove the concept, the integrated microwave filter and antenna at a center frequency of 2 GHz is demonstrated and validated through simulation and laboratory experiments. The experimental performance yielded promising results that were in good agreement with the simulated results. This study is beneficial for microwave systems, given that the reduction of the complexity of design and physical dimension as well as cost are important for applications such as base stations and multiplexers in wireless communication systems.
Analysis and Design of Lead Salt PbSe/PbSrSe Single Quantum Well In the Infra...IJASCSE
There is a considerable interest in studying the energy spectrum changes due to the non parabolic energy band structure in nano structures and nano material semiconductors. Most material systems have parabolic band structures at the band edge, however away from the band edge the bands are strongly non parabolic. Other material systems are strongly parabolic at the band edge such as IV-VI lead salt semiconductors. A theoretical model was developed to conduct this study on PbSe/Pb 0.934 Sr0.066 Se nanostructure system in the infrared region. Moreover, we studied the effects of four temperatures on the analysis and design of this system. It will be shown that the total losses for the system are higher than the modal gain values for lasing to occur and multiple quantum well structures are a better design choice.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
1. Data Mining on Educational Domain
Nikhil Rajadhyax Prof. Rudresh Shirwaikar
Department of Information Technology Department of Information Technology
Shree Rayeshwar Institute of Engineering & Information Shree Rayeshwar Institute of Engineering & Information
Technology Technology
Shiroda, Goa, India Shiroda, Goa, India
e-mail: nikhil.rajadhyax@gmail.com e-mail: rudreshshirwaikar@gmail.com
Abstract— Educational data mining (EDM) is defined as the area
of scientific inquiry centered around the development of methods II. METHODOLOGY
for making discoveries within the unique kinds of data that come
from educational settings , and using those methods to better A. Background
understand students and the settings which they learn in. SRIEIT's undergraduate degree programme - B.E. - consists of
Data mining enables organizations to use their current reporting
three fields of specialization (i.e. Information Technology,
capabilities to uncover and understand hidden patterns in vast
databases. As a result of this insight, institutions are able to
Electronics and Telecommunication, and Computer
allocate resources and staff more effectively. Engineering). Each field of specialization offers students
In this paper, we present a real-world experiment conducted in many subjects during eight different semesters within a period
Shree Rayeshwar Institute of Engineering and Information of four years. A student belongs to a batch and the batch is
Technology (SRIEIT) in Goa, India. Here we found the relevant offered a number of subjects.
subjects in an undergraduate syllabus and the strength of their The performance of students in the different courses offered
relationship. We have also focused on classification of students provides a measure of the students' ability to meet
into different categories such as good, average, poor depending lecturer/institution's expectations. The overall marks obtained
on their marks scored by them by obtaining a decision tree which
by students in the different subjects are utilized in our
will predict the performance of the students and accordingly help
the weaker section of students to improve in their academics.
experiment in finding related subjects. The main objective is
We have also found clusters of students for helping in analyzing to determine the relationships that exist between different
student’s performance and also improvising the subject teaching courses offered as this is required for optimizing the
in that particular subject. organization of courses in the syllabi. This problem is solved
Keywords –Data Mining, Education Domain, India, Association in two steps:
Rule Mining, Pearson Correlation Coefficient. 1. Identify the possible related subjects.
2. Determine the strength of their relationships and determine
I. INTRODUCTION strongly related subjects.
The advent of information technology in various fields has In the first step, we utilized association rule mining [1] to
lead the large volumes of data storage in various formats like identify possibly related two subject combinations in the
records, files, documents, images, sound, videos, scientific syllabi which also reduces our search space. In the second
data and many new data formats. The data collected from step, we applied Pearson Correlation Coefficient [2] to
different applications require proper method of extracting determine the strength of the relationships of subject
knowledge from large repositories for better decision making. combinations identified in the first step.
Knowledge discovery in databases (KDD), often called data Our experiment is based on the following hypothesis:
mining, aims at the discovery of useful information from large "Typically, a student obtains similar marks for related
collections of data [1]. The main functions of data mining are subjects".
applying various methods and algorithms in order to discover This assumption is justified by the fact that similar marks
and extract patterns of stored data. obtained by a student for different subjects imply that the
There are increasing research interests in using data mining in student is meeting the expectations of the course in a similar
education. This new emerging field, called Data Mining on manner. This fact implies an inherent relationship between the
Educational Domain, concerns with developing methods that courses.
discover knowledge from data originating from educational For this experiment, we selected students of batches from
environments. Educational Data Mining uses many techniques 2009-2010 , in semesters 3-6 and 60 student in the three fields
such as Decision Trees, Neural Networks, Naïve Bayes, K- of specialization. The first step, finding possible related
Nearest neighbor, and many others. subjects, requires considering 2-subject combinations.
Using these techniques many kinds of knowledge can be To do this we applied Association Rules Mining
discovered such as association rules, classifications and [1].Association Rule Mining and its application are discussed
clustering. The discovered knowledge can be used for in sections B and C.
prediction regarding the overall performance of the student.
The main objective of this paper is to use data mining B. Association Rule Mining
methodologies to study student’s performance in their Firstly, 2-subject combinations were obtained using Apriori
academics. algorithm by using database of the form TABLE II. Then
1
2. Association rules were applied to the output of Apriori In creating the database, we considered only passed subjects
algorithm due to the fact that no subject had a 100% failure. To identify
Association rules are an important class of regularities that all possible related subjects (not necessarily subjects with high
exists in databases. The classic application of association rules pass rates), we ignored the support and considered only
is the market basket analysis [1]. It analyzes how items confidence measure. The confidence was sufficient to
purchased by customers are associated. determine the possible related subjects (for instance, in the
Table I illustrates a sales database with items purchases in above rule, confidence provides us with the percentage of
each transaction. An example association rule is as follows: students that had passed subjecti also passed subjectj).
We considered the average pass rate as the minimum
pen => ink [support 75%, confidence 80%] confidence:
This rule says that 75% of customers buy pen and ink
together, and those who buy pen buys ink 80% of the time.
TABLE I. INSTANCE OF A SALES DATABASE D. Pearson Correlation Coefficient
The Pearson Correlation Coefficient (r) measures the strength
Transaction Items
of the linear relationship between two continuous variables.
ID
We computed r and selected a threshold value (i.e. γ) to
111 pen, ink, milk
determine strong relationships.
112 pen, ink The Pearson Correlation Coefficient (r) is computed as
113 pen, ink, juice follows:
114 pen, milk
Formally, the association rule-mining model can be stated as
follows. Let I = {il, i2,...,im} be a set of items. Let D be a set of
where:
transactions (the database), where each transaction d is a set of
- X, Y are two continuous variables,
items such that d ⊂ I. An association rule is an implication of
- Sx and Sy are the standard deviations of X and Y, and
the form, X->Y, where X ⊂ I, Y ⊂ I, and X ∩ Y = 0. The rule
- and are the mean values of X and Y.
X -> Y holds in the transaction set D with confidence c if c%
The value of r is such that -1 < r < +1. The + and – signs are
of transactions which contain X in D also contains Y.The rule
used for positive linear correlations and negative linear
has support s in D if s% of the transactions in D contains
correlations, respectively. If there is no linear correlation or a
X∪Y.
weak linear correlation, r is close to 0. A correlation greater
Given a set of transactions D (the database), the problem of
than 0.5 is generally described as strong, whereas a correlation
mining association rules is to discover all association rules that
less than 0.5 is generally described as weak.
have support and confidence greater than or equal to the user
specified minimum support (called minsup) and minimum
confidence (called minconf). E. Application of Pearson Correlation Coefficient
C. Application of Association Rule Mining After experimentation we selected 0.5 for the threshold value
(i.e. γ = 0.5) as a suitable estimate for determining a strong
At SRIEIT, a student earns either a "pass" grade (that is, a
relationship. A subject combination (say subjecti and subjectj
student meets the minimum requirements for successful
where i ≠ j) may contain a strong relationship (that is r ≥ 0.5
completion of the subject) or "failure" grade (that is, a student
for subjecti, subjectj permutation).
fails to meet the minimum requirements for successful
completion of the subject) for every subject the student
followed. A transaction table is considered consisting of F. Classification Algorithm
students with their passed subjects (see TABLE II). Here we make use of decision tree to classify the data and the
Our goal is to find the relationship between two subjects (i.e. tree is obtained by making use of ID3 algorithm. A decision
subjecti and subjectj where i ≠ j) using association rule mining. tree is a tree in which each branch node represents a choice
That is, find association rules with the following format between a number of alternatives, and each leaf node
meeting a certain selection criteria. represents a decision. Decision tree starts with a root node on
subjecti -> subjectj, where i ≠ j which it is for users to take actions. From this node, users split
each node recursively according to decision tree learning
TABLE II. DATABASE INSTANCE OF STUDENT
algorithm. The final result is a decision tree in which each
branch represents a possible scenario of decision and its
Student Student Passed Subjects outcome. We provide the collected data to the algorithm to
S1 subject1, subject2, subject3 create a model called as classifier. Once the classifier is built
S2 subject1, subject2, subject4 we can make use of it and can easily classify any student and
S3 subject1, subject5 can predict its performance.
2
3. ID3 is a simple decision tree learning algorithm. The basic regions where data points are dense. If density falls below a
idea of ID3 algorithm is to construct the decision tree by given threshold, data are regarded as noise.
employing a top-down, greedy search through the given sets to DBSCAN requires three inputs:
test each attribute at every tree node. In order to select the 1. The data source
attribute that is most useful for classifying a given sets, we 2. A parameter, Minpts- which is the minimum number of
introduce a metric - information gain. To find an optimal way points to define a cluster.
to classify a learning set we need some function which 3. A distance parameter, Eps- a distance parameter- if there
provides the most balanced splitting. The information gain are atleast Minpts within Eps of a point is a core point in a
metric is such a function. Given a data table that contains cluster.
attributes and class of the attributes, we can measure
homogeneity (or heterogeneity) of the table based on the Core Object: Object with at least MinPts objects within a
classes. The index used to measure degree of impurity is radius ‘Eps-neighborhood’
Entropy. Border Object: Object that on the border of a cluster
The Entropy is calculated as follows: NEps(p): {q belongs to D | dist(p,q) <= Eps}
Directly Density-Reachable: A point p is directly density-
E(S) = reachable from a point q w.r.t Eps, MinPts if p belongs to
NEps(q)
Splitting criteria used for splitting of nodes of the tree is |NEps (q)| >= MinPts
Information gain. To determine the best attribute for a Density-Reachable: A point p is density-reachable from a
particular node in the tree we use the measure called point q w.r.t Eps, MinPts if there is a chain of points p1, …,
Information Gain. The information gain, Gain (S, A) of an pn, p1 = q, pn = p such that pi+1 is directly density-reachable
attribute A, relative to a collection of examples S, is defined as from pi
Density-Connected: A point p is density-connected to a point
Gain(S,A) = E(S) - E( ) q w.r.t Eps, MinPts if there is a point o such that both, p and q
are density-reachable from o w.r.t Eps and MinPts.
The ID3 algorithm is as follows: It starts with an arbitrary starting point that has not been
- Create a root node for the tree visited. This point's ε-neighborhood is retrieved, and if it
- If all examples are positive, Return the single-node tree contains sufficiently many points, a cluster is started.
Root, with label = +. Otherwise, the point is labeled as noise. Note that this point
- If all examples are negative, Return the single-node tree might later be found in a sufficiently sized ε-environment of a
Root, with label = -. different point and hence be made part of a cluster.
- If number of predicting attributes is empty, then Return the If a point is found to be part of a cluster, its ε-neighborhood is
single node tree Root, with label = most common value of the also part of that cluster. Hence, all points that are found within
target attribute in the examples. the ε-neighborhood are added, as is their own ε-neighborhood.
- Otherwise Begin This process continues until the cluster is completely found.
- A = The Attribute that best classifies examples. Then, a new unvisited point is retrieved and processed, leading
- Decision Tree attribute for Root = A. to the discovery of a further cluster or noise.
- For each possible value, vi, of A, Here too we used attendance and marks of 60 students from 3
- Add a new tree branch below Root, corresponding branches each. (See TABLE III).
to the test A = vi.
- Let Examples (vi) be the subset of examples that
have the value vi for A III. RESULT
- If Examples (vi) is empty
- Then below this new branch add a leaf node A. Observations
with label = most common target value in the The output of association rule mining and later Pearson
examples coefficient correlation provided us with the possibly related 2-
- Else below this new branch add the subtree ID3 subject combination and the strength of their relationship.
(Examples (vi), Target_Attribute, Attributes – The subjects reviewed and the strongly related subjects are
{A}) mentioned in appendix A and E respectively.
- End The results obtained through clustering gained important
-Return Root knowledge and insights that can be used for improving the
performance of students. The yield was different clusters that
Here we used attendance and marks of 60 students from 3 is, cluster1: students attending the classes regularly scored
branches each. (See TABLE IV). high marks and cluster2: students attending regularly scored
less marks. This result helps to predict whether scoring marks
G. Clustering Algorithm in a subject actually depends on attendance or not. It even
DBSCAN is a density-based spatial clustering algorithm. By helps to find out weak students in a particular subject. This
density-based we mean that clusters are defined as connected will help the teachers to improve the performance of the
3
4. students who are weak in those particular subjects. (see subjectC). However, the student may not have acquired the
Appendix F). necessary knowledge and skills required (i.e. pre-requisite
knowledge) for passing subjectB and subjectC. Hence, the
TABLE III. INSTANCE OF A DATABASE FOR CLUSTERING student may fail with a high probability and waste student's
and institution's resources. If there a large percentage of
Stud_id attendance marks students who fail a pre-requisite subject (i.e. subjectA) also
1 93 20 fail the subject (i.e. subjectB), then these subjects are strongly
related (subjectA is strongly related to subjectB) and is
2 100 41 captured in our experimental results.
3 100 41
4 100 25 • Project Course:
5 87 46 At the end of the 6th semester, RIEIT focuses on students
. . . completing a project as a team. The main objective of the
course is to apply knowledge gained from other subjects to
. . . solve a real-world problem. So our experiment will be
beneficial for the students to select project ideas which are
The result obtained from classification is a classifier in the based on present subject and related to past subject.
form of decision tree which classifies the unseen student in
order to predict the performance of the student. Prediction will
help the teachers to pay attention to poor and average students REFERENCES
in order to enhance their capabilities in their academics. [1] Jiawei Han and Micheline Kamber “Data Mining - Concepts and
The result of Clustering and Classification is mentioned in Techniques “, Second Edition
appendix F and G respectively. [2] W.M.R. Tissera, R.I. Athauda, H. C. Fernando ,” Discovery of strongly
related subjects in the undergraduate syllabi using data mining”.
TABLE IV. INSTANCE OF A DATABASE FOR CLASSIFICATION [3] Agrawal R., and Srikant. R., "Fast algorithms for mining association
rules." VLDB-94,1994.
[4] Agrawal, R. Imielinski, T. Swami, A., "Mining association rules
Stud_id Dept Attendance Marks Performance between sets of items in large databases" SIGMOD -1993,1993.
1 ETC Y 310 AVERAGE [5] Roset. S., Murad U., Neumann. E., Idan.Y., and Pinkas.G.,
2 IT N 450 GOOD “Discovery of Fraud Rules for Telecommunications - Challenges and
3 COMP Y 500 GOOD Solutions", KDD-99, 1999.
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between sets of items in large databases" SIGMOD -1993,1993.
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[7] Bayardo,R. Agrawal, R., "Mining the most interesting rules." KDD-
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After applying classification algorithm we get a decision tree
Appendix
which is dependent on the “gain” (see Appendix G).
A. List of subject id and their titles
B. Significance of Results
The results obtained through our experiment gained important id Subject
knowledge and insights that can be used for improving the IT31 Applied Mathematics III
quality of the educational programmes. Some of these insights IT32 Numerical Methods
are outlined below: IT33 Analog And Digital Communication
IT34 Computer Organization And Architecture
• Preconceived notion of a relationship between IT35 Data Structures Using C
Mathematics subjects and programming subjects: IT36 System Analysis And Design
There existed a general notion that mathematics subjects and IT41 Discrete Mathematical Structures
programming subjects are correlated. However, our IT42 Signals And Systems
experiments illustrated that there does not exists a strong IT43 Computer Hardware And Troubleshooting
relationship between these subjects. That is, passing or failing IT44 Microprocessors And Interfaces
a mathematics subject does not determine the ability to IT45 Design And Analysis Of Algorithms
pass/fail a programming subject and vice-versa. IT46 Object Oriented Programming System
IT51 Introduction To Data Communication
• Assist in determining pre-requisite subjects: IT52 Digital Signal Processing
When determining prerequisites it is advantageous to know IT53 Software Engineering
that the existence of the strong relationship between subjects. IT54 Intelligent Agents
A student may fail a particular subject (say subjectA) and IT55 Operating Systems
proceed to taking further subjects (say subjects, subjectB,
4
5. IT56 Database Management System B. Subjects offered in the IT stream for semesters 3-6
IT51 Entrepreneurship Development
IT52 Theory Of Computation 3rd Semester 4th Semester 5th Semester 6th Semester
IT53 Computer Networks IT31 IT41 IT51 IT61
IT54 Computer Graphics IT32 IT42 IT52 IT62
IT55 Web Technology IT33 IT43 IT53 IT63
IT56 Software Testing And Quality Assurance IT34 IT44 IT54 IT64
ETC31 Applied Mathematics III IT35 IT45 IT55 IT65
ETC32 Digital System Design IT36 IT46 IT56 IT66
ETC33 Network Analysis And Synthesis
ETC34 Electronic Devices And Circuits
ETC35 Managerial Economics C. Subjects offered in the ETC stream for semesters 3-6
ETC36 Computer Oriented Numerical Techniques
ETC41 Applied Mathematics IV 3rd Semester 4th Semester 5th Semester 6th Semester
ETC42 Signals And Systems ETC31 ETC41 ETC51 ETC61
ETC43 Electrical Technology ETC32 ETC42 ETC52 ETC62
ETC44 Electromagnetic Field And Waves ETC33 ETC43 ETC53 ETC63
ETC45 Linear Integrated Circuits ETC34 ETC44 ETC54 ETC64
ETC46 Data Structures Using C++ ETC35 ETC45 ETC55 ETC65
ETC51 Probability Theory And Random Processes ETC36 ETC46 ETC56 ETC66
ETC52 Control System Engineering
ETC53 Communication Engineering 1
ETC54 Microprocessors D. Subjects offered in the COMP stream for semesters 3-6
ETC55 Digital Signal Processing
ETC56 Transmission Lines And Waveguides 3rd Semester 4th Semester 5th Semester 6th Semester
ETC61 Communication Engineering 2 COMP31 COMP41 COMP51 COMP61
ETC62 Peripheral Devices And Interfacing COMP32 COMP42 COMP52 COMP62
ETC63 Power Electronics COMP33 COMP43 COMP53 COMP63
ETC64 Antenna And Wave Propagation COMP34 COMP44 COMP54 COMP64
ETC65 Electronic Instrumentation COMP35 COMP45 COMP55 COMP65
ETC66 VLSI Technologies And Design COMP36 COMP46 COMP56 COMP66
COMP31 Applied Mathematics III
COMP32 Basics Of C++
COMP33 Principles Of Programming Languages
E. Strongly Related subjects in the respective streams with
COMP34 Computer Oriented Numerical Techniques γ> 0.5
COMP35 Logic Design
COMP36 Integrated Electronics IT Stream
COMP41 Discrete Mathematical Structures
COMP42 Data Structures
COMP43 Computer Organization
COMP44 Electronic Measurements
COMP45 System Analysis And Design
COMP46 Object Oriented Programming & Design Using C++
COMP51 Organizational Behavior And Cyber Law
COMP52 Automata Language And Computation
COMP53 Microprocessors And Microcontrollers
COMP54 Computer Hardware Design
COMP55 Database Management System
COMP56 Operating System
COMP61 Modern Algorithm Design Foundation
COMP62 Object Oriented Software Engineering
COMP63 Artificial Intelligence
COMP64 Computer Graphics
COMP65 Device Interface And Pc Maintenance
COMP66 Data Communications
5
6. COMP Stream Computer Engg. Stream
ETC Stream
ETC Stream
G. Decision Tree obtained as a result of classification.
F. Clusters obtained in the respective streams
IT Stream
6