scholarly journals Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Dhruvil Shah ◽  
Devarsh Patel ◽  
Jainish Adesara ◽  
Pruthvi Hingu ◽  
Manan Shah
Keyword(s):  
Machine Learning ◽  
Education Sector ◽  
Student Records ◽  
Job Roles ◽  
Student Data ◽  
Disruptive Technologies ◽  
Valid Data ◽  
The Future ◽  
Student Achievements ◽  
The University

AbstractAlthough the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

scholarly journals Obstáculos a la sostenibilización curricular en la universidad española

2021 ◽  
Vol 29 ◽  
pp. 94
Author(s):  
Leire Guerenabarrena-Cortazar ◽  
Jon Olaskoaga-Larrauri ◽  
Ernesto Cilleruelo-Carrasco
Keyword(s):  
Higher Education ◽  
Teacher Training ◽  
Environmental Impact ◽  
Education Sector ◽  
Self Perception ◽  
Slow Pace ◽  
The Future ◽  
University Curricula ◽  
Communication Policies ◽  
The University

A growing concern for sustainability has extended to the higher education sector resulting in institutional statements, specific actions with the goal of reducing the environmental impact, or communication policies aimed at lecturers and students. However, the slow pace by which the institutions operating in this sector are adapting their curricula is frustrating, even more so, when considering the hope towards education and its ability for sensitizing and educating the future leaders of our society. The obstacles hindering the introduction of sustainability in the university curricula have thus become a matter of research. This article presents an investigation on: a) obstacles to curricular sustainability perceived by teachers and b) relationship between teacher training and awareness (attitudes and self-perception of competence for sustainability).

scholarly journals Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Javed Iqbal ◽  
Zeeshan Javed ◽  
Haleema Sadia ◽  
Ijaz A. Qureshi ◽  
Asma Irshad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Disease Risk ◽  
Clinical Applications ◽  
Optimal Decision ◽  
Great Promise ◽  
Time Range ◽  
Base System ◽  
The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

From the Guest Editors: Canadian Perspectives on and Applications of Applied Sociology

2021 ◽  
pp. 193672442110021
Author(s):  
Emily Milne ◽  
Sara J. Cumming
Keyword(s):  
Higher Education ◽  
Teaching Practice ◽  
Education Sector ◽  
Public Confidence ◽  
Applied Sociology ◽  
Application Research ◽  
Special Issue ◽  
The Future ◽  
Sociological Approaches

Public confidence and trust in higher education has declined (Johnson and Peifer 2017) and the future of the higher education sector has been questioned (AGB 2020). More specifically, the discipline of sociology is considered to be in “crisis” and applied sociological approaches are offered as a solution (Graizbord 2019; Weinstein 1997). The purpose of this introduction article as well as the broader special issue is to explore the nature and state of applied sociology in Canada. With a collection of seven articles authored by Canadian sociologists on topics including application research, reflections on process, and teaching practice, this special issue provides a platform to discuss and showcase the distinct nature and contributions of applied sociology in Canada as well as highlight the work of Canadian applied sociologists.

scholarly journals Challenges for Change Agents. Best Practices at the University Library of Freie Universität Berlin

ABI-Technik ◽  
2020 ◽  
Vol 40 (4) ◽  
pp. 357-364
Author(s):  
Martin Lee ◽  
Christina Riesenweber
Keyword(s):  
Best Practices ◽  
Change Agents ◽  
Large Change ◽  
University Library ◽  
The Future ◽  
The University ◽  
Insight Into

AbstractThe authors of this article have been managing a large change project at the university library of Freie Universität Berlin since January 2019. At the time of writing this in the summer of 2020, the project is about halfway completed. With this text, we would like to give some insight into our work and the challenges we faced, thereby starting conversations with similar undertakings in the future.

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

Can machine learning be used to forecast the future uncertainty of military teams?

2021 ◽  
pp. 154851292199911
Author(s):  
Ronald H Stevens ◽  
Trysha L Galloway
Keyword(s):  
Machine Learning ◽  
Sensory Information ◽  
Fundamental Property ◽  
Transition Matrices ◽  
Team Members ◽  
Student Teams ◽  
The Future ◽  
Network States ◽  
Uncertainty Models ◽  
Military Teams

Uncertainty is a fundamental property of neural computation that becomes amplified when sensory information does not match a person’s expectations of the world. Uncertainty and hesitation are often early indicators of potential disruption, and the ability to rapidly measure uncertainty would have implications for future educational and training efforts by targeting reflective discussions about past actions, supporting in-progress corrections, and generating forecasts about future disruptions. An approach is described combining neurodynamics and machine learning to provide quantitative measures of uncertainty. Models of neurodynamic information derived from electroencephalogram (EEG) brainwaves have provided detailed neurodynamic histories of US Navy submarine navigation team members. Persistent periods (25–30 s) of neurodynamic information were seen as discrete peaks when establishing the submarine’s position and were identified as periods of uncertainty by an artificial intelligence (AI) system previously trained to recognize the frequency, magnitude, and duration of different patterns of uncertainty in healthcare and student teams. Transition matrices of neural network states closely predicted the future uncertainty of the navigation team during the three minutes prior to a grounding event. These studies suggest that the dynamics of uncertainty may have common characteristics across teams and tasks and that forecasts of their short-term evolution can be estimated.

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