scholarly journals Student Career Prediction Using Advanced Machine Learning Techniques

2018 ◽  
Vol 7 (2.20) ◽  
pp. 26 ◽  
Author(s):  
K Sripath Roy ◽  
K Roopkanth ◽  
V Uday Teja ◽  
V Bhavana ◽  
J Priyanka
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Recommender Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Science Domain ◽  
Job Role

As students are going through their academics and pursuing their interested courses, it is very important for them to assess their capabilities and identify their interests so that they will get to know in which career area their interests and capabilities are going to put them in. This will help them in improving their performance and motivating their interests so that they will be directed towards their targeted career and get settled in that. Also recruiters while recruiting the candidates after assessing them in all different aspects, these kind of career recommender systems help them in deciding in which job role the candidate should be kept in based on his/her performance and other evaluations. This paper mainly concentrates on the career area prediction of computer science domain candidates.  

What is Machine Learning? A Primer for the Epidemiologist

2019 ◽  
Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computer Science ◽  
Research Methods ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Epidemiologic Research ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

scholarly journals Software Defect Estimation using Machine Learning Algorithms

2021 ◽  
Vol 10 (1) ◽  
pp. 204-208
Author(s):  
Mandi Akif Hussain* ◽  
Revoori Veeharika Reddy ◽  
Kedharnath Nagella ◽  
Vidya S.
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Computer Science ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
System Software ◽  
Software Defect ◽  
Learning Techniques ◽  
And Training

Software Engineering is a branch of computer science that enables tight communication between system software and training it as per the requirement of the user. We have selected seven distinct algorithms from machine learning techniques and are going to test them using the data sets acquired for NASA public promise repositories. The results of our project enable the users of this software to bag up the defects are selecting the most efficient of given algorithms in doing their further respective tasks, resulting in effective results.

The Reverse of Engineering

2020 ◽  
Vol 119 (1) ◽  
pp. 75-93
Author(s):  
Brett Neilson
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Reverse Engineering ◽  
Computer Science ◽  
Science Studies ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Financial Speculation ◽  
Planning Processes

Insofar as planning mediates between the order of what is and the question of what might be, it is not only a matter of philosophy but also one of engineering. Particularly at a time when routines of financial speculation and pattern recognition have colonized the making of futures, planning has become a process of creating architectural opportunities from scattered corpuses of extracted data. Mindful of the importance of machine learning in such processes, this article critically grapples with the proposition that techniques of reverse engineering offer a means of cracking these future making routines and turning them toward projects of social and political ameli oration. I argue that technical practices of reverse engineering need to articulate to radical political projects and modes of organization. Drawing on computer science studies of adversarial machine learning, I also consider the question of whether reverse engineering of machine learning techniques is technically possible. Ultimately, the article contrasts political claims for reverse engineering with what I call the reverse of engineering, or a program that entails the subordination of data to futures rather than planning processes that work from the merely evidential and measurable.

Stress Detection Using Machine Learning Classifiers in Internet of Things Environment

2019 ◽  
Vol 16 (10) ◽  
pp. 4214-4219
Author(s):  
Richa Sharma ◽  
Shalli Rani ◽  
Deepali Gupta
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Recommender Systems ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Healthcare ◽  
Learning Techniques ◽  
Internet Of Things Environment ◽  
Novel Applications

Over the years, Recommender systems have emerged as a means to provide relevant content to the users, be it in the field of entertainment, social-network, health, education, travel, food or tourism. Further,with the expeditious development of Big Data and Internet of Things (IoT), technology has successfully associated with our everyday life activities with smart healthcare being one. The global acceptance towards smart watches, wearable devices or wearable biosensors have paved the way for the evolution of novel applications for personalized eHealth and mHealth technologies. The data gathered by wearables can further be interpreted using Machine learning algorithms and shared with healthcare experts to provide suitable recommendations. In this work, we study the role of recommender systems in IoT and Cloud and vice-versa. Further, we have analyzed the performance of different machine learning techniques on SWELL dataset. Based on the results, it is observed that 2 Class Neural network performs the best with 98% accuracy.

Recommender Systems for E-Learning

2021 ◽  
pp. 221-247
Author(s):  
Mohamed Abdullah Amanullah ◽  
Abdessalem Khedher
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation Systems ◽  
Machine Learning Techniques ◽  
User Needs ◽  
Learning Techniques ◽  
E Learning ◽  
Minimal Deviation ◽  
Learning Platforms

The recommender systems are really important in this phase because the users want to be concentrated and to be focused on the domain in which they are interested. There should be minimal deviation in the topics suggested by the recommendation engines. Some of the famous e-learning platforms suggest recommendations based on tags such as highest rated, bestsellers, and so on in various domains. This ultimately makes the users deviate from the domain in which they have to master, and it results in not satisfying the user needs. So, to address this problem, effective recommendation engines will help provide recommendations according to the users by implementing the machine learning techniques such as collaborative filtering and content-based techniques. In this chapter, the authors discuss the recommendation systems, types of recommendation systems, and challenges.

Withdrawal Notice: Prognosis And Prediction Of Breast Cancer (Bc) Using Growing Mlt (Machine Learning Techniques)

2020 ◽  
Vol 13 ◽  
Author(s):  
Rajesh Kumar Maurya
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Computer Science ◽  
Editorial Policy ◽  
Machine Learning Techniques ◽  
Editorial Policies ◽  
Recent Advances ◽  
Learning Techniques ◽  
Prognosis And Prediction ◽  
Copyright Permission

The article has been withdrawn at the request of the editor of the journal Recent Advances in Computer Science and Communications. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

scholarly journals A Framework for Long-Term Learning Systems

2017 ◽  
Author(s):  
Diana Benavides-Prado
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Efficient Algorithms ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Intelligent Machines ◽  
Learning Techniques ◽  
The World ◽  
Design And Application

Increasing amounts of data have made the use of machine learning techniques much more widespread. A lot of research in machine learning has been dedicated to the design and application of effective and efficient algorithms to explain or predict facts. The development of intelligent machines that can learn over extended periods of time, and that improve their abilities as they execute more tasks, is still a pending contribution from computer science to the world. This weakness has been recognised for some decades, and an interest to solve it seems to be increasing, as demonstrated by recent leading work and broader discussions at main events in the field [Chen and Liu, 2015; Chen et al., 2016]. Our research is intended to help fill that gap.

Sign in / Sign up

Export Citation Format

Share Document