Predictive Models of Student College Commitment Decisions Using Machine Learning

Every year, academic institutions invest considerable effort and substantial resources to influence, predict and understand the decision-making choices of applicants who have been offered admission. In this study, we applied several supervised machine learning techniques to four years of data on 11,001 students, each with 35 associated features, admitted to a small liberal arts college in California to predict student college commitment decisions. By treating the question of whether a student offered admission will accept it as a binary classification problem, we implemented a number of different classifiers and then evaluated the performance of these algorithms using the metrics of accuracy, precision, recall, F-measure and area under the receiver operator curve. The results from this study indicate that the logistic regression classifier performed best in modeling the student college commitment decision problem, i.e., predicting whether a student will accept an admission offer, with an AUC score of 79.6%. The significance of this research is that it demonstrates that many institutions could use machine learning algorithms to improve the accuracy of their estimates of entering class sizes, thus allowing more optimal allocation of resources and better control over net tuition revenue.

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Toward Palmprint Recognition Methodology Based Machine Learning Techniques

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2020.4.4.225 ◽

2020 ◽

Vol 4 (4) ◽

Author(s):

M. M. Ata ◽

K. M. Elgamily ◽

M. A. Mohamed

Keyword(s):

Machine Learning ◽

Hough Transform ◽

Recognition Accuracy ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Palmprint Recognition ◽

Feature Vectors ◽

Learning Techniques

The presented paper proposes an algorithm for palmprint recognition using seven different machine learning algorithms. First of all, we have proposed a region of interest (ROI) extraction methodology which is a two key points technique. Secondly, we have performed some image enhancement techniques such as edge detection and morphological operations in order to make the ROI image more suitable for the Hough transform. In addition, we have applied the Hough transform in order to extract all the possible principle lines on the ROI images. We have extracted the most salient morphological features of those lines; slope and length. Furthermore, we have applied the invariant moments algorithm in order to produce 7 appropriate hues of interest. Finally, after performing a complete hybrid feature vectors, we have applied different machine learning algorithms in order to recognize palmprints effectively. Recognition accuracy have been tested by calculating precision, sensitivity, specificity, accuracy, dice, Jaccard coefficients, correlation coefficients, and training time. Seven different supervised machine learning algorithms have been implemented and utilized. The effect of forming the proposed hybrid feature vectors between Hough transform and Invariant moment have been utilized and tested. Experimental results show that the feed forward neural network with back propagation has achieved about 99.99% recognition accuracy among all tested machine learning techniques.

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Predicting Takeover Success Using Machine Learning Techniques

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v10i10.7264 ◽

2012 ◽

Vol 10 (10) ◽

pp. 547

Author(s):

Mei Zhang ◽

Gregory Johnson ◽

Jia Wang

Keyword(s):

Machine Learning ◽

Learning Community ◽

Binary Classification ◽

Classification Problem ◽

Machine Learning Techniques ◽

Success Prediction ◽

Support Vector ◽

Font Size ◽

Network Support ◽

Learning Techniques

A takeover success prediction model aims at predicting the probability that a takeover attempt will succeed by using publicly available information at the time of the announcement. We perform a thorough study using machine learning techniques to predict takeover success. Specifically, we model takeover success prediction as a binary classification problem, which has been widely studied in the machine learning community. Motivated by the recent advance in machine learning, we empirically evaluate and analyze many state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines with different kernels, decision trees, random forest, and Adaboost. The experiments validate the effectiveness of applying machine learning in takeover success prediction, and we found that the support vector machine with linear kernel and the Adaboost with stump weak classifiers perform the best for the task. The result is consistent with the general observations of these two approaches.

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Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms

The African Journal of Information and Communication ◽

10.23962/10539/31371 ◽

2021 ◽

pp. 1-21

Author(s):

Helper Zhou ◽

Victor Gumbo

Keyword(s):

Machine Learning ◽

Predictive Analytics ◽

Predictive Modelling ◽

Sales Performance ◽

Ordinary Least Squares ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Selection Operator

The emergence of machine learning algorithms presents the opportunity for a variety of stakeholders to perform advanced predictive analytics and to make informed decisions. However, to date there have been few studies in developing countries that evaluate the performance of such algorithms—with the result that pertinent stakeholders lack an informed basis for selecting appropriate techniques for modelling tasks. This study aims to address this gap by evaluating the performance of three machine learning techniques: ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), and artificial neural networks (ANNs). These techniques are evaluated in respect of their ability to perform predictive modelling of the sales performance of small, medium and micro enterprises (SMMEs) engaged in manufacturing. The evaluation finds that the ANNs algorithm’s performance is far superior to that of the other two techniques, OLS and LASSO, in predicting the SMMEs’ sales performance.

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Framework for Providing Security in Private Cloud using Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9121.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 7641-7645

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Learning Algorithms ◽

Feature Reduction ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Cyber Attack ◽

Learning Techniques

The advancement in cyber-attack technologies have ushered in various new attacks which are difficult to detect using traditional intrusion detection systems (IDS).Existing IDS are trained to detect known patterns because of which newer attacks bypass the current IDS and go undetected. In this paper, a two level framework is proposed which can be used to detect unknown new attacks using machine learning techniques. In the first level the known types of classes for attacks are determined using supervised machine learning algorithms such as Support Vector Machine (SVM) and Neural networks (NN). The second level uses unsupervised machine learning algorithms such as K-means. The experimentation is carried out with four models with NSL- KDD dataset in Openstack cloud environment. The Model with Support Vector Machine for supervised machine learning, Gradual Feature Reduction (GFR) for feature selection and K-means for unsupervised algorithm provided the optimum efficiency of 94.56 %.

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Review on Various Machine Learning and Deep Learning Techniques for Prediction and Classification of Quotidian Datasets

Recent Advances in 3D Imaging, Modeling, and Reconstruction - Advances in Multimedia and Interactive Technologies ◽

10.4018/978-1-5225-5294-9.ch014 ◽

2020 ◽

pp. 296-323

Author(s):

Anisha M. Lal ◽

B. Koushik Reddy ◽

Aju D.

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Training Data ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Supervised Methods ◽

Regression Techniques

Machine learning can be defined as the ability of a computer to learn and solve a problem without being explicitly coded. The efficiency of the program increases with experience through the task specified. In traditional programming, the program and the input are specified to get the output, but in the case of machine learning, the targets and predictors are provided to the algorithm make the process trained. This chapter focuses on various machine learning techniques and their performance with commonly used datasets. A supervised learning algorithm consists of a target variable that is to be predicted from a given set of predictors. Using these established targets is a function that plots targets to a given set of predictors. The training process allows the system to train the unknown data and continues until the model achieves a desired level of accuracy on the training data. The supervised methods can be usually categorized as classification and regression. This chapter discourses some of the popular supervised machine learning algorithms and their performances using quotidian datasets. This chapter also discusses some of the non-linear regression techniques and some insights on deep learning with respect to object recognition.

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Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review

Journal of Physical Activity and Health ◽

10.1123/jpah.2019-0088 ◽

2020 ◽

Vol 17 (3) ◽

pp. 360-383 ◽

Cited By ~ 4

Author(s):

Anantha Narayanan ◽

Farzanah Desai ◽

Tom Stewart ◽

Scott Duncan ◽

Lisa Mackay

Keyword(s):

Physical Activity ◽

Machine Learning ◽

Scoping Review ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Movement Behavior ◽

Support Vector ◽

Accelerometer Data ◽

Learning Techniques

Background: Application of machine learning for classifying human behavior is increasingly common as access to raw accelerometer data improves. The aims of this scoping review are (1) to examine if machine-learning techniques can accurately identify human activity behaviors from raw accelerometer data and (2) to summarize the practical implications of these machine-learning techniques for future work. Methods: Keyword searches were performed in Scopus, Web of Science, and EBSCO databases in 2018. Studies that applied supervised machine-learning techniques to raw accelerometer data and estimated components of physical activity were included. Information on study characteristics, machine-learning techniques, and key study findings were extracted from included studies. Results: Of the 53 studies included in the review, 75% were published in the last 5 years. Most studies predicted postures and activity type, rather than intensity, and were conducted in controlled environments using 1 or 2 devices. The most common models were support vector machine, random forest, and artificial neural network. Overall, classification accuracy ranged from 62% to 99.8%, although nearly 80% of studies achieved an overall accuracy above 85%. Conclusions: Machine-learning algorithms demonstrate good accuracy when predicting physical activity components; however, their application to free-living settings is currently uncertain.

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Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

International Journal of Rough Sets and Data Analysis ◽

10.4018/ijrsda.2017010104 ◽

2017 ◽

Vol 4 (1) ◽

pp. 56-74 ◽

Cited By ~ 14

Author(s):

Abinash Tripathy ◽

Santanu Kumar Rath

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Performance Parameters ◽

Linear Discriminant ◽

Learning Techniques

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.

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Opinion Mining using Machine Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4108.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 4287-4292

Keyword(s):

Machine Learning ◽

Opinion Mining ◽

Predictive Ability ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbors ◽

Significant Information ◽

Learning Techniques

Sentiment analysis or opinion mining has gained much attention in recent years.With the constantly evolving social networks and internet marketing sites, reviews and blogs have been obtained among them, they act as an significant source for future analysis and better decision making. These reviews are naturally unstructured and thus require pre processing and further classification to gain the significant information for future use. These reviews and blogs can be of different types such as positive, negative and neutral . Supervised machine learning techniquess help to classify these reviews. In this paper five machine learning algorithms (K-Nearest Neighbors (KNN), Decision Tree, Artificial neural networks (ANNs), Naïve bayes and Support Vector Machine (SVM))are used for classification of sentiments. These algorithms are analyzed usingTwitter dataset. Performance analysis of these algorithms are done by using various performance measures such as Accuracy, precision, recall and F-measure. The evaluation of these techniques on Twitter datasetshowed predictive ability of Machine Learning in opinion mining

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Using real-world transaction data to identify money laundering: Leveraging traditional regression and machine learning techniques

STEM Fellowship Journal ◽

10.17975/sfj-2021-006 ◽

2021 ◽

pp. 1-11

Author(s):

Daniel A. Harris ◽

Kyla L. Pyndiura ◽

Shelby L. Sturrock ◽

Rebecca A.G. Christensen

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Money Laundering ◽

Model Performance ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Binary Outcome ◽

Learning Techniques ◽

Increased Risk

Money laundering is a pervasive legal and economic problem that hides criminal activity. Identifying money laundering is a priority for both banks and governments, thus, machine learning algorithms have emerged as a possible strategy to detect suspicious financial activity within financial institutions. We used traditional regression and supervised machine learning techniques to identify bank customers at an increased risk of committing money laundering. Specifically, we assessed whether model performance differed across varying operationalizations of the outcome (e.g., multinomial vs. binary classification) and determined whether the inclusion of investigator-derived novel features (e.g., averages across existing features) could improve model performance. We received two proprietary datasets from Scotiabank, a large bank headquartered in Canada. The datasets included customer account information (N = 4,469) and customers’ monthly transaction histories (N = 2,827) from April 15, 2019 to April 15, 2020. We implemented traditional logistic regression, logistic regression with LASSO regularization (LASSO), K-nearest neighbours (KNN), and extreme gradient boosted models (XGBoost). Results indicated that traditional logistic regression with a binary outcome, conducted with investigator-derived novel features, performed the best with an F1 score of 0.79 and accuracy of 0.72. Models with a binary outcome had higher accuracy than the multinomial models, but the F1 scores yielded mixed results. For KNN and XGBoost, we observed little change or worsening performance after the introduction of the investigator-derived novel features. However, the investigator-derived novel features improved model performance for LASSO and traditional logistic regression. Our findings demonstrate that investigators should consider different operationalizations of the outcome, where possible, and include novel features derived from existing features to potentially improve the detection of customer at risk of committing money laundering.

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Emotion Detection Analysis using EEG and Physiological Signals for Hybrid Systems

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f3801.049620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 534-543

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Physiological Signals ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Emotion Detection ◽

Detection Analysis ◽

Learning Techniques ◽

Eeg Data ◽

Skin Response

Emotions are an inevitable and integral part of human existence. They form the basis of decisions taken by individuals and the way they perceive their surroundings. Method of articulation of emotions have changed with the increment in dependency between people and innovation. Now the need to recognize emotions has increased with the increasing role of human-Computer Interface (HCI) technology. There are many ways to record and identify human’s emotion using different neurophysiological measurements/ technologies like GSR(Galvanic Skin Response), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper, the focus is on emotion detection using EEG signals and other physiological signals and further analyzing them. There exist various machine learning techniques that have been used to pre-process and classify EEG data, have been reviewed in the paper. The analysis involves major aspects of the emotion recognition process like feature extraction, classification and comparison of the approaches. Different supervised machine learning algorithms have been applied to classify the EEG data. This paper focuses on comprehensive analysis of existing systems and based on the result propose the techniques which when applied will reap high-quality results.

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