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Full-Text Articles in Engineering
Personality Prediction Through Curriculam Vitae Analysis Involving Password Encryption And Prediction Analysis, Gagandeep Kaur, Shruti Maheshwari
Personality Prediction Through Curriculam Vitae Analysis Involving Password Encryption And Prediction Analysis, Gagandeep Kaur, Shruti Maheshwari
Library Philosophy and Practice (e-journal)
A recruitment process requires an eligibility check, an aptitude evaluation and a psychometric analysis of prospective candidates. The work puts forward an application where the system allows employers to post new job offerings and registered candidates can apply. The application estimates applicant’s emotional aptitude through a psychometric analysis based on a test whereas the professional standard is verified via a technical aptitude test. OCEAN Model is used to assess emotional quotient and predict the personality traits. Machine learning techniques such as Logistic Regression are used for modelling the personality predictor. The details of the candidates are kept secure by using …
Bibliometric Survey On Incremental Clustering Algorithms, Archana Chaudhari, Rahul Raghvendra Joshi, Preeti Mulay, Ketan Kotecha, Parag Kulkarni
Bibliometric Survey On Incremental Clustering Algorithms, Archana Chaudhari, Rahul Raghvendra Joshi, Preeti Mulay, Ketan Kotecha, Parag Kulkarni
Library Philosophy and Practice (e-journal)
For clustering accuracy, on influx of data, the parameter-free incremental clustering research is essential. The sole purpose of this bibliometric analysis is to understand the reach and utility of incremental clustering algorithms. This paper shows incremental clustering for time series dataset was first explored in 2000 and continued thereafter till date. This Bibliometric analysis is done using Scopus, Google Scholar, Research Gate, and the tools like Gephi, Table2Net, and GPS Visualizer etc. The survey revealed that maximum publications of incremental clustering algorithms are from conference and journals, affiliated to Computer Science, Chinese lead publications followed by India then United States. …
A Machine Learning Approach To Predicting Alcohol Consumption In Adolescents From Historical Text Messaging Data, Adrienne Bergh
A Machine Learning Approach To Predicting Alcohol Consumption In Adolescents From Historical Text Messaging Data, Adrienne Bergh
Computational and Data Sciences (MS) Theses
Techniques based on artificial neural networks represent the current state-of-the-art in machine learning due to the availability of improved hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents during high school, and encode this semantic content as numeric vectors. These vectors effectively condense the text message data into highly leverageable inputs to a logistic regression classifier in a matter of hours, as compared to the tedious and often quite lengthy task of manually coding data. Using our machine learning approach, we are able to train …
Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar
Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar
Legacy Theses & Dissertations (2009 - 2024)
Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …