scholarly journals Deep Regressor: Cross Subject Academic Performance Prediction System for University Level Students

2019 ◽  
Vol 8 (11S) ◽  
pp. 1265-1267
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Evaluation Process ◽  
Education Institution ◽  
Drop Out ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Current Program ◽  
The Right ◽  
The University

Predicting the academic performance of students has been an important research topic in the Educational field. The main aim of a higher education institution is to provide quality education for students. One way to accomplish a higher level of quality of education is by predicting student’s academic performance and there by taking earlyre- medial actions to improve the same. This paper presents a system which utilizes machine learning techniques to classify and predict the academic performance of the students at the right time before the drop out occurs. The system first accepts the performance parameters of the basic level courses which the student had already passed as these parameters also influence the further study. To pre- dict the performance of the current program, the system continuously accepts the academic performance parame- ters after each academic evaluation process. The system employs machine learning techniques to study the aca- demic performance of the students after each evaluation process. The system also learns the basic rules followed by the University for assessing the students. Based on the present performance of the students, the system classifies the students into different levels and identify the students at high risk. Earlier prediction can help the students to adopt suitable measures in advance to improve the per for- man ce. The systems can also identify the factor saffecting the performance of the same students which helps them to take remedial measures in advance.

scholarly journals Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 38 ◽  
Author(s):  
Fabrizio Balducci ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Agricultural Field ◽  
Heterogeneous Information ◽  
Learning Techniques ◽  
Farm Model ◽  
Machine Learning Applications ◽  
Crop Harvest ◽  
The Right ◽  
The Internet Of Things

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

Application of Machine Learning Techniques for Enuresis Prediction in Children

2020 ◽  
Author(s):  
Baran Tokar ◽  
Mukaddes Baskaya ◽  
Ozer Celik ◽  
Fatih Cemrek ◽  
Ayfer Acikgoz
Keyword(s):  
Machine Learning ◽  
Evaluation Process ◽  
Toilet Training ◽  
Machine Learning Techniques ◽  
School Students ◽  
Importance Value ◽  
Independent Variables ◽  
Learning Techniques ◽  
History Of ◽  
Feature Values

Abstract Introduction As a subset of artificial intelligence, machine learning techniques (MLTs) may evaluate very large and raw datasets. In this study, the aim is to establish a model by MLT for the prediction of enuresis in children. Materials and Methods The study included 8,071 elementary school students. A total of 704 children had enuresis. For analysis of data with MLT, another group including 704 nonenuretic children was structured with stratified sampling. Out of 34 independent variables, 14 with high feature values significantly affecting enuresis were selected. A model of estimation was created by training the data. Results Fourteen independent variables in order of feature importance value were starting age of toilet training, having urinary urgency, holding maneuvers to prevent voiding, frequency of defecation, history of enuresis in mother and father, having child's own room, parent's education level, history of enuresis in siblings, consanguineous marriage, incomplete bladder emptying, frequent voiding, gender, history of urinary tract infection, and surgery in the past. The best MLT algorithm for the prediction of enuresis was determined as logistic regression algorithm. The total accuracy rate of the model in prediction was 81.3%. Conclusion MLT might provide a faster and easier evaluation process for studies on enuresis with a large dataset. The model in this study may suggest that selected variables with high feature values could be preferred with priority in any screening studies for enuresis. MLT may prevent clinical errors due to human cognitive biases and may help the physicians to be proactive in diagnosis and treatment of enuresis.

Prediction and Recommendation System for Diabetes Using Machine Learning Models

2022 ◽  
pp. 316-327
Author(s):  
Nareshkumar Mustary ◽  
Phani Kumar Singamsetty
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Medical Center ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Diabetes Prediction ◽  
Artery Disease ◽  
The Right ◽  
Deep Learning Model ◽  
Machine Learning Models

Diabetes is one of the most deadly diseases on the planet. It is also a cause of a variety of illnesses, such as coronary artery disease, blindness, and urinary organ disease. In this situation, the patient must visit a medical center to obtain their results following consultation. Finding the right combination of characteristics and machine learning techniques for classification is also very critical. However, with the advancement of machine learning techniques, we now have the potential to find a solution to the current problem. The healthcare recommendation system (HRS) may be designed to predict health by evaluating patient lifestyle, physical health, mental health aspects using machine learning. For example, training the model using people's age and diabetes helps to predict new patients without a specific diagnostic for diabetes. The proposed deep learning model with convolutional neural network (D-CNN) achieves an overall accuracy of 96.25%. D-CNN is found to be more successful for diabetes prediction than other machine learning (ML) approaches in the experimental analysis.

Data Analytics and Modeling in IoT-Fog Environment for Resource Constrained IoT-Applications - A Review

2021 ◽  
Vol 14 ◽  
Author(s):  
Omar Farooq ◽  
Parminder Singh
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Data Classification ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Resource Constrained ◽  
Learning Techniques ◽  
Iot Devices ◽  
The Right ◽  
Continuous Use

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously puts tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. It will help optimize the data at the device edge/fog level to better the network's performance in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

scholarly journals Navigating Virtual Environments Using Leg Poses and Smartphone Sensors

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 299 ◽  
Author(s):  
Georgios Tsaramirsis ◽  
Seyed Buhari ◽  
Mohammed Basheri ◽  
Milos Stojmenovic
Keyword(s):  
Machine Learning ◽  
Virtual Environments ◽  
Sensory Information ◽  
Operating Conditions ◽  
Identification Accuracy ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Learning Techniques ◽  
The Right

Realization of navigation in virtual environments remains a challenge as it involves complex operating conditions. Decomposition of such complexity is attainable by fusion of sensors and machine learning techniques. Identifying the right combination of sensory information and the appropriate machine learning technique is a vital ingredient for translating physical actions to virtual movements. The contributions of our work include: (i) Synchronization of actions and movements using suitable multiple sensor units, and (ii) selection of the significant features and an appropriate algorithm to process them. This work proposes an innovative approach that allows users to move in virtual environments by simply moving their legs towards the desired direction. The necessary hardware includes only a smartphone that is strapped to the subjects’ lower leg. Data from the gyroscope, accelerometer and campus sensors of the mobile device are transmitted to a PC where the movement is accurately identified using a combination of machine learning techniques. Once the desired movement is identified, the movement of the virtual avatar in the virtual environment is realized. After pre-processing the sensor data using the box plot outliers approach, it is observed that Artificial Neural Networks provided the highest movement identification accuracy of 84.2% on the training dataset and 84.1% on testing dataset.

scholarly journals Predicting students’ academic performance using a modified kNN algorithm

2021 ◽  
Author(s):  
Moohanad Jawthari ◽  
Veronika Stoffová
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Nearest Neighbors ◽  
Machine Learning Techniques ◽  
Categorical Variables ◽  
Ratio Scale ◽  
K Nearest Neighbors ◽  
Learning Techniques ◽  
Nominal Variables ◽  
Better Than

AbstractThe target (dependent) variable is often influenced not only by ratio scale variables, but also by qualitative (nominal scale) variables in classification analysis. Majority of machine learning techniques accept only numerical inputs. Hence, it is necessary to encode these categorical variables into numerical values using encoding techniques. If the variable does not have relation or order between its values, assigning numbers will mislead the machine learning techniques. This paper presents a modified k-nearest-neighbors algorithm that calculates the distances values of categorical (nominal) variables without encoding them. A student’s academic performance dataset is used for testing the enhanced algorithm. It shows that the proposed algorithm outperforms standard one that needs nominal variables encoding to calculate the distance between the nominal variables. The results show the proposed algorithm preforms 14% better than standard one in accuracy, and it is not sensitive to outliers.

scholarly journals Predicting School Children Academic Performance Using Machine Learning Techniques

2021 ◽  
Vol 6 (5) ◽  
pp. 8-15
Author(s):  
Radwan Qasrawi ◽  
Stephanny VicunaPolo ◽  
Diala Abu Al-Halawa ◽  
Sameh Hallaq ◽  
Ziad Abdeen
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
School Children ◽  
Machine Learning Techniques ◽  
Learning Techniques
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