scholarly journals A FRAMEWORK FOR DATA INTELLIGENCE AND ITS APPLICABILITY IN EDUCATIONAL SYSTEM

2020 ◽  
Vol 4 (3) ◽  
pp. 631-635
Author(s):  
Esther S. Alu ◽  
Joshua Abah ◽  
David O. Adewumi
Keyword(s):  
Educational System ◽  
Machine Learning Techniques ◽  
Educational Institutions ◽  
Contemporary Society ◽  
Effective Management ◽  
Arts And Humanities ◽  
Learning Techniques ◽  
Development Data ◽  
Organizational Problem ◽  
Data Banks

Data as well as information is the most valuable tools used for academic effectiveness and for the organization to achieve its aim and objectives. Data is very essential and the cognitive means to collect and organize this data cannot be achieved without human intelligence. In the contemporary society, the means for reliability, accuracy, effectiveness and for a better recognition of an organization has to do with the reliable information and the means to which data can be collected and organized for future use and for the effective management of the organization. Data intelligence guarantees the integral and sustainable development in all aspect of human endeavors; Science and Technologyy, Arts and Humanities and in every other aspect of human development, data intelligence serves as the master key for decision making. This paper critically reviews the necessity of data intelligence and its application areas in harmony with the effectiveness of academic and educational institutions. This work concludes that data intelligence is integral to the effectiveness of any organization including educational institutions and no organizational problem can be tackled without data intelligence. It is recommended that data intelligence should be applied in the Nigeria educational system and machine learning techniques be applied so as to derive meaning from available data banks. This work developed a framework for application of data intelligence in educational system

scholarly journals Predicting Aggregated User Satisfaction in Software Projects

2018 ◽  
Vol 43 (4) ◽  
pp. 335-357
Author(s):  
Łukasz Radliński
Keyword(s):  
User Satisfaction ◽  
Missing Values ◽  
Cross Validation ◽  
Machine Learning Techniques ◽  
Software Projects ◽  
Learning Techniques ◽  
Prediction Scheme ◽  
Testing Stage ◽  
Development Data ◽  
Research Procedure

Abstract User satisfaction is an important feature of software quality. However, it was rarely studied in software engineering literature. By enhancing earlier research this paper focuses on predicting user satisfaction with machine learning techniques using software development data from an extended ISBSG dataset. This study involved building, evaluating and comparing a total of 15,600 prediction schemes. Each scheme consists of a different combination of its components: manual feature preselection, handling missing values, outlier elimination, value normalization, automated feature selection, and a classifier. The research procedure involved a 10-fold cross-validation and separate testing, both repeated 10 times, to train and to evaluate each prediction scheme. Achieved level of accuracy for best performing schemes expressed by Matthews correlation coefficient was about 0.5 in the cross-validation and about 0.5–0.6 in the testing stage. The study identified the most accurate settings for components of prediction schemes.

Attendance System Using RFID, IOT and Machine Learning: A Two-Factor Verification Approach

2021 ◽  
Vol 12 (6) ◽  
pp. 3285-3297
Author(s):  
RKAR Kariapper
Keyword(s):  
Machine Learning ◽  
Educational System ◽  
Complete System ◽  
Machine Learning Techniques ◽  
Educational Systems ◽  
Technological Solution ◽  
Convolutional Network ◽  
Rfid Tag ◽  
Traditional System ◽  
Learning Techniques

The time and attendance systems help to monitor the employers and students working and attending time. Educational systems are struggling with the traditional system. It affects the pedagogical activities considerably. The traditional system is encountering many problems, and there is a need for a robust technological solution. This study focuses on building a two-factor prototype with RFID, IoT, and machine learning techniques. A microcontroller, GSM module, RFID tag, an RFID reader are used for first step verification. A camera with Multitask Cascaded Convolutional Network (MTCNN) model is used for a second verification. When both are okay, students will get the attendance. If it fails, parents will get a notification about the student’s attendance. When the prototype is developed as a complete system, the educational system will be getting higher advantages.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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