scholarly journals An Effective Recommendation System to Forecast the Best Educational Program Using Machine Learning Classification Algorithms

2020 ◽  
Vol 25 (5) ◽  
pp. 559-568
Author(s):  
Joy Dhar ◽  
Asoke Kumar Jodder
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Educational Program ◽  
Recommendation System ◽  
Academic Program ◽  
Automated System ◽  
Career Goal ◽  
Machine Learning Classification ◽  
Correlation Based Feature Selection ◽  
Feature Selection Approach

After passing the 10th class, every student is eager to know which educational program will be the best for their higher education to match their career goal. Sometimes, they are very much confused to decide the best path for their higher education, and they need help to determine the best suitable academic program to develop their careers and achieve their goal. So, we introduce an effective recommendation system to forecast each student's best educational program for their career development. This proposed research is accomplished by utilizing machine learning (ML) approaches to forecast every student's best academic path based on their past academic performances and recommend them the best suitable academic program for their higher studies. Class 10th standard passing student data are supplied to this automated system, and a correlation-based feature selection approach is applied to extract the relevant features for each academic program. This study utilizes multiple ML algorithms to provide the best results and forecast each student's academic performance and select the best model based on their performance for each educational program. Hence, the best-selected model and related features are involved in the recommendation process to provide the best suitable academic path for achieving every student's career goals.

scholarly journals UniAssist- Implementation of Machine Learning Based Higher Education University Recommendation System

2020 ◽  
pp. 262-266
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Work Experience ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Relevant Information ◽  
Mobile App ◽  
Admission Process ◽  
Software Application ◽  
Tuition Fee

UniAssist project is implemented to help students who have completed their Bachelorette degree and are looking forward to study abroad to pursue their higher education such as Masters. Machine Learning would help identify appropriate Universities for such students and suggest them accordingly. UniAssist would help such individuals by recommending those Universities according to their preference of course, country and considering their grades, work experience and qualifications. There is a need for students hoping to pursue higher education outside India to get to know about proper universities. Data collected is then converted into relevant information that is currently not easily available such as courses offered by their dream universities, the avg. tuition fee and even the avg. expense of living near the chosen university on single mobile app based software platform. This is the first phase of the admission process for every student. The machine-learning algorithm used is Collaborative filtering memory-based approach using KNN calculated using cosine similarity. A mobile-based software application is implemented in order to help and guide students for their higher education.

scholarly journals Book Recommendation System Using Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 1981-1983
Author(s):  
Prof. S. R. Hiray
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Recommendation System ◽  
Support Vector ◽  
System Support ◽  
Machine Learning Classification ◽  
The Subject ◽  
Minor Losses ◽  
Selection Of ◽  
Previous User

Abstract: Users can use book recommendation systems to search and select books from a number of options available on the web or elsewhere electronic sources. They give the user a little bit selection of products that fit the description, given a large group of objects and a description of the user needs. Our system will simply provide recommendations. Recommendations are based on previous user activity, such as purchase, habits, reviews, and likes. These systems gain lot of interest. In the proposed system, we have a big problem: when the user buys book, we want to recommend some books that the user can enjoy. Buyers also have a great deal of options when it comes to recommending the best and most appropriate books for them. User development privacy while placing small and minor losses of accuracy. Recommendations. The proposed recommendation system will provide user's ability to view and search the publications and using the Support Vector Machine (SVM), will list the most purchased and top rated books based on the subject name given as input. Keywords: Recommender System, Support Vector Machine (SVM), Machine Learning, Classification etc.

scholarly journals Chronic Kidney Disease Diagnosis using Decision Tree Algorithms

2020 ◽  
Author(s):  
Hamida Ilyas ◽  
Sajid Ali ◽  
Mahvish Ponum ◽  
Osman Hasan ◽  
Muhammad Tahir Mahmood
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Random Forest ◽  
Kidney Disease ◽  
Early Detection ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Automated System ◽  
Disease Epidemiology ◽  
Machine Learning Classification

Abstract Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages i.e., early stage to the last stage of kidney failure. Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. In particular, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with a 85.5% accuracy. The study also showed that J48 shows improved performance over Random Forest, so, it may be used to build an automated system for the detection of severity of CKD.

scholarly journals The Design of an Automated System for the Analysis of the Activity and Emotional Patterns of Dogs with Wearable Sensors Using Machine Learning

2019 ◽  
Vol 9 (22) ◽  
pp. 4938 ◽  
Author(s):  
Satyabrata Aich ◽  
Sabyasachi Chakraborty ◽  
Jong-Seong Sim ◽  
Dong-Jin Jang ◽  
Hee-Cheol Kim
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Wearable Sensors ◽  
Companion Animals ◽  
Evaluation Process ◽  
Emotional Behavior ◽  
Automated System ◽  
Support Vector ◽  
Human Beings ◽  
Machine Learning Classification

The safety and welfare of companion animals such as dogs has become a large challenge in the last few years. To assess the well-being of a dog, it is very important for human beings to understand the activity pattern of the dog, and its emotional behavior. A wearable, sensor-based system is suitable for such ends, as it will be able to monitor the dogs in real-time. However, the question remains unanswered as to what kind of data should be used to detect the activity patterns and emotional patterns, as does another: what should be the location of the sensors for the collection of data and how should we automate the system? Yet these questions remain unanswered, because to date, there is no such system that can address the above-mentioned concerns. The main purpose of this study was (1) to develop a system that can detect the activities and emotions based on the accelerometer and gyroscope signals and (2) to automate the system with robust machine learning techniques for implementing it for real-time situations. Therefore, we propose a system which is based on the data collected from 10 dogs, including nine breeds of various sizes and ages, and both genders. We used machine learning classification techniques for automating the detection and evaluation process. The ground truth fetched for the evaluation process was carried out by taking video recording data in frame per second and the wearable sensors data were collected in parallel with the video recordings. Evaluation of the system was performed using an ANN (artificial neural network), random forest, SVM (support vector machine), KNN (k nearest neighbors), and a naïve Bayes classifier. The robustness of our system was evaluated by taking independent training and validation sets. We achieved an accuracy of 96.58% while detecting the activity and 92.87% while detecting emotional behavior, respectively. This system will help the owners of dogs to track their behavior and emotions in real-life situations for various breeds in different scenarios.

scholarly journals Chronic kidney disease diagnosis using decision tree algorithms

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hamida Ilyas ◽  
Sajid Ali ◽  
Mahvish Ponum ◽  
Osman Hasan ◽  
Muhammad Tahir Mahmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Random Forest ◽  
Kidney Disease ◽  
Early Detection ◽  
Disease Diagnosis ◽  
Automated System ◽  
Disease Epidemiology ◽  
Machine Learning Classification ◽  
Improved Performance

Abstract Background Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Methods Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Results Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. Conclusions The study concluded that it may be used to build an automated system for the detection of severity of CKD.

scholarly journals A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Muhammad Attique Khan ◽  
Tallha Akram ◽  
Muhammad Sharif ◽  
Majed Alhaisoni ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Feature Selection ◽  
Critical Role ◽  
Rank Correlation ◽  
Automated System ◽  
Support Vector ◽  
Binary Images ◽  
Segmentation Approach ◽  
Correlation Based Feature Selection ◽  
Feature Selection Approach ◽  
Fusion Feature

AbstractAgriculture plays a critical role in the economy of several countries, by providing the main sources of income, employment, and food to their rural population. However, in recent years, it has been observed that plants and fruits are widely damaged by different diseases which cause a huge loss to the farmers, although this loss can be minimized by detecting plants’ diseases at their earlier stages using pattern recognition (PR) and machine learning (ML) techniques. In this article, an automated system is proposed for the identification and recognition of fruit diseases. Our approach is distinctive in a way, it overcomes the challenges like convex edges, inconsistency between colors, irregularity, visibility, scale, and origin. The proposed approach incorporates five primary steps including preprocessing,Standard instruction requires city and country for affiliations. Hence, please check if the provided information for each affiliation with missing data is correct and amend if deemed necessary. disease identification through segmentation, feature extraction and fusion, feature selection, and classification. The infection regions are extracted using the proposed adaptive and quartile deviation-based segmentation approach and fused resultant binary images by employing the weighted coefficient of correlation (CoC). Then the most appropriate features are selected using a novel framework of entropy and rank-based correlation (EaRbC). Finally, selected features are classified using multi-class support vector machine (MC-SCM). A PlantVillage dataset is utilized for the evaluation of the proposed system to achieving an average segmentation and classification accuracy of 93.74% and 97.7%, respectively. From the set of statistical measure, we sincerely believe that our proposed method outperforms existing method with greater accuracy.

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