scholarly journals Student Classification Based on Cognitive Abilities and Predicting Learning Performances using Machine Learning Models

2020 ◽  
Vol 8 (6) ◽  
pp. 3554-3569
Keyword(s):  
Machine Learning ◽  
Cognitive Abilities ◽  
Engineering Students ◽  
Demographic Data ◽  
Learning Preferences ◽  
Learning Ability ◽  
Knowledge Level ◽  
Learning Rates ◽  
Learning Capabilities ◽  
The Individual

Education is the vital parameter of the country for development in divergent areas like cultivation, economic, political, health and so on. Any educational Institute’s (universities, colleges, schools) main goal is to increase the student’s learning capabilities and their skills for their full contribution towards the society. In these days, “student’s learning process and skill development” research topic requires much needed attention for the betterment of the society. The student’s performance depends on his/her learning ability and is influenced by many factors. In this paper, we analyze the different categories of student’s leanings that are very fast, fast, moderate and slow. For this, we conducted the training and tests for attributes like ability, knowledge level, reasoning and core subject abilities for the 313 engineering students in AITAM, Tekkali, affiliated to JNTUK, India from 2017 to 2019. We gathered information about personal, academic, cognitive level and demographic data of students. In this experiment, we are conducting statistical analysis as well as classification of students into 4 types of learners and applying the different Machine Learning (ML) techniques and choose the best ML algorithm for predicting students learning rates. This leads to conducting the remedial classes with new teaching methods for moderate and slow leaning students. The proposed paper accommodates the individual differences of the learners in terms of knowledge level, learning preferences, cognitive abilities etc. For this, we apply 5 ML algorithms that are Naive Bayes, classification Trees (CTs), k-NN, C4.5 and SVM. As per ML analysis, the k-Nearest Neighborhood (k-NN) algorithm is more efficient than other algorithms where the accuracy and prediction values are nearer to 100%.

Measuring Word Learning Ability in Sequential Bilingual Children

2010 ◽  
Vol 17 (1) ◽  
pp. 25-32 ◽  
Author(s):  
Pui Fong Kan
Keyword(s):  
Word Learning ◽  
Learning Ability ◽  
Bilingual Children ◽  
Word Knowledge ◽  
Typically Developing ◽  
Learning Skills ◽  
Word Forms ◽  
The Individual ◽  
Novel Word Learning ◽  
L1 And L2

Abstract The purpose of this article is to look at the word learning skills in sequential bilingual children—children who learn two languages (L1 and L2) at different times in their childhood. Learning a new word is a process of learning a word form and relating this form to a concept. For bilingual children, each concept might need to map onto two word forms (in L1 and in L2). In case studies, I present 3 typically developing Hmong-English bilingual preschoolers' word learning skills in Hmong (L1) and in English (L2) during an 8-week period (4 weeks for each language). The results showed gains in novel-word knowledge in L1 and in L2 when the amount of input is equal for both languages. The individual differences in novel word learning are discussed.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Evidence-based Practice regarding knowledge, attitude and perceived barriers of Mangalore Physical Therapists: A Survey

2020 ◽  
Vol 11 (4) ◽  
pp. 7056-7063
Author(s):  
Vineel P ◽  
Gopala Krishna Alaparthi ◽  
Kalyana Chakravarthy Bairapareddy ◽  
Sampath Kumar Amaravadi
Keyword(s):  
Response Rate ◽  
Evidence Based Practice ◽  
Physical Therapists ◽  
Medical Profession ◽  
Demographic Data ◽  
Perceived Barriers ◽  
Hard Copy ◽  
Evidence Based ◽  
Research Committee ◽  
The Individual

  Evidence-based Practice is defined as usage of current best evidence which is conscientious, explicit and judicious in deciding on the care of the individual. It is one of the vital decision-making processes in the medical profession. Though India is renowned as a center for medical education, there is scarcity regarding the literature on evidence-based practice. The survey aims to identify the prevalence of evidence-based practice among the physical therapists of Mangalore. The study protocol submitted to scientific research committee and Ethical institutional committee, K.M.C. Mangalore Manipal University. On approval, the questionnaire had been distributed among the physical therapists of Mangalore through mails and in the written form. The questionnaire consists of questions divided into eight sections: 1) consent form 2) current practice status; 3) demographic data; 4) behavior; 5) previous knowledge of E.B.P. resources; 6) skills and available resources; 7) Opinions regarding E.B.P.; 8)Perceived barriers regarding E.B.P. The emails were sent through Google forms to all the physical therapists, and hard copies were distributed among the selected physical therapists. The response rate for the emails was 13.1%. The response collected through hard copies was 178, whereas total hard copies distributed was 320, the participants rejected some due to lack of interest. In total, including emails and hard copy questionnaire 205 was the response rate in which all were practicing physical therapy as their primary profession. The findings of the study will pave the way to identify the status of evidence-based practice as well as help in designing promotional programmers for evidence-based practice.

Associations Between Higher-Order Values Vary with Age, Education, and Income

2020 ◽  
Author(s):  
Maksim Rudnev
Keyword(s):  
Cognitive Abilities ◽  
Developed Countries ◽  
European Social Survey ◽  
Higher Order ◽  
Group Differences ◽  
Social Survey ◽  
Value Structures ◽  
Younger Age ◽  
The Individual ◽  
Representative Samples

A theory of basic human values relies on the similarity of value structures across countries. It has been well established that the quasi-circumplex value structure as a whole is indeed universal. However, less attention has been paid to the associations between specific values. This study investigated associations between four higher-order values across age, education, and income groups. We analyzed the data from national representative samples collected in 29 countries as part of the fourth round of the European Social Survey with a series of multilevel regressions. Younger age, higher levels of education and income coincided with higher independence of the four adjacent higher-order values, whereas among older, less educated, and less wealthy groups, values tended to merge into a single dimension of Social versus Person Focus. These differences were slightly weaker in more economically developed countries. The group differences in value associations may follow from corresponding differences in the degree of societal and individual empowerment, cognitive abilities, and socialization experiences. Accounting for the individual differences in relations between values may bring deeper understanding and higher predictive power to the studies of links between values and various behaviors or attitudes. , value structure, value interactions, European Social Survey

scholarly journals Predictive modeling for peri-implantitis by using machine learning techniques

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tomoaki Mameno ◽  
Masahiro Wada ◽  
Kazunori Nozaki ◽  
Toshihito Takahashi ◽  
Yoshitaka Tsujioka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Risk Indicators ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Machine Learning Methods ◽  
Complex Interactions ◽  
Learning Techniques ◽  
Increased Risk ◽  
Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

Assessment of Machine Learning-Based Audiovisual Quality Predictors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Mythili K. ◽  
Manish Narwaria
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Quality Assessment ◽  
Computational Models ◽  
Model Development ◽  
Multimedia Communication ◽  
Current Approach ◽  
Learning Ability ◽  
Data Uncertainty ◽  
Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

A model for the individual empowerment using intrinsic personalization of crowdsourcing tasks

2021 ◽  
pp. 1-6
Author(s):  
Dennis Paulino
Keyword(s):  
Cognitive Abilities ◽  
People With Disabilities ◽  
The Internet ◽  
Human Capabilities ◽  
Financial Compensation ◽  
Social Barriers ◽  
Task Description ◽  
The Individual ◽  
Phd Thesis

Crowdsourcing is a paradigm of outsourcing work that is done using human capabilities through the Internet. Given the various possibilities of overcoming cultural and social barriers, crowdsourcing provides an opportunity for people with disabilities to have a financial compensation and help them feel realised. In crowdsourcing, people with disabilities face problems related with the lack of task description or usability. This article it is presented the main threads for my PhD thesis which main goal is to prove, that it is possible to map crowdsourcing tasks effectively to each individual, focusing particularly on the cognitive abilities.

Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension

Hypertension ◽  
2021 ◽  
Vol 78 (5) ◽  
pp. 1595-1604
Author(s):  
Fabrizio Buffolo ◽  
Jacopo Burrello ◽  
Alessio Burrello ◽  
Daniel Heinrich ◽  
Christian Adolf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arterial Hypertension ◽  
Primary Aldosteronism ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Clinical Score ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Individual Risk ◽  
The Individual

Primary aldosteronism (PA) is the cause of arterial hypertension in 4% to 6% of patients, and 30% of patients with PA are affected by unilateral and surgically curable forms. Current guidelines recommend screening for PA ≈50% of patients with hypertension on the basis of individual factors, while some experts suggest screening all patients with hypertension. To define the risk of PA and tailor the diagnostic workup to the individual risk of each patient, we developed a conventional scoring system and supervised machine learning algorithms using a retrospective cohort of 4059 patients with hypertension. On the basis of 6 widely available parameters, we developed a numerical score and 308 machine learning-based models, selecting the one with the highest diagnostic performance. After validation, we obtained high predictive performance with our score (optimized sensitivity of 90.7% for PA and 92.3% for unilateral PA [UPA]). The machine learning-based model provided the highest performance, with an area under the curve of 0.834 for PA and 0.905 for diagnosis of UPA, with optimized sensitivity of 96.6% for PA, and 100.0% for UPA, at validation. The application of the predicting tools allowed the identification of a subgroup of patients with very low risk of PA (0.6% for both models) and null probability of having UPA. In conclusion, this score and the machine learning algorithm can accurately predict the individual pretest probability of PA in patients with hypertension and circumvent screening in up to 32.7% of patients using a machine learning-based model, without omitting patients with surgically curable UPA.

MOBILE APPLICATION REMOVES SOCIETAL BARRIERS TO P4 MEDICINE

2017 ◽  
pp. 1-3
Author(s):  
J.-P. Michel
Keyword(s):  
Cognitive Abilities ◽  
Mobile Application ◽  
New Technologies ◽  
Global Evaluation ◽  
P4 Medicine ◽  
Fertile Ground ◽  
Definition Of ◽  
The Individual ◽  
The Impact

The overlap between one innovative paradigm (P4 medicine: predictive, personalized, participatory and preventive) and another (a new definition of “Healthy ageing”) is fertile ground for new technologies; a new mobile application (app) that could broaden our scientific knowledge of the ageing process and help us to better analyse the impact of possible interventions in slowing the ageing decline. A novel mobile application is here presented as a game including questions and tests will allow in 10 minutes the assessment of the following domains: robustness, flexibility (lower muscle strength), balance, mental and memory complaints, semantic memory and visual retention. This game is completed by specific measurements, which could allow establishing precise information on functional and cognitive abilities. A global evaluation precedes advice and different types of exercises. The repetition of the tests and measures will allow a long follow up of the individual performances which could be shared (on specific request) with family members and general practitioners.

Sign in / Sign up

Export Citation Format

Share Document